区域PM_(2.5)时空回归建模与预测
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摘要
基于区域PM_(2.5)时空建模和预测的需要及PM_(2.5)浓度呈现明显的时空分布趋势的状况,以苏南地区2014年PM_(2.5)日监测数据为实验数据,使用回归克里格对区域PM_(2.5)进行时空建模和估值。利用最小二乘法建立了PM_(2.5)与时空位置的三元二次回归趋势模型,建模点趋势值与实测值间的平均误差接近于0,表明趋势模型拟合效果较好;拟合了样点残差的理论变异函数模型,表明该地区PM_(2.5)的空间和时间相关性范围分别为150 km和4 d;基于该模型,使用时空普通克里格对残差进行时空插值;插值结果与趋势项相加,得到PM_(2.5)回归克里格估值结果;通过对比不考虑趋势的时空普通克里格估值结果,发现考虑时空趋势的时空回归克里格法精度提高了1. 29%。对所提方法进行了创新性分析,并对不足之处进行了讨论。
PM_(2.5) concentration exhibit significant space-time trends due to geographical and seasonal differences. Thus,concentrations trends cannot be ignored when performing spatiotemporal PM_(2.5) predictions in an area. In this paper,daily monitoring data of PM_(2.5) concentration in southern Jiangsu Province in 2014 were used for modeling and prediction of PM_(2.5) distribution by Spatiotemporal Kriging with Trend model( STKT). Firstly,PM_(2.5) trends were represented by a space-time polynomial functions. The mean error of trend model closed to 0,indicating that the fitting of trend model is excellect. Secondly,the variogram analysis of the residual PM_(2.5) concentrations indicated that there is a varying ST dependence between the PM_(2.5) concentrations in the domain defined by the 150 km,4 days ranges. Based on the variogram and spatiotemporal trend model,the spatiotemporal distribution of PM_(2.5) in the study area was obtained. Plots of the predicted spatiotemporal PM_(2.5) distributions revealed a marked tendency of the PM_(2.5) concentrations to increase from the summer and autumn to spring and winter,and from east to west of the study area. STKT can reduce prediction errors in practically and statistically significant ways. A numerical comparison of the STKT technique vs the mainstream Spatiotemporal Ordinary Kriging( STOK) technique showed that STKT can increase accuracy by 12. 9% than STOK.
PM_(2.5) concentration exhibit significant space-time trends due to geographical and seasonal differences. Thus,concentrations trends cannot be ignored when performing spatiotemporal PM_(2.5) predictions in an area. In this paper,daily monitoring data of PM_(2.5) concentration in southern Jiangsu Province in 2014 were used for modeling and prediction of PM_(2.5) distribution by Spatiotemporal Kriging with Trend model( STKT). Firstly,PM_(2.5) trends were represented by a space-time polynomial functions. The mean error of trend model closed to 0,indicating that the fitting of trend model is excellect. Secondly,the variogram analysis of the residual PM_(2.5) concentrations indicated that there is a varying ST dependence between the PM_(2.5) concentrations in the domain defined by the 150 km,4 days ranges. Based on the variogram and spatiotemporal trend model,the spatiotemporal distribution of PM_(2.5) in the study area was obtained. Plots of the predicted spatiotemporal PM_(2.5) distributions revealed a marked tendency of the PM_(2.5) concentrations to increase from the summer and autumn to spring and winter,and from east to west of the study area. STKT can reduce prediction errors in practically and statistically significant ways. A numerical comparison of the STKT technique vs the mainstream Spatiotemporal Ordinary Kriging( STOK) technique showed that STKT can increase accuracy by 12. 9% than STOK.
引文
[1]王振波，方创琳，许光，等．2014年中国城市PM2. 5浓度的时空变化规律[J]．地理学报，2015,70(11):1 720-1 734.WANG Zhenbo,FANG Chuanglin,XU Guang,et al.Spatial-Temporal Characteristics of the PM2. 5in China in 2014[J]. Acta Geographica Sinica,2015,70(11):1 720-1 734.
