| 基于MODIS气溶胶光学厚度与气象要素的苏锡常地区PM2.5地面浓度分布研究 |
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| 引用本文： | 于文金,于步云,谢涛,苏荣.基于MODIS气溶胶光学厚度与气象要素的苏锡常地区PM2.5地面浓度分布研究[J].环境科学学报,2016,36(10):3535-3542. |
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| 作者姓名： | 于文金 于步云 谢涛 苏荣 |
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| 作者单位： | 南京信息工程大学地理与遥感学院, 南京 210044,南京信息工程大学电子与科学工程学院, 南京 210044,南京信息工程大学海洋科学学院, 南京 210044,南京信息工程大学地理与遥感学院, 南京 210044 |
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| 基金项目： | 国家重大科学研究计划项目（No.2013CB430202）；国家自然科学基金（No.41276187）；江苏高校优势学科建设工程资助项目（PAPD） |
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| 摘 要： | 基于GIS技术和岭回归分析方法,采用苏锡常地区的MODIS高分辨率气溶胶光学厚度资料、PM_(2.5)浓度观测资料和苏锡常及周边地区的气象观测资料,构建了基于气溶胶光学厚度和气象要素的PM_(2.5)地面浓度分布估算模型,模拟了2013年春季苏锡常地区PM_(2.5)的空间分布状况,并将此模型与气象要素多元回归模型、气溶胶光学厚度直接回归模型进行比较.结果表明:该模型将遥感观测资料与地面气象观测资料相结合,能够有效地模拟PM_(2.5)的空间分布状况;2013年春季苏锡常地区PM_(2.5)的空间分布具有整体上西北高、东南低,中心城区高、城郊区低的趋势,局部高浓度区域可能与工业生产、交通等人为因素有关;该模型能够在保持较高精度的前提下,有效地突出局部地区的变化特征,体现出更强的空间分异性,对于研究PM_(2.5)的空间分布规律具有一定的实际应用价值.
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| 关 键 词： | PM2.5 气溶胶光学厚度 雾霾 岭回归分析 |
| 收稿时间： | 2015/12/6 0:00:00 |
| 修稿时间： | 2016/3/24 0:00:00 |
Distribution of PM2.5 in Suzhou-Wuxi-Changzhou area based on modis aerosol optical depth and meteorological factors |
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| YU Wenjin,YU Buyun,XIE Tao and SU Rong.Distribution of PM2.5 in Suzhou-Wuxi-Changzhou area based on modis aerosol optical depth and meteorological factors[J].Acta Scientiae Circumstantiae,2016,36(10):3535-3542. |
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| Authors: | YU Wenjin YU Buyun XIE Tao and SU Rong |
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| Institution: | School of Geography & Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044,School of Electronic and Science, Nanjing University of Information Science and Technology, Nanjing 210044,School of Mare Science, Nanjing University of Information Science and Technology, Nanjing 210044 and School of Geography & Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044 |
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| Abstract: | By using GIS and ridge regression analysis method, MODIS AOD, PM2.5 observation and meteorological data in the Suzhou-Wuxi-Changzhou area are used to built an estimation model of PM2.5. The spatial distribution pattern of PM2.5 in Suzhou-Wuxi-Changzhou area in the spring of 2013 was compared with the model only considering meteorological factors and the model only considering AOD in accuracy. The results show that the model combining remote sensing data with meteorological data can effectively simulate the spatial distribution of PM2.5. PM2.5 in Suzhou-Wuxi-Changzhou area in the spring of 2013 shows higher concentration in the north-west than south-east and higher in urban areas than suburban areas. The model can effectively highlight the characteristics of local areas with high precision, so it can be applied in studying spatial distribution of PM2.5. |
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| Keywords: | PM2 5 aerosol optical depth fog ridge regression analysis |
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