Previous studies in developed countries have found that living in rapidly urbanizing areas is associated with higher risk of mental illness and that social capital had a protective effect on individual mental health. However, the literature is missing empirical studies of the relationship between urbanization, neighborhood social capital and mental health in rapidly urbanizing countries. To bridge this knowledge gap, this study investigated the effects of urbanization on depressive symptoms in China, with an emphasis on the mediating role of neighborhood social capital in the relationship between urbanization and individual-level depressive symptoms. Nationally representative survey data from the 2016 wave of China’s Labor-force Dynamics Survey were used. A sample of 20,861 individuals was obtained from 401 neighborhoods in 158 prefecture-level divisions of 29 provinces. Depressive symptoms were measured using CES-D scores. Neighborhood social capital was assessed by three individual-level variables aggregated to the neighborhood level: perceptions of neighborly trust, the extent of neighborly reciprocity, and membership to neighborhood social groups. Multilevel linear regression and mediation analyses were used to estimate the statistical relationships. The multilevel linear regression analyses found negative relationships between urbanization rate and CES-D score. The mediation analysis found that neighborhood-level social capital was an inconsistent mediator in the relationship between urbanization rate and CES-D score. Interaction terms between urbanization rate and two measures of neighborhood-level social capital were statistically significant, indicating that the protective effects of neighborly reciprocity and membership to neighborhood social groups on CES-D scores (negative relationships) were stronger in the relatively more urbanized areas. Urbanization supports mental health in the Chinese context, although it might undermine residents’ mental health by reducing neighborhood social capital. The protective effect of neighborhood-level reciprocity and social group membership on mental health increased with urbanization.

The frequent outbreak of environmental threats in China has resulted in increased criticism regarding the health effects of China’s urbanization. Urbanization is a double-edged sword with regard to health in China. Although great efforts have been made to investigate the mechanisms through which urbanization influences health, the effect of both economic development and urbanization on health in China is still unclear, and how urbanization-health (or development-health) relationships vary among different income groups remain poorly understood. To bridge these gaps, the present study investigates the impact of both urbanization and economic development on individuals’ self-rated health and its underlying mechanisms in China. We use data from the national scale of the 2014 China Labor-force Dynamics Survey to analyze the impact of China’s urbanization and economic development on health. A total of 14,791 individuals were sampled from 401 neighborhoods within 124 prefecture-level cities. Multilevel ordered logistic models were applied. Model results showed an inverted U-shaped relationship between individuals’ self-rated health and urbanization rates (with a turning point of urbanization rate at 42.0%) and a positive linear relationship between their self-rated health and economic development. Model results also suggested that the urbanization-health relationship was inverted U-shaped for high- and middle-income people (with a turning point of urbanization rate at 0.0% and 49.2%, respectively), and the development-health relationship was inverted U-shaped for high- and low-income people (with turning points of GDP per capita at 93,462 yuan and 71,333 yuan, respectively) and linear for middle-income people. The impact of urbanization and economic development on health in China is complicated. Careful assessments are needed to understand the health impact of China’s rapid urbanization. Social and environmental problems arising from rapid urbanization and economic growth should be addressed. Equitable provision of health services are needed to improve low-income groups’ health in highly urbanized cities.

There is increasing evidence from the developed world that air pollution is significantly related to residents’ depressive symptoms; however, the existence of such a relationship in developing countries such as China is still unclear. Furthermore, although neighbourhood social capital is beneficial for health, whether it is a protective factor in the relationship between health and environment pollution remains unclear. Consequently, we examined the effects of cities’ PM_2.5 concentrations on residents’ depressive symptoms and the moderating effects of neighbourhood social capital, using data from the 2016 wave of China Labourforce Dynamics Survey and the real-time remote inquiry website of Airborne Fine Particulate Matter and Air Quality Index. Results showed that PM_2.5 concentrations and neighbourhood social capital may increase and decrease respondents’ depressive symptoms, respectively. Notably, neighbourhood social capital decreased the negative effect of PM_2.5 concentrations on respondents’ depressive symptoms. These analyses contributed to the understanding of the effect of air pollution on mental health in China and confirmed that neighbourhood social capital were protective factors in the relationship between health and environment hazards.

Generating spatiotemporally continuous land surface temperature (LST) data is in great demand for hydrology, meteorology, ecology, environmental studies, etc. However, the thermal infrared (TIR)-based LST measurements are prone to cloud contamination with missing pixels. To repair the missing pixels, a new XGBoost-based linking approach for reconstructing daytime and nighttime Moderate Resolution Imaging Spectroradiometer (MODIS) LST measurements was introduced. The instantaneous solar radiation and two soil-related predictors from China Data Assimilation System (CLDAS) 0.0625°/1-h data were selected as the linking variables to depict the relationship with instantaneous MODIS LST data. Other land surface properties, including two vegetation indices, the water index, the surface albedo, and topographic parameters, were also used as the predictor variables. The XGBoost method was used to fit an LST linking model by the training datasets from clear-sky pixels and was then applied to the MODIS Aqua-Terra LSTs during summer time (June to August) in 2017 and 2018 across China. The recovered LST data was further rectified with the Savitzky–Golay (SG) filtering method. The results showed the distribution of the reconstructed LSTs present a reasonable pattern for different land-cover types and topography. The evaluation results using in situ longwave radiation measurements showed the RMSE varies from 3.91 K to 5.53 K for the cloud-free pixels and from 4.42 K to 4.97 K for the cloud-covered pixels. In addition, the reconstructed LST products correlated well with CLDAS LST data with similar LST spatial patterns. The variable importance analysis revealed that the two soil-related predictors and the elevation variable are key parameters due to their great contribution to the XGBoost model performance.