Literature DB >> 31096372

Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance.

Yuanyu Xie1, Yuxuan Wang2, Muhammad Bilal3, Wenhao Dong1.   

Abstract

The application of satellite-derived aerosol optical depth (AOD) to infer surface PM2.5 has significantly increased the spatial coverage and resolutions (1-10 km) of ground-level PM2.5 mapping as required for accurate exposure estimation. The remaining challenge is to further increase the mapping resolution to the sub-km level with improved algorithms to minimize misrepresentation of severe haze as clouds. In this study, we provide the first daily PM2.5 estimation over Beijing at a 500 m resolution using AOD from the Simplified Aerosol Retrieval Algorithm (SARA) and linear mixed effects model. A novel cloud screen method is developed which significantly improves data availability during hazy days. The cross-validation R2 for PM2.5 estimations is 0.82 with the cloud-screened SARA AOD. Based on the satellite-predicted high-resolution PM2.5 map, all-day population-weighted PM2.5 is estimated to be 81.4 μg m-3 over Beijing (2.3 times higher than China's NAAQS of 35 μg m-3). Compared to the standard MODIS Dark Target 3 km product which presents a significant percentage of missing data, the 500 m resolution PM2.5 mapping derived from SARA AOD reveals distinct pollution patterns and population exposure conditions during severe hazy days, thereby providing valuable information for pollution control and epidemiological studies.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Aerosol optical depth; Hazy day; PM(2.5); SARA; Sub-km resolution

Year:  2018        PMID: 31096372     DOI: 10.1016/j.scitotenv.2018.12.365

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  2 in total

1.  Contribution of Satellite-Derived Aerosol Optical Depth PM2.5 Bayesian Concentration Surfaces to Respiratory-Cardiovascular Chronic Disease Hospitalizations in Baltimore, Maryland.

Authors:  John T Braggio; Eric S Hall; Stephanie A Weber; Amy K Huff
Journal:  Atmosphere (Basel)       Date:  2020-02-18       Impact factor: 2.686

2.  Hourly Seamless Surface O3 Estimates by Integrating the Chemical Transport and Machine Learning Models in the Beijing-Tianjin-Hebei Region.

Authors:  Wenhao Xue; Jing Zhang; Xiaomin Hu; Zhe Yang; Jing Wei
Journal:  Int J Environ Res Public Health       Date:  2022-07-12       Impact factor: 4.614

  2 in total

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