The nexus between urbanization and PM2.5 related mortality in China

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

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

Reduced inequality in ambient and household PM2.5 exposure in China

The society has high concerns on the inequality that people are disproportionately exposed to ambient air pollution, but with more time spent indoors, the disparity in the total exposure considering both indoor and outdoor exposure has not been explored; and with the socioeconomical development and efforts in fighting against air pollution, it is unknown how the exposure inequality changed over time. Based on the city-level panel data, this study revealed the Concentration Index (C) in ambient PM_2.5 exposure inequality was positive, indicating the low-income group exposed to lower ambient PM_2.5; however, the total PM_2.5 exposure was negatively correlated with the income, showing a negative C value. The low-income population exposed to high PM_2.5 associated with larger contributions of indoor exposure from the residential emissions. The total PM_2.5 exposure caused 1.13 (0.63-1.73) million premature deaths in 2019, with only 14 % were high-income population. The toughest-ever air pollution countermeasures have reduced ambient PM_2.5 exposures effectively that, however, benefited the rich population more than the others. The transition to clean household energy sources significantly affected on indoor air quality improvements, as well as alleviation of ambient air pollution, resulting in notable reductions of the total PM_2.5 exposure and especially benefiting the low-income groups. The negative C values decreased from 2000 to 2019, indicating a significantly reducing trend in the total PM_2.5 exposure inequality over time.

The neighbourhood environment and profiles of the metabolic syndrome

BACKGROUND

There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components.

METHODS

We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO₂ and PM_2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles.

RESULTS

LCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO₂ were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM_2.5 was associated with unhealthier metabolic profiles with MetS.

CONCLUSIONS

This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.

Mortality burden due to long-term exposure to ambient PM2.5 above the new WHO air quality guideline based on 296 cities in China

OBJECTIVE

Quantifying the spatial and socioeconomic variation of mortality burden attributable to particulate matters with aerodynamic diameter ≤ 2.5 µm (PM2.5) has important implications for pollution control policy. This study aims to examine the regional and socioeconomic disparities in the mortality burden attributable to long-term exposure to ambient PM2.5 in China.

METHODS

Using data of 296 cities across China from 2015 to 2019, we estimated all-cause mortality (people aged ≥ 16 years) attributable to the long-term exposure to ambient PM2.5 above the new WHO air quality guideline (5 µg/m3). Attributed fraction (AF), attributed deaths (AD), attributed mortality rate (AMR) and total value of statistical life lost (VSL) by regional and socioeconomic levels were reported.

RESULTS

Over the period of 2015-2019, 17.0% [95% confidence interval (CI): 7.4-25.2] of all-cause mortality were attributable to long-term exposure to ambient PM2.5, corresponding to 1,425.2 thousand deaths (95% CI: 622.4-2,099.6), 103.5/105 (95% CI: 44.9-153.3) AMR, and 1006.9 billion USD (95% CI: 439.8-1483.4) total VSL per year. The AMR decreased from 120.5/105 (95% CI: 52.9-176.6) to 92.7/105 (95% CI:39.9-138.5) from 2015 to 2019. The highest mortality burden was observed in the north region (annual average AF = 24.2%, 95% CI: 10.8-35.1; annual average AMR = 137.0/105, 95% CI: 60.9-198.5). The highest AD and economic loss were observed in the east region (annual average AD = 390.0 thousand persons, 95% CI: 170.3-574.6; annual total VSL = 275.6 billion USD, 95% CI: 120.3-406.0). Highest AMR was in the cities with middle level of GDP per capita (PGDP)/urbanization. The majority of the top ten cities of AF, AMR and VSL were in high and middle PGDP/urbanization regions.

CONCLUSION

There were significant regional and socioeconomic disparities in PM2.5 attributed mortality burden among Chinese cities, suggesting differential mitigation policies are required for different regions in China.

