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P:+Q,P|'P8.XP2p NQ>K,!X,:W,,2PQ,P|L0!Xx,02p NQ,><#X, #2PQP |f@,%X@2p NQe ;,%X[;2x8QXf@,%X @2x\Q>X3' X,E^32PQ^PX3' X/^32x8Q^XY8' X^82x\Q^>5X,62PQP~)I8.X:I2x8QXK,!X+=,,2x8Q,X<#XJ#2x8QX>,Q=3X,&Q2PQ&P">S0%X,"AJ02PQJP2^I0P)U=3X.&U P:+Q&P%M8.X<M P:+QP|/`C7XX`2p NQX>~)I8.X,PI2PQP|?YJXH* 2p NQ >e ;,%X,Jq;2PQP*`=5X8&`X pQ&@)T=3XX&Te xXQ&XV3%XDJ3 P:+QJPPd!0,Xz{0xzP"@QP P<Xz0xzP"@QPd!;,X;zdxDQXFd!=,X="zpfQ2`Dk)U=3X.&U P:+Q&P%M8.X<M P:+QP|/`C7XX`2p NQX >~)I8.X,PI2PQP|?YJXH* 2p NQ >e ;,%X,Jq;2PQP*`=5X8&`X pQ&@)T=3XX&Te xXQ&XV3%XDJ3 P:+QJPPd!0,Xz{0xzP"@QP P<Xz0xzP"@QPd!;,X;zdxDQXFd!=,X="zpfQ|\9(!Xh92p NQh@U2%XX5{J2e xXQJX0 Q 0Q0'Q 03Q 0>Q0VQ00Q0=>x{[3;![. /!>&4,")*-!$/'A5HK='A. <KNTm8][i`2m*ME^#R<K:krMFLD;4kUdOUk}^h_O}Jd UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*JJ+/ƚIGI  7 %%7@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45M"ArialT-  2 2NOxc  2 SO2wc 2 `PM-10w;cd]]%]}# x}# @%}#@%g}#g}#g}#   "--]%7]]]]]]- "-$]%%%%]]"Arial - --U2 ZY4Note: Emissions are taken from EPA Emission Trends.N=<IZ78=<8<$<<7=<#=ZI@II[77<=@$<=<7g"ArialT-  2 1900UUVU  2 I1910UVUU  2  1920VUUV  2  1930UUVU  2 y1940UVUU  2 1950VUUV  2 D1960UVUU  2 1970UVUU  2 1980VUUV  2 v!1990UUVU  "-CC}#  }#33}#}#}#  }#ww}#}#gg}# - 2 t0U  2 P 10VU  2 20VU  2 A30VU  2 40VU}# x}# @%}#@%}#}#  $"- $"-   9 9 <LL44<<<877IP0VA_id||xM`1DK} (at335; ; TE TE [U [X [X c e f J ? 4 D) L< th r            E 2 X <  \  \  _  l  l f f [ \ \ x     =  P  c      !  4  m         W D @  APsy5 H         K 8  P c( r< E f r  4| Gj k n n n t t    % * * ^ o |   0 a ?p R        # # / 6 6 o        S f  < N  7 7 8 J J          - = f y3 A p   0 > B u % 2 9 M U v X j  !*Zm&Q`>Q  EEQVV>O Wj",,ex3C q  #+ENRoe GZddT6/ +>w# 3 @ S      !*!G!.Q!gn!gn!qs!k|!k|!G!:!9!)"$" !"M"`""""!"I1#XD#- x}#  x}#  $"-%_ ^ <  v A)  F SK PU n  Z  S _ P & ?c s  h / Qm r 6wW%p|*+m/YS3q8ki=BHXBGL5 Q V%Z_+ <dDi. \n ; !s!!!"x""x&#K}#x@%}#@%@%}#@% $"- $"-D7OpQ|R]}gz&89Lz0w=uCg|<'7:5B2M$ W    V N J F $ $        <      }   v      M _ ` s  W d js .kc]ibmbm    x9 k k n x     x!!!!!z"p"n"m"f#- - 2 Year\VU;g||"Arial - 42 + CEmissions (million short tons)e+UV*_^U+4*++*_^*V^^;3+4^^U4  "-hh<<hhhAAhhhFFhhhKKhhhPPhhhT T h  h  hY Y h  h  h^ ^ h  h  hc c h  h  hh h h  hhmmhhhrrhh  hwwhh%%h||hh**hhh//hhh33hhh88hhh==hhhBBhhhGGhhhLLhhhQQhhhVVhhh[[hh  h``hhhddhhhiihh  hn n h  h!!hs!s!h!!h!"!"hx"x"h""h&#&#h}#}#HHHZ Z H  H%%HHHVVHH!"!"CpC p 3p3pP P wPwPgPgg}#g}#g}#g}#g}#g}# "-%}#gg}#}#g "--[  $"- $"-(l ( ( ( ( ( (P (c ( ( ( ( (4(G(((((- $"- $"-%(H(- $"- $"-((J((z(%(%(3-|"ArialT- --'2 H Nitrogen oxides (NOx)^%-IHII&H@IIA&,^h@, %2 HSulfur dioxide (SO2)WI%I,&II@II&,WgI- .2 HParticulate matter (PM-10)WI,&BII%I&pH&%I,&,Wm,II- Courier4- &8LDt&MDFd UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+<ƚI _  #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$ h h9 9    )   ) L L!   !        "--$- "-$B$%B$%j$j$"Arial- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arial-  2 1985QRRR  2 1986RRQR  2  1987QRRR  2  1988RRRQ  2 1989RQRR  2 1990QRRR  2 1991RRQR  2 1992QRRR  2 y1993RRRQ  2 m1994RQRR  2 0R  2 P200RRQ  2 P400RRQ  2 P600RRQ  2 P800RRQ 2 1,000R)RRQ 2 $1,200R)RRQ 2 71,400R)RRQ h h9 9    )   ) L L!   !      "-% th [9O6B6)Q h hh h% hth O[OB6e)D  "-- $gNgN $ZZ $N i i 5N 5 $A u uA $5ii5 $(\\( $PPKK $^C^C** $77 $+ + uu9 99 9 "- "-99::;];<<=-=T>>??$@r@r@t73,B ,B #    m     = d h   + 84 Q Q n { R R   ""}JJMt #DUUbb22]-:BGT$rrBBqqsmz=d 4RR!2T"T"{}Mt  1+D bb22  -         "- "-   ]  -  & X  (    m  } = t d s h t q w   h   8    }  M   x % H& O     ]  - : B G   X  (   m  =% B Y hw   ) + 8   } M5T>x! H    -)   ) )   )  "-% ) t( h [W OS Bk 6? ))    "- $2) x )  $t%( tw (  $h 7  h  $[ W [ W  $O S O S  $B k B k  $6? 6 ?  $)) )x x)  $E   l  $ N   ` L LL L "- "-NOTV[]b)d6i]kjpqvx}-:an 1>ert  5 B i v       9 F m z       = J q ~       A N u        E R "y % , . 6 8 ?B"IIKVL[<}6$ &MZzt*bQ\^JD2,!.Ub  %2Yf%(/18:A)D6K]MjTV]`gip-s:tBkagn[XLH=9 -1)>er 5Biv9Fmz =Jq~  $/A3N?uBNR]amqt){ERy"IV}&MZ),6/*Q^!.UbpiSL70%2Yf -!   ! !   !  "- "-# ( 0 0 7 7 @ ]F M M U U ^ -d Tk k s s |  $ X X     (  (  \  \      ,  ,  m       =  d  h       4  h  h       8  8  l  l      < < }      M t      D x x    & H& H' O | |     L L       ]      - : B G T      $ X X     ( ( \ \     , , 6 m      = d      4 h h     8 8 l l     < < }      M t    "  D x x     H H | |     L L    -   "-% t=h [OVB 6y) 3  "-TLr?Y 3 (&!wU Dk^URhF  -- 2 1-YearZRR9k||"Arial- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-ZZuuZh h Z\\ZOOZCCZ66Z**ZZ      !!55JJ^^ZZDnnD  DDDDDD        "-% Z Z  "-- [$K! "- "-%!#- "-%!#- "-- $"""" "- "- "- ! ! )" )" " " " " T# {# # #- "- "-N!N"N"N"NT#N#- "- "-%!#- "- $"B"""# "- "- "-!!!!")"P"]""""""" #-#T#a########- "- "-!!""C"C"""""##T#{#####- "- "-%!Z #Z - "-% # " "-"Arial- -- 2 "ALSF  2 x"GAaT  2 "KYST  2 cx"NCZ[  2 {"SCTZ  2 "TNL[  2 ,"VAKT  2 oi"WV}S Courier4-P6d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+v=ƚI   #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$ !G !R : :  } }[7 [&       "--$- "-$B$%B$%j$j$"ArialT- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arial -  2 b1985RQRR  2 b1986RQRR  2 d 1987QRRR  2 f 1988QRRR  2 g1989RRRQ  2 g1990RRRR  2 i1991RRRQ  2 k1992RRQR  2 k1993RRQR  2 m1994RQRR  2 200RRR  2 ? 300RRR  2 400RRR  2 p500RRR  2 600RRR  2 700RRR  2 7800RRR !G !R : :  } }[7 [&     "-% 1 I     z$ !G !!G !% !    S .H G "-- $; ;  $!! $ " "   $ ##  $$$ $m&m&99 $H'H' $b(b(.. $))rr $a+ a+ --R R  "- "-9  ( 9W9WNWh'h'}Ry~~q" kI _ _ R L @g @g 2 - I7 I7     b : q q 2 Y   FFK H<)wwGGvQQ&rBi%%JZ9zz WW''Ry"I  gg77b-2BYkk)ww FGFGvrBi||ql9ccXS R -: :: : "- "-;FmT_=mw sRK" }  M \ ? = ? A X j b  2   ]--AZrn}Bm=   jM(R "}  M1Tb2]2H-e|prbPBA/!m  -     "-% g   >  b   "- $3U $gVg $   8 W  $  o X  $ >  Y>  $ w   [  $  / \ $3 ] $ b  ^b  $ =   ` } }} } "- "-FSz#JW~'N[+R_"  /  V  c       & 3 Z g      " # # &* '7 +^ ,k / 0 4 5 8 : =. >; Bb Co F G K L O Q T2 U? Yf Zs ] ^ b c ffg ff]6ZCQjNwEB96-*!:Gn{  >KrBOv  FS)z-9>JNZ^#jJnW{~'N[ohVO=7$+ R_"/Vcvo]WD>>&A3LZOgZ]hkvz*7^k.;bo 2?2f9sLRel6Cjw:G1n7{KQdj~ ~ x>uKorlec\ZSQJBGOAv>75.,%#FSz -[7 [[7 [ "- "-]b9jmjmqq{ ==qq  AARy" I } }      M  M     " " 'Q 'Q - 1 6 6 ;! ;! Bb F K K P P V2 ZY _ _ d d jjk ln)s]s]ww}--aa11r Bi{9qmqmhh\U L=L=CqCq70' ' AA Ry"I}}zt t vv}M}MQQ!!b2Y)]]#*-*-1a1a:?FFG1G1FrEDDCCAB@i??>>=<9;m;m9987 7 -& &  "-% C ]    9) & "-j;<x(=  n> ?AG B5Cn D^F [ -- 2 1Year[RR9k||"ArialT- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-ZZZ  Z  Z  Z  Z  ZZZ  %N% N UNUNNNYNYN N !N!NQNQN        "-% NNZ Z  "-- [$K! "- "-%!#- "-%!#- "-- $"""" "- "- "- ! ! )" )" " " " " T# {# # #- "- "-N!N"N"N"NT#N#- "- "-%!#- "- $"B"""# "- "- "-!!!!")"P"]""""""" #-#T#a########- "- "-!!""C"C"""""##T#{#####- "- "-%!Z #Z - "-% # " "-"Arial - -- 2 "ALSF  2 x"GAaT  2 "KYST  2 cx"NCZ[  2 {"SCTZ  2 "TNL[  2 ,"VAKT  2 oi"WV}S Courier4-H& O d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+6xƚI <  #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$w$S"w$q$"q$w$"w$$"$$"$!$"!$8 $"8 $f$ "f$"""   "--$- "-$B$%B$%j$j$V"Arial- --s2 FHFigure . State-level NOx Emissions from Each State in the SAMI Regionh.ggB^../...p9_8_9.^\_..{_.p._^.gg^/9Ah.p^_g.p9^9^./g.9g^.px..{^g/gg 2 k (1985 to 1994)9^^_^.9g._^^^9k"Arial-  2 o85RQ  2 o86RR  2 o 87RR  2 o$88RQ  2 o@89RR  2 o[90RR  2 ow91RR  2 o92RR  2 o93QR  2 o!94RR 2 +200,000RRR)QRR 2 300,000RRR)QRR 2 400,000RRR)QRR 2 .500,000RRR)QRR 2 600,000RRR)QRR 2 700,000RRR)QRR 2 0800,000RRR)QRRw$S"w$q$"q$w$"w$$"$$"$!$"!$8 $"8 $f$ "f$w$S"w$w$S"w$  "-% $w? [ wF"Sq$"q$q$"q$% $q? 7[ +w?" "-- $ >>W W $% QY QY %  $A Eu Eu A  $]33] $xYY%x% $zz $uu $ $ $"7"7"z"zw$"w$w$"w$ "- "-y0W'uuEE  p     @ g    7     U U [   /% /% i    Pw88r GvfEeEe 55l`\Waao0uW'uuEEp@g 7'U'U38B%B%NS]]iPnwxx G 2e2e]o55`0 W   !'!u!u!!!-$"$$"$ "- "-0[+p  @  k Fk  ;    * ?POd qnswt{t{K`   0[|e+HDDDCBpA@@@?>kEd; P<] q{K`0  ![!!""-$"$$"$ "-% $? [ )w]ym!%" "-%n%$$%n%n% %? g? %g % g% ) )%[ [ s%  s% s %-]]%ww%-%-%Hyy%/%H/%H/%dmm%#%d#%d#%!!%k%k%k%%/%%o%/o%o/%K%P%PK%KP%!g"%"w" %!wg" %! g"w!$"!$!$"!$ "- "-#0%=*d,q249;AC H4JAPhRuWY_afhn8pEulwy}  < < ? I I p }       @ M t        D Q x       ! H U [ |       % L Y       )P] -Ta$1Xe(5\i~vsk,h9``^mVW[\_` c0d=hdiqlmpruv y4zA~hu8Elyx<uIlpi}`]TQH E<@9M0t-$!  DQx!HU |%(25%>LAYKNWZdgpt)}P] -Ta-$31DXJe[arx(5\i,9`m 0 = d q     ! !4!A!h!u!!!!!"""-8 $"8 $8 $"8 $ "- "-: 0? WE E L L U Z 'a [a [g g p u | +| + _ _     /  /  p       @  g        7  k  k       ;  ;  o  o     ? ?       Pw##((- 1G5{5{::?BFKFKKKPSWW\O\Oadhhmmr`uvjj[[H0=W.. ' [ [     + + _ _        / / p} y y t t o @l gh h c c ^ [ 7W kW kS S R W ^ ;^ ;d od om r y y  ? ?       P w       G { {     K K      OO `0 W          ! '![![!!!!!-f$ "f$f$ "f$ "-% $f? [ (wi    "  "--1Y-- t E -- ]& --4B--g]--  y--  -- N -- 6? -- R"*! -- 2 Year[QR9k||"Arial- (2 Emissions (short tons)b)RR)Z[R)1R[[82)2[ZR2  "-$W$@ W@ [ W[ wWwWWWWW"W"l l 3 l3 ll5l5l2l2 l l5l5ll""""""  "-%"ll""  "--+~ "- "-%1 - "-% - "-- $     "- "- "-""}Mt- "- "-`0- "- "-%g- "-%H%p%Hp%Hp "- "- "-Vc&3Zg*7^k- "- "-9` 0dd44hh- "- "-%f- "--p "-"Arial- -- 2  ALTE  2  GAbT  2 wKYTS  2 TNCZ[  2 9SCTZ  2  TNMZ  2 VAKT  2 WV}S$ >   s   s " Y (  Y  ^    )          "--$--$B$%B$%j$j$"Arial- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arial-  2 1985QRRR  2 1986RRQR  2  1987QRRR  2  1988RRRQ  2 1989RQRR  2 1990QRRR  2 1991RRQR  2 1992QRRR  2 y1993RRRQ  2 m1994RQRR  2 0R  2 P500RRQ 2 p1,000R)RRQ 2 1,500R)RRQ 2 72,000R)RRQ >   s   s " Y (  Y  ^    )     >   >    "-%  tV h 2[2Oq B 6N ) 8 >  % th &[^O?B6<)F  "-- $gg $Z..Z $N @ @ N  $AxuxuDAD $5YiYi%5% $(\\( $VPVP"" $CC $`7`7,, $+ + s   s s   s  "- "-s q n n j ]h e e a -` T] ] Y W $T rT rT tb h t B t B        m     =  d  h     4       R  R    " " }    M t    D     b bt m _ 2_ 2O H : : ) ]"    - : B G T    $ r r   B B     m    = d    4     R R ! ( "( "0 }4 ; ; C MF tM M U X D_ _ g k q bq by }  2 2      -" "  "- "-w]jY-K:-X,.( 2 4 8m : >= ?d ?h ?q A E Hh L O8 S VT}F4M&x  H O    h ]R 6 -3 :1 B1 G0 . , X* ( (& $ ' m/ 8 =? H P hY ^ _ )_ +b 8{   }  M  x H -Y (  Y Y (  Y  "-% Y t; h c [ O` B 6 ) N  (  "- $ 2Y  Y  $t %; t ;  $h   c h c  $[4  [   $O ` O `  $B  B;   $6b  6   $)z  ) x  $ N  lN  $ (  w ` (  ^    ^    "- "-       ) 6 ] j       - : a n       1 > e r t         5  B  i  v          9  F  m  z         =  J  q  ~         A  N  u           E  R  y        " I V [ }       & M Z        * Q ^       ! . U b   ~ v s j %g 2^ Y[ fR O G D ; 8 / ), 6$ ]! j       - : B a n       1 > e r       5 B i v       9 F m z       = J q ~       A N u        ) E R y       " I V }       & M Z        *! Q" ^% & * + . / 3 !4 .7 U8 b< = @ A E F I %K 2N YO fR T W X \ ]  - )    )    "- "-       ]      - T      $ X X     (  (  \  \      ,  ,  m       =  d  h       4  h  h       8  8  l  l      < < }      M t      D x x     H H O | |x s l l e Le L\ W P P I I A ]; 4 4 . . % -# :" B" G# T& & ( ( , . $1 X1 X3 3 7 9 < (< (> \> \B D G G I ,I ,J 6H mG F F D D B =A d? ? > > < : 49 h9 h7 7 5 4 2 82 80 l0 l, * ' ' % <% <! }      M t      D x x     H H | |    ! L! L$ % ' ' -   "-% th 6[OB6)  "-L? k3 &cYw vkL^RRzF  -- 2 1-YearZRR9k||"Arial- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-ZZuuZh h Z\\ZOOZCCZ66Z**ZZ    o o 66ZZD  D!!DDD        "-% Z Z  "-- [$K! "- "-%!#- "-%!#- "-- $"""" "- "- "- ! ! )" )" " " " " T# {# # #- "- "-M!M"M"M"MT#M#- "- "-%!#- "- $"A"""# "- "- "-!!!!")"P"]""""""" #-#T#a########- "- "-!!""C"C"""""##T#{#####- "- "-%!Y #Y - "-$ # " "-"Arial- -- 2 "ALSF  2 x"GAaT  2 "KYST  2 cx"NCZ[  2 {"SCTZ  2 "TNL[  2 ,"VAKT  2 oi"WV}S Courier4-_d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+ƚI   i #B$i@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$a a a    "--$i- "-$B$B$j$j$"ArialT- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arials -  2 @ALe[  2  GAte  2  KYjd  2 DNCjk  2 SCbk  2 TN\k  2 ]VAWe  2 WVb  "-a X X a   a ^^a a cca a  - 2 $0R  2 "200RQR  2 * "400RQR  2 "600RQR  2 ."800RQR 2 1,000R)RQR 2 51,200R)RQR   "--B( 0v- p  4 $ ~% B2 - @ $VV $p   p  $     $$dd $ $~Q ~% $ $2rr/ 2 $-  $V $p    $4   t  $ $ d  $% ~% Q (Q $B $ 2 r/ / $6-VF   i ndTVrb $,, $F   F  $    i $::n $TT $3 $HHV $bb $V, $ F    $ i i J  $dnn: $T $3X3 $rVVH $b -- 9 w +;+ ;I9 $C $ ] ] e 9 $w>w $W+ $+kk6+  $F $g; $9yy9 $-CmC $ 9 9] e e $ w>!> $;++W{W $ + k66 $F/F $I;;gg $9y--2 1Stateb2R1Rk||"ArialT- 62 Emissions (thousand short tons)a)RQ)[[R(22[Z[RRZ[)RZ[92(2[[Q2  "-ZZ  ZU U ZZ  ZddZZ  $$X X   ^^cca a a a a a   "-%a a a  "--',$   "-- !@!-!_@!-!5@!"Arials ---2 p]"PointSFF#  2 E]"AreaS+FE 2 ]"MobileiEFF Courier4-Xlld UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+ƚI q   #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$      "--$- "-$B$%B$%j$j$"ArialT- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arial -  2 ALe[  2 W GAte  2  KYkd  2 -NCjk  2 SCbj  2 TN]k  2 vVAWe  2 WVb  "-N  N ^N^ N N  - 2 "y0R  2 h 100RRR  2 200RRR  2 300RRR  2 5400RRR   "--B($3U VW3h- G68 ,   ru+ ~EH - @ $tG $8 y y Y8 , $     $ LL  $uX u+ $~ $H H $'-  $6GGtwt $,8 ,y YY $   J  $r L $+ u+ X X $E~~ $ H   $'Y'-  @ y | ? < LO"( $  $ S S m  @ $|   l | ? $&&i < $OO $ $"ccU"( $ $   Q $y @  @ S m m $ ? | ? l $ l $L< < &i i $O $` $("(cUU $3- S V ]&)whce  $ $ - - !  $VFV $] $)jj) $w $==h $e e  $+ $S  - ! ! $ VF F $&]]g $)j $ww: $chh= $ e   --2 1Stateb2Q2Rk||"ArialT- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-Z\\Z  Z/ / ZZZllZZ??wNw N NENEN N ^N^NN        "-% NN   "--)[$!  "--  "o!- "ao!- "7o!"Arial ---2 r"PointTEE#  2 G"AreaT*FE 2 "MobileiEFE Courier4-yCuEPE Ed UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+VƚI    #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$      "--$- "-$B$%B$%j$j$"ArialT- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arial-  2 nALfZ  2  GAse  2  KYkc  2 rNCkj  2 SCbk  2 -TN\k  2 VAWe  2 WVb  "-          - 2 $0R  2 & P200RRQ  2 * P400RRQ  2 .P600RRQ  2 1P800RRQ 2 31,000R)RRQ 2 71,200R)RRQ 2 ;1,400R)RRQ   "--B(YfP/P &0&`[- EO ^  Xc S qaC=%- @ $E{EO $    ^ $ 9 9  X $SS $9  $GG $aoaC $i=-  $OEO{{ $ ^ ^ I  $c X X9   $S $  9W9 $qG $CaCo o $%==iei-u    9 ):  G7i $[[ $u   u  $ .  $)ii) $f : $( $7ww7 $i $[ $ u    $9   . y . $)i $: : f -f $G  (( $7w $ii;-\L %  #jZxh $22; $L   QL % $6  $@@O# $Z,Z $; $NNH $hh $\2;; $ %L % Q Q $  6P6 $j##@OO $Z,, $;^; $xNHH $h--2 1Stateb2R1Rk||"ArialT- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-ZZ* * Z  ZZ99ZZZGGVVY Y ] ] ``ccggjjDD  DDDDDD        "-%    "---[$!  "--  "o!- "eo!- ";o!"Arial---2 v"PointTEE#  2 K"AreaT*FE 2 ""MobileiEFE Courier4-?fj j+*f j+d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+ƚI    #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$      "--$- "-$B$%B$%j$j$"ArialT- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5k"Arial-  2 nALfZ  2  GAse  2  KYkc  2 rNCkj  2 SCbk  2 -TN\k  2 VAWe  2 WVb  "- W W   ^^  dd   - 2 "0R  2 P500RRQ 2 ( 1,000R)RRQ 2 1,500R)RRQ 2 .2,000R)RRQ 2 2,500R)RRQ 2 53,000R)RRQ   "--B(vWh jQvWj>w- Ev -  jc S. qa%- @ $EEv $   Y - $ 9 9  j $SZS. $6  $GG3 $aa $-  $vEv $ - - YI Y $c j j9   $.S.ZZ $  6W6 $qG33 $a  $%e-u   9 )G7t  $[[ $u   6u  $  $)ii) $ $4 $7ww7t $6   $[ $ u  6 6 $9  y $)i $- $G44 $t7tw $  6 ;6 -(\L  jZKnxhA $22T( $L   L  $ $@@ $ZZ $wK $NNn $hmhA $\((2TT $ L    $P $j@ $Z $KKw^w $xnnN $AhAmm--2 1Stateb2R1Rk||"ArialT- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-ZZ* * Z  ZZ99ZZZGG  %%DW W D  D^^DDddDD        "-%    "--([$!  "--  "o!- "`o!- "6o!"Arial---2 p"PointTEE#  2 G"AreaT*FE 2 "MobileiEFE Courier4-^ && u ~bd UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*bb+ƚa!H r  $N;%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45{6{{0{{.{{5!{{q5{{{{u{u"Arial- U2 C54Note: Emissions are taken from EPA Emission Trends.M:;EW65;;5;$:;5;;$:XE?EFW56:;=#;;:6{6{{{{6{{{ -  "- ${6{{{06{{{06{{- ${06{{.0{{{.0{{- ${.0{{5!.{{{5!.{{- ${!5.{{q55!{{{q55!{{-${5q;!5{{{q5{{{{q5{{-\$,{{ mn :  ;$ ~  gCAA  U y ! - X ~  kj{-  "    ; _!9@b@"5q{u{{uuuu{{uuuA-j$3u{ mn :  ;$ ~  gCAA  U y ! - X ~  kj{ug  h T { )  P x  >=;cs7  8 x e d um"Arial-  "--!2 ,Metals ProcessingwR)R I)a0RIRII QR 2 #On-Road VehiclesqR0iRRQ)]RQ I RI 2 @Fuel Comb.-OtherYRQ )iR{Q)0r)RQ0 2 e"Other Industrialr)RQ0))RRRH)0 R  2 Fuel Comb.-Ind.YRR )iQ{R)0)QR) 2 h Fuel Comb.-Elec.YRR )iQ{R)0a RI)"Arial-  2 "#1%Fn  2 "2%Fn  2 8"2%En  2 [!2%En  2 611%<Fn  2 ` 82%EEo Courier4-bd UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*bb+ƚa!H   $N;%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45{{{t{{{{ {{ Q {Q  Q Q {{{u{u"Arial- U2 C54Note: Emissions are taken from EPA Emission Trends.M:;EW65;;5;$:;5;;$:XE?EFW56:;=#;;:6{{{{{{{{ -  "- ${{{{t{{{t{{-${t{{t{{{t{{-${k=*t{{ {{{ {{-$ { =>L>;v&B{{ Q  {{{ Q  {{-"${Q C { h  {k k  7   {Q  Q {uQ Q Q  Q {uQ Q -$ Q Q C { h  {u z d A Q {{ Q {{{{ Q {{-.${{ k /M 7S | E+?J  Q {u{ Q Q uuu{ Q Q uuA-P$&u{ k /M 7S | E+?J  Q Q   ;G'>t3I-E b   um"Arial -  "--2 lFuel Comb.-OtherYRQ )iR{Q)0r)RR0 2 *Natural SourcesiR)Q0R )bRQ0IRI 2 ,;Fuel Comb.-Ind.YRR (iR{R(0)RR) 2 VNonroad VehiclesiQR0RRQ)]RQ I RI 2 rOn-Road VehiclesrQ0iRRR)\RR H RI 2 zFuel Comb.-Elec.YRQ )iR{Q)0b RI)"Arial-  2 d"2%Eo  2 "2"4%Fn  2 #11%=En  2 W12%FEn  2 33%FEn  2 p39%FEn Courier4-t G_^ÍIw7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45{^{{N{{|{{1 {{3{-3-{{{u{u"ArialT- U2 C54Note: Emissions are taken from EPA Emission Trends.M:;EW65;;5;$:;5;;$:XE?EFW56:;=#;;:6{^{{{{^{{{ -  "-${^n{{{N^{{{N^{{-${N\^{{|N{{{|N{{-${|*N{{1 |{{{1 |{{-$ {1 J 3 Q(1*<?p>G|{{31 {{{31 {{-"${3/ z ] e T {-  1 {-3{u---3{u---*$-3/ z ] e T {u S a [ w,-{{3{{{{3{{-.${{aQlP ^ N   W ^ S3{u{3-uuu{3-uuA-P$&u{aQlP ^ N   W ^ S3-PUOJu V hF I X um"Arial-  "--%2 Beef Cattle FeedlotsbRQ))iR)) Q)YRRR Q)I %2 KManaged Burning PreswRRRQRR)bQ0R RR)a0RI %2 Agricultural TillingbR0 IR )Q0R )W RR %2 )Paved Road FugitivesbRIRQ)iRRQ)YRR ) IQI 2 J  Wind Erosion RQ)b0RI QR 2 0 Unpaved RoadsiRRRHRR)iRQRI"ArialT-  2 #3%Eo  2 #4%Fn  2 11%=En  2 !23%EFn  2 @ 28%EEo  2 30%EFn Courier4-EPWjjh0PSbd UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*bb+ƚa!H t  $N;%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45{{{Q{{{{{{+{{{{u{u"ArialT- U2 C54Note: Emissions are taken from EPA Emission Trends.M:;EW65;;5;$:;5;;$:XE?EFW56:;=#;;:6{{{{{{{{ -  "- ${{{{Q{{{Q{{-${Q{{Q{{{Q{{-${U7Q{{{{{{{-${>{U{{+{{{+{{-$ {+5>?I?y={{{+{{{{+{{-J$#{{ mn :  ;$ ~  gCAA  U y ! - X ~  kj{I  B `{ b E +{u{{uuuu{{uuuA-j$3u{ mn :  ;$ ~  gCAA  U y ! - X ~  kj{ug  h T { )  P x  >=;cs7  8 x e d um"Arial-  "--2 Nonroad VehiclesiRR0QRR)\RR I RH !2 u}Chemical & AlliediRQ{ IR )a)b RR 2 ~Waste DisposalQI)R)i IQRIR  $2 Solvent UtilizationbR IRQ))i) CR) RR 2 W On-Road VehiclesrR0iRQR)\RR I RI 2 ^Natural SourcesiR)R0Q )bRR0IQI"ArialT-  2 "3%Eo  2 ky"4%Fn  2 tT 7%Eo  2 8%Fn  2 {9%En  2  69%EFn Courier4-@ !?@'?!?d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+2ƚh`W B  hXz*0u0u0u0uW;t-h "- - 59[z*0u0u0u0u-49 "- ---B(-  $zxl___`NNMMLKGFEEDDA@?=<;:8755543442210//-,,,,--,*)))((()))**+++**)*))(()((&%%%$$! ~}zqmhfeddd dcdcd`bb``_^^^]]]^[ZXVUSRQQS#S$S%T&S&T'U&V(U(V(W(Y)Z(Z)Z)Y)V*T)S)R)S)T(S(R)R(Q'R(S)T)U(T'S&R'Q'P'O(P(O(N'N&M&K;KOKVKkKKKKYgo- -  $&P&N$N$O$N#O$P&P- -  $<==<;:<- -  $9::989- -  $FEDDEF- -   $DEDD- -   $>>=>- -   $7877- ---B( 7-  8$gYKKKKKKHHINNNNOPOOOPPRRQQQRRTSR R S S SSTTTTU W XZ[__`_`efgijmpqssttvwxyz|}                 og- ---B(r?'N-  "$ommmmlmkkjihffecb_Y[[Z[]ijkmnmnprrsstsstuvvtvwxwvustwxyyyzy{z{||}~~}{{z{zyyxvvvvwwxyyxuutststssrqqpqrqppqqppqrqqrssrrrqqppooppoooppppponmmllkjjkkkkjhhgfeddba`_^]ZZSSSX[\]^^]]]~`~c}f~g~hi~l~m~n}p|q|q{qyryqxpwrusttsxp{ooo- -  $- -  $- -  $vwwvv- -   $utvu- -  $srrss- -  $rsrrr- -   $vwvv- -   $]]]- -   $- -   $- ---   $DCCBBA@?=<<<;98865444421000.-,,+**)(''&&$$"!   !!"!"  )34=FGHHHIJLLLLMMNMNNNNOPPQQQRSSRTUVWY[[\\]_acdeefgjmnoqtvvvttsqqqppoppqqpponmmooqppqonllkjiijkkkkjhhgfeddba`_^]ZZSSRQRRRQQQPPONLKJJJIHGGGFFEDD- -   $ wxuutussstuvw- -    $srs- -  $                  - -  $kBCCCCBA@@?>>=<:877634322100010/.,)&!!"!"  )34=FGHHHIJLLMNORRRT VVUTTTUT R"S!SRR R!Q!Q PO N NNMLKJIIHGFD C D D EDDCDDC C CBB- -  $ J%J$K#L#M$L$K$I%J%- -   $O$P#N#O$- -  $453234- -  $- -   $    - -   $Q"P#Q#Q"- -   $- -   $oz*0u0u0u0u- 'Y- -- - $l     ((x(|( zy x!v!u!s"q%m%l$m#n"o!p qrrrqo o n#j$h%h&h&g'h(h(b&b"b _^^^]\\[ZZYYYZ[\]^__` a a ` ` a ` _ ^ ]^_``_`efgijmpqssttvwxyz|}  qz*0u0u0u0u open fi- '- - $S                !"$$&&''((('&#"!  rz*0u0u0u0u- SY'- - $k@@AABBBCDCCDEDDzCxBwAw?x>y=z<{;{:z;x<x<w=v?s?q?p>p>o>n?o>m=l<l:k9j8i6i4h3h2j3k3l1l0m0o0p/q/p/o.q.o/n-o,p*t(x)y(|***)&&h&g'h(h*f+f,g,f-f.f/e3f4g5f7g9f:f;f<g=f<e;e:e9d9b7a6b5b4b2b3_1_/`-`+`(b&b&@sz*0u0u0u0u-S'- - $[&&''()**+,,-.00012444456889;<<<=?@ABBCCDDEFFGGGHIJJJKLNOPPQQQRRRQRSSNLJIHDCA?>>@@AAB*)('&&&tz*0u0u0u0ute exist-49- %^                 !"$$&&''()**+,,-.00012444456889;<<<=?@ABBCCDDEFFGGGHIJJJKLNOPPQQQRRRQRSSNLJIHDCA?>>@@AABBBCDCCDEDDzCxBwAw?x>y=z<{;{:z;x<x<w=v?s?q?p>p>o>n?o>m=l<l:k9j8i6i4h3h2j3k3l1l0m0o0p/q/p/o.q.o/n-o,p*t(x)y(|***)('&#"!  zy x!v!u!s"q%m%l$m#n"o!p qrrrqo o n#j$h%h&h&g'h(h*f+f,g,f-f.f/e3f4g5f7g9f:f;f<g=f<e;e:e9d9b7a6b5b4b2b3_1_/`-`+`(b&b"b _^^^]\\[ZZYYYZ[\]^__` a a ` ` a ` _ ^ ]^_``_`efgijmpqssttvwxyz|}  - -  $5g7h6h5g- -  $ ^ _ _ ^- -  $/h.i/h- -  $&j&k&juz*0u0u0u0ute exist-vz*0u0u0u0u^^^^-h- - 59"Arial-  !GCVTC}<-  !Upper\-  !Plainsr- !SSW-  !Midwest- !SAMI--  !Northmk- !Eastp=d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+VƚI    #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5"Arial- 2 GCVTCb[SN[ 2  Midwesti$LbEF* 2 t North EastZM2*M#TEF*  2 SAMITWi#  2 SSWTTw 2  Upper Plains[LME2$S$E$LE  "-"  "XX""" - 2 0F 2 L 2,000E$EEF 2 4,000E$EEF 2 6,000E$EEF 2 28,000E$EEF   "--B(UUUU- 6   kvM 0  $6O O 6 $ 22  $` $:v $O $ !!b  $ 6 O  $  2  $k`` $Mvv:f: $0  OIO $ !b ,b k"Arial- --2 1RegionjR[)Z[k||"Arial- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-CC  CCCxxC[[dd  JJ   XX   """"""  "-%"""- Courier4-d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+ƚI a   #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- --r2 GNote: Emissions are taken from Trends and were generated using BEIS-2.L;;FW56;:6;#;:6:;#;W<$:;;5;;:L;#;:;;:$;;::6;:EEE$;"Arial- 2 WGCVTCb[SN[ 2  Midwesti$LbEF* 2  North East[L2*M#TEF*  2 @SAMITWi#  2 /SSWSTw 2   Upper Plains[MLE2$S$E$LE  "-"G G "  "ZZ""mm"" - 2 0F 2 1,000E$EEF 2  2,000E$EEF 2 3,000E$EEF 2 $4,000E$EEF 2 5,000E$EEF 2 66,000E$EEF   "--B(ll,U0HUH UDeUaul-    kcMy0\H  $z z $ ] ] #  $@ @  $" "c $y ty $\  \H  $z1 $  ] # # $k  @ $Mcc" $0ytt $H \H   -B(OffceIstaled- $z ] @k@"N1!L $O O $ $ 2 2  $ S $k k@ $N KN $1!! !1!L $z$$O  $]  2  $@SS $"@k@ $NKK $L1!L!t k"Arial---2 1RegionjR[)Z[k||"Arial- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-C44C  CCCC  11 G G   ZZ  mm  """"""  "-%""" "--3k  "-- %y- %L"Arial---2  AnthropogenicSF#F*FEEFEE@ 2 BiogenicTFEEF@ Courier4-VProxyStubClsid32d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+ƚI   #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5"ArialC- 2 RGCVTCbZTN[ 2  Midwesth$MaFE+ 2  North East[M1+L$TEE+  2 SAMITWi$  2 SSWTSw 2  Upper Plains[MLE2$S$E$LE  "-""  """""" - 2  0F 2  2,000E$EEF 2  4,000E$EEF 2 6,000E$EEF 2 8,000E$EEF 2 "10,000EE$EFE 2 +12,000EE$EFE 2 214,000EE$EFE   "--B(UUUU- tK "m  nE Q $t  _t $K aaIK $"88/ "m $d $A $ !! Q $t __ $ K aI I $m "m 8/ / $n  dd $EA[A $Q Q!2k"Arial- --2 1RegionkRZ)[[k||"ArialC- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-C//C  CCCCccAAH H P P WW^^ffmmOO  OOOOOO""""""  "-%"""- Courier4-<d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*<<+vƚ<OH    #$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- -- 2  "Arial- 2 GCVTCb[SN[ 2 R Midwesti#MbEE+ 2  North East[L2+L$SEF*  2 ]SAMISWi$  2 @SSWTSx 2 & Upper PlainsZMLF1$T#E$LF  "-"G G "  "ZZ""mm"" - 2  0F 2 2,000E$EEF 2  4,000E$EEF 2 6,000E$EEF 2 $8,000E$EEF 2 10,000EE$EFE 2 612,000EE$EFE   "--B(WUwWUWwWUrUWh- .d   z QnEa $. .d $   v   $gg z $>>Q $Q $a3 a $d .d  t $    v K v $z z g " $nQQ> $EQQ $a3 3 -B(6s(;PE#=-  b j h?[72!  $  ) $ aa b $88 j $! $[[7 $2!!! 2!  $ )E) $ b ba  $hj j 8  $?!! $7[7 $ 2! ! x  k"Arial---2 1RegionkRZ)[[k||"Arial- 62 Emissions (thousand short tons)b)RR)Z[R)12[[ZRRZ[)RZ[92)1[[R1  "-CttCK K C""CCC  11OG G O  OZZOOmmOO""""""  "-%""" "--3  "-- %- %o"Arial---2  AnthropogenicTE$E+EEFEFE@ 2 BiogenicTEFEE@"Arial- 2 dTNote: Emissions are taken from EPA Emission Trends and were generated using BEIS-2.UB B Nb;;AB; B'A B;BA "'Bb NEO Nb;;BB C(ABB; ABB TA'B BBBA'B BB B:BA ON N'B  Courier4-IndexesSearch-Only Indexe<d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*<<+vƚ<OH   #$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- -- 2  "Arial- 2 mGCVTCa[TNZ 2 P Midwesti$LbEF* 2   North East[L2*M#TEF*  2 SAMISWi$  2 SSWTSw 2  Upper PlainsZMLE2$S$E$LF  "-"G G "  "ZZ""ll"" - 2 0E  2 5E  2  10EE  2 15EE  2 $20EE  2 25EE  2 630EE   "--B(Uu[UjRU[U-  +   A $n $    + $ $ $U  $ !! A $nn $ + +   $ $ $  U U $A A!  k"Arial- --2 1^RegionkR[)Z[k||"Arial- %2 r Tons per Square MileRZ[R)ZR9)bZ[R9R()(R  "-CJJCJ J CJJCJJCJJCJJ  00[G G [  [ZZ[[ll[[""""""  "-%""""Arial- -2 dTNote: Emissions are taken from EPA Emission Trends and were generated using BEIS-2.UB B Nb;;AB; B'A B;BA "'Bb NEO Nb;;BB C(ABB; ABB TA'B BBBA'B BB B:BA ON N'B  Courier4-;w$tw ;w$r G$uHG$H+<g!UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*<<+ƚ<OH >   #$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arials - -- 2  "Arials - 2 GCVTCb[SOZ 2  Midwesti#MbEE+ 2 R North East[L2+L$SEF*  2 SAMISXh$  2 SSWTSx 2  Upper Plains[MLE2$S$E$LE  "-"  "::"xx""" - 2 0E  2 < 5E  2 z10EE  2 15EE  2 20EE  2 425EE   "--B(U@U UUа`- JJ I JII I=  $J"J $J  " J I $J"7J $I"hI $I".I $I" I=  $J $ IJ I   $J77 $Ihh $ I .. $= I=   -B(@࠻ @@- **  *))A)!v $*"j* $*  " *  $*"f* $)"}) $)")A $)!!"!)!v $*jij $ *  i  $*fif $)}h} $A)Ah $v)!v!h k"Arials ---2 1^RegionkR[)Z[k||"Arials - %2 r Tons per Square MileRZ[R)ZR9)bZ[R9R()(R  "-CC  CCCC  YY[  [::[xx[[[""""""  "-%""" "--1  "-- #"q- #D"Arials ---2  AnthropogenicTE$E+EFEEFE@ 2 `BiogenicTFEEF@"Arials - 2 dTNote: Emissions are taken from EPA Emission Trends and were generated using BEIS-2.UB B Nb;;AB; B'A B;BA "'Bb NEO Nb;;BB C(ABB; ABB TA'B BBBA'B BB B:BA ON N'B  Courier4-t<<< **ZZ* VAb>>#2PP#   _J8^'JJ#!2PP#   GCVTC aL8}'JJ#!2PP#   Midwest dO8 'JJ#!2PP#   North East ^I 8* 'JJ#!2PP#   SAMI ]H 8'IJ#!2PP#   SSW fQ\8'JJ#!2PP#   Upper Plains  Vdwd Vw V[w[ V w  VR wR  Vw[FQHJ#!2PP#   0 [FdQSHJ#!2PP#   5 \GhJJJ#!2PP#   10 \Gh[ JJ JJ#!2PP#   15 \Gh J JJ#!2PP#   20 \GhQJ@JJ#!2PP#   25 ,dd  dd 7 7 dd7  g g d d g vvd d pdpdddpdAd!  dq 7  d   gvdTvdddpAOA  ! =! 7 7 q *q  g g   vTTpdpdddd  dd   d d dd}}ddd--d d    d   d}d }-hV  C    0 }   `K WV#'2PP#   Region nZYBWV#'2PP#   Tons per Square Mile  d d d   d d d Vz Vz Vz V z  V z  Vdd V V[[ V   VR R  VwVVdwdw[ [  [,$$  $ $W $W $aLtu dJJ#!2PP#  Anthropogenic\GF u dJJ#!2PP#  Biogenich REF#2PP#  Note: Emissions are taken from EPA Emission Trends and were generated using BEIS-2.d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*+ƚI F  #B$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- --U2 4Note: Emissions are taken from EPA Emission Trends.L;;FW56;:6;#;:6:;#;WE>EEW65;;<$;:;5"Arial- 2 mGCVTCa[TNZ 2 P Midwesti$LbEF* 2   North East[L2*M#TEF*  2 SAMISWi$  2 SSWTSw 2  Upper PlainsZMLE2$S$E$LF  "-"G G "  "ZZ""ll"" - 2 0E  2 5E  2  10EE  2 15EE  2 $20EE  2 25EE  2 630EE   "--B(UUUU- 3  m  0 $ 3 $   5 m $ $L $ $ !! 0 $3 3   $ m m 5 5 $ $LL $ $0 0!k"Arial- --2 1^RegionkR[)Z[k||"Arial- %2 r Tons per Square MileRZ[R)ZR9)bZ[R9R()(R  "-CJJCJ J CJJCJJCJJCJJ  00[G G [  [ZZ[[ll[[""""""  "-%"""- Courier4-<d UUUUUUUUUUUUU ,,,888EEEQQQaaaqqq*<<+vƚ<OH    #$%@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45$"""   "--$- "-$B$%B$%j$j$"Arial- -- 2  "Arial- 2 GCVTCb[SOZ 2  Midwesti#MbEE+ 2 R North East[L2+L$SEF*  2 SAMISXh$  2 SSWTSx 2  Upper Plains[MLE2$S$E$LE  "-""  """""" - 2 0E  2  5E  2  10EE  2 15EE  2 20EE  2 "25EE  2 +30EE  2 235EE   "--B(fU.Uf.Uf0Uf- JJ  J}IUI I $J" J $J  " J  $J"J} $I"IU $I" I $I"+I $J   $ J    $}J} $UIU $ I   $I++-B(  - * * e * ))R)!S $*"/ * $*  " * e $*" * $)"+) $)")R $)!!"!)!S $ * / i/ $ e * e i $ *  i $)+h+ $R)Rh $S)!S!h k"Arial---2 1^RegionkR[)Z[k||"Arial- %2 p Tons per Square MileRZ[R)ZR9)bZ[R9R()(R  "-CC  CCCCAAH H P P WW^^ffmm[[  [[[[[[""""""  "-%""" "--.  "--  "q-  D"Arial---2  AnthropogenicTE$E+EFEEFE@ 2 `BiogenicTFEEF@"Arial- 2 dTNote: Emissions are taken from EPA Emission Trends and were generated using BEIS-2.UB B Nb;;AB; B'A B;BA "'Bb NEO Nb;;BB C(ABB; ABB TA'B BBBA'B BB B:BA ON N'B  Courier4-w2j(xN= '3,,4?k*IN&IN&+k 5 N IN&F&N@`ObRb??b7`^A`BBb`bA`1`!bb!R?bбuҰj|h¼q///R"/ @O@1/"@@@ b!A !!! / Oab B@@AOOO@`B111/" A ` ""@_bBb___`BBBB@#2?/ "@@rrr?bb``<@A@@@@`AP`b`A@"AA% bAbAbA5AA"""+"Y@@B!AB@B@@pA@Ra`b_``qtA2BB?5ŸҿA@@AA5B@"@ AA@A@@BO45 --$.--2+b"Thhh--$h  h444$4rU44333$3 C""33{{{${bE{{--*#'eKe! e4-/"eSWe{eU}- $ b b - -            # 8 ? T [ p w             4 ; P W l s             0 7 L S b b/ b6 bK bR bg bn b b b b b b b b b b b b b+ b2 bG bN bc bj b b b b b b b b b b b bb'b.bCbJb_bfb{bbbbXQ<5 xq\U@9$|u`YD=(! |g`KD/(           k d O H 3 ,             o h S L 7 0    - - - $ k k  - -           % , A H ] d y             !  (  =  D  Y  `  u  |                $  9  @  U  \  k  k / k 6 k K k R k g k n k  k  k  k  k  k  k  k  k  k  k  k  k + k 2 k G k N k c k j k  k  k  k  k  k  k  k  k  k  k  k k 'k .k Ck Jk _k fk {k k k k a Z E > ) "            z e ^ I B - &   ~ibMF1*|g`KD/(           k d O H 3 ,             o h S L 7 0    - -  - $   - -         4 ; P W l s             0 7 L S h o             , 3 H O d k    / 6 K R g n             + 2 G N c j            '.CJ_f{piTM81tmXQ<5 xq\U@9$|g`KD/(           k d O H 3 ,             o h S L 7 0    - - - $ j j - -           $ + @ G \ c x             ' < C X _ t {            # 8 ? T [ j j/ j6 jK jR jg jn j j j j j j j j j j j j j+ j2 jG jN jc jj j j j j j j j j j j j jj'j.jCjJj_jfj{jjjj`YD=(! yd]HA,% }haLE0) |g`KD/(           k d O H 3 ,             o h S L 7 0    -- $p H! H!p - - v }            % : A V ] r y              !  6  =  R  Y  n  u           ! ! ! 2! 9! H! H!/ H!6 H!K H!R H!g H!n H! H! H! H! H! H! H! H! H! H! H! H! H!+ H!2 H!G H!N H!c H!j H! H! H! H! H! H! H! H! H! H! H! H!H!'H!.H!CH!JH!_H!fH!{H!H!H!H!>!7!"!!!         z s ^ W B ; &    ~wb[F?*#{ppp|pgp`pKpDp/p(pp p p p p p p p p p p pk pd pO pH p3 p, p p p p p p p p p p p p po ph pS pL p7 p0 p p p p-- $ZZ% %  - -07LSho,3HOdk (/DKZZZZZZZZZZZ"Z7Z>ZSZZZoZvZZZZZZZZZZ Z Z Z3 Z: ZO ZV Zk Zr Z Z Z Z Z Z Z Z Z Z Z Z Z% P% I% 4% -% % % % % % % % % % % % % p% i% T% M% 8% 1% % % % % % % % % % % % % t% m% X% Q% <% 5% % % % % % % % % % % %             s l W P ; 4    wp[T?8#--\ZN","N"- %"NT$NT$,","P"" %"]$]$P"Pk"Arial Narrow- --2 B$Tiers 1JD*<"D 2 |$and 2DDD"D 2 [ $Tier 3ID)"D 2 HousingXDD=DD 2 StartsP"D)"= 2 2 Fuel PriceKDD"P)=D 2 9 ForecastsKD)D=D="<  2 3!REMIXPf"  2 oCU.S.X"P" 2 NationalXD"DDD 2 ModelfDDD  2 !REMIXPf" 2 RegionalWDDDDD 2 0ModelfDDD 2 5NationalXD"DDD 2 qScenarioQ=DDD)C 2 ~RegionalXDDDDD 2 ScenarioQ=DDD)C  2 5WEFAsPDQ 2 qForecastKD)D=D="  2 ] REMIXPf" 2  Interface!D"D*"C=D  2 u(HPMSXPfQ  2 WDataWD"D 2 HOMESX_fPQ 2  INRAD"XWQW 2 CSEMSWQPfQ  2 ;nVMTPfK 2 w&ModulefDDDD 2 (#Physical OutputPD===D"_D"DD" 2 dModulefDDDD"Arial NarrowP- 2  ResidentialF606655 2 mFossil;510  2 b Fuel;65 2 5DemandF6P665 2 8N ResidentialE605666 2 Electric@60 1 2  DemandF5Q566 2 ; Industrial6651 6 2 Fossil;601  2 b Fuel;56 2 IDemandF5Q566 2 8 Industrial6560 5 2 Electric@51!0 2  DemandF6P665 2 0 CommercialF5QP6 05 2 Fossil;600  2 b Fuel;66 2 {DemandF5Q566 2 8o CommercialF6PQ5!06 2 Electric@51!0 2  DemandE6P665  2 VMTAP; 2 [  Estimation@1P666 2 } Industry-6560 1  2 1Specific@6600 2 I &PhysicalA51015 2 HOutputK656 2 Total066 2  QElectricA60 0 2 y:DemandF5Q656 2  CrosswalkF 600F60  2 ;SCC@FE 2 LGrowthK 6F6 2 GFactors;516 0 2 +ACRONYMS LEGENDAEFKF@Q@6@KAEF  2 REMIF@Q  2 -  42 Regional Economic Models, Inc.F56565@0665Q1Q656160  2 WEFA[A5A  2 -  B2 'Wharton Econometric Forecast Associates[66 56@0665Q5 0;6 6051A0060560  2 ]HPMSF@Q@  2 ]-  ?2 ]%Highway Performance Monitoring SystemF66E60@6 6 P6606Q655 56@105Q 2 HOMESFKQ@@  2 -  :2 "Household Model of Energy by StateF5606655Q5666A56 6060@66 2 )INRADFF@F  2 )-  U2 )4Industrial Regional Activity and Energy Demand Model6560 5E66656A000656@65!50F6P656P665 2 CSEMSF@APA  2 -  B2 'Commercial Sector Energy Model by StateF5QP6 16@606 @65!51Q56660@66  2 VMTAP;  2 -  (2 Vehicle Miles Traveled;6505Q608 51565  2 [SCCAEF  2 [-  .2 [Source Classification Code@66 06E6000665F656WWW --$W1h1hZW --2 n Key@60 %2 User-specified filesF06 06605660 @2 9&Output file goes directly to CrosswalkK666656605 6006E!501F50 K2 -Output file is used as input to another modelK66660606560665666565!Q656 2 Existing Model@1056P656 2 kDeveloped ModuleF5155665Q5666 -- $sHsH - -y  !(=DHHHHHHHHHHHFs?s*s#sssss--5s-s -ns333-%33-O $O3 % $|  %|  $ |  u%u $uN, u:N, %, NRNR::uV $V:u5 < 5 % 5 << $< << %< $<%z3 $3z3b!{b!b!%b!b!{~! $~!b!{E!Z %ZZ= $=Zv %e $ee{pp{p%p{p" $"pS"{,,{,%,{,"H $H","{N N {N %N {N "j $j "N 1 "{  { % { " $ " "{HH{H%H{H"d $d"H+"{{%{" $""{{%{" $""{{%{" $""lil%li$ $$iviv %vvi $i1l01 %1010lL $L0l"Arial Narrow- --:2 "Industry-Specific Value Added Data/./++9/.++4./.:././=./ $2 Regional Population=././.9/.//./ 2 iRegional=.//./ 2 #iHousing=./+/. 2 iStarts9/+ 2 h= Fuel Price4/.9+/ 2 = Adjustments9/.+H./+ $u  -% S S $u$-@ $@u$ u  % u$ $$u .  % .  ! $! .  %1 $11 --2  Regional=/../. 2 ) Income &.+/G/9 2   Population:./../.| -%Q|- $||%Q| $|| %I   | $  |k  t  %  H  | t4 $4  t H2  H%H H  @   2   $ 2   y %  R e  y $yr O 2  O %O  O    :  2   $ 2   --!2 :Disposable Income=+./+.///+.H. 2  Population:./../. !2 x Regional Capital,=././.=./. 2  Labor, Materials/./.G//.+ 2 i Prices9+.+ f  - % f- $f !f  % !f! $f!=-- Courier-w.+ '~!{3 c \Q\\ [b$%[ [xx@xxx 'B';''%'') )   ) ) ) ) ) ) ) ) ) ) 5) =) S) Z) q) x) ) ) ) ) ) ) ) ) ) ) #) *) A) H) ^) f) |) ) ) ) ) ) ) ) ) ) ) ) .) 6) L) S) j) q) ) ) / 7 M T j r            $ : B X ` v }          (0FMdkvoYQ;4kdMF0( }v`XB;$leNG1)     ~ w a Y C < %       s l U N 8 0 0 )  ) )  )    ) ) 5) =) S) Z) q) x) ) ) ) ) ) ) ) )  ) ) # ) * ) A ) H ) ^ ) f ) | ) )  ) )  ) )  ) )  ) )  )  ) . ) 6 ) L ) S ) j ) q )  ) )  ) )  ) )  ) )  )  )  /  7  M  T  j  r               $  :  B  X  `  v  }            ( 0 F M d k                  v o Y Q ; 4             k d M F 0 (   }v`XB;$leNG1)         ~ w a Y C < %            s l U N 8 0 0 )   ) ) )   ) ) ) ) ) )  ) ) )) 1) G) N) e) l) ) ) ) ) ) ) ) ) ) ) ) ) 5) <) R) Z) p) x) ) ) ) ) ) ) ) ) ) ) ") *) @) G) ^) e) {) ) ) ) / 7 M T j r            $ : B X ` v }          (0FMdkkcME/( }u_XA:$qjTL6/leNG1)         ~ w a Y C < %            s l U N 8 0 0 )  ()  ) )    ) ) .) 5) L) S) i) q) ) ) ) ) ) ) ) ) ) ) ) #) 9) A) W) _) u) |) ) ) ) ) ) ) ) )  ) ) ') .) 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Pechan & Associates, Inc. ;3500 Westgate Drive, Suite 103 eLDurham, NC 27707   mOctober 25, 1996   ]Contract Number 003 5 Pechan Report No. 96.10.005/538X)x-x-x-<)3'#!lX  `A%d  yxdddy #;2PQJqP# T ddx !ddx( ( ( T     E.H. PECHAN & ASSOCIATES, INC. Doc. # 96.10.005/538"  J.'r#&h P:+Q.&P##;2PQJqP##FINAL REPORT October 25, 1996ܚ}ġ `A dContents8 # 2p NQH* #CONTENTS (continued) v"Contents   X yxdddxy   P4#X`2p NQX#c hh jSReport 3'3'StandardHPLA4POS.WRS&h3'3'Standardhan formal reportsưh  hh   c (08@HPX!"`$%h' (08@HPX!"`$%h'#&h P:+Q.&P#   dContentsq# 2p NQH* #CONTENTS v"Contents   X yxdddjxy   K'#&U P:+Q.&P#c`T!(# Page ă   `aTABLES AND FIGURES!pN"(# vi `aACRONYMS AND ABBREVIATIONS!pQ"(#vii `aCHAPTER I! INTRODUCTIONpH"(# 1  J} '`A.STUDY OVERVIEW!pH"(# 1  JU '`B.EVALUATION OF HISTORIC AND EXISTING DATA!pH"(# 3  J- '`  1.  Short Term Trends in Emissions in the SAMI States! pH"(# 3  J '`  2.  Source Contributions to Emissions in the SAMI States! pH"(# 3  J '`  3.  Regional Pollutant Emissions! pH"(# 3  J '`C.BIBLIOGRAPHY!p"(# 33 `aCHAPTER II! BASE YEAR INVENTORY EVALUATIONp"(# 37  J'`A.BASE YEAR INVENTORY SELECTION!p"(# 37  J'`  1.  Introduction! p"(# 37  J'`  2.  Potential Options! p"(# 37  J'`     a. 1990 Interim Inventory Overview! p"(# 37  Ju'`     b. National Particulates Inventory (NPI) Overview! p"(# 38  JM'`     c. EPA Emission Trends Inventory Overview! p"(# 38  J%'`B.1990 INTERIM/TRENDS INVENTORY!p"(# 39  J'`  1.  Development Methodology! p"(# 39  J'`     a. Utilities! p"(# 39  J'`     b. Nonutility Point Sources! p"(# 39  J'`     c. Mobile Sources! p"(# 40  J]'`     d. Solvents! p"(# 42  J5'`     e. Other Area Sources! p"(# 42  J '`C.NPI!p"(# 46  J'`  1.  Development Methodology! p"(# 46  J'`     a. Utilities! p"(# 46  J'`     b. Nonutility Point Sources! p"(# 46  Jm'`     c. Mobile sources! p"(# 48  JE '`     d. Solvents! p"(# 48  J!'`     e. Other Area Sources! p"(# 48  J!'`     f. SOA Formation! p"(# 49  J"'`D.DATA GAPS EXISTING IN CURRENT INVENTORIES!p"(# 50  J#'`  1.  Interim/Trends Inventory! p"(# 50  J}$'`  2.  NPI! p"(# 51 XU%x-**=&3'#jX  J'`E.ONGOING DATA COLLECTION EFFORTS!p"(# 51  J'`  1.  Ozone Transport Assessment Group! p"(# 52  J'`  2.  Tennessee Valley Authority Acid Deposition Inventory! p"(# 52  J'`  3.  Interim/Trends Inventory Changes and Modifications! p"(# 53  J`'`  1.  Timing/Schedule! p"(# 54  J8'`  2.  Data Gaps That Will Be Addressed By Ongoing Efforts! p"(# 54  J'`F.REMAINING DATA GAPS!p"(# 55  J'`G.RECOMMENDATION FOR BASE YEAR INVENTORY!p"(# 55  J'`H.RECOMMENDATIONS FOR FILLING DATA GAPS!p"(# 56  J`  1.  Biogenic NOx! p"(# 56  Jp`  2.  SO4 and Primary Nitrate! p"(# 56  JH '`  3.  Elemental Carbon and Organic Carbon! p"(# 57  J `  4.  Nonroad SO2! p"(# 57  J '`  5.  Speciated VOCs! p"(# 57  J '`  6.  Spatial Data Finer than County Level! p"(# 58  J '`  7.  Emissions Estimates for Time Periods Shorter than Seasonal! p"(# 58  J '`I.REFERENCES!p"(# 58 `aCHAPTER III! CONTROL COST ESTIMATION TECHNIQUES FOR REGIONAL CONTROL STRATEGY ANALYSESp"(# 61  J'`A.INTRODUCTION!p"(# 61  J'`B.MODELING TECHNIQUES!p"(# 61  Jh'`  1.  NAPAP Emission Reduction and Cost Estimation Techniques! p"(# 61  J@'`     a. Background! p"(# 61  J'`     b. Summary of the Model! p"(# 62  J'`     c. Coverage of Emission Sources in the NAPAP Emissions Inventory! p"(# 63  J'`     d. Modeling of DemandSide EMOs! p"(# 65  J'`     e. Applicability to the SAMI Region! p"(# 65  Jx'`  2.  Emission Reduction and Cost Analysis Model (ERCAM)! p"(# 66  JP'`     a. Background! p"(# 66  J('`     b. Summary of the Model! p"(# 66  J'`     c. Coverage of Emission Sources in the Interim Inventory! p"(# 69  J'`     d. Use of ERCAM for Modeling DemandSide EMOs! p"(# 70  J'`     e. Applicability of ERCAM to the SAMI Region! p"(# 71  J'`  3.  AIRCOST/PC! p"(# 71  J`'`     a. Background! p"(# 72  J8'`     b. Summary of the Model! p"(# 72  J '`     c. Coverage of Emission Sources in the Interim Inventory! p"(# 73  J '`     d. Use of AIRCOST/PC for Modeling DemandSide EMOs! p"(# 73  J!'`     e. Applicability of AIRCOST/PC to the SAMI Region! p"(# 74 ""x-$$ v$"  J'`  4.  OAQPS Procedures for Preparing Study Estimates of AddOn Air Pollution Control Systems! p"(# 74  J'`  5.  Summary of Modeling and Cost Estimation Techniques! p"(# 75  J'`C.DATA GAPS ASSOCIATED WITH EMISSIONS REDUCTION AND COST DEVELOPMENT!p"(# 77  J8'`D.REFERENCES!p"(# 79 `aCHAPTER IV! EMISSIONS PROJECTIONS FOR REGIONAL ANALYSESp"(# 81  J'`A.GROWTH FACTORS FOR EMISSIONS PROJECTIONS!p"(# 81  Jp'`  1.  BEA Forecasts! p"(# 81  JH '`  2.  E-GAS! p"(# 85  J '`  3.  Comparison of BEA and E-GAS Growth Factors! p"(# 89  J '`B.EMISSION PROJECTION METHODOLOGIES AND TOOLS!p"(# 90  J '`  1.  NAPAP ECIMS! p"(# 90  J '`  2.  Other Emission Projection Tools! p"(# 91  J '`  3.  MPS! p"(# 91  JX'`  4.  ERCAM! p"(# 91  J0'`  5.  AIRCOST/PC! p"(# 92  J'`C.CURRENTLY AVAILABLE EMISSION PROJECTIONS!p"(# 93  J'`  1.  Analyses Comparing Available Emission Projections! p"(# 93  J'`  2.  Overview of Projected PollutantSpecific Trends and Emission Controls! p"(# 95  J'`D.SUMMARY!p"(# 96  Jh'`E.EMISSION PROJECTION DATA GAPS!p"(# 96  J@'`F.REFERENCES!p"(# 97 `aAPPENDIX A! 1990 INTERIM/TRENDS EMISSIONS ESTIMATES (BY SAMI STATE AND TOTALS FOR SAMI REGION)p"(#A-1 `aAPPENDIX B! ECOS/EPA INVENTORY DATA COLLECTION REQUEST PACKAGEp"(#B-1 `aAPPENDIX C! SUMMARY OF CONTROLEFFICIENCY AND COSTEFFECTIVENESS DATA FOR SELECTED STATIONARY AND MOBILE SOURCE CONTROL STRATEGIESp"(#C-1  J8'`A.VOLATILE ORGANIC COMPOUNDS (VOCs)!p"(#C-2  J `B.OXIDES OF NITROGEN (NOx)!p"(#C-3  J '`C.CARBON MONOXIDE (CO)!p"(#C-3  J!'`D.PARTICULATE MATTER (PM)!p"(#C-4  J"`E.AMMONIA (NH3)!p"(#C-4 }"p#x-$$ $"  `Al `A dContents # 2p NQH* #TABLES AND FIGURES (continued) v"Contents   X yxdddxy   J'#&U P:+Q.&P#c% X5Tab/Fig/Refs Heading%# 2p NQH* #; TABLES AND FIGURES+sD ATab/Fig/Refs Heading+   X yxdddy c  K'#&U P:+Q.&P#`a Tables (#`T!(# Page ă  J' (08@HPX!"`$%h' (08@HPX!"`$%h'   aI1.Historical Emission Inventory Data Relevant to SAMI: Published Sources!pH"(# 5  Jq' aI2.Historical Emission Inventory Data Relevant to SAMI: State Sources!p"(# 10  JI' aIII1.Basic Elements of Control Strategy Data Base!p"(# 68  J! aIII2.ERCAMNOx Scenario File!p"(# 68  J' aIII3.ERCAM Report Options!p"(# 69  J  aIII4.AIRCOST/PC Control Strategies for SO2!p"(# 73  J ' aIV1.Economic and Emission Projections and Emission Projection Methodologies/Tools Relevant for SAMI!p"(# 82  JY  aIV2.Alternative Industrial SO2 Emission Estimates, 19852010!p"(# 94  J1 ' aIV3.National Emission Projections from Trends Report, 19902010!p"(# 95  J ' aC1.Control Strategies/Measures for Volatile Organic Compounds!p"(#C-6  J' aC2.Control Strategies/Measures for Nitrogen Oxides!p"(#C-9  J' aC3.Control Strategies/Measures for Carbon Monoxide!p!(#C-15  J' aC4.Control Strategies/Measures for Particulate Matter!p!(#C-16  Ji' aC5.Control Strategies/Measures for Stationary Sources of Ammonia!p!(#C-20  K' a Figures (#`T!(# Page ă  J' %  aI1.Trend in National Emissions, NITROGEN OXIDES, SULFUR DIOXIDE (1900 to 1994), and PARTICULATE MATTER (PM10): nonfugitive dust sources (1940 to 1994)!p"(# 12  Jz aI2.Statelevel SO2 Emissions from Each State in the SAMI Region (1985 to 1994)!p"(# 13  JR aI3.Statelevel NOx Emissions from Each State in the SAMI Region (1985 to 1994)!p"(# 14  J' aI4.Statelevel PM10 Emissions from Each State in the SAMI Region (1985 to 1994)!p"(# 15  J aI5.Point, Area, and Mobile SO2 Emissions in the SAMI States (1990)!p"(# 16  J aI6.Point, Area, and Mobile NOx Emissions in the SAMI States (1990)!p"(# 17  Jb' aI7.Point, Area, and Mobile PM10 Emissions in the SAMI States (1990)!p"(# 18  J:' aI8.Point, Area, and Mobile VOC Emissions in the SAMI States (1990)!p"(# 19  J aI9.Top 6 SO2 Emission Categories for SAMI Region (1990)!p"(# 20  J aI10.Top 6 NOx Emission Categories for SAMI Region (1990)!p"(# 21  J' aI11.Top 6 PM10 Emission Categories for SAMI Region (1990)!p"(# 22  J ' aI12.Top 6 VOC Emission Categories for SAMI Region (1990)!p"(# 23  Jr!' aI13.Regions!p"(# 24  JJ" aI14.SO2 Emissions by Region (1990)!p"(# 25  J"# aI15.NOx Anthropogenic and Biogenic Emissions by Region (1990)!p"(# 26  J#' aI16.PM10 Emissions by Region (1990)!p"(# 27  J$' aI17.VOC Anthropogenic and Biogenic Emissions by Region (1990)!p"(# 28  J% aI18.SO2 Tons per Square Mile by Region (1990)!p"(# 29  J& aI19.NOx Anthropogenic and Biogenic Tons per Square Mile by Region (1990)!p"(# 30  JZ'' aI20.PM10 Tons per Square Mile by Region (1990)!p"(# 31  J2(' aI21.VOC Anthropogenic and Biogenic Tons per Square Mile by Region (1990)!p"(# 32  J )' aIV1.Flowchart for the Economic Growth Analysis System.!p"(# 86lՃX )x-**.3'#X  `AlU `A dContents# 2p NQH* #ACRONYMS AND ABBREVIATIONS (continued) v"Contents   X yxdddxy   J'#&U P:+Q.&P#cjSReport 3'3'Standardhan formal reports3'3'Standardhan formal reportsưh  hh    (08@HPX!"`$%h' (08@HPX!"`$%h'#&h P:+Q.&P#% X5Tab/Fig/Refs Heading%# 2p NQH* # ACRONYMS AND ABBREVIATIONS+{_ ATab/Fig/Refs Heading+   X yxddd>y c  J'#&U P:+Q.&P#2BHDDVh  class 2B heavyduty diesel vehicle(#  J'ACT  alternative control technique(#  Jp'AIM  architectural and industrial maintenance(#  JH'AIRS  Aerometric Information Retrieval System(#  J 'AFR  air fuel ratio(#  J'AQRV  air quality related value(#  J 'ASC  area source category(#  J 'AUSM  advanced utility simulation model(#  J 'BACM  best available control measure(#  JX 'BACT  best available control technology(#  J0 'BEA  Bureau of Economic Analysis(#  J'BEIS  Biogenics Emissions Inventory System(#  J'BENNETh  Btu Efficiency Neural Network(#  J'BLS  Bureau of Labor Statistics(#  J'BOOS  burners out of service(#  Jh'Btu  British thermal unit(#  J@'CAA  Clean Air Act(#  J'CAAA  Clean Air Act Amendments of 1990(#  J'CARB  California Air Resources Board(#  J'CO`  carbon monoxide(#  J'CRESS  Commercial and Residential Emissions Simulation System(#  Jx'CSEMS  Commercial Sector Energy Model by State(#  JP'CTGs  control techniques guideline(#  J('DOE  U.S. Department of Energy(#  J'DOI  U.S. Department of Interior(#  J'ECAM  Emissions and Cost Aggregation Model(#  J'ECIMS  Emissions and Control Integrated Model Set(#  J'ECOS  Environmental Council of States(#  J`'EFIGh  Emission Factors and Inventory Group(#  J8'E-GAS  Economic Growth and Analysis System(#  J'EIA  Energy Information Administration(#  J'EMAD  Emissions, Monitoring, and Analysis Division(#  J'EMO  emission management option(#  J 'EPA  U.S. Environmental Protection Agency(#  Jp!'ERCAMh  Emission Reduction and Cost Analysis Model(#  JH"'EUMODh  electric utility model(#  J #'FAC  fractional aerosol coefficient(#  J#'FGR  fluegas recirculation(#  J$'FHWA  Federal Highway Administration(#  J%'FREDS  Flexible Regional Emissions Data System(#  J&'FTP  Federal test procedure(#  JX''GCVTC  Grand Canyon Visibility Transport Commission(#  J0('GDP  gross domestic product(#  J)'gpm  grams per mile(# X)x-** *3'#>XԌ J'GSP  gross state product(#  J'GT`  gas turbines(#  JH2O  water(#  J'HAP  hazardous air pollutant(#  J`'HDDT  heavyduty diesel truck(#  J8'HDDV  heavyduty diesel vehicle(#  J'HDGT  heavyduty gasoline truck(#  J'HHDDVh  heavy heavyduty diesel vehicle(#  J'HOMESh  Household Model of Energy by State(#  J'HON  hazardous organic NESHAP(#  Jp'hp`  horsepower(#  JH 'HPMS  highway performance monitoring system(#  J 'IC`  internal combustion(#  J 'ICEh  industrial combustion emissions(#  J 'I/M  inspection and maintenance(#  J 'INRAD  Industrial Regional Activity and Energy Demand Model(#  J 'ITR  ignition timing retard(#  JX'km`  kilometers(#  J0'LADCO  Lake Michigan Air Directors Consortium(#  J'LDAR  leak detection and repair(#  J'LDDT  lightduty diesel truck(#  J'LDDV  lightduty diesel vehicle(#  J'LDGT1  lightduty gasoline truck (less than 6,000 pounds in weight)  Jh'LDGT2h  lightduty gasoline truck (6,000 to 8,500 pounds in weight)(#  J@'LDGV  lightduty gasoline vehicle(#  J'LEV  low emission vehicle(#  J'LHDDVh  light heavyduty diesel vehicle(#  JLNB  lowNOx burners(#  J'MACT  maximum achievable control technology(#  Jx'MC  motorcycle(#  JP'MHDDVh  medium heavyduty diesel vehicle(#  J('MPS  Multiple Projections System(#  J'MSA  metropolitan statistical area(#  J'MSW  municipal solid waste(#  J'NAAQS  National Ambient Air Quality Standards(#  J'NAPAP  National Acid Precipitation Assessment Program(#  J`'NESHAPh  National Emission Standard for Hazardous Air Pollutants(#  J8'NGR  natural gas reburning(#  J NH3  ammonia(#  J NOx  oxides of nitrogen(#  J!'NPI  National Particulates Inventory(#  J"'NSCR  nonselective catalytic reduction(#  Jp#'NSPS  new source performance standard(# "p#x-$$ %"Ԍ J'OAQPS  Office of Air Quality Planning and Standards(#  J'OFA  overfire air(#  J'O&M  operations and maintenance  J'OMS  Office of Mobile Sources(#  J`'OPPE  Office of Policy, Planning, and Evaluation(#  J8'OTA  Office of Technology Assessment(#  J'OTAG  Ozone Transport Assessment Group(#  J'Pb`  lead(#  J'PC`  personal computer(#  J'PM-2.5  particles with an aerodynamic diameter less than or equal to a nominal 2.5micrometers(#  JH 'PM-10  particles with an aerodynamic diameter less than or equal to a nominal 10micrometers(#  J 'PROMPTh  Process Model Projection Technique(#  J 'RACT  reasonably available control technology(#  J 'RACM  reasonably available control measure(#  J 'REMI  Regional Economic Models, Inc.