| 大连市大气污染物质量浓度与气溶胶光学厚度的相关性分析 |
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| 引用本文： | 顾吉林,汤宏山,刘淼,耿杨,于月,陶涛.大连市大气污染物质量浓度与气溶胶光学厚度的相关性分析[J].地理科学,2019,39(3):516-523. |
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| 作者姓名： | 顾吉林 汤宏山 刘淼 耿杨 于月 陶涛 |
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| 作者单位： | 辽宁师范大学物理与电子技术学院,辽宁大连,116029;辽宁师范大学物理与电子技术学院,辽宁大连,116029;辽宁师范大学物理与电子技术学院,辽宁大连,116029;辽宁师范大学物理与电子技术学院,辽宁大连,116029;辽宁师范大学物理与电子技术学院,辽宁大连,116029;辽宁师范大学物理与电子技术学院,辽宁大连,116029 |
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| 基金项目： | 大连市高层次人才创新支持计划项目（2017RQ141）、国家自然科学基金项目（11547234）、大学生创新创业训练项目（201610165000050, 201710165000104）资助 |
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| 摘 要： | 分别对2015年6~12月和2016年6~12月大连地区的大气污染物PM2.5、PM10、SO2、NO2、CO和O3的浓度数据进行数据统计分析,基于ENVI软件平台利用MODIS数据反演大连地区的气溶胶光学厚度,通过回归建模研究气溶胶光学厚度与大连地区10个地面监测站点的大气污染物PM2.5、PM10、SO2、NO2、CO和O3的浓度数据的相关性。回归建模以气溶胶光学厚度（AOD）为自变量,以大气污染物PM2.5、PM10、SO2、NO2、CO和O3为因变量,在SPSS软件中分别选取线性、对数、三次、乘幂、指数5种函数类型进行研究,通过对比回归模型的拟合优度R2,选择最优拟合模型,探讨利用遥感数据反演气溶胶光学厚度监测大气污染的相关性。结果表明：气溶胶光学厚度与NO2、PM2.5和PM10的最优拟合模型均为三次模型,其拟合优度R2分别是0.685、0.801和0.845;与O3和SO2的最优拟合模型为指数模型,其R2为0.367和0.482;与CO的最优拟合模型为对数模型,其拟合优度R2为0.810。该结果为分析大气气溶胶污染来源以及治理提供了数据。
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| 关 键 词： | 气溶胶光学厚度 MODIS 大气污染物 |
| 收稿时间： | 2018-04-27 |
| 修稿时间： | 2018-10-25 |
Correlation Analysis Between the Concentration of Atmospheric Pollutant and Aerosol Optical Depth in Dalian City |
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| Jilin Gu,Hongshan Tang,Miao Liu,Yang Geng,Yue Yu,Tao Tao.Correlation Analysis Between the Concentration of Atmospheric Pollutant and Aerosol Optical Depth in Dalian City[J].Scientia Geographica Sinica,2019,39(3):516-523. |
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| Authors: | Jilin Gu Hongshan Tang Miao Liu Yang Geng Yue Yu Tao Tao |
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| Institution: | School of Physics and Electronic Technology, Liaoning Normal University, Dalian 116029, Liaoning, China |
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| Abstract: | The development of the economy is restricted by the gradual increase of the quality concentration of the atmospheric pollutant. The concentration data of PM2.5, PM10, SO2, NO2, CO and O3 of the atmospheric pollutant in Dalian were statistically analyzed from June to December in 2015 and from June to December in 2016. The aerosol optical depth in Dalian was inverted base on ENVI software platform and the data of MODIS. The correlation between aerosol optical depth and concentration data of atmospheric pollutant PM2.5, PM10, SO2, NO2, CO and O3 at 10 ground monitoring stations in Dalian City were researched by regression modeling. The aerosol optical depth was used as an independent variable, and atmospheric pollutants PM2.5, PM10, SO2, NO2, CO, and O3 were dependent variables. The types of functions that were linear, logarithmic, cubic, power and exponential in the SPSS software were selected to research. The best-fit model was selected by comparing the goodness of fit R2 of the regression model. The correlation was discussed between atmospheric pollution by using remote sensing data and aerosol optical depth monitoring. The results showed that the optimal fitting model of aerosol optical depth and NO2, PM2.5 and PM10 are all cubic models, and the goodness of fit R2 is 0.685, 0.801 and 0.845 respectively. The optimal fitting model of O3 and SO2 is the exponential model, whose R2 is 0.367 and 0.482. The optimal fitting model of CO is the logarithmic model, and its fitting optimal R2 is 0.810. The results provided the data for the analysis of sources of atmospheric aerosol pollution and governance. |
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| Keywords: | Aerosol Optical Depth MODIS atmospheric pollutant |
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