基于随机森林模型的中国近地面NO_2浓度估算
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摘要
针对传统近地面NO_2浓度空间模拟过程中NO_2浓度与其影响要素之间关系的复杂非线性机制解释不充分的缺陷,本研究基于随机森林(RF)算法、融合多源地理要素开展了近地面NO_2浓度空间分布模拟研究.以卫星OMI对流层NO_2柱浓度数据和多源地理要素(道路交通、气象因子、土地利用/覆盖、地形高程、人口数量)为输入变量,近地面NO_2浓度为输出变量,利用RF算法构建近地面NO_2浓度反演模型.通过对比地面观测数据与传统土地利用回归模型(LUR)检验RF模型的有效性,基于所构建的最优RF模型在不同时间尺度下模拟分析中国大陆地区近地面NO_2浓度空间分布特征.结果表明:(1)集成多源地理要素的RF回归模型精度高,月均模型整体拟合度R~2 0.85,RMSE 6.08μg/m~3,交叉验证的R~2 0.84,RMSE 6.33μg/m~3,显著高于LUR模型(拟合R~2 0.53,RMSE 10.48μg/m~3,交叉验证的R~2 0.53,RMSE 10.49μg/m~3);(2)地面NO_2浓度与预测变量呈现显著的复杂非线性与时间尺度依赖关系,卫星OMI柱浓度对模型影响程度最大,重要性指标IncMSE介于97.40%～116.54%,多源地理特征变量对RF模型同样具有不可忽视的贡献力(IncMSE在23.34%～47.53%之间);(3)中国大陆地区NO_2污染程度较高,年均模拟浓度为24.67μg/m~3,存在明显季节性空间差异,NO_2浓度冬季(31.85μg/m~3)>秋季(24.86μg/m~3)>春季(23.24μg/m~3)>夏季(18.75μg/m~3),呈现以华北平原为高值中心、向外围逐渐减轻的空间分布格局.较已有研究揭示对流层NO_2柱浓度宏观分布特征,本研究对近地面NO_2污染特征的研究成果对于合理制定污染防控策略、降低居民暴露健康损害具有指导意义.
In order to capture the complex and nonlinear relationship between ground-level NO_2 concentrations and predictor variables,random forest(RF)models combined with multiple types of geographic covariates were developed to estimate ground-level NO_2 concentrations.In this process,satellite-based OMI NO_2 tropospheric columns and multi-source geographic covariates(i.e.,road network,meteorological factors,land use/cover,DEM and population density)were used as potential predictor variables and ground-level NO_2 concentrations were used as the dependent variable for RF models construction.The reliability of the RF models was validated by comparison with ground-measured NO_2 concentrations and typical linear land use regression(LUR)models.Afterwards,the spatial distribution characteristics of NO_2 concentration mapped by RF models across time scales in mainland China were assessed and analyzed.Results showed that RF modeling outperformed LUR modeling with obvious higher model fitting-based R~2 and lower RMSE,which were 0.85 and 6.08μg/m~3 for monthly RF models compared with 0.53 and 10.48μg/m~3 for LUR models.This was confirmed by the cross-validation-based R~2 and RMSE with values of 0.84 and 6.33μg/m~3,while those of LUR models were 0.53 and 10.49μg/m~3.The partial dependence of RF models suggested that the actual relationships between ground-level NO_2 concentrations and predictor variables were nonlinear and time-dependent.OMI NO_2 tropospheric columns contributed most strongly to the RF models of NO_2 concentrations,which had largest percentage of IncMSE(ranged from97.40%to 116.54%).Meanwhile,the importance of different geographic variables could not be disregarded,which had values of IncMSE between 23.34% and 47.53%.Additionally,the NO_2 concentrations simulated by RF models showed that the annual average NO_2 concentrations across mainland China during the study period were 24.67μg/m~3,which had significant seasonal variations with value of 31.85,24.86,23.24 and 18.75μg/m~3 in winter,autumn,spring and summer,respectively.Spatially,higher concentrations of simulated NO_2 concentrations occurred in the North China Plain and decreased to the periphery.Compared with the existing studies focusing on tropospheric NO_2 column density,this study sheds new light on accurate monitoring of spatial-temporal distribution of ground-level NO_2 pollution.Findings from this study will provide new implications for policy making for future national prevention and control of air pollution to reduce the population health burden in China.
