Relevance Analysis on the Variety Characteristics of PM2.5 Concentrations in Beijing, China

Ultra-high-resolution mapping of ambient fine particulate matter to estimate human exposure in Beijing

With the decreasing regional-transported levels, the health risk assessment derived from fine particulate matter (PM2.5) has become insufficient to reflect the contribution of local source heterogeneity to the exposure differences. Here, we combined the both ultra-high-resolution PM2.5 concentration with population distribution to provide the personal daily PM2.5 internal dose considering the indoor/outdoor exposure difference. A 30-m PM2.5 assimilating method was developed fusing multiple auxiliary predictors, achieving higher accuracy (R2 = 0.78-0.82) than the chemical transport model outputs without any post-simulation data-oriented enhancement (R2 = 0.31-0.64). Weekly difference was identified from hourly mobile signaling data in 30-m resolution population distribution. The population-weighted ambient PM2.5 concentrations range among districts but fail to reflect exposure differences. Derived from the indoor/outdoor ratio, the average indoor PM2.5 concentration was 26.5 μg/m3. The internal dose based on the assimilated indoor/outdoor PM2.5 concentration shows high exposure diversity among sub-groups, and the attributed mortality increased by 24.0% than the coarser unassimilated model.

Will Third-Party Treatment Effectively Solve Issues Related to Industrial Pollution in China?

China expanded the application of the third-party treatment model (TPTM) in 2017 for effectively tackling the issues related to industrial pollution on a trial basis, and the model could diversify the government’s toolbox for addressing industrial pollution. With multiple players such as local governments, polluters, and environmental services providers (ESP) involved in the TPTM, appropriate guidance and coordination among the three players are critical to the success of the TPTM. This study constructs an evolutionary game model for the three players to capture their interaction mechanisms and simulates the three-player evolutionary game dynamics with the replicator dynamics equation. The simulation results show that heavier penalties for pollution and lower regulatory costs incurred by local governments could effectively improve the performance of the TPTM. Moreover, although environmental incentives provided by the central government to local levels do not affect the ultimate performance of the TPTM, they do shorten the time needed for the effect of the TPTM to emerge. The study concludes by proposing policy recommendations based on these results.

Spatiotemporal Differences and Dynamic Evolution of PM2.5 Pollution in China

Air pollution, especially the urban haze, has become an urgent issue affecting the sustainable development of cities. Based on the PM2.5 concentration data of 225 Chinese cities collected by satellite remote sensing from 1998 to 2016, we quantitatively analyzed the spatiotemporal distribution characteristics and dynamic evolution trends of PM2.5 concentration in the four regions of China, namely the East, the Central, the West and the Northeast, by using statistical classification, GIS visualization, Dagum Gini coefficient decomposition and kernel density estimation. The results are as follows: First, the PM2.5 pollution in China showed a trend of fluctuation, which appeared to be increasing first and then decreasing, with the year 2007 as an important turning point for PM2.5 pollution changes across the country, as well as in the eastern and central regions. Second, PM2.5 pollution in China had significant spatial agglomeration. The intra-regional difference within the eastern region was the largest, and the inter-regional differences were the main source of overall differences. Third, kernel density estimation showed that the absolute difference of PM2.5 concentration distribution in China was expanding, with a significant phenomenon of polarization and the characteristics of spatial imbalance. This paper aimed to provide a scientific basis and effective reference for further advancing the sustainable development strategy of China in the new era.

