Economic development and fast growing urbanization in China have caused severe air pollution, with frequent pollution episodes endangering the health of inhabitants and disturbing social activities, and as an expanding metropolis, Chengdu has suffered ever since. The concentration variations of main air pollutants, such as PM₁₀, PM_2.5 and NO₂, often show periodicity because of meteorological impact and anthropic activities, and display orientation discrepancies due to influences of wind speed (WS), frequency and pollutant sources. These features have complicated the mechanisms of pollution episodes and deepened the difficulty in pollution control evaluation. The WS has significant influences on the periodicity and orientation variations in pollutant concentrations, and quantifying the influence of which is of high significance and provides sustainable foundations for pollution alleviation strategies. Different time-scale cycles (i.e., Diurnal, weekly, seasonal and annual), along with the WS, wind frequency, wind and spatial orientations in urban areas, were analyzed in this paper. Results show that the periodicity of diurnal, seasonal and annual cycles is remarkable, and weekly cycle is obvious by adding the influence of the WS in 16 orientations. The WS has direct impacts on pollutants varying in the range of 1.5–2.5 m/s, and has a remarkable diffusion effect on pollutants once above 2.5 m/s. Over heavy pollution hours in diurnal, weekly, annual cycles and transitional seasons, the WS had more significant influences on pollutants, and whereas the wind frequency is not the main impact factor for orientation variations. For Chengdu, the northeast orientation is suitable to construct a wind panel with a remarkable diffusion effect on pollutants, while air pollutions in the northwest and southwest orientations were severe with the WS below 1.5 m/s, and pollution diffusion in the north-northwest orientation was the worst. This work can provide guidance and reference for urban planning optimization and air environment protection in cities with air quality control considerations impacted by city wind.

HVAC systems account for a significant portion of building energy consumption, making them a critical factor in achieving energy efficiency and sustainable development in buildings. The thermal comfort environment within kindergarten buildings has a direct impact on children’s health and learning performance while also being closely linked to building energy consumption. Natural ventilation serves as a fundamental passive technology for ensuring indoor air quality. It offers advantages such as energy savings and emissions reduction while improving thermal comfort, making it a key advancement in promoting sustainable building practices. Air change rate serves as a key indicator for measuring indoor air renewal. This study, based on the characteristics of young children being more sensitive to environmental changes and having weaker resistance, investigated the impact of different air change rates on indoor thermal comfort in kindergartens across three distinct climatic zones in China: hot summer and warm winter, hot summer and cold winter, and temperate areas. Thermal comfort was evaluated using the Temperature Humidity Index (THI) and temperature fluctuation rate. And the effects of air change rates on building energy consumption were further examined. Simulation results show that the influence of air change rates on indoor thermal comfort, temperature fluctuations, and energy consumption varies significantly across climate zones. Guided by the sustainable principles of prioritizing children’s health and low-energy operation, the findings indicate that an air change rate of 1.5 is optimal in hot summer and warm winter areas, a rate of 0.5 is most suitable in hot summer and cold winter areas, and a rate of 0.5 is most suitable in temperate areas. This study aims to provide scientific evidence for achieving energy conservation and sustainable ventilation design through natural ventilation in kindergarten buildings across different climate zones, while ensuring children’s health.

Building energy use in the Western Sichuan Plateau exhibits strong seasonal variability owing to its cold climate and intense solar radiation. The objective of this study is to quantify and interpret the contributions of building design parameters to heating, cooling, and total energy use in the Western Sichuan Plateau by integrating parametric energy simulation with explainable machine learning. First, we analyzed the existing hourly energy consumption data of a specific building. Subsequently, 62,208 parametric building schemes were simulated using EnergyPlus (version: 22.1.0) by varying geometry, orientation, window-to-wall ratio (WWR), and envelope characteristics. XGBoost models were developed to predict heating demand, cooling demand, and total energy use intensity (EUI), achieving R² values of 0.9998, 0.9997, and 0.9998, respectively, with MAPE values of 0.45%, 2.75%, and 0.26%. SHAP analysis revealed clear target-dependent effects of design parameters. SHAP analysis revealed target-dependent differences in the importance of design parameters. Wall construction, orientation, and building length were the top three contributors to heating EUI; orientation, wall construction, and WWR ranked highest for cooling EUI; and wall construction, building length, and orientation were most influential for total EUI. Increasing WWR generally reduced its SHAP values for heating EUI, with a weaker effect observed for the insulated-wall configuration, whereas its SHAP values for cooling EUI generally increased. Total EUI showed an overall decreasing trend with increasing WWR. These findings highlight the importance of evaluating building design parameters in relation to heating, cooling, and total energy performance. The identified nonlinear and target-dependent response patterns provide insights into climate-responsive building design in the Western Sichuan Plateau.

Urban greening is central to climate-responsive planning, but month-to-month variation in urban net primary productivity (UNPP) remains insufficiently resolved. We estimated monthly UNPP from the MOD17A2H Gpp band, a gross primary productivity (GPP) product, across Chengdu’s five central districts during 2013–2022 and examined its associations with the normalized difference vegetation index (NDVI), land surface temperature (LST), precipitation, and night-time light intensity. Ordinary least squares regression used 119 overlapping citywide monthly means from January 2013 to November 2022. The model explained 86.1% of UNPP variance (adjusted R² = 0.856). LST showed the largest standardized association (β = 0.596), followed by NDVI (β = 0.286) and precipitation (β = 0.135); night-time light intensity was not significant (β = 0.017, p = 0.632). HC3-robust inference retained the NDVI and LST associations, whereas the precipitation association weakened. District summaries showed the largest stage-wise UNPP increase in Qingyang and the largest NDVI increase in Chenghua. Greener spatial patterns after 2018 coincided temporally with the Park City Initiative but do not establish a causal policy effect. These findings support temporally aligned monitoring while identifying the need for field-based species, management, and impervious surface data.