太阳成8722(中国)有限公司-GREEN NO.1

标题：Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China: Methods and assessment作者：Li, TW (Li, Tongwen); Shen, HF (Shen, Huanfeng); Zeng, C (Zeng, Chao); Yuan, QQ (Yuan, Qiangqiang); Zhang, LP (Zhang, Liangpei)

来源出版物：ATMOSPHERIC ENVIRONMENT 卷：152页码：477-489 DOI：10.1016/j.atmosenv.2017.01.004 出版年： MAR 2017

摘要：Fine particulate matter (PM2.5, particulate matters with aerodynamic diameters less than 2.5 mu m) is associated with adverse human health effects, and China is currently suffering from serious PM2.5 pollution. To obtain spatially continuous ground-level PM2.5 concentrations, several models established by the point-surface fusion of station measurements and satellite observations have been developed. However, how well do these models perform at national scale in China? Is there space to improve the estimation accuracy of PM2.5 concentration? The contribution of this study is threefold. Firstly, taking advantage of the newly established national monitoring network, we develop a national-scale generalized regression neural network (GRNN) model to estimate PM2.5 concentrations. Secondly, different assessment experiments are undertaken in time and space, to comprehensively evaluate and compare the performance of the widely used models. Finally, to map the yearly and seasonal mean distribution of PM2.5 concentrations in China, a pixel-based merging strategy is proposed. The results indicate that the conventional models (linear regression, multiple linear regression, and semi-empirical model) do not obtain the expected results at national scale, with cross-validation R values of 0.49-0.55 and RMSEs of 30.80-31.51 mu g/m(3), respectively. In contrast, the more advanced models (geographically weighted regression, back-propagation neural network, and GRNN) have great advantages in PM2.(5) estimation, with R values ranging from 0.61 to 0.82 and RMSEs from 20.93 to 28.68 mu g/m(3), respectively. In particular, the proposed GRNN model obtains the best performance. Furthermore, the mapped PM2.5 distribution retrieved from 3-km MODIS aerosol optical depth (AOD) products agrees quite well with the station measurements. The results also show that the approach used in this study has the capacity to provide reasonable information for the global monitoring of PM2.5 pollution in China.

入藏号：WOS:000394400000042

文献类型：Article

语种：English

作者关键词：Satellite remote sensing; Point-surface fusion; AOD; PM2.5; GRNN; Assessment

扩展关键词： GROUND-LEVEL PM2.5; REGRESSION NEURAL-NETWORK; PARTICULATE MATTER PM2.5; GEOGRAPHICALLY WEIGHTED REGRESSION; OPTICAL DEPTH MEASUREMENTS; AIR-QUALITY; PM10 CONCENTRATION; URBAN PM10; MODIS; MODEL

通讯作者地址：Shen, HF (reprint author), Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

电子邮件地址：litw@whu.edu.cn; shenhf@whu.edu.cn; zengchaozc@hotmail.com; yqiang86@gmail.com; zlp62@whu.edu.cn

地址：

[Li, Tongwen; Shen, Huanfeng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Hubei, Peoples R China.

[Shen, Huanfeng; Zhang, Liangpei] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan 430079, Hubei, Peoples R China.

[Zeng, Chao] Tsinghua Univ, Dept Hydraul Engn, State Key Lab Hydrosci & Engn, Beijing 100084, Peoples R China.

[Yuan, Qiangqiang] Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Hubei, Peoples R China.

[Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China.

研究方向：Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences

ISSN：1352-2310

eISSN:1873-2844

影响因子：3.459