| 京津冀及周边地区PM2.5时空变化特征遥感监测分析 |
| 摘要点击 4863 全文点击 1483 投稿时间：2018-02-12 修订日期：2018-06-28 |
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| 中文关键词 卫星遥感 京津冀及周边地区 多尺度验证 PM2.5 时空变化 |
| 英文关键词 remote sensing Beijing-Tianjin-Hebei and its surrounding regions multiscale verification PM2.5 spatiotemporal change |
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| 中文摘要 |
| 为分析京津冀及周边地区的PM2.5时空变化特征，先利用MODIS数据反演1 km分辨率的AOT产品，采用地理加权回归模型实现京津冀及周边地区2016~2017年逐日PM2.5浓度的遥感反演，并在此基础上对多种时间尺度PM2.5浓度合成结果进行验证分析，最后从不同时间尺度对2016年和2017年PM2.5时空变化特征进行了对比分析.结果表明本研究反演的日均、月均和年均这3种时间尺度的PM2.5浓度结果总体上效果较为理想，时间尺度越大，遥感估算的PM2.5效果越好，年均PM2.5结果相对精度达80%以上，并且2016年和2017年同一时间尺度的PM2.5遥感结果精度较为接近.京津冀及周边地区PM2.5分布总体均呈现"冬季 > 秋季 ≈ 春季 > 夏季"和"南高北低"的季节变化和空间分布趋势.与2016年相比，2017年京津冀及周边地区PM2.5浓度平均下降约9.2%，且高值区范围明显减小，PM2.5浓度高值一般发生在11月和12月，而低值则一般发生在8月.2017年与2016年PM2.5浓度时空变化与2017年的大气污染综合治理攻坚行动巡查和空气质量专项督查活动密切相关，这也能间接说明大气污染减排的成效. |
| 英文摘要 |
| To analyze the spatial and temporal variation characteristics of PM2.5 in Beijing-Tianjin-Hebei and its surrounding regions, a 1 km resolution AOT product was retrieved from MODIS data and the remote sensing inversion of the PM2.5 concentration in Beijing-Tianjin-Hebei and its surrounding regions was realized using the geographically weighted regression model. On this basis, the synthesis results of multi-timescale PM2.5 concentrations were verified and analyzed. Finally, the spatial and temporal variation characteristics of PM2.5 in Beijing-Tianjin-Hebei and its surrounding regions between 2016 and 2017 were compared and analyzed using different time scales. The results show that the verification of the PM2.5 concentration products of the average daily, monthly, and annual averages are in general good. The larger the time scale is, the better is the PM2.5 effect of the remote sensing estimation. The relative accuracy of the annual average PM2.5 products is higher than 80%. However, the precision of the PM2.5 remote sensing results for 2016 and 2017 is relatively close (at the same time scales). The PM2.5 distribution in Beijing-Tianjin-Hebei and its surrounding regions shows a seasonal variation (winter > autum ≈ spring > summer). The spatial distribution is high in the southern but low in the northern part. Compared with 2016, the average PM2.5 concentration decreased by~9.2% in 2017. The area with high values was significantly reduced. High PM2.5 concentrations occurred in November and December and low concentrations were observed in August. The PM2.5 concentration change between 2017 and 2016 is closely related to the comprehensive control crucial action and specific inspection activities of air pollution in 2017, which indirectly account for the effect of the reduction of the atmospheric pollution. |