(#  JX'RFP  Reasonable Further Progress(#  J0'ROM  Regional Oxidant Modeling(#  J'ROP  rateofprogress(#  J'RVP  Reid vapor pressure(#  J'SAMI  Southern Appalachian Mountains Initiative(#  J'SCC  source classification code(#  Jh'SCR  selective catalytic reduction(#  J@'SEDS  State Energy Data System(#  J'SIC  standard industrial classification (code)(#  J'SNCR  selective noncatalytic reduction(#  JSO2  sulfur dioxide(#  JSO4  sulfate(#  Jx'SOA  secondary organic aerosol(#  JP'SOCMI  synthetic organic chemical manufacturing industry(#  J('SOS  Southern Oxidant Study(#  J'TEEMS  Transportation Energy and Emissions Modeling System(#  J'TLEV  transistional low emission vehicle(#  J'TOC  Technical Oversight Committee(#  J'TSP  total suspended particulates(#  J`'TVA  Tennessee Valley Authority(#  J8'UAM  Urban Airshed Model  J 'ULEV  ultra low emission vehicle(#  J ULNB  ultra lowNOx burner(#  J!'U.S.  United States(#  J"'USDA  U.S. Department of Agriculture(#  Jp#'VMT  vehicle miles traveled(# "p#x-$$ S%"Ԍ J'VOC  volatile organic compound(#  J'VOCM  volatile organic compounds model(#  J'VOL  volatile organic liquid(#  J'WEFA  Wharton Econometric Forecast Associates(# " x-$$ "     ljSReport 3'3'Standardhan formal reports3'3'Standardhan formal reportsưh  hh    (08@HPX!"`$%h' (08@HPX!"`$%h'#&h P:+Q.&P#MChapterheadyxddd y   `As# 2p NQH* #CHAPTER I \INTRODUCTION"Chapterhead"   l yx ddd xy    P4c#&U P:+Q.&P#j1st tier (A)#X`2p NQX#A.`STUDY OVERVIEW#W1st tier (A)#(#  J'#&U P:+Q.&P#The Southern Appalachian Mountain Initiative (SAMI) is a cooperative, coordinating institutional structure established to identify and recommend reasonable measures to remedy existing and prevent future adverse effects from humaninduced air pollution on the air quality related values (AQRVs) of the Southern Appalachians, primarily those of Class I parks and wilderness areas, weighing the environmental and socioeconomic implications of any recommendations. The Mission Statement of the Emission Inventory Subcommittee of SAMIs Technical Oversight Committee (TOC) is as follows: ` The Emission Inventory Subcommittee will support the Technical Oversight Committee, the Policy Committee, and other SAMI subcommittees by assessing and providing information and data on the historic, present, and projected future air emissions within the SAMI region and other geographic areas of interest.! Ready access to information and data regarding emission inventories, as well as projections of future emissions, and control costs from current and past studies and models that relate to the SAMI, is critical to the ability of the Emission Inventory Subcommittee to carry out its mission. The Emission Inventory Subcommittee must have a complete archive of emission inventory and projection information to characterize the base year (1990), and to support emission projections under the Clean Air Act Amendments (CAAA) and alternative emission management options (EMOs) for the SAMI region: Alabama, Georgia, Kentucky, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. These data will assist the work of other elements of SAMI as they assess the effects of the emissions on air quality indices and of the costs on the socioeconomics of the region. SAMIs Policy Committee can also use these data to formulate the EMOs to assess and to evaluate them once assessed. The purpose of this study was to review and gather existing data on emission inventories for the eight SAMI states, source region (within approximately 100 kilometers [km] of Class I regions), and available associated direct control costs, then to assess the quality of the data, and recommend alternative methods to fill critical gaps in the data. The goal was to establish a foundation of current knowledge relevant to the Emission Inventory Subcommittees mission and to identify critical gaps in the available data with respect to that mission. This report details a review of existing emission inventories that could be used as the basis for a base year emission inventory for SAMI. Additionally, it includes a discussion of existing data gaps within these inventories, ongoing studies that can address some of these data gaps, and a recommendation for the development of a base year emission inventory for SAMI.( x-**)e'#  '#+   ԌThis report also includes a review of methods and tools used to produce emission projections and projected control costs. Summary information related to emission projections and control costs are provided. The scope of this study was as follows: Pollutants of interest:  J`1)Sulfur dioxide (SO2) and primary sulfuric acid (SO4)(#  J`2)Nitrogen oxides (NOx) and primary nitrate(#  Jp'`3)Elemental carbon, organic carbon, and speciated volatile organic compounds (VOCs)(#  J '4)Particulate matter with an aerodynamic diameter less than or equal to 2.5/10micrometers (PM2.5, PM10), and total suspended particulates (TSP)(#  J 5)Ammonia (NH3)(#  J '6)Carbon monoxide (CO)(# Inventory averaging times of interest:  J'1)Annual(#  J'2)Seasonal(#  J'3)Weekday/weekend(# Geographic area to be surveyed:  J'Historical Emissions: #The SAMI region (the southern Appalachian mountain physiographic region) and each SAMI State (AL, GA, TN, NC, SC, KY, WV, VA)(#  Jx'Base year Emissions: #The SAMI region (major emphasis) and eastern United States and southern Canada as defined by the National Acid Precipitation Assessment Program (NAPAP) and Regional Oxidant Model (ROM) inventories if needed by the Modeling Subcommittee.(# Geographic scales of interest:  J8'1)State(#  J '2)County(#  J '3)Grid cell(# The source categories studied were those emission categories (whether point, area, mobile, or natural) that emit the pollutants listed above. The inventory data evaluated included both anthropogenic and biogenic sources. Existing information on capital costs, annual operation and maintenance (O&M) expenses, and total levelized annual expenses in a specific years dollars for many emission source categories is also identified. In addition, economic tools that are available, especially those available for developing control costs, and growth factors for projecting emissions are identified. "( x-***"Ԍ P4j1st tier (A)#X`2p NQX#B.`EVALUATION OF HISTORIC AND EXISTING DATA#?1st tier (A)#(#  J'#&U P:+Q.&P#In order to characterize the current status of emission inventories for the SAMI region, an overview of available data on emissions was required. E.H.Pechan & Associates, Inc. (Pechan) developed a summary of existing inventory data in order to select the best candidate(s) for the base year inventory. Tables I1 and I2 summarize the information developed by Pechan as part of that effort. Table I1 provides an overview of inventories developed that encompass more than one State. Table I2 provides the information collected by Pechan during conversations with individual States within the SAMI region concerning historic and current emissions inventory data. The tables provide information detailing the data source, pollutants covered, inventory period covered, geographic coverage, missing inventory source categories, general development methods, purpose for the inventory, temporal characteristics, spatial characteristics, whether on not cost data is included with the inventory, other data of interest that is included and any clarifying notes. The time period covered in these inventories is from  J 19001994. Figure I1 provides a national overview of how emissions of NOx, SO2, and PM10 have varied over this same time period. A bibliography of all of the data evaluated to develop tables I1 and I-2 is presented at the end of this chapter.  P*4Q2nd tier (1)#X`2p NQX#1.Short Term Trends in Emissions in the SAMI States#2nd tier (1)#(#  J#&U P:+Q.&P#Figures I2, I3, and I4 provide short term trends in emissions of SO2, NOx, and PM-10 for 19851994 for each of the States in the SAMI region. In general, emissions for these pollutants have either increased slightly or remained approximately the same over the time period considered. The increase in PM10 emissions for Georgia in 1989 is the result of higher than normal vehicle miles traveled (VMT) estimates for vehicle travel on unpaved roads.  P4Q2nd tier (1)#X`2p NQX#2.Source Contributions to Emissions in the SAMI States#2nd tier (1)#(#  J'#&U P:+Q.&P#Figures I5, I6, I7, and I8 show the contributions of point, area, and mobile sources  Jto the 1990 emissions for each of the SAMI states for SO2, NOx, PM10, and VOC,  Jfrespectively. These figures indicate that SO2 emissions are predominantly due to point  J>sources. For NOx, the majority of emissions are attributable to point source emissions, although several states have contributions from mobile sources that are equivalent to or greater than the point sources. Finally, these graphs show that for VOC and PM10, area sources are the largest contributors to emissions. Figures I9, I10, I11, and I12 show the six largest individual source categories  JN!contributing to SO2, NOx, VOC, and PM10, respectively, across the entire SAMI region.  P"4Q2nd tier (1)#X`2p NQX#3.Regional Pollutant Emissions#2nd tier (1)#(#  J$#&U P:+Q.&P#Figures I14 through I21 present total 1990 emissions of SO2, NOx, PM10, and VOC for various regions in order to compare SAMI emissions with other regions of the United States. The States that comprise each region are shown in figure 13. Figures I-14 through I-17 present total emissions, while figures I-18 through I-21 normalize the  J0(emissions on a square mile basis. Additionally, VOC and NOx emissions are broken out by anthropogenic and biogenic subcategories. All emissions presented in figures I-1") x-**," through I-21 were developed using the United States (U.S.) Environmental Protection Agencys (EPA) Emission Trends data. Use of the Emission Trends data ensures that the methods used to develop the data are consistent across all of the regions covered thus removing any bias that could be associated with differences attributable to differing methodologies."` x-**t"  P4 3'3'Standardhan formal reports'3'3StandardHPLA4POS.WRS&h>ưh  |Rm P4#X`2p NQX#Table I1 (continued)|#X`2p NQX# Table  I1. Historical Emission Inventory Data Relevant to SAMI: Published Sources  & &c XX #0xzP"@Q {P# ddx !X$EEl  & @ H : :  Source/Date Pollutants Time Frame Geographic AreaRR Source Categories Not Inventoried General Methods Purpose for Inventory Temporal Characteristics Spatial Characteristics  Cost Data Included  Other Fields*  Notes& @ Hr  : :   (08@HPX!"`$%h'T\ dlt$!|#%,(*,4/13<68:D=?1. Pechan, 1982xR SO2xR 19761980xR Entire United StatesxR All nonelectric utilitiesxR AP42 emission factors and DOE/EIA data on fuel use/qualityp Providing most accurate inventory available of SO2 emissions from electric utilitiesj Annualj State and plant level j No j TR, DC, S, A, H, CE, U j Fuel use/ fuel qualityr   : :  2. 1980 NAPAP Inventory, 1986  SO2, SO4, NOx, Pb, CO, HCl, HF, NH3, TSP, VOC, and Total HC (modeling inventory disaggregates pollutants into 39 individual classes); no Pb, HCl, or HF for Canadaf 1980f Contiguous United States and 10 Canadian Provinces; study area extends from  dB( 25 to 60- N. latitude and  dBt 50 to 125- W. longitudef f f Fulfillment of the emissions data requirements for development and testing of the Eulerian Regional Acid Deposition Model (RADM)f Annual emissions were allocated to the hourly level for a typical weekday, Saturday, and Sunday in each seasonf National, EPA Region, and State/Province; point source and countylevel area source emissions are allocated to 63,000 20 x 20 kilometer grid cells, each  dB representing 1/6-  dBt latitude by 1/4- longitude f No f SH (CE?) f Inventory contains data for 14,244 plants encompassing 36,807 emission points and 52,904 SCC or process level records; TSP emissions were assigned to alkalinity classes, NOx were split into NO and NO2, and THC were apportioned into as many as 28 photochemical reactivity classes.  j :p :   dBu3.XXG X Gschwandtner, 1985 SO2 and NOx Every 5 years for 19001980, and 1978 Contiguous United States Biogenic Statelevel activity data and AP42 emission factors minus SO2 controls2p Comparison with 1980 NAPAP2p Seasonal2p State 2p No 2p SH (CE?) 2p Different methods for 19001945 and 19501980  f :pp :  4. Gschwandtner, 1988X SO2 and NOxX See aboveX Contiguous United StatesX BiogenicX Revised 1985 report so that national totals are consistent  dBXuw/ Trendsp Reconcile NAPAP national total emission estimatesp Seasonalp State p No p SH (CE?) p See above  2 :pp :  5. Gschwandtner, 1988$ VOC$ Every 5 years for 19001985$ Contiguous United States$ Biogenic$ Allocates national  dBuTrends estimates to States based on activity datap NAPAPp Seasonalp State p No p CE? p Emission factors were estimated for 19001935.   :p :  6. 7. NAPAP Inventory (Version 2), 1989 VOC, NOx, and TSP (all subcategorized), alkaline particulate, SO2, SO4, CO, HCl, HF, NH3, THC (32 HC classes listed in Table 3.24)z 19001985z Contiguous United States, and Canada to  dB60- N. Latitude  dB125- W. Longitudez Nonez FREDS apportions 1985 NAPAP emissions for use in various modeling effortsz Provide inputs to atmospheric chemistry models that simulate source receptor relationships in acidic deposition processesz Hourly for typical weekday, Saturday, and Sunday for all four seasonsz Coordinates for point sources; area sources using Mercator grid  dBand cells of 1/6-  dBlatitude by 1/4- longitude; extending  dBfrom 50125-W  dBlongitude & 2560-N latitude z No z CE z More detailed (e.g., spatially) than the individual Gschwandtner inventories"J!!!>" V  :Y :  8. Kohout (Argonne National Laboratory), 1990p SO2, NOx, and VOCp 19751988 (Spoke with Jeff Camp at ANL he said that they have data up through 1990 for all ESGs in a data base, and data for utilities up through 1992) VY Contiguous United StatesVY BiogenicVY Aggregated SCCs into 68 emission source groups (ESGs), methods differ by ESGVY To provide regional seasonal trend dataVY MonthlyVY State VY No VY  VY Different source of fuel consumption and fuel sulfur content is used before 1985. Additional data available back to 1970, however, these data were not derived using the ESG methods, instead were developed by trending the data back to 1970V   :Y :  9. National Utility Reference File (NURF), 1989" p In addition to data in 1985 NAPAP inventory, includes info. on nonfossil electricity generating units  1985  All electric utilities in contiguous United States  All nonelectric utility source categories  Merging data from many sources, chiefly, 1985 NAPAP (Version 1) and DOE Form EIA767 data  To provide a consistent reference year to meet utilityrelated data needs of NAPAP  Annual  State (data provided for each individual unit)   Fuel cost data from Form FERC423   All except HR, DY, WK, SC, RP, FA, U, DO, and RM   Includes fuel use, fuel quality data   V :; :  10. Heisler, 1985 R SO2, SO4, TEP, alkaline TEP, NO, NO2, NH3, 9 HC reactivity classesp 1982p Contiguous United States, and Canada to  dB50- N. latitude and  dB,6987- W. Longitudex NAPAP report states that they are approximately the same source categories as the 1985 NAPAP Inventoryx Different QC from NAPAP, only the largest SO2, NOx and TEP sources were individually reviewed; better QA for combustion source datax To provide a detailed emissions inventory for atmospheric modeling and assessment activitiesx Annual, seasonal, weekdays and weekends within each season, 8 3hr periods for avg. days in each seasonx Coordinates for over 100,000 point sources, 90 area source categories assigned to  dB1/2- longitude by  dB1/3- latitude grid cells x  x  x Better data for the top utility and nonutility plants than NAPAP; better QA for stack parameters, incl. flow rate, TEP estimates may be better, pointsource VOC probably less reliable (for other diffs. see pp. 342 and 343) in NAPAP      :;; :  11. 12. Husar, 1986R SO2 and NOxR 18901981R Contiguous United States?D Sulfur emissions from anthracite, distillate oil, wood, gasoline, and diesel fuel Materials balance estimates on national and regional level and transfer of fuels to endusers; less source category detail than Gschwandtner(; To develop a dynamic model to facilitate the exploration of various assumptions and uncertainties in longterm emission trends(; Annual(; State (; No (;  (; A detailed comparison of this study with Gschwandtner indicated that the Gschwandtner data should be used for NAPAP (because of more complete source characterization and better reflects recent changes in average sulfur concentration)"!]"    :; :  13. U.S. EPA  dBuNational Trends  dB&uInventory, 1994p All criteria pollutants, biogenics, some air toxicsp 19001993 (no data for Pb before '70, no CO and PM10 data before '40); fugitive dust emissions from 19851993; also has projections to 2010 Contiguous United States, (Canada, Mexico, and Europe for 1985 and/or 1990) Biogenic NOx Different methods for different time periods; recently:  dB& Nonutility point sources and area  dBrsources emissions from 1985 NAPAP projected using BEA growth factors or SEDS fuel consumption;  dB Utilities EIA data on boiler fuel use  dBH  Solvents topdown mass balance;  dB  Motor vehicles MOBILE5.2 and HPMS VMT;  dB  Nonroad per capita emission rates (based on OMS inventories)  dB Fugitive Dust Emission algorithms and meterological data  To estimate national emissions on a consistent basis to indicate national emission trends Annual (and seasonal for 19851993) National and regional (and, for 19851993, lat./long. for point sources, and countylevel for area sources)  No  Depends on time frame and source category  Updated annually; next set of estimates to be published this Fall; different methods used for various time periods (e.g., 19001940, 19401985)"!p" ,  :` :  14. Pechan, National Particulates Inventory, 1994p SO2, NOx, VOC (largely based on data/methods previously developed ); for PM10, PM2.5, NH3, and secondary organic aerosols, largely relied on new methods/data from the National PM studyH  1990H  Contiguous United States, Canada, and MexicoH  Biogenics for Canada and MexicoH  Largely draws from data and methods from 1990 Interim  dB&uInventory, Trends Inventory, and 1985 NAPAP inventory (e.g., growing emission estimates from 1985 NAPAP to 1990 using growth factors). Methods differ greatly by source category.H  Source of baseline emission estimates, which are used to project to 2005, and as input to ambient modeling to develop sourcereceptor relationshipsH  Annual and seasonal (using seasonal throughput data when available for point sources, otherwise NAPAP allocation factors); note: even seasonal distribution for Canada and Mexico due to lack of dataH  United States for point sources, lat./ long., for area sources, county; for Canada state; for Mexico province. H  No H  S, LAT, LON, CE, A, H, PC, and "associated generator data, and associated stack parameters" H  Specific methods for specific source categories; generally based on data and methods developed in 1985 NAPAP, or  dBufor Trends or 1990 Interim Inventory; For sulfate and nitrate: 1) Coefficients were developed, which when applied to the SO2 and NOx inventories, roughly estimates (ammonium) sulfate and nitrate formation from SO2 and NOx emissions; 2) Summing all source contributions for each ambient receptor would yield average concentrations of sulfate and nitrate at each receptor.,   :`  :  15. U.S. EPA, Toxic Release Inventory System, 1995p Over 300 toxic air pollutants in 20 chemical categoriesp 19881993 (limited data is available for 1987)p United States and its territoriesp All non manufacturing industries; manufacturing industries with less than 10 employees Estimates submitted by facilities to EPA Inventory and track toxic pollutant emissions Annual Latitude/ Longitude  No  LAT, LON  Updated annually, new chemicals added from timetotime; nearly all of the 189 HAPs are included  ,  :\ :  16. U.S. EPA, National Screening Inventory... (draft report), April 1995 Benzene, 1,3Butadiene, Formaldehyde,Carbon Tetracholoride, Perchloro ethylene, Methylene Chloride, Trichloro ethylene@Y 1990@Y United States@Y Unclear only readily available data were gathered; likely that some source categories are not included.@Y  dB Stationary Inventories developed for MACT standards; L & E emissions documents; activity data times emission factors; TRI data base.  dB Mobile onroad: MVATS and VMT data; offroad: activity data from NEVES and SPECIATE; aircraft: procedures document and SPECIATE\ To determine what specific pollutants and source categories that need to be controlled.\ Annual\ County and urban1, urban2, and rural percentages \ No \  \ Additional HAP inventories to follow"!@}" t   :\  :  17. 23. Section 112(c)(6) Inventories (various sources/dates)  Extractable Organic Matter, Hexaclorobenzene, Tetraethyl Lead, Tetramethyl Lead, PCBs, Cadmium, and Mercuryt 1990t United Statest Generally, several for each pollutant, additional detail available if neededt Unclear Pechan's information did not provide details t To identify and characterize all source categories that emit any of the seven pollutants listed in section 112(c)(6)t Annualt State (unclear, however as to what level the Cadmium and Mercury emissions are available) t No t  t Inventories for 2,3,7,8tetrachlorodibenzopdioxin and 2,3,7,8tetrachlorodibenzofuran are forthcomingt  &  * SH Stack Height; SD Stack Diameter; ST Stack Gas Exit Temperature; SF Stack Gas Flow Rate; SV Stack Gas Exit Velocity; TR Throughput Rate; HR Normal Operating Hours per day; DY Normal operating Days per week; WK Normal Operating Weeks per year; DC Design Capacity; NC Nameplate Capacity; S Sulfur content; A Ash content; H Heat Content; OR Maximum ozone season rate; LAT Latitude; LON Longitude; PC Primary Control Equipment; SC Secondary Control Equipment; CE Control Efficiency; RE Rule Effectiveness; RP Rule Penetration; FA Facility Age by Source Category; U Utilization/Capacity factor; DO Dispatch Order; RM Reserve Margins; HY Hours per year; UTM Coordinates; %T Percent of throughput in quarters. R"!"  P4| P4# X`2p NQX#Table I2 (continued)| h88  # X`2p NQX#Table  I2. Historical Emission Inventory Data Relevant to SAMI: State Sources   dBu&1X XXXX #0xzP"@Q {P#  !X$EEl  AX ;;,^ t   @ H : :  Source/DateQ PollutantsQ Time FrameQ Geographic Area8 Source Categories Not Inventoried General Methods Purpose for Inventory Temporal Characteristics Spatial Characteristics  Cost Data Included  Other Fields*  Notes @ H  :t :  Alabama Dept. of Env. Mgmnt. Emissions Data System (EDS), current  dBuACO, HC, NO2, NO, Particulates,  dBuASO2, Pb 19901994 Entire State except Jefferson County and the City of Huntsvillet HAPst Annual actual and allowable emissions per pointt Permit fees for Title V programt Yearly ton per year emissions for each point, Allowable hourly rates as wellt  t No. t SH, SD, ST, SF, HR, WK, DY, DC t Stack parameter data generally only updated as needed. Emissions data updated yearly actual emissions.   :tt :  Atlanta area UAM Emission Inventory, 1990   dBuANOx, VOC, CO  1990  43 county area (figure shows boundaries)  Biogenic VOC  dB_uAand NOx except for day specific  According to GA EPD Inventory Preparation Plan; Nonroad mobile emissions furnished by EPA t Establish 1990 base year for ozone SIP t EPA temporal factors, day specific for power plans and Transco, modified for temperature t 43 county modeling inventory allocated to 4 km square grids, major mobile links included  t No  t Source classification code; area classification code  t    :t :  AIRS, AFS SC data 8 PM, SO2, NO2, VOC, CO, Methanol, Methylene Chloride Estimated for 1991, actual emissions for 1993 Entire State of SC Biogenics, mobile and area sources for everywhere except Cherokee Cty. in 1990t Hierarchy: CEM data Massbalance Stack test AP42 FIRE Trade groups Facility estimates Section 105 grant; Title V permit fees Annual (for Cherokee Cty. AMS is also daily) Lat/Long; UTM  No  LAT, LON; Part of 1991 estimated data includes SH,ST, other fields are incomplete (no RE,RP, FA,U,DO, or RM) 5     :! :  NC Modeling Emission Inventory, 1994C  dBQuAVOC, NOx, CO, biogenicsC 1987, 1988 for mobile and power plants. 