Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis

Pavel Kishcha; Sheng Hsiang Wang; Neng Huei Lin; Arlindo da Silva; Tang Huang Lin; Po Hsiung Lin; Gin Rong Liu; Boris Starobinets; Pinhas Alpert

doi:10.1007/978-3-030-22055-6_44

Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis

Pavel Kishcha, Sheng Hsiang Wang, Neng Huei Lin, Arlindo da Silva, Tang Huang Lin, Po Hsiung Lin, Gin Rong Liu, Boris Starobinets, Pinhas Alpert

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

Abstract

An analysis of pollution homogeneity has been conducted to distinguish between remote and local pollution which contributes to month to month changes in aerosol optical depth (AOD) over the Taiwan area. This was carried out using both AERONET measurements at six monitoring sites in Taiwan and NASA MERRA-2 reanalysis, over the 15-year period from 2002 to 2017. As a measure of air pollution homogeneity we used the AOD standard deviation: the more homogeneous the spatial distribution of air pollution, the lower the AOD standard deviation is. Using this approach, we found that, over Taiwan, in autumn, inhomogeneous local air pollution is predominant, while, in spring, homogeneous remote air pollution from south-east Asia dominates. In autumn, when inhomogeneous aerosols from local sources dominate, the AOD standard deviation is essentially higher than that in spring, when homogeneous aerosols from remote sources dominate. Our approach allowed us to distinguish between homogeneous remote and inhomogeneous local sulfate air pollution of similar optical properties and chemical composition.

Original language	English
Title of host publication	Air Pollution Modeling and its Application XXVI
Editors	Clemens Mensink, Wanmin Gong, Amir Hakami
Publisher	Springer
Pages	279-283
Number of pages	5
ISBN (Print)	9783030220549
DOIs	https://doi.org/10.1007/978-3-030-22055-6_44
State	Published - 2020
Event	36th International Technical Meeting on Air Pollution Modeling and its Application, ITM 2018 - Ottawa, Canada Duration: 14 May 2018 → 18 May 2018

Publication series

Name	Springer Proceedings in Complexity
ISSN (Print)	2213-8684
ISSN (Electronic)	2213-8692

Conference

Conference	36th International Technical Meeting on Air Pollution Modeling and its Application, ITM 2018
Country/Territory	Canada
City	Ottawa
Period	14/05/18 → 18/05/18

Keywords

AERONET
Local pollution
MERRA-2 reanalysis
Remote pollution
Taiwan

Access to Document

10.1007/978-3-030-22055-6_44

Cite this

Kishcha, P., Wang, S. H., Lin, N. H., da Silva, A., Lin, T. H., Lin, P. H., Liu, G. R., Starobinets, B., & Alpert, P. (2020). Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis. In C. Mensink, W. Gong, & A. Hakami (Eds.), Air Pollution Modeling and its Application XXVI (pp. 279-283). (Springer Proceedings in Complexity). Springer. https://doi.org/10.1007/978-3-030-22055-6_44

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title = "Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis",

abstract = "An analysis of pollution homogeneity has been conducted to distinguish between remote and local pollution which contributes to month to month changes in aerosol optical depth (AOD) over the Taiwan area. This was carried out using both AERONET measurements at six monitoring sites in Taiwan and NASA MERRA-2 reanalysis, over the 15-year period from 2002 to 2017. As a measure of air pollution homogeneity we used the AOD standard deviation: the more homogeneous the spatial distribution of air pollution, the lower the AOD standard deviation is. Using this approach, we found that, over Taiwan, in autumn, inhomogeneous local air pollution is predominant, while, in spring, homogeneous remote air pollution from south-east Asia dominates. In autumn, when inhomogeneous aerosols from local sources dominate, the AOD standard deviation is essentially higher than that in spring, when homogeneous aerosols from remote sources dominate. Our approach allowed us to distinguish between homogeneous remote and inhomogeneous local sulfate air pollution of similar optical properties and chemical composition.",

keywords = "AERONET, Local pollution, MERRA-2 reanalysis, Remote pollution, Taiwan",

author = "Pavel Kishcha and Wang, {Sheng Hsiang} and Lin, {Neng Huei} and {da Silva}, Arlindo and Lin, {Tang Huang} and Lin, {Po Hsiung} and Liu, {Gin Rong} and Boris Starobinets and Pinhas Alpert",

