Examining the effects of socioeconomic development on fine particulate matter (PM2.5) in China's cities using spatial regression and the geographical detector technique.

The frequent occurrence of extreme smog episodes in recent years has begun to present a serious threat to human health. In addition to pollutant emissions and meteorological conditions, fine particulate matter (PM2.5) is also influenced by socioeconomic development. Thus, identifying the potential effects of socioeconomic development on PM2.5 variations can provide insights into particulate pollution control. This study applied spatial regression and the geographical detector technique for assessing the directions and strength of association between socioeconomic factors and PM2.5 concentrations, using data collected from 945 monitoring stations in 190 Chinese cities in 2014. The results indicated that the annual average PM2.5 concentrations is 61±20μg/m3, and cites with more than 75μg/m3 were mainly located in North China, especially in Tianjin and Hebei province. We also identified a marked seasonal variation in concentrations levels, with the highest level in winter due to coal consumption, lower temperatures, and less rainfall than in summer. Monthly variations followed a "U-shaped" pattern, with a down trend from January and an inflection point in September and then an increasing trend from October. The results of spatial regression indicated that population density, industrial structure, industrial soot (dust) emissions, and road density have a significantly positive effect on PM2.5 concentrations, with a significantly negative influence exerted only by economic growth. In addition, trade openness and electricity consumption were found to have no significant impact on PM2.5 concentrations. Using the geographical detector technique, the strength of association between the five significant drivers and PM2.5 concentrations was further analyzed. We found notable differences among the variables, with industrial soot (dust) emissions playing a greater role in the PM2.5 concentrations than the other variables. These results will be helpful in understanding the dynamics and the underlying mechanisms at work in PM2.5 concentrations in China at the city level, and thereby assisting the Chinese government in employing effective strategies to tackle pollution.

[1]  Xiaoping Liu,et al.  A future land use simulation model (FLUS) for simulating multiple land use scenarios by coupling human and natural effects , 2017 .

[2]  Ma Li-me,et al.  The Spatial Effect of China's Haze Pollution and the Impact from Economic Change and Energy Structure , 2014 .

[3]  Zhen-bo Wang,et al.  Spatial-temporal characteristics and determinants of PM2.5 in the Bohai Rim Urban Agglomeration. , 2016, Chemosphere.

[4]  T. Maggos,et al.  The role of meteorology on different sized aerosol fractions (PM₁₀, PM₂.₅, PM₂.₅-₁₀). , 2012, The Science of the total environment.

[5]  J. Schwartz,et al.  Incorporating local land use regression and satellite aerosol optical depth in a hybrid model of spatiotemporal PM2.5 exposures in the Mid-Atlantic states. , 2012, Environmental science & technology.

[6]  Jun Wang,et al.  Satellite remote sensing of particulate matter and air quality assessment over global cities , 2006 .

[7]  Chuanglin Fang,et al.  Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces , 2014 .

[8]  Shaojian Wang,et al.  The impact of anthropogenic emissions and meteorological conditions on the spatial variation of ambient SO2 concentrations: A panel study of 113 Chinese cities. , 2017, The Science of the total environment.

[9]  Zuo Zhang,et al.  A stochastic equilibrium chance-constrained programming model for municipal solid waste management of the City of Dalian, China , 2017 .

[10]  Jianjun He,et al.  Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in situ air quality monitoring data from China National Environmental Monitoring Center. , 2016, Environment international.

[11]  Zhenshan Lin,et al.  [Interactive Effects of the Influencing Factors on the Changes of PM2.5 Concentration Based on GAM Model]. , 2017, Huan jing ke xue= Huanjing kexue.

[12]  Xiaocong Xu,et al.  A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human–Environment Interactions , 2017 .

[13]  Yang Liu,et al.  Limitations of Remotely Sensed Aerosol as a Spatial Proxy for Fine Particulate Matter , 2009, Environmental health perspectives.

[14]  Yu Hao,et al.  The influential factors of urban PM2.5 concentrations in China: a spatial econometric analysis , 2016 .

[15]  R. Muller,et al.  Air Pollution in China: Mapping of Concentrations and Sources , 2015, PloS one.

[16]  D. Jacob,et al.  Estimating ground-level PM2.5 in the eastern United States using satellite remote sensing. , 2005, Environmental science & technology.

[17]  Dong Jiang,et al.  Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China , 2013, International journal of environmental research and public health.

[18]  S. Christopher,et al.  Remote Sensing of Particulate Pollution from Space: Have We Reached the Promised Land? , 2009, Journal of the Air & Waste Management Association.

[19]  Shaojian Wang,et al.  China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces , 2017 .

[20]  Xiaohui Xu,et al.  Predicting regional space–time variation of PM2.5 with land-use regression model and MODIS data , 2011, Environmental Science and Pollution Research.

[21]  Hui Lin,et al.  A virtual geographic environment system for multiscale air quality analysis and decision making: A case study of SO2 concentration simulation , 2015 .

