Because people spend most of their time indoors, the characterization of indoor air quality is important for exposure assessment. Unfortunately, indoor air data are scarce, leading to a major data gap in risk assessment. In this study, PM2.5 concentrations in both indoor and outdoor air were simultaneously measured using on-line particulate counters in 13 households in Haidian, Beijing for both heating and non-heating seasons. A bimodal distribution of PM2.5 concentrations suggests rapid transitions between polluted and non-polluted situations. The PM2.5 concentrations in indoor and outdoor air varied synchronously, with the indoor variation lagging. The lag time in the heating season was longer than that in the non-heating season. The particle sizes in indoor air were smaller than those in ambient air in the heating season and vice versa in the non-heating season. PM2.5 concentrations in indoor air were generally lower than those in ambient air except when ambient concentrations dropped sharply to very low levels or there were internal emissions from cooking or other activities. The effectiveness of an air cleaner to reduce indoor PM2.5 concentrations was demonstrated. Non-linear regression models were developed to predict indoor air PM2.5 concentrations based on ambient data with lag time incorporated. The models were applied to estimate the overall population exposure to PM2.5 and the health consequences in Haidian. The health impacts would be significantly overestimated without the indoor exposure being taken into consideration, and this bias would increase as the ambient air quality improved in the future.

Indoor and outdoor fine particulate matter (PM2.5) are both leading risk factors for death and disease, but making indoor measurements is often infeasible for large study populations.We developed models to predict indoor PM2.5 concentrations for pregnant women who were part of a randomized controlled trial of portable air cleaners in Ulaanbaatar, Mongolia. We used multiple linear regression (MLR) and random forest regression (RFR) to model indoor PM2.5 concentrations with 447 independent 7-day PM2.5 measurements and 87 potential predictor variables obtained from outdoor monitoring data, questionnaires, home assessments, and geographic data sets. We also developed blended models that combined the MLR and RFR approaches. All models were evaluated in a 10-fold cross-validation.The predictors in the MLR model were season, outdoor PM2.5 concentration, the number of air cleaners deployed, and the density of gers (traditional felt-lined yurts) surrounding the apartments. MLR and RFR had similar performance in cross-validation (R2 = 50.2%, R2 = 48.9% respectively). The blended MLR model that included RFR predictions had the best performance (cross validation R2 = 81.5%). Intervention status alone explained only 6.0% of the variation in indoor PM2.5 concentrations.We predicted a moderate amount of variation in indoor PM2.5 concentrations using easily obtained predictor variables and the models explained substantially more variation than intervention status alone. While RFR shows promise for modelling indoor concentrations, our results highlight the importance of out-of-sample validation when evaluating model performance. We also demonstrate the improved performance of blended MLR/RFR models in predicting indoor air pollution.

Increased public awareness of the health impacts of atmospheric fine particulate matter (PM₂.₅) has led to increased demand and deployment of indoor air cleaners. Yet, questions still remain about the effectiveness of indoor air cleaners on indoor PM₂.₅ concentrations and personal exposure to potentially hazardous components of PM₂.₅. Metals in PM₂.₅ have been associated with adverse health outcomes, so knowledge of their sources in urban indoor and outdoor areas and how exposures are influenced by indoor air cleaners would be beneficial for public health interventions. We collected 48-h indoor, outdoor, and personal PM₂.₅ exposure samples for 43 homes with asthmatic children in suburban Shanghai, China during the spring months. Two sets of samples were collected for each household, one set with a functioning air filter placed in the bedroom (“true filtration”) and the other with a non-functioning (“sham”) air cleaner. PM₂.₅ samples were analyzed for elements, elemental carbon, and organic carbon. The major sources of metals in PM₂.₅ were determined by Positive Matrix Factorization (PMF) to be regional aerosol, resuspended dust, residual oil combustion, roadway emissions, alloy steel abrasion, and a lanthanum (La) and cerium (Ce) source. Under true filtration, the median indoor to outdoor percent removal across all elements increased from 31% to 78% and from 46% to 88% across all sources. Our findings suggest that indoor air cleaners are an effective strategy for reducing indoor concentrations of PM₂.₅ metals from most sources, which could translate into improved health outcomes for some populations.

There is growing pressure to find technically feasible and economically viable solutions in reducing emissions of pollutants from various occupational settings to minimise environmental pollution. Hence, it is essential to develop and test methods for controlling pollutants from occupational backgrounds. We have tested an air cleaner device in reducing aerosol numbers by filtration and airborne bacteria by photocatalysis from an enclosed type dairy barn. Here, we had shown a significant reduction of larger size aerosol numbers (2.0–10.0 µm) and airborne total aerobic bacteria and Staphylococcus aureus (S. aureus) and complete clearance of Escherichia coli (E. coli) in the exhaust air of the air cleaner device. A greater 8.05% and 61.56% reduction of 5.0–10.0 µm aerosol numbers and airborne E. coli, respectively, were observed in the instantly treated central air of the dairy barn. We had found an increasing trend of aerosol numbers and airborne bacteria concentrations in the central air of the dairy barn after stopping the air cleaner device. We also had observed increased bacterial load in the filter paper of the air treatment chamber of the air cleaner device with the advancement of cleaning time. These findings are essential to validate air cleanings from various types of dairy microenvironments.

The particle penetration factor is an important parameter to determine the concentration of indoor particles. In this paper, a mathematical model for calculating this parameter was established by combining with the decay of the indoor PM₂.₅ and CO₂ concentrations measured in a bedroom with an air cleaner. The convergence of the penetration factors was analyzed under different working conditions. The results show that the particle penetration factors converge to stable values within the range of 0.69 to 0.84 close to the value from the empirical formula when the indoor PM₂.₅ concentration decays to stable values. When the role of particle deposition is ignored, the penetration factors at the low and middle airflow modes are 0.78 and 0.69, respectively. The particle penetration factors are mainly determined by the clean air delivery rate (CADR) of the air cleaner, clearance airflow, and I/O ratio during the balanced phase when the roles of indoor particle deposition and exfiltration can be ignored. This work can provide a convenient method for the calculation of the particle penetration factor.

Nowadays, there is an amplified interest in maintaining suitable indoor air quality (IAQ). Besides a wide range of available interventions, air cleaners are considered a valuable tool, since based on inexpensive and easily implementing technologies to improve IAQ. The purpose of this work is to combine the TiO₂-photocatalysis with the electrostatic and adsorption processes, in order to improve efficiency and reliability. A TiO₂-photocatalytic oxidation combined with an electrostatic filter has been studied. Nitrogen oxides reduction and degradation of many VOC over different catalyst support were monitored jointly with CO and CO₂ production. The coupling of photocatalysis with an external electric field enhances efficiency of the process. The choice of materials with diversified adsorptive characteristics plays an important role in the durability of the process over time.