基于GWR降尺度的京津冀地区PM_(2.5)质量浓度空间分布估算
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摘要
卫星遥感估算PM_(2.5)质量浓度研究已较为成熟,但精度还未取得突破性进展.本文利用2017年京津冀地区气溶胶光学厚度(AOD)遥感数据、戈尔德地球观测系统的GEOF气象格网数据以及地面环境监测站PM_(2.5)数据,采用地理加权回归空间降尺度方法,估算京津冀地区的逐月PM_(2.5)质量浓度.基于3种不同的残差插值修正,修正后的PM_(2.5)估算结果均很理想,其中,基于自然邻近残差插值修正后的模型估算结果最优.经验证,在95%的置信水平下,其相关系数r达到0.951,决定系数R~2为0.904,调整后的R~2为0.903,平均预测误差MPE为7.307μg·m~(-3),均方根误差RMSE为11.62μg·m~(-3),相对预测误差RPE为18.35%,说明该模型能客观估算京津冀地区2017年PM_(2.5)质量浓度.2017年PM_(2.5)呈现出南高北低的空间分布特征,南北高低值区域界线与保定市和沧州市的市级行政界线具有较高的一致性.经变异系数分析发现PM_(2.5)在2017年内的稳定性程度与PM_(2.5)质量浓度空间分布呈反向性,即PM_(2.5)质量浓度高的区域稳定性低,年内的变化程度剧烈,而PM_(2.5)质量浓度低的区域稳定性强,年内变化程度弱.
Although the study of estimating PM_(2.5) mass concentrations by satellite remote sensing have been mature, the accuracy of estimating has not made breakthrough progress. In this study, based on the Aerosol Optical Depth(AOD) remote sensing data, the GEOF meteorological grid data of Golden Earth Observation System and PM_(2.5) data from ground environment monitoring station of Beijing-Tianjin-Hebei region in 2017, the monthly PM_(2.5) mass concentrations in Beijing-Tianjin-Hebei region were estimated by using geographically weighted regression spatial downscaling method. The PM_(2.5) estimations results of three different residual interpolation modifications are all ideal, among which the result based on the model of natural neighborhood residual interpolation modification is the best. After verification, at 95% confidence level, the correlation coefficient is 0.951, the determination coefficient R~2 is 0.904, the adjusted R~2 is 0.903, the average prediction error MPE is 7.307 μg·m~(-3), the root mean square error RMSE is 11.62 μg·m~(-3), and the relative prediction error RPE is 18.35%. It shows that the model could objectively estimate PM_(2.5) mass concentrations in Beijing-Tianjin-Hebei in 2017. The annual average PM_(2.5) in 2017 showed the spatial distribution characteristics of high in the south and low in the north. The regional boundaries of PM_(2.5) concentrations between North and South were in good agreement with the municipal administrative boundaries of Baoding and Cangzhou City. In addition, the analysis of coefficient of variation indicated that the stability of PM_(2.5) in 2017 was inverse to the spatial distribution of PM_(2.5) mass concentrations. Namely, the region with high PM_(2.5) mass concentrations had low stability and the degree of changes during the year was intense, while the region with low PM_(2.5) mass concentrations had strong stability and the degree of changes during the year was weak.
Although the study of estimating PM_(2.5) mass concentrations by satellite remote sensing have been mature, the accuracy of estimating has not made breakthrough progress. In this study, based on the Aerosol Optical Depth(AOD) remote sensing data, the GEOF meteorological grid data of Golden Earth Observation System and PM_(2.5) data from ground environment monitoring station of Beijing-Tianjin-Hebei region in 2017, the monthly PM_(2.5) mass concentrations in Beijing-Tianjin-Hebei region were estimated by using geographically weighted regression spatial downscaling method. The PM_(2.5) estimations results of three different residual interpolation modifications are all ideal, among which the result based on the model of natural neighborhood residual interpolation modification is the best. After verification, at 95% confidence level, the correlation coefficient is 0.951, the determination coefficient R~2 is 0.904, the adjusted R~2 is 0.903, the average prediction error MPE is 7.307 μg·m~(-3), the root mean square error RMSE is 11.62 μg·m~(-3), and the relative prediction error RPE is 18.35%. It shows that the model could objectively estimate PM_(2.5) mass concentrations in Beijing-Tianjin-Hebei in 2017. The annual average PM_(2.5) in 2017 showed the spatial distribution characteristics of high in the south and low in the north. The regional boundaries of PM_(2.5) concentrations between North and South were in good agreement with the municipal administrative boundaries of Baoding and Cangzhou City. In addition, the analysis of coefficient of variation indicated that the stability of PM_(2.5) in 2017 was inverse to the spatial distribution of PM_(2.5) mass concentrations. Namely, the region with high PM_(2.5) mass concentrations had low stability and the degree of changes during the year was intense, while the region with low PM_(2.5) mass concentrations had strong stability and the degree of changes during the year was weak.
引文
Chen G, Li S, Knibbs L D, et al. 2018. A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information[J]. Science of the Total Environment, 636: 52-60
陈辉, 厉青, 张玉环,等. 2016. 基于地理加权模型的我国冬季PM2.5遥感估算方法研究[J]. 环境科学学报, 36(6): 2142-2151
杜艳春, 葛察忠, 何理, 等. 2017. 京津冀传统产业绿色转型升级的瓶颈与政策建议[J]. 中国人口资源与环境, (S2): 107-110
Guan D, Su X, Zhang Q, et al. 2014. The socioeconomic drivers of China′s primary PM2.5 emissions[J]. Environmental Research Letters, 9(2):024010
郭建平, 吴业荣, 张小曳, 等. 2013. 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, 34(3): 817-825
Guo Y, Feng N, Christopher S A, et al. 2014. Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS[J]. International Journal of Remote Sensing, 35(17):6522-6544
汉瑞英, 陈健, 王彬. 2016. 利用LUR模型模拟杭州市PM2.5质量浓度空间分布[J]. 环境科学学报, 36(9): 3379-3385
郝静, 孙成, 郭兴宇, 等. 2018. 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, 39(4): 1455-1464
胡艳兴, 潘竟虎, 王怡睿. 2015. 基于ESDA-GWR的1997—2012年中国省域能源消费碳排放时空演变特征[J]. 环境科学学报, 35(6): 1896-1906
黄旭, 郭云霞, 刘剑斌, 等. 2017. 柳州大气PM2.5中糖类物质的分布特征与指示意义[J].中国环境科学, 37(3): 838-843
嵇涛, 刘睿, 杨华, 等. 2015. 多源遥感数据的降水空间降尺度研究——以川渝地区为例[J]. 地球信息科学学报, 17(1):108-117
贾松林, 苏林, 陶金花, 等. 2014. 卫星遥感监测近地表细颗粒物多元回归方法研究[J]. 中国环境科学, 34(3): 565-573
Koelemeijer R, Homan C, Matthijsen J. 2006. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe[J]. Atmospheric Environment, 40(27):5304-5315
Lang J, Zhang Y, Zhou Y, et al. 2017. Trends of PM2.5 and chemical composition in Beijing, 2000—2015[J]. Aerosol & Air Quality Research, 17(2): 412-425
李净, 张晓. 2015. TRMM降水数据的空间降尺度方法研究[J]. 地理科学, 35(9):1164-1169
李荣. 2015. 中国典型地区PM2.5时空分布估算建模方法与应用研究[D]. 北京:中国科学院大学
Liang F, Xiao Q, Wang Y, et al. 2017. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China[J]. Science of the Total Environment, 616-617: 1589-1598
Liang F, Meng G, Xiao Q, et al. 2017. Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China[J]. Environmental Research, 158: 54-60
Lohmann U, Lesins G. 2002. Stronger Constraints on the Anthropogenic Indirect Aerosol Effect[J]. Science, 298(5595): 1012-1015
卢亚灵, 蒋洪强, 黄季夏, 等. 2014. 基于GWR的中国地级城市SO2年均质量浓度模拟[J]. 生态环境学报, 23(8):1305-1310
马宗伟. 2015. 基于卫星遥感的我国PM2.5时空分布研究[D]. 南京:南京大学
Ma Z W, Hu X F, Sayer A M, et al. 2016. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004-2013[J]. Environ Health Perspect, 124(2): 184-192
Menon S, Hansen J, Nazarenko L, et al. 2002. Climate effects of black carbon aerosols in China and India[J]. Science, 297(5590): 2250-2253
Nishizawa T, Asano S, Uchiyama A, et al. 2004. Seasonal variation of aerosol direct radiative forcing and optical properties estimated from ground-based solar radiation measurements[J]. Journal of the Atmospheric Sciences, 61(1): 57-72
