This study examines the microclimate pattern and related spatial perception of urban green stormwater infrastructure (GSI) and the stormwater management landscape, using rain gardens as a case study. It investigates the relationship between different rain garden design factors, such as scale, depth, and planting design, and their effects on microclimate patterns and human spatial perception. Taking an area in Blacksburg, Virginia, as the study site, twelve rain garden design scenarios are generated by combining different design factors. The potential air temperature, relative humidity, and wind speed/direction are analyzed through computational simulation. Additionally, feelings of comfort, the visual beauty of the landscape, and the overall favorite are used as an evaluation index to investigate people’s perception of various rain garden design options. The study found that a multilayer and complex planting design can add more areas with moderate temperature and higher humidity. It also significantly improves people’s subjective perception of a rain garden. Furthermore, a larger scale rain garden can make people feel more comfortable and improve the visual beauty of the landscape, highlighting the importance of designing larger and recreational bioretention cells in GSI systems. Regarding depth, a relatively flatter rain garden with a complex planting design can bring stronger air flow and achieve better visual comfort and visual beauty. Overall, by examining the microclimate pattern and related perception of rain gardens, this study provides insight into better rain garden design strategies for the urban stormwater management landscape. It explores the potential of rain garden design in urban GSI and responds to climate change.

As people spend more than 80% of the day in an indoor built environment, indoor air quality pollution caused by volatile organic compounds (VOCs) emitted from wood-based panels has attracted attention. PVC veneered plywood used in the indoor built environment and relevant VOCs and odor emission under different air exchange rates were studied in this research. Microcell thermal extraction technology was used to sample under the conditions of three different air exchange rates and loading factors: 0.2 m³·h⁻¹·m⁻², 0.5 m³·h⁻¹·m⁻², and 1.0 m³·h⁻¹·m⁻², respectively. Gas Chromatography-Olfactometry-Mass Spectrometry (GC-MS-O) was used to analyze VOCs and their odor release characteristics. The key odor characteristic compounds were analyzed by subjective and objective methods, and the main sources of odor release from the board were analyzed. In this experiment, the time-intensity method is mainly used to analyze the mass concentration of VOCs released from plywood. By comparing the mass concentration and odor intensity of VOCs released from plywood and its components under different air exchange rates and loading factors, the influence of the ratio of air exchange rate to loading factor on plywood is judged. The results show that with three different ratios of air exchange rate to loading factor, the VOCs emission concentration and odor intensity of plywood can be affected greatly and are the highest at 0.2 m³·h⁻¹·m⁻². The components released by PVC veneered plywood are mainly aromatic compounds, alkanes and aldehydes. The research findings can guide the indoor built environment design and construction process to control the emission of VOCs by adjusting the air exchange rate, which helps build more healthy and sustainable living environments for humans.

Urban green space (UGS) accessibility is a cornerstone of equitable and sustainable city planning. However, existing studies focus on large metropolitan areas and rely on limited spatial models that overlook the complexity of urban morphology and socio-demographic diversity. This study shifts the focus to East Lansing, a medium-sized U.S. city that exhibits neither the spatial concentration of major metropolises nor the uniformity of small towns, thereby offering a distinctive context to examine urban green space equity. To this end, we develop a composite accessibility index by integrating four complementary spatial models: Euclidean distance, gravity-based access, two-step floating catchment area (2SFCA), and zone-based analysis. Utilizing high-resolution spatial, demographic, and environmental datasets, the study applies both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) to uncover global patterns and local variations in accessibility determinants. The results reveal pronounced neighborhood-level disparities, with variables such as green coverage, park provision, and commercial density emerging as significant but spatially uneven predictors. The composite index yields a more robust and equitable representation of UGS accessibility than any individual model. This multi-model, spatially explicit framework contributes to methodological advances in accessibility assessment and offers actionable insights for place-based urban greening strategies.