Source-resolved variability of fine particulate matter and human exposure in an urban area

Abstract

Abstract. Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx (Particulate Matter Comprehensive Air quality Model with Extensions) was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36×36, 12×12, 4×4, and 1×1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12×12 km resolves the urban–rural gradient. Increasing resolution to 4×4 km resolves large stationary sources such as power plants, and the 1×1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12×12 km. Predominantly local pollutants such as elemental carbon and primary organic aerosol have gradients that can only be resolved at the 1×1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population-weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis at the county level.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

“…Distinguishing these emission centers and collocating them with their corresponding population at high resolution affirm the highest contributions from the industry sector rather than the residential combustion emissions in western Java. These findings are consistent with recent studies, showing the importance of high-resolution simulations for location-specific sectoral contributions to air quality, 62,69 especially for the Global South with isolated sources. In summary, this study exploits recent developments in a global community model (GCHP) to examine the sensitivity to resolution of the spatial heterogeneity of global population exposure and sectoral contributions, with insights to understudied regions in the Global South.…”

“…Similar effects are found in July (Figure S10). In addition, the fractional contributions of residential combustion are decreased at high resolution due to a greater extent of collocation with rural areas, consistent with a prior study showing higher contributions of residential combustion in suburban areas . For example, in western Java, the contributions of industrial emissions are concentrated in cities especially of Jakarta, Depok, Bogor, and Bandung, while residential combustion contributions shift away from major cities to rural areas with less population density (Figure S11).…”

“…In addition, the fractional contributions of residential combustion are decreased at high resolution due to a greater extent of collocation with rural areas, consistent with a prior study showing higher contributions of residential combustion in suburban areas. 62 For example, in western Java, the contributions of industrial emissions are concentrated in cities especially of Jakarta, Depok, Bogor, and Bandung, while residential combustion contributions shift away from major cities to rural areas with less population density (Figure S11). Distinguishing these emission centers and collocating them with their corresponding population at high resolution affirm the highest contributions from the industry sector rather than the residential combustion emissions in western Java.…”

“…Primary species particularly benefit from better resolving emissions and concentrations at high resolution with large PW-NRMSD of 126% for BC and 62% for POA. For example, the concentrations of BC are particularly enhanced in cities and biomass burning regions, consistent with prior reports. ,, In the Andean region, BC pollution enhancements in central Chile and point sources in Peru, Bolivia, and Argentina are resolved at C360 but obscured at C48 (Figure S1). SS exposure is higher by 14.2% globally at low resolution because of exaggerated intrusion of SS distribution into coastal lands.…”

Cooking emissions are a major source of racial-ethnic air pollution exposure disparities in the United States

“…We believe that this difference is attributable to uncertainty in the county-level National Emissions Inventory (NEI) cooking data; the NEI was used by Tessum et al as part of their modeling [15]. We hypothesize that cooking emissions are not widely recognized as a major contributor to air pollution, so they have not received as much attention when developing emission inventories such as the NEI (especially compared to mobile sources) [29,30].…”