Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1016/j.atmosres.2017.04.019 |
Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia | |
Zaman, Nurul Amalin Fatihah Kamarul1; Kanniah, Kasturi Devi1,3; Kaskaoutis, Dimitris G.2 | |
2017-09-01 | |
发表期刊 | ATMOSPHERIC RESEARCH
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ISSN | 0169-8095 |
EISSN | 1873-2895 |
出版年 | 2017 |
卷号 | 193 |
文章类型 | Article |
语种 | 英语 |
国家 | Malaysia; Greece |
英文摘要 | The insufficient number of ground-based stations for measuring Particulate Matter < 10 mu m (PM10) in the developing countries hinders PM10 monitoring at a regional scale. The present study aims to develop empirical models for PM10 estimation from space over Malaysia using aerosol optical depth (AOD(550)) and meteorological (surface temperature, relative humidity and atmospheric stability) data (retrieved or estimated) from Moderate Resolution Imaging Spectroradiometer (MODIS) during the period 2007-2011. The MODIS retrievals are found to be satisfactorily correlated with ground-based measurements at Malaysia. Multiple linear regressions (MLR) and Artificial Neural Network (ANN) techniques are utilized to develop the empirical models for PM10 estimation. The model development and training are performed via comparison with measured PM10 at 29 stations over Malaysia and reveal that the ANN provides slightly higher accuracy with R-2 = 0.71 and RMSE = 11.61 mu g m(-3) compared to the MLR method (R-2 = 0.66 and RMSE = 12.39 mu g m(-3)). Stepwise regression analysis performed on the MLR method reveals that the MODIS AOD(550) is the most important parameter for PM10 estimations (R-2 = 0.59 and RMSE = 13.61 mu g m-3); however, the inclusion of the meteorological parameters in the MLR increases the accuracy of the retrievals (R-2 = 0.66, RMSE = 12.39 mu g m-3). The estimated PM10 concentrations are finally validated against surface measurements at 16 stations resulting in similar performance from the ANN model (R-2 = 0.58, RMSE = 10.16 mu g m(-3)) and MLR technique (R-2 = 0.56, RMSE = 10.58 mu g m(-3)). The significant accuracy that has been attained in PM10 estimations from space allows us to assess the pollution levels in Malaysia and map the PM10 distribution at large spatial and temporal scales. |
英文关键词 | PM10 Satellite remote sensing Artificial Neural Network Multiple linear regressions Meteorology Malaysia |
领域 | 地球科学 |
收录类别 | SCI-E |
WOS记录号 | WOS:000403995200012 |
WOS关键词 | GROUND-LEVEL PM2.5 ; AIR-QUALITY ASSESSMENT ; CALIBRATION APPROACH ; PM10 CONCENTRATION ; RELATIVE-HUMIDITY ; DUST STORMS ; URBAN PM10 ; MODIS ; POLLUTION ; SURFACE |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/15221 |
专题 | 地球科学 |
作者单位 | 1.Univ Teknol Malaysia, Fac Geoinformat & Real Estate, Trop Map Res Grp, Skudai 81310, Johor Darul Tak, Malaysia; 2.Natl Observ Athens, Inst Environm Res & Sustainable Dev, Atmospher Res Team, GR-11810 Athens, Greece; 3.Univ Teknol Malaysia, Res Inst Sustainable Environm, Ctr Environm Sustainabil & Water Secur IPASA, Utm Johor Bahru 81310, Johor, Malaysia |
推荐引用方式 GB/T 7714 | Zaman, Nurul Amalin Fatihah Kamarul,Kanniah, Kasturi Devi,Kaskaoutis, Dimitris G.. Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia[J]. ATMOSPHERIC RESEARCH,2017,193. |
APA | Zaman, Nurul Amalin Fatihah Kamarul,Kanniah, Kasturi Devi,&Kaskaoutis, Dimitris G..(2017).Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia.ATMOSPHERIC RESEARCH,193. |
MLA | Zaman, Nurul Amalin Fatihah Kamarul,et al."Estimating Particulate Matter using satellite based aerosol optical depth and meteorological variables in Malaysia".ATMOSPHERIC RESEARCH 193(2017). |
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