Assessment of health and economic effects by PM2.5 pollution in Beijing: a combined exposure-response and computable general equilibrium analysis

Intricate synergistic effects between air pollution and carbon emission: An emerging evidence from China

Due to global climate change and intensifying anthropogenic pollution, China confronts the dual challenge of controlling particulate matter 2.5 μm (PM2.5) pollution and reducing carbon emissions. Quantifying the characteristics of PM2.5 concentrations and CO2 emissions, as well as identifying the driving factors and synergistic effects of PM2.5 reduction and CO2 mitigation, are crucial steps in promoting sustainable urban development and achieving the Sustainable Development Goals (SDGs) in China. In this study, we selected 168 cities as our case-study, and quantified spatial characteristics of PM2.5 concentrations and CO2 emissions from 2015 to 2020 in China. Then we analyzed driving factors affecting the spatial heterogeneity of PM2.5 reduction and CO2 mitigation applying Multi-scale Geographically Weighted Regression (MGWR) model. By employing coupling coordination degree (CCD) model, we further detected the spatiotemporal evolution patterns of the synergistic effects between PM2.5 reduction and CO2 mitigation in key Chinese cities. The result showed that: (a) From 2015 to 2020, PM2.5 concentrations experienced a significant reduction from 59.78 μg/m3 to 49.83 μg/m3, while CO2 emissions increased from 44.88 × 106 t in 2015 to 45.77 × 106 t in 2020; (b) Green economy efficiency (gee), government attention (gover), and environmental regulation (envir) demonstrate the most pronounced synergistic effect on pollution reduction and carbon mitigation, with the drivers exhibiting obvious spatial heterogeneity; (c) The overall coupling coordination level of PM2.5 pollution and CO2 emissions in China dropped from 0.49 in 2015 to 0.46 in 2020, and the coupling coordination grade in northern cities was notably higher than that in southern cities. The result enhances our understanding of spatiotemporal patterns of synergistic effects between PM2.5 reduction and CO2 mitigation, and provides the theoretical basis for policy decision-making to realize pollution decrease and carbon neutral and regional environment governance.

Access to sustainable healthcare infrastructure: a review of industrial emissions, coal fires, and particulate matter

Environmental health is critical for the economy's social welfare and environmental sustainability. Using time series data from 1975 to 2020, the research examines the short- and long-run relationship between environmental pollutants and healthcare costs in the context of Pakistan. The study's results reveal that short-term and long-term efforts towards cleaner development in terms of carbon emissions, coal combustion, nitrous oxide (N₂O) emissions, and industrial value-added have resulted in significant reductions in healthcare expenses due to improved management of industrial emissions. However, in the long run, particulate matter (PM_2.5) has a detrimental effect on a country's sustainable healthcare agenda, leading to increased healthcare costs. Furthermore, the increased use of coal-fired power plants that release polycyclic aromatic hydrocarbons (PAH) and revenue generated by contaminated production lead to higher out-of-pocket healthcare costs, increasing a country's risk of morbidity and mortality. The study's Granger causality estimations demonstrate that carbon emissions are responsible for emissions-driven healthcare expenses in a nation. Additionally, economic growth leads to increased carbon emissions and industrial toxins, which are also emission-led. Through variance decomposition analysis (VDA), the study finds that carbon emissions have the highest variance shock of 32.702% on healthcare expenditures in the next ten years. This is followed by polluted income and continued economic growth, which have a variance shock of 13.243% and 8.858%, respectively, over the same period. The findings indicate that the maximum healthcare benefits may be acquired by mitigating environmental pollutants via stringent environmental regulations, reducing industrial toxins through solid waste management techniques, and minimizing coal combustion reliance through renewable fuels. Environmental research is still required to provide more sustainable solutions to the sustainability of the global healthcare agenda.

Removal ability of different underlying surfaces to near-surface particulate matter

Atmospheric particulate matter is a wide-ranging environmental pollutant that can cause serious harm and poses a serious threat to public health. In this study, the near-surface particulate matter removal ability was quantitatively analyzed and compared for different land types under different pollution levels. The results showed that the concentrations of particulate matter 10 μm or less in diameter (PM10) and 2.5 μm or less in diameter (PM2.5) were higher in the morning and lower in the afternoon and that the seasonal variation was autumn > winter > spring > summer at a forest site. The diurnal concentration of particulate matter at a wetland site decreased continuously, with a seasonal variation of winter > autumn > spring > summer. The annual variation in the particulate matter concentration was higher in 2017 than in 2016 at both the forest and wetland sites. Forests remove particulate matter via plant leaves and root absorption, and wetlands rely on the enhancement of the relative air humidity to promote the absorption and accumulation of particles. For different air pollution levels, the deposition flux of PM2.5 increased with the pollution gradient. For the same air quality pollution level, the deposition flux of PM2.5 at the forest site was approximately 1.29 times higher than that at the wetland site. Data concerning PM10 in forests and wetlands are lacking. The results show that the deposition effect of the forest on particulate matter was better than that of the wetland.

