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New directions: Atmospheric chemical mechanisms for the future
Citation:
Kaduwela, A., D. Luecken, W. Carter, AND R. Derwent. New directions: Atmospheric chemical mechanisms for the future. ATMOSPHERIC ENVIRONMENT. Elsevier Science Ltd, New York, NY, 122:609-610, (2015).
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Description:
The chemical reaction scheme or mechanism used to represent atmospheric chemical reactions is at the heart of each air quality model used in research and policy applications to predict and analyse the complex air pollutants: ozone, air toxics and PM2.5. This is necessarily only an approximation of the actual fundamental chemical processes that are occurring but the mechanism should incorporate available information on chemical kinetics and reaction pathways and should be the conduit through which the fundamental science of atmospheric chemistry is applied to solve real-world problems. The efficiency and effectiveness of policies developed to reduce exposure to harmful pollutants depend on how well the mechanisms reflect the actual chemistry. If the mechanism has reaction pathways that are incorrectly characterised or completely missing, the resulting predictions may underestimate emission reduction requirements needed to meet public health and ecosystem protection targets, or may overstate the emission reductions needed and cause unnecessary implementation costs. It is therefore essential that mechanisms utilise the best, most up-to-date atmospheric chemistry information available so that policy development is based on air quality model predictions that are robust, transparent and free from scientific challenge. We are concerned that this may not continue to be the case.
