基于LME/BME的珠江三角洲PM_(2.5)星地融合技术研究
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摘要
收集并处理了遥感反演的气溶胶光学厚度(AOD)、归一化植被指数(NDVI)和气象数据,采用贝叶斯最大熵(BME)结合线性混合模型(LME)估算了2015年10月～2016年3月珠江三角洲地区近地表旬平均PM_(2.5)质量浓度.结果表明,LME+BME模型的预测精度比LME模型有较大提升,LME+BME模型的交叉验证结果 R~2为0.751,RMSE为6.886μg/m~3,MAE为4.52μg/m~3,而LME模型的交叉验证结果 R~2为0.703,RMSE为7.546μg/m~3,MAE为4.927μg/m~3.空间分布看,PM_(2.5)高浓度地区主要集中在广州、佛山、东莞等地区,低浓度地区主要集中在肇庆、惠州、江门的南部等地区;时间变化看,PM_(2.5)污染比较严重的时间为2015年10月中旬、2015年11月下旬以及2016年3月下旬,而2015年10月上旬、2015年12月上旬和2016年1月下旬污染则相对较低.
By combining Linear Mixed Effect(LME) model and Bayesian Maximum Entropy(BME) method, ground-level PM_(2.5) from October 2015 to March 2016 in Pearl River Delta region were estimated in this paper by AOD, NDVI and meteorological data.The results showed that the prediction accuracy of LME+BME method were greatly improved compared with that of the LME method. The cross-validation R~2 of LME+BME model was 0.751, and root mean squared prediction error(RMSE) was 6.886μg/m~3,the mean prediction error(MPE) was 4.52μg/m~3, while R~2=0.703, RMSE=7.546μg/m~3, and MAE=4.927μg/m~3 for the LME method.The high PM_(2.5) concentration was mainly located in Guangzhou, Foshan, Dongguan, and the low PM_(2.5) concentration was mainly distributed in Zhaoqing, Huizhou, Jiangmen. In terms of seasonal variation, PM_(2.5) pollution was more serious in mid-October in 2015,late November in 2015 and late March in 2016, while it was relatively low in early October in 2015, early December in 2015 and late January in 2016.
By combining Linear Mixed Effect(LME) model and Bayesian Maximum Entropy(BME) method, ground-level PM_(2.5) from October 2015 to March 2016 in Pearl River Delta region were estimated in this paper by AOD, NDVI and meteorological data.The results showed that the prediction accuracy of LME+BME method were greatly improved compared with that of the LME method. The cross-validation R~2 of LME+BME model was 0.751, and root mean squared prediction error(RMSE) was 6.886μg/m~3,the mean prediction error(MPE) was 4.52μg/m~3, while R~2=0.703, RMSE=7.546μg/m~3, and MAE=4.927μg/m~3 for the LME method.The high PM_(2.5) concentration was mainly located in Guangzhou, Foshan, Dongguan, and the low PM_(2.5) concentration was mainly distributed in Zhaoqing, Huizhou, Jiangmen. In terms of seasonal variation, PM_(2.5) pollution was more serious in mid-October in 2015,late November in 2015 and late March in 2016, while it was relatively low in early October in 2015, early December in 2015 and late January in 2016.
引文
[1] Yang Y, Christakos G, Huang W, et al. Uncertainty assessment of PM2.5contamination mapping using spatiotemporal sequential indicator simulations and multi-temporal monitoring data[J]. Science Reports,2016,6:24-35.
[2] Zou B, Luo Y Q, Wan N, et al. Performance comparison of LUR and OK in PM2.5 concentration mapping:a multidimensional perspective[J]. Science Reports, 2015,5:86-98.
[3] Lv B, Hu Y, Chang H H, et al. Improving the accuracy of daily PM2.5distributions derived from the fusion of ground-level measurements with aerosol optical depth observations, a case study in north China[J].Environment Science&Technology, 2016,50(9):4752-4759.
