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[Estimation of PM2.5 over eastern China from MODIS aerosol optical depth using the back propagation neural network].
Huan Jing Ke Xue. 2013 Mar; 34(3):817-25.HJ

Abstract

With the fast economic development in China in recent years, air pollutions are becoming increasingly serious. It is, therefore, imperative to develop new technology to solve this issue. Due to the wide spatial coverage of satellite remote sensing, along with the relatively lower cost compared to ground-based in situ aerosol measurements, satellite retrieved aerosol optical depth (AOD) is widely recognized as a good surrogate of surface PM2.5 concentrations. In this study, two years (2007-2008) of AOD data from moderate resolution imaging spectroradiometer (MODIS) onboard Terra at five observational sites of China (Benxi, Zhengzhou, Lushan, Nanning, Guilin), combined with five meteorological factors such as wind speed, wind direction, temperature humidity and planetary boundary height, were used as important input to establish the Back Propagation (BP) neural networks model, which was applied to estimate PM2.5. Afterwards, the model estimated PM2.5 was validated by in situ PM2.5 measurements from the five sites. Specially, scatter analysis showed that the linear correlation coefficient (R) between ground PM2.5 observation and model estimated PM2.5 at Lushan was the highest (R = 0.6), whereas the R values at the four other sites were lower, ranging from 0.43 to 0.49. Time series validations were performed as well, indicating that the R value significantly varied from day to day. However, the R value could be significantly improved by fitting the five-day moving average ground observation values against the model estimated PM2.5 data. Also, the R value at Lushan was the highest (R = 0.83), suggesting that MODIS AOD can be used to monitor PM2.5 by the BP networks model developed in this study.

Authors+Show Affiliations

Guo JP
Institute of Atmospheric Composition, Chinese Academy of Meteorological Sciences, Beijing 100081, China. jpguo@cams.cma.gov.cn
Wu YR
No affiliation info available
Zhang XY
No affiliation info available
Li XW
No affiliation info available

MeSH

AerosolsAir PollutantsAir PollutionChinaEnvironmental MonitoringNeural Networks, ComputerParticulate MatterRemote Sensing TechnologySatellite Communications

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

chi

PubMed ID

23745382

Citation

Guo, Jian-Ping, et al. "[Estimation of PM2.5 Over Eastern China From MODIS Aerosol Optical Depth Using the Back Propagation Neural Network]." Huan Jing Ke Xue= Huanjing Kexue, vol. 34, no. 3, 2013, pp. 817-25.
Guo JP, Wu YR, Zhang XY, et al. [Estimation of PM2.5 over eastern China from MODIS aerosol optical depth using the back propagation neural network]. Huan Jing Ke Xue. 2013;34(3):817-25.
Guo, J. P., Wu, Y. R., Zhang, X. Y., & Li, X. W. (2013). [Estimation of PM2.5 over eastern China from MODIS aerosol optical depth using the back propagation neural network]. Huan Jing Ke Xue= Huanjing Kexue, 34(3), 817-25.
Guo JP, et al. [Estimation of PM2.5 Over Eastern China From MODIS Aerosol Optical Depth Using the Back Propagation Neural Network]. Huan Jing Ke Xue. 2013;34(3):817-25. PubMed PMID: 23745382.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - [Estimation of PM2.5 over eastern China from MODIS aerosol optical depth using the back propagation neural network]. AU - Guo,Jian-Ping, AU - Wu,Ye-Rong, AU - Zhang,Xiao-Ye, AU - Li,Xiao-Wen, PY - 2013/6/11/entrez PY - 2013/6/12/pubmed PY - 2014/6/25/medline SP - 817 EP - 25 JF - Huan jing ke xue= Huanjing kexue JO - Huan Jing Ke Xue VL - 34 IS - 3 N2 - With the fast economic development in China in recent years, air pollutions are becoming increasingly serious. It is, therefore, imperative to develop new technology to solve this issue. Due to the wide spatial coverage of satellite remote sensing, along with the relatively lower cost compared to ground-based in situ aerosol measurements, satellite retrieved aerosol optical depth (AOD) is widely recognized as a good surrogate of surface PM2.5 concentrations. In this study, two years (2007-2008) of AOD data from moderate resolution imaging spectroradiometer (MODIS) onboard Terra at five observational sites of China (Benxi, Zhengzhou, Lushan, Nanning, Guilin), combined with five meteorological factors such as wind speed, wind direction, temperature humidity and planetary boundary height, were used as important input to establish the Back Propagation (BP) neural networks model, which was applied to estimate PM2.5. Afterwards, the model estimated PM2.5 was validated by in situ PM2.5 measurements from the five sites. Specially, scatter analysis showed that the linear correlation coefficient (R) between ground PM2.5 observation and model estimated PM2.5 at Lushan was the highest (R = 0.6), whereas the R values at the four other sites were lower, ranging from 0.43 to 0.49. Time series validations were performed as well, indicating that the R value significantly varied from day to day. However, the R value could be significantly improved by fitting the five-day moving average ground observation values against the model estimated PM2.5 data. Also, the R value at Lushan was the highest (R = 0.83), suggesting that MODIS AOD can be used to monitor PM2.5 by the BP networks model developed in this study. SN - 0250-3301 UR - https://www.unboundmedicine.com/medline/citation/23745382/[Estimation_of_PM2_5_over_eastern_China_from_MODIS_aerosol_optical_depth_using_the_back_propagation_neural_network]_ DB - PRIME DP - Unbound Medicine ER -