The Effect of Economic Growth, Urbanization, and Industrialization on Fine Particulate Matter (PM2.5) Concentrations in China

An estimation of future county-level cement production and associated air pollutant emissions in China through artificial neural networks

Cement production and its air pollutant and carbon dioxides (CO2) emissions in China will be relocated greatly as a joint effect of diverse development of industrial economy and implementation of environmental policies for different regions. The future pathway and spatial pattern of emissions are important for policy making of air quality improvement and CO2 emission abatement, as well as coordinating regional development. In this study, we developed an artificial neural network (ANN) model to predict cement production at the county level and to calculate the associated emissions of air pollutants and CO2 at the county level till 2060. Results show that the cement production will decline from 2327 million metric tons (Mt) in 2015 to 704 Mt. in 2060 under the Shared Socioeconomic Pathways 1 (SSP1). Counties closer to provincial capital will experience greater retirement of cement industry. Likewise, the emissions of air pollutants and CO2 will experience a steady downward trend driven by the declining cement production and the improvement of pollution control technologies. There will be a more significant regional heterogeneity in the reduction of production and emissions at city level compared to the province level. With the clearance for nearly two-thirds of counties, future cement production and emissions will be more intensively distributed in a few cities. The shares of emissions in southwestern regions will grow from 2015 to 2060 while those of eastern regions will continue decreasing. The comparison between the changing spatial distributions of emissions and gross domestic product (GDP) indicates a positive effect of existing policies in reconciling regional economic development and air pollution controls. The outcome could support the analyses on the impact of industrial development on air quality and public health, and the method can be applied widely for other industrial sectors for a more comprehensive understanding of future emission relocation.

Integrated nexus approach to assessing climate change impacts on grassland ecosystem dynamics: A case study of the grasslands in Tanzania

This study addresses the intricate interplay between climate, vegetation, and livestock dynamics in Tanzania within the Climate-Vegetation-Livestock (CVL) nexus through a quantitative assessment. By examining the temporal and spatial relationships between vegetation indices (NDVI, EVI, NPP) and key climatic variables (Precipitation, Temperature, Evapotranspiration) from 2009 to 2019, and projecting to 2050, this research aims to elucidate vegetation responses to climate change and its subsequent impacts on livestock. To this end, the relationship between the vegetation dynamics indicators (NDVI, NPP) and climate parameters is evaluated to quantify the vegetation response to climate change using statistical models. Next, an examination of multicollinearity is conducted to investigate potential interactions (nexus) between variables, incorporating the correlation among independent variables. Notably, the evaluation of performance and accuracy for the mentioned models is conducted through the cross-validation method and validation indices. Ultimately, the variation between projected NPP and NDVI (average for 2040-2060) and the present NPP and NDVI (average for 2009-2020) identifies the regions that are most likely susceptible, showcasing the vegetation cover's reaction to climate change in different emission scenarios. The results unveil significant spatio-temporal variations in vegetation dynamics influenced by climatic factors, where higher precipitation and temperatures correlate with increased vegetation health and productivity. The projected fluctuations in NDVI and NPP values indicate varying trends across different regions, with a general decrease in vegetation density and productivity from the northeast to the west under both RCP2.6 and RCP8.5 scenarios by 2050. This decline is attributed to anticipated changes in precipitation and temperature patterns driven by climate change. Furthermore, significant declines in vegetation density and productivity under emission scenarios, particularly in the southern regions compared to the present, suggest greater vulnerability to climate change impacts. This highlights the need for targeted mitigation strategies in these vulnerable areas. Meanwhile, northeast areas under both NDVI and NPP will remain unchanged across both climate scenarios. Moreover, analysis of livestock distribution maps indicates areas of vulnerability under climate change scenarios, with implications for future livestock management and agricultural practices. These findings underscore the importance of proactive planning and targeted interventions to enhance resilience and sustainable development in vulnerable regions, emphasizing the need for integrated approaches that consider the complex interactions between climate, vegetation, and livestock dynamics.

Recent Advances in Hollow Nanostructures: Synthesis Methods, Structural Characteristics, and Applications in Food and Biomedicine

The development and investigation of innovative nanomaterials stand poised to advance technological progress and meet the contemporary demand for efficient, environmentally friendly, and intelligent products. Hollow nanostructures (HNS), characterized by their hollow architecture, exhibit diverse properties such as expansive specific surface area, low density, high drug-carrying capacity, and customizable structures. These elaborated structures, encompass nanospheres, nanoboxes, rings, cubes, and nanowires, have wide-ranging applications in biomedicine, materials chemistry, food industry, and environmental science. Herein, HNS and their cutting-edge synthesis methods, including solvothermal methods, liquid-interface assembly methods, and the self-templating methods are discussed in-depth. Meanwhile, the potential applications of HNS in food and biomedicine such as food packing, biosensor, and drug delivery over the past three years are summarized, together with a prospective view of future research directions and challenges. This review will offer new insights into designing next generation of hollow nanomaterials for food and biomedicine applications.

Ambient PM2.5 Chemical Composition and Cardiovascular Disease Hospitalizations in China

Little is known about the impacts of specific chemical components on cardiovascular hospitalizations. We examined the relationships of PM2.5 chemical composition and daily hospitalizations for cardiovascular disease in 184 Chinese cities. Acute PM2.5 chemical composition exposures were linked to higher cardiovascular disease hospitalizations on the same day and the percentage change of cardiovascular admission was the highest at 1.76% (95% CI, 1.36-2.16%) per interquartile range increase in BC, followed by 1.07% (0.72-1.43%) for SO42-, 1.04% (0.63-1.46%) for NH4+, 0.99% (0.55-1.43%) for NO3-, 0.83% (0.50-1.17%) for OM, and 0.80% (0.34%-1.26%) for Cl-. Similar findings were observed for all cause-specific major cardiovascular diseases, except for heart rhythm disturbances. Short-term exposures to PM2.5 chemical composition were related to higher admissions and showed diverse impacts on major cardiovascular diseases.

Plastic pollution in terrestrial ecosystems: Current knowledge on impacts of micro and nano fragments on invertebrates

The increasing accumulation of small plastic particles, in particular microplastics (>1 µm to 5 mm) and nanoplastics (< 1 µm), in the environment is a hot topic in our rapidly changing world. Recently, studies were initiated to better understand the behavior of micro- and nanoplastics (MNP) within complex matrices like soil, as well as their characterization, incorporation and potential toxicity to terrestrial biota. However, there remains significant knowledge gaps in our understanding of the wide-extent impacts of MNP on terrestrial invertebrates. We first summarized facts on global plastic pollution and the generation of MNP. Then, we focused on compiling the existing literature examining the consequences of MNP exposure in terrestrial invertebrates. The diversity of investigated biological endpoints (from molecular to individual levels) were compiled to get a better comprehension of the effects of MNP according to different factors such as the shape, the polymer type, the organism, the concentration and the exposure duration. The sublethal effects of MNP are acknowledged in the literature, yet no general conclusion was drawn as their impacts are highly dependent on their characteristic and experimental design. Finally, the synthesis highlighted some research gaps and remediation strategies, as well as a protocol to standardize ecotoxicological studies.

