Effect of urban underlying surface on PM2.5 vertical distribution based on UAV in Xi'an, China.

Fine particulate matter (PM2.5) has become a significant issue of ecological environment. However, few studies have explored the vertical distribution of PM2.5 in cities. The objectives of this paper are to reveal the vertical distribution regular pattern of PM2.5 over urban underlying surfaces near the ground with a hexacopter-type unmanned aerial vehicle (UAV) in winter. Results showed that the maximum vertical gradient of PM2.5 near the ground was typically the greatest in the morning as the stable atmospheric conditions. Moreover, regression model illustrated that relative humidity had the greatest impact on the vertical profile of PM2.5 compared to air temperature and altitude as hygroscopic of PM2.5 aerosols. Curve model shown that vertical profile of PM2.5 over the surfaces of water and green space first increased slowly and then declined, besides, the highest concentration inflection of PM2.5 above the water body (23.7 m) is higher than the green space (14.3 m). Thus, suggesting residents living vertical of 10-30 m from the ground around large water bodies and green spaces should not open windows for ventilation in the morning. Therefore, this study provides insights into the vertical distributions of PM2.5 over different underlying surfaces and should be of reference value to urban planners for designing urban spaces to optimize atmosphere environment to provide a healthy living environment.

Spatial distribution characteristics of PM2.5 and PM10 in Xi'an City predicted by land use regression models.

PM2.5 and PM10 could increase the risk for cardiovascular and respiratory diseases in the general public and severely limit the sustainable development in urban areas. Land use regression models are effective in predicting the spatial distribution of atmospheric pollutants, and have been widely used in many cities in Europe, North America and China. To reveal the spatial distribution characteristics of PM2.5 and PM10 in Xi'an during the heating seasons, the authors established two regression prediction models using PM2.5 and PM10 concentrations from 181 monitoring stations and 87 independent variables. The model results are as follows: for PM2.5, R2 = 0.713 and RMSE = 8.355 µg/m3; for PM10, R2 = 0.681 and RMSE = 14.842 µg/m3. In addition to the traditional independent variables such as area of green space and road length, the models also include the numbers of pollutant discharging enterprises, restaurants, and bus stations. The prediction results reveal the spatial distribution characteristics of PM2.5 and PM10 in the heating seasons of Xi'an. These results also indicate that the spatial distribution of pollutants is closely related to the layout of industrial land and the location of enterprises that generate air pollution emissions. Green space can mitigate pollution, and the contribution of traffic emission is less than that of industrial emission. To our knowledge, this study is the first to apply land use regression models to the Fenwei Plain, a heavily polluted area in China. It provides a scientific foundation for urban planning, land use regulation, air pollution control, and public health policy making. It also establishes a basic model for population exposure assessment, and promotes the sustainability of urban environments.