In order to capture the complex and nonlinear relationship between ground-level NO_2 concentrations and predictor variables,random forest(RF)models combined with multiple types of geographic covariates were developed to estimate ground-level NO_2 concentrations.In this process,satellite-based OMI NO_2 tropospheric columns and multi-source geographic covariates(i.e.,road network,meteorological factors,land use/cover,DEM and population density)were used as potential predictor variables and ground-level NO_2 concentrations were used as the dependent variable for RF models construction.The reliability of the RF models was validated by comparison with ground-measured NO_2 concentrations and typical linear land use regression(LUR)models.Afterwards,the spatial distribution characteristics of NO_2 concentration mapped by RF models across time scales in mainland China were assessed and analyzed.Results showed that RF modeling outperformed LUR modeling with obvious higher model fitting-based R~2 and lower RMSE,which were 0.85 and 6.08μg/m~3 for monthly RF models compared with 0.53 and 10.48μg/m~3 for LUR models.This was confirmed by the cross-validation-based R~2 and RMSE with values of 0.84 and 6.33μg/m~3,while those of LUR models were 0.53 and 10.49μg/m~3.The partial dependence of RF models suggested that the actual relationships between ground-level NO_2 concentrations and predictor variables were nonlinear and time-dependent.OMI NO_2 tropospheric columns contributed most strongly to the RF models of NO_2 concentrations,which had largest percentage of IncMSE(ranged from97.40%to 116.54%).Meanwhile,the importance of different geographic variables could not be disregarded,which had values of IncMSE between 23.34% and 47.53%.Additionally,the NO_2 concentrations simulated by RF models showed that the annual average NO_2 concentrations across mainland China during the study period were 24.67μg/m~3,which had significant seasonal variations with value of 31.85,24.86,23.24 and 18.75μg/m~3 in winter,autumn,spring and summer,respectively.Spatially,higher concentrations of simulated NO_2 concentrations occurred in the North China Plain and decreased to the periphery.Compared with the existing studies focusing on tropospheric NO_2 column density,this study sheds new light on accurate monitoring of spatial-temporal distribution of ground-level NO_2 pollution.Findings from this study will provide new implications for policy making for future national prevention and control of air pollution to reduce the population health burden in China.
引文
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[2]王占山,李云婷,陈添,等.北京城区臭氧日变化特征及与前体物的相关性分析[J].中国环境科学,2014,34(12):3001-3008.Wang Z,Li Y,Chen T,et al.Analysis on diurnal variation characteristics of ozone and correlations with its precursors in urban atmosphere of Beijing[J].China Environmental Science,2014,34(12):3001-3008.
[3]Samoli E,Aga E,Touloumi G,et al.Short-term effects of nitrogen dioxide on mortality:an analysis within the APHEA project[J].Environmental Health Perspectives,2007,115(11):1578-1583.
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[6]张兴赢,张鹏,张艳,等.近10a中国对流层NO2的变化趋势,时空分布特征及其来源解析[J].中国科学:D辑,2007,37(10):1409-1416.Zhang X,Zhang P,Zhang Y,et al.The tropospheric NO2 trends,the temporal and spatial distribution characteristics and source apportionment for nearly 10 years in China[J].Science in China,2007,37(10):1409-1416.
[7]Vinken G C M,Boersma K F,Maasakkers J D,et al.Worldwide biogenic soil NOx emissions inferred from OMI NO2 observations[J].Atmospheric Chemistry&Physics,2014,14(14):10363-10381.
[8]中华人民共和国国家统计局.中国统计年鉴[M].北京:中国统计出版,2016:250-310.National bureau of statistics of the People’s Republic of China,China statistical yearbook[M].Beijing:China Statistics Press,2016:250-310.
[9]Krotkov N A,McLinden C A,Li C,et al.Aura OMI observations of regional SO2 and NO2 pollution changes from 2005 to 2015[J].Atmospheric Chemistry and Physics,2016,16(7):4605-4629.
[10]Zou B,Zheng Z,Wan N,et al.An optimized spatial proximity model for fine particulate matter air pollution exposure assessment in areas of sparse monitoring[J].International Journal of Geographical Information Science,2016,30(4):727-747.
