Indoor Concentrations Of PM2 Research Articles

A study on indoor air quality at daycare centers using IoT environmental sensors

ABSTRACT Indoor air quality (IAQ) is a major concern at daycare centers, due to the weaker immune system of children. Although IAQ is typically measured in accordance with the ISO-16000 standard, environmental sensors have recently become widely used due to their ability to consistently monitor air quality at a lower cost. In this study, We installed IoT sensors indoors and outdoors at ten daycare centers to measure PM(PM2.5, PM10), CO2, temperature, and humidity, as well as window opening-closing sensors to identify ventilation behavior. Indoor PM concentrations were consistently lower than outdoor levels, increasing from 7:00 AM, peaking between 9:00 AM and 12:00 PM, and decreasing during nap time (12:00 PM to 2:00 PM). CO2 levels were generally higher indoors than outdoors, peaking at 2:00 PM before decreasing. Windows were opened more frequently at 7:00 AM, 8:30 AM, and 3:00 PM. The correlation analysis revealed that significant factors influencing CO2 concentration were window opening duration. Ventilation rates per person at the daycare centers ranged from 1.77 to 10.69 m3/h, below both Korean and ASHRAE guidelines. The results of this study can provide valuable insights for developing strategies to improve indoor air quality in daycare centers that depend on window ventilation.

Indoor Air Quality at an Urban Primary School in Madrid (Spain): Influence of Surrounding Environment and Occupancy.

Monitoring indoor air quality (IAQ) in schools is critical because children spend most of their daytime inside. One of the main air pollutant sources in urban areas is road traffic, which greatly influences air quality. Thus, this study addresses, in depth, the linkages of meteorology, ambient air pollution, and indoor activities with IAQ in a traffic-influenced school situated south of Madrid. The measurement period was from 22 November to 21 December 2017. Simultaneous measurements of indoor and outdoor PM1, PM2.5, and PM10 mass concentrations, ultrafine particle number concentration (PNC) and equivalent black carbon (eBC) were analyzed under different meteorological conditions. PNC and eBC outdoor concentrations and their temporal trend were similar among the sampling points, with all sites being influenced in the same way by traffic emissions. Strong correlations were found between indoor and outdoor concentrations, indicating that indoor pollution levels were significantly affected by outdoor sources. Especially, PNC and eBC had the same indoor/outdoor (I/O) trend, but indoor concentrations were lower. The time delay in indoor vs. outdoor concentrations varied between 0.5 and 2 h, depending on wind speed. Significant differences were found between different meteorological conditions (ANOVA p-values < 2.14 × 10-6). Atmospheric stability periods led to an increase in indoor and outdoor pollutant levels. However, the highest I/O ratios were found during atmospheric instability, especially for eBC (an average of 1.2). This might be related to rapid changes in the outdoor air concentrations induced by meteorology. Significant variations were observed in indoor PM10 concentrations during classroom occupancy (up to 230 µg m-3) vs. non-occupancy (up to 19 µg m-3) days, finding levels higher than outdoor ones. This was attributed to the scholarly activities in the classroom. Conversely, PNC and eBC concentrations only increased when the windows of the classroom were open. These findings have helped to establish practical recommendations and measures for improving the IAQ in this school and those of similar characteristics.

Evaluating Indoor Air Quality in Schools: Is the Indoor Environment a Haven during High Pollution Episodes?

Pollution data were collected at five schools in Hong Kong using low-cost, sensor-based monitors both indoors and outdoors during two consecutive high pollution episodes. The pollutants monitored included NO2, O3, PM2.5, and PM10, which were also used as input to a health risk communication protocol known as Air Quality Health Index (AQHI). CO2 was also measured simultaneously. The study aimed to assess the relationship between indoor pollutant concentrations and AQHI levels with those outdoors and to evaluate the efficacy of building operating practices in protecting students from pollution exposure. The results indicate that the regular air quality monitoring stations and outdoor pollutant levels at schools exhibit similar patterns. School AQHI levels indoors were generally lower than those outdoors, with PM10 levels showing a larger proportional contribution to the calculated values indoors. NO2 levels in one school were in excess of outdoor values. CO2 monitored in classrooms commonly exceeded indoor guidelines, suggesting poor ventilation. One school that employed air filtration had lower indoor PM concentrations compared to other schools; however, they were still similar to those outdoors. O3 levels indoors were consistently lower than those outdoors. This study underscores the utility of on-site, sensor-based monitoring for assessing the health impacts of indoor and community exposure to urban air pollutants. The findings suggest a need for improved ventilation and more strategic air intake placement to enhance indoor air quality.

