The effect of outdoor air and indoor human activity on mass concentrations of PM(10), PM(2.5), and PM(1) in a classroom

The influence of activity patterns and relative humidity on particle resuspension in classrooms

This paper investigates the impact of children's recess activity patterns on particulate matter (PM) resuspension in indoor environments, highlighting the complex, multi-dimensional nature of these activities and their interaction with environmental parameters. Despite the recognized role of indoor human activity in PM resuspension, research specifically addressing the effects of children's movements has been sparse. Through experimental scenarios that account for the characteristics of student activities, such as movement speed, trajectory, the number of participants, aisle widths, and varying humidity levels, this study uncovers significant differences in PM resuspension rates. It reveals that not only do movement speed and trajectory have a profound impact, but also the interaction between humidity and these factors plays a critical role, especially under lower humidity conditions. Additionally, the study demonstrates how the combination of people density and spatial configurations can significantly influence resuspension rates. The findings offer valuable insights for designing strategies to mitigate particle pollution in classrooms and similar indoor environments.

To breathe or not to breathe: Inhalational exposure to heavy metals and related health risk

This study reviewed scientific literature on inhalation exposure to heavy metals (HMs) in various indoor and outdoor environments and related carcinogenic and non-carcinogenic risk. A systematic search in Web of Science, Scopus, PubMed, Embase, and Medline databases yielded 712 results and 43 articles met the requirements of the Population, Exposure, Comparator, and Outcomes (PECO) criteria. Results revealed that HM concentrations in most households exceeded the World Health Organization (WHO) guideline values, indicating moderate pollution and dominant anthropogenic emission sources of HMs. In the analyzed schools, universities, and offices low to moderate levels of air pollution with HMs were revealed, while in commercial environments high levels of air pollution were stated. The non-carcinogenic risk due to inhalation HM exposure exceeded the acceptable level of 1 in households, cafes, hospitals, restaurants, and metros. The carcinogenic risk for As and Cr in households, for Cd, Cr, Ni, As, and Co in educational environments, for Pb, Cd, Cr, and Co in offices and commercial environments, and for Ni in metros exceeded the acceptable level of 1 × 10-4. Carcinogenic risk was revealed to be higher indoors than outdoors. This review advocates for fast and effective actions to reduce HM exposure for safer breathing.

Investigation of the Exposure of Schoolchildren to Ultrafine Particles (PM0.1) during the COVID-19 Pandemic in a Medium-Sized City in Indonesia

The health risk of schoolchildren who were exposed to airborne fine and ultrafine particles (PM0.1) during the COVID-19 pandemic in the Jambi City (a medium-sized city in Sumatra Island), Indonesia was examined. A questionnaire survey was used to collect information on schoolchildren from selected schools and involved information on personal profiles; living conditions; daily activities and health status. Size-segregated ambient particulate matter (PM) in school environments was collected over a period of 24 h on weekdays and the weekend. The personal exposure of PM of eight selected schoolchildren from five schools was evaluated for a 12-h period during the daytime using a personal air sampler for PM0.1 particles. The schoolchildren spent their time mostly indoors (~88%), while the remaining ~12% was spent in traveling and outdoor activities. The average exposure level was 1.5~7.6 times higher than the outdoor level and it was particularly high for the PM0.1 fraction (4.8~7.6 times). Cooking was shown to be a key parameter that explains such a large increase in the exposure level. The PM0.1 had the largest total respiratory deposition doses (RDDs), particularly during light exercise. The high level of PM0.1 exposure by indoor sources potentially associated with health risks was shown to be important.

Micro-characteristics of a naturally ventilated classroom air quality under varying air purifier placements