[2]杨冕，王银．长江经济带PM2. 5时空特征及其影响因素研究[J]．中国人口资源与环境，2017,27(1):91-100.YANG Mian, WANG Yin. Spatial-Temporal Characteristics of PM2. 5and Its Influencing Factors in the Yangtze River Economic Belt[J]. China Population, Resources and Environment, 2017, 27(1):91-100.
[3]徐伟嘉，何芳芳，李红霞，等．珠三角区域PM2. 5时空分异特征[J]．环境科学研究，2014,27(9):951-957.XU Weijia,HE Fangfang,LI Hongxia,et al. Spatial and Temporal Variations of PM2. 5in the Pearl River Delta[J]. Research of Environmental Science,2014,27(9):951-957.
[4]徐建辉，江洪．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，2015,36(9):3 119-3 127.XU Jianhui, JIANG Hong. Estimation of PM2. 5Concentration over the Yangtze Delta Using Remote Sensing:Analysis of Spatial and Temporal Variantions[J]. Environmental Science,2015,36(9):3 119-3 127.
[5]刘永林，孙启明，钟明洋，等．重庆市主城区PM2. 5时空分布特征[J]．环境科学，2016,37(4):1 219-1 229.LIU Yonglin SUN Qiming,ZHONG Mingyang,et al.Temporal and Spatial Distribution Characteristics of PM2. 5in Chongqing Urban Areas[J]. Environmental Science,2016,37(4):1 219-1 229.
[6]梅杨，党丽娜，杨勇，等．基于时空克里格的PM2. 5时空预测及分析[J]．环境科学与技术，2016,39(7):157-163.MEI Yang,DANG Lina,YANG Yong,et al. SpatioTemporal Prediction and Analysis of PM2. 5Based on Spatio-Temporal Kriging[J]. Environmental Science&Technology,2016,39(7):157-163.
[7]王库．回归克里格在土壤全氮空间预测上的应用[J]．中国农学通报，2013,29(20):142-147.WANG Ku. Application of Regression Kriging on the Spatial Prediction of Total Soil Nitrogen[J]. Chinese Agricultural Science Bulletin,2013,29(20):142-147.
[8]杨谦，王晓晴，孙孝林，等．基于REML的普通克里格和回归克里格在土壤属性空间预测中的比较[J]．土壤通报，49(2):283-292.YANG Qian,WANG Xiaoqing,SUN Xiaolin,et al.Comparing Prediction Accuracies of Ordinary Kriging and Regression Kriging with REML in Soil Properties Mapping[J]. Chinese Journal of Soil Science,2018,49(2):283-292.
[9]代富强，周启刚，刘刚才．基于回归克里格和遥感的紫色土区土壤有机质含量空间预测[J]．土壤通报，2014,45(3):562-567.DAI Fuqiang, ZHOU Qigang LIU Gangcai. Spatial Prediction of Soil Organic Matter Contents in a Purplish Soil Region with Regression Kriging and Remote Sensing[J]. Chinese Journal of Soil Science,2014,45(3):562-567.
[10]KYRIAKIDIS P C, JOURNEL A G. Geostatistical Space-Time Models:A Review[J]. Mathematical Geology,1999,31:651-684.
[11]周磊，武建军，贾瑞静，等．京津冀PM2. 5时空分布特征及其污染风险因素[J]．环境科学研究，2016,29(4):483-493.ZHOU Lei, WU Jianjun, JIA Ruijing, et al.Investigation of Temporal-Spatial Characteristics and Underlying Risk Factors of PM2. 5Pollution in BeijingTianjin-Hebei Area[J]. Research of Environmental Science,2016,29(4):483-493.
