Spatiotemporal variation and socioeconomic drivers of air pollution in China during 2005-2016

Extracting critical paths for synergistic control of carbon emissions and air pollution: Case of Henan Province

Synergistic reduction of carbon emissions and air pollution is the core means to address the two major strategic tasks of fundamentally improving the ecological environment and the 'Dual-carbon target'. The issue of synergistic reduction at the provincial level needs to be addressed as a matter of urgency. Taking Henan Province, the largest economy in central China, as an example, this study uses environmentally extended input-output analysis and structural path analysis to identify the key sectors that contribute to CO2, SO2, and total particulate matter (TPM) emissions, and to sort out key emission pathways (e.g., Final Demand → Sector…). The results indicate that S2 (Mining of Fossil Energy), S10 (Nonmetal Mineral Products), S11 (Metal Smelting), S13 (Power and Heat) and S17 (Transportation) are mainly responsible for CO2, SO2, and TPM direct emissions on the production side, while S16 (Construction), S12 (Equipment) and S18 (Services) account for more than 45 % of CO2, SO2, and TPM embodied emissions on the consumption side. 32 shared emission pathways are extracted from the top 100 pathways for CO2, SO2, and TPM emissions, which account for 27 %-51 % of total emissions in Henan Province. P9 (Export → Nonmetal Mineral Products), P10 (Export → Metal Smelting) and P21 (Gross Capital Formation → Construction → Nonmetal Mineral Products) are the leading paths responsible for embodied emissions. The research results provide the foundation and guidance for well-designed mitigation policies, as well as a reference for better synergistic control in provinces facing similar situations.

Driving forces of agricultural ammonia emissions in semi-arid areas of China: A spatial econometric approach

Ammonia emissions contribute to PM2.5 formation, posing significant threats to public health, including respiratory and cardiovascular diseases, and causing various ecological issues, such as soil and water acidification. This study investigates ammonia emissions in the semi-arid region of central Gansu Province, China, by establishing a county-level agricultural ammonia emission inventory for 2014-2020 using the emission factor method. A spatial econometric model, integrated with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, is employed to identify key drivers of emissions. This approach is crucial because it accounts for the spatial dependencies of emissions across regions and incorporates socio-economic factors, providing a more comprehensive understanding of emission patterns. Results indicate that livestock and poultry farming (58.76 %) and nitrogen fertilizer application (37.73 %) are major contributors to ammonia emissions. Regional agricultural ammonia emissions are concentrated in the east, river basins, and parts of the southwest. The study also reveals positive spatial clustering and spillover effects of ammonia emissions. In the central region of Gansu Province, a 1 % increase in per capita GDP, population, agricultural structure, and rural electricity consumption leads to changes in agricultural ammonia emissions of 0.059 %, -1.181 %, -0.109 %, and 0.133 %, respectively. Rural electricity consumption, population dynamics, and agricultural structure improvements influence not only locally but also across neighboring regions. The findings emphasize the need for targeted, collaborative regional strategies to mitigate emissions and underscore the importance of considering spatial interactions in emission reduction policies.

How urban growth dynamics impact the air quality? A case of eight Indian metropolitan cities

Air pollution is a matter of great significance that confronts the sustainable progress of urban areas. Against India's swift urbanization, several urban areas exhibit the coexistence of escalating populace and expansion in developed regions alongside extensive spatial heterogeneity. The interaction mechanism between the growth of urban areas and the expansion of cities holds immense importance for the remediation of air pollution. Henceforth, the present investigation utilizes geographically weighted regression (GWR) to examine the influence of urban expansion and population growth on air quality. The examination will use a decade of data on the variation in PM2.5 levels from 2010 to 2020 in eight Indian metropolitan cities. The study's findings demonstrate a spatial heterogeneity between urban growth dynamics and air pollution levels. Urban growth and the expansion of cities demonstrate notable positive impacts on air quality, although the growth of infilling within expanding urban areas can significantly affect air quality. Given the unique trajectories of urban development in developing countries, this research provides many suggestions for urban administrators to foster sustainable urban growth.

Agricultural production and air pollution: An investigation on crop straw fires

In numerous developing nations, the pervasive practice of crop residue incineration is a principal contributor to atmospheric contamination in agricultural operations. This study examines the repercussions of such biomass combustion on air quality during the autumnal harvest season, utilizing data acquired from satellite-based remote sensing of fire events and air pollution measurements. Employing wind direction information alongside difference-in-difference and fixed-effects methodologies, this investigation rectifies estimation inaccuracies stemming from the non-random distribution of combustion occurrences. The empirical findings reveal that agricultural residue burning precipitates an elevation in average PM2.5 and PM10 concentrations by approximately 27 and 22 μg/m3 during the autumnal incineration period, respectively. Furthermore, air pollution attributed to residue burning in prominent grain-producing regions exceeds the national average by approximately 40%. By integrating economic paradigms into agri-environmental inquiries, this study offers novel insights and substantiation of the environmental expenditures engendered by crop residue burning, juxtaposed with extant meteorological and ecological research findings.

Impact of economic development on soil trace metal(loid)s pollution: A case study of China

Recently, intensive anthropogenic activities, while promoting economic growth, have also exacerbated soil trace metal(loid) (TM) pollution. To explore the impact of economic development on soil TM pollution, a time-weighted method was introduced to calculate the average concentrations of eight TMs in Chinese topsoil from 2001 to 2020, and panel data on TMs and economic factors of 31 provinces were used for regression analysis. The results revealed that the average concentrations of soil TMs all exceeded their respective soil background values. Meanwhile, the spatial distribution of soil TMs was characterized by obvious regional heterogeneity, with economically developed areas being heavily polluted and having high ecological risks. In addition, the results derived from panel data models showed that the relationship between soil TM pollution and economic development in China presented a continuous growth curve, but with an N-shaped pattern in eastern China, a U-shaped pattern in central China, and a positive linearity in western China. Four control variables were also introduced to evaluate their impact on TM pollution, and the results indicated that the proportion of secondary industry and the road area per capita were the major influencing factors. Ultimately, the inflection point estimation results suggested that the soil TM pollution level will increase in eastern China, central China and western China with ongoing economic growth. Our findings contribute to the current understanding of the relationship between soil TM pollution and anthropogenic activities, and provide a scientific basis for adjusting and planning industrial development and layout according to the characteristics of soil TM pollution.

How economic development affects healthcare access for people with disabilities: A multilevel study in China

Meeting the healthcare needs of people with disabilities is an important challenge in achieving the central promise of "leave no one behind" during the Sustainable Development Goals era. In this study, we describe the accessibility of healthcare for people living with disabilities, as well as the potential influences of individuals' socioeconomic status and regional economic development. Our data covered 324 prefectural cities in China in 2019 and captured the access to healthcare services for people with disabilities. First, we used linear probability regression models to investigate the association between individual socioeconomic status, including residence, poverty status, education, and healthcare access. Second, we conducted an ecological analysis to test the association between prefectural economic indicators, including GDP (gross domestic product) per capita, urbanization ratio, average years of education, Engel's coefficient, and the overall prevalence of access to healthcare for people with disabilities within prefectures. Third, we used multilevel regression models to explore the association between the individual's socio-economic status, prefectural economic indicators, and access to healthcare at the individual level for people with disabilities. The results showed, first, that higher individual socioeconomic status (urban residence or higher educational level) was associated with better access to healthcare for people with disabilities. Second, regional economic indicators were positively associated with access to healthcare at the aggregate and individual levels. This study suggests that local governments, particularly in low- and middle-income countries, should promote economic development and conduct poverty alleviation policies to improve healthcare access for disadvantaged groups.

