Dataset Information

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

ABSTRACT: Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.

SUBMITTER: Kelly JT

PROVIDER: S-EPMC6859642 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Assessing PM<sub>2.5</sub> Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

Kelly James T JT Koplitz Shannon N SN Baker Kirk R KR Holder Amara L AL Pye Havala O T HOT Murphy Benjamin N BN Bash Jesse O JO Henderson Barron H BH Possiel Norm N Simon Heather H Eyth Alison M AM Jang Carey C Phillips Sharon S Timin Brian B

Atmospheric environment (Oxford, England : 1994) 20190101

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM<sub ...[more]

PMID: 31741655

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Project description:BACKGROUND:Exposure to particulate matter, particularly with aerodynamic diameter <?2.5?µm (PM2.5), may increase inflammation and oxidative stress in pregnant women and affect fetal growth. We examined trimester specific PM2.5 exposure levels and small for gestational age (SGA) using the statewide birth registry of Ohio from 2007 to 2010. METHODS:Exposure to PM2.5 in each trimester and for each gestational week was determined using data from 57 Environmental Protection Agency network monitoring stations across the state of Ohio. We restricted the data to 224,921 singleton live births, with a gestational age of 20-42 weeks, no genetic disorders or congenital abnormalities, and who had home addresses within a 10?km radius of any PM2.5 monitoring station. We estimated odds ratios of SGA using Generalized Linear Models (GLMs) and Distributed Lag Models (DLMs), and adjustment for maternal age, race, education, parity, body mass index, insurance type, tobacco use, prenatal care initiation, birth year, season of birth, and sex of the baby. RESULTS:Mean PM2.5 levels during the entire pregnancy were 13.03?µg/m3 with a standard deviation of 1.57?µg/m3. Covariates adjusted odds ratios and 95% confidence intervals of a 10?µg/m3 increase in PM2.5 levels with a 10?km buffer radius for SGA and trimesters modeled separately were 0.94 (0.88, 1.00) for the first trimester, 0.93 (0.86, 1.00) for the second trimester, 1.07 (1.00, 1.15) for the third trimester, and 0.92 (0.81, 1.06) for the entire pregnancy. When a 5?km buffer radius was used, adjusted odds ratios and 95% confidence intervals for SGA were 0.97 (0.89, 1.05) for the first trimester, 0.96 (0.88, 1.05) for the second trimester, 1.09 (1.02, 1.17) for the third trimester, and 0.99 (0.85, 1.14) for the overall pregnancy, indicating sensitivity to buffer choice. DLMs showed gestational weeks 30-35 to be a particular window of vulnerability. CONCLUSION:Increasing exposure to PM2.5 during the third trimester of pregnancy was associated with a small increase in risk of SGA in this population-based study. Selection of a buffer radius significantly impacted our results in the first trimester, but not in the third trimester.

Dataset Information

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

Publications

Assessing PM<sub>2.5</sub> Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015.

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