PM2.5对大气环境和人体健康危害极大,及时准确地了解具有较高时空分辨率的PM2.5,对大气污染防治具有重要作用。基于多角度实现的大气校正算法(MAIAC),1kmAOD产品,ERA5气象资料,和污染物浓度(CO,2015-2020年粤港澳大湾区的O3,NO2,SO2,PM10和PM2.5),地理和时间加权回归模型(GTWR),BP神经网络模型(BPNN),支持向量机回归模型(SVR),建立了随机森林模型(RF),分别,估算PM2.5浓度。结果表明,射频模型的估计能力优于BPNN,SVR,和GTWR模型。BPNN的相关系数,SVR,GTWR,RF模型分别为0.922、0.920、0.934和0.981。RMSE值分别为7.192、7.101、6.385和3.670μg·m-3。MAE值分别为5.482、5.450、4.849和2.323μg·m-3。射频模型在冬季效果最好,接下来是夏天,在春季和秋季,在不同季节的预测中,相关系数在0.976以上。RF模型可用于预测大湾区PM2.5浓度。在时间上,2021年大湾区城市日ρ(PM2.5)呈“先降后升”趋势,最高值为65.550μg·m-3~112.780μg·m-3,最低值为5.000μg·m-3~7.899μg·m-3。月平均浓度呈U型分布,浓度在1月份开始下降,6月份达到低谷后逐渐上升。季节性,它的特点是冬季浓度最高,夏季最低,以及春季和秋季的过渡。大湾区的年平均ρ(PM2.5)为28.868μg·m-3,低于二级浓度限值。空间上,2021年PM2.5呈“西北向东南”分布减少,高污染地区聚集在大湾区中部,以佛山为代表。低集中区主要分布在惠州东部,香港,澳门,珠海,和其他沿海地区。PM2.5在不同季节的空间分布也表现出异质性和区域性。RF模型对PM2.5浓度的估算精度较高,为大湾区PM2.5污染相关健康风险评估提供科学依据。
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.