Low-Cost Particulate Matter Sensor in Indoor and External Classroom Environments

Indoor PM2.5 and its morphology in a naturally ventilated office in Xi'an, China

Background: The Reducing Air Pollution in Detroit Intervention Study (RAPIDS) was designed to evaluate cardiovascular health benefits and personal fine particulate matter (particulate matter < 2.5 μm in diameter, PM2.5) exposure reductions via portable air filtration units (PAFs) among older adults in Detroit, Michigan. This double-blind randomized crossover intervention study has shown that, compared to sham, air filtration for 3 days decreased 3-day average brachial systolic blood pressure by 3.2 mmHg. The results also showed that commercially available HEPA-type and true HEPA PAFs mitigated median indoor PM2.5 concentrations by 58% and 65%, respectively. However, to our knowledge, no health intervention study in which a significant positive health effect was observed has also evaluated how outdoor and indoor PM2.5 sources impacted the subjects. With that in mind, detailed characterization of outdoor and indoor PM2.5 samples collected during this study and a source apportionment analysis of those samples using a positive matrix factorization model were completed. The aims of this most recent work were to characterize the indoor and outdoor sources of the PM2.5 this community was exposed to and to assess how effectively commercially available HEPA-type and true HEPA PAFs were able to reduce indoor and outdoor PM2.5 source contributions. Methods: Approximately 24 h daily indoor and outdoor PM2.5 samples were collected on Teflon and Quartz filters from the apartments of 40 study subjects during each 3-day intervention period. These filters were analyzed for mass, carbon, and trace elements. Environmental Protection Agency Positive Matrix Factorization (PMF) 5.0 was utilized to determine major emission sources that contributed to the outdoor and indoor PM2.5 levels during this study. Results: The major sources of outdoor PM2.5 were secondary aerosols (28%), traffic/urban dust (24%), iron/steel industries (15%), sewage/municipal incineration (10%), and oil combustion/refinery (6%). The major sources of indoor PM2.5 were organic compounds (45%), traffic + sewage/municipal incineration (14%), secondary aerosols (13%), smoking (7%), and urban dust (2%). Infiltration of outdoor PM2.5 for sham, HEPA-type, and true HEPA air filtration was 79 ± 24%, 61 ± 32%, and 51 ± 34%, respectively. Conclusions: The results from our study showed that intervention with PAFs was able to significantly decrease indoor PM2.5 derived from outdoor and indoor PM2.5 sources. The PAFs were also able to significantly reduce the infiltration of outdoor PM2.5. The results of this study provide insights into what types of major PM2.5 sources this community is exposed to and what degree of air quality and systolic blood pressure improvements are possible through the use of commercially available PAFs in a real-world setting.

The health risks associated with wildfires are expected to increase due to climate change. Children are susceptible to wildfire smoke, but little is known about indoor smoke exposure at childcare facilities. The objective of this analysis was to estimate the effects of outdoor PM2.5 and wildfire smoke episodes on indoor PM2.5 at childcare facilities across British Columbia, Canada. We installed low-cost air-quality sensors inside and outside 45 childcare facilities and focused our analysis on operational hours (Monday–Friday, 08:00–18:00) during the 2022 wildfire season (01 August–31 October). Using random-slope random-intercept linear mixed effects regression, we estimated the overall and facility-specific effects of outdoor PM2.5 on indoor PM2.5, while accounting for covariates. We examined how wildfire smoke affected this relationship by separately analyzing days with and without wildfire smoke. Average indoor PM2.5 increased by 235% on wildfire days across facilities. There was a positive relationship between outdoor and indoor PM2.5 that was not strongly influenced by linear adjustment for meteorological and area-based socio-economic factors. A 1.0 μg m−3 increase in outdoor PM2.5 was associated with a 0.55 μg m−3 [95% CI: 0.47, 0.63] increase indoors on non-wildfire smoke days and 0.51 μg m−3 [95% CI: 0.44, 0.58] on wildfire-smoke days. Facility-specific regression coefficients of the effect of outdoor PM2.5 on indoor PM2.5 was variable between facilities on wildfire (0.18–0.79 μg m−3) and non-wildfire days (0.11–1.03 μg m−3). Indoor PM2.5 responded almost immediately to increased outdoor PM2.5 concentrations. Across facilities, 89% and 93% of the total PM2.5 infiltration over 60 min occurred within the first 10 min following an increase in outdoor PM2.5 on non-wildfire and wildfire days, respectively. We found that indoor PM2.5 in childcare facilities increased with outdoor PM2.5. This effect varied between facilities and between wildfire-smoke and non-wildfire smoke days. These findings highlight the importance of air quality monitoring at childcare facilities for informed decision-making.

