Quantifying the decadal changes of PM2.5 over New York through a combination of satellite, model and in-situ measurements
Abstract
Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. Efforts have been made to regulate PM2.5 precursor emissions across the U.S.A, which are expected to mitigate the air pollution related health impacts. However, quantifying the health outcomes from emission controls requires robust estimates of PM2.5 exposures that accurately describe the spatial and temporal variability of PM2.5. Satellite remote sensing offers the potential to fill the gaps of the sparse, limited sampling of in situ measurement networks and is increasingly being used in health assessments. We provide new estimates of PM2.5 over New York State with 1 km spatial resolution that use Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD and a regional air quality model (CMAQ) to estimate the AOD-PM2.5 scaling factors. Next, we evaluate three major sources of uncertainties of satellite-derived PM2.5 data and their impacts on the derived decadal changes: 1) satellite retrieval of AOD, 2) optical properties of the particles, 3) relationships between the aerosol burden in the planetary boundary layer and full atmospheric column. Finally, we analyze the decadal changes of PM2.5 over New York State using the newly developed PM2.5 data, alongside four other PM2.5 estimates including satellite-derived PM2.5 developed by van Donkelaar et al. (2015), statistical land use regression developed by Beckerman et al. (2013), CMAQ simulations, and a Bayesian fusion of CMAQ and ground-based measurements. By evaluating the decadal changes of PM2.5 from multiple datasets over areas with dense (e.g. New York City area) and sparse ground-based measurements (e.g. upstate New York), we evaluate the extent to which satellite remote sensing could help better quantify the health outcomes of emission controls. References: Beckerman et al., (2013), A Hybrid Approach to Estimating National Scale Spatiotemporal Variability of PM2.5 in the Contiguous United States, Environ. Sci. Technol., 47(13), 7233-7241. van Donkelaar et al. (2015), High-Resolution Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted Regression over North America, Environ. Sci. Technol., 49(17), 10482-10491.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFM.A21G2226J
- Keywords:
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- 0305 Aerosols and particles;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0322 Constituent sources and sinks;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 0345 Pollution: urban and regional;
- ATMOSPHERIC COMPOSITION AND STRUCTURE;
- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES