The pattern and mechanism of air pollution in developed coastal areas of China: From the perspective of urban agglomeration

Healthier lifestyles can modify the air pollutants effect on cardiovascular disease among the middle-aged and elderly

There is increasing evidence that air pollutants significantly increase the risk of cardiovascular disease (CVD). Nevertheless, less research has been conducted to date to reveal protective factors. Therefore, this study aims to indicate whether a healthy lifestyle can modify the effects of environmental pollution on CVD. This study screened 3010 participants from the China Health and Retirement Longitudinal Study (CHARLS) Wave 3 (2015). The study aimed to systematically demonstrate the impact of environmental pollution on CVD and elucidate the role of a healthy lifestyle. Air pollutant data were obtained from the China High Air Pollutant (CHAP) datasets. We analyzed the relationship between these pollutants and cardiovascular disease risk using generalized linear mixed models. In addition, healthy lifestyles were categorized as low, medium, and high; stratified analyses were conducted to estimate the effect of healthy lifestyles on the risk of CVD due to air pollutants. 607 had CVD among 3010 participants, and the three-year mean concentrations of the pollutants chloride ion (Cl-), nitrate ion (NO3-), particulate matter with a diameter of 10 micrometers or less (PM10), particulate matter with a diameter of 10 micrometers or less (PM1), particulate matter with a diameter of 10 micrometers or less (PM2.5) were each linked 1.37 (95%CI:1.22,1.54), 1.03 (95%CI:1.00,1.06), 1.02 (95%CI:1.01,1.03), 1.01 (95%CI:1.00,1.01), and 1.01 (95%CI:1.00,1.01) fold risk of CVD, respectively. For the subgroups of low, medium, and high according to the healthy lifestyle score in model 2, the average concentration of Cl- pollutant was each associated with 1.34 (1.12,1.62), 1.34 (1.12,1.61), and 1.32 (1.03,1.71) times risk with CVD, respectively. The NO3 - was each associated with 1.06 (1.02,1.11), 1.01 (0.97,1.05), and 0.98 (0.93,1.04) times risk with CVD, respectively. The PM1 was each associated with 1.03 (1.01,1.05), 1.01 (0.99,1.02), and 1.00 (0.97,1.02) times risk with CVD, respectively. The PM10 was each associated with 1.01 (1.00,1.01), 1.01 (0.99,1.01), and 1.00 (0.99,1.01) times risk with CVD, respectively. PM2.5 was each associated with 1.02 (1.01,1.03), 1.00 (0.99,1.01), and 1.00 (0.99,1.01) times risk with CVD, respectively. Exposure to these pollutants(Cl-, NO3-, PM10, PM1, PM2.5)is associated with higher risk of CVD, and healthier lifestyles can reduce the risk of CVD due to overall air pollutants.

With Great Ecosystem Services Comes Great Responsibility: Benefits Provided by Urban Vegetation in Brazilian Cities

Ecosystem services (ESs) are extremely important, specifically in urban areas. Urban forests, even representing a pivotal role in global sustainability, have been converted into different human-modified landscapes. This paper aims to analyze the ES provided by the urban areas of 25 cities of the Atlantic Forest in Brazil. We used i-Tree Canopy v.7.1 to classify the land use. We quantified the monetary benefits of the urban vegetation and used socioeconomic variables (i.e., total population, population density, Human Development Index (HDI), and Gross Domestic Product (GDP) per capita) to analyze if the ecosystem services or the land uses are associated with this. Our data reveal that together, the cities studied sequester a significant total of 235.3 kilotonnes of carbon and a substantial 864.82 kilotonnes of CO2 Equivalent (CO2 Equiv.) annually. Furthermore, together, they also store a total of 4861.19 kilotonnes of carbon and 17,824.32 kilotonnes of CO2 Equiv. We found out that the average monetary estimate of annual carbon sequestration was USD 3.57 million, while the average stored estimate was USD 73.76 million. Spearman's correlogram showed a strong positive correlation between density and the percentage of impervious cover non-plantable no trees (IN) in urban areas (p < 0.001). IN was also positively correlated with HDI (p = 0.01), indicating that urban areas with higher HDI tend to have larger impervious areas. Our data suggest essential insights about the ecosystem services provided by urban areas and can serve as significant findings to drive policymakers' attention to whether they want to provide more ecosystem services in cities.