Assessing economic losses of haze with uncertain probabilistic linguistic analytic hierarchy process

With the rapid development of economy and industrialization, environmental problems, especially haze pollution, are being more and more serious. When assessing the economic losses caused by haze, although the traditional quantitative method can show the amount of economic losses visually, there are also some inaccuracies in the calculation process. Based on the situation, we propose a new method called uncertain probabilistic linguistic analytic hierarchy process (UPL-AHP), which combines traditional analytic hierarchy process with uncertain probabilistic linguistic term sets to process decision information in complex problems. Firstly, we propose the concept of uncertain probabilistic linguistic comparison matrix. Then, a new approach is given to check and improve the consistency of an uncertain probabilistic linguistic comparison matrix. After that, we introduce the application of UPL-AHP in group decision making. Finally, the proposed method is used to analyze a practical case concerning the economic losses of haze. Some relevant policy recommendations are given based on the results.

Burden of lung cancer attributable to ambient fine particles and potential benefits from air quality improvements in Beijing, China: A population-based study

OBJECTIVE

We aimed to establish a representative exposure response function between PM_2.5 and lung cancer to evaluate the impact on lung cancer burden and the benefits gained in association with the environmental policy change in Beijing, China.

METHODS

Based on population-based cancer registration data during 2001-2016, using a spatiotemporal Poisson regression model, long-term concentrations of PM_2.5 were linked to sex-age adjusted incidence rates of total lung cancer and its pathological subtypes. We calculated the health and monetary benefits associated with air quality improvement using the cost of illness method.

RESULTS

In the constructed regression model, a 10 μg/m³ increment of PM_2.5 was associated with increases of 6.0% (95% confidence interval [95% CI]: 4.3%, 7.7%), 14.8% (10.3%, 19.4%), and 6.5% (3.3%, 9.8%) in the incidence of total lung cancer, squamous cell carcinoma, and adenocarcinoma, respectively. The estimated associations indicate that long-term exposure to PM_2.5 contributed 1947 to 3059 incident cases of lung cancer per year among the residents in Beijing during the study period. Clean air actions saved 4978 (95% CI: 2711, 7417) lung cancer cases, which brought a savings of 218 (118, 324) million RMB (~31 [17, 46] million US dollars) in direct inpatient medical expenditures. If air quality had met national standards of long-term PM_2.5 (35 μg/m³) in 2014-2016, 10,003 (95% CI: 9325, 10,650) lung cancer cases could have been prevented and direct inpatient medical expenditures of 438 (409, 466) million RMB (~63 [58, 67] million US dollars) could have been saved.

CONCLUSIONS

This study enriches epidemiological study, confirming the association between exposure to PM_2.5 and lung cancer or its subtypes, and provides novel evidence for the notable reduction in lung cancer burden and medical expenditure savings that were achieved through air quality improvements in Beijing from 2014 to 2016.

PM2.5 Pollution: Health and Economic Effect Assessment Based on a Recursive Dynamic Computable General Equilibrium Model

At present particulate matter (PM_2.5) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM_2.5), and then quantifies the health effects of PM_2.5 pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure-response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM_2.5 pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM_2.5 pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335-248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77-4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.

Sources and Temporal Variations of Coarse Particulate Matter (PM) in Central Tehran, Iran

