| 京津冀地区高分辨率PM2.5浓度时空变化模拟与分析 |
| 摘要点击 3109 全文点击 835 投稿时间：2020-12-21 修订日期：2021-03-07 |
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| 中文关键词 MAIAC AOD PM2.5 线性混合效应模型 地理加权回归模型 京津冀地区 |
| 英文关键词 MAIAC AOD PM2.5 linear mixed effect model geographical weight regression model Beijing-Tianjin-Hebei region |
| 作者 | 单位 | E-mail | | 杨晓辉 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | 1131066837@qq.com | | 宋春杰 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | | | 范丽行 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | | | 张凌云 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | | | 魏强 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | | | 李夫星 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境变化遥感识别技术创新中心, 石家庄 050024 | | | 王丽艳 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | | | 王卫 | 河北师范大学资源与环境科学学院, 石家庄 050024
河北省环境演变与生态建设实验室, 石家庄 050024 | wangwei@hebtu.edu.cn |
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| 中文摘要 |
| 为了全覆盖、高分辨率和高精度识别京津冀地区大气PM2.5质量浓度时空变化，选取多角度大气校正算法遥感反演的1km AOD为主要预测因子，多种气象要素和土地利用要素为辅助预测因子，构建了混合效应模型+地理加权回归模型的两阶段统计模型，并针对京津冀地区PM2.5污染较严重的特点，模型中引入了AOD2等独特预测因子.通过上述两阶段模型定量预测了研究区2017年1 km2空间分辨率的每日PM2.5质量浓度.结果表明，模型交叉验证的决定系数R2为0.94，斜率为0.95，均方根预测误差为13.14 μg·m-3，在前人基础上预测精度进一步提升，可用于PM2.5浓度时空变化预测与分析.2017年，京津冀地区PM2.5浓度年均值为44.96 μg·m-3，年均值范围在0~89.89 μg·m-3之间.PM2.5浓度时空变化差异性明显，整体上呈现"平原西南部浓度高、平原东北部浓度中等和山区高原浓度低"的空间分布格局以及"冬季浓度高、夏季浓度低和春秋过渡"的季节变化特点.模型预测结果的高时空分辨率可以支持流行病学研究在较小区域的暴露评估和识别小尺度污染源的时空变化，分析发现在大气污染防治行动计划实施以来，污染较严重的冀中南山麓平原区可能出现了重要污染源的空间变化.模型预测与分析结果可以为京津冀大气污染防治提供科学支撑. |
| 英文摘要 |
| This study developed a two-stage statistical model (linear mixed effect (LME) model+geographical weight regression (GWR) model) to determine the spatio-temporal variation of PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region with full-coverage, high resolution, and high accuracy. The model employs multi-angle implementation of atmospheric correction aerosol optical depth (MAIAC AOD) data, with a 1 km spatial resolution, as the main predictor and meteorological data/land-use data as the auxiliary predictors. To determine the characteristics of heavy PM2.5 pollution in the BTH region, unique predictors such as AOD2 were also introduced into the two-stage model. The two-stage model was used to estimate the daily PM2.5 concentrations with a 1 km resolution. After being cross-validated against ground observations, the R2 of PM2.5 was found to be 0.94, with a slope value of 0.95 and RMSPE value of 13.14 μg·m-3. Compared to previous studies such as LME, the two-stage model has much higher accuracy, suitable for estimating PM2.5 concentrations. The PM2.5 concentration in the BTH region ranged from 0 to 89.89 μg·m-3 in 2017, with a mean value of 44.96 μg·m-3. The spatio-temporal variability of PM2.5 over the BTH region was significant, exhibiting high values over the southwestern plain, moderate values over the northeastern plain, and low values over the mountainous plateau. In terms of seasonal variation, PM2.5 concentrations were high in winter, low in summer, and moderate in spring and autumn. The estimated PM2.5 concentrations, with high spatio-temporal resolution, are useful for exposure assessments in epidemiological studies and identifying the spatio-temporal variation of pollution sources at a fine spatial scale. The results show that the locations of vital pollution sources over the severely polluted south-central Hebei piedmont plain may have changed since the implementation of the Air Pollution Prevention and Control Action Plan. This study could provide a scientific basis for the prevention and control of air pollution in the BHT region. |
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