Indoor air quality modeling for PM 10, PM 2.5, and PM 1.0 in naturally ventilated classrooms of an urban Indian school building

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Assessment of Indoor Particulate Matter and Teacher's Perceived Indoor Climate in Government Schools of Bilaspur District, Chhattisgarh, India: A Cross-Sectional Study

Context

Indoor air pollution (IAP) affects the long-term health, cognitive growth, and academic performance of children. Since children spend most of their time at school, quantifying IAP in classrooms is an important parameter for air pollution.

Aim

To assess the average particulate matter (PM) levels inside and outside of classrooms along with their associated factors and teacher's perceived indoor climate.

Setting and Design

Cross-sectional survey in nine government-run schools.

Methods and Material

PM2.5 and PM10 were measured inside the classroom and outdoors simultaneously during summers, using an Atmos monitor for two consecutive days, along with several school and classroom characteristics. Perception about indoor air quality was captured from teachers (n = 15) using a validated questionnaire.

Statistical Analysis

Mean values of PM using mixed effect linear regression. Perceived indoor air quality is presented using percentages.

Results

Mean indoor PM2.5 and PM10 was 52.5 µg/m3 and 65 µg/m3. Indoor and outdoor PM levels were highly correlated, but the indoor-outdoor ratio of PM concentrations was more than 1. Teachers were mostly bothered by dust, dirt, and noise in the schools.

Conclusion

Indoor air quality was higher than World Health Organization (WHO) standards but within the national standards. Need further research to find the exact cause for higher indoor PM levels compared to outdoor PM levels.

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.

Reducing airborne infection risk of COVID-19 by locating air cleaners at proper positions indoor: Analysis with a simple model

Portable air cleaners (PACs) can remove airborne SARS-CoV-2 exhaled by COVID-19 infectors indoor. However, effectively locating PAC to reduce the infection risk is still poorly understood. Here, we propose a simple model by regressing an equation of seven similarity criteria based on CFD-modeled results of a scenario matrix of 128 cases for office rooms. The model can calculate the mean droplet nucleus concentration with very low computing costs. Combining this model with the Wells-Riley equation, we estimate the airborne infection risk when a PAC is located in different positions. The two similarity criteria, B _p ⁺ and G _p ⁺ , are critical for characterizing the effect of the position and airflow rate of PAC on the infection risk. An infection probability of less than 10% requires B _p   ⁺  to be larger than 144 and G _p   ⁺  to be larger than 0.001. These criteria imply that locating PAC in the center of the room is optimal under the premise that the airflow rate of PAC is greater than a certain level. The model provides an easy-to-use approach for real-time risk control strategy decisions. Furthermore, the placement strategies offer timely guidelines for precautions against the prolonged COVID-19 pandemic and common infectious respiratory diseases.

Indoor air quality of 5,000 households and its determinants. Part A: Particulate matter (PM2.5 and PM10-2.5) concentrations in the Japan Environment and Children's Study

Exposure to particulate matter (PM) is one of the important risk factors for morbidity and mortality. Although PM concentrations have been assessed using air quality monitoring stations or modelling, few studies have measured indoor PM in large-scale birth cohorts. The Japan Environment and Children's Study (JECS) measured indoor and outdoor air quality in approximately 5000 households when the participating children were aged 1.5 and 3 years. PM was collected using portable pumps for 7 days (total of 24 h), inside and outside each home. Prediction models for indoor PM concentrations were built using data collected at age 1.5 years and post-validated against data collected at age 3 years. Median indoor/outdoor PM_2.5 and PM_10-2.5 concentrations at age 1.5 years [3 years] were 12.9/12.7 [12.5/11.3] μg/m³ and 5.0/6.3 [5.1/6.1] μg/m³, respectively. Random forest regression analysis found that the major predictors of indoor PM_2.5 were indoor PM_10-2.5, outdoor PM_2.5, indoor smoking, observable smoke and indoor/outdoor temperature. Indoor PM_2.5, outdoor PM_10-2.5, indoor humidity and opening room windows were important predictors of indoor PM_10-2.5 concentrations. Indoor benzene, acetaldehyde, ozone and nitrogen dioxide concentrations were also found to predict indoor PM_2.5 and PM_10-2.5 concentrations, possibly due to the formation of secondary organic aerosols. These findings demonstrate the importance of reducing outdoor PM concentrations, avoiding indoor smoking, using air cleaner in applicable and diminishing sources of VOCs that could form secondary organic aerosols, and the resulting models can be used to predict indoor PM concentrations for the rest of the JECS cohort.