In this study, we used the positive matrix factorization (PMF) model to evaluate the sources of ambient coarse particulate matter (PM) and their temporal variations in two sampling sites, i.e., a school dormitory and a retirement home, located in central Tehran. 24-h ambient PM samples were collected using low-volume air samplers from May 2012 to June 2013. The collected filters were analyzed for their chemical components, including water-soluble ions, metals, and trace elements, which were used as the input to the PMF model. Our results indicated annual averages of 45.7 ± 3.8 µg/m3 and 36.2. ± 4.0 µg/m3 for coarse PM at the School dormitory and Tohid retirement home, respectively. Moreover, higher ambient coarse PM mass concentrations were observed in the warm season (53.3 ± 5.8 µg/m3 for school dormitory and 43.1 ± 6.1 µg/m3 for Tohid retirement home) as opposed to the cold season (41.4 ± 4.7 µg/m3 for school dormitory and 28.7 ± 4.6 µg/m3 for Tohid retirement home). Our PMF analysis also identified road dust, soil, and industry, and atmospherically processed coarse PM as the three sources of ambient coarse PM in central Tehran. Road dust, soil, and industry were the major sources of ambient coarse PM, contributing respectively to 74 ± 9% and 19 ± 2% of the total coarse PM mass concentration, while atmospherically aged aerosols had a rather minimal contribution of 7 ± 1% to total coarse PM mass concentration. The temporal trends of the resolved factors also revealed higher contributions of road dust to total ambient coarse PM during warm season as opposed to cold season, due to the increased resuspension rate from road surfaces as a result of higher wind speeds, and temperatures, combined with lower relative humidity. Similarly, higher resuspension rate of mechanically originated particulates resulted in higher warm-season time contributions of the soil factor. Results of this study clearly revealed the key role of road dust and non-tail pipe emissions on ambient coarse PM mass concentrations in crowded areas of central Tehran, and have important implications on the potential health impacts that can be caused by these difficult to mitigate sources of coarse PM.

A review on health cost accounting of air pollution in China

Over the last three decades, rapid industrialization in China has generated an unprecedentedly high level of air pollution and associated health problems. Given that China accounts for one-fifth of the world population and suffers from severe air pollution, a comprehensive review of the indicators accounting for the health costs in relation to air pollution will benefit evidence-based and health-related environmental policy-making. This paper reviews the conventional static and the new dynamic approach adopted for air pollution-related health cost accounting in China and analyzes the difference between the two in estimating GDP loss. The advantages of adopting the dynamic approach for health cost accounting in China, with conditions guaranteeing its optimal performance are highlighted. Guidelines on how one can identify an appropriate approach for health cost accounting in China are put forward. Further, we outline and compare the globally-applicable and China-specific indicators adopted by different accounting methodologies, with their pros and cons being discussed. A comprehensive account of the available databases and methodologies for health cost accounting in China are outlined. Future directions to guide health cost accounting in China are provided. Our work provides valuable insights into future health cost accounting research in China. Our study has strengthen the view that the dynamic approach is comparatively more preferred than the static approach for health cost accounting in China, if more data is available to train the dynamic models and improve the robustness of the parameters employed. In addition, future dynamic model should address the socio-economic impacts, including benefits or losses of air pollution polices, to provide a more robust policy picture. Our work has laid the key principles and guidelines for selecting proper econometric approaches and parameters. We have also identified a proper estimation method for the Value of Life in China, and proposed the integration of engineering approaches, such as the use of deep learning and big data analysis for health cost accounting at the fine-grained level (city-district or sub-regional level). Our work has also identified the gap for more accurate health cost accounting at the fine-grained level in China, which will subsequently affect the quality of health-related air pollution policy decision-making at such levels, and the health-related quality of life of the citizens in China.

A systematic review of financial implications of air pollution on health in Asia

Economic losses due to health-related implications of air pollution were huge and incurred significant burdens towards healthcare providers. The objective of this study is to systematically review published literature on the financial implications of air pollution on health in Asia. Four databases: PubMed, Scopus, NHS Economic Evaluation Database (NHS EED), and Web of Science (WoS) were used to identify all the relevant articles. It was limited to all articles that had been published in the respected databases from January 2007 until March 2017. Twenty-four articles were included in this review. Five of the 24 studies (20.8%) reported financial implications of air pollution-related disease through value of statistical life (VOSL) which ranged from USD180 million to USD2.2 billion, six (25%) studies used cost of illness (COI) to evaluate air pollution-related morbidity and found that the cost ranged from USD5.4 million to USD9.1 billion. Another six studies (25%) used a combination of VOSL and COI for both mortality and morbidity valuation and found that the financial implications ranging from USD253 million to USD2.9 billion. Thirteen (54.2%) studies reported healthcare cost associated with both hospital admission and outpatient visit, five (20.1%) on hospital admission only, and one (4.2%) on outpatient visit only. Economic impacts of air pollution can be huge with significant deterioration of health among the Asians.