Source apportionment and emission projections of heavy metals from traffic sources in India: Insights from elemental and Pb isotopic compositions

The study investigates the sources of metals in urban road dusts using elemental concentration and Pb isotopic ratios. The elemental concentrations are also utilized to determine the present heavy metal emissions as well as projected emissions till 2045. Bayesian mixing model for source apportionment highlights the significant contributions of both exhaust and non-exhaust sources to the metal-enriched urban road dusts, with each contributing approximately 40 %. Emission analysis reveals that India's projected electric vehicle (EV) penetration may not be sufficient to suppress the metal emissions from vehicular exhausts. Further challenge is posed by high metal concentrations in the non-exhaust sources, that dominates the emission of some metals compared to exhaust sources. If the metal concentrations remain unchanged, the emission analysis predicts alarming increases in total emissions from all the exhaust and non-exhaust sources by 174 %, 176 %, 163 % and 184 % for Ni, Cu, Zn and Pb, respectively, from 2022 to 2045. Thus, it is crucial to reduce the metal concentrations in traffic emission sources and also impose better regulatory measures to improve the urban metal pollution scenario.

Cross-sectional analysis of socioeconomic drivers of PM2.5 pollution in emerging SAARC economies

Within the intricate interplay of socio-economic, natural and anthropogenic factors, haze pollution stands as a stark emblem of environmental degradation, particularly in the South Asian Association for Regional Cooperation (SAARC) region. Despite significant efforts to mitigate greenhouse gas emissions, several SAARC nations consistently rank among the world's most polluted. Addressing this critical research gap, this study employs robust econometric methodologies to elucidate the dynamics of haze pollution across SAARC countries from 1998 to 2020. These methodologies include the Pooled Mean Group (PMG) and Augmented Mean Group (AMG) estimator, Panel two-stage least squares (TSLS), Feasible Generalized Least Squares (FGLS) and Dumitrescu-Hurlin (D-H) causality test. The analysis reveals a statistically significant cointegrating relationship between PM2.5 and economic indicators, with economic development and consumption expenditure exhibiting positive associations and rainfall demonstrating a mitigating effect. Furthermore, a bidirectional causality is established between temperature and economic growth, both influencing PM2.5 concentrations. These findings emphasize the crucial role of evidence-based policy strategies in curbing air pollution. Based on these insights, recommendations focus on prioritizing green economic paradigms, intensifying forest conservation efforts, fostering the adoption of eco-friendly energy technologies in manufacturing and proactively implementing climate-sensitive policies. By embracing these recommendations, SAARC nations can formulate comprehensive and sustainable approaches to combat air pollution, paving the way for a healthier atmospheric environment for their citizens.

Understanding the Disparities of PM2.5 Air Pollution in Urban Areas via Deep Support Vector Regression

In densely populated urban areas, PM2.5 has a direct impact on the health and quality of residents' life. Thus, understanding the disparities of PM2.5 is crucial for ensuring urban sustainability and public health. Traditional prediction models often overlook the spillover effects within urban areas and the complexity of the data, leading to inaccurate spatial predictions of PM2.5. We propose Deep Support Vector Regression (DSVR) that models the urban areas as a graph, with grid center points as the nodes and the connections between grids as the edges. Nature and human activity features of each grid are initialized as the representation of each node. Based on the graph, DSVR uses random diffusion-based deep learning to quantify the spillover effects of PM2.5. It leverages random walk to uncover more extensive spillover relationships between nodes, thereby capturing both the local and nonlocal spillover effects of PM2.5. And then it engages in predictive learning using the feature vectors that encapsulate spillover effects, enhancing the understanding of PM2.5 disparities and connections across different regions. By applying our proposed model in the northern region of New York for predictive performance analysis, we found that DSVR consistently outperforms other models. During periods of PM2.5 surges, the R-square of DSVR reaches as high as 0.729, outperforming non-spillover models by 2.5 to 5.7 times and traditional spatial metric models by 2.2 to 4.6 times. Therefore, our proposed model holds significant importance for understanding disparities of PM2.5 air pollution in urban areas, taking the first steps toward a new method that considers both the spillover effects and nonlinear feature of data for prediction.

Health benefits of vehicle electrification through air pollution in Shanghai, China

Vehicle electrification has been recognized for its potential to reduce emissions of air pollutants and greenhouse gases in China. Several studies have estimated how national-level policies of electric vehicle (EV) adoption might bring very large environmental and public health benefits from improved air quality to China. However, large-scale adoption is very costly, some regions derive more benefits from large-scale EV adoption than others, and the benefits of replacing internal combustion engines in specific cities are less known. Therefore, it is important for policymakers to design incentives based on regional characteristics - especially for megacities like Shanghai - which typically suffer from worse air quality and where a larger population is exposed to emissions from vehicles. Over the past five years, Shanghai has offered substantial personal subsidies for passenger EVs to accelerate its electrification efforts. Still, it remains uncertain whether EV benefits justify the strength of incentives. The purpose of our study is to evaluate the health and climate benefits of replacing light-duty gasoline vehicles (ICEVs) with battery EVs in the city of Shanghai. We assess health impacts due to ICEV emissions of primary fine particulate matter, NOx, and volatile organic compounds, and to powerplant emissions of NOx and SO2 due to EV charging. We incorporate climate benefits from reduced greenhouse gas emissions based on existing research. We find that the benefit of replacing the average ICEV with an EV in Shanghai is US$6400 (2400-14,700), with health impacts of EVs about 20 times lower than the average ICEV. Larger benefits ensue if older ICEVs are replaced, but replacing newer China ICEVs also achieves positive health benefits. As Shanghai plans to stop providing personal subsidies for EV purchases in 2024, our results show that EVs achieve public health and climate benefits and can help inform policymaking strategies in Shanghai and other megacities.

Association of fine particulate matter (PM2.5) exposure and chronic kidney disease outcomes: a systematic review and meta-analysis

Several studies have reported an increased risk of chronic kidney disease (CKD) outcomes after long-term exposure (more than 1 year) to particulate matter with an aerodynamic diameter of ≤ 2.5 µm (PM2.5). However, the conclusions remain inconsistent. Therefore, we conducted this meta-analysis to examine the association between long-term PM2.5 exposure and CKD outcomes. A literature search was conducted in PubMed, Scopus, Cochrane Central Register of Controlled trials, and Embase for relevant studies published until August 10, 2023. The main outcomes were incidence and prevalence of CKD as well as incidence of end-stage kidney disease (ESKD). The random-effect model meta-analyses were used to estimate the risk of each outcome among studies. Twenty two studies were identified, including 14 cohort studies, and 8 cross-sectional studies, with a total of 7,967,388 participants. This meta-analysis revealed that each 10 μg/m3 increment in PM2.5 was significantly associated with increased risks of both incidence and prevalence of CKD [adjusted odds ratio (OR) 1.31 (95% confidence interval (CI) 1.24 to 1.40), adjusted OR 1.31 (95% CI 1.03 to 1.67), respectively]. In addition, the relationship with ESKD incidence is suggestive of increased risk but not conclusive (adjusted OR 1.16; 95% CI 1.00 to 1.36). The incidence and prevalence of CKD outcomes had a consistent association across all subgroups and adjustment variables. Our study observed an association between long-term PM2.5 exposure and the risks of CKD. However, more dedicated studies are required to show causation that warrants urgent action on PM2.5 to mitigate the global burden of CKD.

