The COVID-19 pandemic has lasted from 2019 to 2022, severely disrupting human health and daily life. The combined effects of spatial, environmental, and behavioral factors on indoor COVID-19 spread and their interactions are usually ignored. Especially, there is a lack of discussion on the role of spatial factors in reducing the risk of virus transmission in complex and diverse indoor environments. This paper endeavours to summarize the spatial factors and their effects involved in indoor virus transmission. The process of release, transport, and intake of SARS-CoV-2 was reviewed, and six transmission routes according to spatial distance and exposure way were classified. The triangular relationship between spatial, environmental and occupant behavioral parameters during virus transmission was discussed. The detailed effects of spatial parameters on droplet-based, surface-based and air-based transmission processes and virus viability were summarized. We found that spatial layout, public-facility design and openings have a significant indirect impact on the indoor virus distribution and transmission by affecting occupant behavior, indoor airflow field and virus stability. We proposed a space-based indoor multi-route infection risk assessment framework, in which the 3D building model containing detailed spatial information, occupant behavior model, virus-spread model and infection-risk calculation model are linked together. It is also applicable to other, similar, respiratory infectious diseases such as SARS, influenza, etc. This study contributes to developing building-level, infection-risk assessment models, which could help building practitioners make better decisions to improve the building’s epidemic-resistance performance.

Global warming has significantly intensified the risks of summer heatwaves, making outdoor thermal comfort during extreme heat periods a critical research focus. Under centralized rural village reconstruction policies, traditional settlements are being replaced by regularized modern communities characterized by new materials and standardized layouts. However, the impact of these morphological transitions on the micro-scale thermal environment remains under-researched, with a notable lack of comparative perspectives between traditional organic and modern standardized typologies. This study identifies six representative zones based on spatial configuration. By integrating UAV photogrammetry (Pix4Dmapper v4.5), AutoCAD 2019, and QGIS (v3.22), morphological characteristics were quantified, followed by microclimate simulations using ENVI-met v5.9. The results reveal that while peak daytime Physiological Equivalent Temperature (PET) in the standardized zones (49.2–51.8 °C) is slightly lower than in traditional zones (53.5–55.2 °C), a phenomenon of thermal homogenization emerges in the former. Specifically, values in standardized zones are highly concentrated around the median (53.5 °C), contributing to a significant upward trend in the minimum PET values, with nearly all sampling points exceeding 47.0 °C. Quantitative analysis identifies green coverage and perviousness as primary cooling drivers, while spatial openness and imperviousness promote thermal homogenization. In contrast, traditional zones retain critical cool refuges due to their spatial heterogeneity. This research provides an empirical foundation and quantitative reference for understanding the thermal performance differences across different rural spatial typologies. The findings offer insights for planners to optimize street layouts and shading strategies, ultimately mitigating heat stress and fostering climate-resilient modern countryside development.

Many ordinary dwellings are not well built in the vast rural areas of China, with poor comfort level and high energy consumption. The China Resources Charity Foundation funded the renovation of more than 200 farmhouses. Compared with the BP neural network model established with the measured data, the BP neural network model established with the simulated data can more quickly estimate the energy saved by the renovation measures of rural dwellings and the improved indoor environment, which is very necessary for the government and enterprises to make decisions to help. In this paper, one of the renovated houses (No. 285) was selected for tracking measurement before and after renovation to obtain real indoor environment and energy consumption data. Simulation software (DesignBuilder) was used to simulate the other 36 renovated houses in order to train the BP neural network model. The energy consumption of No. 285 was calculated by using the trained BP neural model, and the calculated results were compared and analyzed with the simulated data and measured data. Results show that the BP neural network model after training is better than the simple energy consumption simulation software in energy consumption prediction, but there are still errors from the actual measurement values. Regardless of software predictions or actual measurements, the results show that the renovation measures have indeed exerted a very positive impact. As to the overall trend, the software predictions are the same as of the actual measurements, and the variance of day and night temperature variance and the annual extreme values are greatly reduced.