[4] Zhang W, Capps S L, Hu Y, et al. Development of the high-order decoupled direct method in three dimensions for particulate matter:enabling advanced sensitivity analysis in air quality models[J].Geoscientific Model Development Discussions, 2011,4(4):355-368.
[5] Engel-Cox J A., Holloman C H., Coutant B W, et al. Qualitative andquantitative evaluation of MODIS satellite sensor data for regional and urban scaleair quality[J]. Atmospheric Environment, 2004,38:2495-2509.
[6] Engel-Cox J A., Young G S, Hoff R M. Application of satellite remote-sensingdata for source analysis of fine particulate mattertransport events[J]. Journal of the Air&Waste Management Association, 2005,55:1389-1397.
[7] Bucher H U, Baumgartner R, Bucher N, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution[J]. Atmospheric Environment, 2008,42(33):7561-7578.
[8] Kloog I, Chudnovsky A A, Just A C, et al. A new hybrid spatiotemporal model for estimating daily multi-year PM2.5, concentrations across northeastern USA using high resolution aerosol optical depth data[J]. Atmospheric Environment, 2014,95(1):581-590.
[9] Wu J S, Li J C, Peng J, et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing,China[J].Environmental Science Pollution Research, 2015,22(9):7045-7061.
[10] Hu Z Y. Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease[J]. International Journal of Health Geographics, 2009,8(1):27.
[11] Ma Z, Hu X, Huang L, et al. Estimating ground-level PM2.5 in China using satellite remote sensing[J]. Environmental Science&Technology, 2014,48(13):7436.
[12] Song W, Jia H, Huang J, et al. A satellite-based geographically weighted regression model for regional PM2.5, estimation over the Pearl River Delta region in China[J]. Remote Sensing of Environment,2014,154:1-7.
[13]张莹,李正强.利用细模态气溶胶光学厚度估计PM2.5[J].遥感学报,2013,17(4):929-943.Zhang Y, Li Z Q. Estimation of PM2.5 from fine-mode aerosol optical depth[J]. Journal of Remote Sensing, 2013,17(4):929-943.
[14] Zhang Y, Li Z.Remote sensing of atmospheric fine particulate matter(PM2.5)mass concentration near the ground from satellite observation[J]. Remote Sensing of Environment, 2015,160:252-262.
[15] Lin C, Li Y, Yuan Z, et al. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5[J].Remote Sensing of Environment, 2015,156:117-128.
[16] Li Z Q, Zhang Y, Shao J, et al. Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground:Method validation using ground-based measurements[J]. Remote Sensing of Environment, 2016,173:59-68.
[17] Lee H J, Liu Y, Coull B A, et al. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations[J]. Atmospheric Chemistry and Physics, 2011(11):7991-8002.
[18] Lee H J, Coull B A, Bell M L, et al. Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations[J]. Environmental Research, 2012,(118):8-15.
[19] Kloog I, Chudnovsky A A, Just A C, et al. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5concentrations across northeastern USA using high resolution aerosol optical depth data[J]. Atmospheric Environment, 2014,95(1):581-590.
[20] Zheng Y X, Zhang Q, Liu Y, et al. Estimating ground-level PM2.5concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements[J]. Atmospheric Environment, 2016,124:232-242.
[21] Xie Y, Wang Y, Zhang K, et al. Daily estimation of ground-level PM2.5concentrations over Beijing using 3km resolution MODIS AOD[J].Environmental Science&Technology, 2015,49(20):12280-12288.
[22] 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.
[23] Yu H L, Wang C H, Liu M C, et al. Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods[J]. International Journal of Environmental Research&Public Health,2011,8(6):2153-2169.
[24] Beckerman B S, Jerrett M, Serre M, et al. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous united states[J]. Environmental Science&Technology,2013,47(13):7233-7241.
