The influence of air quality model resolution on health impact assessment for fine particulate matter and its components

Abstract

Health impact assessments for fine particulate matter (PM2.5) often rely on simulated concentrations generated from air quality models. However, at the global level, these models often run at coarse resolutions, resulting in underestimates of peak concentrations in populated areas. This study aims to quantitatively examine the influence of model resolution on the estimates of mortality attributable to PM2.5 and its species in the USA. We use GEOS-Chem, a global 3-D model of atmospheric composition, to simulate the 2008 annual average concentrations of PM2.5 and its six species over North America. The model was run at a fine resolution of 0.5 × 0.66° and a coarse resolution of 2 × 2.5°, and mortality was calculated using output at the two resolutions. Using the fine-modeled concentrations, we estimate that 142,000 PM2.5-related deaths occurred in the USA in 2008, and the coarse resolution produces a national mortality estimate that is 8 % lower than the fine-model estimate. Our spatial analysis of mortality shows that coarse resolutions tend to substantially underestimate mortality in large urban centers. We also re-grid the fine-modeled concentrations to several coarser resolutions and repeat mortality calculation at these resolutions. We found that model resolution tends to have the greatest influence on mortality estimates associated with primary species and the least impact on dust-related mortality. Our findings provide evidence of possible biases in quantitative PM2.5 health impact assessments in applications of global atmospheric models at coarse spatial resolutions.

The influence of model spatial resolution on simulated ozone and fine particulate matter for Europe: implications for health impact assessments

“…However in this study, statistical averaging was used to estimate pollutant concentrations at the coarsest resolutions, and therefore differences in emissions and meteorology and their atmospheric processing between the resolutions were not included. In contrast, Li et al (2015) found annual mean PM 2.5 concentrations simulated at a resolution of ∼ 2.5 • in the US to be similar to PM 2.5 concentrations simulated at a resolution of ∼ 0.5 • suggesting that the horizontal scales being compared and the methodology for comparison are important. However maximum PM 2.5 concentrations which occur in highly populated regions were found to be 21 % lower at the coarse resolution (Li et al, 2015).…”

“…For PM 2.5 -related health estimates, studies by Punger and West (2013) and Li et al (2015) both found that attributable mortality associated with long-term exposure to PM 2.5 in the US was lower for their coarser resolution simulations (> 100 km) due to lower simulated PM 2.5 concentrations in densely populated regions. However, Thompson et al (2014) found that using model horizontal resolutions of 36, 12 and S. Fenech et al: The influence of model spatial resolution on simulated ozone and fine particulate matter 5767 4 km had a negligible effect on changes in PM 2.5 concentrations and associated health impacts.…”

“…As outlined above, a number of studies have analysed the effect of model resolution on O 3 and PM 2.5 concentrations but few have looked at the sensitivity of the associated health impacts to model resolution (Punger and West, 2013;Thompson et al, 2014;Li et al, 2015). Punger and West (2013) found mortality associated with long-term exposure to O 3 in the US to be 12 % higher at a 36 km resolution compared to the mortality estimate at 12 km, as a result of higher O 3 simulated at the coarser-scale.…”

Influence of Grid Resolution in Modeling of Air Pollution from Open Burning

“…Air quality modeling is a tool that can be applied to the air quality management system by using various scenarios of related variables (e.g., emission source characteristics, emission rates, climate, population growth, etc.) [9,10]. Because of some limitations of monitoring approach (e.g., locations, instruments and maintenance costs), modeling of air quality is an alternative approach that is widely used for scientific and regulatory purposes [10,11].…”

“…[9,10]. Because of some limitations of monitoring approach (e.g., locations, instruments and maintenance costs), modeling of air quality is an alternative approach that is widely used for scientific and regulatory purposes [10,11]. Modeled concentrations of air pollutants can provide more spatial and temporal variations than monitoring data.…”