Spatiotemporal heterogeneity of PM2.5 and its relationship with urbanization in North China from 2000 to 2017

National and provincial burden of disease attributable to fine particulate matter air pollution in China, 1990-2021: an analysis of data from the Global Burden of Disease Study 2021

BACKGROUND

Fine particulate matter (PM2·5) is the leading environmental risk factor for mortality and disability worldwide. We aimed to evaluate the temporal trend in, and spatial distribution of, the disease burden attributable to PM2·5 in China from 1990 to 2021.

METHODS

Based on the methodology framework and general analytical strategies applied in the Global Burden of Diseases, Injuries, and Risk Factors Study 2021, we calculated the numbers, age-standardised rates, and percentage of deaths and disability-adjusted life-years (DALYs) attributable to PM2·5 air pollution from 1990 to 2021 at the national and provincial level in China, by disease, sex, and age groups. Exposure to PM2·5, including ambient PM2·5 pollution and household PM2·5 pollution from solid fuels, was evaluated across 33 provincial administrative units in China.

FINDINGS

In 2021, 2·3 million (95% uncertainty interval [UI] 1·8-2·9) deaths and 46·7 million (36·6-59·7) DALYs could be attributable to PM2·5 pollution in China, accounting for 19·4% (16·0-23·6) of total deaths and 11·6% (9·4-14·1) of total DALYs. Of these, 1·9 million (95% UI 1·3-2·3) deaths and 37·8 million (26·3-46·5) DALYs resulted from ambient exposure, while 0·4 million (0·1-1·3) deaths and 8·9 million (1·5-27·8) DALYs were due to household exposure from solid fuel use. Stroke, ischaemic heart disease, and chronic obstructive pulmonary disease were the leading three causes. Two peaks in the burden were observed: in children aged younger than 5 years, and in people aged 70 years and older. The percentage of deaths and DALYs due to ambient PM2·5 was higher in men, while that due to household PM2·5 was higher in women. Geographically, the disease burden from ambient PM2·5 was higher in north and northwest China, while that from household PM2·5 was higher in southwest China. From 1990 to 2021, age-standardised death rates attributable to total PM2·5 decreased by 66·0% (95% UI 57·7-73·1) and those attributable to household PM2·5 decreased by 92·2% (76·6-98·7), with larger reductions observed in east and south China. By contrast, the disease burden related to ambient PM2·5 continued to increase and only began to decline in the past decade.

INTERPRETATION

Despite the decline in the disease burden attributable to total PM2·5 in China during 1990-2021, ambient PM2·5 remains a major contributor to mortality and disability. This study highlights considerable spatial heterogeneity across different provinces and provides valuable insights for developing geographically tailored strategies for PM2·5 control and public health promotion in China. Stricter control of ambient air pollution is needed in northern and northwestern regions, while promoting clean cooking energy is more urgently warranted in southwestern areas.

FUNDING

National Natural Science Foundation of China, National Key Research and Development Program of China, Shanghai Municipal Science and Technology Major Project, China Postdoctoral Science Foundation.

Critical windows for exposure to chemical composition of ambient particulate matter and human semen quality decline

BACKGROUND

Critical windows for exposure to chemical components of particulate matter (PM <2.5 μm in diameter [PM2.5]) associated with the human semen quality decline remain unclear.

OBJECTIVES

To address this gap, we developed a new analytical framework by integrating a Linear Mixed Model (LMM) with subject- and center-specific intercepts and a Distributed Lag Model (DLM) to fully account for correlations between finely vulnerable exposure windows based on complete profile of the spermatogenesis cycle.

METHODS

We constructed a multicenter cohort involving 33,234 sperm donors with 78,952 semen samples covering 6 representative regions across China from 2014 to 2020 to investigate the week-scale critical windows for the exposure. Daily exposure to PM2.5 chemical components of donors was derived from grid data based on 1-km spatial resolution surface measurements.

RESULTS

Decreased sperm count was significantly associated with NO3- and SO42- at 9-10 weeks (e.g., β: -0.05 %, 95%CI: [-0.10 %, -0.00 %] at the 9th week) and 0-2 weeks (e.g., β: -0.66 %, 95%CI: [-1.24 %, -0.07 %] at the 1st week), respectively. Critical windows of progressive motility decline were 0-10 weeks for BC (e.g., β: -0.07 %, 95%CI: [-0.11 %, -0.03 %] at the 5th week), Cl- at 1-4 weeks (e.g., β: -2.21 %, 95%CI: [-3.77 %, -0.66 %] at the 2nd week), 0-6 weeks and 9-10 weeks for NO3- (e.g., β: -0.05 %, 95%CI: [-0.09 %, -0.01 %] at the 4th week), 1-3 weeks and the 8th week for NH4+ (e.g., β: -0.06 %, 95%CI: [-0.11 %, -0.01 %] at the 2nd week). Total motility is significantly negatively associated with BC at entire windows, Cl- at 0-3 weeks, the 5th week and 9-10 weeks.

CONCLUSIONS

There are week-scale vulnerable windows of exposure to PM2.5 chemical components for human semen quality. This highlights the need for more targeted pollution control strategies addressing PM2.5 and its chemical components.