Research on the spatial and temporal patterns of ozone concentration and population health effects in the Central Plains Urban Agglomeration from 2017 to 2020

Fine particulate matter (PM2.5) and near-surface ozone (O3) are the main atmospheric pollutants in China. Long-term exposure to high ozone concentrations adversely affects human health. It is of great significance to systematically analyze the spatiotemporal evolution mechanism and health effects of ozone pollution. Based on the ozone data of 91 monitoring stations in the Central Plains Urban Agglomeration from 2017 to 2020, the research used Kriging method and spatial autocorrelation analysis to investigate the spatiotemporal variations of ozone concentration. Additionally, the study assessed the health effects of ozone on the population using the population exposure risk model and exposure-response relationship model. The results indicated that: (1) The number of premature deaths caused by ozone pollution in the warm season were 37,053 at 95% confidence interval (95% CI: 28,190-45,930) in 2017, 37,685 (95% CI: 28,669-46,713) in 2018, and 37,655 (95% CI: 28,647-46,676) in 2019. (2) The ozone concentration of the Central Plains urban agglomeration showed a decreasing trend throughout the year and during the warm season from 2017 to 2020, there are two peaks monthly, one is June, and the other is September. (3) In the warm season, the high-risk areas of population exposure to ozone in the Central Plains Urban Agglomeration were mainly concentrated in urban areas. In general, the population exposure risk of the south is lower than that of the north. The number of premature deaths attributed to ozone concentration during the warm season has decreased, but some southern cities such as Xinyang and Zhumadian have also seen an increase in premature deaths. China has achieved significant results in air pollution control, but in areas with high ozone concentrations and high population density, the health burden caused by air pollution remains heavy, and stricter air pollution control policies need to be implemented.

Changes in PM2.5-related health burden in China's poverty and non-poverty areas during 2000-2020: A health inequality perspective

China suffers from severe PM_2.5 pollution that has resulted in a huge health burden. Such PM_2.5-related health burden has long been suspected to differ between China's poverty-stricken areas (PAs) and non-poverty-stricken areas (NPAs). Yet, evidence-based examination of this long-held belief, which is critical as a barrier of environmental injustice to advancing China's sustainability, is still missing. Here our study shows that the PM_2.5 pollution is more serious in China's NPAs than PAs-with their annual averages being respectively 54.83 μg/m³ and 43.63 μg/m³-causing higher premature mortality in the NPAs. Compared to economic inequality, China's total PM_2.5-related premature mortality was relatively evenly distributed during 2000-2015 across regions of varying levels of gross domestic product (GDP) per capita but increased slightly in 2015-2020 owing to the dramatic change in age structure. The elderly population increased by 31 %. PM_2.5-related premature deaths were more severe for populations of low socioeconomic status, and such environmental health inequalities could be amplified by population aging. Additionally, population migration from China's PAs to developed cities contributed to 638, 779, 303, 954, and 896 premature deaths in 2000, 2005, 2010, 2015, and 2020, respectively. Changes in the age structure (53 %) and PM_2.5 concentration (28 %) had the greatest impact on premature deaths, followed by changes in population (12 %) and baseline mortality (8 %). The contribution rate of changes in the age structure and PM_2.5 concentration was higher in PAs than in NPAs. Our findings provide insight into PM_2.5-related premature death and environmental inequality, and may inform more equitable clean air policies to achieve China's sustainable development goals.

Spatiotemporal Evolution and Driving Forces of PM2.5 in Urban Agglomerations in China

With the rapid development of China's economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation.