Study of the Economic, Environmental, and Social Factors Affecting Chinese Residents' Health Based on Machine Learning

The Healthy China Strategy puts realistic demands for residents' health levels, but the reality is that various factors can affect health. In order to clarify which factors have a great impact on residents' health, based on China's provincial panel data from 2011 to 2018, this paper selects 17 characteristic variables from the three levels of economy, environment, and society and uses the XG boost algorithm and Random forest algorithm based on recursive feature elimination to determine the influencing variables. The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. At the environmental level, coal consumption, energy consumption, total wastewater discharge, and solid waste discharge have a greater impact on the health level of residents. Therefore, the Chinese government should formulate targeted measures at both economic and environmental levels, which is of great significance to realizing the Healthy China strategy.

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.

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

Urbanization has an abundant connotation in dimensions such as population, economy, land, and society and is an important sign to measure regional economic development and social progress. The use of Night Light Data from remote sensing satellites as a proxy variable can significantly improve the accuracy and comprehensiveness of the measurement of urbanization development dynamics. Based on the Night Light Data and statistical data from 2015 to 2019, this paper quantitatively analyzes the spatio-temporal evolution pattern of urbanization in Guangxi and its driving mechanism using exploratory time-space data analysis, GeoDetector and Matrix: Boston Consulting Group, providing an important basis for sustainable urban development planning and scientific decision-making by the government. The findings show that (1) there is a high level of spatial heterogeneity and spatial autocorrelation of urbanization in Guangxi, with the Gini index of urban night light index and urban night light expansion vitality index always greater than 0.5, the global Moran’s I greater than 0.17, the spatial differentiation converging but the spatial correlation increasing. (2) The spatial pattern of urbanization in Guangxi has long been solidified, but there is a differentiation in urban development trend, with the coexistence of urban expansion and shrinkage, requiring differentiated policy design for urban governance. (3) The development and evolution of urbanization in Guangxi present a complex intertwined dynamic mechanism of action, with interaction effects of bifactor enhancement and non-linear enhancement among factors. It should be noted that the influence of factors varies greatly, with the added value of the tertiary industry, gross domestic product, total retail sales of social consumer goods having the strongest direct effect on the urban night light index, while the added value of secondary industry, per capita GDP, gross domestic product having the strongest direct effect on the urban night light expansion vitality index. All of them are key factors, followed by some significant influence factors such as government revenue, population urbanization rate, per government revenue, population urbanization rate, per capita disposable income of urban and rural residents that should not be ignored, and the rest that play indirect roles mainly by interaction.

The Effect of Urbanization on Health Care Expenditure: Evidence From China

This paper investigates the impact and non-linear effects of urbanization on health care expenditure in China. The results indicate that urbanization in both Eastern and Central regions can significantly increase health care expenditure. But the impact of urbanization is not significant, which is related to the backward economic development level and low urbanization rate in the Western region. Taking population aging into consideration, the results of the panel threshold regression model imply that the positive relationship between urbanization and health care expenditure becomes greater when the level of population aging exceeds 10.72% in the Eastern region and 7.00% in the Western region. Therefore, in the urbanization process, the government should pay attention to the positive effect of urbanization on health care expenditure, provide more financial support for the construction of medical facilities, and expand the coverage of medical services and security for residents, especially for elderly people.

The relationship between population heat vulnerability and urbanization levels: A county-level modeling study across China

