| 基于GAM模型分析中国典型区域网格化PM2.5长期变化影响因素 |
| 摘要点击 2391 全文点击 961 投稿时间：2019-05-13 修订日期：2019-10-03 |
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| 中文关键词 PM2.5 典型区域 时空变化 GAM模型 影响因素 |
| 英文关键词 PM2.5 typical regions spatio-temporal variations GAM model influencing factors |
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| 中文摘要 |
| 为探究中国典型区域地表PM2.5浓度长期时空变化及其影响因素，运用广义可加模型（GAM）对1998~2016年均0.01°×0.01°地表PM2.5浓度网格化数据进行分析.典型区域多年平均PM2.5浓度从高到低：华东华中地区（40.5 μg·m-3） > 华北地区（37.4 μg·m-3） > 华南地区（27.8 μg·m-3） > 东北地区（23.7 μg·m-3） > 四川盆地（22.4 μg·m-3）.东北地区PM2.5年际变化呈现明显上升趋势；其他地区1998~2007年呈上升趋势，2008~2016年出现下降趋势.在典型区域PM2.5浓度空间分布上，PM2.5浓度分布呈现显著的空间差异，多年来各区域PM2.5浓度高值分布相对稳定.PM2.5浓度变化的单因素GAM模型中，所有影响因素均通过显著性检验，典型区域中对PM2.5浓度变化影响解释率较高的各个影响因素顺序有所不同.PM2.5浓度变化的多因素GAM模型中，均呈现非线性关系，典型区域方差解释率为87.5%~92%（平均89.0%），模型拟合度较高，对其变化有显著性影响.典型区域YEAR和LON-LAT均对PM2.5浓度变化影响最为显著.除此之外，气象因子对PM2.5的影响大小在各个区域存在不同.东北地区影响PM2.5最重要的3个气象因子排序为：tp > v10 > ssr；华北地区为：temp > tp > msl；华东华中地区为：temp > tp > ssr；华南地区为：temp > RH > blh；四川盆地为：tp > temp > u10.结果表明，运用GAM模型，能够定量分析区域PM2.5浓度长期变化的影响因素，对PM2.5污染评估具有重要意义. |
| 英文摘要 |
| This study investigates annually-averaged surface PM2.5 concentrations with the spatial resolution of 0.01°×0.01° to explore spatio-temporal variations and influencing factors of annual PM2.5 over typical regions in China during the period of 1998-2016, applying the generalized additive model (GAM). Regionally-averaged PM2.5 concentrations of five typical regions are ranked from high to low as follows:East China (40.5 μg·m-3) > North China (37.4 μg·m-3) > South China (27.8 μg·m-3) > Northeast China (23.7 μg·m-3) > Sichuan Basin (22.4 μg·m-3). The PM2.5 over Northeast China showed a linear increasing trend, while in other regions, PM2.5 tended to increase from 1998 to 2007 and decrease after 2007. PM2.5 concentrations over typical regions were all stably distributed which clearly exhibited areas with high PM2.5 values. For the single influencing factor GAM model of PM2.5 concentration, all influencing factors passed the significance test. The most influential factors with regard to the variations in the PM2.5 concentration differed among typical regions. In the multiple-influencing-factors-GAM model of PM2.5 concentration, all factors exhibited a non-linear relationship with PM2.5, and they accounted for 87.5%-92% (average 89.0%) of variations in the PM2.5 concentration, suggesting a good model fit. The most significant influencing factors on PM2.5 concentrations were YEAR and LON-LAT in all typical regions. Meteorological factors have different impacts on PM2.5 concentrations among the typical regions. The three most influential meteorological factors in the five typical regions ranked from high to low are as follows:tp > v10 > ssr for Northeast China; temp > tp > msl for North China; temp > tp > ssr for East and Central China; temp > RH > blh for South China; tp > temp > u10 for the Sichuan Basin. Our results demonstrated that the GAM model could quantitatively analyze influencing factors in long-term variations of the regional PM2.5 concentration, which is important for the assessment of PM2.5 pollution. |
