基于AOD数据和GWR模型估算京津冀地区PM<sub>2.5</sub>浓度

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摘要利用MODIS气溶胶光学厚度（AOD）数据针对不同土地覆盖类型的适用性，提出了一种基于土地覆盖类型的AOD融合方法，生成了一种新的3km AOD数据集.在此基础上，通过地理加权回归（GWR）模型估算了京津冀地区2016年PM_2.5浓度，并用交叉验证的方法对模型性能进行评价.结果表明：利用融合后的AOD数据建立的模型可解释PM_2.594.85%的浓度变化，交叉验证R²为0.94，RMSE为9.27μg/m³，MPE为6.72μg/m³，明显优于多元线性回归（MLR）模型；基于GWR模型估算的京津冀地区2016年年均PM_2.5浓度为58.57μg/m³，其中冬季PM_2.5浓度最高，春秋季次之，夏季浓度最低，PM_2.5月均浓度变化范围32.78~140.83μg/m³，8月份浓度最低，12月份浓度最高；空间分布南北差异显著，衡水市PM_2.5污染最为严重，张家口市PM_2.5浓度较低.利用此方法成功弥补了PM_2.5空间缺失，为城市尺度的健康效应和环境流行病学研究提供数据支持.

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	付宏臣
	孙艳玲
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	陈莉
	张辉
	高爽
	毛健
	景悦
	邵丝露

关键词 ：气溶胶光学原度(AOD), GWR模型, PM_2.5, 京津冀

Abstract：In this study, a MODIS AOD (Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth) combination method was proposed based on land cover type, and a new 3km AOD data set was generated. PM_2.5 concentration in the Beijing-Tianjin-Hebei region for the year of 2016 was estimated by the Geographical Weighted Regression (GWR) model, and was evaluated by the method of cross-validation. The results showed that the model established by AOD data after combination could explain 94.85% of the PM_2.5 concentration change, with the cross-validation R² of 0.94, the RMSE of 9.27μg/m³, and the MPE of 6.72μg/m³. These values were significantly better than that of the multiple linear regression (MLR) model; Based on the GWR model, average annual PM_2.5 concentration in Beijing-Tianjin-Hebei region was 58.57μg/m³, with the highest concentration of PM_2.5 in winter, followed by spring and autumn, and the concentration in summer was the lowest. The monthly average concentration of PM_2.5 varied from 32.78 to 140.83μg/m³, the lowest and the highest concentrations were estimated in August and in December, respectively. The spatial distribution was significant compared with north part from south, the PM_2.5 pollution in Hengshui City was the most serious, PM_2.5 concentration was the lowest in Zhangjiakou. This method successfully compensated for the lack of PM_2.5 space and provided data support for urban-scale health effects and environmental epidemiological studies.

Key words： AOD GWR PM_2.5 Beijing-Tianjin-Hebei

收稿日期: 2019-04-16

PACS:

X513

基金资助:国家重点研发计划（2016YFC0201700）；天津市科技计划项目（16YFXTSF00330）；天津市自然科学基金资助项目（17JCYBJC42900）

通讯作者: 孙艳玲,副教授,flyling99@163.com E-mail: flyling99@163.com

作者简介: 付宏臣(1995-),女,天津人,天津师范大学硕士研究生,主要从事大气环境遥感研究.发表论文2篇.

引用本文:

付宏臣, 孙艳玲, 王斌, 陈莉, 张辉, 高爽, 毛健, 景悦, 邵丝露. 基于AOD数据和GWR模型估算京津冀地区PM_2.5浓度[J]. 中国环境科学, 2019, 39(11): 4530-4537. FU Hong-chen, SUN Yan-ling, WANG Bin, CHEN Li, ZHANG Hui, GAO Shuang, MAO Jian, JING Yue, SHAO Si-lu. Estimation of PM_2.5 concentration in Beijing-Tianjin-Hebei region based on AOD data and GWR model. CHINA ENVIRONMENTAL SCIENCECE, 2019, 39(11): 4530-4537.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2019/V39/I11/4530

