Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures

Zhao Yue Chen, Tian Hao Zhang, Rong Zhang, Zhong Min Zhu, Chun Quan Ou, Yuming Guo

Research output: Contribution to journalArticleResearchpeer-review

8 Citations (Scopus)

Abstract

Background The accurate measurement of particulate matter (PM) provides a crucial basis for health impact assessment and pollution management and control. However, monitoring stations of air pollution are limited worldwide. Recently, some researchers have attempted to estimate the levels of PM based on remote sensing data, but the methods still need to be validated and further improved. Objectives This study aimed to develop a new model, to estimate daily ground-level PM2.5 concentrations using the fused aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectro radiometer and meteorological information. Methods We combined generalized additive mixed-effects model with the log-linked Gaussian error distribution and non-linear exposure-lag-response model for AOD and meteorological measures, to estimate daily ground-level PM2.5 concentrations in 2014–2015 in Guangzhou, China. Results The PM2.5 concentration was significantly associated with AOD and meteorological measures. Compared to the log-linear model, the non-linear exposure-lag-response model had better model performance with a higher temporal (spatial) cross-validation R–square (0.81 (0.81) vs 0.67 (0.67)), and a smaller mean absolute percentage error (17.65% (16.90%) vs 21.22% (21.01%)). AOD explained about 15% variations of PM2.5 in the mixed-effect model. The planetary-boundary -layer-height-revised AOD and relative-humidity-revised PM2.5 did not significantly improve the model performance. Conclusion Considering the non-linear exposure-lag-response association between PM2.5 and AOD and meteorological factors can significantly increase the modelling ability to estimate PM2.5 concentrations.

Original languageEnglish
Pages (from-to)30-37
Number of pages8
JournalAtmospheric Environment
Volume173
DOIs
Publication statusPublished - 1 Jan 2018

Keywords

  • Aerosol optical depth
  • China
  • Fine particulate matter
  • Non-linear exposure-lag-response

Cite this

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title = "Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures",
abstract = "Background The accurate measurement of particulate matter (PM) provides a crucial basis for health impact assessment and pollution management and control. However, monitoring stations of air pollution are limited worldwide. Recently, some researchers have attempted to estimate the levels of PM based on remote sensing data, but the methods still need to be validated and further improved. Objectives This study aimed to develop a new model, to estimate daily ground-level PM2.5 concentrations using the fused aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectro radiometer and meteorological information. Methods We combined generalized additive mixed-effects model with the log-linked Gaussian error distribution and non-linear exposure-lag-response model for AOD and meteorological measures, to estimate daily ground-level PM2.5 concentrations in 2014–2015 in Guangzhou, China. Results The PM2.5 concentration was significantly associated with AOD and meteorological measures. Compared to the log-linear model, the non-linear exposure-lag-response model had better model performance with a higher temporal (spatial) cross-validation R–square (0.81 (0.81) vs 0.67 (0.67)), and a smaller mean absolute percentage error (17.65{\%} (16.90{\%}) vs 21.22{\%} (21.01{\%})). AOD explained about 15{\%} variations of PM2.5 in the mixed-effect model. The planetary-boundary -layer-height-revised AOD and relative-humidity-revised PM2.5 did not significantly improve the model performance. Conclusion Considering the non-linear exposure-lag-response association between PM2.5 and AOD and meteorological factors can significantly increase the modelling ability to estimate PM2.5 concentrations.",
keywords = "Aerosol optical depth, China, Fine particulate matter, Non-linear exposure-lag-response",
author = "Chen, {Zhao Yue} and Zhang, {Tian Hao} and Rong Zhang and Zhu, {Zhong Min} and Ou, {Chun Quan} and Yuming Guo",
year = "2018",
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day = "1",
doi = "10.1016/j.atmosenv.2017.10.055",
language = "English",
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Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures. / Chen, Zhao Yue; Zhang, Tian Hao; Zhang, Rong; Zhu, Zhong Min; Ou, Chun Quan; Guo, Yuming.

In: Atmospheric Environment, Vol. 173, 01.01.2018, p. 30-37.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures

AU - Chen, Zhao Yue

AU - Zhang, Tian Hao

AU - Zhang, Rong

AU - Zhu, Zhong Min

AU - Ou, Chun Quan

AU - Guo, Yuming

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Background The accurate measurement of particulate matter (PM) provides a crucial basis for health impact assessment and pollution management and control. However, monitoring stations of air pollution are limited worldwide. Recently, some researchers have attempted to estimate the levels of PM based on remote sensing data, but the methods still need to be validated and further improved. Objectives This study aimed to develop a new model, to estimate daily ground-level PM2.5 concentrations using the fused aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectro radiometer and meteorological information. Methods We combined generalized additive mixed-effects model with the log-linked Gaussian error distribution and non-linear exposure-lag-response model for AOD and meteorological measures, to estimate daily ground-level PM2.5 concentrations in 2014–2015 in Guangzhou, China. Results The PM2.5 concentration was significantly associated with AOD and meteorological measures. Compared to the log-linear model, the non-linear exposure-lag-response model had better model performance with a higher temporal (spatial) cross-validation R–square (0.81 (0.81) vs 0.67 (0.67)), and a smaller mean absolute percentage error (17.65% (16.90%) vs 21.22% (21.01%)). AOD explained about 15% variations of PM2.5 in the mixed-effect model. The planetary-boundary -layer-height-revised AOD and relative-humidity-revised PM2.5 did not significantly improve the model performance. Conclusion Considering the non-linear exposure-lag-response association between PM2.5 and AOD and meteorological factors can significantly increase the modelling ability to estimate PM2.5 concentrations.

AB - Background The accurate measurement of particulate matter (PM) provides a crucial basis for health impact assessment and pollution management and control. However, monitoring stations of air pollution are limited worldwide. Recently, some researchers have attempted to estimate the levels of PM based on remote sensing data, but the methods still need to be validated and further improved. Objectives This study aimed to develop a new model, to estimate daily ground-level PM2.5 concentrations using the fused aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectro radiometer and meteorological information. Methods We combined generalized additive mixed-effects model with the log-linked Gaussian error distribution and non-linear exposure-lag-response model for AOD and meteorological measures, to estimate daily ground-level PM2.5 concentrations in 2014–2015 in Guangzhou, China. Results The PM2.5 concentration was significantly associated with AOD and meteorological measures. Compared to the log-linear model, the non-linear exposure-lag-response model had better model performance with a higher temporal (spatial) cross-validation R–square (0.81 (0.81) vs 0.67 (0.67)), and a smaller mean absolute percentage error (17.65% (16.90%) vs 21.22% (21.01%)). AOD explained about 15% variations of PM2.5 in the mixed-effect model. The planetary-boundary -layer-height-revised AOD and relative-humidity-revised PM2.5 did not significantly improve the model performance. Conclusion Considering the non-linear exposure-lag-response association between PM2.5 and AOD and meteorological factors can significantly increase the modelling ability to estimate PM2.5 concentrations.

KW - Aerosol optical depth

KW - China

KW - Fine particulate matter

KW - Non-linear exposure-lag-response

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U2 - 10.1016/j.atmosenv.2017.10.055

DO - 10.1016/j.atmosenv.2017.10.055

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JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

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