Spatiotemporal characteristics analysis of multi-factorial air pollution in the Jing-Jin-Ji region based on improved sequential ICI method and novel grey spatiotemporal incidence models

Air pollution shares the attributes of multi-factorial influence and spatiotemporal complexity, leading to air pollution control assistance models easily falling into a state of failure. To address this issue, we design a framework containing improved data fusion method, novel grey incidence models and air pollution spatiotemporal analysis to analyze the complex characteristics of air pollution under the fusion of multiple factors. Firstly, we improve the existing data fusion method for multi-factor fusion. Subsequently, we construct two grey spatiotemporal incidence models to examine the spatiotemporal characteristics of multi-factorial air pollution in network relationships and changing trends. Furthermore, we propose two new properties that can manifest the performance of grey incidence analysis, and we provide detailed proof of the properties of the new models. Finally, in the Jing-Jin-Ji region, the novel models are used to study the network relationships and trend changes of air pollution. The findings are as follows: (1) Two highly polluted belts in the region require attention. (2) Although the air pollution network under multi-factorial fusion obeys the first law of geography, the network density and node density exhibit significant variations. (3) From 2013 to 2021, all pollutants except O3 show improvement. (4) Recommendations for responses are presented based on the above-mentioned results. (5) The parameter analyses, model comparisons, Monte Carlo experiments and model feature summaries illustrate that the proposed models are practical, interpretable and considerably outperform various prevailing competitors with remarkable universality.

Can regional joint prevention and control of atmospheric reduce border pollution? Evidence from China's 12th Five-Year Plan on air pollution

Border pollution is usually a difficult problem in environmental governance. Based on the data at the county level in China from 2005 to 2019, this study takes the 12th Five-Year Plan (FYP) for atmospheric pollution as a policy shock, and uses the difference-in-differences (DID) method to explore the impact of regional joint prevention and control (JPC) of atmospheric pollution policy on air pollution of the border regions. Empirical results show that: (1) After implementing the JPC of atmospheric pollution policy, the PM_2.5 concentration in the border regions is reduced by 3.5%. (2) The mechanism analysis shows that there is a spillover effect in the governing behaviors of local governments. In the border areas under low economic growth pressure and high environmental protection pressure, the reduction effect of the JPC of atmospheric pollution policy is more significant on the PM_2.5 concentration of the border regions. The research conclusions have new insights into the role and effect of macro-regional environmental JPC policy and border pollution control, and provide practical guidance for social green governance.

Long-term cardiometabolic effects of ambient ozone pollution in a large Chinese population

Limited studies investigated the effects of long-term ozone exposure on cardiometabolic health. We aimed to examine the association of long-term ozone exposure with a range of cardiometabolic diseases, as well as the subclinical indicators in Eastern China. The study included 202,042 adults living in 11 prefecture-level areas in Zhejiang Province between 2014 and 2021. Using a satellite-based model with a 1 × 1 km spatial resolution, we estimated residential 5-year average ozone exposures for each subject. Mixed-effects logistic and linear regression models were applied to explore the associations of ozone exposure with cardiometabolic diseases and subclinical indicators, respectively. We found that a 9% [95% confidence interval (95% CI): 7-12%] higher in odds of cardiometabolic disease per 10 μg/m³ increase in ozone exposure. Specifically, we also found higher prevalence of cardiovascular diseases (15%), stroke (19%), hypertension (7%), dyslipidemia (15%), and hypertriglyceridemia (9%) associated with ozone exposure. However, we did not find significant associations between ozone exposure and coronary heart disease, myocardial infarction, or diabetes mellitus. Long-term ozone exposures were also significantly associated with adverse changes in systolic blood pressure, diastolic blood pressure, total serum cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, glucose concentration, and body mass index. Our results showed that people with lower education levels, those over 50 years old, and those who were overweight or obese were more susceptible to the effects of ozone on cardiometabolic diseases. Our findings demonstrated the detrimental effects of long-term ozone exposure on cardiometabolic health, emphasizing the need for ozone control strategies to reduce the burden of cardiometabolic diseases.

Lung cancer risk and exposure to air pollution: a multicenter North China case-control study involving 14604 subjects

BACKGROUND

For North Chinese lung cancer patients, there is limited study on the distribution of air pollution and smoking related features based on analyses of large-scale, high-quality population datasets. The aim of the study was to fully analyze risk factors for 14604 Subjects.

METHODS

Participants and controls were recruited in 11 cities of North China. Participants' basic information (sex, age, marital status, occupation, height, and weight), blood type, smoking history, alcohol consumption, history of lung-related diseases and family history of cancer were collected. PM2.5 concentration data for each year in each city of the study area from 2005 to 2018 were extracted based on geocoding of each person's residential address at the time of diagnosis. Demographic variables and risk factors were compared between cases and matched controls using a univariate conditional logistic regression model. Multivariate conditional logistic regression models were applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for risk factors in univariate analysis. The nomogram model and the calibration curve were developed to predict lung cancer probability for the probability of lung cancer.