[25]江曲图,何俊昱,王占山,等.基于LUR/BME的海岸带地区PM2.5时空特性研究[J].中国环境科学,2017,37(2):424-431.Jiang Q T, He J Y, Wang Z S, et al. Spatiotemporal analysis of PM2.5 in large coastal domains by combining Land Use Regression and Bayesian Maximum Entropy[J]. China Environmental Science, 2017,37(2):424-431.
[26] Christakos G. A Bayesian maximum-entropy view to the spatial estimation problem[J]. Mathematical Geosciences, 1990,22(7):763-777.
[27]杨勇,张若兮.贝叶斯最大熵地统计方法研究与应用进展[J].土壤,2014,(3):402-406.Yang Y, Zhang R X. Review on bayesian maximum entropy geostatistics method[J]. Soils,2014,(3):402-406.
[28] Song W, Jia H, Huang J, et al. A satellite-based geographically weighted regression model for regional PM2.5, estimation over the Pearl River Delta region in China[J]. Remote Sensing of Environment,2014,154:1-7.
[29] Zheng Y, Zhang Q, Liu Y, et al. Estimating ground-level PM2.5concentrations over three megalopolises in China using satellitederived aerosol optical depth measurements[C]//EGU General Assembly Conference. EGU General Assembly Conference Abstracts,2016.
[30]吴健生,王茜,李嘉诚,等.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,2017,38(6):2191-2201.Wu J S, Wang X, Li J C, et al. Comparison of models on spatial variation of PM2.5 concentration:A case of Beijing-Tianjin-Hebei region[J]. Environmental Science,2017,38(6):2191-2201.
[2] Zou B, Luo Y Q, Wan N, et al. Performance comparison of LUR and OK in PM2.5 concentration mapping:a multidimensional perspective[J]. Science Reports, 2015,5:86-98.
[3] Lv B, Hu Y, Chang H H, et al. Improving the accuracy of daily PM2.5distributions derived from the fusion of ground-level measurements with aerosol optical depth observations, a case study in north China[J].Environment Science&Technology, 2016,50(9):4752-4759.
[4] Zhang W, Capps S L, Hu Y, et al. Development of the high-order decoupled direct method in three dimensions for particulate matter:enabling advanced sensitivity analysis in air quality models[J].Geoscientific Model Development Discussions, 2011,4(4):355-368.
[5] Engel-Cox J A., Holloman C H., Coutant B W, et al. Qualitative andquantitative evaluation of MODIS satellite sensor data for regional and urban scaleair quality[J]. Atmospheric Environment, 2004,38:2495-2509.
[6] Engel-Cox J A., Young G S, Hoff R M. Application of satellite remote-sensingdata for source analysis of fine particulate mattertransport events[J]. Journal of the Air&Waste Management Association, 2005,55:1389-1397.
[7] Bucher H U, Baumgartner R, Bucher N, et al. A review of land-use regression models to assess spatial variation of outdoor air pollution[J]. Atmospheric Environment, 2008,42(33):7561-7578.
[8] Kloog I, Chudnovsky A A, Just A C, et al. A new hybrid spatiotemporal model for estimating daily multi-year PM2.5, concentrations across northeastern USA using high resolution aerosol optical depth data[J]. Atmospheric Environment, 2014,95(1):581-590.
[9] Wu J S, Li J C, Peng J, et al. Applying land use regression model to estimate spatial variation of PM2.5 in Beijing,China[J].Environmental Science Pollution Research, 2015,22(9):7045-7061.
[10] Hu Z Y. Spatial analysis of MODIS aerosol optical depth, PM2.5, and chronic coronary heart disease[J]. International Journal of Health Geographics, 2009,8(1):27.
[11] Ma Z, Hu X, Huang L, et al. Estimating ground-level PM2.5 in China using satellite remote sensing[J]. Environmental Science&Technology, 2014,48(13):7436.
[12] Song W, Jia H, Huang J, et al. A satellite-based geographically weighted regression model for regional PM2.5, estimation over the Pearl River Delta region in China[J]. Remote Sensing of Environment,2014,154:1-7.
