Spatiotemporal Variation and Driving Factors for NO2 in Mid-Eastern China

被引:2
|
作者
Yi, Mingjian [1 ,2 ]
Jiang, Yongqing [1 ]
Zhao, Qiang [1 ]
Qiu, Junxia [1 ]
Li, Yi [1 ]
机构
[1] Anhui Jianzhu Univ, Sch Environm & Energy Engn, Hefei 230601, Peoples R China
[2] Anhui Prov Key Lab Environm Pollut Control & Resou, Hefei 230601, Peoples R China
关键词
nitrogen dioxide (NO2); GTWR; spatial heterogeneity; AIR-POLLUTION; WEIGHTED REGRESSION; EMISSIONS; SATELLITE; EXPOSURE;
D O I
10.3390/atmos14091369
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Nitrogen dioxide (NO2) is one of the major air pollutants in cities across mid-eastern China. Comprehending the spatial and temporal dynamics of NO2 drivers in various urban areas is imperative for tailoring effective air control strategies. Using data from ground-based monitoring stations, we investigated the impact of socioeconomic and meteorological factors on NO2 concentrations in cities in mid-eastern China from 2015 to 2021 using the Geographically and Temporally Weighted Regression (GTWR) model. The findings reveal a notable reduction of over 10% in NO2 concentrations since 2015 in most cities, notably a 50.5% decrease in Bozhou. However, certain areas within Anhui and Jiangsu have experienced an increase in NO2 concentrations. Significant spatial heterogeneity is observed in the relationship between NO2 concentrations and influencing factors. The permanent population density (POP) and the electricity consumption (EC) of the entire society exhibited the strongest correlations with NO2 concentrations, with average coefficients of 0.431 and 0.520, respectively. Furthermore, other economic factors such as urbanization rate (UR), the share of secondary sector output in total GDP (IS), and the coverage rate of urban green areas (CG) were predominantly positively correlated, while GDP per capita (PGDP) and civil car vehicles (CV) demonstrated primarily negative correlations. Furthermore, we examined the correlations between four meteorological factors (temperature, relative humidity, wind speed, and precipitation) and NO2 concentrations. All these factors exhibited negative correlations with NO2 concentrations. Among them, temperature exhibited the strongest negative correlation, with a coefficient of -0.411. This research may contribute valuable insights and guidance for developing air emission reduction policies in various cities in mid-eastern China.
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页数:16
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