A naturally-ventilated operational classroom was instrumented at 18 locations to assess spatial variations of classroom air pollution (CRAP), thermal comfort and ventilation indicators under 10 different scenarios (base scenario without air purifier (AP); three single AP scenarios; three scenarios with two APs at same locations; three scenarios with two APs at different locations). Unlike PM_2.5, monitored PM₁₀ and CO₂ concentrations followed the diurnal occupancy profile. Highest vertical variation (38%) in CO₂ was at the classroom entry zone at 40-300 cm height. CO₂ increased until 225 cm before stratifying further. PM₁₀ increased to highest levels at children sitting height (100 cm) before decreasing to adult breathing height (150 cm). Highest horizontal variations in CO₂ (PM₁₀) were 29% (22%) at 40 cm height between the entry and occupied zones. Teachers' exposure to CO₂ (PM₁₀) in breathing zone varied by up to 6% (3%); the corresponding variations across monitored locations were up to 14% (19%). Teachers' exposure to CO₂ was up to 13% higher than that of children and 18% lower for PM₁₀. Traffic emissions (PM_2.5 and NO_x), secondary pollutants (VOCs and O₃), thermal comfort parameters and noise level in the classroom varied insignificantly among scenarios. PM₁₀ reduction was not doubled by using two air purifiers, which were most effective when placed within the highest PM concentration zone. Cross-comparisons of scenarios showed: use of AP reduced classroom's spatial average PM₁₀ up to 14%; PM₁₀ was reduced by increasing the AP's filtration capacity; and AP had insignificant impact on spatial average CO₂. PM₁₀ showed a maximum reduction of 46% (teacher zone), 62% (occupied zone) and 50% (entry zone) at children's breathing height, depending on usage scenario. This study produced high-resolution data for validating the detailed numerical models for classrooms and informing decision-making on AP's placement to minimise children's exposure to CRAP and re-breathed CO₂.

Indoor air quality in a training centre used for sports practice

Background

One of the measures for controlling the coronavirus disease 2019 (COVID-19) pandemic was the mass closure of gyms. This measure leads us to determine the differences between indoor and outdoor air quality. That is why the objective of this study was to analyse the indoor air quality of a sports centre catering to small groups and rehabilitation.

Methods

The study was conducted in a single training centre, where 26 measurements were taken in two spaces (indoors and outdoors). The air quality index, temperature, relative humidity, total volatile compounds, carbon monoxide, ozone, formaldehyde, carbon dioxide, and particulate matter were measured indoors and outdoors using the same protocol and equipment. These measurements were taken twice, once in the morning and once in the afternoon, with all measurements made at the same time, 10 am and 6 pm, respectively. Additionally, four determinations of each variable were collected during each shift, and the number of people who had trained in the room and the number of trainers were counted.

Results

In the different variables analysed, the results show that CO₂ and RH levels are higher indoors than outdoors in both measurement shifts. Temperatures are higher outside than inside and, in the evening, than in the morning. TVOC, AQI and PM show less variation, although they are higher outdoors in the morning. CO is highest indoors. HCHO levels are almost negligible and do not vary significantly, except for a slight increase in the afternoon outside. Ozone levels are not significant. All the variables showed practically perfect reliability in all the measurements, except for ozone measured outside in the morning. On the other hand, the variables exhibit variations between indoors and outdoors during the morning and afternoon, except for the three types of PM. Also, the data show that all the main variables measured inside the sports training centre are similar between morning and afternoon. However, outside, temperature, relative humidity and HCHO levels show significant differences between morning and afternoon while no differences are observed for the other variables.

Conclusion

The indoor air quality of the training centre assessed was good and met current regulations; some of its components even exhibited better levels than fresh air. This article is the first to measure indoor air quality in a sports training centre catering to rehabilitation and small groups.

Development of air purifier operation guidelines using grey box models for the concentrations of particulate matter in elementary school classrooms

Considering the hazardous effects of particulate matter (PM) exposure on students and teachers and the high PM concentration issue in South Korea, air purifiers have recently been installed in most classrooms to improve air quality. However, some on-site challenges, such as operational costs and noise, have been issues with the continuous operation of air purifiers. Therefore, a guideline is needed to dynamically predict the indoor PM concentration based on the changes in outdoor PM concentration and activate the air purifiers only when necessary. This study develops a grey-box model that uses measured data and physical differential equations to perform the given objective and verifies its accuracy using ASTM D5157. Modeling and analysis results have obtained information that can form the basis for developing guidelines to address PM issues in schools: The air purifier should be operated during periods where the predicted values exceed the limit in closed windows and the air purifier is not operating. It was also confirmed that the need for the operation of the air purifier varies between schools and classrooms under the same outdoor PM concentration. Indoor PM concentration increased significantly after students' simultaneous mass movement, necessitating air purifiers' operation before and after the events. The prefilter of the heater also aided in the removal of coarse PM. Additionally, the limitations and future development directions of the model were discussed.

