Long-term particulate matter modeling for health effects studies in California – Part 1: Model performance on temporal and spatial variations

Abstract

Abstract. For the first time, a decadal (9 years from 2000 to 2008) air quality model simulation with 4 km horizontal resolution and daily time resolution has been conducted in California to provide air quality data for health effects studies. Model predictions are compared to measurements to evaluate the accuracy of the simulation with an emphasis on spatial and temporal variations that could be used in epidemiology studies. Better model performance is found at longer averaging times, suggesting that model results with averaging times ≥ 1 month should be the first to be considered in epidemiological studies. The UCD/CIT model predicts spatial and temporal variations in the concentrations of O3, PM2.5, EC, OC, nitrate, and ammonium that meet standard modeling performance criteria when compared to monthly-averaged measurements. Predicted sulfate concentrations do not meet target performance metrics due to missing sulfur sources in the emissions. Predicted seasonal and annual variations of PM2.5, EC, OC, nitrate, and ammonium have mean fractional biases that meet the model performance criteria in 95%, 100%, 71%, 73%, and 92% of the simulated months, respectively. The base dataset provides an improvement for predicted population exposure to PM concentrations in California compared to exposures estimated by central site monitors operated one day out of every 3 days at a few urban locations. Uncertainties in the model predictions arise from several issues. Incomplete understanding of secondary organic aerosol formation mechanisms leads to OC bias in the model results in summertime but does not affect OC predictions in winter when concentrations are typically highest. The CO and NO (species dominated by mobile emissions) results reveal temporal and spatial uncertainties associated with the mobile emissions generated by the EMFAC 2007 model. The WRF model tends to over-predict wind speed during stagnation events, leading to under-predictions of high PM concentrations, usually in winter months. The WRF model also generally under-predicts relative humidity, resulting in less particulate nitrate formation especially during winter months. These issues will be improved in future studies. All model results included in the current manuscript can be downloaded free of charge at http://faculty.engineering.ucdavis.edu/kleeman/.

A Statewide Nested Case–Control Study of Preterm Birth and Air Pollution by Source and Composition: California, 2001–2008

“…A major asset of this large study is the wealth of air pollution metrics. California has the densest ambient PM measurement network of any state in the United States, and detailed emissions inventories ( Hu et al 2015 ). Rich environmental data sets (e.g., PM species measurements and receptor-oriented source apportionment studies at multiple sites) were available to support exposure model application and evaluation.…”

“…The respective strengths and limitations of the various air pollution metrics used in this study, notably for their use in epidemiological studies of pregnancy outcomes, have been discussed extensively in other papers ( Benson 1989 ; Hu et al 2014a , 2014b , 2015 ; Laurent et al 2013 , 2014 ; Wu et al 2009b ) and in a report ( Wu et al 2016 ). Briefly, the interpolation of ambient measurements for PM 2.5 , NO 2 , and O 3 using EBK avoids biases from assigning data from one single monitor to populations living farther away ( Laurent et al 2014 ).…”

“…The chemical transport models capture spatial variability in ambient concentrations better, but are less capable at capturing temporal variability. However, they cover pollutants for which measurement data are very scarce, such as ultrafine PM ( Hu et al 2014b ), chemical species in PM ( Hu et al 2014a , 2015 ), and source-specific primary PM ( Hu et al 2014b ). Validation studies have been conducted for UCD_P ( Hu et al 2014a , 2014b ) and UCD/CIT ( Hu et al 2015 ) to identify those particle size fractions, chemical components, and sources that are suitable for inclusion in epidemiologic studies.…”

“…However, they cover pollutants for which measurement data are very scarce, such as ultrafine PM ( Hu et al 2014b ), chemical species in PM ( Hu et al 2014a , 2015 ), and source-specific primary PM ( Hu et al 2014b ). Validation studies have been conducted for UCD_P ( Hu et al 2014a , 2014b ) and UCD/CIT ( Hu et al 2015 ) to identify those particle size fractions, chemical components, and sources that are suitable for inclusion in epidemiologic studies. Only four major sources of primary PM that passed the direct validation checks (agreement between modeled concentrations and the results of source apportionment studies across several locations and in different episodes in California, as explained above and in “Description of the source constraint checks performed to directly evaluate the accuracy of simulated source contributions using the UCD_P model” in the Supplemental Material) were included in the present study.…”

“…The daily mass concentration of primary PM (PM emitted directly into the atmosphere) and of secondary PM (formed in the atmosphere from gas-phase precursors) was estimated at 4-km spatial resolution across two domains covering 92% of the California population for the period of 2000–2008, using the University of California-Davis/California Institute of Technology (UCD/CIT) chemical transport model ( Hu et al 2015 ). The UCD/CIT model includes a complete description of atmospheric transport, deposition, chemical reaction, and gas-particle transfer ( Hu et al 2015 ). This model provided mass concentration estimates for primary PM total mass and for several chemical species in PM [OC, EC, nitrates, sulfates, ammonium and secondary organic aerosols (SOA)].…”