Patterns and disparities in indoor particulate matter levels in selected primary schools in Kigali, Rwanda

Air pollution is a global environmental and public health challenge. There is limited evidence about the air quality in Rwanda, and the concentrations of particulate matter (PM), namely PM2.5 and PM10 in schools have not been well documented. This study evaluated patterns and disparities in indoor PM levels in selected primary schools in Kigali, Rwanda.The study collected PM2.5 and PM10 concentrations from six classrooms in six selected primary schools during the regular school study period in the dry season. Data were collected using mobile air sensors (purple air/PA–II–SD air quality) and an observation checklist. A Kruskal-Wallis test was performed to assess the difference in PM2.5 and PM10 concentrations between the six schools. The post-hoc Mann-Whitney test was used to compare all group pairs.The results indicated a significant difference in both the indoor PM2.5 concentration (H (5) = 41.01, p < 0.001) and the indoor PM10 concentration (H (5) = 38.5, p < 0.001). The maximum concentration observed was 133.6 μg/m3 for PM2.5 and 158.5 μg/m3 for PM10. Schools in highly exposed areas tended to have higher concentrations of PM than schools in moderately exposed areas. Specifically, the daily average concentration of PM2.5 in schools located in highly exposed areas ranged from 39 μg/m³ to 118 μg/m³, while PM10 levels ranged from 44.0 μg/m³ to 126 μg/m³. In contrast, schools in moderately exposed areas had daily PM2.5 average concentrations ranging from 32.0 μg/m³ to 111.0 μg/m³ and daily PM10 average concentrations ranging from 38.0 μg/m³ to 119 μg/m³.Overall, the recorded values for both PM2.5 and PM10 in all sampled schools were higher than the World Health Organization air quality guidelines. Indoor air quality is poorer in schools situated in highly exposed areas. This study suggests interventions to improve school air quality for the benefit of school communities.

Impacts of daily household activities on indoor particulate and NO2 concentrations; a case study from oxford UK

This study explores indoor air pollutant (PM1, PM2.5 and NO2) concentrations over a 15-week period during the COVID-19 pandemic in a typical suburban household in Oxford, UK. A multi-room intensive monitoring study was conducted in a single dwelling using 10 air quality sensors measuring real-time pollutant concentrations at 10 second intervals to assess temporal and spatial variability in PM1, PM2.5 and NO2 concentrations, identify pollution-prone areas, and investigate the impact of residents' activities on indoor air quality. Significant spatial variations in PM concentrations were observed within the study dwelling, with highest hourly concentrations (769.0 & 300.9 μg m−3 for PM2.5, and PM1, respectively) observed in the upstairs study room, which had poor ventilation. Cooking activities were identified as a major contributor to indoor particulate pollution, with peak concentrations aligning with cooking events. Indoor NO2 levels were typically higher than outdoor levels, particularly in the kitchen where a gas-cooking appliance was used. There was no significant association observed between outdoor and indoor PM concentrations; however, a clear correlation was evident between kitchen PM emissions and indoor levels. Similarly, outdoor NO2 had a limited influence on indoor air quality compared to kitchen activities. Indoor sources were found to dominate for both PM and NO2, with higher Indoor/Outdoor (I/O) ratios observed in the upstairs bedroom and the kitchen. Overall, our findings highlight the contribution of indoor air pollutant sources and domestic activities to indoor air pollution exposure, notably during the COVID-19 pandemic when people were typically spending more time in domestic settings. Our novel findings, which suggest high levels of pollutant concentrations in upstairs (first floor) rooms, underscore the necessity for targeted interventions. These interventions include the implementation of source control measures, effective ventilation strategies and occupant education for behaviour change, all aimed at improving indoor air quality and promoting healthier living environments.