Impact of ventilation methods on indoor particle concentrations in a daycare center

The objective of this study was to quantify the influence of ventilation methods on children's exposure to indoor particles in a daycare center located in an urban area. The ventilation methods applied to the center were monitored for 1 year. It appears that indoor PM₁₀ and PM_2.5 concentrations of the center were basically determined by outdoor conditions. The fluctuations in outdoor particle concentration also affected the ventilation behavior during class. The windows and doors of the classroom were frequently closed during both class hours and nights when the outdoor particle concentrations were at high levels. Statistically significant differences in the I/O ratios were found among the ventilation methods. The PM₁₀ I/O ratio with the closed windows was significantly higher (p < 0.01) than that with the open windows, and when the mechanical fans were operated, the I/O ratio dramatically decreased (p < 0.01). The I/O ratio of PM_2.5 showed a similar trend to that of PM₁₀ except for the mechanical fan operation. The filters rated lower than MERV 11 appear to be insufficient to remove submicron particles from the mechanically supplied outdoor air when the PM_2.5 concentrations are high, such as during the heating period.

A predictive model of indoor PM2.5 considering occupancy level in a hospital outpatient hall

The hospital outpatient hall is more complex and sensitive than other indoor places because of its high density, flow of patients, and risk of infection. The prediction of indoor pollutants, such as PM_2.5, is a critical health risk factor and an important topic in the study of indoor air quality. Numerous black-box models have been built to predict PM_2.5, which are prone to overfitting and low precision in long sequence time prediction due to their limited weighting calculation and factors considered In this study, subject-object weighting incorporates a long sequence time-series model that considers occupancy (SO-LSTS) to predict PM_2.5 concentrations in a hospital outpatient hall. First, the occupancy level was obtained using image recognition technology. Second, both the subjective (improved AHP) and objective (entropy weight) information were coupled by a distance function and then integrated into the LSTS model. Finally, the model performance was compared to six traditional models and the impact on the output length and hyper-parameter confirmation was assessed. The results demonstrate that the occupancy factor can improve the model performance by 54 %, and the model accuracy is improved by 89 % compared to the traditional Informer method. Our study considers real-time environmental and occupancy levels, which can compensate for the difficulty of interpreting the black-box model and identifying an accurate and resource-efficient proactive control model for hospital environmental management compared to conventional approaches.

Airborne fungal and bacterial microbiome in classrooms of elementary schools during the COVID-19 pandemic period: Effects of school disinfection and other environmental factors

The aim of the study was to examine the effects of environmental factors including disinfection on airborne microbiome during the coronavirus disease 2019 pandemic, we evaluated indoor and outdoor air collected from 19 classrooms regularly disinfected. Extracted bacterial and fungal DNA samples were sequenced using the Illumina MiSeq™ platform. Using bacterial DNA copy number concentrations from qPCR analysis, multiple linear regressions including environmental factors as predictors were performed. Microbial diversity and community composition were evaluated. Classrooms disinfected with spray ≤1 week before sampling had lower bacterial DNA concentration (3116 DNA copies/m³ ) than those >1 week (5003 copies/m³ ) (p-values = 0.06). The bacterial DNA copy number concentration increased with temperature and was higher in classrooms in coastal than inland cities (p-values <0.01). Bacterial diversity in outdoor air was higher in coastal than inland cities while outdoor fungal diversity was higher in inland than coastal cities. These outdoor microbiomes affected classroom microbial diversity but bacterial community composition at the genus level in occupied classrooms were similar between coastal and inland cities. Our findings emphasize that environmental conditions including disinfection, climate, and school location are important factors in shaping classroom microbiota. Yet, further research is needed to understand the effects of modified microbiome by disinfection on occupants' health.

A Scoping Review on Wearable Devices for Environmental Monitoring and Their Application for Health and Wellness

This scoping review is focused on wearable devices for environmental monitoring. First, the main pollutants are presented, followed by sensing technologies that are used for the parameters of interest. Selected examples of wearables and portables are divided into commercially available and research-level projects. While many commercial products are in fact portable, there is an increasing interest in using a completely wearable technology. This allows us to correlate the pollution level to other personal information (performed activity, position, and respiratory parameters) and thus to estimate personal exposure to given pollutants. The fact that there are no univocal indices to estimate outdoor or indoor air quality is also an open problem. Finally, applications of wearables for environmental monitoring are discussed. Combining environmental monitoring with other devices would permit better choices of where to perform sports activities, especially in highly polluted areas, and provide detailed information on the living conditions of individuals.

