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Improving Estimates of PM_2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm.

ABSTRACT: Improved characterization of ambient PM_2.5 mass concentration and chemical speciation is a topic of interest in air quality and climate sciences. Over the past decades, considerable efforts have been made to improve ground-level PM_2.5 using remotely sensed data. Here we present two new approaches for estimating atmospheric PM_2.5 and chemical composition based on the High Spectral Resolution Lidar (HSRL)-retrieved aerosol extinction values and types and Creating Aerosol Types from Chemistry (CATCH)-derived aerosol chemical composition. The first methodology (CMAQ-HSRL-CH) improves EPA's Community Multiscale Air Quality (CMAQ) predictions by applying variable scaling factors derived using remotely-sensed information about aerosol vertical distribution and types and the CATCH algorithm. The second methodology (HSRL-CH) does not require regional model runs and can provide atmospheric PM_2.5 mass concentration and chemical speciation using only the remotely sensed data and the CATCH algorithm. The resulting PM_2.5 concentrations and chemical speciation derived for NASA DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) Baltimore-Washington, D.C. Corridor (BWC) Campaign (2011) are compared to surface measurements from EPA's Air Quality Systems (AQS) network. The analysis shows that the CMAQ-HSRL-CH method leads to considerable improvement of CMAQ's predicted PM_2.5 concentrations (R² value increased from 0.37 to 0.63, the root mean square error (RMSE) was reduced from 11.9 to 7.2 μg m^-3, and the normalized mean bias (NMB) was lowered from -46.0 to 4.6%). The HSRL-CH method showed statistics (R²=0.75, RMSE=8.6 μgm^-3, and NMB=24.0%), which were better than the CMAQ prediction of PM_2.5 alone and analogous to CMAQ-HSRL-CH. In addition to mass concentration, HSRL-CH can also provide aerosol chemical composition without specific model simulations. We expect that the HSRL-CH method will be able to make reliable estimates of PM_2.5 concentration and chemical composition where HSRL data are available.

SUBMITTER: Meskhidze N

PROVIDER: S-EPMC8353958 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Previous research has highlighted the importance of major atmospheric aerosols such as sulfate, through its precursor sulfur dioxide (SO2), black carbon (BC), and organic carbon (OC), and their effect on global climate regimes, specifically on their impact on particulate matter measuring ? 2.5 ?m (PM2.5). Policy regulations have attempted to address the change in these major active aerosols and their impact on PM2.5, which would presumably have a cascading effect toward the change of health risks. Objective: This study aimed to determine how the change in the global emissions of anthropogenic aerosols affects health, particularly through the change in attributable mortality (AN) and years of life lost (YLL). This study also aimed to explore the importance of using AM/YLL in conveying air pollution health impact message. Methods: The Model for Interdisciplinary Research on Climate was used to estimate the gridded atmospheric PM2.5 by changing the emission of SO2, BC, and OC. Next, the emissions were utilized to estimate the associated cause-specific risks via an integrated exposure-response function, and its consequent health indicators, AM and YLL, per country. Results: OC change yielded the greatest benefit for all country income groups, particularly among low-middle-income countries. Utilizing either AM or YLL did not alter the order of benefits among upper-middle and high-income countries (UMIC/HIC); however, using either health indicator to express the order of benefit varied among low- and low-middle-income countries (LIC/LMIC). Conclusions: Global and country-specific mitigation efforts focusing on OC-related activities would yield substantial health benefits. Substantial aerosol emission reduction would greatly benefit high-emitting countries (i.e. China and India). Although no difference is found in the order of health outcome benefits in UMIC/HIC, caution is warranted in using either AM or YLL for health impact assessment in LIC/LMIC.

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Improving Estimates of PM_2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm.

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Improving Estimates of PM2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm.

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OmicsDI is part of the ELIXIR infrastructure

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Improving Estimates of PM_2.5 Concentration and Chemical Composition by Application of High Spectral Resolution Lidar (HSRL) and Creating Aerosol Types from Chemistry (CATCH) Algorithm.