[11]胡晨霞,邹滨,李沈鑫,等.城市微环境PM2.5浓度空间分异特征分析[J].中国环境科学,2018,38(3):910-916.Hu C,Zou B,Li S,et al.Spatial heterogeneity analysis of PM2.5concentrations in intra-urban microenvironments[J].China Environmental Science,2018,38(3):910-916.
[12]周春艳,厉青,何颖霞,等.山东省近10年对流层NO2柱浓度时空变化及影响因素[J].中国环境科学,2015,35(8):2281-2290.Zhou C,Li Q,He Y,et al.Spatial-temporal change of tropospheric NO2 column density and its impact factors over Shandong province during 2005~2014[J].China Environmental Science,2015,35(8):2281-2290.
[13]张杰,李昂,谢品华,等.基于卫星数据研究兰州市NO2时空分布特征以及冬季NOx排放通量[J].中国环境科学,2015,35(8):2291-2297.Zhang J,Li A,Xie P,et al.Research on the spatial/temporal patterns of NO2 concentration and NOx emissions of Lanzhou by applying satellite data[J].China Environmental Science,2015,35(8):2291-2297.
[14]章吴婷,张秀英,刘磊,等.多源卫星遥感的华北平原大气NO2浓度时空变化[J].遥感学报,2018,22(2):335-346.Zhang W,Zhang X,Liu L,et al.Spatial variations in NO2 trend in North China Plain based on multi-source satellite remote sensing[J].Journal of Remote Sensing,2018,22(2):335-346.
[15]高晋徽,朱彬,王言哲,等.2005~2013年中国地区对流层二氧化氮分布及变化趋势[J].中国环境科学,2015,35(8):2307-2318.Gao J,Zhu B,Wang Y,et al.Distribution and long-term variation of tropospheric NO2 over China during 2005 to 2013[J].China Environmental Science,2015,35(8):2307-2318.
[16]Van der A R J,Peters D,Eskes H,et al.Detection of the trend and seasonal variation in tropospheric NO2 over China[J].Journal of Geophysical Research:Atmospheres,2006,111(D12):317.
[17]Zoogman P,Liu X,Suleiman R M,et al.Tropospheric emissions:Monitoring of pollution(TEMPO)[J].Journal of Quantitative Spectroscopy and Radiative Transfer,2017,186:17-39.
[18]李令军,王英.基于卫星遥感与地面监测分析北京大气NO2污染特征[J].环境科学学报,2011,31(12):2762-2768.Li L,Wang Y.The characterization of NO2 pollution in Beijing based on satellite and conventional observation data[J].Acta Scientiae Circumstantiae,2011,31(12):2762-2768.
[19]Lamsal L N,Krotkov N A,Celarier E A,et al.Evaluation of OMIoperational standard NO2 column retrievals using in situ and surface-based NO2 observations[J].Atmospheric Chemistry and Physics,2014,14(21):11587-11609.
[20]浦静姣,张艳,余琦,等.上海地区O3与NO2时空特征数值模拟个例研究[J].中国环境科学,2009,29(5):461-468.Pu J,Zhang Y,Yu Q,et al.Case study on the numerical simulations of the characteristics of temporal and spatial distributions of O3 and NO2in Shanghai Area[J].China Environmental Science,2009,29(5):461-468.
[21]丁宇宇,彭丽,冉靓,等.利用OMI卫星资料计算NO2地面浓度的方法研究[J].北京大学学报:自然科学版,2011,47(4):671-676.Ding Y,Peng L,Ran L,et al.A method of inferring ground level NO2using satellite-borne OMI observations[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2011,47(4):671-676.
[22]陈良富,顾坚斌,王甜甜,等.近地面NO2浓度卫星遥感估算问题[J].环境监控与预警,2016,8(3):1-5.Chen L,Gu J,Wang T,et al.Scientific problems for ground NO2concentration estimation using doas method from satellite observation[J].Environmental Monitoring and Forewarning,20106,8(3):1-5.
[23]Geddes J A,Martin R V,Boys B L,et al.Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations[J].Environmental Health Perspectives,2016,124(3):281.
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