Estimations of infiltration factors of diurnal PM2.5 and heavy metals in children's bedrooms

Children are the sensitive population to fine particulate matter (PM_2.5 ) exposure and spend most of their time in bedroom. Infiltration factor (F_inf ) can be used to calculate the fraction of total indoor PM_2.5 with outdoor origin to increase the accuracy of exposure assessment. However, studies have ignored the diurnal variations of PM_2.5 F_inf values, and a few studies have estimated F_inf values for heavy metals in PM_2.5 in children's bedrooms. To calculate the PM_2.5 F_inf , real-time indoor and outdoor PM_2.5 concentrations and occupants' activities were collected in 56 study bedrooms. At 22 of the 56 study bedrooms, PM_2.5 samples were also collected for heavy metals analysis. We noted the PM_2.5 F_inf was higher during the daytime (0.70 ± 0.23) than nighttime (0.54 ± 0.27) during the hot season, and the time of air conditioner use was longer at nighttime. The largest F_inf value of heavy metal was V (0.88 ± 0.25), followed by Pb (0.85 ± 0.28), Mn (0.72 ± 0.26), Cr (0.69 ± 0.35), and Zn (0.61 ± 0.32), with a larger variation. Our findings suggest that the estimations of diurnal PM_2.5 and heavy metals F_inf values are necessary to increase the accuracy of exposure assessment.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality

BACKGROUND

School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children.

OBJECTIVE

We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States.

METHODS

Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates.

RESULTS

LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively.

DISCUSSION

The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Design and Implementation of an IoT-Based Smart Classroom Incubator

Ambient conditions influence human health, emotions, and mental power. Therefore, numerous studies have been conducted in different disciplines on the measurement and control of ambient conditions in classrooms. Moreover, a number of studies identify some physical and mental performances of students simultaneously or for informative purposes by utilizing various hardware and software. However, these studies did not consider automatic control processes and individuality in fulfilling classroom ambient conditions, which influence students’ behavior. This study aims to reduce the adverse impacts of environmental factors on learning and control more necessary physical parameters with higher accuracy using the latest technology and methods. Thus, a new smart classroom incubator (SCI) algorithm, including hardware, software, and experimental studies, in which individual differences could be considered even in the same classroom environment, and its implementation were presented. The system enables access and monitoring of data wherever there is Internet connection. Moreover, it was designed based on IoT because it allows for data transfer over web services or data-dependent operations. All the necessary equipment was placed in the classroom without affecting the learning environment and distracting the class. Cronbach’s α coefficient, which indicates the reliability of the implemented model, was 0.891.

PM10, PM2.5, PM1, and PM0.1 resuspension due to human walking

Indoor air quality has become a major concern in recent years due to the adverse effects of poor air quality, caused by the presence of several sources of pollutants, on the building occupants' health. Particle resuspension has been identified as a major indoor particle matter (PM) source in indoor environments. The present work investigated the human walking-induced PM resuspension in a full-scale laboratory experimental chamber. The PM mass concentration was monitored using a Miniwras Grimm counter. The floor of the test chamber was covered with a tufted synthetic carpet and uniformly loaded with neutralized alumina dust. Using the mass-based balance equation and the well-mixed condition hypothesis, resuspension rates were estimated after 10 min of walking activity. Results show that human walking significantly increases the indoor PM10, PM2.5, PM1, and PM0.1 concentrations. The average estimated PM10, PM2.5, PM1, and PM0.1 resuspension rates were (2.5 ± 0.6) × 10^-1 h^-1, (1.9 ± 0.5) × 10^-2 h^-1, (6.5 ± 0.3) × 10^-3 h^-1, and (4.3 ± 0.3) × 10^-3 h^-1, respectively.

Contributions of internal emissions to peaks and incremental indoor PM2.5 in rural coal use households

