Uncovering temporal-spatial drivers of vehicular NOx emissions in China

Wang, Junfeng; Li, Xinjing; Ding, Shihao; Xu, Xiaoya; Liu, Lingxuan; Dong, Liang; Feng, Yinchang

doi:10.1016/j.jclepro.2020.125635

Cited by 8 publications

(1 citation statement)

References 53 publications

(65 reference statements)

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“…Social factors, including industrial emissions, traffic emissions, energy consumption, industrial structure, and population density, have been recognized as essential influencers affecting NO 2 concentrations [20][21][22]. Wang et al [23] analyzed the factors driving vehicle NO x emissions from 2005 to 2015 and identified economic development and road vehicle carrying capacity as the primary drivers of emissions growth. Xu et al [24] discovered that different levels of urbanization have varying effects on NOx emissions.…”

Section: Introductionmentioning

confidence: 99%

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

Yi,

Jiang,

Zhao

et al. 2023

Atmosphere

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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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Section: Introductionmentioning

confidence: 99%

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

Yi,

Jiang,

Zhao

et al. 2023

Atmosphere

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Probabilistic Transport-Induced Emissions and Health Risks for Adelaide, South Australia

Iqbal,

Rahman

2024

Environ Model Assess

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The research aims to assess the risks associated with the transport-induced emissions (major criteria pollutants) in Adelaide, South Australia. A stochastic emission modelling strategy was implemented for a probabilistic risk assessment of the transport system for current and future scenarios, by analysing the road specific transport system data. The simulated result suggests that NOx and PM2.5 risks are significant at about 35% of city areas, especially along the roads with heavy vehicle concentration and higher traffic. The risk will significantly increase in 10 years if the current practice persists, demonstrating a high probability (more than 60%) of emissions above 150% of the air quality standard. The research suggests that appropriate planning is necessary to cope with the development needs for the system’s future sustainability.

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Spatiotemporal variations of atmospheric nitrogen deposition in China during 2008–2020

Chen,

Wang

et al. 2023

Atmospheric Environment

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Uncovering temporal-spatial drivers of vehicular NOx emissions in China

Cited by 8 publications

References 53 publications

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

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

Probabilistic Transport-Induced Emissions and Health Risks for Adelaide, South Australia

Spatiotemporal variations of atmospheric nitrogen deposition in China during 2008–2020

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