Outdoor particulate matter (PM(10)) is associated with detrimental health effects. However, individual PM(10) exposure occurs mostly indoors. We therefore compared the toxic effects of classroom, outdoor, and residential PM(10). Indoor and outdoor PM(10) was collected from six schools in Munich during teaching hours and in six homes. Particles were analyzed by scanning electron microscopy and X-ray spectroscopy (EDX). Toxicity was evaluated in human primary keratinocytes, lung epithelial cells and after metabolic activation by several human cytochromes P450. We found that PM(10) concentrations during teaching hours were 5.6-times higher than outdoors (117 ± 48 μg/m(3) vs. 21 ± 15 μg/m(3), P < 0.001). Compared to outdoors, indoor PM contained more silicate (36% of particle number), organic (29%, probably originating from human skin), and Ca-carbonate particles (12%, probably originating from paper). Outdoor PM contained more Ca-sulfate particles (38%). Indoor PM at 6 μg/cm(2) (10 μg/ml) caused toxicity in keratinocytes and in cells expressing CYP2B6 and CYP3A4. Toxicity by CYP2B6 was abolished with the reactive oxygen species scavenger N-acetylcysteine. We concluded that outdoor PM(10) and indoor PM(10) from homes were devoid of toxicity. Indoor PM(10) was elevated, chemically different and toxicologically more active than outdoor PM(10). Whether the effects translate into a significant health risk needs to be determined. Until then, we suggest better ventilation as a sensible option. Indoor air PM(10) on an equal weight base is toxicologically more active than outdoor PM(10). In addition, indoor PM(10) concentrations are about six times higher than outdoor air. Thus, ventilation of classrooms with outdoor air will improve air quality and is likely to provide a health benefit. It is also easier than cleaning PM(10) from indoor air, which has proven to be tedious.

Indoor PM2.5 in an urban zone with heavy wood smoke pollution: The case of Temuco, Chile

The relationship between indoor and outdoor particulate air pollution was investigated at an urban background site on the Payambar Azam Campus of Mazandaran University of Medical Sciences in Sari, Northern Iran. The concentration of particulate matter sized with a diameter less than 1μm (PM1.0), 2.5μm (PM2.5), and 10μm (PM10) was evaluated at 5 outdoor and 12 indoor locations. Indoor sites included classrooms, corridors, and office sites in four university buildings. Outdoor PM concentrations were characterized at five locations around the university campus. Indoor and outdoor PM measurements (1-min resolution) were conducted in parallel during weekday mornings and afternoons. No difference found between indoor PM10 (50.1±32.1μg/m3) and outdoor PM10 concentrations (46.5±26.0μg/m3), indoor PM2.5 (22.6±17.4μg/m3) and outdoor PM2.5 concentration (22.2±15.4μg/m3), or indoor PM1.0 (14.5±13.4μg/m3) and outdoor mean PM1.0 concentrations (14.2±12.3μg/m3). Despite these similar concentrations, no correlations were found between outdoor and indoor PM levels. The present findings are not only of importance for the potential health effects of particulate air pollution on people who spend their daytime over a period of several hours in closed and confined spaces located at a university campus but also can inform regulatory about the improvement of indoor air quality, especially in developing countries.

Influence of atmospheric fine particulate matter (PM2.5) pollution on indoor environment during winter in Beijing

