Investigating the vertical distribution patterns of urban air pollution based on unmanned aerial vehicle gradient monitoring
Abstract
Understanding the vertical distribution patterns of air pollution is crucial to elucidate the formation mechanism of extreme air pollution events and explore the air pollution exposure risks of residents. The vertical air pollutant (SO2, NO2, PM1, PM2.5 and PM10) concentrations along a west-east sampling belt of 0–120 m height were investigated using a multirotor unmanned aerial vehicle (UAV) equipped with mobile sensors on 4 monitoring days. Vertical distribution patterns were explored by ordinary least-squares regression and Pearson correlation coefficient. The results indicated that the concentration of particulate matter decreased gradually from west to the east (S1 to S8) on monitoring Days 2 and 3. The ground cumulative effects of atmospheric particulate matter (PM1, PM2.5, PM10) were significantly higher than those of gaseous pollutants (SO2, NO2). The vertical variation ranges of pollutants from large to small were PM10 (k = 0.18), PM2.5 (k = 0.16), PM1 (k = 0.07), NO2 (k = 0.06) and SO2 (k = 0.01). Atmospheric particulate matter tends to change significantly with vertical height, and the concentration decreases gradually with increasing height. The proposed UAV based gradient monitoring approach and vertical pollutant change trend analysis method could promote the urban air pollution researches and urban sustainable development.
Abstract
Understanding the vertical distribution patterns of air pollution is crucial to elucidate the formation mechanism of extreme air pollution events and explore the air pollution exposure risks of residents. The vertical air pollutant (SO2, NO2, PM1, PM2.5 and PM10) concentrations along a west-east sampling belt of 0–120 m height were investigated using a multirotor unmanned aerial vehicle (UAV) equipped with mobile sensors on 4 monitoring days. Vertical distribution patterns were explored by ordinary least-squares regression and Pearson correlation coefficient. The results indicated that the concentration of particulate matter decreased gradually from west to the east (S1 to S8) on monitoring Days 2 and 3. The ground cumulative effects of atmospheric particulate matter (PM1, PM2.5, PM10) were significantly higher than those of gaseous pollutants (SO2, NO2). The vertical variation ranges of pollutants from large to small were PM10 (k = 0.18), PM2.5 (k = 0.16), PM1 (k = 0.07), NO2 (k = 0.06) and SO2 (k = 0.01). Atmospheric particulate matter tends to change significantly with vertical height, and the concentration decreases gradually with increasing height. The proposed UAV based gradient monitoring approach and vertical pollutant change trend analysis method could promote the urban air pollution researches and urban sustainable development.