Multi-scale spatiotemporal trends and corresponding disparities of PM2.5 exposure in China

Despite the effectiveness of targeted measures to mitigate air pollution, China-a developing country with high PM2.5 concentration and dense population, faces a high risk of PM2.5-related mortality. However, existing studies on long-term PM2.5 exposure in China have not reached a consensus as to which year it peaked during the "initially pollution, then mitigation" process. Furthermore, analyses in these studies were rarely undertaken from multi-spatial scales. In this study, a piecewise linear regression model was employed to detect the turning point of population-weighted exposure (PWE) to PM2.5 for the period 2000-2020. Multi-scale spatiotemporal patterns of PM2.5 exposure were evaluated during upward and downward periods at the province, city and county levels, and their corresponding disparities were estimated using the Gini index. The results showed that 2013 was the breakpoint year for PM2.5 PWE across China from 2000 to 2020. Cities and counties where PM2.5 PWE displayed increasing trends during the mitigation stage (2013-2020) basically became the heaviest PM2.5 exposure regions in 2020. High PM2.5 exposure was observed in Beijing-Tianjin-Hebei, Central China, and the Tarim Basin in Xinjiang, whereas lower PM2.5 exposure regions were mainly concentrated in Hainan Province, the Hengduan Mountains, and northern Xinjiang. These cross-provincial patterns might have been overlooked when conducting macro-scale analyses. Province-level PM2.5 exposure inequality was less than the city- and county-levels estimations, and regional inequalities were high in eastern and western China. In this study, multi-scale PM2.5 exposure trends and their disparities over a prolonged period were investigated, and the findings provide a reference for pollution mitigation and regional inequality reduction.

Chemical composition, sources, and health risks of PM2.5 in small cities with different urbanization during 2020 Chinese Spring Festival

To investigate the impact of quarantine measures and fireworks banning policy on chemical composition and sources of PM2.5 and associated health risks in small, less developed cities, we sampled in Guigang (GG), Shaoyang (SY), and Tianshui (TS), located in eastern, central, and north-western China, in 2020 Spring Festival (CSF). Mass concentration, carbonaceous, metals, and WSIIs of PM2.5 were analyzed. The study found high levels of PM2.5 pollution with the average concentration of 168.05 µg/m3 in TS, 134.59 µg/m3 in SY, and 125.71 µg/m3 in GG. A negative correlation was found between the urbanization level and PM2.5 pollution. Lockdown measures reduced PM2.5 mass and industrial elements. In non-control period (NCP), combustion and fireworks were the major sources of PM2.5 in GG and TS, and industry source accounted for a significant proportion in the relatively more urbanized SY. Whereas on control period (CP), soil dust, combustion, and road dust were the main source in GG, secondary aerosols dominated in SY and TS. Our health risk assessment showed unacceptable levels of non-carcinogenic and carcinogenic risks over the study areas, despite lockdown measures reducing health risks. As and Cr(VI), as the major pollutants, their associated sources, industry sources, and fireworks sources, posed the greatest risk to people at the sampling sites after exposure to PM2.5. This work supports the improvement of PM2.5 control strategies in small Chinese cities during the CSF.

Analysis of diurnal to seasonal variations and trends in air pollution potential in an urban area

Air pollution is the world's largest environmental health threat to humans and has wide-ranging adverse effects on the environment. The term ventilation coefficient (VC), which is a function of the average wind speed through the planetary boundary layer (PBL) and the PBL height (PBLH), can be used to estimate air pollution potential. We analyzed PBLH, wind speed through PBL, and VC over Tehran using ERA5, and PM2.5 surface concentration using MERRA-2 during 1991-2020. Both PBLH and VC undergo substantial diurnal variations, with higher values during the day and much lower values at night. As a result, PM2.5 concentration in Tehran is the maximum in the early morning, while it is relatively lower in the afternoon. The average wind speed through PBL shows the same diurnal variation in all seasons, except in winter when winds in PBL are stronger at night than during the day. Both PBLH and VC over Tehran show substantial seasonal variations, with much higher values in summer followed in decreasing order by spring, autumn, and winter, highlighting an extremely high air pollution potential in winter. Hence, due to high pollutant emissions, the occurrence of severe air pollution is expected to be a common feature in Tehran in winter. PBLH has significantly increased over Tehran both during the day and at night for the period 1991-2020, primarily in response to the surface warming in recent decades, while wind speed through PBL has significantly declined only at night. The overall impact of such changes is an increase in VC over Tehran both during the day and at night, although the increasing trend of VC is statistically significant only at night. Our results highlight the urgent need for the implementation of effective sustainable policies to reduce air pollution and its adverse effects in winter when air pollution potential is high in Tehran.

Effects of urbanisation on PM2.5 concentrations: A systematic review and meta-analysis

While urbanisation greatly improves a population's quality of life, it also has significant effects on urban air pollution. Previous studies have determined how urbanisation affects PM2.5 concentrations; the findings, however, have not been consistent. This study conducts a meta-analysis to systematically organise existing research and draw more conclusive and broadly applicable results regarding the impact of different factors of urbanisation on PM2.5 concentrations. The main research findings are as follows: (1) the Environmental Kuznets Curve (EKC) is proven to hold true in terms of the effect of population and land urbanisation on PM2.5 concentrations, while there is no consistent conclusion on the non-linear relationship between economic urbanisation and PM2.5 concentrations; (2) publication bias is evident in research on the economic and comprehensive urbanisation dimensions under linear assumptions; (3) there are notable heterogeneities in existing research in this field. The meta-regression model further indicates that model design, sample design, and publication characteristics might be responsible for these heterogeneities. This study innovatively applies a meta-analysis to investigate the effect of urbanisation on PM2.5 concentrations. The findings will contribute to scholars designing more rigorous research frameworks in this field.

Measuring the urban-rural and spatiotemporal heterogeneity of the drivers of PM2.5-attributed health burdens in China from 2008 to 2021 using high-resolution dataset

Urbanization has been considered a driver of PM2.5 pollution and the attributed health burden. This study systematically measured the spatiotemporal and urban-rural heterogeneity of PM2.5-attributed health burden drivers, including income, population, baseline mortality rate, and PM2.5 level. The results reveal the significantly positive contribution of disposable income and the periodical and urban-rural differentiation of population contribution to PM2.5-attributed health burden. The difference in driver performance due to socioeconomic development and urbanization stages might be an important determinant for different or even opposite results of previous studies. Policymaking for mitigating PM2.5-attributed health risk could incorporate the re-assessment and driver determination for PM2.5-attributed health burden into the construction and development plan from the overall urbanization perspective. The urbanization-perspective driver decomposition could be synergized with the flow analysis, equality evaluation, and policy benefit estimation to achieve further direction-determining and quantitative assessment of the urban-rural PM2.5 health risk management strategies.

Impacts of industrial production and air quality by remote sensing on nitrogen dioxide concentration and related effects: An econometric approach

The high concentration of nitrogen dioxide (NO2) is to blame for West Java's poor Air Quality Index (AQI). So, this study aims to determine the influence of industrial activity as reflected by the value of its imports and exports, wind speed, and ozone (O3) on the high concentration of tropospheric NO2. The method used is the econometric Vector Error Correction Model (VECM) approach to capture the existence of a short-term and long-term relationship between tropospheric NO2 and its predictor variables. The data used in this study is in the form of monthly time series data for the 2018-2022 period sourced from satellite images (Sentinel-5P and ECMWF Climate Reanalysis) and publications of the Central Bureau of Statistics (BPS-Statistics Indonesia). The results explained that, in the short-term, tropospheric NO2 and O3 influence each other as they would in a photochemical reaction. In the long-term, exports from the industrial sector and wind speed have a significant effect on the concentration of tropospheric NO2. The short-term effect occurs directly in the first month after the shock, while the long-term effect occurs in the second month after the shock. Wind gusts originating from industrial areas cause air conditions to be even more alarming because tropospheric NO2 pollutants spread throughout the region in West Java. Based on the coefficient correlation result, the high number of pneumonia cases is one of the impacts caused by air pollution.

