Main Title |
Regional Modeling Analysis of the Dependencies of Atmospheric Oxidants to Perturbations in NOx and Hydrocarbon Emissions. |
Author |
Mathur, R. ;
Schere, K. L. ;
|
CORP Author |
Computer Sciences Corp., Research Triangle Park, NC.;Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Research and Exposure Assessment Lab. |
Publisher |
1993 |
Year Published |
1993 |
Report Number |
EPA-68-WO-0043; EPA/600/A-93/080; |
Stock Number |
PB93-180925 |
Additional Subjects |
Atmospheric chemistry ;
Air pollution ;
Oxidizers ;
Regional analysis ;
Nitrogen oxides ;
Hydrocarbons ;
Photochemical reactions ;
Ozone ;
Trends ;
Three-dimensional calculations ;
Concentration(Composition) ;
Temporal distribution ;
Troposphere ;
Northeast Region(United States) ;
Regional Oxidant Model
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
NTIS |
PB93-180925 |
Some EPA libraries have a fiche copy filed under the call number shown. |
|
07/26/2022 |
|
Collation |
10p |
Abstract |
Atmospheric distribution of photochemical oxidants has been a subject of interest and concern not only because of their deleterious effects on human health and vegetation, but also because of their crucial role in determining the chemical composition of the atmosphere. The paper examines some issues related to the distribution and production of photochemical species and presents an analysis of results obtained from applications of a comprehensive three-dimensional regional scale photochemical model over the northeast United States. The Regional Oxidant Model (ROM) is used to simulate the response of various photochemical species to specific anthropogenic emissions strategies involving NOx and hydrocarbon reductions. Domain and temporal averages of predicted concentrations are examined for various species. Their relative influence on oxidant chemistry over the modeled domain is investigated. The relative benefits of reductions in NOx and hydrocarbon emissions on predicted ozone levels are also examined. Overall, model predictions show good qualitative agreement with expected trends. |