[1]	郭建平,吴业荣,张小曳,等.BP网络框架下MODIS气溶胶光学厚度产品估算中国东部PM2.5[J]. 环境科学, 2013,34(3):817-825. Guo J P, Wu Y R, Zhang X Y, et al. Estimation of PM2.5 over Eastern China from MODIS aerosol optical depth using the back propagation neural network[J]. Environmental Science, 2013,34(3):817-825.
[2]	林海峰,辛金元,张文煜,等.北京市近地层颗粒物浓度与气溶胶光学厚度相关性分析研究[J]. 环境科学, 2013,34(3):826-834. Lin H F, Xin J Y, Zhang W Y, et al. Comparison of atmospheric particulate matter and aerosol optical depth in Beijing City[J]. Environmental Science, 2013,34(3):826-834.
[3]	王旭,胡引翠,田冰,等.京津冀地区AOD和PM2.5质量浓度的特征及相关性分析[J]. 环境工程学报, 2016,10(9):5069-5074. Wang X, Hu Y C, Tian B, et al. Analysis of characteristic and correlation of mass concentration of aerosol optical depth retrievals and ground-level PM2.5 in the Beijing-Tianjin-Hebei Region, China[J]. Chinese Journal of Environmental Engineering, 2016,10(9):5069-5074.
[4]	Xin J Y, Zhang Q, Wang L, et al. The empirical relationship between the PM2.5concentration and aerosol optical depth over the background of North China from 2009 to 2011[J]. Atmospheric Research, 2014,138:179-188.
[5]	Hu X F, Waller L A, Al-Hamdan M Z, et al. Estimating ground-level PM2.5 concentrations in the southeastern U.S. using geographically weighted regression[J]. Environmental Research, 2013,121:1-10.
[6]	景悦,孙艳玲,徐昊,等.基于混合效应模型的京津冀地区PM2.5日浓度估算[J]. 中国环境科学, 2018,38(8):2890-2897. Jing Y, Sun Y L, Xu H, et al. Daily estimation of PM2.5 concentrations based on mixed effects model in Beijing-Tianjin-Hebei region.[J]. China Environmental Science, 2018,38(8):2890-2897.
[7]	Jiao L M, Xu G, Zhao S L, et al. LUR-based Simulation of the Spatial Distribution of PM2.5 of Wuhan[J]. Geomatics and Information Science of Wuhan University, 2015,40(8):1088-1094.
[8]	Koelemeijer R, Homan C D, Matthijsen J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe[J]. Atmospheric Environment, 2006,40(27):5304-5315.
[9]	Kloog I, Nordio F, Coull B A, et al. Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic States[J]. Environmental Science & Technology, 2012,46(21):11913-11921.
[10]	Hsu N C. Changes to MODIS deep blue aerosol products between collection 6and collection 6.1.[EB/OL]. https://modis-atmosphere.gsfc.nasa.gov/sites/default/files/ModAtmo/modis_deep_blue_c61_changes2.pdf.
[11]	Mattoo S. Aerosol Dark Target (10km & 3km) Collection 6.1Changes.[EB/OL]. https://modis-atmosphere.gsfc.nasa.gov/sites/default/files/ModAtmo/C061_Aerosol_Dark_Target_v2.pdf.
[12]	Ma Z W, Liu Y, Zhao Q Y, et al. Satellite-derived high resolution PM2.5 concentrations in Yangtze River Delta Region of China using improved linear mixed effects model[J]. Atmospheric Environment, 2016,133:156-164.
[13]	You W, Zang Z L, Zhang L F, et al. Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count[J]. Remote Sensing of Environment, 2015,168:276-285.
[14]	Li T W, Shen H F, Zeng C, et al. Point-surface fusion of station measurements and satellite observations for mapping PM2.5 distribution in China:methods and assessment[J]. Atmospheric Environment, 2016,152:477-489.
[15]	Guo Y X, Tang Q H, Gong D Y, et al. Estimating ground-level PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model[J]. Remote Sensing of Environment, 2017,198:140-149.
[16]	He Q Q, Huang B. Satellite-based mapping of daily high-resolution ground PM2.5, in China via space-time regression modeling[J]. Remote Sensing of Environment, 2018,206:72-83.