Background: Indoor air quality (IAQ) in shopping malls is an interesting case of study since a shopping mall is a public place where people favor to spend their time. This study was conducted to investigate the IAQ of shopping malls in Kota Kinabalu, Sabah, whereby three shopping malls were selected as investigation sites. Methods: The parameters being studied include particulate matter (PM0.3-∞, PM0.5-∞, PM2-∞ and PM5-∞) and ozone. Indoor and outdoor air measurements were performed in the three shopping malls on weekdays and weekends to determine the I/O ratios. Results: In this study, overall average indoor PM concentrations on weekends were higher than weekdays, reaching maxima average concentrations of 421.44 ± 102.96 µg/m3 for PM0.3-∞, 41.75 ± 15.54 µg/m3 for PM0.5-∞, 1.30 ± 0.41 µg/m3 for PM2-∞, and 0.21 ± 0.09 µg/m3 for PM5-∞. Correlation between indoor and outdoor PM concentrations mostly showed poor relationship in the three shopping malls, showing that indoor sources such as re-suspension phenomena due to occupant's activities were clearly the main contributors to indoor PM concentrations. Poor ventilation system also affected IAQ by increasing the PM accumulation. However, I/O ratios were often less than 1.0, indicating that PM in indoor air arises predominantly from outdoor air transported to indoors. Average indoor ozone concentration at all the shopping malls was measured to be below the 0.05 ppm of ICOP-IAQ 2010. Conclusion: The overall assessment of IAQ in the three shopping malls showed that SM2 has a better IAQ compared to SM1 and SM3.

Network of low-cost air quality sensors for monitoring indoor, outdoor, and personal PM2.5 exposure in Seattle during the 2020 wildfire season

Haze is a common phenomenon during winter (December- February) in Dhaka, Bangladesh. Fine particulate matter or PM2.5 is considered the major component of haze. This study was conducted to evaluate the effect of haze on the concentration, chemical composition, source contribution and associated health risks of indoor PM2.5 in a residential home of Dhaka Cantonment, located at the center of Dhaka city. During haze, the 24- hour average indoor PM2.5 concentration was 139 &amp;#177; 57.7 &amp;#181;gm-3, whereas that during non- haze period was 96.3 &amp;#177; 17.7 &amp;#181;gm-3; both significantly exceeded the WHO guideline value (15 &amp;#181;gm-3). Despite the same indoor environment, haze- time PM2.5 concentration in Cantonment was 1.44 times higher than its non- haze counterpart. Mean I/O ratio (I/Ohaze= 0.92 and I/Onon- haze= 0.96) and strong positive correlation (R&amp;#178;haze= 0.89 and R2non- haze= 0.85) between indoor and outdoor PM2.5 suggested infiltration of polluted outdoor air into indoor environment. NOAA- HYSPLIT Backward Air Mass Trajectory Analysis indicated that north and northwestern air passing over the highly polluted Indo-Gangetic Plain (IGP) probably carried excessive air pollutants to Dhaka during winter, which might be responsible for creating haze and further increase of indoor PM2.5 level owing to infiltration. 24- hour gravimetric sampling of Indoor PM2.5 was carried out to quantify six heavy metals (Fe, Mn, Cu, Zn, Pb and Cr) by AAS analysis. Except Zn, haze- time concentration of all the heavy metals were 1.32 to 71.3 times higher than their non- haze concentrations. During haze, Pb (1022 &amp;#177; 195 ngm-3) and Mn (693 &amp;#177; 62.3 ngm-3) exceeded the WHO guideline values significantly. Enrichment factor analysis and source apportionment by Positive Matrix Factorization (PMF) revealed four sources of indoor PM2.5, namely- industrial emission (4.4%), crustal sources (17.6%), vehicular emission (44.6%) and fugitive lead (32.9%). Health risk assessment indicated that hazard index (HI) for children during haze period was 31.19, whereas that during non- haze period was 0.57 only. Moreover, haze- time HI for adults was 13.68, which was 29 times higher than its non- haze value (0.47). Hence, indoor PM2.5 exposure put children at much greater risk than the adults during haze, despite staying inside their home. Total cancer risk during haze and non- haze episodes exceeded the USEPA regulated target value (1 &amp;#215; 10-6). The total cancer risk during haze was 1.33 times higher than that during non- haze weather. Therefore, haze increased the probability of developing cancer from 1 in 2146 individuals to 1 in 1608 individuals. The hazard ratio (HR) during haze (9.25) and non- haze (6.42) period indicated severely compromised indoor air quality in the sampling household of Dhaka Cantonment, so effective measures should be adopted to control indoor air pollution.

The objective of this study is to analyze the characteristic of indoor particles and the connection between indoor particles and outdoor particles. A long-term sampling and measurements of fine particulate matter (PM2.5) has been carried out around an resident area in Harbin (a typical urban in Chinese severe cold regions), which including indoor PM2.5 concentrations in two different types of residences, outdoor PM2.5 concentrations in the residential area, and PM2.5 concentrations near the regional heat source which supply heat for the residential area. This study analyses the affection of people's behavior on the indoor PM2.5 characteristic, the characteristic of PM2.5 in different seasons especially the uncommon characteristic during the heating season, and the influence of weather conditions (temperature, relative humidity, wind speed and solar radiation) on the characteristic of PM2.5. Besides, it shows the influence of outdoor atmospheric PM2.5 on the indoor environment is very strong based on the analysis of I/O ratios, outdoor PM2.5 is the main source of indoor PM2.5. If the outdoor haze intensifies, indoor PM2.5 concentrations will increase significantly.

