中文关键词空气质量中国PM_2.5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 environmentChinaPM_2.5 estimationtwo-phase hybrid modeltrend testchange-point test

作者	单位	E-mail
时燕	云南师范大学信息学院, 昆明 650500 云南师范大学西部资源环境地理信息技术教育部工程研究中心, 昆明 650500 云南电网有限责任公司信息中心, 昆明 650217	xdshyan@163.com
刘瑞梅	云南师范大学信息学院, 昆明 650500 云南师范大学西部资源环境地理信息技术教育部工程研究中心, 昆明 650500
罗毅	云南师范大学信息学院, 昆明 650500 云南师范大学西部资源环境地理信息技术教育部工程研究中心, 昆明 650500	lysist@ynnu.edu.cn
杨昆	云南师范大学信息学院, 昆明 650500 云南师范大学西部资源环境地理信息技术教育部工程研究中心, 昆明 650500

中文摘要 本研究基于国控监测网络的PM_2.5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PM_2.5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PM_2.5浓度组合估算模型.在此基础上，分析了2000~2017年中国PM_2.5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PM_2.5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PM_2.5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，PM_2.5浓度较高的北部沿海、东部沿海和长江中游地区的PM_2.5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PM_2.5年均浓度超过了二级限值（35 μg·m^-3），虽然全国PM_2.5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 (PM_2.5) 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 PM_2.5. We propose a two-stage combined estimation model of PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 concentration, as its trend changed from one of continuous increase to one of rapid decrease. The PM_2.5 concentration decreases more rapidly in areas with high concentrations of PM_2.5 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 PM_2.5 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 PM_2.5 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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