Is there an EKC between economic growth and air pollutant emissions in SAARC countries? Evidence from disaggregated analysis

The manufacturing and construction (M&C) sector not only plays a vital role in promoting economic growth, but is also a significant contributor to global air pollution. Growing concerns regarding air pollutant emissions necessitate a more disaggregated (i.e., sectoral) investigation in order to identify the major contributors. This study employs aggregated and disaggregated data to determine the fundamental effects of economic growth (i.e., overall growth and sectoral growth) on air pollutant emissions (APE) (specifically, PM2.5 and PM10 released by the M&C sector) in SAARC economies between 1995 and 2018. It assesses the environmental Kuznets curve (i.e., inverted U-shaped and N-shaped) using the feasible generalized least squares (FGLS), panel-corrected standard errors (PCSE), and generalized method of moments (GMM) techniques. The sectoral analysis reveals the presence of an N-shaped EKC while the overall analysis indicates an inverted U-shaped EKC. Population, financial development (FD), and merchandise exports (MX) have no influence on the estimates. Population and FD increase APE in all models, whereas the effects of MX vary between models. As SAARC economies are capital-deficient, these economies can adopt unbalanced environmental protection policies. First, focus on major contributing sectors (e.g., M&C sector) to curb APE, then focus on less emitting sectors in turn. By implementing pollution reduction strategies on M&C sector activities, governments may reach their threshold (peak) points earlier than expected. A reduction in APE is impossible without rigorous monitoring and application. Being capital-deficient nations and given the collective nature of the problem, a Transboundary Haze/Pollution agreement is required to solve this issue.

The air we breathe: An In-depth analysis of PM2.5 pollution in 1312 cities from 2000 to 2020

In recent decades, the phenomenon of rapid urbanization in various parts of the world has led to a significant increase in PM2.5 concentration, which has emerged as a growing social concern. In order to achieve the objective of sustainable development, the United Nations Global Sustainable Development Goals (SDGs) have established the goal of creating inclusive, safe, resilient, and sustainable cities and human habitats (SDG 11). Goal 11.6 aims to decrease the negative environmental impact per capita in cities, with an emphasis on urban air quality and waste management. However, the global distribution of PM2.5 pollution varies due to disparities in urbanization development in different regions. The purpose of this paper is to explore the global spatial distribution and temporal variation of PM2.5 in cities with populations greater than 300,000 from 2000 to 2020, to gain insight into the issue. The findings indicate that PM2.5 concentrations are expected to continue increasing as urbanization progresses, but the rate of evolution of PM2.5 concentration varies depending on the continent, country, and city. From 2000 to 2020, PM2.5 concentration increased significantly in Asia and Africa, with the majority of the increased concentrations located in Asian countries and some African countries. On the other hand, most European and American countries had lower PM2.5 concentrations. The results of this study have the potential to inform urbanization policy formulation by providing knowledge about the spatial distribution of PM2.5 pollution during global urbanization. Addressing the issue of PM2.5 pollution is critical in achieving SDG 11.6 and promoting sustainable and coordinated development in cities worldwide.

Governance matters: Urban expansion, environmental regulation, and PM2.5 pollution

Increasing PM2.5 pollution in urban expansion threatens citizens' health. Environmental regulation has proven to be an effective tool to directly combat PM2.5 pollution. However, whether it can moderate the impacts of urban expansion on PM2.5 pollution, in the context of rapid urbanization, is an interesting and unexplored topic. Therefore, this paper constructs a Drivers-Governance-Impacts framework and explores in depth the interactions among urban expansion, environmental regulation, and PM2.5 pollution. Based on 2005-2018 sample data from the Yangtze River Delta region, the estimation results of the Spatial Durbin model imply that (1) urban expansion has an inverse U-shaped association with PM2.5 pollution. The positive correlation may reverse when the ratio of urban built-up land area hits 0.21. (2) Of the three environmental regulations, investment in pollution control has little impact on PM2.5 pollution. Pollution charges and public attention exhibit a U-shaped and inverted U-shaped relationship with PM2.5 pollution, respectively. (3) In terms of moderating effects, pollution charges can exacerbate PM2.5 pollution from urban expansion, while public attention can inhibit it through its monitoring role. Therefore, we suggest that cities adopt differentiated strategies of urban expansion and environmental protection according to their urbanization levels. Meanwhile, appropriate formal regulation and strong informal regulation will help improve air quality.

Development zone policy and urban carbon emissions: empirical evidence from the construction of national high-tech industrial development zones in China

As a key strategy to promote system reform, improve the investment environment, and encourage industrial agglomeration, the national high-tech industrial development zone (NHTDZ) policy in China can not only reduce energy consumption through the scale effect but also improve energy efficiency by modernizing industrial structure and fostering technological innovation, thereby alleviating environmental pollution. Existing studies, however, focus solely on the effects of NHTDZ policy on social and economic development, ignoring their impact on the ecological environment, especially carbon (CO₂) emissions that contribute to global warming. Thus, this article analyzes a panel data of 285 prefecture-level cities and above in China from 2003 to 2019 to assess the influence of NHTDZ policy on CO₂ emissions, treating the NHTDZ construction since 1988 as a quasi-natural experiment. The results indicate that the NHTDZ policy would mitigate urban carbon emissions, particularly in middle, southeastern, medium-sized, resource-based (RB), non-key environmental protection (non-KEP), and non-two control zone (non-TCZ) cities. In addition, the mediation mechanism test demonstrates that the environmental benefits of the NHTDZ policy in China are attributable to the scale effect, the structural upgrading effect, and the technology innovation effect. The NHTDZ policy would lower per capita CO₂ emissions by reducing energy consumption, upgrading industrial structure, and promoting green technology innovation.

Exploring the magnitude threshold of urban PM2.5 concentration: evidence from prefecture-level cities in China

As major carriers of modern economy and population, cities and towns are vortex centers of pollution migration, and the environmental effects brought about by China's unprecedented urbanization can be imagined, although the specific scale is still a mystery. This paper focuses on the nonlinear response mechanism of urban PM2.5 concentration to the urbanization population scale, considering that China's urbanization development path is dominated by large- and medium-sized cities. The panel data of PM2.5 concentration of Chinese cities observed by satellite during 1998-2016 are used to capture the nonlinear characteristics of panel threshold model (PTM). The estimation results of the double-threshold PTM including the quadratic term of urbanization population show that the U-shaped relationship between urbanization population and PM2.5 concentration is nonlinear adjusted by urban GDP per capita with the two thresholds of 6777 Yuan and 10,296 Yuan at 2010 constant price. When the urban GDP per capita exceeds 10,296 Yuan, the urbanized population at the turning point of the U-shaped curve is 12.967 million people, which only appears in a few super-large cities such as Beijing, Tianjin, Shanghai and Chongqing. The size matching of urban economy and population is an important follow-up of environmental policies.

Reconsidering the effects of urban form on PM2.5 concentrations: an urban shrinkage perspective

The phenomenon of urban shrinkage is currently occurring worldwide; however, the "growth-oriented" planning paradigm is not suitable for these shrinking cities. Reconsidering the relationship between urban form and PM_2.5 concentrations from the perspective of urban shrinkage can help provide a research reference for controlling air pollution and optimizing the spatial layout of shrinking cities. This study takes shrinking areas in China as the research subject, which are divided into four research groups according to their shrinkage degree. The empirical results indicate that the average PM_2.5 concentrations decrease with the aggravation of urban shrinkage. In terms of the effect of urban form on PM_2.5 concentrations, the urban size is always positively related to PM_2.5 concentrations, while the impact of urban fragmentation on PM_2.5 concentrations is negligible. Further, urban shape positively affects PM_2.5 concentrations only in moderately and severely shrinking cities. Cities with sprawling urban forms have higher PM_2.5 concentrations, except for those facing severe shrinking trends. This study suggests that governments in shrinking cities should reasonably adjust both the urban form and land use to improve air quality based on the degree of urban shrinkage.