[22]  M. Brauer,et al.  Use of Satellite Observations for Long-Term Exposure Assessment of Global Concentrations of Fine Particulate Matter , 2014, Environmental health perspectives.

[23]  Jiansheng Wu,et al.  Spatiotemporal patterns of remotely sensed PM2.5 concentration in China from 1999 to 2011 , 2016 .

[24]  A. Cohen,et al.  Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution. , 2012, Environmental science & technology.

[25]  Ke-Bin He,et al.  Review on recent progress in observations, source identifications and countermeasures of PM2.5. , 2016, Environment international.

[26]  D. Reiner,et al.  Emissions affected by trade among developing countries. , 2009, Nature.

[27]  Chen Yizhen.  Study on Air Quality Impact in Beijing from Thermal Power Planning for the Northern Passageway of the West-east Power Transfer Project , 2003 .

[28]  Yongxian Su,et al.  Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique , 2017 .

[29]  Jiming Hao,et al.  Status and characteristics of ambient PM2.5 pollution in global megacities. , 2016, Environment international.

[30]  Hong-yu Liu,et al.  Socioeconomic Drivers of PM2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China , 2016, International journal of environmental research and public health.

[31]  M. G. Estes,et al.  Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression. , 2013, Environmental research.

[32]  Zhen Cheng,et al.  Characteristics and source apportionment of PM2.5 during a fall heavy haze episode in the Yangtze River Delta of China , 2015 .

[33]  W. Meng,et al.  Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing , 2009 .

[34]  H. Barker.  Isolating the Industrial Contribution of PM2.5 in Hamilton and Burlington, Ontario , 2013 .

[35]  Brian Stone,et al.  Urban sprawl and air quality in large US cities. , 2008, Journal of environmental management.

[36]  A. Papayannis,et al.  Influence of Saharan Dust Transport Events on PM2.5 Concentrations and Composition over Athens , 2012, Water, Air, & Soil Pollution.

[37]  Chuanglin Fang,et al.  The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China. , 2016, The Science of the total environment.

[38]  Jianxin Cui,et al.  [Shifting path of industrial pollution gravity centers and its driving mechanism in Pan-Yangtze River Delta]. , 2014, Huan jing ke xue= Huanjing kexue.

[39]  Weiqi Zhou,et al.  Impact of urbanization level on urban air quality: a case of fine particles (PM(2.5)) in Chinese cities. , 2014, Environmental pollution.

[40]  Jiming Hao,et al.  Air quality impacts of power plant emissions in Beijing. , 2007, Environmental pollution.

[41]  T. Gärling,et al.  Quality attributes of public transport that attract car users : A research review , 2013 .

[42]  Giancarlo Rampazzo,et al.  A procedure to assess local and long-range transport contributions to PM2.5 and secondary inorganic aerosol , 2012 .

[43]  Shilu Tong,et al.  Ambient air pollution, climate change, and population health in China. , 2012, Environment international.

[44]  Itai Kloog,et al.  Long-term Exposure to PM2.5 and Incidence of Acute Myocardial Infarction , 2012, Environmental health perspectives.

[45]  K. Hubacek,et al.  The characteristics and drivers of fine particulate matter (PM2.5) distribution in China , 2017 .

[46]  Xiaoping Liu,et al.  Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities , 2017 .

[47]  Hai-long Ma,et al.  Quantifying the relationship between urban development intensity and carbon dioxide emissions using a panel data analysis , 2015 .

[48]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[49]  Atul Srivastava,et al.  Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: Influence of meteorology , 2013 .

[50]  C. Fang,et al.  Spatiotemporal variations of energy-related CO2 emissions in China and its influencing factors: An empirical analysis based on provincial panel data , 2016 .

[51]  Min Zhou,et al.  Input–Output Efficiency of Urban Agglomerations in China: An Application of Data Envelopment Analysis (DEA) , 2013 .

[52]  Yang Liu,et al.  Estimating ground-level PM2.5 in China using satellite remote sensing. , 2014, Environmental science & technology.

[53]  Jiansheng Wu,et al.  VIIRS-based remote sensing estimation of ground-level PM2.5 concentrations in Beijing–Tianjin–Hebei: A spatiotemporal statistical model , 2016 .

[54]  Burcu Onat,et al.  Personal exposure of commuters in public transport to PM2.5 and fine particle counts , 2013 .

[55]  Pentti Paatero,et al.  Advanced factor analysis of spatial distributions of PM2.5 in the eastern United States. , 2003, Environmental science & technology.

[56]  G. Peters,et al.  The socioeconomic drivers of China’s primary PM2.5 emissions , 2014 .

[57]  Jun Wang,et al.  Improved algorithm for MODIS satellite retrievals of aerosol optical thickness over land in dusty atmosphere: Implications for air quality monitoring in China , 2010 .

[58]  J. List,et al.  The Effects of Environmental Regulations on Foreign Direct Investment , 2000 .

[59]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[60]  Itai Kloog,et al.  Long-term Exposure to PM2.5 and Incidence of Acute Myocardial Infarction , 2012, Environmental Health Perspectives.