Exposure assessment of indoor particulate matter during pregnancy: a narrative review of the literature

OBJECTIVE

The aim of this review was to summarize the evidence of the exposure assessment approaches of indoor particulate matter (PM) during pregnancy and to recommend future focus areas.

CONTENT

Exposure to indoor PM during pregnancy is associated with adverse birth outcomes. However, many questions remain about the consistency of the findings and the magnitude of this effect. This may be due to the exposure assessment methods used and the challenges of characterizing exposure during pregnancy. Exposure is unlikely to remain constant over the nine-month period. Pregnant females' mobility and activities vary - for example, employment status may be random among females, but among those employed, activities are likely to be greater in the early pregnancy than closer to the delivery of the child.

SUMMARY

Forty three studies that used one of the five categories of indoor PM exposure assessment (self-reported, personal air monitoring, household air monitoring, exposure models and integrated approaches) were assessed. Our results indicate that each of these exposure assessment approaches has unique characteristics, strengths, and weaknesses. While questionnaires and interviews are based on self-report and recall, they were a major component in the reviewed exposure assessment studies. These studies predominantly used large sample sizes. Precision and detail were observed in studies that used integrated approaches (i. e. questionnaires, measurements and exposure models).

OUTLOOK

Given the limitations presented by these studies, exposure misclassification remains possible because of personal, within and between household variability, seasonal changes, and spatiotemporal variability during pregnancy. Therefore, using integrated approaches (i. e. questionnaire, measurements and exposure models) may provide better estimates of PM levels across trimesters. This may provide precision for exposure estimates in the exposure-response relationship.

Metal-containing Particulate Matter and Associated Reduced Olfactory Identification Ability in Children from an Area of High Atmospheric Exposure in Mexico City

Air pollution has been linked to poor olfactory function in human adults. Among pollutants, particulate matter (PM) is especially relevant, as it may contain toxic metal ions that can reach the brain via olfactory pathways. Our purpose was to investigate the relation between atmospheric PM and olfactory identification performance in children. Using a validated method, we tested the olfactory identification performance of 120 children, 6–12 years old, from two locations in Mexico City: a focal group (n = 60) from a region with high PM levels and a control group of equal size and similar socioeconomic level from a region with markedly lower PM concentrations. Groups were matched for age and sex. Concentrations of manganese and lead in the hair of participants were determined as biomarkers of exposure. Daily outdoor PM levels were obtained from official records, and indoor PM levels were measured in the children’s classrooms. Official records confirmed higher levels of outdoor PM in the focal region during the days of testing. We also found higher classroom PM concentrations at the focal site. Children from the focal site had on average significantly lower olfactory identification scores than controls, and hair analysis showed significantly higher levels of manganese for the focal children but no difference in lead. Children appear to be vulnerable to the effects of air pollution on olfactory identification performance, and metal-containing particles likely play a role in this. Olfactory tests provide a sensitive, noninvasive means to assess central nervous function in populations facing poor air quality.

Machine learning and statistical models for predicting indoor air quality

Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically based mechanistic models or statistical models that are driven by measured data. In comparison with mechanistic models mostly used in unoccupied or scenario-based environments, statistical models have great potential to explore IAQ captured in large measurement campaigns or in real occupied environments. The present study carried out the first literature review of the use of statistical models to predict IAQ. The most commonly used statistical modeling methods were reviewed and their strengths and weaknesses discussed. Thirty-seven publications, in which statistical models were applied to predict IAQ, were identified. These studies were all published in the past decade, indicating the emergence of the awareness and application of machine learning and statistical modeling in the field of IAQ. The concentrations of indoor particulate matter (PM_2.5 and PM₁₀ ) were the most frequently studied parameters, followed by carbon dioxide and radon. The most popular statistical models applied to IAQ were artificial neural networks, multiple linear regression, partial least squares, and decision trees.

Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city

BACKGROUND

Indoor and outdoor fine particulate matter (PM_2.5) are both leading risk factors for death and disease, but making indoor measurements is often infeasible for large study populations.

METHODS

We developed models to predict indoor PM_2.5 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 PM_2.5 concentrations with 447 independent 7-day PM_2.5 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.

RESULTS

The predictors in the MLR model were season, outdoor PM_2.5 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 PM_2.5 concentrations.

CONCLUSIONS

We predicted a moderate amount of variation in indoor PM_2.5 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.