Spatial-temporal variability of PM2.5 air quality in Beijing, China during 2013-2018

This study investigates spatial-temporal variability and trends of ambient PM_2.5 in Beijing, China, using data collected from eight urban and four suburban stations. During 2013-2018, the city-wide annual PM_2.5 concentrations decreased significantly by 40% (84 μg/m³ in 2013 vs. 50 μg/m³ in 2018). The decreasing PM_2.5 trend is more pronounced in winter and during the heating season (November-March), in urban areas, and at the median and upper percentiles of PM_2.5 concentrations. The 95th percentile PM_2.5 concentrations had decreased by 20 μg/m³/yr in the heating season and 16 μg/m³/yr in the non-heating season. During the six-year study period, there was a significant increase in excellent air quality days (PM_2.5 concentration < 35 μg/m³) and a significant decrease in heavy pollution days (PM_2.5 concentration > 150 μg/m³). PM_2.5 concentrations were strongly correlated across the 12 stations. Urban areas in south Beijing experienced higher PM_2.5 levels than suburban sites at every hour-of-day, day-of-week, and month-of-year. PM_2.5 levels were higher during winter and the heating season, when PM_2.5 emission was high due to space heating and mixing layer heights were low. PM_2.5 was higher at weekends than during weekdays, when 20% of private passenger vehicles are prohibited, and higher at night than during the day, when heavy duty delivery vehicles are not permitted. These temporal and spatial trends suggest that Beijing's PM_2.5 is strongly impacted by local emissions. Our results indicate, control strategies implemented were successful in Beijing's air quality improvement, but further reduction of PM_2.5 concentrations in Beijing could be challenging due to significant contribution from its neighboring cities, calling for comprehensive and collaborative efforts in regional/national scale.

Spatial and Temporal Variabilities of PM2.5 Concentrations in China Using Functional Data Analysis

As air pollution characterized by fine particulate matter has become one of the most serious environmental issues in China, a critical understanding of the behavior of major pollutant is increasingly becoming very important for air pollution prevention and control. The main concern of this study is, within the framework of functional data analysis, to compare the fluctuation patterns of PM2.5 concentration between provinces from 1998 to 2016 in China, both spatially and temporally. By converting these discrete PM2.5 concentration values into a smoothing curve with a roughness penalty, the continuous process of PM2.5 concentration for each province was presented. The variance decomposition via functional principal component analysis indicates that the highest mean and largest variability of PM2.5 concentration occurred during the period from 2003 to 2012, during which national environmental protection policies were intensively issued. However, the beginning and end stages indicate equal variability, which was far less than that of the middle stage. Since the PM2.5 concentration curves showed different fluctuation patterns in each province, the adaptive clustering analysis combined with functional analysis of variance were adopted to explore the categories of PM2.5 concentration curves. The classification result shows that: (1) there existed eight patterns of PM2.5 concentration among 34 provinces, and the difference among different patterns was significant whether from a static perspective or multiple dynamic perspectives; (2) air pollution in China presents a characteristic of high-emission “club” agglomeration. Comparative analysis of PM2.5 profiles showed that the heavy pollution areas could rapidly adjust their emission levels according to the environmental protection policies, whereas low pollution areas characterized by the tourism industry would rationally support the opportunity of developing the economy at the expense of environment and resources. This study not only introduces an advanced technique to extract additional information implied in the functions of PM2.5 concentration, but also provides empirical suggestions for government policies directed to reduce or eliminate the haze pollution fundamentally.

Does Whistleblowing Work for Air Pollution Control in China? A Study Based on Three-party Evolutionary Game Model under Incomplete Information

During China’s air pollution campaign, whistleblowing has become an important way for the central government to discover local environmental issues. The three parties involved in whistleblowing are: the central government environmental protection departments, the local government officials, and the whistleblowers. Based on these players, this paper has constructed an Evolutionary Game Model under incomplete information and introduced the expected return as well as replicator dynamics equations of various game agents based on analysis of the game agents, assumptions, and payoff functions of the model in order to study the strategic dynamic trend and stability of the evolutionary game model. Furthermore, this paper has conducted simulation experiments on the evolution of game agents’ behaviors by combining the constraints and replicator dynamics equations. The conclusions are: the central environmental protection departments are able to effectively improve the environmental awareness of local government officials by measures such as strengthening punishment on local governments that do not pay attention to pollution issues and lowering the cost of whistleblowing, thus nurturing a good governance and virtuous circle among the central environmental protection departments, local government officials, and whistleblowers. Based on the study above, this paper has provided policy recommendations in the conclusion.