With rapid urbanization and increase in car ownership, ambient noise pollution resulting from diversified sources (e.g., road traffic, railway, commercial services) has become a severe environmental problem in the populated areas in China. However, research on the spatial variation of noise pollution and its potential effects on urban residents’ mental health has to date been quite scarce in developing countries like China. Using a health survey conducted in Beijing in 2017, we for the first time investigated the spatial distributions of multiple noise pollution perceived by residents in Beijing, including road traffic noise, railway (or subway) noise, commercial noise, and housing renovation (or construction) noise. Our results indicate that there is geographic variability in noise pollution at the neighborhood scale, and road traffic and housing renovation/construction are the principal sources of noise pollution in Beijing. We then employed Bayesian multilevel logistic models to examine the associations between diversified noise pollution and urban residents’ mental health symptoms, including anxiety, stress, fatigue, headache, and sleep disturbance, while controlling for a wide range of confounding factors such as socio-demographics, objective built environment characteristics, social environment and geographic context. The results show that perceived higher noise-pollution exposure is significantly associated with worse mental health, while physical environment variables seem to contribute little to variations in self-reported mental disorders, except for proximity to the main road. Social factors or socio-demographic attributes, such as age and income, are significant covariates of urban residents’ mental health, while the social environment (i.e., community attachment) and housing satisfaction are significantly correlated with anxiety and stress. This study provides empirical evidence on the noise-health relationships in the Chinese context and sheds light on the policy implications for environmental pollution mitigation and healthy city development in China.

In the context of population ageing in many developed and developing countries, encouraging active transport behaviors of older adults, is a key public health priority. However, many cross-sectional studies assessing the impact of built environment characteristics on travel behavior fail to address residential self-selection bias, and hence the causal relationship is uncertain. A large-scale public housing scheme provided this study with a unique research opportunity to distinguish residential self-selection from the effects of built environment characteristics on the travel behaviors of older adults (N = 13,468 and 3,961 in two analyses respectively) in Hong Kong, because public housing residents have little freedom to choose their residential locations. The results showed that the elderly living in public housing estates generally have fewer trips, shorter overall travel times and distances, and fewer motorized trips including those by rail or private car than those living in private housing estates. In addition, the results for walking, walking times, numbers of trips, and travel distance for elderly people in public and private housing all exhibited markedly different associations with built environment characteristics. Strength of built environment-travel behavior associations dropped by approximately 30-50% after controlling for the effect of residential self-selection. The results indicate that both built environment characteristics and residential self-selection affect travel behaviors.

With an increasing awareness of global climate change, the effect of urban spatial organization, at both city and neighborhood scales, on urban CO₂ emission reduction has attracted much scholarly and practical attention. Using Beijing as a case study, this article examines the extent to which neighborhood-scale urban form may contribute to reduction of travel-related CO₂ emissions in the context of rapid urbanization and spatial transformation. We derive complete travel-activity records of 1,048 residents from an activity diary survey conducted in 2007. Analysis using structural equation models finds that residents living in a neighborhood with higher land use mix, public transit accessibility, and more pedestrian-friendly street design tend to travel in a “low-carbon” manner and emit less CO₂ in daily travel, even controlling for residential and travel preferences. This article offers empirical evidence that sheds light on debates about policy measures to facilitate China’s transition toward sustainable and low-carbon urban development.

Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and individual activity-travel microenvironments affect the assessment of individual-level pollution exposure in space and time at a very fine resolution. It compares three different types of individual-level exposure estimates generated by using residence-based monitoring station assessment, mobility-based monitoring station assessment, and mobility-based real-time assessment. Further, it examines the differences in personal exposure to PM_2.5 associated with different activity places and travel modes across various environmental conditions. The results show that the exposure estimates generated by monitoring station assessment and real-time sensing assessment vary substantially across different activity locations and travel modes. Individual-level daily exposure for residents living in the same community also varies significantly, and there are substantial differences in exposure levels using different approaches. These results indicate that residence- or mobility-based monitoring station assessments, which cannot account for the differences in air pollutant exposures between outdoor and indoor environments and between different travel-related microenvironments, could generate considerably biased estimates of personal pollution exposure. Key Words: indoor environment, real-time exposure to air pollution, smart technologies, travel modes, the uncertain geographic context problem.

Environmental pollution is a major problem in China, subjecting people to significant health risk. Surprisingly little is known, though, about how these risks are distributed spatially or socially. Drawing on a large-scale survey conducted in Beijing in 2013, we examine how environmental hazards and health, as perceived by residents, are distributed at a fine (subdistrict) scale in urban Beijing and investigate the association between hazards, health, and geographical context. A Bayesian spatial multilevel logistic model is developed to account for spatial dependence in unobserved contextual influences (neighborhood effects) on health. The results reveal robust associations between exposure to environmental hazards and health. A unit decrease on a five-point Likert scale in exposure is associated with increases of 15.2 percent (air pollution), 17.5 percent (noise), and 9.3 percent (landfills) in the odds of reporting good health, with marginal groups including migrant workers reporting greater exposure. Health inequality is also evident and is associated with age, income, educational attainment, and housing characteristics. Geographical context (neighborhood features like local amenities) also plays a role in shaping the social distribution of health inequality. The results are discussed in the context of developing environmental justice policy within a Chinese social market system that experiences tension between its egalitarian roots and its pragmatic approach to tackling grand public policy challenges.

Background: Some studies have reported that air pollution exposure can have adverse effects on pregnancy outcomes. However, the disparity between urban and rural areas in the risk of preterm birth (PTB) has yet to be elucidated. Considering geographic contexts as homogeneous or ignoring urban–rural differences cannot accurately reveal the disparities in the health effects of air pollution under different geographic contexts. The aims of this study were to examine the disparities in the risks of PTB in three different regions and five urban–rural types and to investigate the extent to which fine particulate matter (PM_2.5) exposure during the entire pregnancy can explain the variations.Methods: We collected data on 429,865 singleton newborns born in 2014 in Hubei Province, China, and divided Hubei Province into three regions. Spatial correlation methods were employed to measure the associations between the rate of PTB and air pollution using average annual indexes for the entire province and regions. A series of multilevel logistic models were conducted to examine disparities in the risks of PTB with decreases in urbanity and the effects of air pollution exposure on the occurrence of preterm births.Results: The PM_2.5concentration was significantly different across the regions. The eastern region had the most wide-ranged and serious level of pollution, whereas the levels in the middle and western regions weakened. The odds of PTB and air pollution exhibited a positive spatial correlation for the entire province and in the east (BiMoran’s I = 0.106 and 0.697, respectively). Significant urban–rural disparities in the risks of PTB were noted in the east and middle regions, and the mean PM_2.5exposure during the entire pregnancy was positively associated with PTB risk. However, in the west, the results showed weak differences in the risks of PTB among the five urban–rural types and an insignificant effect of PM_2.5exposure. The direction of the effect of district/county-level income on PTB varied by region.Conclusions: This study finds that air pollution exposure and PTB have significant and positive spatial relationships in areas with a serious air pollution burden. The risks of PTB in three regions of Hubei Province follow the same W-shaped pattern as urbanity decreases and rurality increases. High levels of air pollution exposure may be an important disadvantage for urban pregnant women in this setting.