1990 for point, area, and nonroad; projections to  dB1999, 2005.1 NC modeling domain  Followed EPA's "Procedures" manual for the most part.  dBC Utilities provided hour by hour for episode days.  dB5 Mobile MOBILE5a & HPMS VMT Modeling Analysis (UAM) Typical summer day and episode day specific; point is annual, except in EPA output format UTM coordinates for point sources; countylevel for area and nonroad; linkbased mobile for major road types.  No  Point in EPA format includes SH, SD, ST, SV, HR, DY, HY, PC, CE, RE, UTM, %T; area and mobile are in EPS format '! Are currently working on 1993 emissions for 2 episodes in 1993.  5 :! :  NC 1990 Emission Inventory, 19955 VOC, NOx, CO, biogenics5 19905 State of NC5 5 Followed EPA's "Procedures" manual for the most part.  dB5 Mobile Mobile 5a & HPMS VMT To support OTAG and other regional modeling efforts Typical summer day. Point is annual UTM coordinates for point sources, countylevel for area, nonroad, biogenics. Nonlink mobile by county. 't No 't Point in EPS format; includes SH, SD, ST, SV, HR, DY, HY, PC, CE, RE, UTM, %T; area and mobile in EPS format also  Expect to complete within 2 weeks (from 6/30/95)"hhh88" Z ' : :  NC's CO Emission Inventory, 1994 CO 1990 projected to 1993, 1996, 1999, 2002,  dB20051c Wake, Durham, Forsyth, and Mecklenberg CountiesZ Z Followed EPA's "Procedures" manual for the most part.  dB Mobile ĩ MOBILE5a & HPMS VMTZ Redesignation demonstration and maintenance planZ Typical winter dayZ UTM coordinates for point sources; countylevel for all other source types Z No Z  Z Z   : :  EPA NEDS/AIRS Point Source Data8 Criteria pollutants8 19721990 (soon  dB19911993)1E N.C.E Generally only larger point sources are includedV Emission factors and mass balanceV Track large sources of criteria pollutantsV Annual (usually also provides daily)V UTM or long/lat; pre1990 liable to have errors V No V NEDS/AIRS required fields V 1985 and later data are believed to be accurate but some sources may be omitted for some years; Pre1985 is spotty for frequency of inclusion of sources accuracy should be double checked by current standards. Do not make yeartoyear comparisons other than for individual sources to develop individual projections. Z Z : :  Base Year Inventory, 1993 8 VOC, NOx, CO 8 1990 8 Knoxville Nonattainment AreaL  ܩL  Questionnarires to point sources; emission factors; census and traffic data Section 182(a)(1) of CAA Annual and peak ozone season daily Seventeen counties: sixteen in TN and one in NC  No    This inventory was uploaded into AIRSZ  >  :V :  Nonattainment Area Inventory, 1994 SO2 1990 Copper Basin Nonattainment Area (Polk County)LV ܩLV Survey of sourcesLV Inventory for modelingLV Three hour and dailyLV Copper basin LV No LV  LV Included in request for rclassification to attainment x   :VE :  NAPAP, mid1980sE Particulate, VOC, SO2, NO2, COE Mid 80'sE StatewideE Smaller than 100 tons per yearE Questionnaries to sourcesE NAPAPE AnnualE Statewide E No E  E Updated NEDSx  L E* SH Stack Height; SD Stack Diameter; ST Stack Gas Exit Temperature; SF Stack Gas Flow Rate; SV Stack Gas Exit Velocity; TR Throughput Rate; HR Normal Operating Hours per day; DY Normal operating Days per week; WK Normal Operating Weeks per year; DC Design Capacity; NC Nameplate Capacity; S Sulfur content; A Ash content; H Heat Content; OR Maximum ozone season rate; LAT Latitude; LON Longitude; PC Primary Control Equipment; SC Secondary Control Equipment; CE Control Efficiency; RE Rule Effectiveness; RP Rule Penetration; FA Facility Age by Source Category; U Utilization/Capacity factor; DO Dispatch Order; RM Reserve Margins; HY Hours per year; UTM Coordinates; %T Percent of throughput in quarters.  dB\1 Inventories after 1990 omit some smaller sources included in 1990. Also, some nonpermitted sources in the 1990 inventory (such as landfills and POTWs) may be omitted."\h|"  P4 '3'3StandardHPLA4POS.WRS&h'3Letter Landscape'3StandardRegulation Outline>XX88  XX# # &h P:+Q.&P# 1  1 !,,XXJdd| ,, J'؄#X`2p NQX# Figure 1 ! Figure 1 <A,n#28IZZ  0*x(a JFigure I1. Trend in National Emissions, NITROGEN OXIDES, SULFUR DIOXIDE (1900 to 1994), and PARTICULATE  MATTER (PM10): nonfugitive dust sources (1940 to 1994)<$##..##..A#.$hhh88!e K{!X|#8.#A#2  J' ## #&h P:+Q.&P# 1 ! 1 A,,XXJddP ,, J'؄ a,n#28ZZ  0*x(驔 PhwFigure I2. Statelevel SO2 Emissions from Each State in the SAMI Region (1985 to 1994) $##..##..a#.$hhh88!e K{AXP #8.#a#2  J' ## #&h P:+Q.&P# 1 A 1 a,,XXJdd$ ,, J'؄ ,n#28ZZ  0*x(驔 PhrFigure I3. Statelevel NOx Emissions from Each State in the SAMI Region (1985 to 1994) $##..##..#.$hhh88!e K{aX$  #8.##2  J' ## #&h P:+Q.&P# 1 a 1 ,,XXJdd ,, J'؄,n#28ZZ b 0*x(|Figure I4. Statelevel PM10 Emissions from Each State in the SAMI Region (1985 to 1994)$##..##..#.$hhh88!e K{X  #8.##2b  J' ## #&h P:+Q.&P# 1  1 ,,XXJdd ,, J'؄,n#28ZZ 6 0*x(驈 PhU Figure I5. Point, Area, and Mobile SO2 Emissions in the SAMI States >(1990)$##..##..#.$hhh88!e K{X#8.##26  J' ## #&h P:+Q.&P# 1  1 ,,XXJdd ,, J'؄,n#28ZZ  0*x(驈 PhP Figure I6. Point, Area, and Mobile NOx Emissions in the SAMI States >(1990)$##..##..#.$hhh88!e K{X#8.##2   J' ## #&h P:+Q.&P# 1  1 ,,XXJddt ,, J'؄ ,n&<&zZZ ,n(pFigure I7. Point, Area, and Mobile PM10 Emissions in the SAMI States >(1990)$##..##..^/$hhh88#e K{Xt^z/q%&<  J' ## #&h P:+Q.&P# 1  1 ,,XXJddH ,, J'؄!,n#28ZZ  0*x(n. Figure I8. Point, Area, and Mobile VOC Emissions in the SAMI States >(1990)$##..##..!#.$hhh88!e K{XH#8.#!#2  J' ## #&h P:+Q.&P# 1  1 !,,XXJdd  ,, J'؄A,n#28add  0*x( } Ph  Figure I9. Top 6 SO2 Emission Categories for SAMI Region >(1990)$##..##..A#.$hhh88!e K{!X #8.#A#2   ## #&h P:+Q.&P# 1 ! 1 A,,XXJdd  ,, J'؄a,n#28add Z  0*x( ~ Ph Figure I10. Top 6 NOx Emission Categories for SAMI Region >(1990)$##..##..a#.$hhh88!e K{AX #8.#a#2Z   J' ## #&h P:+Q.&P# 1 A 1 a,,XXJdd= ,,ؐ,n#28add d> 0*x( fp Figure I11. Top 6 PM10 Emission Categories for SAMI Region >(1990)$##..##..#.$hhh88!e K{aX=#8.##2d>  J' ## #&h P:+Q.&P# 1 a 1 ,,XXJdd ? ,, J'ؐ,n#28add  @ 0*x( d Figure I12. Top 6 VOC Emission Categories for SAMI Region >(1990)$##..##..#.$ hhh88!e K{ X ?#8.# #2 @  ## #&h P:+Q.&P# 1  1 ,,XXJdd. A !,, J'ؐ,W#I8ddD:\SAMI\SAMIREGN.WMF B !0*x( +xFigure I13. Regions$##..##..#.$!hhh88 e K{!X. A#8.#!#I B  ## #&h P:+Q.&P# 1  1 ,,XXJdd C ",, J'ؐ,n#28ZZ l D "0*x(h PhW0Figure I14. SO2 Emissions by Region >(1990)$##..##..#.$"hhh88!e K{"X C #8.#"#2l D!  ## #&h P:+Q.&P# 1  1 ,,XXJdd E #,, J'ؐ,n#28ZZ @ F #0*x(驃 Ph Figure I15. NOx Anthropogenic and Biogenic Emissions by Region >(1990)$##..##..#.$#hhh88!e K{#X E"#8.###2@ F#  ## #&h P:+Q.&P# 1  1 ,,XXJdd~G $,,ؐ!,n#28ZZ H $0*x(POFigure I16. PM10 Emissions by Region >(1990)$##..##..!#.$$hhh88!e K{$X~G$#8.#!$#2H%  ## #&h P:+Q.&P# 1  1 !,,XXJddRI %,,ؐA,n#28;ZZ J %0*x(i Figure I17. VOC Anthropogenic and Biogenic Emissions by Region >(1990)$##..##..A#.$%hhh88!e K{!%XRI&#8.#A%#2J'  ## #&h P:+Q.&P# 1 ! 1 A,,XXJdd&K &,,ؐa,n#28;ZZ L &0*x(s Ph/ Figure I18. SO2 Tons per Square Mile by Region >(1990)$##..##..a#.$&hhh88!e K{A&X&K(#8.#a&#2L)  J' ## #&h P:+Q.&P# 1 A 1 a,,XXJddM ',,ؐ 1 aKKKK Figure 1 ,n#28;ZZ N ',h(Ƒ PhFigure I19. NOx Anthropogenic and Biogenic Tons per Square Mile by Region >(1990)$##..##..#.$'hhh88!e K{a'XM*#8.#'#2N+  J' ## #&h P:+Q.&P#KKKK Figure 1 a 1 ,,XXJdd O (,,ؐ,n#28ZZ P (0*x([& Figure I20. PM10 Tons per Square Mile by Region >(1990)$##..##..#.$(hhh88!e K{(X O,#8.#(#2P-  J' ## #&h P:+Q.&P# 1  1 ,,XXJdd8Q ),,ؐ0*ni28;ZZ vR )0*x(t2Figure I21. VOC Anthropogenic and Biogenic Tons per Square Mile by Region (1990)$##..##..i-$)hhh88!e K{)X8Q.i8-#)i2vR/  P4jSReport '3Letter Landscape'3StandardRegulation Outline3'3'Standardhan formal reports#88*  88h  # T\ dlt$!|#%,(*,4/13<68:D=? (08@HPX!"`$%h'#&h P:+Q.&P#n  yxdddy #;2PQJqP#  AX ;;,^  !ddx( ( ( x    E.H. PECHAN & ASSOCIATES, INC. Doc. # 96.10.005/538"  J.'r#&h P:+Q.&P##;2PQJqP##FINAL REPORT October 25, 1996j1st tier (A)#X`2p NQX#C.`BIBLIOGRAPHY#q1st tier (A)#(#  J'#&U P:+Q.&P#1.`E.H. Pechan & Associates, Inc. Estimates of Sulfur Oxide Emissions from the Electric  J'Utility Industry, Volumes I and II. Prepared for U.S. Environmental Protection Agency, Washington, DC. March 1982.(#  J2'2.`U.S. Environmental Protection Agency, Office of Research and Development, Air and  J 'Energy Engineering Research Laboratory. Development of the 1980 NAPAP  J'Emissions Inventory. EPA600/786057a. December 1986.(#  J'3.`Gschwandtner, G., et al.  Historic Emissions of Sulfur and Nitrogen Oxides in the  Jj 'United States from 1900 to 1980. Journal of the Air Pollution Control Association. Vol. 36, No. 2, February 1986.(#  J '4.`Gschwandtner, G., K.C. Gschwandtner, K. Eldridge. Historic Emissions of Sulfur and  J 'Nitrogen Oxides in the United States from 1900 to 1980, Volume II. Data. EPA600/785009b. Prepared for the U.S. Environmental Protection Agency, Washington, D.C. April 1985.(#  J*'5.`Gschwandtner, G., and J.K. Wagner. Historic Emissions of Volatile Organic  J'Compounds in the United States from 1900 to 1985. EPA600/788008a. Prepared for the U.S. Environmental Protection Agency, Washington, D.C. May1988.(#  J'6.`Zimmerman, et al. Anthropogenic Emissions Data for the 1985 NAPAP Inventory. EPA-600/788022. Prepared for the U.S. Environmental Protection Agency, Washington, D.C. November 1988.(#  J'7.`Saeger, M. et al. The 1985 NAPAP Emissions Inventory (Version 2): Development of  J'the Annual Data and Modelers Tapes. EPA600/789012a. Prepared for U.S. Environmental Protection Agency. November 1989.(#  JJ'8.`Kohout, E.J. et al.  Current Emission Trends for Nitrogen Oxides, Sulfur Dioxide, and  J"'Volatile Organic Compounds by Month and State: Methodology and Results. Prepared by Argonne National Laboratory for National Acid Precipitation Assessment Program. August 1990.(#  J'9.`U.S. Environmental Protection Agency. The 1985 NAPAP Emissions Inventory  JZ'(Version2): Development of the National Utility Reference File. EPA600/789013a, Research Triangle Park, NC. 1989.(#  J!'10.`Heisler, et al. The EPRI 1982 National Air Pollutant Emissions Inventory,  J"'Proceedings, Air Pollution Control Association 78th Annual Meeting, Detroit, MI. 1985.(#  JB%'11.`Husar, R.B. Emissions of Sulfur Dioxide and Nitrogen Oxides and Trends for  J&'Eastern North America, in Acid Deposition LongTerm Trends. National Academy Press, Washington, DC. 1986.(# "'*x-**("Ԍ J12.`Husar, R.B. Manmade SOx Emission Trends for the United States. Report Submitted to E.H. Pechan & Associates. October 4, 1986.(#  J'13.`U.S. Environmental Protection Agency. National Air Pollutant Emission Trends,  J`'19001993. EPA454/R94027. Office of Air Quality Planning and Standards, Research Triangle Park, NC. October 1994.(#  J'14.`E.H. Pechan & Associates, Inc. Development of the OPPE Particulate Programs  J'Implementation Evaluation System. Prepared for the Office of Policy, Planning, and Evaluation/Office of Policy Analysis, U.S. Environmental Protection Agency, Washington, DC. September, 1994.(#  J '15.`U.S. Environmental Protection Agency, Toxic Release Inventory System files, Technology Transfer Network Bulletin Board, Research Triangle Park, NC. Various years.(#  J '16.`U.S. Environmental Protection Agency. National Screening Inventory of Sources and Emissions of Candidate 112(k) Pollutants to Support the Urban Area Source Program, Benzene, 1,3Butadiene, Perchloroethylene, Trichloroethylene, Methylene Chloride,  J'Carbon Tetrachloride, and Formaldehyde, Review Draft Report and background data, Research Triangle Park, NC. April 1995.(#  J'17.`U.S. Environmental Protection Agency, Emissions Inventory of Section 112(c)(6)  Jh'Pollutants, Extractable Organic Matter (EOM), Draft Report, Research Triangle Park, NC. September 1993.(#  J'18.`U.S. Environmental Protection Agency, Estimation of National Hexachlorobenzene  J'Emissions for 1990 Final Report, Research Triangle Park, NC. October 1993. (#  Jx'19.`U.S. Environmental Protection Agency, Estimation of Alkylated Lead Emissions, Final  JP'Report, Research Triangle Park , NC. September 1993.(#  J'20.`U.S. Environmental Protection Agency, Emissions Inventory of Section 112(c)(6)  J'Pollutants, Polychlorinated Biphenyl Compounds (PCBs) Draft Report, Research Triangle Park, NC. September 1993.(#  J`'21.`U.S. Environmental Protection Agency, Locating and Estimating Air Emissions from  J8'Sources of Cadmium and Cadmium Compounds, EPA454/R93040, Research Triangle Park, NC. September 1993.(#  J!'22.`U.S. Environmental Protection Agency, Locating and Estimating Air Emissions from  J"'Sources of Mercury and Mercury Compounds, EPA454/R93023, Research Triangle Park, NC. September 1993.(#  J %'23.`Emissions Inventory of Section 112(c)(6) Pollutants: Polycyclic Organic Matter (POM), 2,3,7,8Tetrachlorodibenzopdioxin (TCDD)/2,3,7,8Tetrachlorodibenzofuran (TCDF),  J&'and Polychlorinated Biphenyl Compounds (PCBs), Final Report, Radian Corporation, Research Triangle Park, NC. March 1995.(# "(+x-**)"Ԍ K' Other Sources Examined:  JGschwandtner, G.  Description of the Analysis of Historic SO2 and NOx emissions Stack  J'Height and Season. Prepared for U.S. Environmental Protection Agency, Research Triangle Park, NC. June 1985.  JGschwandtner, G. J.K. Wagner, and R.B. Husar. Comparison of Historic SO2 and NOx  J'Emission Data Sets. EPA600/788009a. Prepared for the U.S. Environmental Protection Agency, Washington, D.C. May 1988.  Jp'Misenheimer, D.C., et al. Ammonia Emission Factors for the NAPAP Emission Inventory. Prepared for U.S. Environmental Protection Agency, Washington, DC. December 1985.  J 'Wilson, J.H., E.H. Pechan, and K. Graves. Assessment of National and Regional Acid  J 'Deposition Precursor Emission Trends. EPA600/889042. Prepared for the U.S. Environmental Protection Agency, Washington, D.C. March 1989.  JX'Placet, M. et al. Emissions Involved in Acidic Deposition Processes, Stateof J0'Science/Technology, Report 1. National Acid Precipitation Assessment Program. January 1990.  J'Herrick, C.N. Acidic Deposition: An Inventory of NonFederal Research, Monitoring, and  J'Assessment Activity. National Acid Precipitation Assessment Program. Washington, DC. January 1990.  J'Gschwandtner, G.  Special Report on the Outcome of Comparing U.S. EPA and Argonne  J'Emission Trend Estimates. Prepared for U.S. Environmental Protection Agency, Emission Inventory Branch, Research Triangle Park, NC. June 24, 1991.  Jx'Irving, P.M. Acidic Deposition: State of Science and Technology, Summary Report of the  JP'U.S. National Acid Precipitation Assessment Program. National Acid Precipitation Assessment Program, Washington, D.C. September 1991."(,x-**y" MChapterhead"-x-**"Ԓ  %y0xddd.y   `Aq# 2p NQH* #CHAPTER II  BASE YEAR INVENTORY EVALUATION"Chapterhead"   l y1x dddJ.xy    P4c#&U P:+Q.&P#j1st tier (A)#X`2p NQX#A.`BASE YEAR INVENTORY SELECTION#1st tier (A)#(#  P4#&U P:+Q.&P#Q2nd tier (1)#X`2p NQX#1.Introduction#2nd tier (1)#(#  Jx '#&U P:+Q.&P#This chapter provides a broad overview of three emission inventories that are suitable for use as the foundation of the base year (1990) SAMI inventory. Although each provides certain aspects of the information required for development of the 1990 base year SAMI inventory, none provides all of the requisite information required for SAMI. A detailed description of these inventories, including a discussion of the methods used in their development, is provided below. However, since their initial development, the differences between the inventories have largely disappeared, primarily as the result of the EPA Emission Factors and Inventory Group (EFIG) efforts to update the annual Emission  J8'Trends (Trends) report to incorporate improvements in emission inventory estimation techniques developed for these inventories. Additionally, several ongoing efforts are discussed that should provide a means for melding these inventories together to provide a composite inventory that is more likely to supply the majority of the information required to perform the various SAMI analyses. These ongoing efforts should provide a mechanism for providing either higher quality inventory data for some source categories, or supplemental information for source categories that currently are not covered by other inventories. Although inventory data is available from the Aerometric Information Retrieval System (AIRS), as well as State operating permit and compliance information systems, the inventories reviewed here provide overall coverage of all emission sources (including area and mobile sources) for the entire SAMI region. Data included in AIRS may only include emissions for nonattainment areas. In addition, the inventories reviewed here were developed using a consistent methodology for each source category. As such, they provide a consistent basis for evaluating the impact of regulatory programs across broad geographic regions.  Ph 4Q2nd tier (1)#X`2p NQX#2.Potential Options#j2nd tier (1)#(#  P:"4#&U P:+Q.&P#%3rd tier (a)#X`2p NQX#a.h  1990 Interim Inventory Overview#q3rd tier (a)#(#  J $'#&U P:+Q.&P#Because the States are not required to develop statewide emission inventories for all source categories, the EPA tasked Pechan to develop a comprehensive emission inventory  J%'for 1990 called the Interim Inventory. This inventory, along with similar inventories for ozone episodes in other years (1987, 1988, 1989, and 1991) was developed to support ROM for much of the Eastern United States. The inventory provides countylevel emissions estimates on an annual and ozone season daily basis for the following pollutants: VOCs;D(.x-**")e'#0.Ex0ansfer Operations  '#+ 1.ag J120,000 capacity)  JNOx; CO; and sulfur dioxide (SO2). The Interim methodology is described in Regional  J'Interim Emission Inventories (19871991), Volume I: Development Methodologies.1  P4%3rd tier (a)#X`2p NQX#b.h  National Particulates Inventory (NPI) Overview#V3rd tier (a)#(#  JZ'#&U P:+Q.&P#The EPAs Office of Policy, Planning, and Evaluation (OPPE) is currently assessing the impacts of lower ambient air quality standards for PM-10 and of new standards based on PM-2.5. OPPE tasked Pechan to develop a base year (1990) emissions inventory for use in the National PM study. This inventory, the National Particulates Inventory (NPI),  J'was first documented in the Development of the OPPE Particulate Programs  J'Implementation Evaluation System.2 Updates to this inventory are documented in the  Jj 'draft report entitled The National Particulates Inventory: Phase II Emission Estimates.3 This inventory includes countylevel emissions estimates on an annual and seasonal basis for the following pollutants:  J '`PM-10;(#  J '`PM-2.5;(#  Jz`SO2;(#  JR`NOx;(#  J*'`VOC; (#  J`NH3; and(#  J'`Secondary organic aerosols (SOA).(#  J'The methodologies used in preparing the NPI were similar to the Interim/Trends methodology. These methodologies are described below.  P4%3rd tier (a)#X`2p NQX#c.h  EPA Emission Trends Inventory Overview#,3rd tier (a)#(#  J'#&U P:+Q.&P#Since 1973, EPA has prepared annual national emissions estimates to assess historic  J'trends in criteria pollutant emissions. This Trends Inventory, which is updated each  Jyear, currently provides NOx, SO2, and VOC estimates for 19001994; CO and particulate  Jl'matter (PM-10) estimates for 19401994; and lead (Pb) estimates for 19701994.4 Pechan  JD'has assisted EPA in preparing the Trends emission estimates for several years.  J'For emission estimates since 1985, the Trends methodology has been revised to be  J'consistent with the methods used in developing the Interim inventories. Although the  J'methods used to develop emission estimates in both the Interim and Trends inventories are consistent with respect to sources, temporal, and geographic coverage, new and  JT 'revised methods have now been incorporated into the Trends methodology for some source  J,!'categories. These new methods supersede those used in the Interim Inventory  J"'development and the Trends estimates are now used as the basis for ROM modeling. Additionally, many of the methods and emissions estimates developed for use in the NPI  J#'have now been incorporated into the annual Trends estimates, particularly those used for  J$'particulate estimates. On a temporal basis, the Trends inventory contains annual, seasonal, and ozone season daily (for ozone related pollutants) emissions. It should be  J<&'noted however, that the current Trends inventory is confined to the criteria pollutants. Expansion of the inventory to other pollutants in the future is likely. "'/x-***+"Ԍ1990 emission estimates for each of the SAMI States and summary information for  Jthe entire SAMI region for VOC, NOx, CO, SO2, and PM-10 from the Interim/Trends Inventory are presented in appendix A.  P`4j1st tier (A)#X`2p NQX#B.`1990 INTERIM/TRENDS INVENTORY#1st tier (A)#(#  J2'#&U P:+Q.&P#Because of the close connections between the Interim and the Trends inventories for the majority of the pollutants, the estimation methodologies are discussed together. NPI methods that are either different from or supplemental to those included in the discussion  J'of the Interim or Trends methods are discussed separately.  Pj 4Q2nd tier (1)#X`2p NQX#1.Development Methodology#2nd tier (1)#(#  P< 4#&U P:+Q.&P#%3rd tier (a)#X`2p NQX#a.h  Utilities#3rd tier (a)#(#  J '#&U P:+Q.&P#Emissions from the combustion of fuel by electric utilities were divided into two classifications: (1) steam generating fossilfuel units (an electric utility unit is a boiler) and (2) nonsteam generating fossilfuel units such as gas turbines (GT) and internal combustion (IC) engines. The emissions from fossilfuel steam electric utility units for 1990 were based on four basic factors: (1) fuel consumption, (2) emission factor, which relates the quantity of fuel consumed to the quantity of pollutant emitted, (3) fuel characteristics, such as sulfur content, ash content, and heating value of fuels, and (4) control efficiency, which indicates the amount of pollutant not removed by the use of control devices. The fuel consumption  J~'characteristics and control efficiencies were obtained at the boilerlevel from the Steam JV'Electric Plant Operation and Design Report (Form EIA767), collected and published annually by the Energy Information Administration (EIA) of the U.S. Department of  J'Energy (DOE). Emission factors were obtained from the Compilation of Air Pollutant  J'Emission Factors (AP-42), and were both Source Classification Code (SCC) and pollutantspecific. The 1990 emissions for GT and IC engines were estimated from the point source portion of the 1985 NAPAP Emissions Inventory for the appropriate sources. The methodology used to develop the emissions for the 1990 base year for these sources is identical to that used for nonutility point sources and is discussed below.  P4%3rd tier (a)#X`2p NQX#b.h  Nonutility Point Sources#3rd tier (a)#(#  Jp!'#&U P:+Q.