Spatiotemporal characteristics and influencing factors for joint events of air pollution wave and cold wave in China

Climate change triggered more environmental extremes. The joint events of air pollution wave and cold wave showed higher health risks than independent events, but little evidence is available for the spatiotemporal features of their co-occurrence. To better understand and forecast the joint events, a method framework was developed in this study. The temporal trend and spatial distribution of count and duration for joint events were measured at each grid cell (0.5°×0.5°) by integrating the PM2.5 air pollution wave and cold wave. The generalized linear mixed model was used to screen influencing variables that took into account socioeconomic characteristics, meteorological variables, and annual PM2.5 levels. During 2000 and 2018, the average annual count of joint events was 4.1 ± 6.8 days and the average duration ranged from 1.0 to 9.7 days. High spatial heterogeneity was observed throughout China, with a significant increase in joint events observed in Xinjiang area (the largest province in China). The most average count of joint events was observed in Henan province (one of the most populous provinces), while the longest duration was in Chongqing (a municipality, one of the megacities). Areas with higher PM2.5 levels, prolonged air pollution wave, and cold wave durations would experience more joint events. These findings can assist China in locating vulnerable areas and establishing effective local early warning systems. The method framework offers broader perspectives on mitigating health risks associated with extreme events in other countries and regions.

An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables

Forecasting the air quality index (AQI) is a critical and pressing challenge for developing nations worldwide. With air pollution emerging as a significant threat to the environment, this study considers seven study sites of the sub-tropical region in Bangladesh and introduces a novel hybrid deep-learning model. The proposed model, expressed as CLSTM-BiGRU, integrates a convolutional neural network (CNN), a long-short term memory (LSTM), and a bi-directional gated recurrent unit (BiGRU) network. Leveraging nineteen remotely sensed predictor variables and harnessing the grey wolf optimization (GWO) algorithm, the CLSTM-BiGRU model showcases its superiority in air quality forecasting. It consistently outperforms the benchmark models, yielding lower forecasting errors and higher efficiency (i.e., correlation coefficient ~1) values. Hence, this study underscores the feasibility and substantial potential of the hybrid deep learning model, which can provide precise forecasts of air quality index, and will be highly useful for relevant stakeholders and decision-makers. Furthermore, the adaptability and potential utility of this innovative model may be ascertained for air quality monitoring and effective public health risk mitigation in urban environments.

Ambient air pollution and the health-related quality of life of older adults: Evidence from Shandong China

Ambient air pollution is a major public health concern impacting all aspects of human health. There is a lack of studies on the impact of ambient air pollution on health-related quality of life (HRQoL) of older Chinese adults. Our study answers two questions: How concentrations of ambient air pollutants are associated with HRQoL among older adults in China and, second, what are the possible mechanisms through which ambient air pollution affects HRQoL. From the 2018 National Health Service Survey, we sampled 5717 aged 65 years or older residents for the eastern province of Shandong, China. Data on individual exposures to PM_2.5 and PM₁₀ (particulate matter with diameter less than or equal to 2.5 μm and 10 μm) and sulfur dioxide (SO₂) were collected from the ChinaHighAirPollutants (CHAP) datasets. Mixed-effects Tobit regression models and mixed-effects ordered Probit regression models were employed to examine the associations of long-term exposure to ambient air pollution with the European Quality of Life 5 Dimensions 3 Level Version (EQ-5D-3L) scale comprising mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Socioeconomic, demographic and behavioral factors relating to HRQoL were also examined. The results show that for each 1 μg/m³ increase, EQ-5D-3L scores fell 0.002 for PM_2.5; 0.001 for PM₁₀ and 0.002 for SO₂. Long term exposure to PM_2.5, PM₁₀ and SO₂ were also associated with increased prevalence of pain/discomfort and anxiety/depression. The reduced HRQoL effects of ambient air pollution were exacerbated by higher socioeconomic status (affluent, urban and higher level of education). Our findings suggested that HRQoL of older Chinese adults was not only associated with demographic, socioeconomic, and health-related factors, but also negatively correlated with air pollution, especially through increased pain/discomfort and anxiety/depression. The paper proposes policy recommendations.

A new approach of air pollution regionalization based on geographically weighted variations for multi-pollutants in China

Air pollution regionalization is a key and necessary action to identify pollution regions for implementing control measures. Here we present a new approach called Geographically Weighted Rotation Empirical Orthogonal Function (GWREOF) for air pollution regionalization in China. Compared with previous methods, such as EOF, REOF, and K-mean, GWREOF better accounts for the variability of air pollution conditions driven by emission patterns and meteorology with centralized spatial locations. We apply GWREOF to multiple air pollutants (such as PM_2.5, O₃, and other monitored air pollutants) and air quality metrics using their measured spatial and temporal variations in 337 Chinese cities over 2015-2020. We find that the regionalization results for different air pollutants are highly similar, primarily determined by topography and meteorological conditions in China. Therefore, we propose an integrated regionalization result, which identifies 18 air pollution control regions in China and can be applied to multiple pollutants and different years. We further analyze PM_2.5, O₃, and O_X (O₃ + NO₂) pollution levels and their correlations in these regions. PM_2.5 and O₃ correlations are generally strongly positive in southern China while negative in northern China. However, PM_2.5 and O_X correlations are broadly positive in China, reflecting the crucial role of atmospheric oxidizing capacity. Regional-specific and coordinated control measures are in need as China's air pollution strategy transits from PM_2.5-focused to PM_2.5-O₃ synergic control.

Assessing the effects of short-term traffic restriction policies on traffic-related air pollutants

The implementation of short-term traffic restriction policies (TRPs) during major events positively influences the traffic emission reduction. However, few studies explore the impact of diesel vehicle emissions on air quality during short-term TRP. In particular, the intertwined influences of short-term TRP and Spring Festival remains unclear. Based on Beijing 2022 Olympic Games, this study analyzed the spatiotemporal changes in urban air quality and diesel vehicle emission during short-term TRP. The results showed that the TRPs and Spring Festival contributed equally to the improvement of air quality and reduction of diesel vehicle emissions. The "interruption-recovery" pattern of short-term TRPs is characterized by a longer duration and a slower decline/recovery rate. Additionally, the individual contribution rate of diesel vehicle emissions to urban air pollutants was 15-20 % higher than that of meteorological factors during short-term TRPs.

Financial reform and haze pollution: A quasi-natural experiment of the financial reform pilot zones in China

Financial reform becomes a new tool for environmental governance because it can indirectly affect the environment by promoting economic and financial agglomeration and technological innovation. Despite China's aggressive financial reform pilot (FRP) policy since 2012, little is known about whether and how such policy affects haze pollution (HP). We exploit geographic and temporal variations in China's FRP policy and compile a dataset covering 284 cities over the period from 2003 to 2019. Employing a difference-in-differences (DID) approach, we document that China's FRP policy has a negative causal effect on HP in the pilot cities. The estimates obtained from an instrumental variable constructed by religious temples also support the haze-abatement effect of such policy. This effect is largely driven by advances in technological innovation and increases in economic agglomeration, while financial agglomeration is proven to have little effect. Finally, our estimate is particularly pronounced in cities with high levels of economic development, financial development and technological innovation, and that in large-sized and non-mineral resourced cities. Overall, our findings shed light on the importance of financial reform in environmental governance in a developing country.

Spatio-Temporal Distribution Characteristics and Drivers of PM2.5 Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic

PM_2.5 is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM_2.5 pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM_2.5 concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM_2.5 concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM_2.5 concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM_2.5 concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.