RESULTS

There was a total of 14604 subjects, comprising 7124 lung cancer cases and 7480 healthy controls included in the study. Marital status of unmarried persons, people with a history of lung-related disease, corporate personnel and production /service personnel were protective factors for lung cancer. People younger than 50 years old, people who were smoking and quit smoking, people who had been drinking consistently, people with family history of cancer and PM2.5 exposure were proven to be a risk factor for lung cancer. The risk of lung cancer varied with sex, smoking status and air pollution. Consistent alcohol consumption, persistent smoking and smoking quit were risk factors for lung cancer in men. By smoking status, male was risk factor for lung cancer in never smokers. Consistent alcohol consumption added risk for lung cancer in never smokers. The combined effects of PM2.5 pollution exposure and ever smoking aggravated the incidence of lung cancer. According to air pollution, lung cancer risk factors are completely different in lightly and heavily polluted areas. In lightly polluted areas, a history of lung-related disease was a risk factor for lung cancer. In heavily polluted areas, male, consistent alcohol consumption, a family history of cancer, ever smokers and smoking quit were all risk factors for lung cancer. A nomogram was plotted and the results showed that PM2.5 was the main factor affecting the occurrence of lung cancer.

CONCLUSIONS

The large-scale accurate analysis of multiple risk factors in different air quality environments and various populations, provide clear directions and guidance for lung cancer prevention and precise treatment.

Potential heterogeneity of urban ecological resilience and urbanization in multiple urban agglomerations from a landscape perspective

Rapid urbanization has reduced the capacity of cities to mitigate and withstand disasters. Strengthening urban ecological resilience (ER) is important for improving urban self-organization. Geographical characteristics and developmental status of different cities lead to a more complex relationship between urbanization and ER. Using the three major urban agglomerations in China, we constructed a new framework for assessing the ER from a landscape and ecological processes perspective, and analyzed the driving heterogeneity of urbanization on ER. The results indicated that the ER of Yangtze River Delta (YRD) and Pearl River Delta (PRD) decreased continuously from 2000 to 2018, while the ER of Beijing-Tianjin-Hebei (BTH) decreased from 2000 to 2010, and then increased from 2010 to 2018. The resilience level of PRD was significantly lower than those of BTH and YRD. The urbanization process had a negative impact on ER, and the contribution of urbanization factors to ER varied significantly across cities, and population factors have the most direct influence. Curve fitting analysis further deepened our understanding of heterogeneity, investigating from the perspective of landscape and driving factors, and suggesting improvement measures. This study can deepen the understanding of the impact of urbanization on resilience and provide scientific guidance for achieving regional sustainability.

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.

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

As a key node city of the "Silk Road Economic Belt" Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. This study analyzed the air quality before and during the COVID-19 (Coronavirus disease 2019) pandemic and its potential health effects based on the data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h levels from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021. As per the results, the concentrations of the air pollutants PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h in Urumqi from 2017 to 2021 showed a cyclical trend, and the implementation of COVID-19 prevention and control measures could effectively reduce the concentration(ρ) of air pollutants. The mean value of ρ(PM_2.5) decreased from 2017 to 2021, whereas ρ(O₃_8h) showed a waveform change trend (increased in 2017-2018, decreased in 2018-2020, and increased after 2020). Meanwhile, the maximum annual average values of ρ(PM_2.5) and ρ(O₃_8h) for the six monitoring stations during 2017-2021 occurred at sites S2 (74.37 µg m^-3) and S6 (91.80 µg m^-3), respectively; rapid industrialization had a greater impact on PM_2.5 and O₃_8h concentrations compared to commercial and residential areas. In addition, the air quality index data series can characterize the fluctuation trend of PM_2.5. The high pollution levels (Class IV and V) of the air pollutants PM_2.5 and O₃_8h in Urumqi have been decreasing annually, and good days can account for 80-95% of the total number of days in the year, indicating that the number of days with a potential threat to residents' health is gradually decreasing. Therefore, more attention should be paid in controlling and managing air pollution in Urumqi.

Short-term health impacts related to ozone in China before and after implementation of policy measures: A systematic review and meta-analysis

This paper presents a meta-analysis of the impacts of short-term exposure to ozone (O₃) on three health endpoints: all-cause, cardiovascular, and respiratory mortality in China. All relevant studies from January 1990 to December 2021 were searched from four databases. After screening, 30 studies were included for the meta-analysis. The results showed that a significant rise of 0.41 % (95 % confidence interval (CI): 0.35 %-0.48 %) in all-cause, 0.60 % (95 % CI: 0.51 %-0.68 %) in cardiovascular and 0.45 % (95 % CI: 0.28 %-0.62 %) in respiratory mortality for each 10 μg m^-3 increase in the maximum daily 8 h average O₃ concentration (MDA8 O₃). Moreover, results stratified by heterogeneous time periods before and after implementing a policy measure in 2013, showed that the pooled effects for all-cause and respiratory mortality before were greater than those after, while the pooled effects for cardiovascular mortality before 2013 were slightly smaller than those after. The finding that short-term exposure to O₃ was positively related to the three health endpoints was validated by means of a sensitivity analysis. Furthermore, we did not observe any publication bias. Our results present an updated and better understanding of the relationship between short-term exposure to O₃ and the three health endpoints, while providing a reference for further assessment of the impact of short-term O₃ exposure on human health.