Polycentric agglomeration and haze pollution: evidence from China

Polycentric agglomeration has gradually become a salient feature of rapid growth in urbanization in China. Using province-level balanced panel data over the period 2000-18, we examine the impact of polycentric agglomeration on haze pollution and its mechanism of action. The results show that the impact of polycentric agglomeration on haze pollution exhibits a significant inverted U-shaped feature. Nevertheless, except for a few provinces where polycentric agglomeration exceeds the turning point, the degree of polycentric concentration in most provinces lies to the left of the turning point. Further, a mediating effect model illustrates that industrial structure rationalization and technological progress are effective paths through which polycentric agglomeration affects haze pollution. Finally, we demonstrate that the effect of polycentric agglomeration on haze pollution is influenced by transportation and communication infrastructure; improved transportation and communication infrastructure contributes to the haze control effect of polycentric agglomeration.

Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China's Cities Based on Spatial Autocorrelation Analysis and MGWR Model

Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.

Effect of urban form on PM2.5 concentrations in urban agglomerations of China: Insights from different urbanization levels and seasons

Planned urban form has become an important strategy to improve air quality in urban agglomerations (UAs), especially pollution due to PM_2.5, but the influencing mechanisms are not yet clear. This study explores the relationship between four metrics of urban form (size, fragmentation, shape, and dispersion) as determined by analysis of remotely sensed images at 30-m resolution and PM_2.5 concentrations in 19 Chinese UAs. The influence of level of urban development and season is examined. Five control variables, including population density, temperature, precipitation, wind speed, and the normalized difference vegetation index (NDVI) are selected for use in multiple linear regression models. Size, fragmentation, and shape of urban form, but not dispersion, were found to have significant effects on PM_2.5 concentrations of different urbanization-level UAs. Urban size and fragmentation have stronger impacts on PM_2.5 concentrations in UAs with lower urbanization levels while urban shape has a greater impact in higher-level UAs. In terms of seasonal variation in all UAs, urban form is more pronouncedly associated with PM_2.5 concentrations during spring and autumn than summer and winter. Urban size and fragmentation are positively associated with PM_2.5 concentrations whereas urban shape and dispersion are on the contrary. The relationships between urban form and PM_2.5 uncovered here underscore the importance of urban planning as a tool to minimize PM_2.5 pollution. Specifically, local government should encourage polycentric urban form with lower fragmentation in urban agglomerations. UAs with lower urbanization levels should control the disordered expansion of construction land and higher-level UAs should promote the mix of green land and construction land. Moreover, measures to control air pollution from anthropogenic activities in spring, autumn and winter are likely to be more effective in decreasing PM_2.5 concentrations in UAs.

Nexus between environmental vulnerability and agricultural productivity in BRICS: what are the roles of renewable energy, environmental policy stringency, and technology?

This study aims to examine the effect of carbon dioxide emission and air pollution on agricultural productivity while accounting for the effect of renewable energy use, ICT, technological innovation, environmental policy stringency, and democracy for Brazil, Russia, India, China, and South Africa (BRICS) during the period 1990-2019. Several econometric procedures including mean group estimates are employed. The result suggests that both carbon dioxide emission and air pollution negatively affect the productivity of the agricultural sector. The effects of renewable energy, ICT, technological innovation, and democracy are found to be increasing agricultural productivity. Environmental policy stringency coefficient confirms the porter hypothesis. The result from the causality test suggests that bidirectional causality exists between CO2, PM2.5, renewable energy, technological innovation, ICT, and agricultural productivity. Finally, the study provides several policy suggestions for the governments of the BRICS economies in order to increase agricultural productivity while tackling the environmental vulnerability.

Who Needs to Save Energy and Reduce Emissions? Perspective of Energy Misallocation and Economies of Scale

With the rapid development of the economy, human survival and socio-economic development are facing the severe challenges of climate threats. Global warming is one of the greatest threats to human survival and political stability that has occurred in human history. The main factor causing global warming is the extensive use of energy; therefore, it is imperative to spend more effort in energy conservation and emission reduction. In this context, this paper provides a reference and basis for decision making on emission-reduction paths through the perspective of energy input misallocation and economies of scale of CO2 emissions. The results show that for cities with relatively low energy inputs, the impact of excessive energy input on CO2 emissions is stronger than the effect of the scale of energy input on reducing CO2 emissions. Therefore, these cities need to prioritize energy conservation and emission reduction. On the other hand, in cities with large energy inputs, the impact of the scale of energy input on reducing CO2 emissions is more significant than the effect of excessive energy input on CO2 emissions. Therefore, these areas should also focus on energy conservation and emission reduction. The results of this paper have theoretical value and practical significance for scientifically implementing energy conservation and emission reduction strategies, as well as reasonably planning energy conservation pathways.

Defining the role of renewable energy, economic growth, globalization, energy consumption, and population growth on PM2.5 concentration: evidence from South Asian countries

Rapid industrialization and economic development in South Asia (SA) caused serious air pollution-related issues. Air pollutants, particularly fine particulate matter (PM_2.5), have negative effects on health, instigating widespread concern. The current study is an attempt to analyze the impact of non-renewable energy (NRE), globalization (GLO), GDP, renewable energy (RE), and population (POP) on PM_2.5 concentration in SA from 1998 to 2020. In doing so, this study incorporated advanced and robust econometric techniques, i.e., Pesaran (Economet Rev 34(6-10), 1089-1117, 2015), to check the cross-sectional dependency, and the unit root presence checked through Cross-sectional Im, Pesaran, and Shin (CIPS) and Cross-sectionally Augmented Dickey-Fuller (CADF) unit root tests. Moreover, the long and short-run association among the selected variables was analyzed through Westerlund and Edgerton (Econ Lett 97(3), 185-190, 2007), cointegration test, and cross-sectional augmented ARDL (CS-ARDL). The empirical results indicate that the panel was cross-sectionally correlated, stationary at the first difference, and co-integrated in the long run. Moreover, the CS-ARDL model indicates a positive association between GDP and PM_2.5 concentration. Similarly, NRE and POP contribute significantly to increasing the PM_2.5 concentration in SA. However, RE and GLO play an important role to decrease the PM_2.5 concentration in SA.

Understanding Anthropogenic PM2.5 Concentrations and Their Drivers in China during 1998-2016

Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM_2.5 pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM_2.5 concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen's slope estimator and the Mann-Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM_2.5 concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM_2.5 in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m³ from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM_2.5 concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m³) gradually decreased and gradient 3 (≥35 μg/m³) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM_2.5 concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM_2.5 emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM_2.5 concentrations and the mechanisms influencing anthropogenic PM_2.5 concentrations, which can help the Chinese government develop effective abatement strategies.

