| 粤港澳大湾区大气PM2.5浓度的遥感估算模型 |
| 摘要点击 1127 全文点击 265 投稿时间：2023-02-28 修订日期：2023-04-03 |
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| 中文关键词 粤港澳大湾区 MAIAC AOD PM2.5 时空地理加权模型（GTWR） BP神经网络模型（BPNN） 支持向量机回归模型（SVR） 随机森林模型（RF） |
| 英文关键词 Guangdong-Hong Kong-Macao Greater Bay Area MAIAC AOD PM2.5 geographically and temporally weighted regression model （GTWR） BP neural network model （BPNN） support vector machine regression model （SVR） random forest model （RF） |
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| 中文摘要 |
| PM2.5对大气环境和人类健康危害极大,及时准确地掌握高时空分辨率的PM2.5浓度对空气污染防治起着重要作用.基于粤港澳大湾区2015~2020年多角度大气校正算法（MAIAC）1 km AOD产品、ERA5气象资料和站点污染物浓度（CO、O3、NO2、SO2、PM10和PM2.5）,分别建立了估算PM2.5浓度的时空地理加权模型（GTWR）、BP神经网络模型（BPNN）、支持向量机回归模型（SVR）和随机森林模型（RF）.结果表明,RF模型的估算能力优于BPNN、SVR和GTWR模型,BPNN、SVR、GTWR和RF模型的相关系数依次为0.922、0.920、0.934和0.981,均方根误差（RMSE）分别为7.192、7.101、6.385和3.670 μg·m-3,平均绝对误差（MAE）分别为5.482、5.450、4.849和2.323 μg·m-3；RF模型在季节PM2.5的预测中以冬季效果最佳、夏季次之、春季和秋季再次,预测值与实测值的相关系数在0.976以上；RF模型可用于大湾区PM2.5浓度的预测分析研究.在时间上,大湾区各市2021年逐日ρ（PM2.5）呈“先减后增”的变化趋势,最高值在65.550~112.780 μg·m-3,最低值介于5.000~7.899 μg·m-3；月均浓度变化呈“U”型分布,1月开始降低至6月达到谷值后逐渐升高；季节上表现为冬季浓度最高、夏季最低、春秋季节过渡的特点；大湾区年均ρ（PM2.5）为28.868 μg·m-3,低于年均二级浓度限值.空间上,2021年PM2.5呈“西北-东南”递减的特征,高污染区域聚集在大湾区的中部,以佛山为代表；低浓度区主要分布在惠州东部、港澳和珠海等沿海地区；不同季节PM2.5浓度在空间分布上也表现出异质性和区域性.RF模型估算了高精度PM2.5浓度,为大湾区PM2.5污染相关的健康风险评估提供了科学依据. |
| 英文摘要 |
| PM2.5 is extremely harmful to the atmospheric environment and human health, and a timely and accurate understanding of PM2.5 with high spatial and temporal resolution plays an important role in the prevention and control of air pollution. Based on multi-angle implementation of atmospheric correction algorithm (MAIAC), 1 km AOD products, ERA5 meteorological data, and pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) in the Guangdong-Hong Kong-Macao Greater Bay Area during 2015-2020, a geographically and temporally weighted regression model (GTWR), BP neural network model (BPNN), support vector machine regression model (SVR), and random forest model (RF) were established, respectively, to estimate PM2.5 concentration. The results showed that the estimation ability of the RF model was better than that of the BPNN, SVR, and GTWR models. The correlation coefficients of the BPNN, SVR, GTWR, and RF models were 0.922, 0.920, 0.934, and 0.981, respectively. The RMSE values were 7.192, 7.101, 6.385, and 3.670 μg·m-3. The MAE values were 5.482, 5.450, 4.849, and 2.323 μg·m-3, respectively. The RF model had the best effect during winter, followed by that during summer, and again during spring and autumn, with correlation coefficients above 0.976 in the prediction of different seasons. The RF model could be used to predict the PM2.5 concentration in the Greater Bay Area. In terms of time, the daily ρ(PM2.5) of cities in the Greater Bay Area showed a trend of "decreasing first and then increasing" in 2021, with the highest values ranging from 65.550 μg·m-3 to 112.780 μg·m-3 and the lowest values ranging from 5.000 μg·m-3 to 7.899 μg·m-3. The monthly average concentration showed a U-shaped distribution, and the concentration began to decrease in January and gradually increased after reaching a trough in June. Seasonally, it was characterized by the highest concentration during winter, the lowest during summer, and the transition during spring and autumn. The annual average ρ(PM2.5) of the Greater Bay Area was 28.868 μg·m-3, which was lower than the secondary concentration limit. Spatially, there was a "northwest to southeast" decreasing distribution of PM2.5 in 2021, and the high-pollution areas clustered in the central part of the Greater Bay Area, represented by Foshan. Low concentration areas were mainly distributed in the eastern part of Huizhou, Hong Kong, Macao, Zhuhai, and other coastal areas. The spatial distribution of PM2.5 in different seasons also showed heterogeneity and regionality. The RF model estimated the PM2.5 concentration with high accuracy, which provides a scientific basis for the health risk assessment associated with PM2.5 pollution in the Greater Bay Area. |
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