&P#1990 nonutility point source emissions were developed by projecting the 1985 NAPAP Emission Inventory estimates for these sources to the year 1990 based on the growth in Bureau of Economic Affairs (BEA) historic earnings for the appropriate State and industry, as identified by the 2digit Standard Industrial Classification (SIC) code. State and SIClevel growth factors were calculated as the ratio of the 1990 earnings data to the 1985 earnings data. When creating the 1990 emissions inventory, changes were made to emission factors, control efficiencies, and emissions from the 1985 NAPAP inventory for the nonutility point  J)sources. The CO, NOx, SO2, and VOC emissions were calculated for 1990 using the")0x-**3*8" following steps: (1)project 1985 controlled emissions to 1990 using the appropriate growth factors, (2) calculate the uncontrolled emissions using control efficiencies from the 1985 NAPAP Emission Inventory, and (3) calculate the final 1990 controlled emissions using revised control efficiencies and the appropriate rule effectiveness. The 1990 PM-10 emissions were calculated using the TSP emissions from the 1985 NAPAP Emission Inventory. The 1990 uncontrolled TSP emissions were estimated in the same manner as the other pollutants. From these TSP emissions, the 1990 uncontrolled PM-10 estimates were calculated by applying SCCspecific uncontrolled particle size distribution factors. The controlled PM-10 emissions were estimated in the same manner as the other pollutants. PM-10 control efficiencies were obtained from revisions made to the PM-10 Calculator. Details on the PM-10 calculator updates are given in chapter 5 of reference 3. In addition, rule effectiveness was applied to the 1990 emissions estimated for the  J nonutility point sources. The CO, NOx, and VOC point source controls were assumed to be 80 percent effective. For a few plants, the rule effectiveness was changed from 80percent to 100 percent, based upon comments received from State air agencies. PM-10  J and SO2 controls were assumed to be 100percent effective.  P04%3rd tier (a)#X`2p NQX#c.h  Mobile Sources#3rd tier (a)#(#  P4#&U P:+Q.&P#4th tier (i)#X`2p NQX#  i. OnRoad Vehicles#-4th tier (i)#(#  J'#&U P:+Q.&P#The 1990 onroad vehicle emissions estimates were based on countylevel VMT and emission factors. Emissions were estimated for eight vehicle categories including: lightduty gasoline vehicles (LDGV); lightduty diesel vehicles (LDDV); lightduty gasoline trucks1 (LDGT1 [trucks less than 6,000 pounds in weight]); lightduty gasoline trucks2 (LDGT2 [6,000 to 8,500 pounds in weight]); lightduty diesel trucks (LDDT); heavyduty diesel trucks (HDDT); heavyduty gasoline trucks (HDGT); and motorcycles (MC).  N'0ǽ5th tier#vz  xC,&#   VMT Data05th tier  Jl'#&U P:+Q.&P#Annual 1990 VMT were obtained from the Federal Highway Administrations (FHWA) highway performance monitoring system (HPMS) data base. The data are specified by State, vehicle type, and roadway type. Using population data from the 1980 census, the data were distributed among the counties. The data were then apportioned from the HPMS vehicle categories to the eight vehicle classes listed above using allocations provided by the EPAs Office of Mobile Sources (OMS). The resulting 1990 countylevel vehicle and roadway type specific VMT data were temporally allocated to months. Seasonal NAPAP allocation factors were used to apportion the VMT to the four seasons. Monthly VMT data were obtained using a ratio between the number of days in a month and the number of days in the corresponding season.  Nd%0ǽ5th tier#vz  xC,&#   CO, NOx, and VOC Emission Factors=05th tier  J'#&U P:+Q.&P#Countylevel emission factors for CO, NOx, and VOC were calculated using the MOBILE5a model, which is designed to estimate exhaust and evaporative emission factors for onroad vehicles. To calculate the emission factors for 1990 vehicles, the model"(1x-**+" utilized information on statelevel monthly maximum and minimum temperatures, nine vehicle speeds, national vehicle registration distributions, gasoline volatility given in terms of inuse Reid vapor pressure (RVP), and countylevel inspection and maintenance (I/M) and oxygenated fuels programs. The Federal Test Procedure (FTP) operating mode was modeled at all speeds.  N0ǽ5th tier#vz  xC,&#   PM-10 and SO2 Emission Factors05th tier  J'#&U P:+Q.&P#The EPAs particulate matter emission factor model for highway vehicles, PART5, was used to calculate PM-10 emission factors from vehicle exhaust, brake wear, tire wear, and  Jpreentrained road dust from paved and unpaved roads, and SO2 vehicle exhaust emission factors. The vehicle registration distribution used was also common to all PART5 model runs. PART5 uses the same vehicle classifications as the MOBILE model, except that the MOBILE heavy duty diesel vehicle (HDDV) class is broken into five subclasses in PART5.  JX'To maintain consistency with the 1990 Trends emissions, the 1990 vehicle  J0'registration distribution used in the MOBILE modeling for the 1990 Trends emissions was adapted for this inventory development. This registration distribution was modified by distributing the MOBILE HDDV vehicle class distribution among the five PART5 HDDV subclasses (2BHDDV, LHDDV, MHDDV, HHDDV, and Buses). This was accomplished using HDDV subclassspecific sales, survival rates, and diesel market shares. Monthly, countylevel, SCCspecific PM-10 emissions from highway vehicle exhaust components were calculated by multiplying 1990 monthly countylevel, SCCspecific VMT by 1990 state level, SCCspecific exhaust PM-10 emission factors generated using PART5. None of the inputs affecting the calculation of the PM-10 exhaust emission factors vary by month, so only annual PM-10 exhaust emission factors were calculated. PART5 total exhaust emission factors are the sum of Pb, soluble organic fraction, remaining carbon  J(portion, and SO4 emission factors.  JNational SO2 highway vehicle exhaust emission factors by vehicle type and speed were calculated using PART5. These emission factors calculated within PART5 vary according to fuel density, the weight percent of sulfur in the fuel, and the fuel economy of the vehicle (which varies by speed). None of these parameters vary by month or State.  J8Monthly/county/SCCspecific SO2 emissions were then calculated by multiplying each  J countys monthly VMT at the road type and vehicle type level by the SO2 emission factor (calculated for each vehicle type and speed) that corresponds to the vehicle type and road type. The PART5 PM-10 emission factor for brake wear is 0.013 grams per mile (gpm). This value was applied to estimate brake wear emissions for all vehicle types. PART5 emission factors for tire wear are proportional to the average number of wheels per vehicle. The emission factor is 0.002 gpm per wheel. Therefore, separate tire wear emission factors were calculated for each vehicle type. Tire wear PM-10 emissions"'2x-***" were then calculated at the monthly/county/SCC level by multiplying the monthly/county/SCC levelVMT by the tire wear emission factor for the appropriate vehicle type.  JA detailed description of the methodology to estimate PM-10 and SO2 emissions from  J`'onroad vehicles for 1990 is located in chapter 11 of the National Particulates Inventory:  J8'Phase II Emission Estimates (reference 3).  P44th tier (i)#X`2p NQX#  ii. Nonroad Sources#0-4th tier (i)#(#  J'#&U P:+Q.&P#The nonroad sources category includes emissions from aircraft, commercial marine vessels, railroads, and all other nonroad vehicles and equipment. The 1990 base year emissions from aircraft, commercial marine vessels, and railroads have been estimated from the area source portion of the 1985 NAPAP Emission Inventory by the process described under the nonutility point source section. The basis for the 1990 nonroad emissions for all other nonroad sources was emission inventories prepared by OMS for 27nonattainment areas. These inventories were combined and used to create national countylevel emissions for all nonroad sources. These emissions in the OMS inventories were classified by equipment and engine type and were distributed to the appropriate  JRnonroad SCCs. Because the OMS inventories did not contain SO2 emissions, SO2 emissions were not estimated for the nonroad SCCs. It was assumed, based on the  Jemissions from the 1985 NAPAP Emissions Inventory, that the SO2 emissions for these SCCs were very small (less than92,000 short tons per year).  P4%3rd tier (a)#X`2p NQX#d.h  Solvents#3rd tier (a)#(#  J\'#&U P:+Q.&P#Emissions from area source solvent utilization were based on a national material balance of the total point and area source solvent consumption. The national solvent emissions were calculated by subtracting the quantity of solvent transferred to waste management operations and the quantity of solvent destroyed by air pollution controls from the total national solvent consumption in 1989. The 1989 national solvent emissions were apportioned to States and counties using data from the 1988 census data base. The 1989 countylevel solvent emissions were then projected to 1990 using BEA earnings data. The resulting 1990 solvent emission inventory included emissions from both area and point sources. The 1990 countylevel point source solvent emissions were subtracted from the total solvent inventory to yield the 1990 area source solvent emissions.  PT 4%3rd tier (a)#X`2p NQX#e.h  Other Area Sources#@3rd tier (a)#(#  J&"#&U P:+Q.&P#Excluding the source categories listed below, all other area source CO, NOx, VOC,  J"SO2, and PM-10 emissions were calculated using the methodology described under the nonutility point source section. Because the majority of area source emissions for all pollutants represented uncontrolled emissions, the second and third steps described in the section on calculating emissions from nonutility point sources were not required to estimate the 1990 nonsolvent area source emissions. The area source emissions from the 1985 NAPAP Emissions Inventory that fall within this category, with the exception of those listed below, have been projected to the year"(3x-**+" 1990 based on BEA historic earnings data, BEA historic population data, DOE State Energy Data Systems (SEDS) data, or other growth indicators. The specific growth indicator was assigned based on the source category. The BEA earnings data were converted to 1982 dollars. The 1990 SEDS data were extrapolated from data for the years 1985 through 1989. All growth factors were calculated as the ratio of the 1990 data to the 1985 data for the appropriate growth indicator.  P44th tier (i)#X`2p NQX#  i. Residential Wood Combustion#%-4th tier (i)#(#  J'#&U P:+Q.&P#The 1990 residential wood combustion estimates were made using recently revised AP-42 emission factors and the EPA wood burning model. Five different types of residential wood combustion have emission factors included in AP42. Since no data were available to weight these emission factors, and because conventional woodstoves constitute the majority of woodstoves nationwide, the emission factor for conventional woodstoves was used to calculate all residential wood combustion emissions (this results in a high/conservative estimate). The wood burning model estimates consumption for all counties in the United States based on survey data from the northeast and several western States. The revised residential wood combustion emission estimates were  JR'developed as part of the NPI, and are also incorporated into the 1990 Interim/Trends Inventory.  P44th tier (i)#X`2p NQX#  ii. Natural Sources, Geogenic, Wind Erosion#-4th tier (i)#(#  J'#&U P:+Q.&P#PM-10 emissions from agricultural land wind erosion were estimated using a modified version of the NAPAP methodology. Monthly emissions were estimated from the acres of crops planted, the number of seconds, and the dust flux. The expected dust flux was based on the probability distribution of wind energy, the mean wind speed and the coefficient of drag. Allocation of the wind erosion emissions to the county level are based upon land use.  P44th tier (i)#X`2p NQX#  iii. Miscellaneous, Agriculture and Forestry#-4th tier (i)#(#  Jf'#&U P:+Q.&P#PM-10 emissions from agricultural crops were estimated using the AP-42 emission factor equation for agricultural tilling. The activity data for this calculation were the acres of land planted. The emission factor, expressed in terms of the mass of TSP produced per acretilled was corrected by parameters including the silt of the surface soil, the particle size multiplier, and the number of tillings per year. Allocation of the emissions to counties was achieved by using acres of cropland harvested as reported in the Census of Agriculture. The 1990 emissions from agricultural livestock were determined from activity data, expressed in terms of the number of head of cattle per county, and a national PM-10 emission factor.  P%44th tier (i)#X`2p NQX#  iv. Miscellaneous, Other Combustion#-4th tier (i)#(#  JX''#&U P:+Q.&P#The miscellaneous, other combustion category includes emissions from agricultural burning, forest fires/wildfires, prescribed/slash and managed burning, and structural fires. "0(4x-**(" The emissions from agricultural burning and structural fires were produced using the methodology described under the nonutility point source section. Forest fires/wildfires emissions were generated using information on the number of forest fires, their location, and the acreage burned. These data were obtained from the Department of Interior (DOI) and the U.S. Department of Agriculture (USDA) Forest Service. The amount of biomass used to determine the quantity of vegetation burned was estimated by the EPA. Average emission factors were applied to the estimated quantities of vegetation burned. Emissions from prescribed burning were based on the 1989 USDA Forest Service Inventory of particulate matter from prescribed burning. This inventory contained statelevel totals for PM-10, PM-2.5, nonmethane (used as a surrogate for VOC), CO, and several air toxics. The pollutants in the Forest Service Inventory (PM-10, PM-2.5, VOC, and CO) were distributed to the countylevel using the same countylevel distribution as was used in the 1985 NAPAP Inventory where forest acreage per county was obtained  J from local officials and State land usage maps. Emission estimates for NOx and SO2 were developed by assuming the ratio of CO to each pollutant in the Forest Service Inventory was the same as the ratio of CO to pollutant in the 1985 NAPAP Inventory. The  Jresulting 1989 emissions for CO, NOx, VOC, SO2, and PM-10 were used to represent emissions in 1990.  P44th tier (i)#X`2p NQX#  v. Miscellaneous, Fugitive Dust# -4th tier (i)#(#  Jb'#&U P:+Q.&P#PM-10 fugitive dust emissions arise from construction activities, mining and quarrying, paved road resuspension, and unpaved roads. The general methodology used to estimate emissions for each of these categories required an activity indicator, an emission factor, and one or more correction factors. The activity indicator for a given category varied from year to year, as may the overall correction factor.  Nr'0ǽ5th tier#vz  xC,&#   Construction Activities 05th tier  J"'#&U P:+Q.&P#The PM-10 emissions for 1990 were calculated from an emission factor, an estimate of the acres of land under construction, and the average duration of construction activity. The acres of land under construction were estimated from the dollars spent on construction. The PM-10 emission factor was calculated from the TSP emission factor for construction obtained from AP-42 and the PM-10/TSP ratio. Allocation of emissions to the county level was made using construction payroll statistics obtained from the County Business Patterns available from the Census Bureau.  N!'0ǽ5th tier#vz  xC,&#   Mining and Quarrying05th tier  J#'#&U P:+Q.&P#PM-10 emissions for mining and quarrying were the sum of the emissions from metallic ore, nonmetallic ore, and coal mining operations. These PM-10 emissions arise from the following activities: (1)overburden removal, (2) drilling and blasting, (3)loading and unloading, and (4) overburden replacement. Emissions from transfer and conveyance operations, crushing and screening operations, and storage and travel on haul roads were not included because of the lack of adequate activity data. "(5x-**@*"ԌTo calculate the emissions from metallic ore mining, the PM-10 emission factors for copper ore processing operations were applied to all metallic ores. The PM-10 emission factors for western surface coal mining were used to estimate the emissions from both nonmetallic ore and coal mining. Emissions were allocated to each county equally.  N8'0ǽ5th tier#vz  xC,&#   Paved Road Resuspension05th tier  J'#&U P:+Q.&P#The calculation of total PM-10 emissions for 1990 was based on the paved road VMT, the PART5 particulate matter emission factor model, and two correction factors: road surface silt loading and the number of dry days. A dry day is defined as any day with less than 0.1 inches of precipitation. This term attempts to account for the effect of precipitation on emissions. Surface silt loading values by paved road functional classes and EPA region were determined using an empirical model based on traffic volume. Total 1990 VMT data were obtained by State and road functional class. The VMT from paved roads was calculated by subtracting the unpaved road VMT (see following section) from the total VMT for 1990. The base emission factor used in the calculation of total PM-10 emissions from paved roads accounts for the emissions from the vehicle (tailpipe, brake wear, and tire wear) as well as from the interaction between the vehicle and the road surface. The fugitive dust category includes only those emissions from the road surface and not the vehicle. For this reason, the PM-10 emissions for onroad vehicles calculated as described under onroad vehicles and distributed to paved roads using VMT data were subtracted from the total PM-10 emissions for paved roads. The results were the PM-10 fugitive dust emissions for paved roads. County level emissions allocations were made using county VMT for paved roads developed for onroad vehicle sources.  N'0ǽ5th tier#vz  xC,&#   Unpaved Roadsz05th tier  JP'#&U P:+Q.&P#Total 1990 PM-10 emissions were based on the unpaved roads VMT data, the PART5 emission factor model, and the following correction factors: particle size multiplier, silt content of road surface material, mean vehicle speed, mean vehicle weight, mean number of wheels, and the number of dry days. Mean vehicle speeds were assigned to each unpaved road functional class. The number of dry days is defined in the same manner as for estimating the paved road estimates. The VMT data for unpaved roads were obtained for rural and urban road functional classes excluding local types and for local road types. As with the PM-10 emissions from paved roads, the emissions from onroad vehicles subtracted from the total emissions determined by the method described above in order to yield the PM-10 fugitive dust emissions from unpaved roads and to prevent the doublecounting of vehicle emissions. County emissions levels were allocated from State totals based upon the fraction of the total State rural land area contained in each county.  P %44th tier (i)#X`2p NQX#  vi. Biogenic Emissions#!-4th tier (i)#(#  J&'#&U P:+Q.&P#Originally, estimates for biogenic VOC emissions included in the Interim/Trends data  J''base were statelevel estimates taken from a report by Lamb et al.5 However, in late 1995, the 1990 biogenic emission estimates were replaced by countylevel biogenic"(6x-**)" emission estimates produced by EPA using the revised Biogenics Emissions Inventory System (BEIS2). Summary countylevel emissions estimates from biogenic sources for total VOC and NO were provided to Pechan as part of the Trends report effort in late July 1995, with final incorporation into the Trends data base in August 1996.  P84j1st tier (A)#X`2p NQX#C.`NPI#Y%1st tier (A)#(#  J '#&U P:+Q.&P#The NPI provides a national countylevel emissions inventory of primary particulate  J(PM-10 and PM-2.5) and precursors to secondary particulate formation " SO2, NOx,  JVOCs, SOA, and NH3 " for the base year of 1990. For nearly all source categories, the  JNPI uses VOC, CO, SO2, and NOx emissions from the 1990 Interim/Trends Inventory (the  Jj Interim/Trends Inventory does not include NH3 or SOA). However, for fugitive dust emissions from mining and quarrying, the NPI does not have emission estimates while  J the Interim/Trends Inventory does. For the NPI, revised PM-10, PM-2.5, and SO2 emissions estimates for motor vehicles were calculated based on a recently released PART5 model. The revised emission estimates have been incorporated into the latest  J 'Trends inventory.  PR4Q2nd tier (1)#X`2p NQX#1.Development Methodology#+*2nd tier (1)#(#  J$'#&U P:+Q.&P#The same methodologies that were used to develop the 1990 Interim/Trends Inventory were used to estimate NPI emissions for nearly all source categories. This section describes the additional information and different methodologies that were  J'employed in developing the NPI. For example, s part of Phase II of the NPI Study, new sizespecific information was incorporated into the PM-10 and PM-2.5 emission estimates.  J\'The most recent version of the Interim/Trends Inventory (draft version now under review) replaces previous PM emission estimates with these revised estimates.  P4%3rd tier (a)#X`2p NQX#a.h  Utilities#-3rd tier (a)#(#  J#&U P:+Q.&P#PM-10, PM-2.5, SO2, NOx, VOC, and SOA emissions were estimated for 1990 (NH3 emissions were not considered for electric utility sources). The NPIs steam electric utility  Jf'emission inventory for 1990 is the same as that in the Interim/Trends Inventory (i.e.,based on the aggregated monthly electric utility steam boilerlevel data from the DOEs Form EIA767).  P4%3rd tier (a)#X`2p NQX#b.h  Nonutility Point Sources#03rd tier (a)#(#  J #&U P:+Q.&P#The nonutility point source PM-10, PM-2.5, and NH3 emissions were calculated using  Jp!'a methodology consistent with emission estimates in the 1990 Interim/Trends Inventory. This means that nonutility point source emissions are calculated based on emission estimates from the 1985 NAPAP Inventory projected to 1990 using BEA industrial earnings data. Because annual PM-10 and PM-2.5 are not available from the 1985 NAPAP files, annual TSP emissions were used as the starting point for estimating PM-10 and PM-2.5 emissions. The following two sections outline the steps used in estimating  J&'PM-10 and PM-2.5 emissions for both the Interim/Trends and the NPI inventories. "X'7x-***"Ԍ P44th tier (i)#X`2p NQX#  i. PM-10#4-4th tier (i)#(#  J'#&U P:+Q.&P#PM-10 emissions were calculated from TSP emissions in five steps. The five steps were as follows:  JZ'Step 1:h  Controlled TSP emissions were projected to 1990 using BEA data and emission estimates from each point source in the 1985 NAPAP Inventory (excluding steam electric utilities). Emissions estimates were projected to 1990 based on the growth in earnings by 2digit SIC code industry. No growth was assumed for point sources for which the matching BEA earnings data were not complete;(#  JB 'Step 2:h  1990 uncontrolled TSP emissions were calculated from the controlled emissions and the control efficiency from the 1985 NAPAP Inventory;(#  J 'Step 3:h  1990 uncontrolled PM-10 emissions were calculated by applying an SCCspecific (uncontrolled) particle size distribution factor to the uncontrolled TSP emissions. The SCCspecific (uncontrolled) particle size distribution factors were developed based on information contained in AP42, engineering judgment, and other sources (these are the same as used in the PM-10 calculator program);(#  J'Step 4:h  The PM-10 control efficiency was determined using the PM-10 calculator program. Where SCCspecific control information was not available, the control efficiencies were based on general particulate control information found in appendix C of AP42;(#  J'Step 5:h  Controlled PM-10 emissions for 1990 were calculated by multiplying uncontrolled 1990 PM-10 emissions by the PM-10 control efficiency for the given SCC and control equipment (caveat here with problem of PM-10 emissions calculated based on the particle size distribution and PM-10 control efficiency being higher than the TSP emissions).(#  P44th tier (i)#X`2p NQX#  ii. PM-2.5# =-4th tier (i)#(#  J'#&U P:+Q.