Spatiotemporal Pattern of Fine Particulate Matter and Impact of Urban Socioeconomic Factors in China

Research progress on the effects of gut microbiome on lung damage induced by particulate matter exposure

Air pollution is one of the top five causes of death in the world and has become a research hotspot. In the past, the health effects of particulate matter (PM), the main component of air pollutants, were mainly focused on the respiratory and cardiovascular systems. However, in recent years, the intestinal damage caused by PM and its relationship with gut microbiome (GM) homeostasis, thereby affecting the composition and function of GM and bringing disease burden to the host lung through different mechanisms, have attracted more and more attention. Therefore, this paper reviews the latest research progress in the effect of PM on GM-induced lung damage and its possible interaction pathways and explores the potential immune inflammatory mechanism with the gut-lung axis as the hub in order to understand the current research situation and existing problems, and to provide new ideas for further research on the relationship between PM pollution, GM, and lung damage.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Regional Differences, Distribution Dynamics, and Convergence of Air Quality in Urban Agglomerations in China

The urban agglomeration (UA), with a high concentration of population and economy, represents an area with grievous air pollution. It is vital to examine the regional differences, distribution dynamics, and air quality convergence in UAs for sustainable development. In this study, we measured the air quality of ten UAs in China through the Air Quality Index (AQI). We analyzed regional differences, distribution dynamics, and convergence using Dagum’s decomposition of the Gini coefficient, kernel density estimation, and the convergence model. We found that: the AQI of China’s UAs shows a downward trend, and the index is higher in northern UAs than in southern UAs; the differences in air quality within UAs are not significant, but there is a gap between them; the overall difference in air quality tends to decrease, and regional differences in air quality are the primary contributor to the overall difference; the overall distribution and the distribution of each UA move rightward; the distribution pattern, ductility, and polarization characteristics are different, indicating that the air quality has improved and is differentiated between UAs; except for the Guanzhong Plain, the overall UA and each UA have obvious σ convergence characteristics, and each UA presents prominent absolute β convergence, conditional β convergence, and club convergence.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Industrial structure, urban governance and haze pollution: Spatiotemporal evidence from China

As a negative external product of China's rapid development, haze pollution has seriously affected the quality of economic development and people's quality of life. This paper firstly explores the important reasons for the uncoordinated industrial structure caused by haze pollution, and puts forward the purpose of promoting the adjustment of industrial structure through urban governance in order to tackle with the urgent problem of haze pollution. Using panel data from 287 cities in China, this paper analyzes the relationship among industrial structure, urban governance and haze pollution using the Geographically and Temporally Weighted Regression (GTWR) model. The innovations are: (1) this paper focuses on the topic of industrial structure, urban governance and haze pollution simultaneously. (2) this paper uses the method of GTWR to comprehensively consider the spatial and temporal tendency at the same time. (3) Conclusions are helpful to provide targeted policy recommendations. And the results show that: (1) the spatial clustering characteristics of haze pollution are very prominent, and have been suppressed to a certain extent under the measures of urban governance; (2) the spatial and temporal differences of industrial structure on haze pollution are large; (3) corporate governance plays an important role in slowing down haze pollution; (4) in public governance, the green coverage rate of built-up areas, the innocuous disposal rate of domestic garbage and the increase of public transport will have a negative impact on haze pollution, while highly concentrated urban population, high level of economic development, large number of industrial enterprises above designated size, and increased thermal power generation capacity will increase the degree of haze pollution; (5) cities with steadily decreasing of the proportion of the secondary industry, the proportion of the tertiary industry, the comprehensive treatment rate of industrial solid materials, the green coverage rate of the built-up area and the industrial enterprises above designated size are mainly lie in southeastern China, respectively; and cities with decline in innocuous disposal rate of domestic garbage are concentrated in the western region, while cities with significant changes of the number of buses per unit are mainly distributed in the northeastern region, the other variables are not obvious.

How Do Economic Growth, Urbanization, and Industrialization Affect Fine Particulate Matter Concentrations? An Assessment in Liaoning Province, China

With China's rapid development, urban air pollution problems occur frequently. As one of the principal components of haze, fine particulate matter (PM2.5) has potential negative health effects, causing widespread concern. However, the causal interactions and dynamic relationships between socioeconomic factors and ambient air pollution are still unclear, especially in specific regions. As an important industrial base in Northeast China, Liaoning Province is a representative mode of social and economic development. Panel data including PM2.5 concentration and three socio-economic indicators of Liaoning Province from 2000 to 2015 were built. The data were first-difference stationary and the variables were cointegrated. The Granger causality test was used as the main method to test the causality. In the results, in terms of the causal interactions, economic activities, industrialization and urbanization processes all showed positive long-term impacts on changes of PM2.5 concentration. Economic growth and industrialization also significantly affected the variations in PM2.5 concentration in the short term. In terms of the contributions, industrialization contributed the most to the variations of PM2.5 concentration in the sixteen years, followed by economic growth. Though Liaoning Province, an industry-oriented region, has shown characteristics of economic and industrial transformation, policy makers still need to explore more targeted policies to address the regional air pollution issue.