Dynamic Expansion of Urban Land in China’s Coastal Zone since 2000

Although a major region with strong urbanization, there is not yet a systematic and comprehensive understanding of urban expansion during the last 20 years for China’s coastal zone. In this paper, based on remote sensing techniques, and using indicators such as new urban land proportion, annual urban increase, and annual growth rate, as well as a landscape expansion index reflecting the urban expansion type (e.g., edge-expansion, infilling, and outlying), we measured the dynamic expansion of urban land in China’s coastal zone since 2000. The results indicated that: (1) China’s coastal zone experienced rapid urbanization from 2000 to 2020, with the new urban land and annual urban growth rate at 17,979.72 km2 and 4.83%, respectively. The new urban land was mainly concentrated in economically advanced regions, such as Bohai Rim, Shandong Peninsula, the Yangtze River delta, and the Pearl River delta. (2) The urban growth rates of coastal cities in Liaoning, Hebei, Shandong, southeast Fujian, and Taiwan became slower over time, with a sharp decline during 2015–2020. In the mid and south of China’s coastal zone, such as coastal cities in Jiangsu, Guangxi, and Hainan, there was slow urbanization before 2015, and urban land expanded dramatically during 2015–2020. (3) The urban expansion of China’s coastal zone was dominated by edge-expansion after 2000, but it went through a low-speed and intensive development stage during 2010–2015, with an increase in urban land less than 50% of that in the other three five-year periods, and the most significant filling of urban space compared with the other three five-year periods, which was probably caused by the global financial crisis. (4) The spatial-temporal differences in the urbanization process in China’s coastal zone were largely consequent on national economic development strategies and regional development plans implemented in China’s coastal zone.

Exposure Risk of Global Surface O3 During the Boreal Spring Season

Surface ozone (O₃) is an oxidizing gaseous pollutant; long-term exposure to high O₃ concentrations adversely affects human health. Based on daily surface O₃ concentration data, the spatiotemporal characteristics of O₃ concentration, exposure risks, and driving meteorological factors in 347 cities and 10 major countries (China, Japan, India, South Korea, the United States, Poland, Spain, Germany, France, and the United Kingdom) worldwide were analyzed using the MAKESENS model, Moran' I analysis, and Generalized additive model (GAM). The results indicated that: in the boreal spring season from 2015 to 2020, the global O₃ concentration exhibited an increasing trend at a rate of 0.6 μg/m³/year because of the volatile organic compounds (VOCs) and NOx changes caused by human activities. Due to the lockdown policies after the outbreak of COVID-19, the average O₃ concentration worldwide showed an inverted U-shaped growth during the study period, increasing from 21.9 μg/m³ in 2015 to 27.3 μg/m³ in 2019, and finally decreasing to 25.9 μg/m³ in 2020. According to exposure analytical methods, approximately 6.32% of the population (31.73 million people) in the major countries analyzed reside in rapidly increasing O₃ concentrations. 6.53% of the population (32.75 million people) in the major countries were exposed to a low O₃ concentration growth environment. Thus, the continuous increase of O₃ concentration worldwide is an important factor leading to increasing threats to human health. Further we found that mean wind speed, maximum temperature, and relative humidity are the main factors that determine the change of O₃ concentration. Our research results are of great significance to the continued implementation of strict air quality policies and prevention of population hazards. However, due to data limitations, this research can only provide general trends in O₃ and human health, and more detailed research will be carried out in the follow-up.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12403-022-00463-7.

Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China

The complex correlation between regions caused by the externality of air pollution increases the difficulty of its governance. Therefore, analysis of the spatio-temporal network of air pollution (STN-AP) holds great significance for the cross-regional coordinated governance of air pollution. Although the spatio-temporal distribution of air pollution has been analyzed, the structural characteristics of the STN-AP still need to be clarified. The STN-AP in the Yangtze River Delta urban agglomeration (YRDUA) is constructed based on the improved gravity model and is visualized by UCINET with data from 2012 to 2019. Then, its overall-individual-clustering characteristics are analyzed through social network analysis (SNA) method. The results show that the STN-AP in the YRDUA was overall stable, and the correlation level gradually improved. The centrality of every individual city is different in the STN-AP, which reveals the different state of their interactive mechanism. The STN-AP could be subdivided into the receptive block, overflow block, bidirectional block and intermediary block. Shanghai, Suzhou, Hangzhou and Wuxi could be key cities with an all above degree centrality, betweenness centrality and closeness centrality and located in the overflow block of the STN-AP. This showed that these cities had a greater impact on the STN-AP and caused a more pronounced air pollution spillovers. The influencing factors of the spatial correlation of air pollution are further determined through the quadratic assignment procedure (QAP) method. Among all factors, geographical proximity has the strongest impact and deserves to be paid attention in order to prevent the cross-regional overflow of air pollution. Furthermore, several suggestions are proposed to promote coordinated governance of air pollution in the YRDUA.