Multidimensional effects of urbanization on PM2.5 concentration in China

Recently, the contradiction between urbanization and the air environment has gradually attracted attention. However, most existing studies have explored the impact of single urbanization factors, such as population, the economy, or land, on PM_2.5 and ignored the impact of multidimensional urbanization on PM_2.5 concentration. Moreover, the heterogeneity in the mechanisms responsible for the PM_2.5 concentration caused by multidimensional urbanization has not been thoroughly studied in different regions in China. Therefore, we investigate the spatial-temporal evolution characteristics of PM_2.5 concentration in China during 1998-2019 by spatial analysis and dynamic panel models based on the environmental Kuznets curve (EKC). Then, we study the effects of multidimensional urbanization on PM_2.5 concentration, and analyze the dominant factors in China's eight economic regions. During the study period, the PM_2.5 concentration in China fluctuated before 2013 and gradually decreased thereafter. The PM_2.5 concentration has significant regional differences in China. Spatially, the PM_2.5 concentration is higher in the north than in the south and higher in the east than in the west. Additionally, there is a significant spatial spillover effect. Both population urbanization and economic urbanization show an inverted U-shaped relationship with PM_2.5 concentration in China, which is consistent with the classical EKC theory. Due to other socioeconomic factors, the PM_2.5 concentration tends to decrease linearly with increasing land urbanization rate. The effects of urbanization on the PM_2.5 concentration in the eight economic regions in China show significant differences. The effect of land urbanization on the PM_2.5 concentration is dominant in the Middle Yangtze River region, that of economic urbanization is dominant in northwestern China, and that of population urbanization is dominant in the remaining regions in China.

Chemical characterization, source apportionment, and health risk assessment of PM2.5 in a typical industrial region in North China

To clarify the chemical characteristics, source contributions, and health risks of pollution events associated with high PM_2.5 in typical industrial areas of North China, manual sampling and analysis of PM_2.5 were conducted in the spring, summer, autumn, and winter of 2019 in Pingyin County, Jinan City, Shandong Province. The results showed that the total concentration of 29 components in PM_2.5 was 53.4 ± 43.9 μg·m^-3, including OC/EC, water-soluble ions, inorganic elements, and metal elements. The largest contribution was from the NO₃^- ion, at 14.6 ± 14.2 μg·m^-3, followed by organic carbon (OC), SO₄^2-, and NH₄⁺, with concentrations of 9.3 ± 5.5, 9.1 ± 6.4, and 8.1 ± 6.8 μg·m^-3, respectively. The concentrations of OC, NO₃^-, and SO₄^2- were highest in winter and lowest in summer, whereas the NH₄⁺ concentration was highest in winter and lowest in spring. Typical heavy metals had higher concentrations in autumn and winter, and lower concentrations in spring and summer. The annual average sulfur oxidation rate (SOR) and nitrogen oxidation rate (NOR) were 0.30 ± 0.14 and 0.21 ± 0.12, respectively, with the highest SO₂ emission and conversion rates in winter, resulting in the SO₄^2- concentration being highest in winter. The average concentration of secondary organic carbon in 2019 was 2.8 ± 1.9 μg·m^-3, and it comprised approximately 30% of total OC. The concentrations of 18 elements including Na, Mg, and Al were between 2.3 ± 1.6 and 888.1 ± 415.2 ng·m^-3, with Ni having the lowest concentration and K the highest. The health risk assessment for typical heavy metals showed that Pb poses a potential carcinogenic risk for adults, whereas As may pose a carcinogenic risk for adults, children, and adolescents. The non-carcinogenic risk coefficients for all heavy metals were lower than 1.0, indicating that the non-carcinogenic risk was negligible. Positive matrix factorization analysis indicated that coal-burning emissions contributed the largest fraction of PM_2.5, accounting for 35.9% of the total. The contribution of automotive emissions is similar to that of coal, at 32.1%. The third-largest contributor was industrial sources, which accounted for 17.2%. The contributions of dust and other emissions sources to PM_2.5 were 8.4% and 6.4%, respectively. This study provides reference data for policymakers to improve the air quality in the NCP.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

Do We Need More Urban Green Space to Alleviate PM2.5 Pollution? A Case Study in Wuhan, China

Urban green space can help to reduce PM2.5 concentration by absorption and deposition processes. However, few studies have focused on the historical influence of green space on PM2.5 at a fine grid scale. Taking the central city of Wuhan as an example, this study has analyzed the spatiotemporal trend and the relationship between green space and PM2.5 in the last two decades. The results have shown that: (1) PM2.5 concentration reached a maximum value (139 μg/m3) in 2010 and decreased thereafter. Moran’s I index values of PM2.5 were in a downward trend, which indicates a sparser distribution; (2) from 2000 to 2019, the total area of green space decreased by 25.83%. The reduction in larger patches, increment in land cover diversity, and less connectivity led to fragmented spatial patterns of green space; and (3) the regression results showed that large patches of green space significantly correlated with PM2.5 concentration. The land use/cover diversity negatively correlated with the PM2.5 concentration in the ordinary linear regression. In conclusion, preserving large native natural habitats can be a supplemental measure to enlarge the air purification function of the green space. For cities in the process of PM2.5 reduction, enhancing the landscape patterns of green space provides a win-win solution to handle air pollution and raise human well-being.