The purpose of this work was to assess population vulnerability to heat-related health risks and its relationship with urbanization levels to provide essential information for the future development and policy-making for climate change adaptation. We constructed a heat vulnerability index (HVI), quantified the population heat vulnerability in each county across China by a principal component analysis (PCA) of multiple factors, and assessed urbanization levels in each county using multisource data. Then, the HVI was validated using the heat-attributable fraction (heat-AF) of nonaccidental mortality based on death monitoring data and meteorological data from 95 counties across China. The results showed that our HVI was significantly positively associated with the heat AF of nonaccidental mortality. A negative correlation was observed between the urbanization level and the HVI. The HVI was generally higher in less urbanized western China and lower in the more urbanized eastern regions. The baseline mortality occupies the top position in the importance ranking of the heat-vulnerability indicators at all three urbanization levels, but the other indicators, including the aging rate, agricultural population rate, education, ethnic structure, economic status, air conditioner ownership rate, and number of hospitals, ranked differently among different urbanization levels. This finding indicates that to reduce population heat vulnerability, the most important approach is to improve the health status of the whole population and reduce baseline mortality; additionally, regional-specific measures and emphasis should be adjusted reasonably along with the process of urbanization according to the characteristics and key factors of local heat vulnerability.

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.

Dietary patterns and metabolic syndrome among urbanized Tibetans: A cross-sectional study

The prevalence of metabolic syndrome (MetS) and the major dietary patterns among urbanized Tibetans are unclear. The present study aimed to investigate the prevalence of MetS among Jiarong Tibetans in Aba Plateau, identify the major dietary patterns, and evaluate their association with the risk of MetS. In this cross-sectional study on 476 subjects, 18-80-years-old, dietary intakes were evaluated using a simplified food frequency questionnaire (SFFQ). MetS was defined according to the International Diabetes Federation (IDF) guidelines. Principal component analysis was performed to assess the major dietary patterns. Multivariate logistic regression analysis examined the associations between dietary patterns and the risk of MetS. The prevalence of Mets in the population was 37.6%. Herein, three major dietary patterns were extracted: traditional Tibetan, urbanized, and healthy dietary patterns. After adjusting for potential confounders (Model 1: adjusted for sex and age; Model 2: adjusted for sex, age, smoking status, drinking situation, physical activity level and total energy intake), subjects in the highest tertile of the healthy dietary had a lower risk of MetS compared to those from the lowest tertile. Also, no significant statistical association was established between the risk of MetS and the traditional Tibetan and urbanized diet.

Air pollution and cause-specific mortality: A comparative study of urban and rural areas in China

Air pollution increases the risks of all-cause mortality, cardiovascular mortality and respiratory mortality across China. However, the urban-rural differences in the associations between air pollution and mortality have not been clearly identified. In this study, a distributed lag nonlinear model was used to examine whether the air pollutants-mortality associations vary between urban and rural areas. Then, we used logistic regression analyses to evaluate the air pollutants-mortality relations. Also, generalized additive models were simulated to evaluate the nonlinear curves. Our results showed that the relative risks of air pollution-related mortality were generally higher in rural areas, where PM_2.5 pollution was the dominant factor (p-value < 0.05). Mortality risks for all-cause, cardiovascular and respiratory will increase when average annual PM_2.5 concentrations exceed approximately 38 μg/m³, 41 μg/m³ and 41 μg/m³, respectively, all of which exceed the annual Grade II standards. In urban areas, PM_10-2.5 and NO₂ were associated with mortality (p-value < 0.05). We proposed some area-specific strategies for controlling the NO₂ pollution and PM_10-2.5 pollution in urban areas and the PM_2.5 pollution in rural areas to eliminate the gaps. Our findings identify that rural residents are more sensitive to air pollution than urban residents in China, and this result challenges previous assumptions about the more adverse effects of urbanization on residents' health in developing countries.

Socioeconomic disparity in the association between long-term exposure to PM2.5 and mortality in 2640 Chinese counties

BACKGROUND

Although the association between long-term exposure to PM_2.5 and mortality has been evaluated intensively, little is known about the socioeconomic disparity in the association.

METHODS

We collected data on annual all-cause mortality, PM_2.5 concentration, socioeconomic and demographic characteristics of 2640 counties from the two most recent Chinese censuses in 2000 and 2010. We applied the difference-in-differences (DID) method to estimate PM_2.5-mortality association for counties at different quartiles of literacy rate, college rate, urbanization rate and GDP per capita, respectively.

