| 近20年来中国PM2.5污染演变的时空过程 |
| 摘要点击 4055 全文点击 1646 投稿时间：2019-05-15 修订日期：2019-08-27 |
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| 中文关键词 空气质量 中国 PM2.5估算 二阶段组合估算模型 趋势检验 突变检验 |
| 英文关键词 air environment China PM2.5 estimation two-phase hybrid model trend test change-point test |
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| 中文摘要 |
| 本研究基于国控监测网络的PM2.5实测数据、MODIS AOD数据以及气象参数（温度、风速、风向、边界层高度和相对湿度），综合考虑AOD与PM2.5关系的季节性和区域性差异，构建了基于支持向量回归机（ε-SVR）与思维进化算法优化后的BP神经网络（MEC-BP）的二阶段PM2.5浓度组合估算模型.在此基础上，分析了2000~2017年中国PM2.5浓度的时空变化过程.结果表明，本研究提出的二阶段组合估算模型提供了中国2000~2017年内空间分辨率为1°×1°的月度近地面PM2.5浓度的可靠估算，有效地弥补了中国地面监测网络在时间和空间上的空白（模型的决定系数R2为0.838，均方根误差RMSE为11.512 μg·m-3，平均绝对百分比误差MAPE为14.905%，均方百分比误差MSPE为0.243%，绝对误差MAE为6.476 μg·m-3，均方误差MSE为132.519 μg·m-3）.时间变化过程分析结果表明：①2014年是2000~2017年内中国PM2.5浓度从持续缓慢上升到快速下降的关键转折点，其中，从2014年开始，PM2.5浓度较高的北部沿海、东部沿海和长江中游地区的PM2.5污染情况改善较明显.②然而，在研究时间范围内，全国仍有超过65%的区域PM2.5年均浓度超过了二级限值（35 μg·m-3），虽然全国PM2.5污染情况有一定程度地改善，但是空气污染形势依然严峻. |
| 英文摘要 |
| We use measured aerosol fine particulate matter (PM2.5) data, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and meteorological parameters (temperature, wind speed, wind direction, boundary layer height, and relative humidity) from the Chinese national control monitoring network, to consider seasonal and regional differences in the relationship between AOD and PM2.5. We propose a two-stage combined estimation model of PM2.5 concentrations based on the ε-support vector regression (ε-SVR/Epsilon-SVR) and the Mind Evolutionary Computation-BP neural network (MEC-BP) for analyzing spatiotemporal variations in PM2.5 concentrations in China between 2000 and 2017. The results showed that the two-stage combined estimation model provided a reliable estimation of the monthly ground-level PM2.5 concentrations at a spatial resolution of 1°×1° during 2000-2017 in China. This effectively offsets the time and space gaps in the current data sets of the ground monitoring network (R2=0.838, root mean square errors (RMSE)=11.512 μg·m-3, mean absolute percentage error (MAPE)=14.905%, mean squared percentage error (MSPE)=0.243%, mean absolute error (MAE)=6.476 μg·m-3, mean squared error (MSE)=132.519 μg·m-3). The preliminary spatiotemporal analysis results showed that:① Over the period 2000-2017, 2014 represented an important demarcation point for the annual PM2.5 concentration, as its trend changed from one of continuous increase to one of rapid decrease. The PM2.5 concentration decreases more rapidly in areas with high concentrations of PM2.5 in particular, including the northern coastal area, the eastern coastal area, and the middle reaches of the Changjiang River. ② During the studied period, the annual average PM2.5 concentration exceeded the second level criterion of the Chinese national air quality standard (35 μg·m-3) over more than 65% of China. Although the PM2.5 pollution situation in China improved to a certain extent in the latter years of the studied period, the air pollution situation remained poor. |
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