Impact of the Digital Economy on PM2.5: Experience from the Middle and Lower Reaches of the Yellow River Basin

The development of the digital economy holds great significance for alleviating haze pollution. To estimate the impact of the digital economy on haze pollution, this paper explores the spatiotemporal evolutionary characteristics of the digital economy and PM2.5 concentration in the middle and lower reaches of the Yellow River Basin from 2011 to 2019 and conducts regression analysis by combining a fixed effect (FE) model and the spatial Durbin model (SDM). Moreover, this study divides the mitigation effect of haze pollution into a direct effect and a spatial spillover effect, and it further analyzes the mechanism from the perspectives of technological innovation and the industrial structure. The empirical results show that the development level of the digital economy increases year by year and that the concentration of PM2.5 decreases year by year. The digital economy level and PM2.5 concentration in the downstream region are higher than those in the middle region, and the digital economy is negatively correlated with haze pollution. Similarly, the spatial spillover effect of the digital economy is conducive to curbing haze pollution. The robustness test also supports this conclusion. In addition, there is regional heterogeneity in the impact of the digital economy on haze pollution. The direct effect and spatial spillover effect of the digital economy on haze pollution in the downstream region are greater than those in the middle region. This study suggests that to realize air pollution prevention and control, it is necessary to strengthen the construction of digital infrastructure and create a good digital economy development environment based on local conditions. Encouraging the development of digital technological innovation and promoting industrial digital transformation hold great significance for alleviating haze pollution.

Analysis of the Green Development Effects of High-Speed Railways Based on Eco-Efficiency: Evidence from Multisource Remote Sensing and Statistical Data of Urban Agglomerations in the Middle Reaches of the Yangtze River, China

As part of the modern transport infrastructure, high-speed railways (HSRs) have been considered an important factor affecting eco-efficiency (EE). This study used multisource remote sensing and statistical data from 185 counties representing urban agglomerations in the middle reaches of the Yangtze River (UAMRYR) in China from 2009 to 2018. The study integrated ArcGIS analysis, the Super-SBM (super slack-based measure) model, and the DSPDM (dynamic spatial panel Durbin model) to explore the spatial effects of HSRs on EE. The results showed that the coordinates of the interannual centers of gravity for EE and HSRs both fell in the same county, possessing similar parameter values for the standard deviation elliptical, a negative spatial mismatch index, and obvious spatial mismatch characteristics. In different spatially dislocated areas, the spatial effects of HSRs on EE are variable. Overall, the short-term effects are more intense than the long-term effects, and both the long-term and short-term effects are dominated by the effects of spatial spillover. A new perspective is proposed to explore the green development effects of HSRs, with a view to providing policy implications for the enhancement of EE and the planning of HSRs.

Spatial and temporal variations in PM2.5 and associated health risk assessment in Saudi Arabia using remote sensing

Air pollutants, especially ambient particulate matter (PM_2.5), detrimentally impact human health and cause premature deaths. The dynamic characteristics and associated health risks of PM_2.5 are analyzed based on the standard deviational ellipse (SDE) and trend analysis in Saudi Arabia (SAU) from 1998 to 2018 by utilizing recently updated satellite-derived PM_2.5 concentrations (V4.GL.03). The outcomes show that the national average PM_2.5 concentration increased from 28 μg/m³ to 45 μg/m³ with a growth rate of 2.3 μg/m³/year. The center of median PM_2.5 concentrations moved to the southeast over the years studied due to the presence of vast sandy deserts, sand dunes, a busy port, and coastal and industrial areas in this region. The areas of SAU that experienced PM_2.5 concentrations above 35 μg/m³ increased from 20% to 70%. The rapid-fast growth (RFG) class acquired from the unsupervised classification has the fastest growth rate of 2.5 μg/m³/yr, occurring in southeastern SAU, namely Ash-Sharqiyah, Ar-Riyad, and Najran. It covered ∼27% of the total area of SAU over the study period. Whereas, the slow growth (SG) class with a less than 0.2 μg/m³/yr growth rate covered 12% of the total area of SAU, distributed in northwestern regions. The extent of extremely-high risk areas corresponding to greater than 1 × 10³ μg·person/m³ increased from 4% to 11%, particularly in Makkah, Central Al-Madinah, and western Asir, Jizan, mid-eastern Najran, Al-Quassim, and mid-eastern Ar-Riyad and Ash Sharqiyah.

Do anthropogenic and natural factors elevate the haze pollution in the South Asian countries? Evidence from long-term cointegration and VECM causality estimation

Anthropogenic and natural factors lead to substantial environmental degradation. This shift is aligned with the country's overall development, resulting in high demand for energy resources and a dramatic shift in human activities that contribute to haze pollution. Some of the countries in the South Asian region are ranked between one and twenty on the list of countries with the highest levels of PM2.5 pollution. The member countries have taken many steps to tackle global warming, but concern about haze pollution was found limited. Moreover, very little research was conducted on haze pollution, which led us to conduct this research in this region. This study used the panel data from 1998 to 2018 and a set of econometric models like long-term cointegrating relationship, fully modified ordinary least squares, and vector error-correction model Granger causality tests to examine the major drivers like anthropogenic and natural factors that might elevate haze pollution. Furthermore, our empirical results depict that (1) there is a long-term cointegrating relation between haze and the factors studied. (2) Energy consumption, urbanisation, and economic growth are the primary drivers of environmental degradation. (3) Rainfall has the most substantial influence on reducing haze pollution. The study concluded that (a) if the countries continue to develop at the same pace, all factors studied will continue to drive haze pollution to rise. (b) A decrease in PM2.5 pollution requires improvements in regional rainfall through vegetation, reducing reliance on fossil fuel-based energy sources, and increasing environmental education. (c) Slowing down the drive for urbanisation would not be cost-effective in reducing haze pollution in the region in the short run. Thus, reducing haze by adjusting the factors studied would not be easy in the short run and require the careful adoption of long-term policies.

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial-temporal heterogeneity of PM (PM_2.5 and PM₁₀) concentration in Heilongjiang Province during 2014-2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO₂, NO₂, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM.

Elevated urbanization-driven plant accumulation and human intake risks of polycyclic aromatic hydrocarbons in crops of peri-urban farmlands

As an ubiquitous carcinogen, polycyclic aromatic hydrocarbons (PAHs) are closely related to anthropogenic activities. The process of urbanization leads to the spatial interlacing of farmlands and urbanized zones. However, field evidence on the influence of urbanization on the accumulation of PAHs in crops of peri-urban farmlands is lacking. This study comparatively investigated the urbanization-driven levels, compositions, and sources of PAHs in 120 paired plant and soil samples collected from the Yangtze River Delta in China and their species-specific human intake risks. The concentrations of PAHs in crops and soils in the peri-urban areas were 2407.92 ng g^-1 and 546.64 ng g^-1, respectively, which are significantly higher than those in the rural areas. The PAHs in the root were highly relevant to those in the soils (R² = 0.63, p < 0.01), and the root bioconcentration factors were higher than 1.0, implying the contributions of root uptake to plant accumulations. However, the translocation factors in the peri-urban areas (1.57 ± 0.33) were higher than those in the rural areas (1.19 ± 0.14), indicating the enhanced influence through gaseous absorption. For the congeners, the 2- to 3-ring PAHs showed a higher plant accumulation potential than the 4- to 6-ring PAHs. Principal component analysis show that the PAHs in the peri-urban plants predominantly resulted from urbanization parameters, such as coal combustion, vehicle emissions, and biomass burning. The mean values of estimated dietary intake of PAHs from the consumption of peri-urban and rural crops were 9116 ng day^-1 and 6601.83 ng day^-1, respectively. The intake risks of different crops followed the order rice > cabbage > carrot > pea. Given the significant input of PAHs from urban to farmland, the influence of many anthropogenic pollutants arising from rapid urbanization should be considered when assessing the agricultural food safety.