RESULTS

Overall, every 10 µg/m³ increase in annual average PM_2.5 was associated with 3.8% (95% confidence interval [CI]: 3.0-5.0) increase of all-cause mortality. The stratified analysis suggested higher health impact of exposure in counties with lower socioeconomic status. For counties of the lowest quartile (Q₁) of literacy rate, college rate, urbanization rate and GDP per capita, the effect estimates were 6.0% (95% CI: 4.2-7.7), 4.4% (95% CI: 2.8-6.0), 3.5% (95% CI: 2.0-5.1) and 4.9% (95% CI: 2.7-7.1), respectively. There was strong evidence for elevated risk in mortality associated with PM_2.5 of all socioeconomic factors in the lowest quartile (Q₁) compared with the highest quartile counties (Q₄) (p-value for difference < 0.05).

CONCLUSIONS

There was socioeconomic disparity in the PM_2.5-mortality association in China. Dwellers living in less developed counties are more vulnerable to long-term exposure to ambient PM_2.5 than those living in developed counties.

The contribution of PM2.5 to cardiovascular disease in China

China is experiencing rapid urbanization and industrialization with correspondingly high levels of air pollution. Although the harm of PM2.5 has been long reported, it is only quite recently that there is increasing concern in China for its possible adverse health effects on cardiovascular disease. We reviewed the epidemiologic evidence of potential health effects of PM2.5 on cardiovascular disease reported from recent studies in China (2013 onwards). There is clear evidence for the contribution of PM2.5 to cardiovascular outcomes, including mortality, ischemic heart disease, and stroke from studies based in various regions in China. This evidence adds to the global evidence that PM2.5 contributes to adverse cardiovascular health risk and highlights the need for improved air quality in China.

A Framework to Classify Environmental Inequity in Absolute and Relative Terms, and Its Application in Beijing

Although reducing environmental inequities is widely recognized as an essential step towards sustainable cities, decision-makers frequently lack the tools to identify inequity distribution patterns and designing effective intervention policies. This study seeks to present a framework that can help decision-making processes by classifying environmental inequity districts in multiple perspectives, especially in absolute and relative terms. This framework includes four steps: (A) variable selection, (B) data normalization, (C) ranking indicators, (D) summarizing inequity classification, which then assign results to selected areas. The framework aims to classify and compare environmental inequities in multiple perspectives, and can be applied in various environmental problems, with advantages such as high acceptability and clear comprehensibility. To show the potential use of this framework, a case application in Beijing, China, was conducted to evaluate the environmental inequity of air pollution. The results suggest that decision-makers should focus on the central urban area and some southern regions of Beijing to implement various improvement policies. Based on the results from Beijing, how the framework can be used to help decision-makers, the future roles of this framework with the government and the public, as well as the framework’s limitations are further discussed.

Whither the roads lead to? Estimating association between urbanization and primary healthcare service use with chinese prefecture-level data in 2014

With the rapid economic development across China over recent decades, examining how urbanization may affect healthcare service use and its implications is more than urgent. This study estimates the association between urbanization and primary healthcare services use in China. We construct a prefecture-level dataset on healthcare services utilization and urbanization. We regress the proportion of residents using healthcare services in primary healthcare centers versus secondary or tertiary hospitals on a set of prefecture-level control variables. Results suggest that use of primary healthcare centers outpatient service is positively associated with being in the proximity of a provincial capital, but negatively correlated with the percentage of the urban population and the availability of public transportation. Higher likelihood of seeking care in major hospitals instead of primary healthcare centers is associated with urbanization, justifying a need for primary care physicians as gatekeepers in China's healthcare delivery system.