Indoor and outdoor air quality in street corner kiosks in a large metropolitan area

Poor air quality in workplaces constitutes a great concern on human health as a good fraction of our time is spent at work. In Greece, very unique workplaces are the street corner kiosks, which are freestanding boxes placed on sidewalks next to city streets and vehicular traffic, where one can find many consumer goods. As such, its employees are exposed to both outdoor and indoor air pollutants. Very few studies have examined the occupational exposure of kiosk workers to air pollutants, and thus the magnitude of this unique indoor and outdoor exposure remains unknown. The objective of this study is to investigate and compare the levels of indoor and outdoor particulate matter (PM10 and PM2.5), ultrafine particles (UFPs) and black carbon (BC) in different kiosks located in Athens, Greece, in urban-traffic and urban-background environments. Continuous measurements of the above-mentioned pollutants were carried out on a 24-h basis over 7 consecutive days at three kiosks from September to October 2019. Indoor PM10 concentrations in the urban kiosk ranged from 19.0 to 44.0 μg/m3, PM2.5 values ranged from 14.0 to 33.0 μg/m3, whereas BC concentrations ranged from 1.2 to 7.0 μg/m3 and UFPs from almost 9.5 to 47.0 × 103 pt/cm3. Outdoor PM10 and PM2.5 measurements ranged from 29.0 to 59.0 μg/m3 and from 22.0 to 39.0 μg/m3, respectively. BC outdoor concentrations ranged from 1.1 to 2.2 μg/m3. The mean hazard quotient (HQ) for PM10 (4.9) and PM2.5 (4.7) among all participants was >1. The health risk of exposure to PM10 and PM2.5 was found to be at moderate hazard levels, although in some cases we observed HQ values higher than 10 due to high PM10 and PM2.5 concentrations in the kiosks. Overall our study indicates that people working at kiosks can be exposed to very high concentrations on particulate pollution depending on a number of factors including the traffic that strongly depends on location and the time of the day.

Hybrid models of machine-learning and mechanistic models for indoor particulate matter concentration prediction

While indoor PM2.5 concentrations are generally lower than those outdoors, daily exposure indoors can significantly exceed outdoor levels, given that people spend over 90% of their time inside. This study systematically compares traditional methods with machine learning(ML)-based approaches in the context of indoor particulate matter concentration prediction and control research, with the aim of combining their strengths for more effective outcomes.One-year indoor PM concentration data from a non-interference-operating target office were used to develop ML models. Input variables, including background concentrations (outdoor and hallway), office conditions, and weather parameters, were employed with the CNN-LSTM algorithm. The ML models, enhanced with ensemble techniques, achieved a prediction accuracy of R2 = 0.943 on the test set, not considered during model development.Using predictions under step-change events in outdoor PM2.5 levels, the ML model determined the infiltration factor, PM2.5 removal rates, and particle penetration rates, hence mechanistic model coefficients. Collaborative hybrid models, integrating ML and mechanistic approaches, yielded dynamic models (R2 = 0.928) applicable under general conditions, whereas conventional mechanistic models derived from controlled experiments were limited to controlled conditions.

The impact of vehicle parameters on road PM10 vehicle resuspended emissions: A case in South African low-income settlement

About 70% of the roads in low-income settlements are unpaved and close (≤15m) to residents, thus a major source of ambientand indoor PM10 concentrations. International studies have suggested that decreasing vehicle speed and managing vehicle type onpaved or unpaved roads can reduce vehicle dust emissions. These mitigation strategies should be examined before being adoptedand gazetted into South African air quality management plans. This study aimed to characterise roads and traffic, emphasisingdetermining the impact of vehicle type on PM10 emissions. GIS was used to determine the proportion of paved and unpaved roads. Atraffic counter was used to monitor vehicles and to determine traffic composition, diurnal cycles, and average speed per road type.Field campaigns (summer; 6 days; 15 hrs per day) were carried out in Bokamoso to monitor PM10 concentration usinga TSI DustTrak DRX® real-time optical aerosol counter. The Box Model method quantifies vehicle PM10 emission factors for heavy-duty, medium-duty, and motor vehicles. About 0.88 km of road within Bokamoso is paved with a daily traffic volume of &gt;2000, and 3.6 km is unpaved with &gt;250 daily traffic volume. Paved road heavy-duty vehicle non-exhaust PM10 emissions reported a positive medium to strong coefficient of determination to vehicle speed increase with a coefficient of determination of 0.59. Even though there is a positive coefficient of determination between heavy-duty vehicle non-exhaust PM10 emission factors and speed on unpaved roads, it is weak (0.2) due to low and less variable vehicle speed. Motor vehicle paved and unpaved road non-exhaust emission factors showed no significant coefficient of determination with increased vehicle speed. Paved road non-exhaust emission factors were not significantly variable with vehicle type. They ranged between an average of 0.15-0.18 g/km/h, with motor vehicles reporting an average of 0.18 g/km/h while heavy-duty vehicles reported an average of 0.15 g/km/h. On the contrary, unpaved road non-exhaust traffic PM10 emissions ranged between 0.16-0.3 g/km/h. The emission factors presented may be used to model vehicle traffic emissions, improve on-road vehicle dust emissions impact assessment and as a guide when deciding on local applicable road dust mitigation strategies

Indoor air quality in remote first nations communities in Ontario, Canada.