[12]周亮，周成虎，杨帆，等．2000—2011年中国PM2. 5时空演化特征及驱动因素解析[J]．地理学报，2017,72(11):2 079-2 092.ZHOU Liang,ZHOU Chenghu,YANG Fan,et al.Spatio-Temporal Evolution and the Influencing Factors of PM2. 5in China Between 2000 and 2011[J]. Acta Geographica Sinica,2017,72(11):2 079-2 092.
[13]苏维，张帅珺，赖新云，等．南昌市空气PM2. 5和PM10的时空动态及其影响因素[J]．应用生态学报，2017,28(1):257-265.SU Wei, ZHANG Shuaijun, LAI Xinyun, et al.Spatiotemporal Dynamics of Atmospheric PM2. 5and PM10and Its Influencing Factors in Nanchang,China[J]. Chinese Journal of Applied Ecology,2017,28(1):257-265.
[14]HOFF R M,CHRISTOPHER S A. Remote Sensing of Particulate Pollution from Space:Have We Reached the Promised Land?[J]. Journal of the Air&Waste Management Association,2009,59(6):645-675.
[15]GUO J,XIA F,ZHANG Y,et al. Impact of Diurnal Variability and Meteorological Factors on the PM2. 5-AOD Relationship:Implications for PM2. 5Remote Sensing[J]. Environmental Pollution, 2017, 221(94):94-104.
[16]HE Q Q,HUANG B. Satellite-Based Mapping of Daily High-Resolution Ground PM2. 5in China Via SpaceTime Regression Modeling[J]. Remote Sensing of Environment,2018,206:72-83
[17]郭婉臻，夏楠，塔西甫拉提·特依拜，等．基于AOD数据的新疆大型露天煤炭开采区PM2. 5和PM10反演[J]．农业工程学报，2017,33(19):216-222.GUO Wanzhen,XIA Nan,TASHPOLAT Tiyip,et al.Inversion of PM2. 5and PM10Content Based on AOD Data in Large Opencast Coal Mining Area of Xinjiang[J]. Transactions of the Chinese Society of Agricultural,2017,33(19):216-222.
[2]杨冕，王银．长江经济带PM2. 5时空特征及其影响因素研究[J]．中国人口资源与环境，2017,27(1):91-100.YANG Mian, WANG Yin. Spatial-Temporal Characteristics of PM2. 5and Its Influencing Factors in the Yangtze River Economic Belt[J]. China Population, Resources and Environment, 2017, 27(1):91-100.
[3]徐伟嘉，何芳芳，李红霞，等．珠三角区域PM2. 5时空分异特征[J]．环境科学研究，2014,27(9):951-957.XU Weijia,HE Fangfang,LI Hongxia,et al. Spatial and Temporal Variations of PM2. 5in the Pearl River Delta[J]. Research of Environmental Science,2014,27(9):951-957.
[4]徐建辉，江洪．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，2015,36(9):3 119-3 127.XU Jianhui, JIANG Hong. Estimation of PM2. 5Concentration over the Yangtze Delta Using Remote Sensing:Analysis of Spatial and Temporal Variantions[J]. Environmental Science,2015,36(9):3 119-3 127.
[5]刘永林，孙启明，钟明洋，等．重庆市主城区PM2. 5时空分布特征[J]．环境科学，2016,37(4):1 219-1 229.LIU Yonglin SUN Qiming,ZHONG Mingyang,et al.Temporal and Spatial Distribution Characteristics of PM2. 5in Chongqing Urban Areas[J]. Environmental Science,2016,37(4):1 219-1 229.
[6]梅杨，党丽娜，杨勇，等．基于时空克里格的PM2. 5时空预测及分析[J]．环境科学与技术，2016,39(7):157-163.MEI Yang,DANG Lina,YANG Yong,et al. SpatioTemporal Prediction and Analysis of PM2. 5Based on Spatio-Temporal Kriging[J]. Environmental Science&Technology,2016,39(7):157-163.