Indoor air quality is critically important to the human as people spend most time indoors. Indoor PM_2.5 is related to the outdoor levels, but more directly influenced by internal sources. Severe household air pollution from solid fuel use has been recognized as one major risk for human health especailly in rural area, however, the issue is significantly overlooked in most national air quality controls and intervention policies. Here, by using low-cost sensors, indoor PM_2.5 in rural homes burning coals was monitored for ~4 months and analyzed for its temporal dynamics, distributions, relationship with outdoor PM_2.5, and quantitative contributions of internal sources. A bimodal distribution of indoor PM_2.5 was identified and the bimodal characteristic was more significant at the finer time resolution. The bimodal distribution maxima were corresponding to the emissions from strong internal sources and the influence of outdoor PM_2.5, respectively. Indoor PM_2.5 was found to be correlated with the outdoor PM_2.5, even though indoor coal combustion for heating was thought to be predominant source of indoor PM_2.5. The indoor-outdoor relationship differed significantly between the heating and non-heating seasons. Impacts of typical indoor sources like cooking, heating associated with coal use, and smoking were quantitatively analyzed based on the highly time-resolved PM_2.5. Estimated contribution of outdoor PM_2.5 to the indoor PM_2.5 was ~48% during the non-heating period, but decreased to about 32% during the heating period. The contribution of indoor heating burning coals comprised up to 47% of the indoor PM_2.5 during the heating period, while the other indoor sources contributed to ~20%. The study, based on a relatively long-term timely resolved PM_2.5 data from a large number of rural households, provided informative results on temporal dynamics of indoor PM_2.5 and quantitative contributions of internal sources, promoting scientific understanding on sources and impacts of household air pollution.

Indoor Particulate Matter in Urban Households: Sources, Pathways, Characteristics, Health Effects, and Exposure Mitigation

Particulate matter (PM) is a complex mixture of solid particles and liquid droplets suspended in the air with varying size, shape, and chemical composition which intensifies significant concern due to severe health effects. Based on the well-established human health effects of outdoor PM, health-based standards for outdoor air have been promoted (e.g., the National Ambient Air Quality Standards formulated by the U.S.). Due to the exchange of indoor and outdoor air, the chemical composition of indoor particulate matter is related to the sources and components of outdoor PM. However, PM in the indoor environment has the potential to exceed outdoor PM levels. Indoor PM includes particles of outdoor origin that drift indoors and particles that originate from indoor activities, which include cooking, fireplaces, smoking, fuel combustion for heating, human activities, and burning incense. Indoor PM can be enriched with inorganic and organic contaminants, including toxic heavy metals and carcinogenic volatile organic compounds. As a potential health hazard, indoor exposure to PM has received increased attention in recent years because people spend most of their time indoors. In addition, as the quantity, quality, and scope of the research have expanded, it is necessary to conduct a systematic review of indoor PM. This review discusses the sources, pathways, characteristics, health effects, and exposure mitigation of indoor PM. Practical solutions and steps to reduce exposure to indoor PM are also discussed.

Elemental and microbiota content in indoor and outdoor air using recuperation unit filters

This paper presents the results of a twelve-month measurement campaign conducted at a rural single-family house in Poland. The external and internal filters of a recuperator used to mechanically ventilate the building were used to separate the total suspended particles (TSPs), and the concentrations of fifteen elements and abundance of fungi and bacteria were determined. Lower annual mean concentrations were observed indoors, and the concentrations of most elements did not significantly change between seasons. There were some differences between winter and summer, which may have resulted from changes in the ventilation regimes in the house. The number of bacteria was similar outdoors and indoors, while the amounts of fungi were higher indoors (p < 0.05). The order of metal concentrations outdoors agreed well with observations in other countries, while indoors the metal concentrations order indicated the individual characteristics of the building. The species diversity of fungi was higher than that of bacteria, and different species were found indoors and outdoors, while bacteria were typically present both indoors and outdoors. Different TSP sources were identified indoors and outdoors, suggesting limited penetration between the two environments. However, both environments were affected by traffic. Mechanical ventilation systems with built-in filters (such as recuperators) were useful in assessing the air quality within the building, and the changeable recuperation filters offer an approach to assess the air quality in several houses without any additional cost or discomfort to the residents.

Using Low-Cost Sensors to Assess Fine Particulate Matter Infiltration (PM2.5) during a Wildfire Smoke Episode at a Large Inpatient Healthcare Facility

Wildfire smoke exposure is associated with a range of acute health outcomes, which can be more severe in individuals with underlying health conditions. Currently, there is limited information on the susceptibility of healthcare facilities to smoke infiltration. As part of a larger study to address this gap, a rehabilitation facility in Vancouver, Canada was outfitted with one outdoor and seven indoor low-cost fine particulate matter (PM_2.5) sensors in Air Quality Eggs (EGG) during the summer of 2020. Raw measurements were calibrated using temperature, relative humidity, and dew point derived from the EGG data. The infiltration coefficient was quantified using a distributed lag model. Indoor concentrations during the smoke episode were elevated throughout the building, though non-uniformly. After censoring indoor-only peaks, the average infiltration coefficient (range) during typical days was 0.32 (0.22–0.39), compared with 0.37 (0.31–0.47) during the smoke episode, a 19% increase on average. Indoor PM_2.5 concentrations quickly reflected outdoor conditions during and after the smoke episode. It is unclear whether these results will be generalizable to other years due to COVID-related changes to building operations, but some of the safety protocols may offer valuable lessons for future wildfire seasons. For example, points of building entry and exit were reduced from eight to two during the pandemic, which likely helped to protect the building from wildfire smoke infiltration. Overall, these results demonstrate the utility of indoor low-cost sensors in understanding the impacts of extreme smoke events on facilities where highly susceptible individuals are present. Furthermore, they highlight the need to employ interventions that enhance indoor air quality in such facilities during smoke events.