A recent study of the health of Indigenous children in four First Nations Communities in remote northwestern Ontario found that 21% of children had been admitted to hospital for respiratory infections before age 2 years. Here we report a detailed analysis of the housing conditions in these communities. We employed a variety of statistical methods, including linear regression, mixed models, and logistic regression, to assess the correlations between housing conditions and loadings of biocontaminants (dust mite allergens, fungal glucan, and endotoxin) and indoor concentrations of PM2.5, CO2, benzene, and formaldehyde. The houses (n = 101) were crowded with an average of approximately 7 people. Approximately 27% of the homes had sustained CO2 concentrations above 1500 ppm. Most homes had more than one smoker. Commercial tobacco smoking and the use of non-electric heating (e.g., wood, oil) were associated with increased fine particle concentrations. Over 90% of the homes lacked working Heat Recovery Ventilators (HRVs), which was associated with increased fine particle concentrations and higher CO2. Of the 101 homes, 12 had mold damage sufficient to increase the relative risk of respiratory disease. This resulted from roof leaks, through walls or around the windows due to construction defects or lack of maintenance. A similar percentage had mold resulting from condensation on windows. Endotoxin loadings were much higher than any previous study in Canada. This work provides evidence for the need for more effort to repair existing houses and to ensure the HRVs are properly installed and maintained.

Influence of stove locations and ventilation conditions on kitchen air quality and thermal comfort during oil-cooking activities

This paper documented the effect of ventilation conditions and stove locations on air quality and thermal comfort in a kitchen where fish was fried via African cooking styles using palm oil. CO, CO2, PM2.5, PM10, HCHO, TVOC, relative humidity (RH), and temperature were measured using an air quality detector. The position of the kerosene stove (at the window and adjacent wall) and conditions of the door and windows (fully and partly open) were altered to study their effect. Data of the pollutants concentrations and thermal comfort parameters were acquired and analyzed for the background, bleaching, frying, and after-frying durations for the different cases of stove locations and door and windows conditions. Bleaching and frying were noticed to increase the background PM2.5, PM10, CO2, HCHO, TVOC, temperature, and CO by 604–1301 μg/m3, 729–2760 μg/m3, 78–266 ppm, 0.03–0.13 mg/m3, 0.04–0.16 mg/m3, 0.83–8.33 °C and 18–189 ppm, respectively and reduced RH by 1.67–13.33%, for all the studied test conditions. PM10 followed by PM2.5 exhibited the slowest concentration decay. Opening the windows and door and placing the stove at the window area were observed to lower the indoor temperature, PM2.5, PM10, CO2, and CO concentrations, and heat index of the studied environment as the RH, HCHO, and TVOC were increased. Bleaching + frying using palm oil is found to be injurious to human health and thermal comfort (as heat index >32) and should be carried out with serious safety precautions as provided herein.

Classification prediction model of indoor PM2.5 concentration using CatBoost algorithm

It is increasingly important to create a healthier indoor environment for office buildings. Accurate and reliable prediction of PM2.5 concentration can effectively alleviate the delay problem of indoor air quality control system. The rapid development of machine learning has provided a research basis for the indoor air quality system to control the PM2.5 concentration. One approach is to introduce the CatBoost algorithm based on rank lifting training into the classification and prediction of indoor PM2.5 concentration. Using actual monitoring data from office building, we consider previous indoor PM2.5 concentration, indoor temperature, relative humidity, CO2 concentration, and illumination as input variables, with the output indicating whether indoor PM2.5 concentration exceeds 25 μg/m3. Based on the CatBoost algorithm, we construct an intelligent classification prediction model for indoor PM2.5 concentration. The model is evaluated using actual data and compared with the multilayer perceptron (MLP), gradientboosting decision tree (GBDT), logistic regression (LR), decision tree (DT), and k-nearest neighbors (KNN) models. The CatBoost algorithm demonstrates outstanding predictive performance, achieving an impressive area under the ROC curve (AUC) of 0.949 after hyperparameters optimition. Furthermore, when considering the five input variables, the feature importance is ranked as follows: previous indoor PM2.5 concentration, relative humidity, CO2, indoor temperature, and illuminance. Through verification, the prediction model based on CatBoost algorithm can accurately predict the indoor PM2.5 concentration level. The model can be used to predict whether the indoor concentration of PM2.5 exceeds the standard in advance and guide the air quality control system to regulate.