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.

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.

Study on the Spatial and Temporal Distribution Characteristics and Influencing Factors of Particulate Matter Pollution in Coal Production Cities in China

In recent years, with the continuous advancement of China's urbanization process, regional atmospheric environmental problems have become increasingly prominent. We selected 12 cities as study areas to explore the spatial and temporal distribution characteristics of atmospheric particulate matter in the region, and analyzed the impact of socioeconomic and natural factors on local particulate matter levels. In terms of time variation, the particulate matter in the study area showed an annual change trend of first rising and then falling, a monthly change trend of "U" shape, and an hourly change trend of double-peak and double-valley distribution. Spatially, the concentration of particulate matter in the central and southern cities of the study area is higher, while the pollution in the western region is lighter. In terms of social economy, PM_2.5 showed an "inverted U-shaped" quadratic polynomial relationship with Second Industry and Population Density, while it showed a U-shaped relationship with Generating Capacity and Coal Output. The results of correlation analysis showed that PM_2.5 and PM₁₀ were significantly positively correlated with NO₂, SO₂, CO and air pressure, and significantly negatively correlated with O₃ and air temperature. Wind speed was significantly negatively correlated with PM_2.5, and significantly positively correlated with PM₁₀. In terms of pollution transmission, the southwest area of Taiyuan City is a high potential pollution source area of fine particles, and the long-distance transport of PM_2.5 in Xinjiang from the northwest also has a certain contribution to the pollution of fine particles. This study is helpful for us to understand the characteristics and influencing factors of particulate matter pollution in coal production cities.

A Health Impact and Economic Loss Assessment of O3 and PM2.5 Exposure in China From 2015 to 2020

China is in a critical air quality management stage. Rapid industrial development and urbanization has resulted in non-ignorable air pollution, which seriously endangers human health. Assessment of the health impacts and economic losses of air pollution is essential for the prevention and control policy formulation. Based on ozone (O3) and fine particulate matter concentration (PM2.5) monitoring data in 331 Chinese cities from 2015 to 2020, this study evaluated the health effects and the corresponding economic losses of O3 and PM2.5 pollution on three health endpoints. The ratio of population exposed to O3 levels that exceeded the Chinese Ambient Air Quality Standards (CAAQS) increased from 13.35% in 2015 to 14.15% in 2020, which resulted in 133,415 (2015) - 156,173 (2020) all-cause deaths, 88,941 (2015) - 104,051 (2020) cardiovascular deaths, and 28,614 (2015) - 33,456 (2020) respiratory deaths. The ratio of population exposed to PM2.5 levels that exceeded the CAAQS decreased, but in many regions, especially in North China and the Yangtze River Delta, the PM2.5 concentration remained high. By 2020, nearly half of the population in China was still exposed to PM2.5 levels that exceeded the CAAQS, and the corresponding economic losses reached CNY 3.46 and 3.05 billion, respectively. These results improved the understanding of the spatial-temporal variation trends of major air pollutants at city scale in China, and emphasize the continued coordination urgently needed for controlling O3 and PM2.5 following the implementation of the 2013 policy to mitigate air pollution to protect human health.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000-2018

In recent years, air pollution caused by PM_2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM_2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM_2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM_2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM_2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM_2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM_2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM_2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM_2.5 pollution in China and provide an important reference for the future direction of PM_2.5 pollution control.

Local emissions and secondary pollutants cause severe PM2.5 elevation in urban air at the south edge of the North China Plain: Results from winter haze of 2017-2018 at a mega city

Severe haze occurrence in the north of the North China Plain (NCP) is recognized as a consequence of the regional transport of pollutants initially from the south and then the rapid formation of secondary pollutants in the local air. However, the origin of pollutants causing haze in the southern NCP has not yet been elucidated even through careful data observation. Based on the contents of water-soluble inorganic ions in PM_2.5 samples collected during two severe haze episodes in Zhengzhou, a mega city located on the southern edge of the NCP, we estimated the contributions of local primary emissions and secondary pollutants to haze occurrence. On average, Na⁺, K⁺, and Ca²⁺ mainly originated from anthropogenic sources, and their anthropogenic fractions had proportions of 97.5%, 93.9%, and 76.5% in their respective total mass. Anions Cl^- and SO₄^2- substantially originated from not only produced substantially via secondary formation but also from primary emissions, and their primary proportions in their respective total mass were 51.1% and 30.8%. In contrast, NH₄⁺ and NO₃^- were dominated by secondary formation. The increase in PM_2.5 was mainly caused by the formation of secondary inorganic (29.1%) and organic species (57.2%) and the primary anthropogenic emissions (12.5%). These results indicated that the haze at the southern edge of the NCP was mainly caused by pollutants in the local areas. Compared to the haze in the northern NCP, the haze in the southern NCP edge had a higher PM_2.5 mass concentration and a higher proportion of secondary species, but a lower proportion of primary species, indicating the high heterogeneity of winter haze over the NCP.