Quantitative Assessment of Relationship between Population Exposure to PM2.5 and Socio-Economic Factors at Multiple Spatial Scales over Mainland China

Analyzing the association between fine particulate matter (PM_2.5) pollution and socio-economic factors has become a major concern in public health. Since traditional analysis methods (such as correlation analysis and geographically weighted regression) cannot provide a full assessment of this relationship, the quantile regression method was applied to overcome such a limitation at different spatial scales in this study. The results indicated that merely 3% of the population and 2% of the Gross Domestic Product (GDP) occurred under an annually mean value of 35 μg/m³ in mainland China, and the highest population exposure to PM_2.5 was located in a lesser-known city named Dazhou in 2014. The analysis results at three spatial scales (grid-level, county-level, and city-level) demonstrated that the grid-level was the optimal spatial scale for analysis of socio-economic effects on exposure due to its tiny uncertainty, and the population exposure to PM_2.5 was positively related to GDP. An apparent upward trend of population exposure to PM_2.5 emerged at the 80th percentile GDP. For a 10 thousand yuan rise in GDP, population exposure to PM_2.5 increases by 1.05 person/km² at the 80th percentile, and 1.88 person/km² at the 95th percentile, respectively.

Effect of calcium formate as an additive on desulfurization in power plants

SO₂ in flue gas needs to be eliminated to alleviate air pollution. As the quality of coal decreases and environmental standard requirements become more stringent, the high-efficiency desulfurization of flue gas faces more and more challenges. As an economical and environmentally friendly solution, the effect of calcium formate as an additive on desulfurization efficiency in the wet flue gas desulfurization (WFGD) process was studied for the first time. Improvement of the desulfurization efficiency was achieved with limited change in pH after calcium formate was added into the reactor, and it was found to work better than other additives tested. The positive effects were further verified in a power plant, which showed that adding calcium formate could promote the dissolution of calcium carbonate, accelerate the growth of gypsum crystals and improve the efficiency of desulfurization. Thus, calcium formate was proved to be an effective additive and can potentially be used to reduce the amount of limestone slurry required, as well as the energy consumption and operating costs in industrial desulfurization.

China's Air Quality and Respiratory Disease Mortality Based on the Spatial Panel Model

Background: Air pollution has become an important factor restricting China's economic development and has subsequently brought a series of social problems, including the impact of air pollution on the health of residents, which is a topical issue in China. Methods: Taking into account this spatial imbalance, the paper is based on the spatial panel data model PM_2.5. Respiratory disease mortality in 31 Chinese provinces from 2004 to 2008 is taken as the main variable to study the spatial effect and impact of air quality and respiratory disease mortality on a large scale. Results: It was found that there is a spatial correlation between the mortality of respiratory diseases in Chinese provinces. The spatial correlation can be explained by the spatial effect of PM_2.5 pollutions in the control of other variables. Conclusions: Compared with the traditional non-spatial model, the spatial model is better for describing the spatial relationship between variables, ensuring the conclusions are scientific and can measure the spatial effect between variables.

PM2.5, Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data

This paper adopts the PM_2.5 concentration data obtained from 1497 station-based monitoring sites, population and gross domestic product (GDP) census data, revealing population exposure and economic effects of PM_2.5 in four typical urban agglomerations of China, i.e., Beijing-Tianjin-Hebei (BTH), the Yangtze River delta (YRD), the Pearl River delta (PRD), and Chengdu-Chongqing (CC). The Cokriging interpolation method was used to estimate the PM_2.5 concentration from station-level to grid-level. Next, an evaluation was conducted mainly at the grid-level with a cell size of 1 × 1 km, assisted by the urban agglomeration scale. Criteria including the population-weighted mean, the cumulative percent distribution and the correlation coefficient were applied in our evaluation. The results showed that the spatial pattern of population exposure in BTH was consistent with that of PM_2.5 concentration, as well as changes in elevation. The topography was also an important factor in the accumulation of PM_2.5 in CC. Moreover, the most polluted urban agglomeration based on the population-weighted mean was BTH, while the least was PRD. In terms of the cumulative percent distribution, only 0.51% of the population who lived in the four urban agglomerations, and 2.33% of the GDP that was produced in the four urban agglomerations, were associated with an annual PM_2.5 concentration smaller than the Chinese National Ambient Air Quality Standard of 35 µg/m³. This indicates that the majority of people live in the high air polluted areas, and economic development contributes to air pollution. Our results are supported by the high correlation between population exposure and the corresponding GDP in each urban agglomeration.