Change in Air Quality during 2014-2021 in Jinan City in China and Its Influencing Factors

Air pollution affects climate change, food production, traffic safety, and human health. In this paper, we analyze the changes in air quality index (AQI) and concentrations of six air pollutants in Jinan during 2014-2021. The results indicate that the annual average concentrations of PM10, PM2.5, NO2, SO2, CO, and O3 and AQI values all declined year after year during 2014-2021. Compared with 2014, AQI in Jinan City fell by 27.3% in 2021. Air quality in the four seasons of 2021 was obviously better than that in 2014. PM2.5 concentration was the highest in winter and PM2.5 concentration was the lowest in summer, while it was the opposite for O3 concentration. AQI in Jinan during the COVID epoch in 2020 was remarkably lower compared with that during the same epoch in 2021. Nevertheless, air quality during the post-COVID epoch in 2020 conspicuously deteriorated compared with that in 2021. Socioeconomic elements were the main reasons for the changes in air quality. AQI in Jinan was majorly influenced by energy consumption per 10,000-yuan GDP (ECPGDP), SO2 emissions (SDE), NOx emissions (NOE), particulate emissions (PE), PM2.5, and PM10. Clean policies in Jinan City played a key role in improving air quality. Unfavorable meteorological conditions led to heavy pollution weather in the winter. These results could provide a scientific reference for the control of air pollution in Jinan City.

Watershed Horizontal Ecological Compensation Policy and Green Ecological City Development: Spatial and Mechanism Assessment

Green ecological development has become an inevitable choice to achieve sustainable urban development and carbon neutrality. This paper evaluates the level of green ecological city development in the Xin'an watershed as measured by green total factor productivity (GTFP), analyzes the direct and spatial effects of the Watershed Horizontal Ecological Compensation policy on GTFP, and further examines the moderating effect of the Research and Development (R&D) incentives, industrial structure, and income gap. This paper conducts difference-in-differences (DID) and spatial regression analysis on 27 cities from 2007 to 2019. The results show that GTFP progresses to varying degrees across cities over time, especially in the pilot cities. Crucially, the Watershed Horizontal Ecological Compensation policy significantly improved GTFP, although the effect was slight. Interestingly, the increase in GTFP in pilot cities that implemented the policy spatially suppressed the increase in GTFP in cities that did not implement the policy. Our evidence also shows that the positive effect of the policy is higher in regions with higher R&D incentives and industrial structure upgrading, which indicates that R&D incentives and industrial upgrading are crucial. In comparison, the income gap has not made the expected negative adjustment effect under the Chinese government's poverty alleviation policy. However, the positive policy effect is heterogeneous in the downstream and upstream pilot cities. The "forcing effect" of the policy on the downstream cities is more favorable than the "compensating effect" on the upstream cities. Therefore, policymakers should pay more attention to ensuring the effectiveness of the Watershed Horizontal Ecological Compensation policy in enhancing GTFP as a long-term strategy to guarantee the sustainability of green ecological development in Chinese cities.

Is air pollution politics or economics? Evidence from industrial heterogeneity

This paper checks the asymmetrical impact of Beijing's and Shanghai's air quality (AQ) on cross-industries stock returns (SR) by using the quantile-on-quantile (QQ) regression method. The major empirical findings as shown as followings. There are heterogeneous responses from SR to AQ within the same city. Different links are discovered for Beijing and Shanghai within the same industry. Air pollution does not have political or economic properties for all industries. Our research provides useful contributions compared with past literature. First of all, we distinguish whether air pollution is political or economic. Apart from psychology and physiology, government intervention and economic expectation are also important components in interpreting the influence from AQ to SR. Second, this study adequately considers the heterogeneity of industries. Industries differently react to the identical extrinsic shock, depending on the nature of their industry. Besides, the QQ approach captures quantile-varying relationship between variables, and does not need to consider structural fracture and time lag effects. The practical significance is that investors need to focus on national industrial policies, and avoiding biased decisions in stock market from air pollution.

Associations of socioeconomic status with infectious diseases mediated by lifestyle, environmental pollution and chronic comorbidities: a comprehensive evaluation based on UK Biobank

BACKGROUND

Socioeconomic status (SES) inequity was recognized as a driver of some certain infectious diseases. However, few studies evaluated the association between SES and the burden of overall infections, and even fewer identified preventable mediators. This study aimed to assess the association between SES and overall infectious diseases burden, and the potential roles of factors including lifestyle, environmental pollution, chronic disease history.

METHODS

We included 401,009 participants from the UK Biobank (UKB) and defined the infection status for each participant according to their diagnosis records. Latent class analysis (LCA) was used to define SES for each participant. We further defined healthy lifestyle score, environment pollution score (EPS) and four types of chronic comorbidities. We used multivariate logistic regression to test the associations between the four above covariates and infectious diseases. Then, we performed the mediation and interaction analysis to explain the relationships between SES and other variables on infectious diseases. Finally, we employed seven types of sensitivity analyses, including considering the Townsend deprivation index as an area level SES variable, repeating our main analysis for some individual or composite factors and in some subgroups, as well as in an external data from the US National Health and Nutrition Examination Survey, to verify the main results.

RESULTS

In UKB, 60,771 (15.2%) participants were diagnosed with infectious diseases during follow-up. Lower SES [odds ratio (OR) = 1.5570] were associated with higher risk of overall infections. Lifestyle score mediated 2.9% of effects from SES, which ranged from 2.9 to 4.0% in different infection subtypes, while cardiovascular disease (CVD) mediated a proportion of 6.2% with a range from 2.1 to 6.8%. In addition, SES showed significant negative interaction with lifestyle score (OR = 0.8650) and a history of cancer (OR = 0.9096), while a significant synergy interaction was observed between SES and EPS (OR = 1.0024). In subgroup analysis, we found that males and African (AFR) with lower SES showed much higher infection risk. Results from sensitivity and validation analyses showed relative consistent with the main analysis.

CONCLUSIONS

Low SES is shown to be an important risk factor for infectious disease, part of which may be mediated by poor lifestyle and chronic comorbidities. Efforts to enhance health education and improve the quality of living environment may help reduce burden of infectious disease, especially for people with low SES.

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

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

Spatiotemporal Variation Air Quality Index Characteristics in China's Major Cities During 2014-2020

The temporal and spatial variation characteristics of air quality index (AQI) in major cities in China were explored in this paper using statistical analysis, hot spot analysis, spatial autocorrelation, mean center, and geographic detector based on the daily AQI data from 2014 to 2020. The results show that ① the annual AQI average value dropped from 94 to 67 from 2014 to 2020. The percentage of cities with daily AQI excellent rates between 0.8 and 1 is significantly increasing, reaching 77% in 2020. ② AQI is highest and lowest in winter and summer, respectively. The trend of the monthly AQI average value is roughly in a U shape. Moreover, the AQI in January and December is high, and the AQI in August and September is low. ③ The spatial distribution of the annual AQI average in China's major cities shows agglomeration effects. The hot spots are distributed in North China and Xinjiang, and the cold spots are mainly distributed in the northeast and southern regions of China. ④ The average center of the annual AQI average of major cities in China was distributed in Sanmenxia City and Luoyang City, Henan Province, from 2014 to 2020 with a relatively small mean center migration range. ⑤ Based on the geographical detector model, the impact of total precipitation, 10-m u component of wind, 10-m v component of wind, surface pressure, and 2-m temperature on AQI is analyzed, and it is concluded that 2-m temperature has the greatest impact on AQI. Meanwhile, it is explored that GDP and population density have a certain impact on air quality. Therefore, analyzing the temporal and spatial characteristics of air quality provides some scientific basis for the regional collaborative governance of air pollution and the in-depth fight against pollution in China.

Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM2.5 concentrations in adjacent areas weakened the temporal convergence of PM2.5 concentrations. This paper introduced policy implications for haze prevention and control.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM2.5, nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO2 concentration in 2021 in Beijing. Notably, the O3 concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM2.5, NO2, SO2, and CO in February of 2020, but PM2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

Spatial temporal patterns and driving factors of industrial pollution and structures in the Yangtze River Economic Belt

The conflict between industrial development and environmental pollution is global. This study quantitatively analyzes the temporal and spatial changes, spatial effects and determinants of industrial pollution discharge in the Yangtze River Economic Belt through two main indicators: wastewater and SO₂. Analyze the spatial coupling relationship between industrial agglomeration and pollution emission and the characteristics of industrial structure in different regions. The analysis shows that industrial emissions in the Yangtze River Economic Belt first increased and then decreased during the period 2003-2019. Industrial pollution spread from large to small and medium cities and shifted from downstream to upstream. Moreover, a positive correlation exists between industrial pollution discharge and per capita GDP, secondary industry proportion, population density, and energy use. Meanwhile, scientific and technological progress and environmental regulations are associated with industrial pollution reduction. Since the Yangtze River Economic Belt was still in the industrialization stage and had not yet reached a turning point on the Environmental Kuznets Curve. The "pollution refuge" phenomenon was evident in the Belt, where underdeveloped areas in the central and western regions accommodated portions of highly polluting industries from the eastern areas through "regional competition" and "policy depression." The industrial agglomeration and pollution antagonistic zones were dominated by polluting industries; environmental risks were greatest in these areas. The upstream and downstream of the YREB play the negative and positive environmental externalities of industrial agglomeration, respectively. Thus, differential control measures should be formulated according to different regions, industrial pollutants, and polluting industries to improve environmental quality.

Does Rent-Seeking Affect Environmental Regulation?

Resource utilization not only meets the needs of economic development, but also has a far-reaching negative impact on the environment. Environmental regulation is regarded as the key measure to solve environmental pollution. However, the rent-seeking behavior of local enterprises will seriously weaken the implementation effect of environmental regulations. Under the background of the development of big data era, the massive micro enterprise data provided by China's private enterprise survey database provides favorable conditions for this paper to study its impact effect from the direction of big data. This paper uses OLS model and Tobit model to investigate the impact of rent-seeking on the implementation effect of environmental regulation. The results show that environmental regulation will make honest enterprises actively reduce output to control the emission level. However, rent-seeking enterprises will further expand their output to gain greater profits because they are sheltered by local governments.

Ventilation Capacities of Chinese Industrial Cities and Their Influence on the Concentration of NO2

Most cities in China, especially industrial cities, are facing severe air pollution, which affects the health of the residents and the development of cities. One of the most effective ways to alleviate air pollution is to improve the urban ventilation environment; however, few studies have focused on the relationship between them. The Frontal Area Index (FAI) can reflect the obstructive effect of buildings on wind. It is influenced by urban architectural form and is an attribute of the city itself that can be used to accurately measure the ventilation capacity or ventilation potential of the city. Here, the FAIs of 45 industrial cities of different sizes in different climatic zones in China were computed, and the relationship between the FAI and the concentration of typical pollutants, i.e., NO2, were analyzed. It was found that (1) the FAIs of most of the industrial cities in China were less than 0.45, indicating that most of the industrial cities in China have excellent and good ventilation capacities; (2) there were significant differences in the ventilation capacities of different cities, and the ventilation capacity decreased from the temperate to the tropical climate zone and increased from large to small cities; (3) there was a significant difference in the ventilation capacity in winter and summer, indicating that that with the exception of building height and building density, wind direction was also the main influencing factor of FAI; (4) the concentration of NO2 was significantly correlated with the FAI, and the relative contribution of the FAI to the NO2 concentration was stable at approximately 9% and was generally higher than other socioeconomic factors. There was a turning point in the influence of the FAI on the NO2 concentration (0.18 < FAI < 0.49), below which the FAI had a strong influence on the NO2 concentration, and above which the influence of the FAI became weaker. The results of this study can provide guidance for suppressing urban air pollution through urban planning.

Restricted Anthropogenic Activities and Improved Urban Air Quality in China: Evidence from Real-Time and Remotely Sensed Datasets Using Air Quality Zonal Modeling

The study aims to examine the major atmospheric air pollutants such as NO2, CO, O3, PM2.5, PM10, and SO2 to assess the overall air quality using air quality zonal modeling of 15 major cities of China before and after the COVID-19 pandemic period. The spatio-temporal changes in NO2 and other atmospheric pollutants exhibited enormous reduction due to the imposition of a nationwide lockdown. The present study used a 10-day as well as 60-day tropospheric column time-average map of NO2 with spatial resolution 0.25 × 0.25° obtained from the Global Modeling and Assimilation Office, NASA. The air quality zonal model was employed to assess the total NO2 load and its change during the pandemic period for each specific region. Ground surface monitoring data for CO, NO2, O3, PM10, PM2.5, and SO2 including Air Quality Index (AQI) were collected from the Ministry of Environmental Protection of China (MEPC). The results from both datasets demonstrated that NO2 has drastically dropped in all the major cities across China. The concentration of CO, PM10, PM2.5, and SO2 demonstrated a decreasing trend whereas the concentration of O3 increased substantially in all cities after the lockdown effect as observed from real-time monitoring data. Because of the complete shutdown of all industrial activities and vehicular movements, the atmosphere experienced a lower concentration of major pollutants that improves the overall air quality. The regulation of anthropogenic activities due to the COVID-19 pandemic has not only contained the spread of the virus but also facilitated the improvement of the overall air quality. Guangzhou (43%), Harbin (42%), Jinan (33%), and Chengdu (32%) have experienced maximum air quality improving rates, whereas Anshan (7%), Lanzhou (17%), and Xian (25%) exhibited less improved AQI among 15 cities of China during the study period. The government needs to establish an environmental policy framework involving central, provincial, and local governments with stringent laws for environmental protection.

Ambient Air Pollution and Socioeconomic Status in China

BACKGROUND

Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.

OBJECTIVE

Our objective was to test the relationship between ambient air pollution exposures and SES in China.

METHODS

We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.

RESULTS

Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9   μ g / m 3 NO 2 and 3 - 6   μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.

CONCLUSIONS

Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

Partitioning for “Common but Differentiated” Precise Air Pollution Governance: A Combined Machine Learning and Spatial Econometric Approach

Effective governance of air pollution requires precise identification of its influencing factors. Most existing studies attempt to identify the socioeconomic factors but lack consideration of multidimensional heterogeneous characteristics. This paper fills this long-ignored research gap by differentiating governance regions with regard to multidimensional heterogeneity characteristics. Decision tree recursive analysis combined with a spatial autoregressive model is used to identify governance factors in China. Empirical results show several interesting findings. First, geographic location, administrative level, economic zones and regional planning are the main heterogeneous features of accurate air pollution governance in Chinese cities. Second, significant influencing factors of air pollution in different delineated regions are identified, especially significant differences between coastal and non-coastal cities. Third, the trends of heterogeneity in urban air governance in China are to some extent consistent with national policies. The approach identifies factors influencing air pollution, thus providing a basis for accurate air pollution governance that has wider applicability.

Examining the Potential Scaling Law in Urban PM2.5 Pollution Risks along with the Nationwide Air Environmental Effort in China

Urban scaling law provides a quantitative understanding of the fundamental nonlinear properties of how cities work. Addressing this, this study intended to examine the potential scaling law that may lie in urban air pollution. With ground-monitored PM2.5 data and statistical socioeconomic factors in 265 Chinese cities (2015–2019), a targeted analysis, based on the scaling power-law model and scale-adjusted metropolitan indicator (SAMI) was conducted. The main findings of this study were summarized as follows: (1) A significant sublinear scaling relationship between PM2.5 and urban population size indicated that air quality degradation significantly lagged behind urban growth, affirming the remarkable effectiveness of national efforts on atmospheric environment improvement. (2) SAMI analysis expressed the relative conflict risk between PM2.5 pollution and urbanization and showed significant spatial cluster characteristics. Cities in central China showed higher potential risk than other regions, and there was a clear southward tendency for the city clusters with increasing SAMIs during the study period. (3) During the study period, urbanization was not the reason affecting the human-land conflict in terms of air pollution. This study is significant in that it marked the first innovative incorporation of the scaling law model into an urban environmental risk study. It also offered a new perspective from which to reframe the urban PM2.5 pollution risk, along with the nationwide air environmental effort in China, which will benefit future research on multi-types of urban environmental issues.