Foliar dust as a reliable environmental monitor of heavy metal pollution in comparison to plant leaves and soil in urban areas

Pollution of atmospheric particulate matter carrying heavy metals has posed a great threat to various ecosystem compartments. Here, a total of 540 samples from four ecosystem compartments (plant leaves, foliar dust, surface soil, and subsoil) were collected in urban soil-plant systems to characterize the heavy metal concentration and composition of foliar dust, to verify the suitability of foliar dust as an environmental monitor, and to explore the importance of foliar dust in shaping the heavy metal composition in plant leaves. We found that the concentrations of all detected elements (lead, zinc, copper, chromium, nickel, and manganese) in foliar dust were the highest among the four ecosystem compartments. The mass of element per unit leaf area, considering both the dust retention amount and the heavy metal concentration of foliar dust, had significant positive correlations with the degree of heavy metal pollution in soil. Foliar dust could reflect ambient elemental composition most reliably among the four ecosystem compartments. The above findings show that foliar dust is more suitable for environmental monitoring than soil and plant materials in urban areas. In addition, the elemental composition of plant leaves differed significantly with different soil-plant systems although species identity dominated the leaf elemental composition. The variation partitioning model and the partial correlation analysis confirm that foliar dust plays a more important role in shaping the elemental composition of plant leaves than soil. This study provides a new way for environmental pollution monitoring and contributes to a comprehensive understanding of atmospheric particulate matter.

Effects of air pollutant emission on the prevalence of respiratory and circulatory system diseases in Linyi, China

As a typical industrial city, Linyi has suffered severe atmospheric pollution in recent years. Meanwhile, a high incidence of respiratory and circulatory diseases has been observed in Linyi. The relationship between air pollutants and the prevalence of respiratory and circulatory system diseases in Linyi is still unclear, and therefore, there is an urgent need to assess the human health risks associated with air pollutants. In this study, the number of outpatient visits and spatial distribution of respiratory and circulatory diseases were first investigated. To clarify the correlation between diseases and air pollutant emissions, the residential intake fraction (IF) of air pollutants was calculated. The results showed that circulatory and respiratory diseases accounted for 62.32% of the total causes of death in 2015. The incidence of respiratory diseases was high in the winter, and outpatient visits were observed for more males (60.9%) than females (39.1%). The spatial distribution suggested that outpatient visits for respiratory and circulatory diseases were concentrated in the main urban area of Linyi, including the Hedong District, Lanshan District, and Luozhuang District, and especially at the junction of these three areas. After calculating the IF combined with the characteristics of pollution sources, meteorological conditions, and population data, a high IF value was concentrated in urban and suburban areas, which was consistent with the high incidence of diseases. Moreover, high R values and a significant correlation (R > 0.6, p < 0.05) between outpatient visits and residential IF of air pollutants imply similar spatial distributions of outpatient visits and IF value of residents. The spatial similarity of air pollution and outpatient visits suggested that future air pollution control policies should better reflect the health risks of spatial hotspots. This study can provide a potentially important reference for environmental management and air pollution-related health interventions.

Analysis of Spatio-Temporal Heterogeneity and Socioeconomic driving Factors of PM2.5 in Beijing–Tianjin–Hebei and Its Surrounding Areas

Due to rapid urbanization and socio-economic development, fine particulate matter (PM2.5) pollution has drawn very wide concern, especially in the Beijing–Tianjin–Hebei region, as well as in its surrounding areas. Different socio-economic developments shape the unique characteristics of each city, which may contribute to the spatial heterogeneity of pollution levels. Based on ground fine particulate matter (PM2.5) monitoring data and socioeconomic panel data from 2015 to 2019, the Beijing–Tianjin–Hebei region, and its surrounding provinces, were selected as a case study area to explore the spatio-temporal heterogeneity of PM2.5 pollution, and the driving effect of socioeconomic factors on local air pollution. The spatio-temporal heterogeneity analysis showed that PM2.5 concentration in the study area expressed a downward trend from 2015 to 2019. Specifically, the concentration in Beijing–Tianjin–Hebei and Henan Province had decreased, but in Shanxi Province and Shandong Province, the concentration showed an inverted U-shaped and U-shaped variation trend, respectively. From the perspective of spatial distribution, PM2.5 concentrations in the study area had an obvious spatial positive correlation, with agglomeration characteristics of “high–high” and “low–low”. The high-value area was mainly distributed in the junction area of Henan, Shandong, and Hebei Provinces, which had been gradually moving to the southwest. The low values were mainly concentrated in the northern parts of Shanxi and Hebei Provinces, and the eastern part of Shandong Province. The results of the spatial lag model showed that Total Population (POP), Proportion of Urban Population (UP), Output of Second Industry (SI), and Roads Density (RD) had positive driving effects on PM2.5 concentration, which were opposite of the Gross Domestic Product (GDP). In addition, the spatial spillover effect of the PM2.5 concentrations in surrounding areas has a positive driving effect on local pollution levels. Although the PM2.5 levels in the study area have been decreasing, air pollution is still a serious problem. In the future, studies on the spatial and temporal heterogeneity of PM2.5 caused by unbalanced social development will help to better understand the interaction between urban development and environmental stress. These findings can contribute to the development of effective policies to mitigate and reduce PM2.5 pollutions from a socio-economic perspective.