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Kishcha, P, Wang, SH , Lin, NH, da Silva, A, Lin, TH, Lin, PH, Liu, GR, Starobinets, B & Alpert, P 2020, Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis. in C Mensink, W Gong & A Hakami (eds), Air Pollution Modeling and its Application XXVI. Springer Proceedings in Complexity, Springer, pp. 279-283, 36th International Technical Meeting on Air Pollution Modeling and its Application, ITM 2018, Ottawa, Canada, 14/05/18. https://doi.org/10.1007/978-3-030-22055-6_44

Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis. / Kishcha, Pavel; Wang, Sheng Hsiang ; Lin, Neng Huei et al.
Air Pollution Modeling and its Application XXVI. ed. / Clemens Mensink; Wanmin Gong; Amir Hakami. Springer, 2020. p. 279-283 (Springer Proceedings in Complexity).

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

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AU - Lin, Neng Huei

AU - da Silva, Arlindo

AU - Lin, Tang Huang

AU - Lin, Po Hsiung

AU - Liu, Gin Rong

AU - Starobinets, Boris

AU - Alpert, Pinhas

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2020.

PY - 2020

Y1 - 2020

N2 - An analysis of pollution homogeneity has been conducted to distinguish between remote and local pollution which contributes to month to month changes in aerosol optical depth (AOD) over the Taiwan area. This was carried out using both AERONET measurements at six monitoring sites in Taiwan and NASA MERRA-2 reanalysis, over the 15-year period from 2002 to 2017. As a measure of air pollution homogeneity we used the AOD standard deviation: the more homogeneous the spatial distribution of air pollution, the lower the AOD standard deviation is. Using this approach, we found that, over Taiwan, in autumn, inhomogeneous local air pollution is predominant, while, in spring, homogeneous remote air pollution from south-east Asia dominates. In autumn, when inhomogeneous aerosols from local sources dominate, the AOD standard deviation is essentially higher than that in spring, when homogeneous aerosols from remote sources dominate. Our approach allowed us to distinguish between homogeneous remote and inhomogeneous local sulfate air pollution of similar optical properties and chemical composition.

AB - An analysis of pollution homogeneity has been conducted to distinguish between remote and local pollution which contributes to month to month changes in aerosol optical depth (AOD) over the Taiwan area. This was carried out using both AERONET measurements at six monitoring sites in Taiwan and NASA MERRA-2 reanalysis, over the 15-year period from 2002 to 2017. As a measure of air pollution homogeneity we used the AOD standard deviation: the more homogeneous the spatial distribution of air pollution, the lower the AOD standard deviation is. Using this approach, we found that, over Taiwan, in autumn, inhomogeneous local air pollution is predominant, while, in spring, homogeneous remote air pollution from south-east Asia dominates. In autumn, when inhomogeneous aerosols from local sources dominate, the AOD standard deviation is essentially higher than that in spring, when homogeneous aerosols from remote sources dominate. Our approach allowed us to distinguish between homogeneous remote and inhomogeneous local sulfate air pollution of similar optical properties and chemical composition.

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KW - MERRA-2 reanalysis

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SN - 9783030220549

T3 - Springer Proceedings in Complexity

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BT - Air Pollution Modeling and its Application XXVI

A2 - Mensink, Clemens

A2 - Gong, Wanmin

A2 - Hakami, Amir

PB - Springer

Y2 - 14 May 2018 through 18 May 2018

ER -

Kishcha P, Wang SH , Lin NH, da Silva A, Lin TH, Lin PH et al. Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis. In Mensink C, Gong W, Hakami A, editors, Air Pollution Modeling and its Application XXVI. Springer. 2020. p. 279-283. (Springer Proceedings in Complexity). doi: 10.1007/978-3-030-22055-6_44

Distinguishing Between Remote and Local Air Pollution Over Taiwan: An Approach Based on Pollution Homogeneity Analysis

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