A novel framework for decomposing PM2.5 variation and demographic change effects on human exposure using satellite observations

Human exposure to PM_2.5, represented by population-weighted mean PM_2.5 concentration (c_ρ), declines under three conditions: (1) mean PM_2.5 concentration declines, (2) PM_2.5 concentration within urban areas goes through more of a decrease than within rural areas, or (3) city planning relocates people into cleaner areas. Decomposing these effects on human exposure is essential to guide future environmental policies. The lack of ground PM_2.5 observations limits the assessment of human exposure to PM_2.5 over China. This study proposed a novel diagnostic framework using satellite observations to decompose the variation in c_ρ resulting from change in the mean PM_2.5 concentration, spatial difference in PM_2.5 change, and demographic change. In this framework, we decomposed c_ρ into mean PM_2.5 concentration (c₀) and pollution-population-coincidence induced PM_2.5 exposure (PPCE). We then used this framework to decompose the variation in c_ρ over China within three recent Five-Year Plans (FYPs) (2001-2015). The results showed that the decline in c₀ reduced c_ρ in most provinces within the eleventh and twelfth FYPs. The spatial difference in PM_2.5 change reduced the PPCE and c_ρ in most provinces within the tenth and twelfth FYPs, with the most substantial reduction rate of -3.64 μg m^-3·yr^-1 in Tianjin within the twelfth FYP. Rural-to-urban migration resulting from rapid urbanization, however, increased the PPCE and c_ρ (by as much as 0.22 μg m^-3·yr^-1) in all provinces except Taiwan within all three FYPs. The demographic change reduced c_ρ in Taiwan because of the migration of population into less polluted areas. To better reduce human exposure, it is recommended that control efforts further target populous residential areas and urbanization planning relocates people into less polluted areas. Our decomposition framework paves a new way to decompose the human exposure to other air pollutants in China and other regions.

Impact of Urbanization on PM2.5-Related Health and Economic Loss in China 338 Cities

According to the requirements of the Healthy China Program, reasonable assessment of residents' health risks and economic loss caused by urban air pollution is of great significance for environmental health policy planning. Based on the data of PM2.5 concentration, population density, and urbanization level of 338 Chinese cities in the year of 2015, the epidemiological relative risk (RR) was adopted to estimate the negative health effects caused by exposure to PM2.5. Meanwhile, the Value of Statistical Life (VSL) and Cost of Illness (COI) methods were used to calculate economic loss. The results show that PM2.5 pollution remains serious in 2015, which brings about many people suffering from all kinds of fearful health problems especially premature death and related diseases. The mortality and morbidity increase dramatically, and the total direct economic loss related to PM2.5 pollution in 2015 was 1.846 trillion yuan, accounting for 2.73% of total annual GDP. In addition, there was a strong correlation between urbanization level and health risks as well as economic loss, which implies that people who live in highly urbanized cities may face more severe health and economic losses. Furthermore, 338 cities were divided into four categories based on urbanization level and economic loss, of which the key areas (type D) were the regions where an increase in monitoring and governance is most needed. In the process of urbanization, policy makers should pay more attention to health costs and regional differentiated management, as well as promote the construction of healthy cities more widely.

Emission of intermediate volatility organic compounds from a ship main engine burning heavy fuel oil

Intermediate volatility organic compounds (IVOCs) are crucial precursors of secondary organic aerosol (SOA). In this study, gaseous IVOCs emitted from a ship main engine burning heavy fuel oil (HFO) were investigated on a test bench, which could simulate the real-world operations and emissions of ocean-going ships. The chemical compositions, emission factors (EFs) and volatility distributions of IVOC emissions were investigated. The results showed that the main engine burning HFO emitted a large amount of IVOCs, with average IVOC EFs of 20.2-201 mg/kg-fuel. The IVOCs were mainly comprised of unspeciated compounds. The chemical compositions of exhaust IVOCs were different from that of HFO fuel, especially for polycyclic aromatic compounds and alkylcyclohexanes. The volatility distributions of IVOCs were also different between HFO exhausts and HFO fuel. The distinctions in IVOC emission characteristics between HFO exhausts and HFO fuel should be considered when assessing the IVOC emission and related SOA formation potentials from ocean-going ships burning HFO, especially when using fuel-surrogate models.