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM_2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM_2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM_2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM_2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM_2.5 concentration is inverted. The results show that the PM_2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM_2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM_2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM_2.5 concentration estimation and prediction based on satellite remote sensing technology.

Does the EU emissions trading system help reduce PM2.5 damage? A research based on PSM-DID method

Air quality issues, especially haze pollution, have become an important aspect that threatens the sustainable development and health of human beings. Previous studies on the environmental effects of emissions trading system (ETS) mainly focused on carbon emission reduction, instead of focusing on the synergistic governance effect between carbon emission and PM2.5 reduction. Based on the PSM-DID method and the World Development Index (WDI) database, this paper examines whether the EU ETS has a spillover effect on PM2.5 damage reduction, and discusses the related impact mechanisms. The research results show that the EU ETS promotes the reduction of PM2.5 damage, and in different phases of implementation, the impact of the EU ETS on the reduction of PM2.5 damage has a dynamic effect. The robustness test results also show that the research conclusions of this paper are highly reliable. Finally, this paper gives relevant policy suggestions, which can encourage countries to achieve carbon emission reduction targets while helping to reduce PM2.5 damage, and eventually achieve a win-win situation between economic growth and environmental improvement.

Hybridization of hierarchical clustering with persistent homology in assessing haze episodes between air quality monitoring stations

Haze has been a major issue afflicting Southeast Asian countries, including Malaysia, for the past few decades. Hierarchical agglomerative cluster analysis (HACA) is commonly used to evaluate the spatial behavior between areas in which pollutants interact. Typically, using HACA, the Euclidean distance acts as the dissimilarity measure and air quality monitoring stations are grouped according to this measure, thus revealing the most polluted areas. In this study, a framework for the hybridization of the HACA technique is proposed by considering the topological similarity (Wasserstein distance) between stations to evaluate the spatial patterns of the affected areas by haze episodes. For this, a tool in the topological data analysis (TDA), namely, persistent homology, is used to extract essential topological features hidden in the dataset. The performance of the proposed method is compared with that of traditional HACA and evaluated based on its ability to categorize areas according to the exceedance level of the particulate matter (PM₁₀). Results show that additional topological features have yielded better accuracy compared to without the case that does not consider topological features. The cluster validity indices are computed to verify the results, and the proposed method outperforms the traditional method, suggesting a practical alternative approach for assessing the similarity in air pollution behaviors based on topological characterizations.

Explaining the longevity characteristics in China from a geographical perspective: A multi-scale geographically weighted regression analysis

Longevity is a near-universal human aspiration that can affect moral progress and economic development at the social level. In rapidly developing China, questions about the geographical distribution and environmental factors of longevity phenomenon need to be answered more clearly. This study calculated the longevity index (LI), longevity index for females (LIF) and longevity index for males (LIM) based on the percentage of the long-lived population among the total number of elderly people to investigate regional and gender characteristics at the county level in China. A new multi-scale geographically weighted regression (MGWR) model and four possible geographical environmental factors were applied to explore environmental effects. The results indicate that the LIs of 2838 counties ranged from 1.3% to 16.3%, and the distribution showed obvious regional and gender differences. In general, the LI was high in the East and low in the West, and the LIF was higher than the LIM in 2614 counties (92.1%). The MGWR model performed well explaining that geographical environmental factors, including topographic features, vegetation conditions, human social activity and air pollution factors have a variable influence on longevity at different spatial scales and in different regions. These findings enrich our understanding of the spatial distribution, gender differences and geographical environmental effects on longevity in China, which provides an important reference for people interested in the variations in the associations between different geographical factors.

Spatio-temporal heterogeneity of air pollution and its key influencing factors in the Yellow River Economic Belt of China from 2014 to 2019

The Yellow River Economic Belt (YREB) plays an important role in China's socio-economic development and ecological security. However, this region has suffered from serious atmospheric pollution in recent years due to intense human activity. Identifying and qualifying the spatio-temporal characteristics of the region's air pollution and its driving forces would help in the formulation of effective mitigation policies. Here, the YREB's spatio-temporal characteristics of air quality were meticulously investigated using air pollution observation, synchronous meteorological, and socio-economic data from 103 cities, for the period of 2014-2019. Furthermore, the factors influencing air pollution were analyzed and qualified. Although air quality improved in the cities of the YREB following the implementation of the Air Pollution Action Plan, the region's quality index (AQI) remained higher than the national average. Annual variations of AQI in the YREB followed a U-shaped pattern, being high in autumn and winter and low in spring and summer; this U shape became shallower following improvements in air quality during autumn and winter. From 2014 to 2019, the annual average AQI values of cities in the eastern, middle, and western YREB dropped from 109.66, 111.70, and 94.65 to 92.00, 103.85, and 73.95, respectively. The air pollution trends of cities revealed obvious spatial agglomeration, and those cities with poor air quality were primarily the western cities of Shandong province, most cities in Henan province, and the eastern cities of Shanxi province. Due to the improvement of air quality in eastern cities, the pollution center of gravity moved gradually from Changzhi (113°3411"E, 36°040"N) to Linfen (110°5222″E, 36°2344″N). The results of the spatial Durbin model (SDM) indicated that air pollution had an apparent spillover effect in the YREB at the watershed scale, and that government technical expenditure, gross domestic product (GDP) per capita, population density, annual wind speed, and relative humidity all had significant negative overall effects on the AQI values of cities. The green cover rate, ratio of secondary industry, and temperature, meanwhile, all had significant positive total effects. Due to differences the natural conditions and stages of socio-economic development between the eastern, middle, and western cities of the YREB, the impact directions and functional strengths of their key factors differed greatly.

Association between long-term exposure to ambient air pollution and clinical outcomes among patients with heart failure: Findings from the China PEACE Prospective Heart Failure Study

BACKGROUND

The health effects of air pollution on heart failure (HF) patients have not been adequately studied.

OBJECTIVES

We assessed the associations between long-term air pollution exposure and prognosis in HF patients.

METHODS

HF patients were prospectively recruited from 52 hospitals throughout China between August 2016 and May 2018. The participants were followed up for 12 months after discharge from index hospitalization. Long-term air pollution was calculated as annual average level of air pollution before the date of the index hospitalization. Outcomes were defined as HF readmission, cardiovascular death, and composite events. Cox proportional hazards model was applied to quantify the associations between air pollution exposure and clinical outcomes.

RESULTS

Of 4866 patients included in the analysis, mean age was 65.2 ± 13.5 years, and 62.5% were male. During 1-year follow-up, 1577 (32.4%) participants were readmitted for HF and 550 (11.3%) died from cardiovascular disease. Though no associations between long-term air pollution and HF readmission in the overall participants, geographic and age heterogeneity in the long-term effects of air pollutants on HF readmission was observed. Air pollutants included PM_2.5 [HR (hazard ratio) = 1.146, 95% CI (confidence interval): 1.044, 1.259], PM₁₀ (HR = 1.120, 95% CI: 1.043, 1.203), SO₂ (HR = 1.808, 95% CI: 1.190, 2.747), and CO (HR = 3.596, 95% CI: 1.792, 7.218) were associated with higher risk of HF readmission in South China, but not in North China, where people spend less time outdoors and have limited indoor-outdoor ventilation. PM_2.5, PM_10, O₃, and CO among patients ≥ 65 years were found to be associated with higher risk of HF readmission. The effects on composite outcomes were broadly consistent with that of HF readmission. Cardiovascular death was not significantly associated with air pollution in the overall or subgroups.

