Over the past two decades, the rate of urbanization has increased significantly worldwide, with more than half of the population already living in cities; this trend continues in numerous countries and regions. Tunisia is a North African country with a rich history and diverse cultural heritage. In Greater Tunis, its capital city, urbanization has accelerated since 1960. Rapid urbanization has increased the demand for grey infrastructure and led to changes in land-use patterns and the destruction of the environment. This study aims to understand and depict the relationship between urban expansion and the green infrastructure in the Greater Tunis area. This study uses land-use data, administrative boundaries vector data, and Google satellite imagery datasets to calculate and analyze the changes in the land use transfer matrix and landscape pattern index of built-up land and green spaces in the Tunisian capital for three periods: 2000, 2010, and 2020. We found that the expansion of built-up areas in Tunis has increased from 8.8% in 2000 to 12.1% in 2020, and changes in green spaces have decreased from 23% in 2000 to 20.9% in 2020. Without planning guidelines, the layout of green spaces has become more fragmented and disorganized. For this reason, we provide programs and suggestions for building a complete ecological network of green spaces in order to provide references and lessons for related studies and cities facing the same problems.

While extensive research has examined the contribution of urban parks to well-being, empirical evidence on the role of cultural attributes in historical urban parks and their impact on visitors’ well-being remains limited. This study explores the impact of physical characteristics of historical urban parks on well-being from the perspective of human settlement environment. Quantitative data were collected from 11 urban parks in Wuhan, China, combining online crowdsourcing for physical characteristic assessments and questionnaire surveys for psychological evaluations. Machine learning techniques, spatial analysis, and statistical methods including multistep regression and Bootstrap sampling were employed to test our hypotheses. Our results demonstrate that objective physical features—including park area, green coverage rate, green space shape index, and the proportion of heritage landmarks—positively influence well-being, whereas road density exhibits a negative association. Cultural perception and place attachment serve as significant mediators between physical characteristics and well-being outcomes, with the proportion of heritage landmarks influencing well-being through a dual mediation path. Additionally, we found interaction effects between physical and psychological factors, with education level moderating the relationship between cultural perception and well-being. These findings advance environmental psychology theory by elucidating how historical elements foster unique pathways to well-being, distinct from those offered by conventional green spaces. Our research provides evidence-based guidance for historical urban park design and renovation in the context of urban renewal, where balancing preservation and modernization presents significant challenges.

Air pollution, especially PM_2.5 pollution, still seriously endangers the health of urban residents in China. The built environment is an important factor affecting PM_2.5; however, the key factors remain unclear. Based on 37 neighborhoods located in five Chinese megacities, three relative indicators (the range, duration, and rate of change in PM_2.5 concentration) at four pollution levels were calculated as dependent variables to exclude the background levels of PM_2.5 in different cities. Nineteen built environment factors extracted from green space and gray space and three meteorological factors were used as independent variables. Principal component analysis was adopted to reveal the relationship between built environment factors, meteorological factors, and PM_2.5. Accordingly, 24 models were built using 32 training neighborhood samples. The results showed that the adj_R² of most models was between 0.6 and 0.8, and the highest adj_R² was 0.813. Four principal factors were the most important factors that significantly affected the growth and reduction of PM_2.5, reflecting the differences in green and gray spaces, building height and its differences, relative humidity, openness, and other characteristics of the neighborhood. Furthermore, the relative error was used to test the error of the predicted values of five verification neighborhood samples, finding that these models had a high fitting degree and can better predict the growth and reduction of PM_2.5 based on these built environment factors.

Air pollution, particularly fine particulate matter (PM_2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM_2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM_2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM_2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM_2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R² value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM_2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM_2.5, revealing effective approaches for addressing urban environmental issues.