Health burdens of ambient PM2.5 pollution across Chinese cities during 2006-2015

With frequent severe haze and smog episodes in Chinese cities, an increasing number of studies have focused on estimating the impact of fine particulate matter (PM_2.5) on public health. However, the current use of national and provincial demographic data might mask regional differences and lead to inaccurate estimations of pollution-related health impacts across cities. We applied the Global Burden of Disease methodology to develop a dataset of premature deaths attributed to ambient PM_2.5 in 129 Chinese cities in 2006, 2010 and 2015, based on the information of baseline mortality rates and population densities at the city level. Our results suggested that ambient PM_2.5 pollution led to 631,230 (95% confidence interval: 281,460-873,800) premature deaths in those cities in 2015, which was similar to that in 2010, but 42.8% higher than that in 2006. The reduction of premature deaths was not as obvious as the improvement in air quality in recent years, primarily owing to the aging Chinese population. For large and medium/small cities, the effects of PM_2.5 abatement on alleviating public health burdens were lower than those for megalopolises and metropolises; however, such large and medium/small cities are at risk of increasing future PM_2.5 pollution levels due to rapid development. Significant differences in PM_2.5-induced premature deaths indicated the need for specific policies to mitigate the health burden of air pollution in different types of Chinese cities.

Are cities good for health? A study of the impacts of planned urbanization in China

Background

Urbanization in developing countries is usually accompanied by migration to cities, making it a challenge to unpack the independent relationships between migration, urbanization and health, particularly in the presence of health-selective migration. Since 1978, unprecedented planned urbanization has taken place in China and further increases to the urban population are expected. This paper explored the impacts of urbanization in China through a comparative study of in situ urbanized population.

Methods

Using the China Health and Retirement Longitudinal Study (CHARLS), a nationally representative dataset for people aged 45 years or older, we compared self-assessed general health, depressive symptoms and waist circumference among three groups: (i) in situ urbanized-rural residents; (ii) rural residents; and (iii) urban residents. Using a model informed by the literature on the social determinants of health in later life, we investigated the patterning and drivers of differences in health outcomes between these three groups, in order to explore the impact of urbanization independent of the impact of migration.

Results

There are consistent advantages in health and less depression among urbanized-rural residents compared with the rural group; and this group has even better health outcomes than the urban group after adjusting for early life differences. However, this relationship is reversed for waist circumference. Socioeconomic circumstances and factors related to a planned urbanization partly explain these effects.

Conclusions

Urbanization in China has, on average, had an independent and positive effect on health and well-being. Planned urbanization could benefit people’s health in developing countries. It is likely that improved infrastructure is a key driver.

PM2.5-related health and economic loss assessment for 338 Chinese cities

China is in a critical stage of ambient air quality management after global attention on pollution in its cities. Industrial development and urbanization have led to alarming levels of air pollution with serious health hazards in densely populated cities. The quantification of cause-specific PM_2.5-related health impacts and corresponding economic loss estimation is crucial for control policies on ambient PM_2.5 levels. Based on ground-level direct measurements of PM_2.5 concentrations in 338 Chinese cities for the year 2016, this study estimates cause-specific mortality using integrated exposure-response (IER) model, non-linear power law (NLP) model and log-linear (LL) model followed by morbidity assessment using log-linear model. The willingness to pay (WTP) and cost of illness (COI) methods have been used for PM_2.5-attributed economic loss assessment. In 2016 in China, the annual PM_2.5 concentration ranged between 10 and 157 μg/m³ and 78.79% of the total population was exposed to >35 μg/m³ PM_2.5 concentration. Subsequently, the national PM_2.5-attributable mortality was 0.964 (95% CI: 0.447, 1.355) million (LL: 1.258 million and NPL: 0.770 million), about 9.98% of total reported deaths in China. Additionally, the total respiratory disease and cardiovascular disease-specific hospital admission morbidity were 0.605 million and 0.364 million. Estimated chronic bronchitis, asthma and emergency hospital admission morbidity were 0.986, 1.0 and 0.117 million respectively. Simultaneously, the PM_2.5 exposure caused the economic loss of 101.39 billion US$, which is 0.91% of the national GDP in 2016. This study, for the first time, highlights the discrepancies associated with the three commonly used methodologies applied for cause-specific mortality assessment. Mortality and morbidity results of this study would provide a measurable assessment of 338 cities to the provincial and national policymakers of China for intensifying their efforts on air quality improvement.