Air pollution, residents' happiness, and environmental regulation: evidence from China

This study investigates the impact of air pollution on residents' subjective happiness, using data from the China General Social Survey for 2013, 2015, and 2017, regional air pollution, and socioeconomic indicators. We find that air pollution has a negative effect on residents' subjective happiness. Specifically, the average marginal effect of the logarithm of SO₂ emissions on happiness is -0.0099 and significant at the 1% level; namely, a one-unit increase in [Formula: see text] will reduce the likelihood of residents feeling happy by 0.99%. This negative effect is greater for those who have children, are old, or have a higher level of education. We also empirically test two mechanisms by which air pollution affects subjective happiness-depressed mood and leisure activities outside the home-and demonstrate that environmental regulation can moderate the negative impact of air pollution on happiness, but the moderating effects are nonlinear. Environmental governance investments are more effective at the low level, pollutant discharge fees are more effective at the medium level, and complaints about environmental pollution are more effective at the high level. As well as enriching theoretical insights into the relationship between air pollution and happiness, this study provides a valuable reference for developing more suitable policies in relation to environmental management and national happiness.

Haze pollution reduction in Chinese cities: Has digital financial development played a role?

Based on the exogenous shock of digital financial development in China in 2013, a difference-in-differences (DID) model is set up in this paper to investigate the causal relationship between digital financial development and haze pollution reduction. The finding of the paper is that a one standard deviation increase in digital finance after 2013 decreases the PM2.5 concentrations by 0.2708 standard deviations. After a number of robustness checks, like placebo tests, instrumental variable (IV) estimations, eliminating disruptive policies, and using alternative specifications, this causal effect is not challenged. In addition, this paper explores three potential mechanisms of digital finance to reduce haze pollution: technological innovation, industrial upgrading, and green development. Moreover, the heterogeneous effects signify that the usage depth of digital finance works best in haze pollution reduction. Digital finance has more positive effects in cities in the north and those with superior Internet infrastructure and higher levels of traditional financial development. However, the quantile regression estimates suggest that for cities with light or very serious haze pollution, the positive impact of digital finance is limited. These findings supplement the research field on the environmental benefits of digital finance, which provides insights for better public policies about digital financial development to achieve haze pollution reduction.

Quality of Life and Mental Health Benefits of Public Participation in Forest Conservation Activities in Urban Areas

The purpose of this study is to investigate the effect of forest conservation activities on the physical and psychological wellbeing of participants. The experiment was conducted in a forest near an urban area and involved 61 participants (average age: 22.5 ± 1.8). The participants selected one of three activities (pruning, stacking cut branches, and removing vines) in the forest conservation program. The effects of these activities on the musculoskeletal system were assessed using the Ovako Working Posture Assessment System (OWAS); the physical intensity of the activities was evaluated using heart rate data. The psychological evaluation measurement indexes used the Positive and Negative Affect Schedule, Rosenberg Self-Esteem scale, World Health Organization Quality of Life assessment instrument, and the Perceived Restorativeness Scale. As a result of the OWAS assessment, forest conservation activities were found to be action categories 1 and 2, which were less burdensome to the musculoskeletal system. All forestry activities were found to be light levels of physical intensity. Psychological evaluation of the participants revealed that positive emotions such as self-esteem, quality of life, and perceived restorativeness increased significantly, whereas negative emotions decreased significantly. This forest conservation program, that involved low-intensity activities which were less burdensome to the musculoskeletal system, had positive physical and psychological effects on the local residents who participated.

How does biased technological progress affect haze pollution? Evidence from APEC economies

Biased technological progress is the act of energy conservation and emission reduction by changing the marginal rate of substitution. In this study, we introduced renewable energy into a production function, and proposed a method of identifying biased characteristics of technological progress, based on marginal productivity theory. A panel dataset for the Asia-Pacific Economic Cooperation (APEC) economies from 2000 to 2017 was analyzed to explore the effect of biased technological progress in reducing particulate matter (PM_2.5). We found that input biased technological progress tended to use more non-renewable energy. Input biased technological progress aggravated haze pollution; however, this effect decreased as the PM_2.5 concentration increased. Output biased technological progress significantly reduced haze pollution in high-income economies, but increased it in low-income economies. The effect of neutral technological progress on haze pollution was the opposite of the effect from output biased technological progress. We also found that increasing renewable energy consumption and reducing energy intensity were separate effective paths for input and output biased technological progress, respectively, to mitigate haze pollution. For neutral technological progress, improving total factor productivity was an important way to mitigate haze pollution. Finally, several policy recommendations are proposed to mitigate haze pollution in APEC economies.

Identifying Spatiotemporal Heterogeneity of PM2.5 Concentrations and the Key Influencing Factors in the Middle and Lower Reaches of the Yellow River

Fine particulate matter (PM2.5) is a harmful air pollutant that seriously affects public health and sustainable urban development. Previous studies analyzed the spatial pattern and driving factors of PM2.5 concentrations in different regions. However, the spatiotemporal heterogeneity of various influencing factors on PM2.5 was ignored. This study applies the geographically and temporally weighted regression (GTWR) model and geographic information system (GIS) analysis methods to investigate the spatiotemporal heterogeneity of PM2.5 concentrations and the influencing factors in the middle and lower reaches of the Yellow River from 2000 to 2017. The findings indicate that: (1) the annual average of PM2.5 concentrations in the middle and lower reaches of the Yellow River show an overall trend of first rising and then decreasing from 2000 to 2017. In addition, there are significant differences in inter-province PM2.5 pollution in the study area, the PM2.5 concentrations of Tianjin City, Shandong Province, and Henan Province were far higher than the overall mean value of the study area. (2) PM2.5 concentrations in western cities showed a declining trend, while it had a gradually rising trend in the middle and eastern cities of the study area. Meanwhile, the PM2.5 pollution showed the characteristics of path dependence and region locking. (3) the PM2.5 concentrations had significant spatial agglomeration characteristics from 2000 to 2017. The “High-High (H-H)” clusters were mainly concentrated in the southern Hebei Province and the northern Henan Province, and the “Low-Low (L-L)” clusters were concentrated in northwest marginal cities in the study area. (4) The influencing factors of PM2.5 have significant spatiotemporal non-stationary characteristics, and there are obvious differences in the direction and intensity of socio-economic and natural factors. Overall, the variable of temperature is one of the most important natural conditions to play a positive impact on PM2.5, while elevation makes a strong negative impact on PM2.5. Car ownership and population density are the main socio-economic influencing factors which make a positive effect on PM2.5, while the variable of foreign direct investment (FDI) plays a strong negative effect on PM2.5. The results of this study are useful for understanding the spatiotemporal distribution characteristics of PM2.5 concentrations and formulating policies to alleviate haze pollution by policymakers in the Yellow River Basin.

The Influence of Air Pollution on Happiness and Willingness to Pay for Clean Air in the Bohai Rim Area of China

Air pollution imposes detrimental impacts on residents’ health and the general quality of life. Quantifying the influential mechanism of air pollution on residents’ happiness and the economic value brought by environmental quality improvement could provide a scientific basis for the construction of livable cities. This study estimated urban residents’ willingness to pay for air pollution abatement by modeling the spatial relationship between air quality and self-rated happiness with a Bayesian multi-level ordinal categorical response model. Using large-scale geo-referenced survey data, collected in the Bohai Rim area of China (including 43 cities), we found that a standard deviation decrease in the number of polluted days over a year was associated with about a 15 percent increase in the odds of reporting a higher degree of happiness, after controlling for a wide range of individual- and city-scale covariate effects. On average, urban residents in the Bohai Rim region were willing to pay roughly 1.42 percent of their average monthly household income for mitigating marginal reductions in air pollution, although great spatial variability was also presented. Together, we hoped that these results could provide solid empirical evidence for China’s regional environmental policies aiming to promote individuals’ well-being.