Spatiotemporal variations of ambient air pollutants and meteorological influences over typical urban agglomerations in China during the COVID-19 lockdown

To investigate the air quality change during the COVID-19 pandemic, we analyzed spatiotemporal variations of six criteria pollutants in nine typical urban agglomerations in China using ground-based data and examined meteorological influences through correlation analysis and backward trajectory analysis under different responses. Concentrations of PM_2.5, PM₁₀, NO₂, SO₂ and CO in urban agglomerations respectively decreased by 18%-45% (30%-62%), 17%-53% (22%-39%), 47%-64% (14%-41%), 9%-34% (0%-53%) and 16%-52% (23%-56%) during Lockdown (Post-lockdown) period relative to Pre-lockdown period. PM_2.5 pollution events occurred during Lockdown in Beijing-Tianjin-Hebe (BTH) and Middle and South Liaoning (MSL), and daily O₃ concentration rose to grade Ⅱ standard in Post-lockdown period. Distinct from the nationwide slump of NO₂ during Lockdown period, a rebound (∼40%) in Post-lockdown period was observed in Cheng-Yu (CY), Yangtze River Middle-Reach (YRMR), Yangtze River Delta (YRD) and Pearl River Delta (PRD). With slightly higher wind speed compared with 2019, the reduction of PM_2.5 (51%-62%) in Post-lockdown period is more than 2019 (15%-46%) in HC (Harbin-Changchun), MSL, BTH, CP (Central Plain) and SP (Shandong-Peninsula), suggesting lockdown measures are effective to PM_2.5 alleviation. Although O₃ concentrations generally increased during the lockdown, its increment rate declined compared with 2019 under similar sunlight duration and temperature. Additionally, unlike HC, MSL and BTH, which suffered from additional (> 30%) air masses from surrounding areas after the lockdown, the polluted air masses reaching YRD and PRD mostly originated from the long-distance transport, highlighting the importance of joint regional governance.

Ingestion of GNSS-Derived ZTD and PWV for Spatial Interpolation of PM2.5 Concentration in Central and Southern China

With the increasing application of global navigation satellite system (GNSS) technology in the field of meteorology, satellite-derived zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data have been used to explore the spatial coverage pattern of PM_2.5 concentrations. In this study, the PM_2.5 concentration data obtained from 340 PM_2.5 ground stations in south-central China were used to analyze the variation patterns of PM_2.5 in south-central China at different time periods, and six PM_2.5 interpolation models were developed in the region. The spatial and temporal PM_2.5 variation patterns in central and southern China were analyzed from the perspectives of time series variations and spatial distribution characteristics, and six types of interpolation models were established in central and southern China. (1) Through correlation analysis, and exploratory regression and geographical detector methods, the correlation analysis of PM_2.5-related variables showed that the GNSS-derived PWV and ZTD were negatively correlated with PM_2.5, and that their significances and contributions to the spatial analysis were good. (2) Three types of suitable variable combinations were selected for modeling through a collinearity diagnosis, and six types of models (geographically weighted regression (GWR), geographically weighted regression kriging (GWRK), geographically weighted regression—empirical bayesian kriging (GWR-EBK), multiscale geographically weighted regression (MGWR), multiscale geographically weighted regression kriging (MGWRK), and multiscale geographically weighted regression—empirical bayesian kriging (MGWR-EBK)) were constructed. The overall R² of the GWR-EBK model construction was the best (annual: 0.962, winter: 0.966, spring: 0.926, summer: 0.873, and autumn: 0.908), and the interpolation accuracy of the GWR-EBK model constructed by inputting ZTD was the best overall, with an average RMSE of 3.22 μg/m³ recorded, while the GWR-EBK model constructed by inputting PWV had the highest interpolation accuracy in winter, with an RMSE of 4.5 μg/m³ recorded; these values were 2.17% and 4.26% higher than the RMSE values of the other two types of models (ZTD and temperature) in winter, respectively. (3) The introduction of the empirical Bayesian kriging method to interpolate the residuals of the models (GWR and MGWR) and to then correct the original interpolation results of the models was the most effective, and the accuracy improvement percentage was better than that of the ordinary kriging method. The average improvement ratios of the GWRK and GWR-EBK models compared with that of the GWR model were 5.04% and 14.74%, respectively, and the average improvement ratios of the MGWRK and MGWR-EBK models compared with that of the MGWR model were 2.79% and 12.66%, respectively. (4) Elevation intervals and provinces were classified, and the influence of the elevation and the spatial distribution of the plane on the accuracy of the PM_2.5 regional model was discussed. The experiments showed that the accuracy of the constructed regional model decreased as the elevation increased. The accuracies of the models in representing Henan, Hubei and Hunan provinces were lower than those of the models in representing Guangdong and Guangxi provinces.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM_2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM_2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM_2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM_2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM_2.5 pollution.