[7]王库．回归克里格在土壤全氮空间预测上的应用[J]．中国农学通报，2013,29(20):142-147.WANG Ku. Application of Regression Kriging on the Spatial Prediction of Total Soil Nitrogen[J]. Chinese Agricultural Science Bulletin,2013,29(20):142-147.
[8]杨谦，王晓晴，孙孝林，等．基于REML的普通克里格和回归克里格在土壤属性空间预测中的比较[J]．土壤通报，49(2):283-292.YANG Qian,WANG Xiaoqing,SUN Xiaolin,et al.Comparing Prediction Accuracies of Ordinary Kriging and Regression Kriging with REML in Soil Properties Mapping[J]. Chinese Journal of Soil Science,2018,49(2):283-292.
[9]代富强，周启刚，刘刚才．基于回归克里格和遥感的紫色土区土壤有机质含量空间预测[J]．土壤通报，2014,45(3):562-567.DAI Fuqiang, ZHOU Qigang LIU Gangcai. Spatial Prediction of Soil Organic Matter Contents in a Purplish Soil Region with Regression Kriging and Remote Sensing[J]. Chinese Journal of Soil Science,2014,45(3):562-567.
[10]KYRIAKIDIS P C, JOURNEL A G. Geostatistical Space-Time Models:A Review[J]. Mathematical Geology,1999,31:651-684.
[11]周磊，武建军，贾瑞静，等．京津冀PM2. 5时空分布特征及其污染风险因素[J]．环境科学研究，2016,29(4):483-493.ZHOU Lei, WU Jianjun, JIA Ruijing, et al.Investigation of Temporal-Spatial Characteristics and Underlying Risk Factors of PM2. 5Pollution in BeijingTianjin-Hebei Area[J]. Research of Environmental Science,2016,29(4):483-493.
[12]周亮，周成虎，杨帆，等．2000—2011年中国PM2. 5时空演化特征及驱动因素解析[J]．地理学报，2017,72(11):2 079-2 092.ZHOU Liang,ZHOU Chenghu,YANG Fan,et al.Spatio-Temporal Evolution and the Influencing Factors of PM2. 5in China Between 2000 and 2011[J]. Acta Geographica Sinica,2017,72(11):2 079-2 092.
[13]苏维，张帅珺，赖新云，等．南昌市空气PM2. 5和PM10的时空动态及其影响因素[J]．应用生态学报，2017,28(1):257-265.SU Wei, ZHANG Shuaijun, LAI Xinyun, et al.Spatiotemporal Dynamics of Atmospheric PM2. 5and PM10and Its Influencing Factors in Nanchang,China[J]. Chinese Journal of Applied Ecology,2017,28(1):257-265.
[14]HOFF R M,CHRISTOPHER S A. Remote Sensing of Particulate Pollution from Space:Have We Reached the Promised Land?[J]. Journal of the Air&Waste Management Association,2009,59(6):645-675.
[15]GUO J,XIA F,ZHANG Y,et al. Impact of Diurnal Variability and Meteorological Factors on the PM2. 5-AOD Relationship:Implications for PM2. 5Remote Sensing[J]. Environmental Pollution, 2017, 221(94):94-104.
[16]HE Q Q,HUANG B. Satellite-Based Mapping of Daily High-Resolution Ground PM2. 5in China Via SpaceTime Regression Modeling[J]. Remote Sensing of Environment,2018,206:72-83
[17]郭婉臻，夏楠，塔西甫拉提·特依拜，等．基于AOD数据的新疆大型露天煤炭开采区PM2. 5和PM10反演[J]．农业工程学报，2017,33(19):216-222.GUO Wanzhen,XIA Nan,TASHPOLAT Tiyip,et al.Inversion of PM2. 5and PM10Content Based on AOD Data in Large Opencast Coal Mining Area of Xinjiang[J]. Transactions of the Chinese Society of Agricultural,2017,33(19):216-222.
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