Indoor air quality in Hungarian Passive Houses

The indoor air quality (IAQ) was investigated in 15 Passive Houses in the heating and non-heating seasons between 2019 and 2021 in Hungary. The concentrations of volatile organic compounds (VOCs), aldehydes, ozone, nitrogen dioxide, PM2.5 mass, carbon dioxide, bacteria, fungi, and pollen were measured together with the monitoring of temperature, relative humidity and air change rate (ACR). The IAQ varied considerably among the investigated buildings. Significant seasonal differences were obtained for all physical parameters (temperature, relative humidity, ACR), certain VOCs (benzene, α-pinene, limonene), acetaldehyde, and airborne fungi. Considerable health concern was associated with the indoor concentrations of PM2.5 mass and nitrogen dioxide in many cases based on the evaluation of IAQ in relation to potential adverse health effects, while the peak concentrations of other pollutants (e.g. trichloroethylene, α-pinene, certain aldehydes, fungi) were also of concern in a couple of cases. The median indoor/outdoor concentration ratios of benzene, PM2.5 mass, nitrogen dioxide, ozone and fungi indicated that these pollutants are mainly of outdoor origin, while the other VOCs, aldehydes and bacteria showed higher concentration indoors. Overheating, the lack of proper particle filters in the mechanical ventilation system, and low ACR and relative humidity were identified as frequent problems related to the building characteristics. The emissions from building materials and furniture, the proximity of construction works and unpaved roads, and the allergenic vegetation might considerably influence IAQ. The results highlight that risk reduction measures are needed to create healthier indoor environment in the Passive Houses.

LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction.

Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies focus on outdoor air quality, while few studies on indoor air quality. Current neural network-based methods for indoor air quality prediction ignore the optimization of input variables, process input features serially, and still suffer from loss of information during model training, which may lead to the problems of memory-intensive, time-consuming and low-precision. We present a novel concurrent indoor PM prediction model based on the fusion model of Least Absolute Shrinkage and Selection Operator (LASSO) and an Attention Temporal Convolutional Network (ATCN), together called LATCN. First, a LASSO regression algorithm is used to select features from PM1, PM2.5, PM10 and PM (>10) datasets and environmental factors to optimize the inputs for indoor PM prediction model. Then an Attention Mechanism (AM) is applied to reduce the redundant temporal information to extract key features in inputs. Finally, a TCN is used to forecast indoor particulate concentration in parallel with inputting the extracted features, and it reduces information loss by residual connections. The results show that the main environmental factors affecting indoor PM concentration are the indoor heat index, indoor wind chill, wet bulb temperature and relative humidity. Comparing with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) approaches, LATCN systematically reduced the prediction error rate (19.7% ~ 28.1% for the NAE, and 16.4% ~ 21.5% for the RMSE) and improved the model running speed (30.4% ~ 81.2%) over these classical sequence prediction models. Our study can inform the active prevention of indoor air pollution, and provides a theoretical basis for indoor environmental standards, while laying the foundations for developing novel air pollution prevention equipment in the future.

Hazard Evaluation of Indoor Air Quality in Bank Offices

IAQ is a crucial factor affecting the health, comfort, and productivity of workers, particularly those working in enclosed spaces like bank offices. This study aimed to evaluate the IAQ of a bank office’s operational area and vault by analyzing concentrations of CO2, TVOC, PM10, and PM2.5, as well as temperature, relative humidity, and air movement. Two different ventilation systems were compared to assess their impact on IAQ. The acquired data were statistically analyzed using mean comparison t-tests and hazard ratio analysis. The results revealed that indoor concentrations of PM2.5 and CO2 significantly contribute to the total hazard ratio, indicating the need to reduce their levels below reference values. The study also found that the ventilation system significantly affects indoor air quality, and concentrations of TVOC, CO2, PM10, and PM2.5 in the air are considerable. Significantly, the study found that bank offices with split unit air-conditioners had the highest mean CO2 levels, indicating poor ventilation. Overall, the study reveals that the building, activities, and ventilation in bank offices have a profound influence on IAQ parameters, primarily PM2.5 and CO2. Further research is required to formulate strategies for enhancing IAQ in these settings.

