To evaluate the effects of air purifiers on the concentrations of indoor air pollutants and on asthma control in children. In this randomized crossover trial, daily use of an air purifier filter was compared with a matched placebo with the filter off. Thirty elementary school students who had asthma were enrolled and randomly allocated to one of two groups. The primary endpoints were changes in indoor air quality, asthma severity, lung function, airway inflammatory, urine microbiome, and phthalate after the installation of air purifiers. PM2.5 and CO₂ were measured as indoor air pollutants. Asthma severity was assessed in terms of both symptom and medication scores acquired using a daily questionnaire. The higher the score, the better the symptom or the less frequent the use of medication. Peak expiratory flow rate and fractional exhaled nitric oxide were also measured. The mean age of the enrolled patients was 9.2±1.98 years. The mean concentration of PM2.5 was 17.0 μg/m³ in the filter-off condition, but significantly lower at 9.26 μg/m³ in the filter-on condition. Medication scores were 6.9 for the filter-off and 7.12 for the filter-on conditions, reflecting a statistically significant decrease in the frequency of medications used during air purifier operation. Bacterial richness, as determined using the Chao 1 index, was markedly lower in the filter-on than the filter-off condition. This study suggests that air purifiers benefit medication burden in children with asthma by reducing PM2.5 levels.

Various studies have indicated that particulate matter <2.5 μm (PM2.5) could cause adverse health effects on pulmonary functions in susceptible groups, especially asthmatic children. Although the impact of ambient PM2.5 on children’s lower respiratory health has been well-established, information regarding the associations between indoor PM2.5 levels and respiratory symptoms in asthmatic children is relatively limited. This randomized, crossover intervention study was conducted among 26 asthmatic children’s homes located in Incheon metropolitan city, Korea. We aimed to evaluate the effects of indoor PM2.5 on children’s peak expiratory flow rate (PEFR), with a daily intervention of air purifiers with filter on, compared with those groups with filter off. Children aged between 6–12 years diagnosed with asthma were enrolled and randomly allocated into two groups. During a crossover intervention period of seven weeks, we observed that, in the filter-on group, indoor PM2.5 levels significantly decreased by up to 43%. (p < 0.001). We also found that the daily or weekly unit (1 μg/m³) increase in indoor PM2.5 levels could significantly decrease PEFR by 0.2% (95% confidence interval (CI) = 0.1 to 0.5) or PEFR by 1.2% (95% CI = 0.1 to 2.7) in asthmatic children, respectively. The use of in-home air filtration could be considered as an intervention strategy for indoor air quality control in asthmatic children’s homes.

This study aimed to compare the distribution of indoor, outdoor, and personal PM_2.5 (particulate matter ≤ 2.5 μm) hourly concentrations measured simultaneously among 81 nonsmoking elderly participants (65 years or older) living in urban, industrial, or rural areas over 4 seasons (2 weeks per season) from November 2021 to July 2022). PM_2.5 measurements were conducted using low-cost sensors with quality control and quality assurance tests. Seasonal outdoor PM_2.5 levels were 16.4 (9.1–29.6) μg/m³, 20.5 (13.0–38.0) μg/m³, 18.2 (10.2–31.8) μg/m³, and 9.5 (3.8–18.7) μg/m³ for fall, winter, spring, and summer, respectively. For indoor PM_2.5, the median seasonal range was 5.9–7.5 μg/m³, and the median personal PM_2.5 exposure concentration was 8.0–9.4 μg/m³. This study provided seasonal distributions of IO (ratio of indoor to outdoor PM_2.5 concentration) and PO (ratio of personal to outdoor PM_2.5 concentration) using a total of 94,676 paired data points. The median seasonal IO ranged from 0.30 to 0.51 in fall, winter, and spring; its value of summer was 0.70. The median PO by season and study area were close to 1.0 in summer while it ranged 0.5 to 0.7 in other seasons, statistically significantly lower (p < 0.05) than that in summer. Our study has revealed that the real-world exposure level to PM_2.5 among our elderly study participants might be lower than what was initially expected based on the outdoor data for most of the time. Further investigation may need to identify the reasons for the discrepancy, personal behavior patterns, and the effectiveness of any indoor air quality control system.

Dampness deteriorates indoor air quality. This study aims at finding out the relationship between dampness score and levels of common indoor air quality indicators including CO₂, PM2.5, temperature, relative humidity and concentrations of indoor fungi. A survey was used to develop a dampness score and the indoor air quality was measured using a real-time indoor air quality sensing monitor (ESCORT, South Korea) and a collector for fungi measurement (KAS-110, Kemik Corporation, Seongnam, SouthKorea) among asthmatics’ home. A statistical package was used to evaluate the relationship between dampness score and indoor air quality variables. The spearman correlation coefficient between the dampness score and carbon dioxide was .642 indicating a strong correlation (p<.05). A significant correlation between the dampness score and the concentration of mold in the bedroom was also found ( .315, p<.05). The correlation coefficient between the dampness score and the relative humidity was .40 (p<.05). The correlation coefficients between the dampness score and PM2.5 or temperature were .02 or .014, respectively. This study found that dampness score was mainly determined by indoor CO₂, fungi concentration and relative humidity.