[17]	Kaufman Y J, Wald A E, Remer L A, et al. The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997,35(5):1286-1298.
[18]	Gupta P, Levy R C, Mattoo S, et al. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm[J]. Atmospheric Measurement Techniques, 2016,9(7):3293-3308.
[19]	Hsu N C, Jeong M J, Bettenhausen C, et al. Enhanced Deep Blue aerosol retrieval algorithm:The second generation[J]. Journal of Geophysical Research Atmospheres, 2013,118(16):9296-9315.
[20]	Sayer A M, Hsu N C, Bettenhausen C, et al. Validation and uncertainty estimates for MODIS Collection 6 "Deep Blue" aerosol data[J]. Journal of Geophysical Research:Atmospheres, 2013,118(14):7864-7872.
[21]	Dimitriou K, Kassomenos P. A study on the reconstitution of daily PM10 and PM2.5 levels in Paris with a multivariate linear regression model[J]. Atmospheric Environment, 2014,98:648-654.
[22]	郝静,孙成,郭兴宇,等.京津冀内陆平原区PM2.5浓度时空变化定量模拟[J]. 环境科学, 2018,39(4):1455-1465. Hao J, Sun C, Guo X Y, et al. Simulation of the Spatio-temporally Resolved PM2.5 Aerosol Mass Concentration over the Inland Plain of the Beijing-Tianjin-Hebei Region[J]. Environmental Science, 2018, 39(4):1455-1465.
[23]	Xie Y Y, Wang Y X, Zhang K, et al. Daily estimation of ground-level PM2.5 concentrations over Beijing using 3km resolution MODIS AOD[J]. Environmental Science & Technology, 2015:49(20):12280-12288.
[24]	Brunsdon C, Fotheringham A S, Charlton M E. Geographically Weighted Regression:A Method for Exploring Spatial Nonstationarity[J]. Geographical Analysis, 1996,28(4):281-298.
[25]	Zhang T H, Gong W, Wang W, et al. Ground level PM2.5 estimates over China using satellite-based geographically weighted regression (GWR) Models are improved by including NO2 and enhanced vegetation index (EVI)[J]. International Journal of Environmental Research and Public Health, 2016,13(12):1215-1227.
[26]	Zhai L, Li S, Zou B, et al. An improved geographically weighted regression model for PM 2.5 concentration estimation in large areas[J]. Atmospheric Environment, 2018,181:145-154.
[27]	Engel-Cox J, Kim Oanh N T, Van Donkelaar A, et al. Toward the next generation of air quality monitoring:Particulate Matter[J]. Atmospheric Environment, 2013,80:584-590.
[28]	Rodriguez J D, Perez A, Lozano J A. Sensitivity analysis of k-Fold cross validation in prediction error estimation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010,32(3):569-575.
[29]	GB 3095-2012环境空气质量标准[S]. GB 3095-2012 Ambient Air Quality Standards[S].
[30]	杨兴川,赵文吉,熊秋林,等.2016年京津冀地区PM2.5时空分布特征及其与气象因素的关系[J]. 生态环境学报, 2017,26(10):1747-1754. Yang X C, Zhao W J, Xiong Q L, et al. Spatio-temporal distribution of PM2.5 in Beijing-Tianjin-Hebei (BTH) Area in 2016 and its relationship with meteorological factors[J]. Ecology and Environmental Sciences, 2017,26(10):1747-1754.
[31]	李珊珊,程念亮,徐峻,等.2014年京津冀地区PM2.5浓度时空分布及来源模拟[J]. 中国环境科学, 2015,35(10):2908-2916. Li S S, Cheng N L, Xu J, et al. Spatial and temporal distributions and source simulation of PM2.5 in Beijing-Tianjin-Hebei region in 2014.[J]. China Environmental Science, 2015,35(10):2908-2916.
[32]	贾佳,韩力慧,程水源,等.京津冀区域PM2.5及二次无机组分污染特征研究[J]. 中国环境科学, 2018,38(3):801-811. Jia J, Han L H, Cheng S Y, et al. Pollution characteristic of PM2.5 and secondary inorganic ions in Beijing-Tianjin-Hebei region.[J]. China Environmental Science, 2018,38(3):801-811.