Residential kitchens are major reservoirs for foodborne pathogens that threaten public health. Effective hygiene management is often hampered by the absence of predictive models for microbial growth under real-world conditions. To this end, this study introduces a novel Gompertz model that incorporates cooking and cleaning effects (Gompertz-CC), developed through a year-long field investigation across eight Chinese cities and controlled laboratory experimentation. Statistical analyses of field data identified air temperature and user behaviors, specifically cooking and cleaning activities, as the most influential factors for bacterial growth. By incorporating cooking and cleaning correction factors that adjust to temperature variations, the new model outperforms the original Gompertz model across all tested conditions (Adj.R² > 0.902, RMSE < 0.127). It allows for estimation of the critical bacterial rebound times post-cooking and cleaning, which shorten as temperatures rise. In winter, Staphylococcus aureus and Salmonella enterica remain below the safe thresholds for 15–30 hours following disinfection, while during summer months these windows narrow to 3–6 hours. By bridging the gap between laboratory microbiology and real-world kitchen dynamics, the Gompertz-CC model provides a robust framework for adaptive cleaning protocols.

Accurate analysis of interstitial condensation for walls could influence the energy efficiency and durability of buildings. The Chinese standard adopts the classic 1-D steady-state vapor diffusion model to assess the risk of interstitial condensation in the heating period in severe cold and cold regions, where the indoor and outdoor boundary conditions could be approximated as a steady state. However, its applicability is subject to doubt in other scenarios where indoor and outdoor boundary conditions are highly dynamic. The transient hygrothermal model subsequently provides more accurate results but is also more complicated. This paper aims to investigate the advisable interstitial condensation calculation method for office buildings in various climate zones in China. The hygrothermal properties of four typical building materials were tested first. Next, the steady-state and transient calculation methods were compared. Finally the optimized vapor diffusion resistance factors of vapor barriers were proposed. Results show that the constant and variable material properties have a negligible effect on interstitial condensation analysis, with a maximum difference of 2.5% in relative humidity (RH). However, the steady-state and transient calculations have discrepant results, with the maximum difference in RH exceeding 10%. Wind-driven rain also plays an important role, with the maximum difference in RH exceeding 15%. Besides, the EPS internal thermal insulation composite system (ITICS) and rock wool external thermal insulation composite system (ETICS) require an additional vapor barrier in Shanghai and Guangzhou. Since the reduction of building’s sensible and latent heat consumption caused by the vapor barrier is limited, the vapor permeability resistance of the vapor barrier only needs to consider moisture control.

In hygrothermal simulations, uncertainties in input parameters affect the reliability of outputs, potentially leading to erroneous judgments about building performance. This study proposes a probabilistic method to comprehensively evaluate input uncertainties in the simulated hygrothermal performance of 1-D composite walls in five thermal design zones in China. Specifically, input parameters are categorized into discrete (e.g., insulation type) and continuous (e.g., thermal conductivity) parameters. The discrete parameters form 160 basic simulation scenarios. For each scenario, 400 uniform random samples are generated within given ranges of continuous parameters using Latin hypercube sampling, resulting in 64,000 simulation cases. Heat flux, moisture flux, moisture content, interstitial condensation, mold growth and frost damage are used as indicators for the hygrothermal performance, and multiple linear regression analysis is used to determine the most influential continuous parameters. The results indicate that the thermal conductivity of insulation materials has the greatest influence on the average heat flux, interstitial condensation risk, mold growth risk, and freeze/thaw cycles. The moisture retention curve and vapor diffusion resistance factor of structural materials significantly influence the average moisture flux. The moisture retention curve of structural materials has the most influence on the average moisture content of the structural layer. This study elucidates the most critical input parameters for different thermal design zones in China and other similar climate regions.