Approximation of personal exposure to fine particulate matters (PM2.5) during cooking using solid biomass fuels in the kitchens of rural West Bengal, India

More than 85% of the rural Indian households use traditional solid biofuels (SBFs) for daily cooking. Burning of the easily available unprocessed solid fuels in inefficient earthen cooking stoves produce large quantities of particulate matters. Smaller particulates, especially with aerodynamic diameter of 2.5 μm or less (PM_2.5), largely generated during cooking, are considered to be health damaging in nature. In the present study, kitchen level exposure of women cooks to fine particulate matters during lunch preparation was assessed considering kitchen openness as surrogate to the ventilation condition. Two-way ANCOVA analysis considering meal quantity as a covariate revealed no significant interaction between the openness and the seasons explaining the variability of the personal exposure to the fine particulate matters in rural kitchen during cooking. Multiple linear regression analysis revealed the openness as the only significant predictor for personal exposure to the fine particulate matters. In the present study, the annual average fine particulate matter exposure concentration was found to be 974 μg m^-3.

Developing a predictive model for fine particulate matter concentrations in low socio-economic households in Durban, South Africa

In low-resource settings, there is a need to develop models that can address contributions of household and outdoor sources to population exposures. The aim of the study was to model indoor PM_2.5 using household characteristics, activities, and outdoor sources. Households belonging to participants in the Mother and Child in the Environment (MACE) birth cohort, in Durban, South Africa, were randomly selected. A structured walk-through identified variables likely to generate PM_2.5 . MiniVol samplers were used to monitor PM_2.5 for a period of 24 hours, followed by a post-activity questionnaire. Factor analysis was used as a variable reduction tool. Levels of PM_2.5 in the south were higher than in the north of the city (P < .05); crowding and dwelling type, household emissions (incense, candles, cooking), and household smoking practices were factors associated with an increase in PM_2.5 levels (P < .05), while room magnitude and natural ventilation factors were associated with a decrease in the PM_2.5 levels (P < .05). A reasonably robust PM_2.5 predictive model was obtained with model R² of 50%. Recognizing the challenges in characterizing exposure in environmental epidemiological studies, particularly in resource-constrained settings, modeling provides an opportunity to reasonably estimate indoor pollutant levels in unmeasured homes.

Status of indoor air pollution (IAP) through particulate matter (PM) emissions and associated health concerns in South Asia

Exposure to particulate emissions poses a variety of public health concerns worldwide, specifically in developing countries. This review summarized the documented studies on indoor particulate matter (PM) emissions and their major health concerns in South Asia. Reviewed literature illustrated the alarming levels of indoor air pollution (IAP) in India, Pakistan, Nepal, and Bangladesh, while Sri Lanka and Bhutan are confronted with relatively lower levels, albeit not safe. To our knowledge, data on this issue are absent from Afghanistan and Maldives. We found that the reported levels of PM₁₀ and PM_2.5 in Nepal, Pakistan, Bangladesh, and India were 2-65, 3-30, 4-22, 2-28 and 1-139, 2-180, 3-77, 1-40 fold higher than WHO standards for indoor PM₁₀ (50 μg/m³) and PM_2.5 (25 μg/m³), respectively. Regarding IAP-mediated health concerns, mortality rates and incidences of respiratory and non-respiratory diseases were increasing with alarming rates, specifically in India, Pakistan, Nepal, and Bangladesh. The major cause might be the reliance of approximately 80% population on conventional biomass burning in the region. Current review also highlighted the prospects of IAP reduction strategies, which in future can help to improve the status of indoor air quality and public health in South Asia.

Modeling indoor particulate exposures in inner-city school classrooms

Outdoor air pollution penetrates buildings and contributes to total indoor exposures. We investigated the relationship of indoor to outdoor particulate matter in inner-city school classrooms. The School Inner City Asthma Study investigates the effect of classroom-based environmental exposures on students with asthma in the northeast United States. Mixed effects linear models were used to determine the relationships between indoor PM2.5 (particulate matter) and black carbon (BC), and their corresponding outdoor concentrations, and to develop a model for predicting exposures to these pollutants. The indoor-outdoor sulfur ratio was used as an infiltration factor of outdoor fine particles. Weeklong concentrations of PM2.5 and BC in 199 samples from 136 classrooms (30 school buildings) were compared with those measured at a central monitoring site averaged over the same timeframe. Mixed effects regression models found significant random intercept and slope effects, which indicate that: (1) there are important PM2.5 sources in classrooms; (2) the penetration of outdoor PM2.5 particles varies by school and (3) the site-specific outside PM2.5 levels (inferred by the models) differ from those observed at the central monitor site. Similar results were found for BC except for lack of indoor sources. The fitted predictions from the sulfur-adjusted models were moderately predictive of observed indoor pollutant levels (out of sample correlations: PM2.5: r2=0.68, BC; r2=0.61). Our results suggest that PM2.5 has important classroom sources, which vary by school. Furthermore, using these mixed effects models, classroom exposures can be accurately predicted for dates when central site measures are available but indoor measures are not available.