Spatiotemporal Changes in PM2.5 and Their Relationships with Land-Use and People in Hangzhou

Increases in the extent and level of air pollution in Chinese cities have become a major concern of the public and burden on the government. While ample literature has focused on the status, changes and causes of air pollution (particularly on PM_2.5 and PM₁₀), significantly less is known on their effects on people. In this study we used Hangzhou, China, as our testbed to assess the direct impact of PM_2.5 on youth populations that are more vulnerable to pollution. We used the ground monitoring data of air quality and Aerosol optical thickness (AOT) product from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the spatiotemporal changes of PM_2.5 by season in 2015. We further explored these distributions with land cover, population density and schools (kindergarten, primary school and middle school) to explore the potential impacts in seeking potential mitigation solutions. We found that the seasonal variation of PM_2.5 concentration was winter > spring > autumn > summer. In Hangzhou, the percentage of land area exposed to PM_2.5 > 50 µg m⁻³ accounted for 59.86% in winter, 56.62% in spring, 40.44% in autumn and 0% in summer, whereas these figures for PM_2.5 of <35 µg m⁻³ were 70.01%, 5.28%, 5.17%, 4.16% in summer, winter, autumn and spring, respectively. As for land cover, forest experienced PM_2.5 of 35–50 µg m⁻³ (i.e., lower than those of other cover types), likely due to the potential filtering and absorption function of the forests. More importantly, a quantitative index based on population-weighted exposure level (pwel) indicated that only 9.06% of the population lived in areas that met the national air quality standards. Only 1.66% (14,055) of infants and juveniles lived in areas with PM_2.5 of <35 µg m⁻³. Considering the legacy effects of PM_2.5 over the long-term, we highly recommend improving the monitoring systems for both air quality and people (i.e., their health conditions), with special attention paid to infants and juveniles.

Risk Reduction Behaviors Regarding PM2.5 Exposure among Outdoor Exercisers in the Nanjing Metropolitan Area, China

Aims: This study aimed to describe risk reduction behaviors regarding ambient particulate matter with a diameter of 2.5 μm or less (PM_2.5) among outdoor exercisers and to explore potential factors influencing those behaviors in the urban area of Nanjing, China. Method: A cross-sectional convenience sample survey was conducted among 302 outdoor exercisers in May 2015. Descriptive analysis was used to describe demographics, outdoor physical activity patterns, knowledge of PM_2.5 and risk reduction behaviors. Multivariate logistic regression analysis was then used to explore factors that influence the adoption of risk reduction behaviors. Results: The most common behavior to reduce PM_2.5 exposure was minimizing the times for opening windows on hazy days (75.5%), and the least common one was using air purifiers (19.3%). Two thirds of respondents indicated that they wore face masks when going outside in the haze (59.5%), but only 13.6% of them would wear professional antismog face masks. Participants adopting risk reduction behaviors regarding PM_2.5 exposure tended to be females, 50–60 year-olds, those with higher levels of knowledge about PM_2.5 and those who had children. Conclusions: These findings indicate the importance of improving knowledge about PM_2.5 among outdoor exercisers. Educational interventions should also be necessary to guide the public to take appropriate precautionary measures when undertaking outdoor exercise in high PM_2.5 pollution areas.