DISCUSSION

Among HF patients who were older, living in South China, more HF readmissions occurred with higher long-term air pollution exposure. The findings suggest that the elderly patients and those living in South China should particularly enhance their personal protection against air pollution.

Investigation on the Relationship between Satellite Air Quality Measurements and Industrial Production by Generalized Additive Modeling

The development of the green economy is universally recognized as a solution to natural resource shortages and environmental pollution. When exploring and developing a green economy, it is important to study the relationships between the environment and economic development. As opposed to descriptive and qualitative research without modeling or based on environmental Kuznets curves, quantitative relationships between environmental protection and economic development must be identified for exploration and practice. In this paper, we used the generalized additive model (GAM) regression method to identify relationships between atmospheric pollutants (e.g., NO2, SO2 and CO) from remote sensing and in situ measurements and their driving effectors, including meteorology and economic indicators. Three representative cities in the Anhui province, such as Hefei (technology-based industry), Tongling (resource-based industry) and Huangshan (tourism-based industry), were studied from 2016 to 2020. After eliminating the influence of meteorological factors, the relationship between air quality indexes and industrial production in the target cities was clearly observed. Taking Hefei, for example, when the normalized output of chemical products increases by one unit, the effect on atmospheric NO2 content increases by about 20%. When the normalized output of chemical product increases by one unit, the effect on atmospheric SO2 content increases by about 10%. When chemical and steel product outputs increase by one unit, the effect on atmospheric CO content increases by 25% and 20%, respectively. These results can help different cities predict local economic development trends varying by the changes in air quality and adjust local industrial structure.

The air quality changes and related mortality benefits during the coronavirus disease 2019 pandemic in China: Results from a nationwide forecasting study

Air quality changes during the coronavirus disease 2019 (COVID-19) pandemic in China has attracted increasing attention. However, more details in the changes, future air quality trends, and related death benefits on a national scale are still unclear. In this study, a total of 352 Chinese cities were included. We collected air pollutants (including fine particulate matter [PM_2.5], inhalable particulate matter [PM₁₀], nitrogen dioxide [NO₂], and ozone [O₃]) data for each city from January 2015 to July 2020. Convolutional neural network-quantile regression (CNN-QR) forecasting model was used to predict pollutants concentrations from February 2020 to January 2021 and the changes in air pollutants were compared. The relationships between the socioeconomic factors and the changes and the avoided mortality due to the changes were further estimated. We found sharp declines in all air pollutants from February 2020 to January 2021. Specifically, PM_2.5, PM₁₀, NO₂, and O₃ would drop by 3.86 μg/m³ (10.81%), 4.84 μg/m³ (7.65%), 0.55 μg/m³ (2.18%), and 3.14 μg/m³ (3.36%), respectively. The air quality changes were significantly related to many of the socioeconomic factors, including the size of built-up area, gross regional product, population density, gross regional product per capita, and secondary industry share. And the improved air quality would avoid a total of 7237 p.m._2.5-related deaths (95% confidence intervals [CI]: 4935, 9209), 9484 p.m.₁₀-related deaths (95%CI: 5362, 13604), 4249 NO₂-related deaths (95%CI: 3305, 5193), and 6424 O₃-related deaths (95%CI: 3480, 9367), respectively. Our study shows that the interventions to control COVID-19 would improve air quality, which had significant relationships with some socioeconomic factors. Additionally, improved air quality would reduce the number of non-accidental deaths.

Spatial characteristics of the PM2.5/PM10 ratio and its indicative significance regarding air pollution in Hebei Province, China

Particulate matter (PM) is the primary air pollutant in northern China. The PM_2.5/PM₁₀ ratio has been used increasingly as an indicator to reflect anthropogenic PM pollution, but its advantages compared with individual PM_2.5 or PM₁₀ concentrations have not been proven sufficiently by experimental data. By dividing Hebei Province (China) into seven natural ecological regions, this study investigated the spatial characteristics of the PM_2.5/PM₁₀ ratio and its relationships with PM_2.5, PM₁₀, economic density, and wind speed. Results showed that the PM_2.5/PM₁₀ ratio decreased from east to west and from south to north, with an annual average value in 2019 of 0.439-0.559. The characteristics of the spatial variation of the PM_2.5/PM₁₀ ratio were different to those of either PM_2.5 or PM₁₀ concentration, indicating that PM pollution reflected by the PM_2.5/PM₁₀ ratio is not entirely consistent with that by PM_2.5 and PM₁₀ concentrations. In comparison with PM_2.5 or PM₁₀ concentration, the PM_2.5/PM₁₀ ratio had higher (lower) correlation with economic density (wind speed), indicating that the PM_2.5/PM₁₀ ratio is a better indicator used to reflect the intensity of anthropogenic emissions of PM pollutants. According to the characteristics of the spatial variations of the PM_2.5/PM₁₀ ratio and the PM_2.5 and PM₁₀ concentrations, the seven ecological regions of Hebei Province were categorized into four different types of atmospheric PM pollution: "three low regions," "three high regions," "one high and two low regions," and "one low and two high regions." This reflects the comprehensive effect of the intensity of anthropogenic PM emissions and the atmospheric diffusion conditions.

Short-Term Effects of Particle Size and Constituents on Blood Pressure in Healthy Young Adults in Guangzhou, China

Background Although several studies have focused on the associations between particle size and constituents and blood pressure, results have been inconsistent. Methods and Results We conducted a panel study, between December 2017 and January 2018, in 88 healthy university students in Guangzhou, China. Weekly systolic blood pressure and diastolic blood pressure were measured for each participant for 5 consecutive weeks, resulting in a total of 440 visits. Mass concentrations of particles with an aerodynamic diameter of ≤2.5 µm (PM2.5), ≤1.0 µm (PM1.0), ≤0.5 µm (PM0.5), ≤0.2 µm (PM0.2), and number concentrations of airborne particulates of diameter ≤0.1 μm were measured. Linear mixed-effect models were used to estimate the associations between blood pressure and particles and PM2.5 constituents 0 to 48 hours before blood pressure measurement. PM of all the fractions in the 0.2- to 2.5-μm range were positively associated with systolic blood pressure in the first 24 hours, with the percent changes of effect estimates ranging from 3.5% to 8.8% for an interquartile range increment of PM. PM0.2 was also positively associated with diastolic blood pressure, with an increase of 5.9% (95% CI, 1.0%-11.0%) for an interquartile range increment (5.8 μg/m3) at lag 0 to 24 hours. For PM2.5 constituents, we found positive associations between chloride and diastolic blood pressure (1.7% [95% CI, 0.1%-3.3%]), and negative associations between vanadium and diastolic blood pressure (-1.6% [95% CI, -3.0% to -0.1%]). Conclusions Both particle size and constituent exposure are significantly associated with blood pressure in the first 24 hours following exposure in healthy Chinese adults.