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m³ increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Spatiotemporal heterogeneity of PM2.5 and its driving difference comparison associated with urbanization in China's multiple urban agglomerations

The development of urban agglomeration further deteriorates the air pollution status arising from urbanization. However, disparities in the urbanization process across different urban agglomerations may shape unique regional air pollution characters, and further complicate its driving mechanism. In this study, 11 urban agglomerations with different urbanization levels in China thus were chosen as the case study areas, to explore the spatiotemporal heterogeneity of PM2.5 and its potential driving difference related to the urbanization process from a multi-urban agglomeration perspective. The ground-monitored PM2.5 data and socioeconomic panel data (2015-2018) were processed using multiple statistical analysis methods, and the main findings of this study can be generated as followed: (1) significant spatial heterogeneity characteristics of PM2.5 pollution were recognized across the study area. And even though obvious improvement in the air quality during the study period, PM2.5 concentration remains at a high level for most of the urban agglomerations. (2) Urbanization process has a substantial contribution to regional PM2.5 pollution, and significant differences of the urbanization factors on PM2.5 concentration across the urban agglomerations assigned with various urbanization levels were emphasized. The significance of this study is to provide insight into the relationship of the urbanization process on PM2.5 pollution in different urban agglomerations and to offer a scientific basis for regional cooperation for air pollution regulation among multiple urban agglomerations.

Air pollutant emissions induced by rural-to-urban migration during China's urbanization (2005-2015)

As the world's most populous country, China has witnessed rapid urbanization in recent decades, with population migration from rural to urban (RU) regions as the major driving force. Due to the large gap between rural and urban consumption and investment level, large-scale RU migration impacts air pollutant emissions and creates extra uncertainties for air quality improvement. Here, we integrated population migration assessment, an environmentally extended input-output model and structural decomposition analysis to evaluate the NO_x, SO₂ and primary PM_2.5 emissions induced by RU migration during China's urbanization from 2005 to 2015. The results show that RU migration increased air pollutant emissions, while the increases in NO_x and SO₂ emissions peaked in approximately 2010 at 2.4 Mt and 2.2 Mt, accounting for 9.2% and 8.7% of the national emissions, respectively. The primary PM_2.5 emissions induced by RU migration also peaked in approximately 2012 at 0.3 Mt, accounting for 2.8% of the national emissions. The indirect emissions embodied in consumption and investment increased, while household direct emissions decreased. The widening gap between urban and rural investment and consumption exerted a major increasing effect on migration-induced emissions; in contrast, the falling emission intensity contributed the most to the decreasing effect benefitting from end-of-pipe control technology applications as well as improving energy efficiency. The peak of air pollutant emissions induced by RU migration indicates that although urbanization currently creates extra environmental pressure in China, it is possible to reconcile urbanization and air quality improvement in the future with updating urbanization and air pollution control policies.

Assessing face masks in the environment by means of the DPSIR framework

The use of face masks outside the health care facility dates back a century ago. However, face masks use noticeably soared due to the COVID-19 (Coronavirus disease 2019) pandemic. As a result, an unprecedented influx of discarded face masks is ending up in the environment. This review paper delves into face masks in the environment using the DPSIR (driving forces, pressures, states, impacts, and responses) framework to simplify and communicate the environmental indicators. Firstly, the historical, and briefly the economic trajectory of face masks are discussed. Secondly, the main driving forces of face masks use with an emphasis on public health are explored. Then, the pressures exerted by efforts to fulfill the human needs (driving forces) are investigated. In turn, the state of the environment due to the influx of masks along with the impacts are examined. Furthermore, the upstream, and downstream societal responses to mitigate the environmental damages of the driving forces, pressures, states, and impacts are reviewed. In summary, it has been shown from this review that the COVID-19 pandemic has been causing a surge in face mask usage, which translates to face masks pollution in both terrestrial and aquatic environments. This implies proper usage and disposal of face masks is paramount to the quality of human health and the environment, respectively. Moreover, further research on eco-friendly face masks is indispensable to mitigating the environmental damages occurring due to the mass use of surgical masks worldwide.

Factors Influencing PM2.5 Concentrations in the Beijing–Tianjin–Hebei Urban Agglomeration Using a Geographical and Temporal Weighted Regression Model

Air pollution is the environmental issue of greatest concern in China, especially the PM2.5 pollution in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA). Based on sustainable development, it is of interest to study the spatiotemporal distribution of PM2.5 and its influencing mechanisms. This study reveals the temporal evolution and spatial clustering characteristic of PM2.5 pollution from 2015 to 2019, and quantifies the drivers of its natural and socioeconomic factors on it by using a geographical temporal weighted regression model. Results show that PM2.5 concentrations reached their highest level in 2015 before decreasing in the following years. The monthly averages all present a U-shaped change trend. Relative to the traditional high concentrations in the northern part of the BTHUA domain in 2015, the gap in pollution between the north and south has reduced since 2018. The obvious spatial heterogeneity was demonstrated in both the strength and direction of the variables. This study may help identify reasons for high PM2.5 concentrations and suggest appropriate targeted control and prevention measures.

Characteristics of airborne bacterial communities and antibiotic resistance genes under different air quality levels

Pathogenic bacteria and antibiotic resistance genes (ARGs) in bioaerosols are major threats to human health. In this study, the microbial community structure and ARG distribution characteristics of airborne bacteria in total suspended particulates (TSP) and PM_2.5 were investigated under different air quality levels in Xinxiang, Central China. The results revealed that with the deterioration of air quality, the concentrations of airborne bacteria in both TSP and PM_2.5 decreased; however, the relative amounts of pathogenic bacteria increased. The predominant genera in pathogenic bacteria of Bacillus, Sphingomonas, Corynebacterium, Rhodococcus, and Staphylococcus were identified in both TSP and PM_2.5. Although the airborne bacteria concentrations and absolute abundances of ARGs in TSP were higher than those in PM_2.5 under identical air quality conditions, the bacterial community structure and relative amounts of pathogenic bacteria were similar. In addition, the relationship between environmental factors of ions, metal elements, and meteorological parameters and the community structures of airborne bacteria and pathogenic bacteria were also analyzed. The effects of soluble ions and metal elements on several dominant genera of total bacteria and pathogenic bacteria differed, probably due to the strong tolerance of pathogenic bacteria to harsh atmospheric environments Different subtypes of ARGs showed various distribution characteristics with variations in air quality. The deterioration of air quality can inhibit the dissemination of ARGs, as the minimum values of all ARGs and class 1 integrase intI1 were observed under Severely Polluted conditions. This study provides a comprehensive understanding of the effect of air pollution levels on the airborne bacteria community composition and ARG distribution.

Effect of Battery-Electric and Plug-In Hybrid Electric Vehicles on PM2.5 Emissions in 29 European Countries

The contribution of battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) to mitigating/reducing fine particulate matter (PM2.5) emissions was researched through a panel of 29 European countries from 2010 to 2019, using the econometric technique of method of moments quantile regression (MM-QR). This research is innovative by connecting the increasing use of electric vehicles with PM2.5 emissions and using the MM-QR to explore this relationship. Two models were estimated to analyse their contribution to reducing PM2.5 in European countries. The nonlinearity of the models were confirmed. The statistical significance of the variables is strong for the upper quantiles (75th and 90th), resulting from the effectiveness of European policies to improve the environment. Electric vehicles (BEVs and PHEVs), economic growth, and urbanisation reduce the PM2.5 problem, but energy intensity and fossil fuel consumption aggravate it. This research sheds light on how policymakers and governments can design proposals to encourage electric vehicle use in European countries. To achieve the long-term climate neutral strategy by 2050, it is imperative to implement effective policies to reduce the consumption of fossil fuels and promote the adoption of electric vehicles using renewable energy sources.