Observed inequality in urban greenspace exposure in China.

Given the important role of green environments playing in healthy cities, the inequality in urban greenspace exposure has aroused growing attentions. However, few comparative studies are available to quantify this phenomenon for cities with different population sizes across a country, especially for those in the developing world. Besides, commonly used inequality measures are always hindered by the conceptual simplification without accounting for human mobility in greenspace exposure assessments. To fill this knowledge gap, we leverage multi-source geospatial big data and a modified assessment framework to evaluate the inequality in urban greenspace exposure for 303 cities in China. Our findings reveal that the majority of Chinese cities are facing high inequality in greenspace exposure, with 207 cities having a Gini index larger than 0.6. Driven by the spatiotemporal variability of human distribution, the magnitude of inequality varies over different times of the day. We also find that exposure inequality is correlated with low greenspace provision with a statistical significance (p-value < 0.05). The inadequate provision may result from various factors, such as dry cold climate and urbanization patterns. Our study provides evidence and insights for central and local governments in China to implement more effective and sustainable greening programs adjusted to different local circumstances and incorporate the public participatory engagement to achieve a real balance between greenspace supply and demand for developing healthy cities.

Spatiotemporal mapping and assessment of daily ground NO2 concentrations in China using high-resolution TROPOMI retrievals.

Nitrogen dioxide (NO2) is an important air pollutant that causes direct harms to the environment and human health. Ground NO2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 µg/m3, with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 µg/m3). The ground NO2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO2 data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Identifying the driving factors of NO2 pollution of One Belt One Road countries: satellite observation technique and dynamic spatial panel data analysis.

To recover the global economy, China in 2013 called for a new global strategy, namely, "One Belt and One Road Initiative" (BRI), which aims at reinforcing regional economic cooperation, enhancing regional collaboration of economic policy, and realizing the goal of rapid economic development of member countries. Accelerating industrialization not only has been recognized as an effective way to stimulate economic development, but also lead to the serious issue of environmental pollution, which challenges the environmental sustainability. In this study, we focus on the industrializing region as a study area to investigate the driving factors of environmental pollution. Technically, we utilized satellite observation technique to obtain NO2 columns data to denote environmental pollution and then applied dynamic spatial panel data models to evaluate what affects NO2 pollution levels. The findings are the following. (1) NO2 pollution exhibits significant and positive spatial autocorrelation, indicating spatial spillovers of NO2 pollution. (2) Lebanon, Bangladesh, Kyrgyzstan, and India experienced the largest increase of NO2 pollution while NO2 pollution in Singapore, Hungary, Greece, and Ukraine was substantially reduced. (3) The results of the dynamic spatial panel data models show that both the time dynamics effects and the spatial spillover effects are found to be significant and positive. In other words, both effects should be considered. Population is the foremost contributor to increase NO2 pollution while urbanization is an effective way to reduce pollution. An EKC relationship between NO2 pollution and per capita income was verified. Besides, industrialization, foreign direct investment, and trade openness have positive impacts on NO2 pollution.

Impact of Housing and Community Conditions on Multidimensional Health among Middle- and Low-Income Groups in Hong Kong.

With decades of urbanization, housing and community problems (e.g., poor ventilation and lack of open public spaces) have become important social determinants of health that require increasing attention worldwide. Knowledge regarding the link between health and these problems can provide crucial evidence for building healthy communities. However, this link has heretofore not been identified in Hong Kong, and few studies have compared the health impact of housing and community conditions across different income groups. To overcome this gap, we hypothesize that the health impact of housing and community problems may vary across income groups and across health dimensions. We tested these hypotheses using cross-sectional survey data from Hong Kong. Several health outcomes, e.g., chronic diseases and the SF-12 v. 2 mental component summary scores, were correlated with a few types of housing and community problems, while other outcomes, such as the DASS-21⁻Stress scores, were sensitive to a broader range of problems. The middle- and low-income group was more severely affected by poor built environments. These results can be used to identify significant problems in the local built environment, especially amongst the middle- and low-income group.