Land use change has an important influence on the spatial and temporal distribution of PM_2.5 concentration. Therefore, based on the particulate matter (PM_2.5) data from remote sensing instruments and land use change data in long time series, the Getis-Ord Gi* statistic and SP-SDM are employed to analyze the spatial distribution pattern of PM_2.5 and its response to land use change in China. It is found that the average PM_2.5 increased from 25.49 μg/m³ to 31.23 μg/m³ during 2000-2016, showing an annual average growth rate of 0.97%. It is still greater than 35 μg/m³ in nearly half of all cities. The spatial distribution pattern of PM_2.5 presents the characteristics of concentrated regional convergence. PM_2.5 is positively correlated with urban land and farmland, negatively correlated with forest land, grassland, and unused land. Furthermore, the average PM_2.5 concentrations show the highest values for urban land and decrease in the order of farmland > unused land > water body > forest > grassland. The impact of land use change on PM_2.5 is a non-linear process, and there are obvious differences and spillover effects for different land types. Thus, reasonably controlling the scale of urban land and farmland, optimizing the spatial distribution pattern and development intensity, and expanding forest land and grassland are conducive to curbing PM_2.5 pollution. The research conclusions provide a theoretical basis for the management of PM_2.5 pollution from the perspective of optimizing land use.

The lockdown of cities in the Yangtze River Delta (YRD) during COVID-19 has provided many natural and typical test sites for estimating the potential of air pollution control and reduction. To evaluate the reduction of PM_2.5 concentration in the YRD region by the epidemic lockdown policy, this study employs big data, including PM_2.5 observations and 29 independent variables regarding Aerosol Optical Depth (AOD), climate, terrain, population, road density, and Gaode map Point of interesting (POI) data, to build regression models and retrieve spatially continuous distributions of PM_2.5 during COVID-19. Simulation accuracy of multiple machine learning regression models, i.e., random forest (RF), support vector regression (SVR), and artificial neural network (ANN) were compared. The results showed that the RF model outperformed the SVR and ANN models in the inversion of PM_2.5 in the YRD region, with the model-fitting and cross-validation coefficients of determination R² reached 0.917 and 0.691, mean absolute error (MAE) values were 1.026 μg m⁻³ and 2.353 μg m⁻³, and root mean square error (RMSE) values were 1.413 μg m⁻³, and 3.144 μg m⁻³, respectively. PM_2.5 concentrations during COVID-19 in 2020 have decreased by 3.61 μg m⁻³ compared to that during the same period of 2019 in the YRD region. The results of this study provide a cost-effective method of air pollution exposure assessment and help provide insight into the atmospheric changes under strong government controlling strategies.

The evaluation and dynamic monitoring of urban sprawl is essential to the sustainable development of cities and therefore attracts enthusiasm from numerous scholars. This study conducted a thorough review of the literature on the multidimensional and multiscale measurement of urban sprawl. Firstly, it provides a definition based on the common characteristics of urban sprawl to contribute to a relatively uniform definition and judging criteria. Secondly, indicators of growth, morphology, density, land use mixture, and accessibility dimensions are sorted out, as well as dimensions that are not widely operationalized but make sense. Thirdly, the review spotlights single-dimensional measures in large-sample comparative studies and booming comparative studies based on multidimensional measures. Furthermore, another focus lies on different spatiotemporal combinations of temporal and spatial scales for the measurement. Overall, there are large gaps in comparative studies on the multidimensional measurement of urban sprawl under multiple spatiotemporal scales. In particular, few micro-scale studies focus on inner-city units, and few measure urban sprawl at multiple scales simultaneously. Finally, the challenges and future of multidimensional and multiscale measurements are discussed: relativity and uncertainty of sprawl criteria; strong dependence on the choice of spatiotemporal scales; comparability of sprawl measurements that remains to be improved; the necessity of long-term international cooperation on the measurement of urban sprawl at the global and regional levels. The article appeals for more multidimensional and multiscale urban sprawl measurement studies based on multi-case comparisons in the future, especially in the developing context.

The spatial mismatch between affordable housing neighborhoods and public services/facilities significantly reduces the well-being of low-income dwellers, which has risen to a typical issue of spatial injustice. Previous studies on accessibility evaluation most focus on a single type of service, lacking empirical studies exploring the integrated spatial accessibility of multiple services for low-income residents. Taking Harbin City in northeast China as an example, this study assessed the spatial accessibility of transit, education, healthcare, shopping, and recreation facilities for affordable housing neighborhoods, using the Gaussian-based 2SFCA method. The pattern of accessibility for each type of facility in Harbin showed obvious spatial differentiation between the urban core and the city periphery, for both affordable housing neighborhoods and other neighborhoods. The sample household survey indicated that low-income households who were generally characterized as elderly, less-educated, under-employed or unemployed had extremely restricted and passive residence choices. In comparison to non-low-income households, the spatial accessibility of higher-level facilities for low-income households was relatively poorer. Particularly, affordable housing neighborhoods had much lower accessibility of subway stations, though more than 97% of low-income respondents living in affordable housing neighborhoods chose to solve the daily trip demand by taking a bus or the subway. With respect to equity-oriented urban planning, more importance should be attached to the spatial accessibility of public services and facilities when planning affordable housing projects for low-income households.

Nowadays, urban development prioritizes improving quality over mere quantitative expansion, which requires a shift in land use from previous extensive use patterns to a more refined and efficient intensive use pattern. The intensive use of industrial land (IUIL) is a resource-utilization strategy for industrial land that aims at maximizing the dual growth of ecological and economic benefits by minimizing resource consumption and factor costs. In this research, IUIL is deconstructed into four aspects: input intensity, utilization intensity, economic benefits, and environmental benefits. Grounded in a qualitative analysis of the interconnected casual feedback among the various factors influencing IUIL, a system dynamics model with 36 indicators for IUIL evaluation and prediction is constructed. Policy impacts on IUIL from the supply side, utilization side, and supervision side are analyzed and embedded for a scenario simulation based on the theory of the entire life cycle of land use. The simulation shows its efficiency in reporting the variation tendency of industrial land use with varied policy impact strength. The results reveal a dynamic understanding of the evolving patterns of land intensification and present different characteristics of IUIL. This study further proposes policy recommendations to provide experience and references for the authorities to promote high-quality urbanization development in practice.

Territorial spatial planning involves fixability and flexibility in different driving factors related to control and development orientation, and they play an important role in regional sustainable development, especially in developing countries such as China. With rapid urbanisation and industrialisation, China has been impacted by conflicts between development and protection in territorial space. To integrate the contradictions among different territorial spatial planning measures, planners and scholars have started to focus on studies regarding fixability–flexibility relationships and integration. However, the relationship between and integration of fixability and flexibility in territorial spatial planning have yet to be clearly summarised. This paper explores an innovative research direction for the fixability–flexibility relations in territorial spatial planning from a new perspective, the Food–Energy–Water Nexus, which is a dynamic and comprehensive framework for Sustainable Development Goals (SDGs) studies. This paper covers the existing research on fixability and flexibility in territorial spatial planning. Moreover, after summarising the conflicts of fixability and flexibility, the dialectical relationship between and the integration of fixability and flexibility are researched.