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Atmospheric Modeling and Analysis Research


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EPA and the states are responsible for implementing the National Ambient Air Quality Standards (NAAQS) for ozone and particulate matter (PM).  New standards for eight hour average ozone and daily average PM2.5 concentrations implemented recently.  Air quality simulation models, such as the Community Multiscale Air Quality (CMAQ) model, are central components of the air quality management process at national, state, and local levels. 

Research Projects

Community Multi-scale Air Quality Model (CMAQ)
CMAQ is a powerful computational tool used by EPA for air quality management, and by the National Weather Service to produce daily forecasts for ozone air quality. The model is also used by states to assess implementation actions needed to attain National Ambient Air Quality Standards. CMAQ includes emission, meteorology, and chemical modeling components which help scientists reduce uncertainties in model simulations.

Community Modeling and Analysis System (CMAS)
EPA instituted the Community Modeling Analysis System (CMAS) Center in 2001 to provide community air quality modeling support, share ideas and techniques, and encourage the growth of the modeling community. The center is currently operated under contract by the University of North Carolina at Chaplel Hill's Institute for the Environment.

Air Quality Model Evaluation International Initiative (AQMEII)
The Air Quality Model Evaluation International Initiative (AQMEII) is a joint project with the European Commission Joint Research Centre based in Brussels, Belgium. EPA scientists are co-leading the international research collaboration in an effort to build a common strategy for regional and global model development and evaluation.

Air Quality and Climate Change Interactions
EPA scientsts are developing techniques for dynamically downscaling future global climate scenarios to create regional and local climate scenarios. These techniques will help EPA assess effects of future climate change on air quality, water quality and availability, heat stress, health, ecosystem exposures, and changes in extreme events.

Modeling Air Quality Impacts on Terrestrial and Water Quality
Clean water and air come from healthy, sustainable ecosystems. To restore and protect ecosystems, EPA scientists are bringing together air quality models, meteorolgy models, and hydrology models to determine the effects air quality can have on ecosystem health. Workng together, these models can help scientists and policy makers beter understand factors that contribute to air pollution-driven impacts on ecosystems.

Development of a Local-to-Global Air Quality Modeling System
As allowable levels of air pollutants become lower, it's becoming more important for scientists to understand exactly where pollution is coming from on a local-to-global scale. To address this need, EPA scientists are developing modeling frameworks representing complex interactons between physical, chemical and dynamical processes.

Researchers Examine Nanoparticles' Impact on Fuel Emissions and Air Pollution
EPA scientists are working with colleagues in the U.K. to sample air in areas in England where cerium oxide-based fuel additives are being used in diesel buses. Using data collected in this study, the scientists are building a mdoel to examine how cerium oxide changes fuel emissions.

Emissions Modeling Research
Six common pollutants are found in the environment from three major sources: man-made, vegetation, and natural sources. EPA scientists estimate pollution from these sources by using data from maps, models, satellites, field measurements, laboratory studies, and other sources. These estimations help inform decision makers and protect human health.

Fine-Scale Atmospheric Modeling for Use in Human Exposure and Health Studies
EPA scientists are developing pollutant dispersion models that simulate the way pollutants move through and collect in the air around us. This research will give air quality managers enhanced science and a new suite of modeling tools for developing and implementing ambient air quality policies that protect human health. 

CMAQ Aerosol Module
CMAQ Ecosystem ExAtmospheric particulate matter (PM) is linked with acute and chronic health effects, visibility degradation, acid and nutrient deposition, and climate change. Accurate predictions of the PM mass concentration, composition, and size distribution are necessary for assessing the potential impacts of future air quality regulations and future climate on these health and environmental outcomes.  The objective of this research is to improve predictions of PM by advancing the scientific algorithms, computational efficiency, and numerical stability of the CMAQ aerosol module.

GLIMPSE is an EPA modeling tool used to find U.S. policy scenarios that simultaneously improve air quality, human health, reduce impacts to ecosystems, and mitigate climate change. It is designed to be fast as well as comprehensive — to allow decision-makers to explore a range of options, to maximize benefits to air quality and reduce climate change impacts.

CMAQ for Air Toxics and Multipollutant Modeling
The Multipollutant version of the Community Multiscale Air Quality model (CMAQ-MP) predicts many air toxics including acrolein, formaldehyde and mercury compounds. Chemial and aerosol processes in CMAQ-MP allow scientists to determine how emission control strategies affect air pollutants and toxics, such as ozone or mercury, withing a single model application.

Coupled WRF-CMAQ Modeling System
EPA is developing a coupled atmospherics-chemistry models - the two-way coupled WRF-CMAQ modeling systems - to address the needs of emerging assessments for air quality-climate interactions and for finer-scale air quality applications.

Computational Science
Distributed or so-called "massively parallel" processing enables efficient computation of problems with very large problem sizes. However, programming for effective distributed processing requires careful code design and structuring.

Dynamic Evaluation of a Regional Air Quality Model
Air quality models are often used to predict changes in air quality due to changes in emissions (e.g. potential emission control measures) or changes in meteorology (e.g. predicted climate change in future years). A dynamic evaluation approach focuses on model-predicted pollutant responses comin from changes in emissions or meteorology, and introduce challenges that- traditional operational and diagnostic evaluation methods don't present.

Linking Air Quality to Aquatic and Terrestrial Ecosystems
Ecosystem exposure occurs when stressors and receptors occur at the same time and place. In order to model the exposure, models for different media (e.g. air, water, land) must be linked together. Linkages between models for air, water, and land can occur through the use of consistent input data such as land use and meteorology and through the appropriate exchange of data at relevant spatial and temporal scales.

Linking to Ecosystem Serivces
Humans benefit from many resources supplied by natural and managed ecosystems. Collectively, these benefits are known as ecosystem services and include products like clean air, clean water, food and fiber. Ecosystem services are distinct from other ecosystem products and functions because there is human demand for these natural assets.

Multiscale Meteorological Modeling for Air Quality
Meteorological modles are an important part of air quality modeling systems that evolve with the stat of science. Because of this evollution, EPA scientsts frequently challenge established models and configurations, examining not only new physics schemes, but also data assimilation strategies. By challenging established methodolgy, scientists aim to improve meteorologiccal model simulations and reduce uncertainty in air quality simulations.

Operational Performance Evaluation of Air Quality Model Simulations
To help EPA evaluate operation performance of meteorological of air quality simulations, researchers develped the Atmospheric Model Evaluation Tool (AMET). AMET uses open-source database and R statistics software, providing a powerful system for processing meteorological and air quality model output and then evaluating the performance of model predictions.

Planetary Boundary Layer Modeling for Meteorology and Air Quality
EPA has a new Asymmetric Convective Model (ACM2) that can represent both super-grid-scale and sub-grid-scale components of turbulent transport in the convective boundary layer of earth's atmosphere. ACM2 is in the latest releases of the Weather Research and Forecast (WRF) model and Community Multiscale Air Quality (CMAQ) model and is being extensively used by the air quality and research communities.

Probabilistic Model Evaluation
During probabilistic evaluation, EPA uses a combination of deterministic air quality models and statistical methods to determine probabilistic estimates of air quality. These models and methods work together to inform and strengthen confidence in air quality management decisions.

Watershed Deposition Tool
Developed by EPA to provide an easy to use tool for mapping the deposition estimates from CMAQ to watersheds to provide the linkage between air and water needed for TMDL (Total Maximum Daily Load) and related nonpoint-source watershed analyses.

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