基于移动式监测的道路PM2.5浓度精细化时空模拟

作为主要的大气污染指标，PM_2.5浓度常来源于固定环境监测站点的监测与遥感影像数据，但时空精度普遍不足，难以揭示微尺度下城市内部PM_2.5时空分布情况。本文利用移动式监测方法，选择典型工作日(2017年11月27日)，对广州市主城区道路以1 s和1 m为时空粒度进行PM_2.5浓度数据采集，并以早、晚出行高峰时段为对象，通过机器学习方法模拟道路PM_2.5精细化时空分布格局。结果表明，主城区早高峰道路PM_2.5浓度值相近的平均范围为24 m，晚高峰为15 m,PM_2.5浓度存在微尺度的时空异质性。利用多层感知器(MLP)构建的早、晚高峰PM_2.5浓度模型，拟合度分别达到0.70和0.68，明显优于传统的普通最小二乘法(OLS)线性回归模型。模型揭示出早高峰主城区全路网PM_2.5平均浓度为30.19μg/m3，晚高峰达到44.55μg/m3，部分高达94.82μg/m3，且“西高东低”的分布特征显著。本文提出的PM

Precise spatiotemporal simulation of on-road PM2.5 concentration based on mobile monitoring

As the main air pollution indicator, PM_2.5 concentration often comes from monitoring data of fixed environmental monitoring stations and remote sensing image data. The spatial and temporal accuracy is generally insufficient, which makes it difficult to reveal the spatial and temporal distribution of PM_2.5 in urban interior at microscale. In this study, using the mobile monitoring method of cycling, the typical working day (November 27, 2017) was selected to collect PM_2.5 concentration data of roads in the main urban area of Guangzhou at a time and space granularity of 1 m·s. The machine learning method is utilized to simulate the refined spatiotemporal distribution pattern of on-road PM_2.5 during the morning and evening peak hours. The results show that the average spatial range of PM_2.5 concentration values close to each other in the morning peak hours is 24 m, which is larger than that in the evening peak hours of 15 m. There was a microscale spatial and temporal heterogeneity of PM_2.5 concentration. The fitting degrees of morning and evening peaks' PM_2.5 models constructed by Multilayer Perceptron (MLP) reached 0.70 and 0.68, respectively, which is obviously superior to the traditional Ordinary Least Square (OLS) linear regression model. The model reveals that the average concentration of PM_2.5 in the whole road network of the main urban area was 30.19 μg/m3 in the morning peak, and reached 44.55 μg/m3 in the evening peak, with the maximum up to 94.82 μg/m3. The spatial distribution characteristics of "high in the west and low in the east" are significant. The refined mapping method of PM_2.5 concentration proposed in this paper has a spatial accuracy of 1 m and can better describe the spatial heterogeneity. The method is proved to be feasible and can provide reference for public health travel and targeted pollution prevention.