&P#PM-2.5 emissions estimates were estimated in two additional steps following development of the PM-10 estimates. The two steps were as follows:  JT 'Step 1:h  Uncontrolled PM-2.5 emissions were calculated by applying an SCCspecific particle size distribution factor to the final uncontrolled PM-10 emissions (i.e., after any replacements with TSP);(#  J#'Step 2:h  PM-2.5 emissions were calculated by repeating steps 4 and 5 from the PM-10 calculation procedure and substituting PM-2.5 for PM-10.(#  J<&NH3 emissions are not currently included in the Interim/Trends Inventory. However,  J'NH3 emissions were calculated in the NPI by projecting the 1985 NAPAP NH3 emissions to 1990 using BEA growth factors in a manner similar to that used for nonutility point  J('sources of TSP in the NPI and for other pollutants in the Interim/Trends Inventory."(8x-**Z*"Ԍ P4ԙ%3rd tier (a)#X`2p NQX#c.h  Mobile sources#`B3rd tier (a)#(#  P4#&U P:+Q.&P#4th tier (i)#X`2p NQX#  i. OnRoad Vehicles#mC-4th tier (i)#(#  J'#&U P:+Q.&P#In 1994, EPA released a computer model with the acronym PART5, which can be used to estimate particulate emission rates from inuse gasoline and dieselfueled motor vehicles. It calculates particle emission factors in gpm from onroad automobiles, trucks, and MCs, for particle sizes up to 10 microns. The EPAs PART5 model was used to calculate highway vehicle PM-10 emission factors from vehicle exhaust, brake wear, tire  Jwear, and reentrained road dust from paved and unpaved roads, and SO2 vehicle exhaust  Jemission factors. This new models PM-10 and SO2 estimates are incorporated into both  J 'the NPI and the Interim/Trends Inventory.  J< To estimate NH3 emissions from motor vehicles, NH3 emission factors for each of three different engine types (gasoline with catalyst, gasoline without catalyst, and diesel) were applied to vehicle mileage data for each of eight different vehicle types (e.g., LDGV, LDGT1). These 3 emission factors were calculated from the results of Volkswagens emission testing program for 18 different vehicles and 3 engine types. The highway vehicle emissions were estimated at the county/SCClevel using VMT estimates developed  JLfor the 1990 Interim/Trends Inventory. These NH3 estimates are not currently included  J$'in the Interim/Trends Inventory.  P44th tier (i)#X`2p NQX#  ii. Nonroad Mobile Sources#NJ-4th tier (i)#(#  J'#&U P:+Q.&P#Nonroad mobile source emissions in the NPI are based on 1990 nonroad county emission estimates compiled by EPAs EFIG Branch. These estimates include all nonroad sources except aircraft, commercial marine vessels, railroads, and fugitive road dust. Emissions from these sources were estimated by growing the applicable NAPAP source  Jcategories. Because no data were available for estimating nonroad SO2 emissions, the  JNPI and the Interim/Trends Inventory do not include SO2 estimates for this source category.  Pf4%3rd tier (a)#X`2p NQX#d.h  Solvents#M3rd tier (a)#(#  J8'#&U P:+Q.&P#Emissions from solvents are taken from the estimates provided in the 1990  J'Interim/Trends Inventory; no changes were made to this inventory for the NPI.  P4%3rd tier (a)#X`2p NQX#e.h  Other Area Sources#O3rd tier (a)#(#  J!'#&U P:+Q.&P#The basis for the emission estimates for most (nonfugitive dust) area source categories was the 1985 NAPAP Area Source Emissions Inventory, with the exception of nonroad mobile sources, and prescribed burning. As with the point sources, the 1985 NAPAP Inventory contained TSP emissions. Except where noted, these TSP emissions were forecast to 1990 using growth factors and then particle size multipliers were applied to estimate PM-10 and PM-2.5 emissions. Emissions estimates from the 1985 NAPAP Inventory were forecast to 1990 based on historical BEA earnings data, historical estimates of fuel consumption, or other categoryspecific growth indicators. "R(9x-**7+"Ԍ J'The main contribution of the Trends Inventory to the NPI is the fugitive dust PM  J'emission estimates. The Trends Inventory contains PM emission estimates for paved road dust, unpaved road dust, construction fugitive dust, agricultural tilling, cattle feed lots, and wind erosion from agricultural land. New PM-10 and PM-2.5 emission estimates for cattle feed lots were developed for the NPI, and have been incorporated into the latest  J8'version of the Trends Inventory. In addition, the previous Trends Inventory for PM-10 was estimated at the state level, with the exception of construction which was developed at the EPAregion level. Countylevel PM-10 emission estimates for cattle feed lots were  J'estimated for the NPI using activity data from the Census of Agriculture (head of cattle per county) and a PM-10 emission factor of 17 tons per 1,000 head. PM-2.5 emissions were calculated from the countylevel PM-10 emissions by applying the AP42 particle size multiplier for agricultural tilling of 0.10 or (0.476 of PM-10). These revised estimates  J 'have been incorporated into the latest version of the Trends Inventory.  J The 1990 Interim/Trends Inventory does not include estimates of NH3 emissions. For the NPI, new estimates were derived for 1990 to update those available in the 1985 NAPAP Inventory. Agricultural sources (i.e., livestock operations and fertilizer  JXapplication) account for approximately 90 percent of NH3 in current inventories. For livestock operations, activity data (countylevel estimates of number of head of cattle and  J'calves, hogs and pigs, poultry, sheep, horses, goats, and minks) were from the 1992  JCensus of Agriculture.6 Emission factors were obtained from a study of NH3 emissions  J'conducted in the Netherlands.7 For fertilizer application, which may contribute up to  J10percent of total NH3 emissions nationally, activity data were from the Commercial Fertilizers Data Base compiled by the Tennessee Valley Authority (TVA) and now  J@'maintained by the Association of American Plant Food Control Officials.8 This data base includes countylevel use of over 100 different types of fertilizers including those that emit  JNH3. The emission factors used for fertilizer application come from the same source as the livestock operations emission factors. This source lists emission factors for 10different types of fertilizers (whereas NAPAP only listed an emission factor for one type of fertilizer). The other combustion source category includes agricultural (field) burning, wildfires, structural fires, and prescribed (forest and range management) burning. Emissions for  J'agricultural burning, wildfires, and structural burning were derived using the Trends  Jmethods described above. For prescribed burning, PM-10, PM-2.5, SO2, NOx, and VOC emissions were based on a 1989 USDA Forest Service inventory of PM and air toxics from prescribed burning.  J 'As with Trends, estimates for biogenic VOC emissions at the state level were taken  J 'from a report by Lamb et al.5  P"4%3rd tier (a)#X`2p NQX#f.h  SOA Formation#3a3rd tier (a)#(#  Jj$'#&U P:+Q.&P#The 1990 Interim/Trends Inventory does not include estimates of SOA. Methods used in the NPI for assessing SOA formation draw heavily from the work of Grosjean and  J&'Seinfeld (1989).9 In that study, the researchers assigned fractional aerosol coefficients (FACs) to a wide variety of organic species to express the fraction of emissions that may form SOA. FACs are based on the reactivity of an organic compound with atmospheric oxidants and the vapor pressure of the resulting products. The FAC is expressed as a"(:x-**o," dimensionless fraction that can be multiplied by the total mass of the organic compound released, resulting in a mass of secondary aerosol formed. In order to determine SOA formation potentials for a source, the general approach was to obtain a speciation profile that listed each organic compound and weight fraction. Next, the weight fraction of each compound was multiplied by the appropriate FAC and the resulting individual formation potentials were summed to provide a sourcespecific FAC. For use with VOC inventories that contain estimates of nonmethane organic compounds, the sourcespecific FACs are adjusted to account for the presence of methane, where applicable. In determining sourcespecific FACs, an effort was made to provide conservative estimates of formation potential that would result in conservative (high) estimates of SOA formation. Additional details on the data sources and assumptions used in creating SOA formation potentials for  JH 'anthropogenic and biogenic sources can be found in the NPI report.2  P 4j1st tier (A)#X`2p NQX#D.`DATA GAPS EXISTING IN CURRENT INVENTORIES#h1st tier (A)#(#  J '#&U P:+Q.&P#The information provided below indicates the current data gaps existing in the inventories reviewed above. In the information presented below, data gaps represent missing information. However, there are also issues related to improvement of the overall quality of the data even where emission estimates already exist. These issues would be better termed weaker data elements. A brief discussion of some of these weaker data elements is also included.  P4Q2nd tier (1)#X`2p NQX#1.Interim/Trends Inventory#k2nd tier (1)#(#  J'#&U P:+Q.&P#As indicated above, the Interim/Trends Inventory was developed primarily for use in either ROM modeling or for use in providing historical trends in emissions for criteria air pollutants. A small amount of data is available for toxic and greenhouse gas emissions, however the spatial scale of those emissions is generally different than that available for criteria pollutants. With the exception of lead emissions, criteria emission estimates are available at the county level for all source categories for the majority of States. With respect to those pollutants of particular interest to SAMI the following information is  Jl'missing from the Interim/Trends Inventory:  J`Biogenic NOx (except for NO)(#  J`SO4,(#  J'`primary nitrate,(#  J'`elemental carbon,(#  J|'`organic carbon,(#  JT '`speciated VOC,(#  J,!`nonroad engine SO2 emissions (leaf blowers, lawnmowers, etc.),(#  J"'`no emissions estimates available on a finer spatial basis than county level, and(#  J"'`no emissions estimates for time periods shorter than seasonal (except ozone J#'related pollutants).(# In addition to data gaps resulting from either missing pollutants or pollutant availability at a geographic scale that is not amenable to the uses that SAMI has planned, there are also issues related to relative data quality even where emission estimates exist.  J''For example, the current nonutility point source emission estimates in the Interim/ J('Trends Inventory were derived by projecting the 1985 NAPAP emissions for these sources"(;x-***" to 1990 based upon growth indicators. As a consequence, these emissions estimates may not adequately capture facility shutdowns, new facilities, changes in operations relative to 1985 levels, or additions of new controls. Thus, while there are emission estimates available for these sources, their quality would not be of the same level as the emission estimates developed for utilities or for mobile sources, for example.  P4Q2nd tier (1)#X`2p NQX#2.NPI#v2nd tier (1)#(#  J'#&U P:+Q.&P#In many respects, the NPI inventory has the same data deficiencies as the Interim/ J'Trends Inventory because of their common heritage with respect to the methodology used  Jto develop each inventory. Thus, the NPI inventory does not included biogenic NOx, SO4,  Jj CO, primary nitrate, elemental carbon, organic carbon, or nonroad SO2 emissions. It also does not include speciated VOC per se, however speciated VOC is calculated as an intermediate in the determination of SOA. As a consequence, Pechan could fairly readily estimate speciated VOC by recalculating SOA and extracting the speciated VOC emissions during the intermediate calculational step. The NPI report listed several recommendations for improving the NPI. These recommendations are listed in order of the potential level of effort that would be needed to accomplish the tasks and/or the probable impact of the enhancements on the emission inventory:  J'If new/updated data become available, some enhancements could be made:(#  Jb'`  obtain necessary data to estimate State totals for other, nonagricultural land types (countylevel data could also be used, but this would represent a  J'deviation from methods currently used to compile the Trends Inventory).(#  J'Certain requirements would require more effort to accomplish due to limited availability of existing data and the need to do research to locate more data, or perform analyses to develop the data. An example of this would be where source testing may be needed in order to obtain data to develop new emission factors. Areas that would require more effort than those listed above include:(#  J'`  expand and/or enhance the speciation profiles needed to estimate SOA emissions from nonbiogenic VOC emissions.(#  JZ'Enhancements to the PM-2.5 emission factors, especially for the fugitive dust category would be advisable, since the area source fugitive dust category dominates the total PM-2.5 (and PM-10) emissions inventory:(# `(#  J"'h  develop source specific PM-2.5 particle size distribution data or emission factors for area sources of fugitive dust.(# "j$<x-**%"Ԍ P4j1st tier (A)#X`2p NQX#E.`ONGOING DATA COLLECTION EFFORTS#1st tier (A)#(#  P4#&U P:+Q.&P#Q2nd tier (1)#X`2p NQX#1.Ozone Transport Assessment Group#2nd tier (1)#(#  J'#&U P:+Q.&P#There are several ongoing data collection efforts that have the potential to significantly enhance the inventory data available for use by SAMI. These efforts include the inventory and/or modeling efforts by the Lake Michigan Air Directors Consortium (LADCO), the TVA, the Southern Oxidant Study (SOS), and EPAs ROM. Of greatest potential impact to SAMI is an inventory data collection effort initiated in May, 1995 by the Environmental Council of States (ECOS) and EPA. In early May, a memorandum was sent to all State Air Directors from Mary Gade (Vice President of ECOS) and John Seitz (Director of EPAs Office of Air Quality Planning and Standards [OAQPS])  Jd 'requesting their cooperation in updating the Interim Inventory with State data. Accompanying this memorandum was a second memorandum from William Hunt (Director of EPAs Emissions, Monitoring, and Analysis Division [EMAD]) indicating the types and format of data requested via the ECOS/EPA effort. Although this data collection effort was primarily targeted towards ozone modeling, the data requested included information relative to all pollutants. In addition, information concerning emission projections and control strategies for future years were also requested. A copy of the Gades/Seitz memorandum, the Hunt memorandum, the technical approach for  J$'incorporating this data into the Interim Inventory, and the requested data elements is included in appendix B. It should be noted that there is some discussion in attachment A to the Hunt memorandum of the National Inventory. This inventory is identical to the  J'Interim/Trends Inventory discussed here. The ECOS/EPA data collection effort later became known as the Ozone Transport  J4'Assessment Group (OTAG) emission inventory.  The OTAG data collection effort is  J 'functionally equivalent to the ECOS/EPA data collection effort. Only the name changed.  The OTAG inventory collection effort included collection of emissions data from the 38eastern States. It included all data collected and developed as part of the TVA ozone inventory effort. Pechan was responsible for developing the inventory for the LADCO States, as well as compiling all of the data from the remaining States into a common data base format, development of all of the quality assurance reports, and implementation of any changes in the inventory data as a result of the quality assurance procedure.  P4Q2nd tier (1)#X`2p NQX#2.Tennessee Valley Authority Acid Deposition Inventory#!2nd tier (1)#(#  J'#&U P:+Q.&P#In addition to the inventory effort oriented towards ozone modeling, the TVA is  Jv currently compiling inventory data on SO2 emissions for the OTAG domain. These emissions are being allocated to the OTAG 36 km grid cell domain and include all States in the OTAG domain, not just States associated with the TVA. The inventory is primarily being used in support of acid deposition assessment. The data being collected for the TVA Acid Deposition effort is being compiled from  J%'existing inventories.  No new emissions estimates are being developed for this inventory  J^&'effort . The inventory being compiled will include data for 3years: 1977, 1990 and a projection inventory for 2010. The 1977 inventory is derived from the Section 812 Retrospective inventory developed by Argonne National Laboratory, ICF Resources Incorporated, Abt Associates, and the Environmental Law Institute. That inventory"(=x-***" estimated emissions from each of six principal emission sectors: industrial combustion, industrial processes, electric utilities, onhighway vehicles, offhighway vehicles, and commercial/residential sources. Additionally, that inventory started as a statelevel inventory that was then allocated to the county level using the Flexible Regional Emissions Data System (FREDS) developed for NAPAP. An overview of the methodologies used to estimate emissions for the Section 812 Retrospective analysis can be found in the report The Impact of the Clean Air Act on 1970 to 1990 Emissions; Section 812 Retrospective Analysis, dated March 1, 1995 and prepared by Pechan. It should be noted that several of the source category emission estimates for that inventory are based on national topdown emission estimates developed for the EPA Emission Trends report and allocated to the statelevel using information developed by Argonne National Laboratory.  J The 1990 inventory data were taken directly from the NPI. The NPI utilized the SO2 emissions developed as part of the 1990 Interim Inventory. Both the NPI and the 1990 Interim Inventory were developed by Pechan. No changes or additions were made to the inventory for the TVA Acid Deposition inventory, other than allocating emissions to the OTAG grid. The 2010 projection inventory was compiled using two previous inventory efforts. For electric utilities, the TVA Acid Deposition inventory uses a 2010 projection developed by ICF for use in assessing emission trading among utilities. That inventory, which is at the boiler level, was developed for use in the RADM model and was prepared approximately  Jhtwo years ago. Only SO2 data were extracted from that inventory. The nonutility point source and the area source projections were developed using the 1990 Interim Inventory coupled with BEA growth factors prepared by E.H. Pechan and Associates. The TVA Acid Deposition inventory also includes some lowlevel estimates of uncertainty. The uncertainty estimates primarily involve evaluation of the uncertainty associated with parameters affecting fuel emission factors. For example the uncertainty analysis includes uncertainty evaluations of the sulfur and ash contents of various fuels. In summary, the TVA Acid Deposition inventory offers gridded emissions estimates  Jusing existing emission inventory data for SO2 only. The inventory was compiled using a variety of existing inventories and thus provides no consistency between the methods used to estimate emissions for various sources. The inventory is largely derivative of both the Emission Trends and 1990 Interim Inventory efforts. It does not include any new information collected as part of the OTAG inventory effort. While it does include some uncertainty analyses, these analyses are largely limited to parameters affecting fuel combustion emission factors. Nonfuel combustion sources were not evaluated.  P"4Q2nd tier (1)#X`2p NQX#3.Interim/Trends Inventory Changes and Modifications#Z2nd tier (1)#(#  Jj$'#&U P:+Q.&P#As indicated earlier, the nonutility point source data in the original 1990  JB%'Interim/Trends Inventory were developed by projecting 1985 NAPAP emission inventory  J&'data to 1990. EPA is currently in the process of updating the Interim/Trends Inventory with data taken from the AIRS point source data base. As part of the Trends effort,  J'Pechan attempted to develop both SO2 and PM emissions estimates from data collected as part of the OTAG effort. As indicated previously, most of the data collected for OTAG"(>x-***"  Jwas oriented towards ozone season daily emissions. Where States provided SO2 and PM data, Pechan is incorporating that information into the Trends data base. Pechan also developed a mechanism to develop estimates from activity data if emissions data for these pollutants was not provided. Unfortunately, since control information was not available for these sources, many of the emission estimates developed in this manner were significantly higher than existing emission estimates for these sources. As a consequence, Pechan has begun replacing the nonutility point source information in the Trends inventory with data from AIRS. This data effort is oriented towards replacement of all  Jsources of SO2 and PM with emissions levels greater than or equal to 250 tons/year. This criteria accounts for the majority of emissions from these sources.  PH 4Q2nd tier (1)#X`2p NQX#1.Timing/Schedule#2nd tier (1)#(#  J '#&U P:+Q.&P#Collection of emission inventory information as a result of the Seitz/Gades (OTAG) memorandum was completed in the spring of 1996. A significant quality assurance review procedure was performed involving development of emission summaries that were reviewed by each State that provided information. The review of the inventory data was completed in the summer of 1996 and the final version of the inventory was submitted in September 1996. EPA has always planned to include much of the data collected as part of the OTAG inventory effort in the Trends data base. Pechan is in the process of incorporating this data into the Trends data base. The final incorporation of this data into the data base is scheduled for the end of 1996. Incorporation of the nonutility point source emissions data extracted from AIRS for inclusion in the Trends data base is scheduled to be completed in the end of 1996. Biogenic emissions estimates produced using the BEIS2 model were completed in  J'July 1995, however they were not incorporated into the Interim/Trends Inventory data base until August 1996. These estimates should be consistent with those developed for OTAG since OTAG decided to use BEIS2 estimates in their inventory effort.  PJ4Q2nd tier (1)#X`2p NQX#2.Data Gaps That Will Be Addressed By Ongoing Efforts#2nd tier (1)#(#  J'#&U P:+Q.&P#Of the ongoing efforts discussed above, the one that has the greatest potential to address a data gap (i.e., missing or absent data) is the replacement of the nonutility  J'emissions in the Interim/Trends Inventory with data obtained from the AIRS data base or from data submitted as part of the OTAG inventory effort. Other source categories are likely to improve as the result of the OTAG (ECOS/EPA) data collection effort. For example, solvent emissions will be available as estimated directly by the States. The current solvent estimates are based upon a national mass balance approach using 1989 data. Additionally, the incorporation of State data from the OTAG (ECOS/EPA) data collection effort may provide a means for ensuring that any missing major sources within 100km of Class I areas are incorporated into the inventory, especially if these sources are new sources. "'?x-**})"Ԍ P4j1st tier (A)#X`2p NQX#F.`REMAINING DATA GAPS#1st tier (A)#(#  J'#&U P:+Q.