Indoor air quality of low and middle income urban households in Durban, South Africa

INTRODUCTION

Elevated levels of indoor air pollutants may cause cardiopulmonary disease such as lower respiratory infection, chronic obstructive lung disease and lung cancer, but the association with tuberculosis (TB) is unclear. So far the risk estimates of TB infection or/and disease due to indoor air pollution (IAP) exposure are based on self-reported exposures rather than direct measurements of IAP, and these exposures have not been validated.

OBJECTIVE

The aim of this paper was to characterize and develop predictive models for concentrations of three air pollutants (PM₁₀, NO₂ and SO₂) in homes of children participating in a childhood TB study.

METHODS

Children younger than 15 years living within the eThekwini Municipality in South Africa were recruited for a childhood TB case control study. The homes of these children (n=246) were assessed using a walkthrough checklist, and in 114 of them monitoring of three indoor pollutants was also performed (sampling period: 24h for PM₁₀, and 2-3 weeks for NO₂ and SO₂). Linear regression models were used to predict PM₁₀ and NO₂ concentrations from household characteristics, and these models were validated using leave out one cross validation (LOOCV). SO₂ concentrations were not modeled as concentrations were very low.

RESULTS

Mean indoor concentrations of PM₁₀ (n=105)_, NO₂ (n=82) and SO₂ (n=82) were 64μg/m³ (range 6.6-241); 19μg/m³ (range 4.5-55) and 0.6μg/m³ (range 0.005-3.4) respectively with the distributions for all three pollutants being skewed to the right. Spearman correlations showed weak positive correlations between the three pollutants. The largest contributors to the PM₁₀ predictive model were type of housing structure (formal or informal), number of smokers in the household, and type of primary fuel used in the household. The NO₂ predictive model was influenced mostly by the primary fuel type and by distance from the major roadway. The coefficients of determination (R²) for the models were 0.41 for PM₁₀ and 0.31 for NO₂. Spearman correlations were significant between measured vs. predicted PM₁₀ and NO₂ with coefficients of 0.66 and 0.55 respectively.

CONCLUSION

Indoor PM₁₀ levels were relatively high in these households. Both PM₁₀ and NO₂ can be modeled with a reasonable validity and these predictive models can decrease the necessary number of direct measurements that are expensive and time consuming.

Microbiota and Particulate Matter Assessment in Portuguese Optical Shops Providing Contact Lens Services

The aim of this work was to assess the microbiota (fungi and bacteria) and particulate matter in optical shops, contributing to a specific protocol to ensure a proper assessment. Air samples were collected through an impaction method. Surface and equipment swab samples were also collected side-by-side. Measurements of particulate matter were performed using portable direct-reading equipment. A walkthrough survey and checklist was also applied in each shop. Regarding air sampling, eight of the 13 shops analysed were above the legal requirement and 10 from the 26 surfaces samples were overloaded. In three out of the 13 shops fungal contamination in the analysed equipment was not detected. The bacteria air load was above the threshold in one of the 13 analysed shops. However, bacterial counts were detected in all sampled equipment. Fungi and bacteria air load suggested to be influencing all of the other surface and equipment samples. These results reinforce the need to improve air quality, not only to comply with the legal requirements, but also to ensure proper hygienic conditions. Public health intervention is needed to assure the quality and safety of the rooms and equipment in optical shops that perform health interventions in patients.

Enhancing indoor air quality -The air filter advantage

Air pollution has become the world's single biggest environmental health risk, linked to around 7 million deaths in 2012 according to a recent World Health Organisation (WHO) report. The new data further reveals a stronger link between, indoor and outdoor air pollution exposure and cardiovascular diseases, such as strokes and ischemic heart disease, as well as between air pollution and cancer. The role of air pollution in the development of respiratory diseases, including acute respiratory infections and chronic obstructive pulmonary diseases, is well known. While both indoor and outdoor pollution affect health, recent statistics on the impact of household indoor pollutants (HAP) is alarming. The WHO factsheet on HAP and health states that 3.8 million premature deaths annually - including stroke, ischemic heart disease, chronic obstructive pulmonary disease (COPD) and lung cancer are attributed to exposure to household air pollution. Use of air cleaners and filters are one of the suggested strategies to improve indoor air quality. This review discusses the impact of air pollutants with special focus on indoor air pollutants and the benefits of air filters in improving indoor air quality.

