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

Abstract

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62–126%) and secondary (26–35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Changing Responses of PM2.5 and Ozone to Source Emissions in the Yangtze River Delta Using the Adjoint Model

“…Those uncertainties are expected to be reduced along with the improvement of chemistry mechanisms in the GEOS-Chem adjoint. Besides, the model horizontal resolution (≈25–30 km) may misrepresent emission impacts on air pollution to some extent as it could not fully capture the detailed pollution distributions and colocated air pollutant hotspots, especially for urban regions. − Finally, the effect of the COVID-19 lockdown on short-term emission change was not quantified in this work. As presented in Figure S20, there was no steep reduction in emissions or sudden change in sectoral proportion in 2020, implying that air pollution controls dominated the reductions of anthropogenic emissions from a long-term perspective.…”

Impact of Model Spatial Resolution on Global Geophysical Satellite-Derived Fine Particulate Matter

“…7−9 Recent advances in global fine-resolution CTMs have demonstrated better capability of resolving fine-scale air pollution patterns. 5,6,10,11 Nonetheless, the spatial heterogeneity of the simulated surface PM 2.5 to AOD relationship is complicated by the coupled effects of the spatial variation of PM 2.5 and of the covariation extent between the surface and total column aerosol abundance. The effects of model resolution on the accuracy of geophysical PM 2.5 inference are largely unknown and require further investigation.…”

“…3,16 Nonlinear chemical mechanisms further deteriorate an accurate representation of secondary aerosols at coarse resolution. 6,10,17 For example, a prior study found that enhanced mixing between oxidants and precursors at coarse resolution (∼200 km) leads to 18% more sulfate production over southeastern Europe than at fine resolution (∼25 km). 6 Spatial gradients of air pollution are especially better resolved at fine resolution over mountainous regions with strong local pollution and weak dispersion.…”

“…6,10,17 For example, a prior study found that enhanced mixing between oxidants and precursors at coarse resolution (∼200 km) leads to 18% more sulfate production over southeastern Europe than at fine resolution (∼25 km). 6 Spatial gradients of air pollution are especially better resolved at fine resolution over mountainous regions with strong local pollution and weak dispersion. 3,18,19 T h i s c o n t e n t i s Despite large spatial gradients of ground-level PM 2.5 , variations of the simulated surface to column relationship are modulated by the covariation extent between total column AOD and surface PM 2.5 , which depends on various factors including vertical distribution, composition-and size-dependent properties including hygroscopicity and extinction cross section, and local meteorological conditions such as relative humidity (RH) and boundary layer (BL) dynamics.…”

“…Exposure to ambient fine particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5 ) has been identified as the leading environmental risk factor for the Global Burden of Disease in 2019 . However, challenges remain for accurate ambient PM 2.5 exposure due to limited ground monitoring networks and pronounced spatial gradients of surface PM 2.5 . − To bridge this gap, prior studies have employed a geophysical approach that combines satellite retrievals of aerosol optical depth (AOD) and chemical transport model (CTM) outputs to derive long-term surface PM 2.5 concentrations worldwide. , The performance of geophysical PM 2.5 inference relies on concurrent improvements of satellite retrievals of AOD and of CTMs. Although the resolution of satellite AOD can be as fine as 1 km, global CTMs at a resolution as coarse as 200 km have been applied to simulate the relationship of surface PM 2.5 to AOD. − Recent advances in global fine-resolution CTMs have demonstrated better capability of resolving fine-scale air pollution patterns. ,,, Nonetheless, the spatial heterogeneity of the simulated surface PM 2.5 to AOD relationship is complicated by the coupled effects of the spatial variation of PM 2.5 and of the covariation extent between the surface and total column aerosol abundance.…”