Analysis of Spatial Heterogeneity and the Scale of the Impact of Changes in PM2.5 Concentrations in Major Chinese Cities between 2005 and 2015

Deteriorating air quality is one of the most important environmental factors posing significant health risks to urban dwellers. Therefore, an exploration of the factors influencing air pollution and the formulation of targeted policies to address this issue are critically needed. Although many studies have used semi-parametric geographically weighted regression and geographically weighted regression to study the spatial heterogeneity characteristics of influencing factors of PM2.5 concentration change, due to the fixed bandwidth of these methods and other reasons, those studies still lack the ability to describe and explain cross-scale dynamics. The multi-scale geographically weighted regression (MGWR) method allows different variables to have different bandwidths, which can produce more realistic and useful spatial process models. By applying the MGWR method, this study investigated the spatial heterogeneity and spatial scales of impact of factors influencing PM2.5 concentrations in major Chinese cities during the period 2005–2015. This study showed the following: (1) Factors influencing changes in PM2.5 concentrations, such as technology, foreign investment levels, wind speed, precipitation, and Normalized Difference Vegetation Index (NDVI), evidenced significant spatial heterogeneity. Of these factors, precipitation, NDVI, and wind speed had small-scale regional effects, whose bandwidth ratios are all less than 20%, while foreign investment levels and technologies had medium-scale regional effects, whose bandwidth levels are 23% and 32%, respectively. Population, urbanization rates, and industrial structure demonstrated weak spatial heterogeneity, and the scale of their influence was predominantly global. (2) Overall, the change of NDVI was the most influential factor, which can explain 15.3% of the PM2.5 concentration change. Therefore, an enhanced protection of urban surface vegetation would be of universal significance. In some typical areas, dominant factors influencing pollution were evidently heterogeneous. Change in wind speed is a major factor that can explain 51.6% of the change in PM2.5 concentration in cities in the Central Plains, and change in foreign investment levels is the dominant influencing factor in cities in the Yunnan-Guizhou Plateau and the Sichuan Basin, explaining 30.6% and 44.2% of the PM2.5 concentration change, respectively. In cities located within the lower reaches of the Yangtze River, NDVI is a key factor, reducing PM2.5 concentrations by 9.7%. Those results can facilitate the development of region-specific measures and tailored urban policies to reduce PM2.5 pollution levels in different regions such as Northeast China and the Sichuan Basin.

Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China

PM_2.5 pollution has produced adverse effects all over the world, especially in fast-developing China. PM_2.5 pollution in China is widespread and serious, which has aroused widespread concern of the government, the public and scholars. This paper evaluates the evolution trend and spatial pattern of PM_2.5 pollution in China based on the data of 281 prefecture-level cities in China from 2007 to 2017, and reveals the pollution situation of PM_2.5 and its relationship with industrial restructuring and technological progress by using spatial dynamic panel model. The results show that China's PM_2.5 pollution has significant path dependence and spatial correlation, and the industrial restructuring and technological progress have significant positive effects on alleviating PM_2.5 pollution. As a decomposition item of technological progress, technical change effectively alleviates PM_2.5 pollution. Another important discovery is that the interaction between industrial restructuring and technological progress will aggravate PM_2.5 pollution. Finally, in order to effectively improve China's air quality, while advocating the Chinese government to pursue high-quality development, this paper puts forward a regional joint prevention mechanism.

Towards the Healthy Community: Residents’ Perceptions of Integrating Urban Agriculture into the Old Community Micro-Transformation in Guangzhou, China

In the renewal of old communities, one of the development directions is to improve health and enhance well-being. A healthy community includes four aspects of health, namely, healthy production, healthy lifestyle, healthy environment and ecosystem, and healthy physical and mental states of residents living in the community. Urban agriculture (UA), as a form of the community garden, is a supplementary form for the lack of production function in the urban community. It also has the potential to contribute to sustainable and resilient urban communities. This study focuses on analysing the health benefits of UA and attempts to identify old community residents’ attitudes and perceptions towards UA and understand their confusion and worry. The purpose of this study is to promote the healthy and sustainable development of old communities by integrating UA into the micro-transformation of old communities and provide planning and design strategies and community development ideas for the micro-transformation. Surveys were conducted on 10 old communities in Yuexiu district, located in Guangzhou, China. Statistical analysis was conducted using IBM Statistical SPSS version 26 to obtain information on the factor structure of residents’ perceptions towards the health benefits of UA. The analysis results showed significant differences between gender groups and the status of children on old community residents’ perceptions towards general UA benefits. The main factors accounting for old community residents’ perceptions towards the health benefits of UA were environmental health benefits, physical and psychological health benefits, and community health benefits. When developing UA in old communities, co-construction and co-sharing mode, public participation mode, and promotion mode are three important development strategies. Construction location, design style, and seasonal design are also critical for the construction of UA in old communities.