Environment and Health in Children Day Care Centres (ENVIRH) - Study rationale and protocol

BACKGROUND

Indoor air quality (IAQ) is considered an important determinant of human health. The association between exposure to volatile organic compounds, particulate matter, house dust mite, molds and bacteria in day care centers (DCC) is not completely clear. The aim of this project was to study these effects.

METHODS – STUDY DESIGN

This study comprised two phases. Phase I included an evaluation of 45 DCCs (25 from Lisbon and 20 from Oporto, targeting 5161 children). In this phase, building characteristics, indoor CO2 and air temperature/relative humidity, were assessed. A children's respiratory health questionnaire derived from the ISAAC (International Study on Asthma and Allergies in Children) was also distributed. Phase II encompassed two evaluations and included 20 DCCs selected from phase I after a cluster analysis (11 from Lisbon and 9 from Oporto, targeting 2287 children). In this phase, data on ventilation, IAQ, thermal comfort parameters, respiratory and allergic health, airway inflammation biomarkers, respiratory virus infection patterns and parental and child stress were collected.

RESULTS

In Phase I, building characteristics, occupant behavior and ventilation surrogates were collected from all DCCs. The response rate of the questionnaire was 61.7% (3186 children).Phase II included 1221 children. Association results between DCC characteristics, IAQ and health outcomes will be provided in order to support recommendations on IAQ and children's health. A building ventilation model will also be developed.

DISCUSSION

This paper outlines methods that might be implemented by other investigators conducting studies on the association between respiratory health and indoor air quality at DCC.

Environment and Health in Children Day Care Centres (ENVIRH) - Study rationale and protocol

BACKGROUND

Indoor air quality (IAQ) is considered an important determinant of human health. The association between exposure to volatile organic compounds, particulate matter, house dust mite, molds and bacteria in day care centers (DCC) is not completely clear. The aim of this project was to study these effects.

METHODS - STUDY DESIGN

This study comprised two phases. Phase I included an evaluation of 45 DCCs (25 from Lisbon and 20 from Oporto, targeting 5161 children). In this phase, building characteristics, indoor CO2 and air temperature/relative humidity, were assessed. A children's respiratory health questionnaire derived from the ISAAC (International Study on Asthma and Allergies in Children) was also distributed. Phase II encompassed two evaluations and included 20 DCCs selected from phase I after a cluster analysis (11 from Lisbon and 9 from Oporto, targeting 2287 children). In this phase, data on ventilation, IAQ, thermal comfort parameters, respiratory and allergic health, airway inflammation biomarkers, respiratory virus infection patterns and parental and child stress were collected.

RESULTS

In Phase I, building characteristics, occupant behavior and ventilation surrogates were collected from all DCCs. The response rate of the questionnaire was 61.7% (3186 children). Phase II included 1221 children. Association results between DCC characteristics, IAQ and health outcomes will be provided in order to support recommendations on IAQ and children's health. A building ventilation model will also be developed.

DISCUSSION

This paper outlines methods that might be implemented by other investigators conducting studies on the association between respiratory health and indoor air quality at DCC.

Characterizing and predicting coarse and fine particulates in classrooms located close to an urban roadway

The PM10, PM2.5, and PM1 (particulate matter with aerodynamic diameters < 10, < 2.5, and < 1 microm, respectively) concentrations were monitored over a 90-day period in a naturally ventilated school building located at roadside in Chennai City. The 24-hr average PM10, PM2.5, and PM1 concentrations at indoor and outdoor environments were found to be 136 +/- 60, 36 +/- 15, and 20 +/- 12 and 76 +/- 42, 33 +/- 16, and 23 +/- 14 microg/m3, respectively. The size distribution of PM in the classroom indicated that coarse mode was dominant during working hours (08:00 a.m. to 04:00 p.m.), whereas fine mode was dominant during nonworking hours (04:00 p.m. to 08:00 a.m.). The increase in coarser particles coincided with occupant activities in the classrooms and finer particles were correlated with outdoor traffic. Analysis of indoor PM10, PM2.5, and PM1 concentrations monitored at another school, which is located at urban reserved forest area (background site) indicated 3-4 times lower PM10 concentration than the school located at roadside. Also, the indoor PM1 and PM2.5 concentrations were 1.3-1.5 times lower at background site. Further, a mass balance indoor air quality (IAQ) model was modified to predict the indoor PM concentration in the classroom. Results indicated good agreement between the predicted and measured indoor PM2.5 (R2 = 0.72-0.81) and PM1 (R2 = 0.81-0.87) concentrations. But, the measured and predicted PM10 concentrations showed poor correlation (R2 = 0.17-0.23), which may be because the IAQ model could not take into account the sudden increase in PM10 concentration (resuspension of large size particles) due to human activities. Implications: The present study discusses characteristics of the indoor coarse and fine PM concentrations of a naturally ventilated school building located close to an urban roadway and at a background site in Chennai City, India. The study results will be useful to engineers and policymakers to prepare strategies for improving the IAQ inside classrooms. Further, this study may help in the development of IAQ standards and guidelines in India.

