Literature DB >> 31051362

Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8.

Taixin Zhang1, Lin Zang2, Youchuan Wan1, Wei Wang3, Yi Zhang1.   

Abstract

High concentrations of particulate matter with diameter of <2.5 μm (PM2.5) demonstrate severe effects on human health, especially in the metropolitan agglomerations of China. Estimating PM2.5 based on satellite aerosol optical depth (AOD) is a widely used method. AOD data from Himawari-8, a geostationary satellite, enable improvement of the temporal resolution of PM2.5 estimates to the hourly level, thereby reflecting diurnal variations of pollutants compared with AOD products from polar orbit satellites, which only have one value per day. In this study, PM2.5 concentrations are estimated based on Himawari-8 AOD and other ancillary data by constructing spatiotemporal linear mixed effects model in Central China (CCH), Beijing-Tianjin-Henan (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. The determination coefficient (R2) between the measurements and estimates of PM2.5 calculated with the tenfold cross-validation method are 0.82, 0.84, 0.80 and 0.74 in CCH, BTH, YRD and PRD, respectively. The spatial distributions of PM2.5 present large regional variation, which is highly correlated with land-use type. Heavily polluted zones are mainly located in urban or rural areas, which have dense population and high anthropogenic emissions. Comparisons among different seasons show that particle pollution during the cold seasons (autumn and winter) is relatively severe with an average PM2.5 of >60 μg/m3 in CCH, BTH and YRD, whereas the level does not greatly change throughout the year in the PRD region. During the daytime, particulate pollution levels are generally high in the morning.
Copyright © 2019. Published by Elsevier B.V.

Entities:  

Keywords:  Himawari-8 AOD; Hourly PM2.5; Linear mixed-effects model; Spatiotemporal variation

Year:  2019        PMID: 31051362     DOI: 10.1016/j.scitotenv.2019.04.299

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


  4 in total

1.  Estimating ground-level PM2.5 concentrations by developing and optimizing machine learning and statistical models using 3 km MODIS AODs: case study of Tehran, Iran.

Authors:  Saeed Sotoudeheian; Mohammad Arhami
Journal:  J Environ Health Sci Eng       Date:  2021-02-02

2.  Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping.

Authors:  Huanfeng Shen; Man Zhou; Tongwen Li; Chao Zeng
Journal:  Int J Environ Res Public Health       Date:  2019-10-24       Impact factor: 3.390

3.  Best Water Vapor Information Layer of Himawari-8-Based Water Vapor Bands over East Asia.

Authors:  You Wu; Feng Zhang; Kun Wu; Min Min; Wenwen Li; Renqiang Liu
Journal:  Sensors (Basel)       Date:  2020-04-23       Impact factor: 3.576

4.  PM2.5 Prediction with a Novel Multi-Step-Ahead Forecasting Model Based on Dynamic Wind Field Distance.

Authors:  Mei Yang; Hong Fan; Kang Zhao
Journal:  Int J Environ Res Public Health       Date:  2019-11-14       Impact factor: 3.390

  4 in total

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