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| 基于遥感数据的京津冀地区PM2.5时空分布特征 |
| Spatial and Temporal Distribution Characteristics of PM2.5in Beijing Tianjin Hebei Region Based on Remote Sensing Data |
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| DOI： |
| 中文关键词: PM2.5 时空分布 气溶胶光学厚度 多元线性回归模型 京津冀地区 |
| 英文关键词: PM2.5 Spatial and temporal distribution Aerosol optical depth Multiple linear regression model Beijing Tianjin Hebei region |
| 基金项目:教育部天津市大学生创新创业训练计划基金资助项目(201710065056) |
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| 中文摘要: |
| 基于MODIS AOD遥感数据，采用多元线性回归模型对PM25地面监测数据进行模拟估算，同时加入降水量、相对湿度等气象因子以提高模型精度，结合GIS空间分析技术，得到2015—2016年京津冀地区空间连续的PM25浓度分布。结果表明：利用多元线性回归模型反演PM25浓度效果较好，R2均在059～084之间。在时间上，京津冀地区PM25浓度呈现出夏季最低、秋季稍高、冬春两季最高的变化趋势；在空间上，2015年和2016年京津冀地区PM25浓度有明显的区域差异，均呈现出西北低、东南高的分布格局，大致与燕山山脉和太行山脉走向一致。 |
| 英文摘要: |
| Based on MODIS AOD remote sensing data， using multiple linear regression model for simulating and estimating
PM2.5 concentration， adding some meteorological factors such as precipitation， relative humidity for improving model accuracy， adopting GIS spatial analysis technology， the distribution of PM2.5 spatial continuous concentrations from 2015 to 2016 in Beijing Tianjin Hebei region were obtained. The results showed that the multiple linear regression model was effective in inverting PM2.5 concentration， and R2 were all between 0.59 and 0.84. In terms of time， PM2.5 concentration in Beijing Tianjin Hebei region had a trend of being the lowest in summer， slightly high in autumn， the highest in winter and spring. In terms of space， PM2.5concentration had significant regional difference in 2015 and 2016， showing a distribution pattern of low in the northwest and high in the southeast of Beijing Tianjin Hebei region. It roughly lined up with the Yanshan mountain and Taihang mountain. |
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