&P#Despite the incorporation of new and updated information, there are still data gaps that exist that no current inventory addresses. The remaining data gaps include:  JZBiogenic NOx (with the exception of NO)(#  J2SO4(#  J 'primary nitrate(#  J'elemental carbon(#  J'organic carbon(#  J'speciated VOC (with the possible exception of biogenic sources)(#  Jj nonroad SO2 emissions(#  JB 'no emissions estimates available on a finer spatial basis than county level(#  J 'no emissions estimates for time periods shorter than seasonal (except ozone J 'related pollutants).(# Additionally, incorporation of the OTAG inventory data and the nonutility point  Jz'source data derived from AIRS will create a disconnect between the current Interim/ JRTrends Inventory and the NPI for NH3 and PM2.5. By incorporating new and/or revised  J*emission sources into the Trends inventory based on OTAG/AIRS data, corresponding NH3 and PM2.5 emissions will be either incorrect or absent. EPA is funding the update of these emissions, however, these revisions will not be completed until the end of 1996.  Pb4j1st tier (A)#X`2p NQX#G.`RECOMMENDATION FOR BASE YEAR INVENTORY#01st tier (A)#(#  J4'#&U P:+Q.&P#Pechan recommends that the following approach be adopted to develop a 1990 base year inventory for use by SAMI:  J'`1.The starting point for the inventory should be the 1990 Interim/Trends Inventory with revisions developed from the OTAG inventory effort as well as supplemental data derived from AIRS for the nonutility point source data. This is the most comprehensive inventory currently available for the SAMI region and it is amenable for the majority of the efforts anticipated as part of SAMI.Ƥ!  J'`2.Supplement the 1990 Interim/Trends Inventory with data on TSP, PM-2.5,  J|and NH3 emission information developed as part of the NPI. Where necessary, develop revised estimates for these pollutants for sources added in number1 from either the OTAG or AIRS data. Develop speciated VOC emissions using intermediate calculation from SOA determination for nonbiogenic sources.Ƥ!  J$'`3.Add speciated biogenic emissions from BEIS2. Although BEIS2 is not the official EPA model, it is widely recognized as an enhanced biogenic emissions estimation model. Depending upon the speciation requirements of SAMI, either the mainframe or the personal computer (PC)based BEIS2 model could be used to develop these estimates.Ƥ! "(@x-**+"ԌThe approach outlined above will produce a countylevel annual emission inventory  Jfor all criteria pollutants (except Pb), NH3, speciated VOCs, PM-2.5, and TSP. Ozone season daily emissions would be available for ozone related pollutants, and seasonal emissions could be calculated for all criteria pollutants. There are additional positive aspects to the approach outlined above for producing a base year inventory. First, the majority of the inventory data is already available for 1990 in a computerized format. Second, the format of the computerized inventory is consistent with producing an input file that can be handled by the ROM model preprocessing system (which could be used to produce a gridded inventory). Finally, the  Jp'majority of the enhancements to the 1990 Interim/Trends Inventory that are the result of the OTAG/AIRS data incorporation efforts should be complete by the end of 1996 and will have been funded by other organizations.  P 4j1st tier (A)#X`2p NQX#H.`RECOMMENDATIONS FOR FILLING DATA GAPS#1st tier (A)#(#  J '#&U P:+Q.&P#It should be understood that data gaps would still exist with regard to SAMIs requirements following development of the base year inventory using the steps above. Pechans recommendations for filling these remaining data gaps, data gaps that must be filled within one year, and data gaps that would lead to better data or reduced uncertainty (but are not crucial to fulfillment of SAMIs mission) are provided in this section.  PhQ2nd tier (1)#X`2p NQX#1.Biogenic NOx#2nd tier (1)#(#  J\#&U P:+Q.&P#Total biogenic NOx emissions will be difficult to estimate with existing emission inventory tools for biogenic sources. A possible solution would be to perform a literature  J search to determine whether literature information on NO/NOx ratios for different types of biogenic sources are available. If available, this ratio could then be applied to existing  JNO emissions estimates to develop total biogenic NOx estimates. Without an indepth knowledge of the availability of this information, it is impossible to determine at this point whether or not the effort required to obtain such data and apply it would be small or large. Since biogenic NO estimates already exist and probably account for a great deal of the  Jbiogenic NOx emissions, this data gap is considered a lower priority that could probably be filled on a time frame longer than 1 year.  PT hQ2nd tier (1)#X`2p NQX#2.SO4 and Primary Nitrate#2nd tier (1)#(#  J&"#&U P:+Q.&P#If the interest in SO4 is for primary sulfate emissions, then the EPAdeveloped SPECIATE program could be utilized in conjunction with existing inventories of total suspended particulate to determine the fraction of particulate mass that is sulfate. A similar approach could be utilized to develop primary nitrate emissions. If sulfate  J%emissions resulting from the combination of SO2 and water (H2O) vapor in the atmosphere are the desired information (i.e., secondary sulfate emissions), then estimates of relative humidity would be required. If this is the desired emissions estimate, then air quality  J(models would probably provide a better estimate of SO4 formation than would an inventory effort."(Ax-**,"ԌIf visibility and/or acid deposition assessments are the primary driving force behind the need for these emissions estimates, then these data gaps need to be filled within a 1year time frame.  P`4Q2nd tier (1)#X`2p NQX#3.Elemental Carbon and Organic Carbon#2nd tier (1)#(#  J2'#&U P:+Q.&P#As with primary nitrate and sulfate, EPAs SPECIATE program could be used in conjunction with total suspended particulate emissions to develop an emission inventory for elemental and organic carbon. This approach was used to develop an elemental/organic carbon inventory for the Grand Canyon Visibility Transport Commission (GCVTC). In order to properly assess visibility, this data gap needs to be fulfilled within 1year.  P hQ2nd tier (1)#X`2p NQX#4.Nonroad SO2#2nd tier (1)#(#  J '#&U P:+Q.&P#Although the Interim/Trends Inventory does contain emission estimates for aircraft, locomotive, and commercial marine vessels, it does not contain emission estimates from smaller engines (lawn mowers, leaf blowers, etc.). Emissions estimates for the small  JLengine nonroad SO2 sources will be difficult to develop. One potential approach would be to develop ratios of emissions from older emission inventory efforts (perhaps the NAPAP inventory) with a pollutant that currently has a reasonable nonroad emission inventory  Jvalue (CO, NOx, or VOC would be the prime candidates). This ratio would then be used  Jwith the emission estimates for that pollutant to provide nonroad SO2 estimates.  JAlternatively, estimates of SO2 emissions from this source category could be developed using estimates for this source that were developed as part of the NAPAP emission inventory. This would require a projection of the emissions from 1985 to 1990, in addition to development of a method of allocating the total emissions (which were all that were estimated for NAPAP) to individual engine categories. Total 1985 NAPAP emissions from this category were 92,000 short tons. As a consequence, Pechan feels that this source  Jcategory is not likely to be a very significant contributor to overall SO2 emissions in the SAMI region, and probably would not be significant on smaller scales (such as Class I areas) as well. This data gap can be filled on a time frame longer than 1 year.  P4Q2nd tier (1)#X`2p NQX#5.Speciated VOCs#>2nd tier (1)#(#  Jv '#&U P:+Q.&P#As indicated in the draft interim report, speciation information for VOCs was utilized in an intermediate step towards producing secondary organic aerosol information for the NPI. As a consequence, these speciation profiles could be applied to the SAMI inventory to develop speciated VOC emissions estimates. Unless there is an overriding requirement for speciated VOC emissions for a particular assessment, this data gap could probably be filled on a time frame longer than 1 year. "6'Bx-**)"Ԍ P4Q2nd tier (1)#X`2p NQX#6.Spatial Data Finer than County Level#t2nd tier (1)#(#  J'#&U P:+Q.&P#Without a decision on the types of air quality modeling that will be carried out, it is impossible to assess whether or not emissions information at a spatial level below that of counties is required for SAMI. In its current format, the data in the National Trends Inventory (and the potential updates discussed in the draft interim report) could be allocated to grid levels if those grids are the same as those currently required for ROM. Allocation to other grid sizes could be performed, but the effort required would be greater (since a methodology for allocating the National Trends Inventory to the ROM grids already exists). The degree of effort required would be dependent upon: 1) the spatial resolution required; and 2) the availability of information and/or techniques with which to perform the allocation. This is especially true if microscale inventories in Class I areas are required. If important for air quality modeling, this data gap should be filled on a 1 year time frame.  Pz4Q2nd tier (1)#X`2p NQX#7.Emissions Estimates for Time Periods Shorter than Seasonal#2nd tier (1)#(#  JL'#&U P:+Q.&P#Without a decision on the types of air quality modeling that will be carried out, it is impossible to assess whether or not seasonal emissions estimates are adequate for the purposes of SAMI. Peak ozone season daily emissions for ozonerelated pollutants are already available. If shorter time periods are required for other pollutants, then allocation factors available from the ROM efforts could be used to allocate annual emissions to other time periods, as long as these time periods are not less than whole days. These allocation factors are revisions to those developed for the 1985 NAPAP emission inventory. If important for the air quality modeling effort (or for another assessment), this data gap should be filled within 1 year.  Pl4j1st tier (A)#X`2p NQX#I.`REFERENCES#1st tier (A)#(#  J>'#&U P:+Q.&P#1.`U.S. Environmental Protection Agency, Source Receptor Analysis Branch, Regional  J'Interim Emission Inventories (19871991), Volume I: Development Methodologies, EPA454/R93021a, Research Triangle Park, NC, May 1993.(#  J'2.`E.H. Pechan & Associates, Inc, Development of the OPPE Particulate Programs  Jv 'Implementation Evaluation System, prepared for U.S. Environmental Protection Agency, Office of Policy Planning, and Evaluation/Office of Policy Analysis, September1994.(#  J#'3.`E.H. Pechan & Associates, Inc, Phase II Regional Particulate Strategies, Task 2:  J$'Emission Estimates, Draft Report, prepared for U.S. Environmental Protection Agency, Office of Policy, Planning, and Evaluation, May 1995.(#  J6''4.`U.S. Environmental Protection Agency, National Air Pollutant Emission Trends, 1900 J('1994, EPA454/R95011, Research Triangle Park, NC, October 1995.(# "(Cx-**)"Ԍ J'5.`Lamb B., D. Gay, H. Westberg, and T. Pierce, A Biogenic Hydrocarbon Emission  J'Inventory for the USA Using a Simple Forest Canopy Model, Atmospheric  J'Environment, Volume 27A, pp. 16731690, 1993.(#  J`'6.`Bureau of the Census, 1992 Census of Agriculture Geographic Area Series 1A, 1B,  J8'and 1C, U.S. Department of Commerce, 1992.(#  J'7.`Asman, William A.H., Ammonia Emissions in Europe: Updated Emission and Emission Variations, National Institute of Public Health and Environmental Protection, Biltoven, The Netherlands, May 1992.(#  JH '8.`Tennessee Valley Authority, Commercial Fertilizers Data 1989 and 1990, National Fertilizer Research Center, Muscle Shoals, AL, 1990.(#  J '9.`Grosjean, D. and J.H. Seinfeld, Parameterization of the Formation Potential of  J 'Secondary Organic Aerosols, Atmospheric Environment, Volume 23, No. 8, pp. 17331747, 1989.(#" Dx-** " Chapterhead"Ex-**"Ԓ  =y2xdddFy   `An# 2p NQH* #CHAPTER III yCONTROL COST ESTIMATION TECHNIQUES FOR fREGIONAL CONTROL STRATEGY ANALYSES"Chapterhead"    y3xH dddFxy    P4c#&U P:+Q.&P#j1st tier (A)#X`2p NQX#A.`INTRODUCTION#1st tier (A)#(#  J '#&U P:+Q.&P#The Clean Air Act (CAA) Amendments of 1990 will require major reductions in air  J pollutant emissions including PM-10, VOC, NOx, SO2, CO, and hazardous air pollutants (HAPs). One of SAMIs objectives is to evaluate the emission reductions and costs associated with controls required by the CAA and then determine if additional emission reductions will be required to protect and preserve the delicate ecosystems and natural resources of the Southern Appalachians, especially Class I areas. This chapter of the report summarizes available modeling techniques for estimating emission reductions and costs associated with CAArequired controls applicable to the SAMI States, and the applicability of the techniques for estimating emission reductions and costs for controls beyond those required by the CAA. Appendix C of this report presents text and tables that summarize controlefficiency and costeffectiveness data for  Jselected control strategies for stationary and mobile sources of VOC, NOx, CO, PM, and  JNH3. The information is not a comprehensive list of available control strategies for all stationary and mobile source categories; however, it does provide perspective on the range of control efficiencies and costs for various source categories.  P*4j1st tier (A)#X`2p NQX#B.`MODELING TECHNIQUES#c1st tier (A)#(#  J'#&U P:+Q.&P#The discussion of modeling techniques for estimating emission reductions and control costs provides an overview of cost data and cost estimation techniques presented in NAPAP and models which have been developed since NAPAP. The section also discusses the applicability and limitations of the techniques for the SAMI region.  P44Q2nd tier (1)#X`2p NQX#1.NAPAP Emission Reduction and Cost Estimation Techniques#2nd tier (1)#(#  P 4#&U P:+Q.&P#%3rd tier (a)#X`2p NQX#a.h  Background#3rd tier (a)#(#  J!'#&U P:+Q.&P#NAPAP Report 25 addresses the applicability, performance, and costs of control  J"technologies and other measures for SO2, NOx, and VOCs from stationary and mobile sources in the utility, industrial, residential/commercial/institutional, and transportation  J`$sectors.1 For utilities, the report focuses on SO2 and NOx controls. For stationary  J8%industrial and mobile sources, the report focuses on SO2 and NOx controls, with some emphasis on VOC controls. To support impact analyses associated with alternative control policies, a system of models was developed to calculate emission reductions, control costs, and emission projections. The following briefly summarizes the models and control technologies in the report.(Fx-**+e'#2FEx2ansfer Operations H '#W 3FagH 320,000 capacity) Ԍ P4ԙ%3rd tier (a)#X`2p NQX#b.h  Summary of the Model#3rd tier (a)#(#  J'#&U P:+Q.&P#The Emissions and Control Integrated Model Set (ECIMS) was developed for the NAPAP Integrated Assessment to model emission reductions, control costs, and emission projections associated with alternative control policies for the U.S. utility, industrial, residential/commercial/institutional, and transportation sectors. ECIMS consists of a system of mainframe computer models. The Emissions and Cost Aggregation Model (ECAM) is a submodel of ECIMS that aggregates control strategy costs and emission  J'reductions from the sectoral models to calculate total direct control costs.aY  FJ 'ԍFor this chapter, control strategy and control measure are used interchangeably to mean a method or technique for controlling emissions from stationary or mobile sources. This aggregation eliminates doublecounting of costs and the omission of major costs. Several sectormodels are used to calculate the direct cost of an emissioncontrol policy. The Advanced Utility Simulation Model (AUSM) was developed to calculate control costs for electric utilities. AUSM allows switching or blending of different types of coal but does not allow switching from coal to natural gas. The Industrial Combustion Emissions (ICE) model was developed to calculate control costs for industrial boilers. This model chooses the boiler fuel and a corresponding emissions control based on a comparison of the aftertax, discounted, net cash flows for the various alternatives. The Process Model Projection Technique (PROMPT) was developed to calculate control costs for process energy use sources. The Volatile Organic Compounds Model (VOCM) was developed to calculate control costs for VOC emission sources. The PROMPT and VOCM models use step functions to calculate costs for various levels of control based on engineering estimates.  J'Adetailed description of ECIMS is presented in NAPAP Report 26.2 In discussions with personnel at Argonne National Laboratories, it was learned that all of the sectoral models and economic driver computer programs were never completely linked onto a single computer platform. This linkage is necessary to fully automate data transfer and feedback between the individual models. Instead, ECIMS was used only once " to develop the NAPAP Integrated Assessment. Since that analysis, there has not been any effort to further refine ECIMS. Although some of the work that was completed on individual sectormodels has been extended and refined, many of the models have not been updated, and ECIMS has not been fully integrated or made more userfriendly. Additional model refinements and data that would improve ECIMS are discussed in NAPAP Report 26. The mainframe computer ICE model has been converted for use on a PC. ICE  Jestimates annualized costs associated with control strategies for SO2, NOx, PM, and SO4 emissions from industrial boilers. The model generates annualized costs in 1980 dollars. A preliminary review of the documentation for ICE was not clear on the sources of the data inputs. Further review of the documentation will be necessary to assess the userfriendliness of the model (e.g., identifying the sources of data inputs, procedures for entering the data inputs into the model, and modeling control strategy impacts for sources located in the SAMI region). Also, information was not available to assess the types of control strategies and sources of cost data for the control strategies. The control strategy  Jj$information for NOx is more than likely outdated and should be updated with information published in EPAs Alternative Control Techniques (ACT) documents and other"B%G x-**'" information sources published since 1990. Further review of the model will be needed to determine if the control strategies and associated control cost data will need to be updated  J'to model CAArequired controls and controls not required by the CAA.3  P`4%3rd tier (a)#X`2p NQX#c.h  Coverage of Emission Sources in the NAPAP Emissions Inventory#3rd tier (a)#(#  J2'#&U P:+Q.&P#Documentation on ECIMS is not available; consequently, the types of control strategies or control measures included in the model and the source categories to which they are applied could not be evaluated. Presumably, the model includes the commercially available control technologies and other control strategies such as emissions trading programs. Further evaluation will be necessary to assess the comprehensiveness of the control strategies included in ECIMS relative to the regional models discussed below. The NAPAP report is an excellent reference for information on commercially available  J and emerging technologies and other control measures particularly for SO2 and NOx emissions from existing and new utility and industrial boilers. The summary of the report (pages 25384 through 25387) provides tables that summarize control efficiencies and costs for alternative control technologies for utility boilers and industrial processes.  J*The SO2 and NOx control technologies summarized for utility boilers include the following:  J'`Fuel changes (i.e., substitution and coal cleaning/benefication); (#  J'`Flue gas cleaning (i.e., wet flue gas desulfurizaton, lime spray drying, WellmanLord, sorbent injection, and selective catalytic reduction); and(#  Jb`Combustion modification (i.e., gas reburning overfire air and low NOx burners, urea/ammonia injection).(#  JThe SO2 and NOx control technologies summarized for industrial fuel combustion sources include the following:  Jr'`Coalfired boilers(#  JJ'`  Fuel changes (i.e., substitution and coal cleaning/benefication); (#  J"'`  Flue gas cleaning (i.e., sodium and dual alkali flue gas desulfurizaton, lime spray drying, and urea injection); and (#  J'`  Combustion modification (i.e., lowexcess air).(#  J'`Oilfired boilers(#  JZ'`  Flue gas cleaning (i.e., sodium and dual alkali flue gas desulfurizaton); and(#  J2 '`  Combustion modification (i.e., lowexcess air, fluegas recirculation, and stagedair combustion).(#  J"'`Natural gasfired boilers(#  J#'`  Flue gas cleaning (i.e., selective catalytic reduction and ammonia injection); and (#  JB%'`  Combustion modification (i.e., fluegas recirculation and stagedair combustion).(# "&Hx-**c)"Ԍ J'`Process heaters(#  J`  Combustion modification (i.e., lowexcess air and low NOx burners).(#  J'`Municipal waste combustors(#  J`'`  Flue gas cleaning (i.e., lime spray drying, duct sorbent injection, ammonia injection, and urea injection).