Holistic methodology to reduce energy use and improve indoor air quality for demand-controlled ventilation

Ventilation control logics are usually based on the control indicators of occupancy. However, strategies including control of contaminants not linked to occupancy are requested and more feasible with the introduction in the market of low-cost sensors (LCS).In this work, a methodology for the improvement of demand-controlled ventilation (DCV) using measurements of IAQ parameters with LCS, correlation analysis, and co-simulation EnergyPlus/CONTAM is presented. Its goal was reduced annual energy use and the fraction of time with room air concentration of IAQ parameters outside thresholds.The ventilation control sequences of supply airflow rates and recirculation of return air focused on the significant parameters chosen by cross-correlation functions in the de-trended measurements.The results revealed that the methodology successfully developed control sequences that simultaneously reduced annual energy use and the number of hours outside the recommended IAQ guidelines compared to the baselines. In cold cities with excellent outdoor air quality, recirculation could reduce energy use and increase the RH in winter. Further simulations demonstrated that the use of recirculation had a protective effect on the indoor concentrations of PM2.5, assuming low outdoor air quality. However, when using recirculation, it is essential to control the IAQ to avoid excessive pollutants, RH, and temperatures.

ASSOCIATION BETWEEN RESPIRATORY COMPLAINTS AND PM 10 CONCENTRATION IN HOUSEHOLDS IN PALEMBANG

Background: Acute Respiratory Infections (ARI) cause around 4 million under-five deaths in developing countries, including Indonesia. Air quality is strongly influenced by human activities. Purpose: This study was to analyze the relationship between PM10 concentrations in the house and the risk factors for the health conditions of the household environment and human characteristics on the incidence of respiratory disorders. Methods: This research is an analytic observational with a cross-sectional design. The number of samples calculated as many as 60 respondents were taken randomly. This research was conducted in Pulokerto Village, Gandus District, Palembang from August to December 2009. Data were collected through observation and interviews using a questionnaire, while indoor air samples were measured using the EPAM 5000 instrument by BTKL PP Palembang officers. Data were analyzed by univariate, bivariate using chi-square test, and multivariate using multiple logistic regression. Results: The results of statistical tests showed that the usage of insect repellent (p v = 0.0005, OR = 13.095, 95% CI = 3,821-44,882), smoking habits (p v = 0.0005, OR = 15.536, 95% CI = 3.114-77.519), Indoor PM10 concentration (pv=0.021, OR=3.463, 95% CI (1.186 – 10.108) While the most dominant variable affecting respiratory disorders is the usage of insect repellent. Conclusion: The factors that have the greatest influence in causing respiratory disorders are age, gender, education, population density and the use of mosquito repellent.

Effectiveness of a Protocol to Reduce Children's Exposure to Particulate Matter and NO2 in Schools during Alert Days.

Reducing children’s exposure to air pollutants should be considered a primary goal, especially for the most vulnerable subjects. The goal of this study was to test the effectiveness of applying a protocol in the event of alert days, i.e., days with forecasted PM10 levels above the EU limit value (50 µg/m3). The test was conducted, before the onset of SARS-CoV-2 restrictions, in a classroom of a primary school in Parma (Italy)—a highly polluted area in Northern Italy. The protocol included indications for the frequency of opening windows and doors, as well as the activation of an air purifier. Teachers and students were asked to apply the protocol only in the event of alert days, while no indications were provided for non-alert days. A monitoring system measuring PM1, PM2.5, PM10, CO2, and NO2 was deployed in the classroom. Measurements of the same parameters were also performed outdoors near the school. The application of the protocol reduced the indoor/outdoor (I/O) ratio for all toxic pollutants. The reduction was also remarkable for PM10—the most critical air quality parameter in the study area (1.5 and 1.1 for non-alert and alert days, respectively). Indoor concentrations of PM10—especially during non-alert days—were often higher than outdoors, showing a major contribution from resuspension due to the movement of people and personal cloud. The protocol did not cause any increase in indoor CO2 levels. Our findings showed that the application of a ventilation protocol together with the contribution of an air purifier may represent an effective way to reduce children’s exposure to air pollution during severe air pollution episodes. Considering the onset of COVID-19 and the airborne transmission of pathogens, this protocol now has more meaningful implications for children’s welfare, and can be integrated with protocols designed as measures against the spread of SARS-CoV-2.