Progressing towards Environmental Health Targets in China: An Integrative Review of Achievements in Air and Water Pollution under the “Ecological Civilisation and the Beautiful China” Dream

Despite the positive effect of industrialisation on health and quality of life indicators across the globe, it is also responsible for the release of chemical toxins into the environment. Thus, the pursuit of economic development through industrialisation has equally nurtured numerous environmental disasters with accompanying catastrophic health effects. China is one of the countries with high carbon emissions, but new policy changes have resulted in massive gains in controlling environmental damage while enhancing the environment-related quality of life. This paper combines the six-step integrative review strategy with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) strategy to determine appropriate exclusion and inclusion criteria to explore the available stock of literature. We note that overall pollution in China fell by 10% between 2014 and 2019 whereas the average fine particulate matter (PM2.5) concentration of 93 micrograms per cubic meter reduced by 47% by 2019. Beijing exhibited the top 200 most polluted cities in 2019 after recording the lowest PM2.5 ever. All cities that implemented the 2012 Environmental Air Quality Standards reduced the average concentration of PM2.5 and sulfur dioxide by 42–68% by the end of 2018. Improvements in freshwater quality and a decline in water pollution levels were recorded despite increases in economic growth, urbanisation, energy use, trade openness, and agriculture, all of which are major stimulants of pollution. Deterring environmental tariff, tight ecological inspections, closing down of non-compliant producers, heavy investment in environmental control, and the ambitious five year-plan to revitalise renewable energy goals emanating from China’s ecological civilisation masterplan are responsible for these improvements in air and water pollution. China needs to work more aggressively to consolidate the gains already made in order to quicken the actualisation of the ecological civilisation and beautiful China dream.

Association between urbanisation and the risk of hyperuricaemia among Chinese adults: a cross-sectional study from the China Health and Nutrition Survey (CHNS)

OBJECTIVE

To explore the association between urbanicity and hyperuricaemia (HUA) and whether urbanicity is an independent risk factor for HUA in Chinese adults.

DESIGN

Data analysis from a cross-sectional survey.

SETTING AND PARTICIPANTS

8579 subjects aged 18 years or older were enrolled in the study from the 2009 wave of the China Health and Nutrition Survey to analyse the association between urbanicity and HUA. We divided them into three categories according to urbanisation index (low, medium and highly urbanised groups).

MAIN OUTCOME MEASURES

HUA was defined as serum uric acid ≥7 mg/dL in men and ≥6 mg/dL in women.

RESULTS

The prevalence of HUA in low, medium and highly urbanised groups was 12.2%, 14.6% and 19.8%, respectively. The independent factors influencing serum uric acid included age, gender, hypertension, diabetes, chronic kidney disease, drinking, obesity and community-level urbanisation index (β=0.016, p<0.001). The risk of HUA in the highly urbanised group was significantly higher than that of the low urbanised group (OR 1.771, 95% CI 1.545 to 2.029, p<0.001), even after adjusting for other covariates (OR 1.661, 95% CI 1.246 to 2.212, p=0.001). In a subgroup analysis, we found that age, gender, comorbidity (such as hypertension, diabetes, obesity and chronic kidney disease) and physical activity affected the association between urbanisation and the risk of HUA.

CONCLUSIONS

Our findings suggest that living in highly urbanised areas is linked with higher risk of HUA independent of cardiometabolic and health-related behavioural risk factors, which have been shown to increase along with urbanisation.

COVID-19's lockdown effect on air quality in Indian cities using air quality zonal modeling

The complete lockdown due to COVID-19 pandemic has contributed to the improvement of air quality across the countries particularly in developing countries including India. This study aims to assess the air quality by monitoring major atmospheric pollutants such as AOD, CO, PM_2.5, NO₂, O₃ and SO₂ in 15 major cities of India using Air Quality Zonal Modeling. The study is based on two different data sources; (a) grid data (MODIS- Terra, MERRA-2, OMI and AIRS, Global Modeling and Assimilation Office, NASA) and (b) ground monitoring station data provided by Central Pollution Control Board (CPCB) / State Pollution Control Board (SPCB). The remotely sensed data demonstrated that the concentration of PM_2.5 has declined by 14%, about 30% of NO₂ in million-plus cities, 2.06% CO, SO₂ within the range of 5 to 60%, whereas the concentration of O₃ has increased by 1 to 3% in majority of cities compared with pre lockdown. On the other hand, CPCB/SPCB data showed more than 40% decrease in PM_2.5 and 47% decrease in PM₁₀ in north Indian cities, more than 35% decrease in NO₂ in metropolitan cities, more than 85% decrease in SO₂ in Chennai and Nagpur and more than 17% increase in O₃ in five cities amid 43 days pandemic lockdown. The restrictions of anthropogenic activities have substantial effect on the emission of primary atmospheric pollutants.

Spatiotemporal impact of major events on air quality based on spatial differences-in-differences model: big data analysis from China

In an attempt to investigate the impact of major events on urban air quality in terms of the extent, duration and spatial scope, data on the daily air quality index and the concentrations of individual pollutants are collected in 140 cities of China from January 2, 2015, to November 28, 2017. Based on a spatial differences-in-differences, the impact of major events, such as political conferences, sporting events at the national level, on urban air quality in the dimensions of time and space are explored. It is concluded that major events not only affected the air quality of the host city, but also exercised influence on the air quality of the surrounding areas. Recommendations for mitigating the impact of major events on urban air quality have been proposed, such as establish regional atmospheric environment management system and formulate regional unified standards for pollutant discharge, industrial access and law enforcement.

Impacts of electricity generation on air pollution: evidence from data on air quality index and six criteria pollutants

We estimate impacts of electricity generation (total power output and thermal power output) on air pollution (air quality index (AQI) and six criteria air pollutants), with a particular emphasis on industry and city heterogeneity. To identify this relationship, we combine detailed monthly data on electricity production, air pollution, economy and weather for a six-year period in four biggest cities in China. Our fundamental identification strategy employs Ordinary Least Squares Regression of panel data with city–month fixed effects and addresses confounding variations between electricity generation and economy or weather conditions. We find that one unit (100 million kwh) increase in power output is associated with a 0.3-unit (representing value) increase in AQI, nearly all of which is driven by increases in thermal power output. We notice a robust positive impact of increased electricity generation (specifically thermal power output) on PM2.5 and PM10, also positive relationships between increases in other power output (total power output minus thermal power output) and SO2, NO2, while changes in power output have no statistically significant effect on CO and O3. The heterogeneous pollution effects of electricity generation are present in specific cities with different weather conditions. The results indicate that a reduction policy in power industry differentiating among cities might enhance effectiveness by considering each city’s particular backgrounds, a previously overlooked aspect associated with pollution reduction policies.

Evaluation and Treatment Analysis of Air Quality Including Particulate Pollutants: A Case Study of Shandong Province, China

At present, China’s air pollution and its treatment effect are issues of general concern in the academic circles. Based on the analysis of the development stages of air pollution in China and the development history of China’s air quality standards, we selected 17 cities of Shandong Province, China as the research objects. By expanding China’s existing Air Quality Index System, the air quality of six major pollutants including PM_2.5 and PM₁₀ in 17 cities from February 2017 to January 2020 is comprehensively evaluated. Then, with a forecast model, the air quality of the above cities in the absence of air pollution control policies since June 2018 was simulated. The results of the error test show that the model has a maximum error of 4.67% when simulating monthly assessment scores, and the maximum mean error of the four months is 3.17%. Through the comparison between the simulation results and the real evaluation results of air quality, we found that since June 2018, the air pollution control policies of six cities have achieved more than 10% improvement, while the air quality of the other 11 cities declined. The different characteristics of pollutants and the implementation of governance policies are perhaps the main reasons for the above differences. Finally, policy recommendations for the future air pollution control in Shandong and China were provided.