[13]张莹,李正强.利用细模态气溶胶光学厚度估计PM2.5[J].遥感学报,2013,17(4):929-943.Zhang Y, Li Z Q. Estimation of PM2.5 from fine-mode aerosol optical depth[J]. Journal of Remote Sensing, 2013,17(4):929-943.
[14] Zhang Y, Li Z.Remote sensing of atmospheric fine particulate matter(PM2.5)mass concentration near the ground from satellite observation[J]. Remote Sensing of Environment, 2015,160:252-262.
[15] Lin C, Li Y, Yuan Z, et al. Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5[J].Remote Sensing of Environment, 2015,156:117-128.
[16] Li Z Q, Zhang Y, Shao J, et al. Remote sensing of atmospheric particulate mass of dry PM2.5 near the ground:Method validation using ground-based measurements[J]. Remote Sensing of Environment, 2016,173:59-68.
[17] Lee H J, Liu Y, Coull B A, et al. A novel calibration approach of MODIS AOD data to predict PM2.5 concentrations[J]. Atmospheric Chemistry and Physics, 2011(11):7991-8002.
[18] Lee H J, Coull B A, Bell M L, et al. Use of satellite-based aerosol optical depth and spatial clustering to predict ambient PM2.5 concentrations[J]. Environmental Research, 2012,(118):8-15.
[19] Kloog I, Chudnovsky A A, Just A C, et al. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5concentrations across northeastern USA using high resolution aerosol optical depth data[J]. Atmospheric Environment, 2014,95(1):581-590.
[20] Zheng Y X, Zhang Q, Liu Y, et al. Estimating ground-level PM2.5concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements[J]. Atmospheric Environment, 2016,124:232-242.
[21] Xie Y, Wang Y, Zhang K, et al. Daily estimation of ground-level PM2.5concentrations over Beijing using 3km resolution MODIS AOD[J].Environmental Science&Technology, 2015,49(20):12280-12288.
[22] 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.
[23] Yu H L, Wang C H, Liu M C, et al. Estimation of fine particulate matter in Taipei using landuse regression and bayesian maximum entropy methods[J]. International Journal of Environmental Research&Public Health,2011,8(6):2153-2169.
[24] Beckerman B S, Jerrett M, Serre M, et al. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous united states[J]. Environmental Science&Technology,2013,47(13):7233-7241.
[25]江曲图,何俊昱,王占山,等.基于LUR/BME的海岸带地区PM2.5时空特性研究[J].中国环境科学,2017,37(2):424-431.Jiang Q T, He J Y, Wang Z S, et al. Spatiotemporal analysis of PM2.5 in large coastal domains by combining Land Use Regression and Bayesian Maximum Entropy[J]. China Environmental Science, 2017,37(2):424-431.
[26] Christakos G. A Bayesian maximum-entropy view to the spatial estimation problem[J]. Mathematical Geosciences, 1990,22(7):763-777.
[27]杨勇,张若兮.贝叶斯最大熵地统计方法研究与应用进展[J].土壤,2014,(3):402-406.Yang Y, Zhang R X. Review on bayesian maximum entropy geostatistics method[J]. Soils,2014,(3):402-406.
[28] Song W, Jia H, Huang J, et al. A satellite-based geographically weighted regression model for regional PM2.5, estimation over the Pearl River Delta region in China[J]. Remote Sensing of Environment,2014,154:1-7.
[29] Zheng Y, Zhang Q, Liu Y, et al. Estimating ground-level PM2.5concentrations over three megalopolises in China using satellitederived aerosol optical depth measurements[C]//EGU General Assembly Conference. EGU General Assembly Conference Abstracts,2016.
[30]吴健生,王茜,李嘉诚,等.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,2017,38(6):2191-2201.Wu J S, Wang X, Li J C, et al. Comparison of models on spatial variation of PM2.5 concentration:A case of Beijing-Tianjin-Hebei region[J]. Environmental Science,2017,38(6):2191-2201.