Common Problem Areas
Referencing: The referencing problems are generally oversights or incorrect pages. Other problems include secondary and tertiary references (for example, the SI is a secondary reference, but the analytical data package, well logs, or field notebooks attached to the SI are primary references), listing a reference not cited in the text, wrong reference numbers, and not referencing statements of fact.
Maps:The primary problem is the absence of detailed maps. Also problematic is illegibility and missing information such as scale, north arrows, direction of ground water flow, sample locations, and detailed legends for inserted boundaries, points of interest, and shaded areas. Also. All information added to a map needs to be supported by a reference.
Aquifer Interconnection: This is one of the most commonly disputed and questioned portions of the ground water pathway. Problems include; insufficient documentation; incorrect/inaccurate interpretation and documentation of technical reports; not mentioning conflicting references; not meeting the HRS aquifer definition; and confusing geologic descriptions.
Analytical Data: The most common problem is the absence of good quality analytical data to document HRS factors. Other problems include: lack of a data validation report; incorrect use or interpretation; matching the appropriate HRS factor with level of data quality; misunderstanding qualifiers; and incomplete presentation in the text. CLP data is a known quality, but may not be the best. If QA/QC reports are not included in the scoring package, they should be made readily available.
Background Levels: Background locations are commonly inappropriate. Background levels should ideally reflect conditions in the media minus influences caused by the site you are evaluating. Therefore, background and release samples should be as similar as possible, and the degree of similarity/dissimilarity should be documented. Background levels for ubiquituous or naturally occurring hazardous substances should be established using more data than simply the results of a single background sample.