Risk assessment of inhalation exposure to polycyclic aromatic hydrocarbons in school children

Polycyclic aromatic hydrocarbons (PAHs) associated with the inhalable fraction of particulate matter were determined for 1 year (2009-2010) at a school site located in proximity of industrial and heavy traffic roads in Delhi, India. PM10 (aerodynamic diameter ≤10 μm) levels were ∼11.6 times the World Health Organization standard. Vehicular (59.5%) and coal combustion (40.5%) sources accounted for the high levels of PAHs (range 38.1-217.3 ng m(-3)) with four- and five-ring PAHs having ∼80 % contribution. Total PAHs were dominated by carcinogenic species (∼75%) and B[a]P equivalent concentrations indicated highest exposure risks during winter. Extremely high daily inhalation exposure of PAHs was observed during winter (439.43 ng day(-1)) followed by monsoon (232.59 ng day(-1)) and summer (171.08 ng day(-1)). Daily inhalation exposure of PAHs to school children during a day exhibited the trend school hours > commuting to school > resting period in all the seasons. Vehicular source contributions to daily PAH levels were significantly correlated (r = 0.94, p < 0.001) with the daily inhalation exposure level of school children. A conservative estimate of ∼11 excess cancer cases in children during childhood due to inhalation exposure of PAHs has been made for Delhi.

"Air pollution in Delhi: Its Magnitude and Effects on Health"

Air pollution is responsible for many health problems in the urban areas. Of late, the air pollution status in Delhi has undergone many changes in terms of the levels of pollutants and the control measures taken to reduce them. This paper provides an evidence-based insight into the status of air pollution in Delhi and its effects on health and control measures instituted. The urban air database released by the World Health Organization in September 2011 reported that Delhi has exceeded the maximum PM10 limit by almost 10-times at 198 μg/m3. Vehicular emissions and industrial activities were found to be associated with indoor as well as outdoor air pollution in Delhi. Studies on air pollution and mortality from Delhi found that all-natural-cause mortality and morbidity increased with increased air pollution. Delhi has taken several steps to reduce the level of air pollution in the city during the last 10 years. However, more still needs to be done to further reduce the levels of air pollution.

Levels and indoor-outdoor relationships of size-specific particulate matter in naturally ventilated Portuguese schools

Indoor exposure to particulate matter (PM) has received great interest due to the epidemiological evidence of its health impact, particularly in susceptible populations such as children. The present study investigated indoor concentrations of three PM size fractions in 11 naturally ventilated schools with static heating systems, and the relationship between indoor and outdoor PM concentrations. The study was performed in Porto, Portugal, during winter and included school buildings and individual classrooms with walk-through surveys, as well as indoor and outdoor air monitoring. Mean 12-h indoor daytime concentrations PM₁₀, PM(2.5), and PM₁ were 140, 95, and 91 μg/m³, respectively. During the day, PM(2.5) and PM₁ concentrations were lower indoors than outdoors (indoor/outdoor ratios of 0.83 and 0.8, respectively), whereas PM₁₀ showed the opposite trend. Concentrations decreased significantly during the night, 49% for PM₁₀ and 27% for PM(2.5) and PM₁. These findings reflect the significant contribution from the activities of occupants inside classrooms to higher indoor levels of PM₁₀ levels, whereas the fine fraction of PM(2.5) and PM₁ is primarily influenced by outdoor concentrations. This study provides a link between size-specific PM in Portuguese schools with contribution of outdoor versus indoor air. Our results suggest that exposure to PM is high and highlights the need for strategies that provide healthier school environments.