Quantifying public health benefits of environmental strategy of PM2.5 air quality management in Beijing–Tianjin–Hebei region, China

Chen, Li; Shi, Mengshuang; Li, Suhuan; Gao, Shuang; Zhang, Hui; Sun, Ying; Mao, Jian; Zhang, Bai; Wang, Zhongliang; Zhou, Jian

doi:10.1016/j.jes.2016.11.014

Cited by 43 publications

(18 citation statements)

References 22 publications

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“…In 2018, the permanent population of the BTH region was 110 million, with a total GDP of 8.5 trillion yuan which accounted for 9.44% of the GDP of the entire country. The BTH region accounted for 11.7% of the total number of motor vehicles in China [33]. In recent years, fog and haze have been frequent in this region.…”

Section: Study Areamentioning

confidence: 99%

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing–Tianjin–Hebei Region, China

Zhang

Cheng

et al. 2019

IJERPH

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Heavy-duty diesel trucks (HDDTs) contribute significantly to NOX and particulate matter (PM) pollution. Although existing studies have emphasized that HDDTs play a dominant role in vehicular pollution, the spatial distribution pattern of HDDT emissions and their related socioeconomic factors are unclear. To fill this research gap, this study investigates the spatial distribution pattern and spatial autocorrelation characteristics of NOX, PM, and SO2 emissions from HDDTs in 200 districts and counties of the Beijing–Tianjin–Hebei (BTH) region. We used the spatial lag model to calculate the significances and directions of the pollutants from HDDTs and their related socioeconomic factors, namely, per capita GDP, population density, urbanization rate, and proportions of secondary and tertiary industries. Then, the geographical detector technique was applied to quantify the strengths of the significant socioeconomic factors of HDDT emissions. The results show that (1) NOX, PM, and SO2 pollutants emitted by HDDTs in the BTH region have spatial heterogeneity, i.e., low in the north and high in the east and south. (2) The pollutants from HDDTs in the BTH region have significant spatial autocorrelation characteristics. The spatial dependence effect was obvious; for every 1% increase in the HDDT emissions in the surrounding districts and counties, the local HDDT emissions increased by 0.39%. (3) Related factors analysis showed that the proportion of tertiary industries had a significant negative correlation, whereas the proportion of secondary industries and urbanization rate had significant positive correlations with HDDT emissions. Population density and per capita GDP did not pass the significance test. (4) The order of effect intensities of the significant socioeconomic factors was proportion of tertiary industry > proportion of secondary industry > urbanization rate. This study guides scientific decision making for pollution control of HDDTs in the BTH region.

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Section: Study Areamentioning

confidence: 99%

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing–Tianjin–Hebei Region, China

Zhang

Cheng

et al. 2019

IJERPH

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“…To date, several studies have been conducted at regional and national levels to evaluate the effectiveness of the Action Plan. These studies have consistently indicated the health benefits of the improved air quality following the implementation of the Action Plan [12][13][14][15][16][17][18][19]. However, such studies had limitations, as follows.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the short-term mortality effect of the national action plan on air pollution in Beijing, China

Han

Sun

Gong

et al. 2020

Environ. Res. Lett.

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The Air Pollution Prevention and Control Action Plan (referred to as the Action Plan henceforth) provides a golden opportunity for China to evaluate whether the remarkable air quality improvements as a result of the plan have brought about health benefits to residents. Based on the ground-level particulate matter with an aerodynamic diameter of less than 2.5 μm (PM 2.5 ) concentrations and the daily respiratory-cardiovascular mortality, we aimed to assess changes in the mortality effect associated with short-term exposure to PM 2.5 due to the implementation of the Action Plan in Beijing. We analyzed the changes in PM 2.5 concentrations and air quality during the pre-and post-emission reduction periods. We then used the generalized additive model to estimate the changes in mortality risk associated with PM 2.5 exposure during both periods. We found that following the introduction of the Action Plan, the annual average PM 2.5 concentrations declined from 101.7 μg m −3 in 2013 to 58.6 μg m −3 in 2017, attaining the target of the plan (60 μg m −3 ). The remarkable reduction in PM 2.5 concentrations has led to a marked decrease in mortality risk. Compared with the pre-emission reduction level, total respiratory mortality decreased from 0.56% (95% CI: 0.40%-0.73%) to 0.43% (95% CI: 0.23%-0.63%), while the total cardiovascular mortality decreased from 0.44% (95% CI: 0.37%-0.52%) to 0.29% (95% CI: 0.19%-0.39%). Significant decreases were also observed in sex-specific subgroups. Our study implied that the significant efforts of the nation to clean China's air has yielded positive results of air quality improvement and human health protection in Beijing. However, the ambient air pollution in Beijing remains severe. The PM 2.5 concentrations still exceed the level (annual average of 10 μg m −3 ) recommended by the World Health Organization. Thus, consistent efforts are required to implement the emission abatement measures to continuously curb air pollution and protect public health.

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“…Chen et al used Voronoi spatial interpolation method to estimate the PM 2.5 concentration in the BTH region. They showed that up to 14,051 deaths and 6574 million yuan loss would be avoided when the PM 2.5 concentration fell by 25% in BTH [19]. Zhu et al studied the spatial impacts of foreign direct investment (FDI) on SO 2 emissions in the BTH region by spatial panel data from 2000 to 2013.…”

Section: Introductionmentioning

confidence: 99%

Evaluating China’s Air Pollution Control Policy with Extended AQI Indicator System: Example of the Beijing-Tianjin-Hebei Region

Yuan

Yang

2019

Sustainability

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This paper calculated and evaluated the air quality of 13 cities in China’s Beijing-Tianjin-Hebei (BTH) region from February 2015 to January 2018 based on the extended AQI (Air Quality Index) Indicator System. By capturing the heterogeneous information in major pollutant indicators and the standardization process, we depicted the important effect of other relevant features of pollutant indicators beyond single-point data. Based on that, we further calculated the assessment value of the air quality of different cities in the BTH region by using the Collaborative Filtering Backward Cloud Model to construct differentiated weights of different indicators. With help of the Back Propagation (BP) Neutral Network, we simulated the effect of the pollution control policies of the Chinese government targeting air pollution since March 2016. Our conclusion is: the pollution control policies have improved the air quality of Beijing by 55.74%, and improved the air quality of Tianjin by 34.38%; while the migration of polluting enterprises from Beijing and Tianjin has caused different changes in air quality in different cities of Hebei province—we saw air quality deterioration by 58.60% and 38.68% in Shijiazhuang and Handan city respectively.

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Quantifying public health benefits of environmental strategy of PM2.5 air quality management in Beijing–Tianjin–Hebei region, China

Cited by 43 publications

References 22 publications

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing–Tianjin–Hebei Region, China

Spatial Characteristics and Factor Analysis of Pollution Emission from Heavy-Duty Diesel Trucks in the Beijing–Tianjin–Hebei Region, China

Assessment of the short-term mortality effect of the national action plan on air pollution in Beijing, China

Evaluating China’s Air Pollution Control Policy with Extended AQI Indicator System: Example of the Beijing-Tianjin-Hebei Region

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