The impact of air pollution on school children's health is currently one of the key foci of international and national agencies. Of particular concern are ultrafine particles which are emitted in large quantities, contain large concentrations of toxins and are deposited deeply in the respiratory tract. In this study, an intensive sampling campaign of indoor and outdoor airborne particulate matter was carried out in a primary school in February 2006 to investigate indoor and outdoor particle number (PN) and mass concentrations (PM(2.5)), and particle size distribution, and to evaluate the influence of outdoor air pollution on the indoor air. For outdoor PN and PM(2.5), early morning and late afternoon peaks were observed on weekdays, which are consistent with traffic rush hours, indicating the predominant effect of vehicular emissions. However, the temporal variations of outdoor PM(2.5) and PN concentrations occasionally showed extremely high peaks, mainly due to human activities such as cigarette smoking and the operation of mower near the sampling site. The indoor PM(2.5) level was mainly affected by the outdoor PM(2.5) (r = 0.68, p < 0.01), whereas the indoor PN concentration had some association with outdoor PN values (r = 0.66, p < 0.01) even though the indoor PN concentration was occasionally influenced by indoor sources, such as cooking, cleaning and floor polishing activities. Correlation analysis indicated that the outdoor PM(2.5) was inversely correlated with the indoor to outdoor PM(2.5) ratio (I/O ratio; r = -0.49, p < 0.01), while the indoor PN had a weak correlation with the I/O ratio for PN (r = 0.34, p < 0.01). The results showed that occupancy did not cause any major changes to the modal structure of particle number and size distribution, even though the I/O ratio was different for different size classes. The I/O curves had a maximum value for particles with diameters of 100-400 nm under both occupied and unoccupied scenarios, whereas no significant difference in I/O ratio for PM(2.5) was observed between occupied and unoccupied conditions. Inspection of the size-resolved I/O ratios in the preschool centre and the classroom suggested that the I/O ratio in the preschool centre was the highest for accumulation mode particles at 600 nm after school hours, whereas the average I/O ratios of both nucleation mode and accumulation mode particles in the classroom were much lower than those of Aitken mode particles. The findings obtained in this study are useful for epidemiological studies to estimate the total personal exposure of children, and to develop appropriate control strategies for minimising the adverse health effects on school children.

Estimating hourly average indoor PM2.5 using the random forest approach in two megacities, China

Air pollutant emissions from wildfires on Indonesian peatlands lead to poor regional air quality across south-east Asia. Fine particulate matter (PM2.5) emissions are particularly high for peat fires leading to substantial population exposure to PM2.5. Despite this, air quality monitoring is limited in regions close to peat fires meaning the impacts of peatland fires on air quality is poorly understood and it is difficult to evaluate predictions from atmospheric chemistry models. To address this, we deployed a network of low-cost (Purple Air) PM2.5 sensors at 8 locations across Central Kalimantan, where peat fires are frequent. The sensors measured indoor and outdoor PM2.5 concentrations during August to December 2023. During the haze season (September 1st to October 31st), daily mean outdoor concentrations were 120 mg m-3 but peaked at &gt;400 mg m-3. Indoor PM2.5 concentrations were only ~10% lower (mean 110 mg m-3), indicating that is difficult for the population to reduce their exposure to PM2.5 from fires. The reduction in mean PM2.5 concentrations between outdoor and indoor environments was larger in urban locations (-11%) compared with rural locations (-3%), suggesting urban housing may provide better protection from outdoor air pollution. To generate an updated assessment for the population&amp;#8217;s exposure to peatland fire PM2.5 we combine the information from monitoring both indoor and outdoor PM2.5 concentrations with modelled ambient (outdoor) PM2.5 concentrations from the WRF-Chem atmospheric chemistry transport model. Our updated exposure assessment accounts for the population&amp;#8217;s personal exposure to peatland fire PM2.5 for the first time.

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations. In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models. The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables. In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.