(# The report also presents information on advanced combustion technologies for the utility and industrial sectors. These technologies include the atmospheric fluidized bed combustor, pressurized fluidized bed combustor, and integrated coalgasification combined cycle process. At the time Report 25 was published, these technologies were being designed to increase energy efficiency resulting in lower emissions than conventional combustion technologies. The atmospheric fluidized bed combustor is widely used in industrial facilities, particularly in international markets, and was being demonstrated on a scale appropriate for utility applications. The other two technologies were undergoing field demonstration at scales appropriate for utility application. Since the report was published 5 years ago, information would have to be obtained on the results of the demonstration projects to evaluate the applicability of these technologies for repowering existing or installation at new facilities. Other advanced combustion technologies discussed include the natural gasfired combined cycle process, steam injection turbines, coalfired turbines, fuel cells, and slagging combustors. The natural gasfired combined cycle process is used in both the utility and industrial sectors, where the basic technology has been modified for cogeneration of electricity and steam. This technology offers increased efficiency and  Jlower SO2 and NOx emissions relative to conventional pulverized coal boilers. The steam injection turbine has been developed as a lower cost alternative to the natural gasfired combined cycle process. Coalfired turbines, fuel cells, and slagging combustors are in the developmental stages. Fuel cells offer the potential of very high efficiencies combined  Jxwith low levels of SO2 and NOx emissions. In 1990, a fuel cell demonstration project was under way; however, the report indicates that fuel cell technology will not likely be commercially available until well past the turn of the century. The slagging combustor is being developed as an alternative for reducing oil consumption. The technology allows a new coal combustor to replace an existing oil combustor while maintaining and using most of the other equipment in the plant. The slagging combustor is commercially available for smallscale units and was undergoing demonstration on a scale applicable to utilities in 1990. The slagging combustor offers the potential for converting from oil to coal if the price of oil increases to the point that it is more economically attractive to use coal. Section 7 of the report discusses alternative emission costcontrol strategies for electric utilities. These include emissions rollback strategies (i.e., uniform rollback, leastcost, and leastemissions dispatching); emissions trading (i.e., marketable permits, bubbles, and banking); and emission targeting strategies. The focus of the discussion is on alternative strategies for consideration during the CAAA debates for controlling emissions from electric utilities. Since section 7 was prepared, Title IV of the CAAA have  J&been implemented which establishes an emissions trading program for controlling SO2  J'emissions from electric utility boilers. Industrial sources of SO2 emissions may elect to  J(opt into the program. Consequently, additional regulatory initiatives for controlling SO2"(Ix-**7+8" emissions from sources subject to TitleIV will need to be designed within the framework  Jof the Title IV program. In addition, the average cost of SO2 allowances traded under the TitleIV program is significantly less than originally anticipated. Additional research on  Jestimating the costs of SO2 controls for sources affected by Title IV should account for the actual cost of allowances. Section 10 of the Report 25 discusses VOC emission controls for industrial processes. The discussion on VOC controls includes adsorption, absorption, condensation, combustion/incineration, equipment standards for floatingroof tanks, maintenance/special equipment, and VOC control technique performance and costs. The discussion on VOC controls, including performance and costs, is very general and not process specific. Section 11 of the report discusses mobile source control technologies and techniques. Since section 11 was prepared, many of the CAArequired control programs for mobile sources have been or are being implemented. In addition, significant developments may have been made on technologies and techniques that were considered in the developmental stages prior to 1990. Additional research on estimating the costs of mobile source controls will need to account for more recent cost and emission reduction estimates associated with the CAArequired control programs and any new technological developments that may have occurred since section 11 was prepared.  P4%3rd tier (a)#X`2p NQX#d.h  Modeling of DemandSide EMOs##83rd tier (a)#(#  J'#&U P:+Q.&P#Section 8 of the report provides a comprehensive discussion of demandside EMOs. The discussion focuses on conservation, load shaping, selfgeneration, and fuel switching. Section 8 also summarizes the results of studies at the power pool/State and regional/national levels. Barriers to and issues associated with demandside EMOs (i.e.,regulatory constraints, market failures, policy barriers, and other barriers) are also discussed. Documentation on the ECIMS model was not available to determine if the model was used to evaluate the demandside EMOs for the NAPAP Integrated Assessment.  PJ4%3rd tier (a)#X`2p NQX#e.h  Applicability to the SAMI Region#;3rd tier (a)#(#  J'#&U P:+Q.&P#The sectoral models and economic driver computer programs for ECIMS were never completely linked onto a single computer platform, many of the models have not been updated, and ECIMS has not been fully integrated or made more userfriendly. Consequently, the model is not readily adaptable for evaluating impacts associated with control strategies for the SAMI region. Further investigation will be necessary to determine if ECIMS or any of its sectoral models can be adapted for SAMIs purposes. The information presented in NAPAP Report 25 on control technologies and measures that were commercially available in the late 1980s is probably outdated if the source categories to which they apply are affected by the CAAA. Information published on emission reductions and control costs associated with regulations developed to implement the CAAA should be reviewed for more accurate estimates of emission reductions and costs. A useful component of Report25is that it does identify emerging technologies for stationary fuel combustion and mobile sources which may have become commercially available since 1990. Further research on these technologies may be useful in identifying opportunities for additional controls on sources in the SAMI region. "(Jx-**Z*"ԌA source category which is not mentioned in Report 25 but has been receiving significant attention recently is municipal solid waste (MSW) landfills. The EPA is planning to finalize a new source performance standard (NSPS) for new, and guidelines for existing, MSW landfills in the Fall of 1995 to control nonmethane organic compound emissions. States are expected to use the guidelines to develop rules for existing landfills. The NSPS and guidelines will also achieve significant reductions in methane emissions. EPA has been conducting research on the utilization of methane from landfills to produce electricity, steam, pipeline quality gas, and motor vehicle fuel (i.e., compressed natural gas and methanol). To offset costs associated with the NSPS, affected MSW landfills may be seeking opportunities for landfill gas utilization. In addition, in some States, landfill gas utilization projects have been implemented to offset closure costs. SAMI may want to consider opportunities for MSW landfill gas utilization projects as an alternative energy source while reducing emissions from landfills, and possibly offsetting emissions from conventional energy sources (e.g., coalfired boilers) that would otherwise be used to produce energy.  P 4Q2nd tier (1)#X`2p NQX#2.Emission Reduction and Cost Analysis Model (ERCAM)#F2nd tier (1)#(#  PR4#&U P:+Q.&P#%3rd tier (a)#X`2p NQX#a.h  Background#$H3rd tier (a)#(#  J$'#&U P:+Q.&P#Initially, ERCAM was developed under contract to EPA in 1988 to support regional and national impact analyses of ozone control programs. The model uses parameters contained in emission inventories to provide a technique for quickly estimating the emission reduction and cost impacts associated with alternative control strategies or measures for stationary and mobile sources. ERCAMVOC was developed first to analyze the emission reductions and costs associated with the various Congressional alternatives during the debate over the CAAA. Themodel was subsequently used to analyze the impacts associated with the General Preamble for implementing the ozone and CO requirements of Title I of the CAAA. The model has been continually updated with new control strategies and to refine the techniques for estimating emission reduction and cost  J'impacts of the techniques.4 Since 1994, the model has been undergoing revisions to support EPAs reviews of the  J'national ambient air quality standards (NAAQS) for ozone and PM-10.49 The revisions have involved updating information on controls included in the model, and adding information on new controls associated with EPA and State and local agency regulatory efforts. For example, information for the Title III maximum achievable control technology (MACT) standards for bulk terminals, coke oven batteries, dry cleaning, halogenated solvent cleaners, pulp and paper, marine vessel loading, and the hazardous organic National Emission Standard for Hazardous Air Pollutants (NESHAP [HON]) for the synthetic organic chemical manufacturing industry (SOCMI) was incorporated into the model. Source categories for which control strategies were updated include wood furniture coating, plastic parts coating, industrial adhesives, architectural and industrial  J$'maintenance coatings, commercial bakeries, and pesticides.4  P<&4%3rd tier (a)#X`2p NQX#b.h  Summary of the Model#P3rd tier (a)#(#  J('#&U P:+Q.&P#ERCAM consists of separate data bases that are linked with emission inventory data to estimate costs and emission reductions for the utility, nonutility point source, motor"(Kx-**3*" vehicle, and area/nonroad source sectors. The data bases consist of control strategy files  Jfor stationary and mobile sources of VOC, NOx, CO, PM-10, and PM-2.5. The modeling objectives of ERCAM are:  J8'Provide quick response estimates of emission reductions and costs for alternative control strategies;(#  J'Incorporate multiple control strategies for each source category so that the costs and emission reductions of different levels of control can be examined;(#  J'Design the model so that SIP inventories for 1990 can be easily incorporated as they become available;(#  JH 'Estimate control costs and emission reductions at the national, regional, State, nonattainment area, or attainment area levels;(#  J 'Provide cost estimates in terms of 1990 dollars so that projected costs can be easily updated using price indices;(#  J 'Provide accurate results at the nonattainment area level as well as at the national level; and(#  JX'Provide results in a spreadsheet format.(#  J'The model is currently programmed to use the Interim Inventory data bases as direct  J'input. In this way, as the Interim Inventory data bases are updated to incorporate State inventories or other changes, no programming changes will be needed to incorporate the revised inventories into ERCAM. Control and cost information for the model is organized by cost pod in the control strategy data bases to facilitate linkage of each control strategy to individual emission sources. A pod is a group of source types, as defined by SCCs or area source categories (ASCs), which have similar process and emission characteristics, control techniques, and control cost methodologies. A cost pod may have one or several control strategies which consist of the control technique, efficiency, and cost input parameters for each strategy. The basic structure of the control strategy data bases is shown in tableIII-1. A unique ERCAM simulation is defined by:  J'Projection year;xbY  F'ԍChapter IV of this report discusses the emissions projection component of ERCAM.x(#  J'Scenario file name (specifies a set of control strategies);(#  J`Whether Title IV NOx controls for utilities are included;(#  J8'Point source size cutoffs for reasonably available control technology (RACT) for ozone nonattainment areas; and(#  J 'Motor vehicle scenario name (specifies control strategies for motor vehicles at the county level).(# "p#LXx-**%"Ԍ P4#X`2p NQX#  Table  III1. Basic Elements of Control Strategy Data Base #I2PQPP#у | AX ;;,^  add<M< < | x L   Pod Source category or grouping of SCCs for control purposes   Pod Name Descriptive name of pod CS Control strategy code CS Name Control strategy name (e.g., selective catalytic reduction)   Reduction Percent emission reduction associated with the control strategyl   Cost Parameters Sizespecific cost equation parameters for capital and operations and maintenance (O&M) costs; cost per ton estimate for sources where sizespecific equations are not applicable or available.l J '#&h P:+Q.&P# The scenario file designates which control strategies will be applied to a pod. For  Jiexample, table III2 shows a sample of an ERCAMNOx scenario file applied to ozone  JA'nonattainment areas.7 The variable ATTCAT specifies the ozone nonattainment area classification to which the control strategies are applied. The control strategy code specifies which control strategy will be applied to each ozone nonattainment category and pod combination, and can be varied to examine the impacts of alternative levels of control on a source category. A complete set of attainment category/pod/control strategy combinations is referred to by a unique three character string such as CAA or MAX. The emission reduction and cost parameters associated with the control strategy are stored in the control strategy file.  Ph#X`2p NQX# #Table  III2. ERCAMNOx Scenario File #I2PQPP#у ^ add<M< < ddTbM9" T" ^  l <A  "8" "wATTCAT8" POD8" CS8"A8"PCTRD8"SPODNAME"<A@b" "8" T2 1" 2  21 33 39 45 53 79 Utility BoilerCoal, Wall"@@" "" T2 2" 3  33 42 53 72 79 83 Utility BoilerCoal, Tangential"@@" "" T2 3" 4  28 33 39 42 61 72 75 Utility BoilerGas/Oil, Wall"@@" "" T52 20" 1  80 Industrial BoilerPulverized Coal"@@" "" T5* 2 21* " 1*  *  80*  Industrial BoilerStoker"@,A" "+" T5V!+2 22V!+" 1V!+ V!+ 80V!+ Industrial BoilerResidual Oil,A**  "+?"  eA!'*?Notes#;2PQJqP#NOTES:ATTCAT = ozone nonattainment classification"  eAP"'`ttPOD| |  = source category code"  eA"'`ttCS| |  = control strategy identifier " used to match to the appropriate control strategy" `tt| |  in the cost equation file"  eAB$'`ttPCTRD = percentage reduction associated with the control strategy levels"  eA$'`ttPodname = source category name" In this scenario, a 33 percent reduction (CS = 2) would be applied to wallfired coal utility boilers (POD = 1) in marginal  eA&'nonattainment areas (ATTCAT = 2)p*ӌNotes#I2PQPP#*V!? J''#&h P:+Q.&P# "X(Mx-**)""ԌThe CAA major source size definitions are chosen as the default RACT source size cutoffs. Other cutoffs may be specified, with separate cutoffs for each ozone nonattainment area classification. Currently, ERCAM is not programmed to specify the major source size cutoffs for moderate and serious PM-10 nonattainment areas. However, the model can be easily programmed to incorporate the PM-10 nonattainment area classification cutoffs for major sources. Similar to the stationary source scenario, a motor vehicle scenario is also chosen for a model simulation. This is specified by a unique threecharacter string. The motor vehicle scenario file specifies which set of MOBILE5a emission factors to apply to each county. Flags are included to designate the types of control strategies. In addition, the control strategies can be overlapped to assess the cumulative impacts of combinations of strategies. This file also drives which cost parameters to apply to estimate the cost of motor vehicle controls. The motor vehicle cost file contains costs for each of these options in terms of dollars per registered vehicle, dollar per mile traveled, or dollar per new vehicle. All output is retained at the county/SCC level and a number of reports can be generated from these files. Reports can be generated as machine readable (dBase, LOTUS, text files) or hard copy text. Options for reporting are shown in table III3. Reports include baseline, projected, and controlled emissions, annual costs, and cost per ton of pollutant reduced.  Ph4A#X`2p NQX#O ,Table  III3. ERCAM Report Options #I2PQPP#у ^ddTbM9" T"  ddlN\ ^ *TV! R  ParametermR OptionsT4 R8  Emissions8 Annual or ozone season daily4m 8  Geographic Level8 National, State, Region, Nonattainment Classification, Nonattainment Area, or Northeast Ozone Transport RegionD E  Source CategoryE Pod, Tier 1, or Tier 2 LevelDE J'A#&h P:+Q.&P# The cost algorithms and control efficiencies in ERCAM are developed from EPA reports, technical reports prepared by State or local air agencies to support development  JA'of regulations, journal articles, or the OAQPS Control Cost Manual. The form of the cost algorithms depend on how published data can be linked to emission inventory parameters. For example, for stationary VOC sources, ERCAM generally uses average costeffectiveness and emission reduction values developed for control strategies to estimate  J!control costs. For NOx control strategies for boilers, the cost algorithms use the design capacity, fuel heat content, and fuel consumption rate. For PM-10 control strategies for boilers, the cost algorithms use the stack gas flow rate to estimate costs.  P%4%3rd tier (a)#X`2p NQX#c.h  Coverage of Emission Sources in the Interim Inventory#3rd tier (a)#(#  J&'#&U P:+Q.&P#The coverage of source categories by control strategies included in ERCAM varies depending on the pollutant. ERCAM includes control strategies that cover all of the  J('stationary VOC sources in the Interim Inventory. Control strategies for stationary VOC"(Nx-**)'" sources include all of the major Federal regulatory programs such as RACT, NSPS, NESHAPs, and MACT standards. In addition, several control strategies based on State and local regulations and the California Federal Implementation Plans for ozone nonattainment areas have been included. For stationary source categories for which controls will be initiated as required by the CAA and for which control cost data are not available, a generic cost effectiveness of $2,000 per ton of VOC reduction is used until cost data become available to replace the generic estimate. A generic cost effectiveness of $2,000 to $10,000, and an average of  J$5,000, is used to estimate the costs of additional VOC and NOx reductions needed by ozone nonattainment areas when their adopted control measures fall short of achieving  JH 'the reductions needed to meet their rateofprogress target.4 These generic values can be changed depending on the existing level of control for different source categories.  J For stationary NOx sources, control strategies are included for source categories  J covered by an ACT document, NSPS, or Title IV CAA requirements. Title IV NOx controls represent the standards for utility boilers mandated under the CAA. Each existing unit has been identified as a phase 1 or phase 2 unit and control strategies have been selected  J0to bring units into compliance with the Title IV NOx standards.7 For stationary PM-10 sources, the model includes control strategies representative of reasonably available control measures and techniques (RACM/RACT) and best available control measures and techniques (BACM/BACT), where cost data are available. As a part of EPAs review of the NAAQS for PM-10, methods are also being developed to estimate emission reductions, costs, and cost effectiveness associated with control strategies for  JPM-2.5.8,9 Control strategies for stationary sources of SO2, CO, and NH3 have not been  J'incorporated into ERCAM. Additional work on including control strategies for industrial  Jsources of SO2 is anticipated in the near future. For stationary CO sources, emission reduction and cost algorithms for iron and steel manufacturing, aluminum production, solid waste disposal, and chemicals manufacturing are available and can be programmed  JP'for use in ERCAM.10  JOnroad motor vehicle control strategies for VOC, NOx, CO, PM-10, and PM-2.5 included in ERCAM are basic and enhanced I/M programs, reformulated gasoline and diesel fuels, oxygenated fuels, CAAtailpipe standards, and California low emission vehicle (LEV) program. Control strategies for nonroad motor vehicles include Federal initiatives for compression and sparkignition engines, reformulated gasoline and diesel fuels, and additional controls based on information obtained from the California Air Resources Board  J '(CARB).49  P!4%3rd tier (a)#X`2p NQX#d.h  Use of ERCAM for Modeling DemandSide EMOs#3rd tier (a)#(#  J#'#&U P:+Q.&P#Currently, ERCAM is not programmed to model demandside EMOs for the electric utility industry. An offline analysis would have to be completed to assess the potential effects of demandside EMOs on the operating capacity and emissions of affected boilers (at the SCC level). The results of the analysis would then be reviewed to determine if the EMOs can be programmed into ERCAM. "'Ox-**+'"Ԍ P4%3rd tier (a)#X`2p NQX#e.h  Applicability of ERCAM to the SAMI Region#ߓ3rd tier (a)#(#  J'#&U P:+Q.&P#Provided that the Interim Inventory (or an inventory that is derivative of the Interim Inventory) is used as the base inventory for the SAMI States, ERCAM is currently available to provide quickresponse emission reduction and control cost estimates for CAArequired control strategies. The model can also provide estimates of postcontrol emissions at the SCC level which can then be used in ambient models for estimating residual emission impacts associated with controls. The model also contains nonCAA required control strategies which can be used to estimate the impacts of additional control strategies if needed, or can be easily revised to include new control strategies for source categories for which additional controls may be needed. Relative to other models, ERCAM provides the most comprehensive coverage of both stationary and mobile source categories for the pollutants of interest to SAMI.  J 'To adapt ERCAM to an inventory format different from that of the Interim Inventory, it will be necessary to review the content and format of the inventory to determine if any changes are necessary. Potential problems include the following:  JR'`Data fields in the inventory needed for input to ERCAM may not align with ERCAMs structure. The inventory data fields could be changed to align them with ERCAMs structure;(#  J'`Pods would have to be matched with source categories if the inventory contains  J'SCCs or ASCs different from those used in the Interim Inventory;(#  J:'`For mobile sources, base year VMT estimates for the inventory may be different from the base year from which ERCAM projects VMT estimates. This problem would need to be corrected; and (#  J'`Some State inventories may not contain the data required for inputs to ERCAM equations. Where data are lacking, generic costeffectiveness values may have to be used in lieu of sourcespecific parameters to estimate control strategy impacts. (# One limitation of the model is that it is not programmed to automatically optimize for the leastcost control strategy for individual sources to which more than one strategy is applied. However, the model can be used to generate the cost and cost effectiveness of alternative control strategies for sources, and then the results can be analyzed for a leastcost solution through a postprocessing step. A second limitation is that ERCAM currently is not programmed to model demandside EMOs for the electric utility industry. An offline analysis would have to be completed to assess the potential effects of demandside EMOs on the operating capacity and emissions of affected boilers (at the SCC level). The results of the analysis would then be reviewed to determine if the EMOs can be programmed into ERCAM. "j$Px-**9%'"Ԍ P4Q2nd tier (1)#X`2p NQX#3.AIRCOST/PC#2nd tier (1)#(#  P4#&U P:+Q.&P#%3rd tier (a)#X`2p NQX#a.h  Background#3rd tier (a)#(#  J'#&U P:+Q.&P#The model was developed under contract to the Congress Office of Technology Assessment (OTA) and EPA to simulate the effects of different emission control strategies on each major steam electric generating unit in the contiguous United States. The model was developed to replace the AUSM mainframe computer model developed for the NAPAP Integrated Assessment program. The AUSM model was complicated and difficult to operate. Consequently, AIRCOST/PC was developed to streamline the methods used in the AUSM model to calculate impacts associated with alternative control policies for electric utility boilers. AIRCOST/PC has been widely used to examine proposed acid rain control policies in studies under the NAPAP and for analyses of CAA Amendments of 1990. The model was used by OTA in its analysis of proposed acid rain mitigation legislation (H.R. 3400) and in  J 'the preparation of its publication on the acid rain phenomenon, Acid Rain and  J'Transported Air Pollutants: Implications for Public Policy, as well as in OTA responses to Congressional requests. This work included estimates of base case and control strategy  JLprojections of SO2 and NOx emission rates for electric utilities over the period 1980 through 2030. Modifications were made to AIRCOST to allow analysis of all States and estimation of several alternative control strategies with one model run. In addition to  Jusing the model to analyze the effects of alternative SO2 controls on electric utilities, the  Janalyses included the extension o