Combined Heat, Air, Moisture and Pollutant Simulations (CHAMPS) research for building and urban energy efficiency and environmental quality analysis

The energy and environmental performance of buildings and communities depends on how the heat, air, moisture and pollutant flows are managed and controlled in the system. Combined Heat, Air, Moisture and Pollutant Simulations (CHAMPS) research and development is vital to the optimal design and operation of building and urban energy and environmental systems. The 16th International Forum and Workshop on Combined Heat, Air, Moisture and Pollutant Simulations (CHAMPS 2019) returned to Nanjing University, China on October 20–22, 2019. Previously, Nanjing University held the 8th forum and workshop in 2011 and a topical issue was published on Building Simulation journal in December 2011 (Volume 4, Number 4). CHAMPS 2019 covered research on multiple scales ranging from materials and microenvironment around a person to buildings and their surrounding urban environments. The papers published in this topical issue include selected papers presented at the forum and additional papers submitted directly to the journal. All papers went through the rigorous peer review process of the journal. A brief overview of each paper is given below:  The paper by Shen et al. (2021) developed a new modularbased Green Design Studio (GDS) platform for fast and accurate performance analysis for early stage green building design. A simplified physics-based model, the ResistanceCapacitance (RC) model was proposed as a generic simulation model, which is significantly faster than conventional simulation tools such as EnergyPlus, and hence more practical for use in real-time design interaction and optimization. The proposed GDS approach has the potential to provide a fast and reliable feedback on performance prediction and makes it easier to modify the building design and evaluate the performance of each “green” feature in near-real time in design.  The paper by Liu et al. (2021) developed a procedure to estimate the parameters of mechanistic volatile organic compound (VOC) emission source models from chamber testing data. Results indicated that estimates converged to the original parameter values used for the data generation and the error of estimated parameters Dm (in-material diffusion coefficient), Cm0 (initial concentration) and Kma (partition coefficient) were within ±10%, ±23%, and ±25% of the true values, respectively. The adoption of data analysis would result in a database of mechanistic model parameters for evaluating the impact of material emissions on indoor pollution load and indoor air quality (IAQ).  The paper by Zhao et al. (2021) developed a probabilisticbased method to evaluate the hygrothermal performance of building components. The Latin hypercube sampling technique, incorporated with the correlation structure among the inputs, was applied to generate random samples that follow the intrinsic relations. The performance of an internally insulated masonry wall was evaluated by applying the proposed approach against different criteria. The method can provide a comprehensive risk analysis and support the decision-maker and engineer in the design and optimization of building components.  The paper by Zhou et al. (2021b) proposed an action-based Markov chain modeling approach to predict the window operating behavior in office spaces. They found that this approach can simulate occupants’ window operating behavior dynamically within the building and heating, ventilation, and air conditioning (HVAC) domains effectively. Compared with the state-based Markov chain BUILD SIMUL (2021) 14: 237–239 https://doi.org/10.1007/s12273-020-0760-9