Indoor air quality depends on the interaction of site, building and building envelope characteristics, occupant behaviour and ventilation performance. The main objective of the study is to analyse the ventilation performance based on measurements and non-stationary simulations of carbon dioxide (CO2) and radon (222Rn) concentrations performed on two example ventilation zones: a room in a single-family house (DH) and a room in a multi-apartment building (VS). The method included: i) modelling of DH and VS; ii) continuous measurements of CO2 and 222Rn concentrations in the air of DH and VS in autumn and winter, monitoring of microclimatic conditions and recording of the ventilation schedule; iii) nonstationary simulations of the required and recommended ventilation rates (scenarios 1‒6) with CONTAM 3. 4. (National Institute of Standards and Technology, 2020); iv) evaluation of the ventilation performance by determining the optimal ventilation rates for the DH model, provided that the concentrations of both pollutants do not exceed the limit values (CCO2 <1000 ppm and CRn <100 Bq m‒3 ). Using non-stationary data analysis, we have determined the optimal ventilation rates to ensure CRn <100 Bq m‒3 and CCO2 <1000 ppm for the whole period. The optimal ventilation rate in the DH model is 1.4 h ‒1 (69.9 m3 h ‒1 ) in autumn, 1.8 h‒1 (89.9 m3 h ‒1 ) in winter; and 0.5 h‒1 (34.6 m3 h ‒1 ) in autumn in the VS model. The values of the optimal rates for effective ventilation are higher by a factor of 2.5‒9.0 than the recommended or required rates under national legislation. The lowest ventilation rate for occupancy times is in Scenario 3 (0.5 h‒1 ; 25 m3 h ‒1 for the DH model and 34.6 m3 h ‒1 for the VS model) and leads to an exceedance of the limit values for CO2 of 1000 ppm, for 8 % of the time in the DH model in autumn (average concentration of 621 242 ppm) and 7 % of the time in winter (average concentration of 630 230 ppm) and for 0 % of the time in the VS model in autumn (average concentration of 745 154 ppm). The CRn limit of 100 Bq m‒3 is exceeded less than 1 % of the time in the DH model in autumn (mean concentration 49 16 Bq m‒3 ) and 8 % of the time in winter (mean concentration 74 18 Bq m‒3 ). Also, in the VS model, the CRn limit of 100 Bq m‒3 is exceeded less than 1 % of the time in autumn (mean concentration 35 15 Bq m‒3 ). The presented approach for ventilation performance analysis is also applicable to buildings of other uses, as it allows the determination of the optimal ventilation rate based on all influencing factors and ventilation performance indicators. CO2 and 222Rn proved to be useful ventilation performance indicators in our study, which are complementary as they have different origins (cellular respiration, soil), and both show a diurnal and seasonal cycle of concentration variation. An integrated approach to data analysis using a non-stationary simulation model, considering the interaction of location, building design, occupant specificities (occupancy, activities) and the cycle of pollutant concentration changes, leads to an improvement in indoor air quality.
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