China’s tremendous economic growth during the past three decades has resulted in worsening air quality in most of its cities. However, the spatiotemporal patterns and underlying drivers of air pollution in China remain poorly understood. To address this issue, we used stepwise regression to identify major socioeconomic, climatic, and urban form factors influencing air pollution in 69 major cities across China. Our results showed that social factors such as population size and density were positively correlated with emissions of PM_2.5, PM₁₀, NO_x, and SO₂. Economic factors such as Gross Domestic Product (GDP) and GDP of secondary industry were positively correlated with industry and transportation emissions but negatively correlated with residential emissions of air pollutants. Urban form attributes such as measures of urban fragmentation and contiguity were important in explaining patterns of emissions from residential, power generation, and transportation sectors. As for climatic factors, higher precipitation, higher wind speed, and higher temperatures were all negatively correlated with air pollution levels. We also found that the effects of socioeconomic, climatic, and urban from factors on air pollution levels varied considerably among seasons and between the annual and seasonal scales. Our findings have useful implications for urban planning and management for controlling air pollution in China and beyond.

Understanding building metabolism is critical for guiding urban resource management and addressing challenges in urban sustainable development. Key attributes of buildings, including geolocation, footprint, height, and vintage, are crucial to characterizing spatiotemporal patterns of building metabolism. However, these attributes are usually challenging to obtain broadly and automatically, which obscures a comprehensive understanding and accurate assessment of urban metabolism. Moreover, the lack of a finer spatial pattern of these attributes shadows a spatially explicit characterization of material stock and flow in cities. In this study, we took Shenzhen—whose urbanization over the past three decades has been unprecedented in China and even around the world— has been taken as an example to develop a city-level building dataset based on a random-forest model and quantify the spatiotemporal patterns of material metabolism at relatively high spatial resolution (in 500 m × 500 m grids) by combing material flow analysis (MFA) with geographic information system (GIS). The results show that Shenzhen grew from a small town with 281.02 × 10⁶ m³ of buildings in the 1990s to a mega-city with 3585.5 × 10⁶ m³ of buildings in 2018 and expanded both outward and upward from downtown to suburban areas. The urban “weight” (material stock) increased from 92.69 Mt in the 1990s to 1667.8 Mt in 2018 and tended to be saturated, with an average growth rate of 9.5% per year. Spatially, the south-central areas were the largest container of material stocks and generated the most demolition waste. The spatially explicit maps of building three-dimensional (3-D) form and vintage provide detailed information for architectural conservation and could support the decision-making for urban renewal planning. The spatiotemporal patterns of in-use material stocks and potential generation of construction and demolition waste (CDW) provide a benchmark of environmental risk assessment and potential secondary resources to reduce “original” material consumption, which could help alter urban renewal to an environmental-friendly and sustainable trajectory.

Urbanization and related construction activities generate massive construction and demolition (C&D) waste, which poses considerable risks to the environment and human health. However, it is difficult to assess the significance of these issues without a quantitative understanding of spatial patterns of C&D waste generation (source), transportation (flow), and treatment (sink). This paper maps source, sinks, and flows of C&D waste by coupling a geographic information system and material flow analysis. The methodology is new in the field of C&D waste quantification at the city level, especially downscaling to 5 km × 5 km grids. The results showed that a total of 37.72 million metric tonnes (Mt) of C&D wastes were generated in Beijing, 2019, and ~72% of them were reused and recycled. In space, C&D waste generation in suburban and rural districts (28.73 Mt) was over three times more than that in the downtown area (8.99 Mt). However, the downtown area was the net source region and transported massive amounts of C&D waste to suburban and rural districts. In comparison, several suburban and rural districts had self-sufficient treatment capacity. Our study highlights that a series of C&D waste maps on multiple spatial scales are of great help to design effective policies for waste management by providing spatial details of magnitude and components, and explicitly recognizing primary source and sink areas in cities.