Penner J E, Dong X, Chen Y. 2004. Observational evidence of a change in radiative forcing due to the indirect aerosol effect[J]. Nature, 427(6971): 231-234
Rob B, Gerard H, Danielle V, et al. 2013. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe-The ESCAPE project[J]. Atmospheric Environment, 72(2): 10-23
邵龙义, 时宗波, 黄勤. 2000. 都市大气环境中可吸入颗粒物的研究[J]. 环境保护, (1): 24-26
吴健生, 王茜, 李嘉诚, 等. 2017. PM2.5浓度空间分异模拟模型对比:以京津冀地区为例[J]. 环境科学, 38(6): 2191-2201
谢元博, 陈娟, 李巍. 2014. 雾霾重污染期间北京居民对高浓度PM2.5持续暴露的健康风险及其损害价值评估[J]. 环境科学, 35(1): 1-8
徐建辉, 江洪. 2015. 长江三角洲PM2.5质量浓度遥感估算与时空分布特征[J]. 环境科学, 36(9): 3119-3127
杨维, 赵文吉, 宫兆宁, 等. 2013.北京城区可吸入颗粒物分布与呼吸系统疾病相关分析[J]. 环境科学, 34(1): 237-243
叶辉, 王军邦, 黄玫, 等. 2012. 青藏高原植被降水利用效率的空间格局及其对降水和气温的响应[J]. 植物生态学报, 36(12): 1237-1247
殷永文, 程金平, 段玉森, 等. 2011. 上海市霾期间PM2.5 、PM10污染与呼吸科、儿呼吸科门诊人数的相关分析[J]. 环境科学, 32(7): 1894-1898
尤加俊, 安如. 2015. 基于CCI和MODIS数据的淮河流域地表土壤湿度降尺度方法研究[J]. 测绘与空间地理信息, 38(2):30-34
张世乔, 江洪, 王祎鑫, 等. 2017. 京津唐地区PM2.5遥感估算与区域传输[J]. 遥感信息, 32(4):11-23
Zhang T, Gong W, Wang W, et al. 2016. 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, 13(12): E1215
张文, 潘竟虎. 2016. 2013年大范围雾霾期间京津冀PM2.5质量浓度遥感估算及时空变化的经验正交函数分析[J]. 兰州大学学报(自然科学版), 52(3): 350-356
张予燕. 2006. β射线法与振荡天平法测定环境中PM10的对比分析[J]. 仪器仪表与分析监测, (1): 45-46
朱一川, 张晶, 周文刚, 等. 2003. LD—3C型微电脑激光粉尘仪及其质量浓度转换系数K值的测定[J]. 中国环境卫生, (1): 103-107
陈辉, 厉青, 张玉环,等. 2016. 基于地理加权模型的我国冬季PM2.5遥感估算方法研究[J]. 环境科学学报, 36(6): 2142-2151
杜艳春, 葛察忠, 何理, 等. 2017. 京津冀传统产业绿色转型升级的瓶颈与政策建议[J]. 中国人口资源与环境, (S2): 107-110
Guan D, Su X, Zhang Q, et al. 2014. The socioeconomic drivers of China′s primary PM2.5 emissions[J]. Environmental Research Letters, 9(2):024010
郭建平, 吴业荣, 张小曳, 等. 2013. 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, 34(3): 817-825
Guo Y, Feng N, Christopher S A, et al. 2014. Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS[J]. International Journal of Remote Sensing, 35(17):6522-6544
汉瑞英, 陈健, 王彬. 2016. 利用LUR模型模拟杭州市PM2.5质量浓度空间分布[J]. 环境科学学报, 36(9): 3379-3385
郝静, 孙成, 郭兴宇, 等. 2018. 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, 39(4): 1455-1464
胡艳兴, 潘竟虎, 王怡睿. 2015. 基于ESDA-GWR的1997—2012年中国省域能源消费碳排放时空演变特征[J]. 环境科学学报, 35(6): 1896-1906
黄旭, 郭云霞, 刘剑斌, 等. 2017. 柳州大气PM2.5中糖类物质的分布特征与指示意义[J].中国环境科学, 37(3): 838-843
嵇涛, 刘睿, 杨华, 等. 2015. 多源遥感数据的降水空间降尺度研究——以川渝地区为例[J]. 地球信息科学学报, 17(1):108-117
贾松林, 苏林, 陶金花, 等. 2014. 卫星遥感监测近地表细颗粒物多元回归方法研究[J]. 中国环境科学, 34(3): 565-573
Koelemeijer R, Homan C, Matthijsen J. 2006. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe[J]. Atmospheric Environment, 40(27):5304-5315
Lang J, Zhang Y, Zhou Y, et al. 2017. Trends of PM2.5 and chemical composition in Beijing, 2000—2015[J]. Aerosol & Air Quality Research, 17(2): 412-425
