| 华北平原AOD时空演化与影响因素 |
| 摘要点击 1739 全文点击 522 投稿时间：2021-09-04 修订日期：2021-12-08 |
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| 中文关键词 气溶胶光学厚度(AOD) 华北平原 时空变化 统计建模 影响因素分析 |
| 英文关键词 aerosol optical depths(AOD) North China Plain spatiotemporal variation statistical modeling analysis of influencing factors |
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| 中文摘要 |
| 深入了解大气气溶胶时空变化及其影响因素，对控制大气污染，改善大气环境具有重要意义.首先利用2013~2019年的VIIRS IP气溶胶光学厚度（AOD）数据分析华北平原AOD的时空变化规律.其次，选取SO2、NO2、PM2.5、气象数据、NDVI、DEM、GDP和POPU作为影响因素，基于XGBoost模型分别建立华北平原5个代表城市的AOD与其影响因素之间的连接模型，定量估算并揭示AOD时空分布规律背后各个影响因素的贡献.结果表明在空间分布上，华北平原AOD以太行山脉为界，呈现东南高西北低的格局.在时间变化上，5个城市AOD年均值整体呈下降趋势，AOD月均值先上升后下降，最高值出现在7月，最低值出现在12月.此外，建立的华北地区5个城市AOD估算模型精度较高，R2在0.60~0.67之间.华北平原的AOD影响因素中，NO2和SO2是对5个城市AOD贡献最大的影响因素，此外，PM2.5是另外一种重要的污染排放影响因素.气象因素方面，温度（T）、相对湿度（RH）、风速（WS）和风向（WD）是其他4个重要的影响因素.华北5个代表城市AOD影响因素的贡献和重要性排序既有共性也有差异. |
| 英文摘要 |
| A better knowledge of the spatial and temporal variation in atmospheric aerosol and its influencing factors is of great significance to controlling atmospheric pollution and improving the atmospheric environment. First, the visible infrared imaging radiometer suite (VIIRS) intermediate product (IP) aerosol optical depth (AOD) data from 2013 to 2019 were used to analyze the temporal and spatial variation in AOD in the North China Plain. Secondly, SO2, NO2, PM2.5, meteorological data, NDVI, DEM, GDP, and POPU were selected as influencing factors, and the linkage models between AOD and its influencing factors were established based on the XGBoost model for each of the five representative cities in the North China Plain to quantitatively estimate and reveal the contribution of various influencing factors behind the temporal and spatial distribution in AOD. The results showed that in terms of spatial distribution, the AOD of the North China Plain was bounded by the Taihang Mountains, showing a pattern of high AOD in the southeast and low AOD in the northwest. In terms of temporal changes, the annual average value of AOD in the five cities showed an overall decreasing trend, and the monthly average value of AOD first increased and then decreased, with the highest value appearing in July and the lowest value in December. In addition, the AOD estimation model established in this paper for the five cities in North China had high accuracy, with R2 ranging from 0.60 to 0.67. Among the factors influencing AOD in the North China Plain, NO2 and SO2 were the most influential factors contributing to AOD in the five cities. In addition, PM2.5 was another important pollutant emission factor. In terms of meteorological factors, temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) were the other four important influencing factors. There were both commonalities and differences in the rankings of the contribution and importance of AOD influencing factors in the five representative cities in North China. |
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