Survey of residential indoor particulate matter measurements 1990-2019.

We surveyed literature on measurements of indoor particulate matter in all size fractions, in residential environments free of solid fuel combustion (other than wood for recreation or space heating). Data from worldwide studies from 1990 to 2019 were assembled into the most comprehensive collection to date. Out of 2752 publications retrieved, 538 articles from 433 research projects met inclusion criteria and reported unique data, from which more than 2000 unique sets of indoor PM measurements were collected. Distributions of mean concentrations were compiled, weighted by study size. Long-term trends, the impact of non-smoking, air cleaners, and the influence of outdoor PM were also evaluated. Similar patterns of indoor PM distributions for North America and Europe could reflect similarities in the indoor environments of these regions. Greater observed variability for all regions of Asia may reflect greater heterogeneity in indoor conditions, but also low numbers of studies for some regions. Indoor PM concentrations of all size fractions were mostly stable over the survey period, with the exception of observed declines in PM2.5 in European and North American studies, and in PM10 in North America. While outdoor concentrations were correlated with indoor concentrations across studies, indoor concentrations had higher variability, illustrating a limitation of using outdoor measurements to approximate indoor PM exposures.

Indoor PM10 in university classrooms: Chemical composition and source behaviour

The indoor atmosphere of five university environments, ranging from a research laboratory to a vast lecture hall, was studied during a seven-week measurement campaign. The study was carried out in real-world conditions and was designed to distinguish periods when the environments were occupied from periods when they were empty. A comprehensive chemical characterisation of PM10 was carried out (elements as total, extractable and residual fractions, ions, elemental carbon, organic carbon), which allowed the study of the main PM sources (soil, sea, secondary inorganic species, traffic emission, organics). Other sources (heavy oil combustion, biomass burning and non-exhausts traffic emission) were well traced by the extractable or residual fractions of some elements (V, Rb, Mo).During classes, indoor PM10 concentration exceeded outdoor values. The main differences between indoor and outdoor chemical composition were due to soil components and to the organics. Soil particles were carried in by the students and re-suspended by their movements. The organics were mainly due to bioparticles released by the occupants. An increase inside classrooms was also observed for sulphate and attributed to the used of mineral gypsum as blackboard chalk. Sea-salt particles and ammonium nitrate, instead, showed a sharp decrease when entering the indoor environments, due to their large size and a shift in its thermodynamic equilibrium, respectively.The concentration of particles released by anthropogenic combustion sources (exhaust and non-exhaust traffic emission, domestic heating) was generally lower than outdoors, and the degree of their infiltration was determined by their dimensions.

Levels of indoor particulate matter and association with asthma in children in Benin City, Nigeria.

The relationship between indoor particulate matter and asthma in children was assessed in this study. Forty-five (45) locations were randomly selected across the five local government areas in Benin City, Edo State, for air quality assessment. Indoor and outdoor particulates (PM1.0, PM2.5, and PM10) were monitored monthly using a handheld BLATN particulate sampler (Br-Smart-126S series). Reported clinical cases of asthma in children from 2008 to 2017 were collected from two major hospitals in the metropolis. The data obtained were analysed with SPSS for Windows version 21.0. The average concentrations of indoor and ambient PM ranged between 10.7-26.2 and 19.0-49.4µg/m3 (PM1.0), 27.4-59.6 and 45.6-93.0µg/m3 (PM2.5), and 33.5-67.9 and 60.9-106.1µg/m3 (PM10) in the wet and dry seasons. PM2.5 and PM10 concentrations were observed above the WHO standards. Indoor particulate concentration was significantly (p = 0.001-0.012) higher in the dry season across the locations. Outdoor PM correlated positively (R = 0.568-0.855, p < 0.05; R2 = 0.322-0.724, p < 0.001) with their corresponding indoor PM concentration. The hazard ratio (HR) values of PM2.5 and PM10 exceeded 1 in all the sampling locations during the dry season, while the mean total hazard ratio (THR) of both PM metrics was considerably higher during the dry season than in the wet season. Indoor PM concentrations showed a significant positive correlation with reported cases of asthma (R = 0.498-0.542, p < 0.001) and accounted for 40.6% of the asthma cases during the dry season. The study showed that children in the selected households are at risk of increased asthma exacerbation due to exposure to particulate matter pollution.