Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach

Xiaoli Wei; Runhe Shi; Wei Gao; Deying Zhang

doi:10.1117/12.2272937

Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach

Xiaoli Wei
, Runhe Shi^*
, Wei Gao
, Deying Zhang

^*Corresponding author for this work

School of Geographic Sciences

East China Normal University
Colorado State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Fine particulates less than 2.5 microns in aerodynamic diameter (PM_2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM_2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM_2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM_2.5 concentration in Northeastern China from 2013-2016, then ground-level PM_2.5monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM_2.5 is 63.98μg/m2, AOD values agreed well with estimated PM_2.5 concentration, 2) the spatial variations of PM_2.5 were not clear, while the temporal dynamic of PM_2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM_2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM_2.5 estimations in northeastern China.

Original language	English
Title of host publication	Remote Sensing and Modeling of Ecosystems for Sustainability XIV
Editors	Wei Gao, Ni-Bin Chang, Jinnian Wang
Publisher	SPIE
ISBN (Electronic)	9781510612679
DOIs	https://doi.org/10.1117/12.2272937
State	Published - 2017
Event	Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017 - San Diego, United States Duration: 9 Aug 2017 → …

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	10405
ISSN (Print)	0277-786X
ISSN (Electronic)	1996-756X

Conference

Conference	Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017
Country/Territory	United States
City	San Diego
Period	9/08/17 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Aerosol optical depth
MODIS-Aqua
Multilinear correlation model
Northeastern China
PM 2.5 concentration

Access to Document

10.1117/12.2272937

Cite this

Wei, X., Shi, R., Gao, W., & Zhang, D. (2017). Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach. In W. Gao, N.-B. Chang, & J. Wang (Eds.), Remote Sensing and Modeling of Ecosystems for Sustainability XIV Article 104050U (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10405). SPIE. https://doi.org/10.1117/12.2272937

Wei, Xiaoli ; Shi, Runhe ; Gao, Wei et al. / Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China : A model approach. Remote Sensing and Modeling of Ecosystems for Sustainability XIV. editor / Wei Gao ; Ni-Bin Chang ; Jinnian Wang. SPIE, 2017. (Proceedings of SPIE - The International Society for Optical Engineering).

@inproceedings{a37c0a7c6eaf4b1cabe78142407d036a,

title = "Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach",

abstract = "Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.",

keywords = "Aerosol optical depth, MODIS-Aqua, Multilinear correlation model, Northeastern China, PM 2.5 concentration",

author = "Xiaoli Wei and Runhe Shi and Wei Gao and Deying Zhang",

note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017 ; Conference date: 09-08-2017",

year = "2017",

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language = "英语",

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Wei, X, Shi, R, Gao, W & Zhang, D 2017, Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach. in W Gao, N-B Chang & J Wang (eds), Remote Sensing and Modeling of Ecosystems for Sustainability XIV., 104050U, Proceedings of SPIE - The International Society for Optical Engineering, vol. 10405, SPIE, Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017, San Diego, United States, 9/08/17. https://doi.org/10.1117/12.2272937

Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach. / Wei, Xiaoli; Shi, Runhe; Gao, Wei et al.
Remote Sensing and Modeling of Ecosystems for Sustainability XIV. ed. / Wei Gao; Ni-Bin Chang; Jinnian Wang. SPIE, 2017. 104050U (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10405).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China

T2 - Remote Sensing and Modeling of Ecosystems for Sustainability XIV 2017

AU - Wei, Xiaoli

AU - Shi, Runhe

AU - Gao, Wei

AU - Zhang, Deying

PY - 2017

Y1 - 2017

N2 - Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.

AB - Fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) has been widely considered to be one of the main pollutant threating human health. Ground-level PM2.5 monitoring can provide accurate point data, but its value is hard to scale up to large scale. In this respects, satellite data with large coverage areas and long term range, could enhance our ability to estimate PM2.5 concentration. In this study, a Multilinear correlation model (MLC) based on MODIS AOD level 2 data was developed to estimate PM2.5 concentration in Northeastern China from 2013-2016, then ground-level PM2.5monitoring data from 15 stations covering study area were used for validation. Results showed that 1) the annual PM2.5 is 63.98μg/m2, AOD values agreed well with estimated PM2.5 concentration, 2) the spatial variations of PM2.5 were not clear, while the temporal dynamic of PM2.5 were observed, the highest values were observed in winter, opposite to what were observed in fall. 3) the MLC model coupled with meteorological data could improve the precision of PM2.5 estimations. Therefore, we suggest that the developed MLC model is useful for the PM2.5 estimations in northeastern China.

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KW - Northeastern China

KW - PM 2.5 concentration

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DO - 10.1117/12.2272937

M3 - 会议稿件

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T3 - Proceedings of SPIE - The International Society for Optical Engineering

BT - Remote Sensing and Modeling of Ecosystems for Sustainability XIV

A2 - Gao, Wei

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A2 - Wang, Jinnian

PB - SPIE

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ER -

Wei X, Shi R, Gao W, Zhang D. Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach. In Gao W, Chang NB, Wang J, editors, Remote Sensing and Modeling of Ecosystems for Sustainability XIV. SPIE. 2017. 104050U. (Proceedings of SPIE - The International Society for Optical Engineering). doi: 10.1117/12.2272937

Estimating fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) in Northeastern China: A model approach

Abstract

Publication series

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UN SDGs

Keywords

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