The Modeling Study about Impacts of Emission Control Policies for Chinese 14th Five-Year Plan on PM2.5 and O3 in Yangtze River Delta, China

The Chinese government has made great efforts to combat air pollution through the reductions in SO2, NOx and VOCs emissions, as part of its socioeconomic Five-Year Plans (FYPs). China aims to further reduce the emissions of VOCs and NOx by 10% in its upcoming 14th FYP (2021–2025). Here, we used a regional chemical transport model (e.g., WRF/CMAQ) to examine the responses of PM2.5 and O3 to emission control policies of the 14th FYP in the Yangtze River Delta (YRD) region. The simulation results under the 4 emission control scenarios in the 2 winter months in 2025 indicate that the average concentrations of city mean PM2.5 in 41 cities in the YRD were predicted to only decrease by 10% under both S1 and S1_E scenarios, whereas the enhanced emission control scenarios (i.e., S2_E and S3_E) could reduce PM2.5 in each city by more than 20%. The model simulation results for O3 in the 3 summer months in 2025 show that the O3 responses to the emission controls under the S1 and S1_E scenarios show different control effects on O3 concentrations in the YRD with the increase and decrease effects, respectively. The study found that both enhanced emission control scenarios (S2_E and S3_E) could decrease O3 in each city by more than 20% with more reductions in O3 under the S3_E emission control scenario because of its higher control strengths for both NOx and VOCs emissions. It was found that emission reduction policies for controlling high emission sectors of NOx and VOCs such as S2_E and S3_E were more effective for decreasing both PM2.5 and O3 in the YRD. This study shows that O3 controls will benefit from well-designed air pollution control strategies for reasonable control ratios of NOx and VOCs emissions.

The Direct and Spillover Effect of Multi-Dimensional Urbanization on PM2.5 Concentrations: A Case Study from the Chengdu-Chongqing Urban Agglomeration in China

The Chengdu-Chongqing urban agglomeration (CUA) faces considerable air quality concerns, although the situation has improved in the past 15 years. The driving effects of population, land and economic urbanization on PM_2.5 concentrations in the CUA have largely been overlooked in previous studies. The contributions of natural and socio-economic factors to PM_2.5 concentrations have been ignored and the spillover effects of multi-dimensional urbanization on PM_2.5 concentrations have been underestimated. This study explores the spatial dependence and trend evolution of PM_2.5 concentrations in the CUA at the grid and county level, analyzing the direct and spillover effects of multi-dimensional urbanization on PM_2.5 concentrations. The results show that the mean PM_2.5 concentrations in CUA dropped to 48.05 μg/m³ at an average annual rate of 4.6% from 2000 to 2015; however, in 2015, there were still 91% of areas exposed to pollution risk (>35 μg/m³). The PM_2.5 concentrations in 92.98% of the area have slowly decreased but are rising in some areas, such as Shimian County, Xuyong County and Gulin County. The PM_2.5 concentrations in this region presented a spatial dependence pattern of "cold spots in the east and hot spots in the west". Urbanization was not the only factor contributing to PM_2.5 concentrations. Commercial trade, building development and atmospheric pressure were found to have significant contributions. The spillover effect of multi-dimensional urbanization was found to be generally stronger than the direct effects and the positive impact of land urbanization on PM_2.5 concentrations was stronger than population and economic urbanization. The findings provide support for urban agglomerations such as CUA that are still being cultivated to carry out cross-city joint control strategies of PM_2.5 concentrations, also proving that PM_2.5 pollution control should not only focus on urban socio-economic development strategies but should be an integration of work optimization in various areas such as population agglomeration, land expansion, economic construction, natural adaptation and socio-economic adjustment.