基于移动监测的城市PM2.5污染时空模式研究——以广州市中心区为例

Analysis of Spatio-Temporal PM2.5 Patterns Obtained Using Mobile Monitoring:Case Study Conducted in Central District of Guangzhou

采用便携式空气污染监测设备对广州市环城高速内的中心城区PM2.5污染情况进行移动监测,获取225.7万条频率为1 Hz的PM2.5监测数据,基于此对研究区进行10 m×10 m高时空分辨率的PM2.5污染模拟,并分析移动采集的可靠性及城市中心区PM2.5污染时空模式。结果显示:天气状况稳定条件下移动监测的城市PM2.5数据在时间维度与固定监测站点数据呈现较显著相关性(R^2为0.72~0.86);广州市中心城区的PM2.5污染时空分布在短时间内具有显著的时空分异特征:时间上,干、湿季的平均逐时极差分别为27和11μg/m^3,质量浓度最高值和最低值出现的时段与当天的背景质量浓度值有关;空间上,交通枢纽、商业中心、工业园和大型商贸市场附近PM2.5污染风险高,公园绿地、高校、高级住宅区等风险相对较低,且呈干季西高东低、南高北低,湿季东高西低的空间分异特征。

It is crucial to address the global risk of disease caused by PM2.5 air pollution,which requires largescale monitoring of PM2.5 pollution.Simulations of pollutant patterns are also necessary;however,it is currently difficult to accurately depict the spatial and temporal distribution patterns of PM2.5 pollution in cities using traditional air pollutant simulation methods.In this study,basic low-cost air quality monitoring equipment was used to conduct mobile monitoring of PM2.5 pollution in the central urban area of Guangzhou within the ring expressway,and 2,257,000 PM2.5 monitoring data were obtained at a frequency of 1 Hz.Using these data,simulation of PM2.5 pollution within the study area was conducted at a spatial and temporal resolution of 10 m×10 m,and the reliability of collecting spatial and temporal patterns of PM2.5 pollution via the mobile device in the urban center was analyzed.The mobile monitoring data results showed the following:under stable weather conditions,there was a significant temporal correlation between PM2.5 data obtained under mobile monitoring and that from the fixed monitoring station(R^2:0.72-0.86).The spatial and temporal distributions of PM2.5 pollution in the central area of Guangzhou showed significant spatial and temporal differentiations over short time periods.Temporally,the hourly average ranges in dry and wet seasons were 27μg/m^3and 11μg/m^3,respectively,where the temporal periods of the highest and lowest concentrations occur depends were related to the background concentrations on the day.Spatially,there were higher values of PM2.5 near transportation hubs,commercial centers,industrial parks,and large commercial markets;however,lower values were found in parks,green areas,and high-end residential areas and on university campuses.Furthermore,spatial differentiation characteristics were evident,with values higher in the west and south and lower in the east and north during the dry season,but higher in the east and lower in the west during the wet season.Although there was no temporal correlation between high PM2.5 values during the day and peak traffic periods,pollution was spatially concentrated in the vicinity of important traffic nodes within the city,and the amount increased during peak traffic periods.These results show that the mobile monitoring method can be used to describe the spatial and temporal patterns of pollutants and key areas of exposure can be identified,which is of great significance for optimizing and adjusting the layout structure of monitoring sites and associated maintenance costs.Implementation of this method could enable the identification of high risk pollution routes,which would prevent and control pollution,improve the ecological environment,and enable targeted protection measures to be effectively evaluated.As such,use of mobile monitoring is important in the construction of smart cities and for realizing the long-term and highprecision air quality monitoring within cities under the support of smart geospatial technology.