In the construction of resilient cities,regional air pollution prevention plays a pivotal role.Building on the previous research experience,the relationship between air pollution concentration and urban size exhibits ...In the construction of resilient cities,regional air pollution prevention plays a pivotal role.Building on the previous research experience,the relationship between air pollution concentration and urban size exhibits a sublinear allometric growth pattern.To identify effective strategies for mitigating particulate matter air pollution,this study quantitatively explored 6 variables influencing urbanization in China’s cities and established an allometry model.Empirical analysis was conducted using data from 293 prefecturelevel cities and 1,827 county-level cities to examine the relationship between annual concentrations of fine particulate matter PM_(2.5) and PM_(10) in the atmosphere.①The findings of this study provided partial validation for the Kuznets curve and demonstrated a reverse‘U’-shaped association between urbanization and levels of PM_(2.5) and PM_(10) pollution.②By partitioning the Hu Huanyong line,this study identified the spatial distribution pattern of PM_(2.5) and PM_(10).In central and western regions,as urban size expands,inhalable particle concentrations tended to increase further;whereas in the southeast region,inhalable particle concentrations gradually decreased and stabilized after a certain threshold of urban scale expansion was reached.Among the factors influencing urban size,green coverage within built-up areas exerted the most significant impact on both PM_(2.5) and PM_(10) concentrations,followed by the extent of built-up areas and the scale of secondary industries.This study presented an effective strategy for reconciling conflicts between urban expansion and air pollution management,while concurrently promoting resilient cities characterized by high levels of modernization and superior quality.展开更多
Based on the monitoring data of PM_(2.5) concentration in Bengbu Environmental Monitoring Station and precipitation observation data of Bengbu National Meteorological Observation Station from 2016 to 2019, the influen...Based on the monitoring data of PM_(2.5) concentration in Bengbu Environmental Monitoring Station and precipitation observation data of Bengbu National Meteorological Observation Station from 2016 to 2019, the influence of precipitation on PM_(2.5) mass concentration in Bengbu City was analyzed. The results show that precipitation had a washing and removal effect on PM_(2.5) in the air, and the removal effect was related to precipitation level, precipitation intensity, precipitation duration and PM_(2.5) concentration. The removal effect of precipitation on PM_(2.5) increased with the increase of precipitation level, and the seasonal difference was obvious. Precipitation intensity was positively correlated with the removal effect of PM_(2.5) , but the average removal rate began to decline when precipitation intensity exceeded 10 mm. With the increase of precipitation intensity, the proportion of positive removal showed an overall upward trend, but there was a low-value area as precipitation intensity was 3-10 mm. Precipitation duration was also positively correlated with the removal effect of PM_(2.5) , and there was a low-value area when precipitation duration was 10-15 h. When PM_(2.5) concentration was low before the precipitation process began, the removal effect was not good, and the average removal rate was negative. As PM_(2.5) concentration was high before the precipitation process started, the removal effect was obvious.展开更多
文摘In the construction of resilient cities,regional air pollution prevention plays a pivotal role.Building on the previous research experience,the relationship between air pollution concentration and urban size exhibits a sublinear allometric growth pattern.To identify effective strategies for mitigating particulate matter air pollution,this study quantitatively explored 6 variables influencing urbanization in China’s cities and established an allometry model.Empirical analysis was conducted using data from 293 prefecturelevel cities and 1,827 county-level cities to examine the relationship between annual concentrations of fine particulate matter PM_(2.5) and PM_(10) in the atmosphere.①The findings of this study provided partial validation for the Kuznets curve and demonstrated a reverse‘U’-shaped association between urbanization and levels of PM_(2.5) and PM_(10) pollution.②By partitioning the Hu Huanyong line,this study identified the spatial distribution pattern of PM_(2.5) and PM_(10).In central and western regions,as urban size expands,inhalable particle concentrations tended to increase further;whereas in the southeast region,inhalable particle concentrations gradually decreased and stabilized after a certain threshold of urban scale expansion was reached.Among the factors influencing urban size,green coverage within built-up areas exerted the most significant impact on both PM_(2.5) and PM_(10) concentrations,followed by the extent of built-up areas and the scale of secondary industries.This study presented an effective strategy for reconciling conflicts between urban expansion and air pollution management,while concurrently promoting resilient cities characterized by high levels of modernization and superior quality.
文摘Based on the monitoring data of PM_(2.5) concentration in Bengbu Environmental Monitoring Station and precipitation observation data of Bengbu National Meteorological Observation Station from 2016 to 2019, the influence of precipitation on PM_(2.5) mass concentration in Bengbu City was analyzed. The results show that precipitation had a washing and removal effect on PM_(2.5) in the air, and the removal effect was related to precipitation level, precipitation intensity, precipitation duration and PM_(2.5) concentration. The removal effect of precipitation on PM_(2.5) increased with the increase of precipitation level, and the seasonal difference was obvious. Precipitation intensity was positively correlated with the removal effect of PM_(2.5) , but the average removal rate began to decline when precipitation intensity exceeded 10 mm. With the increase of precipitation intensity, the proportion of positive removal showed an overall upward trend, but there was a low-value area as precipitation intensity was 3-10 mm. Precipitation duration was also positively correlated with the removal effect of PM_(2.5) , and there was a low-value area when precipitation duration was 10-15 h. When PM_(2.5) concentration was low before the precipitation process began, the removal effect was not good, and the average removal rate was negative. As PM_(2.5) concentration was high before the precipitation process started, the removal effect was obvious.