李净, 张晓. 2015. TRMM降水数据的空间降尺度方法研究[J]. 地理科学, 35(9):1164-1169
李荣. 2015. 中国典型地区PM2.5时空分布估算建模方法与应用研究[D]. 北京:中国科学院大学
Liang F, Xiao Q, Wang Y, et al. 2017. MAIAC-based long-term spatiotemporal trends of PM2.5 in Beijing, China[J]. Science of the Total Environment, 616-617: 1589-1598
Liang F, Meng G, Xiao Q, et al. 2017. Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China[J]. Environmental Research, 158: 54-60
Lohmann U, Lesins G. 2002. Stronger Constraints on the Anthropogenic Indirect Aerosol Effect[J]. Science, 298(5595): 1012-1015
卢亚灵, 蒋洪强, 黄季夏, 等. 2014. 基于GWR的中国地级城市SO2年均质量浓度模拟[J]. 生态环境学报, 23(8):1305-1310
马宗伟. 2015. 基于卫星遥感的我国PM2.5时空分布研究[D]. 南京:南京大学
Ma Z W, Hu X F, Sayer A M, et al. 2016. Satellite-based spatiotemporal trends in PM2.5 concentrations: China, 2004-2013[J]. Environ Health Perspect, 124(2): 184-192
Menon S, Hansen J, Nazarenko L, et al. 2002. Climate effects of black carbon aerosols in China and India[J]. Science, 297(5590): 2250-2253
Nishizawa T, Asano S, Uchiyama A, et al. 2004. Seasonal variation of aerosol direct radiative forcing and optical properties estimated from ground-based solar radiation measurements[J]. Journal of the Atmospheric Sciences, 61(1): 57-72
Penner J E, Dong X, Chen Y. 2004. Observational evidence of a change in radiative forcing due to the indirect aerosol effect[J]. Nature, 427(6971): 231-234
Rob B, Gerard H, Danielle V, et al. 2013. Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe-The ESCAPE project[J]. Atmospheric Environment, 72(2): 10-23
邵龙义, 时宗波, 黄勤. 2000. 都市大气环境中可吸入颗粒物的研究[J]. 环境保护, (1): 24-26
吴健生, 王茜, 李嘉诚, 等. 2017. PM2.5浓度空间分异模拟模型对比:以京津冀地区为例[J]. 环境科学, 38(6): 2191-2201
谢元博, 陈娟, 李巍. 2014. 雾霾重污染期间北京居民对高浓度PM2.5持续暴露的健康风险及其损害价值评估[J]. 环境科学, 35(1): 1-8
徐建辉, 江洪. 2015. 长江三角洲PM2.5质量浓度遥感估算与时空分布特征[J]. 环境科学, 36(9): 3119-3127
杨维, 赵文吉, 宫兆宁, 等. 2013.北京城区可吸入颗粒物分布与呼吸系统疾病相关分析[J]. 环境科学, 34(1): 237-243
叶辉, 王军邦, 黄玫, 等. 2012. 青藏高原植被降水利用效率的空间格局及其对降水和气温的响应[J]. 植物生态学报, 36(12): 1237-1247
殷永文, 程金平, 段玉森, 等. 2011. 上海市霾期间PM2.5 、PM10污染与呼吸科、儿呼吸科门诊人数的相关分析[J]. 环境科学, 32(7): 1894-1898
尤加俊, 安如. 2015. 基于CCI和MODIS数据的淮河流域地表土壤湿度降尺度方法研究[J]. 测绘与空间地理信息, 38(2):30-34
张世乔, 江洪, 王祎鑫, 等. 2017. 京津唐地区PM2.5遥感估算与区域传输[J]. 遥感信息, 32(4):11-23
Zhang T, Gong W, Wang W, et al. 2016. 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, 13(12): E1215
张文, 潘竟虎. 2016. 2013年大范围雾霾期间京津冀PM2.5质量浓度遥感估算及时空变化的经验正交函数分析[J]. 兰州大学学报(自然科学版), 52(3): 350-356
张予燕. 2006. β射线法与振荡天平法测定环境中PM10的对比分析[J]. 仪器仪表与分析监测, (1): 45-46
朱一川, 张晶, 周文刚, 等. 2003. LD—3C型微电脑激光粉尘仪及其质量浓度转换系数K值的测定[J]. 中国环境卫生, (1): 103-107
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