Revisiting the impact of vehicle emissions and other contributors to air pollution in urban built-up areas: A dynamic spatial econometric analysis

Whether vehicle emissions are the primary source of PM_2.5 in urban China remains controversial, which may be attributable to the insufficient consideration of the spatial autocorrelation and the spatial spillover effects of PM_2.5. We employ data from built-up areas of 285 prefecture-level cities in China spanned 2001-2016 and dynamic spatial panel data analysis to resolve this controversy. Our results show that the direct and indirect effects of vehicles on PM_2.5 concentration (annual mean and spatial variation within the city) in urban China are not significant in the short- and long-term. Alternatively, SO₂ emission directly increases the mean and spatial variation of PM_2.5 within the city in the short- and long-term. Short-term direct and indirect positive association and long-term indirect positive association are found relative to economic growth and PM_2.5. Population density increases PM_2.5 directly and indirectly in the short-term and yet, directly decreases and indirectly increases PM_2.5 in the long-term. In the short- and long-term, the spatial spillover effect of secondary industry increases PM_2.5, and industry also directly increases the spatial variation of PM_2.5 within the city. Although real estate investment directly increases PM_2.5 in the long-term, the spatial spillover effect of investment reduces PM_2.5 in the short- and long-term. Our results show that other factors, rather than vehicle emissions, are the major contributors to PM_2.5 in urban China. Furthermore, the Environmental Kuznets Curve hypothesis does not apply to the relationship between economic growth and PM_2.5 proliferation in urban China. When tackling air pollution, owing to the significant spatial spillover of PM_2.5 that is driven by multiple contributing factors, short- and long-term inter-regional coordination is required to achieve an effective positive outcome.

Spatial spillover effects of environmental regulations on air pollution: Evidence from urban agglomerations in China

Environmental regulations affects the environmental quality of not only local areas but also surrounding regions. It remains unknown whether the effect of environmental regulations on the surrounding regions is free riding or pollution shelter. Based on the data from 2006 to 2018, the spatial correlation of PM_2.5 in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations in China was examined in this study. In addition, the spatial spillover effects of environmental regulation on PM_2.5 concentrations were explored while the socio-economic driving factors of the heterogeneity of pollution spillover were identified via SDM based STIRPAT framework. Results showed that the characteristics of PM_2.5 concentrations spatial correlations varies from one urban agglomeration to another. This study revealed that the air pollution is affected by not only local environmental regulations, but also regulations implemented in surrounding cities. The PM_2.5 concentration of BTH, YRD and PRD increased by 0.76, 0.147 and 0.109 for each unit increase in environmental regulation of surrounding cities, respectively. In fact, cities with loose regulation become the pollution shelters. The spatial spillover effects offset the improvement effects of local environmental regulations on the air quality. Furthermore, the comparison amongst three urban agglomerations showed that the spatial spillover effects of PM_2.5 concentration in BTH and YRD are higher than that of PRD. This is attributed to differences in industrial structure, population density, economic development, FDI and geographical location. Therefore, the spatial spillover effects should be taken into consideration and joint regulation should be strengthened to address air pollution issues in urban aggregations.

Spatial Association Effect of Haze Pollution in Cheng-Yu Urban Agglomeration

This study takes a network perspective to examine the spatial spillover effects of haze pollution in Cheng-Yu urban agglomeration which is the fourth largest urban agglomeration and a comprehensive demonstration zone of new urbanization in China. Firstly, we use Granger causality test to construct haze pollution spatial association network, and then we apply social network analysis to reveal the structural characteristics of this network. The results show that: haze pollution in Cheng-Yu urban agglomeration is a complex multithreaded network. Chongqing, Chengdu, Guang’an, Luzhou, Deyang and Nanchong are the centers of the network, sending and transmitting the most relationships. The haze pollution spatial association network can be divided into net beneficiary block, net overflow block, bilateral overflow block and broker block. These four blocks present obvious geographical distribution characteristics and are partly related to the difference of urbanization. The above results contribute by illustrating the current spatial spillover situation of haze pollution and provide a theoretical foundation for the government that it should simultaneously consider cities’ statues and their spatial spillover effects in the network rather than simple geographic proximity when formulating future haze pollution control policies in urban agglomeration.

Evaluation of urban comprehensive carrying capacity: case study of the Beijing-Tianjin-Hebei urban agglomeration, China

An evaluation indexing system based on the theory of coordination was constructed to estimate the urban carrying capacity (UCC) of the Beijing-Tianjin-Hebei (BTH) region with four subsystems: social, economic, environmental, and transportation. This indexing system revealed the interactions between "supply" and "demand." The improved entropy method was adopted to calculate the weight of 17 indicators and evaluate the comprehensive UCC of 13 cities in the BTH region using data covering the period 1990-2018. The results showed that two cities, Tangshan (UCC of - 0.0021) and Handan (UCC of - 0.0009), were "overloaded" in 2018, while the other 11 cities were "loadable." The social and transportation subsystems played the most crucial roles in the evaluation. Based on the results, Baoding achieved the highest UCC, while that of Tangshan was the lowest. The results could play a significant role in decision-making relating to the sustainable development of the BTH region. Three policy implications are proposed based on these findings: (i) the efficiency of resource utilization and scientific allocation should be enhanced and industrial optimization and upgrading should be promoted, (ii) the coordinated development of urbanization and environment in the region should be improved, and (iii) the integration of traffic decongestion measures should be faster, and industrial docking systems should be enhanced.

Vehicle emissions in a middle-sized city of China: Current status and future trends

Vehicle emissions are regarded as an important contributor to urban air pollution in China and most previous studies focused on megacities. However, the vehicle pollution in middle-sized cities becomes more severe due to the increasing vehicle population (VP) and lagged control policy. This study takes Langfang, a typical middle-sized city bordered by two megacities (Beijing and Tianjin), as the target domain to investigate vehicle emissions. The speed correction curves (SCC) are introduced to improve the vehicle emission factors (EF) simulation in official technical guidelines on emission inventory (GEI). A multi-year vehicle emission inventory (from 2011 to 2025) is developed in Langfang. From 2011 to 2017, the total vehicle emissions in Langfang decrease for carbon monoxide (CO), but increase for volatile organic compounds (VOCs), nitrogen oxides (NO_x), and inhalable particles (PM₁₀), respectively. From 2018 to 2025, the emissions would increase more rapidly in Langfang than in Beijing and Tianjin, indicating the middle-sized cities may become a significant contributor to air pollution in China. Four possible control policies, including VP constrained (VPC), public transportation promotion (PTP), new energy vehicles promotion (NEP), and freight transportation structure optimization (FTO) are evaluated. The most significant emissions reductions are observed under the FTO for CO, NO_x, and PM₁₀, and under the VPC for VOCs. The spatial distributions of vehicle emissions show a high order of heterogeneity, indicating that local conditions should be considered in policy formulation in addition to national consistency. More comprehensive policies should be implemented to mitigate the vehicle pollution in middle-sized cities.

Spatial Association Pattern of Air Pollution and Influencing Factors in the Beijing-Tianjin-Hebei Air Pollution Transmission Channel: A Case Study in Henan Province

The Beijing–Tianjin–Hebei (BTH) air pollution transmission channel and its surrounding areas are of importance to air pollution control in China. Based on daily data of air quality index (AQI) and air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃) from 2015 to 2016, this study analyzed the spatial and temporal characteristics of air pollution and influencing factors in Henan Province, a key region of the BTH air pollution transmission channel. The result showed that non-attainment days and NAQI were slightly improved at the provincial scale during the study period, whereas that in Hebi, Puyang, and Anyang became worse. PM_2.5 was the largest contributor to the air pollution in all cities based on the number of non-attainment days, but its mean frequency decreased by 21.62%, with the mean occurrence of O₃ doubled. The spatial distribution of NAQI presented a spatial agglomeration pattern, with high-high agglomeration area varying from Jiaozuo, Xinxiang, and Zhengzhou to Anyang and Hebi. In addition, the NAQI was negatively correlated with sunshine duration, temperature, relative humidity, wind speed, and positively to atmospheric pressure and relative humidity in all four clusters, whereas relationships between socioeconomic factors and NAQI differed among them. These findings highlight the need to establish and adjust regional joint prevention and control of air pollution as well as suggest that it is crucially important for implementing effective strategies for O₃ pollution control.