The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis

Diao, Beidi; Ding, Lei; Su, Panda; Cheng, Jinhua

doi:10.3390/ijerph15071405

Cited by 41 publications

(30 citation statements)

References 62 publications

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“…Beijing and its surrounding areas differ greatly in their variations in NO 2 columns, which may be due to the following reasons: On the one hand, Beijing has implemented stricter environmental control policies, including motor vehicle management and factory relocations since 2008, which has fundamentally curbed the emissions of NO x . This inference is affirmed by Diao et al [64]. However, NO 2 has a short life, especially in summer, so pollutants cannot be transported over long distances.…”

Section: Monthly and Seasonal Patterns Of Tropospheric No2 Vcdmentioning

confidence: 74%

The Spatial–Temporal Variation of Tropospheric NO2 over China during 2005 to 2018

Wang

2019

Atmosphere

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In recent years, new and strict air quality regulations have been implemented in China. Therefore, it is of great significance to evaluate the current air pollution situation and effectiveness of actions. In this study, Ozone Monitoring Instrument (OMI) satellite data were used to detect the spatiotemporal characteristics of tropospheric NO2 columns over China from 2005 to 2018, including spatial distribution, seasonal cycles and long-term trends. The averaged NO2 pollution is higher in southeastern China and lower in the northwest, which are well delineated by the Heihe–Tengchong line. Furthermore, the NO2 loadings are highest in the North China Plain, with vertical column density (VCD) exceeding 13 × 1015 molec cm−2. Regarding the seasonal cycle, the NO2 loadings in eastern China is highest in winter and lowest in summer, while the western region shows the opposite feature. The amplitude of annual range increase gradually from the south to the north. If the entire period of 2005–2018 is taken into account, China has experienced little change in NO2. In fact, however, there appears to be significant trends of an increase followed by a downward tendency, with the turning point in the year 2012. In the former episode of 2005–2012, increasing trends overwhelm nearly the whole nation, especially in the Jing–Jin–Tang region, Shandong Province, and Northern Henan and Southern Hebei combined regions, where the rising rates were as high as 1.0–1.8 × 1015 molec cm−2 year−1. In contrast, the latter episode of 2013–2018 features remarkable declines in NO2 columns over China. Particularly, the regions where the decreased degree was remarkable in 2013–2018 were consistent with the regions where the upward trend was obvious in 2005–2012. Overall, this upward–downward pattern is true for most parts of China. However, some of the largest metropolises, such as Beijing, Shanghai and Guangzhou, witnessed a continuous decrease in the NO2 amounts, indicating earlier and more stringent measures adopted in these areas. Finally, it can be concluded that China’s recent efforts to cut NO2 pollution are successful, especially in mega cities.

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Section: Monthly and Seasonal Patterns Of Tropospheric No2 Vcdmentioning

confidence: 74%

The Spatial–Temporal Variation of Tropospheric NO2 over China during 2005 to 2018

Wang

2019

Atmosphere

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“…In addition, environmental pollution is greatly influenced by adjacent regions depending on wind and relative humidity. For example, when the wind is strong enough, pollutants can spread to adjacent regions [51]. Wind and diffusion cause particulates to migrate between regions, resulting in a spatial spillover effect.…”

Section: Theoretical Paths Of the Effects Of Fdi And Corruption On Enmentioning

confidence: 99%

Regional Difference in Spatial Effects: A Theoretical and Empirical Study on the Environmental Effects of FDI and Corruption in China

Tang

Yang

et al. 2020

Discrete Dynamics in Nature and Society

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Environmental pollution has aroused extensive concern worldwide in recent years. Existing studies on the relationship between foreign direct investment (FDI) and environmental pollution have, however, paid little attention to spatial effects and regional corruption’s environmental performance from a spatial perspective. To address this gap, we investigate the spatial agglomeration effects of environmental pollution in China and the environmental effects of FDI and regional corruption using spatial econometric analysis method. The results indicate significant spatial agglomeration effects in environmental pollution. The results of spatial panel data models reveal that the estimation coefficient of FDI is significantly negative, and FDI inflows reduce China’s environmental pollution. Regional corruption is shown to increase environmental pollution, thereby contributing further to environmental degradation. The interaction coefficient of FDI and regional corruption is significantly positive, indicating that regional corruption reduces the environmental benefits derived from FDI. In addition, regional differences in spatial effects verify that regional corruption also reduces the environmental performance of FDI in the central region. Meanwhile, regional corruption increases the environmental aggravation effects of FDI in the eastern region but weakens it in the western region. Our findings lead to some policy recommendations with regard to environmental protection and pollution control.

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“…Its toxicity posed a threat to the health of residents [39,40]. Exposure to excessive NO x can result in diseases in the short term, and even death in the long term [41]. Besides, NO x can lead to the generation of PM 2.5 and O 3 by reacting with other elements in the atmosphere [42,43].…”

Section: Introductionmentioning

confidence: 99%

Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis

et al. 2020

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The Chinese government has identified air pollution transmission points in Beijing-Tianjin-Hebei region and its surrounding areas under 2 + 26 initiative. This study introduces a modified Gravity Model to construct the spatial correlation network of industrial NO x in 2 + 26 policy region from 2011 to 2015, and further explores network characteristics and socioeconomic factors of this spatial correlation network by Social Network Analysis. Results indicate significant correlation of industrial NO x emission in 2 + 26 policy cities. The spatial correlation network of industrial NO x has remained stable within 5 years, implying no pollution exacerbation of interregional transmission. According to the effect of output and input in the correlation network of industrial NO x , cities in 2 + 26 policy region can be categorized into four types: high-high, high-low, low-low, and low-high, as each should adopt the corresponding strategies for emission reduction. Shijiazhuang, Liaocheng, Cangzhou, Heze and Handan should be key monitored during implementation of emission reduction. Taiyuan, Hebi, Langfang, Tangshan and Yangquan, should give priority to local emission reduction although less associated with other cities, based on city type and current emission situation. Environmental regulation and geographical distance have significant influence on the spatial correlation network of industrial NO x , of which the indicator of environmental regulation difference matrix has become significantly negative since 2014, while the indicator of geographical effect has been significantly positive all along. Urban industrial emission has significant correlation between cities with distance of 0-300 km, while no significant correlation between cities with distance exceeding 300 km.

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The Spatial-Temporal Characteristics and Influential Factors of NOx Emissions in China: A Spatial Econometric Analysis

Cited by 41 publications

References 62 publications

The Spatial–Temporal Variation of Tropospheric NO2 over China during 2005 to 2018

The Spatial–Temporal Variation of Tropospheric NO2 over China during 2005 to 2018

Regional Difference in Spatial Effects: A Theoretical and Empirical Study on the Environmental Effects of FDI and Corruption in China

Spatial Correlation of Industrial NOx Emission in China’s 2 + 26 Policy Region: Based on Social Network Analysis

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