Distribution Levels of Particulate Matter Fractions (<2.5 µm, 2.5-10 µm and >10 µm) at Seven Primary Schools in a European Ceramic Cluster

This study addresses the concentration of particulate matter and their size using a statistical analysis of data obtained inside seven schools located in the towns of Castellón (S1, S2, and S3), Alcora (S4, S5, and S6) and Lucena (S7) in northeast Spain. Samples were taken for five to eight hours, depending on school hours, to obtain a monthly sample for each school. The main goal of this study is to assess the differences depending on the type of location and the sampling point to be able to design corrective measures that improve the habitability and safety of the teaching spaces analyzed. The lowest concentrations of fine particulate matter, less than 2.5 µm, were registered at the rural location. The values of these particles found in industrial and urban locations were not substantially different. In the case of particulate matter between 2.5 and 10 µm, significant differences were observed between the three types of locations. The lowest concentrations of particles larger than 10 µm were registered at the rural location, and the highest concentrations were found at the industrial locations. Among the urban stations, the particle concentration of this fraction in station S2 was significantly higher than that in stations S1 and S3, which had similar concentrations. These values are also similar to those registered at school S6, which is at an industrial location. The resuspension of particles from both indoor sources as well as those transported from the outside is an important factor in the concentrations of particles inside classrooms.

Could Air Quality Get Better during Epidemic Prevention and Control in China? An Analysis Based on Regression Discontinuity Design

Though many scholars and practitioners are paying more attention to the health and life of the public after the COVID-19 outbreak, extant literature has so far failed to explore the variation of ambient air quality during this pandemic. The current study attempts to fill the gap by disentangling the causal effects of epidemic prevention on air quality in China, measured by the individual pollutant dimensionless index, from other confounding factors. Using the fixed effects model, this article finds that five air indicators, PM2.5, PM10, CO, NO2, and SO2, significantly improved during the shutdown period, with NO2 showing the most improvement. On the contrary, O3 shows an inverse pattern, that is, O3 gets worse unexpectedly. The positive impact of epidemic prevention on air quality, especially in terms of PM2.5, PM10, and NO2, become manifest five days after the resumption of labor, indicated by the result of a regression discontinuity design. These findings are still robust and consistent after the dataset of 2019 as a counterfactual sample is utilized. The findings of this paper make contributions to both environmental governance and pandemic prevention, with relevant guidelines regarding the health and life of the public and governmental behavioral management strategies discussed.

From spontaneous to strategic natural window ventilation: Improving indoor air quality in Swiss schools

Natural window ventilation is frequently employed in schools in Europe and often leads to inadequate levels of human bioeffluents. However, intervention studies that verify whether recommended ventilation targets can be achieved in practice with reasonable ventilation regimes and that are also suitable for countries with cold winters are practically non-existent. To explore the initial situation in Switzerland we carried out carbon dioxide (CO₂) measurements during the winter in 100 classrooms, most of which (94%) had natural window ventilation. In more than two thirds of those, the hygienic limit value of 2000 ppm specified for CO₂ in the Swiss Standard SN 520180 (2014) was exceeded. To improve ventilation behavior, an intervention was implemented in 23 classrooms during the heating season. Ventilation was performed exclusively during breaks (to avoid discomfort from cold and drafts), efficiently, and only for as long as was necessary to achieve the ventilation objective of compliance with the hygienic limit value (strategic ventilation). The intervention included verbal and written instructions, awareness-raising via a school lesson and an interactive tool for students, which was also used to estimate the required duration of ventilation. CO₂ exposure was significantly reduced in pilot classes (Wilcoxon signed-rank test, p = 3.815e^-06). Median CO₂ levels decreased from 1600 ppm (control group) to 1097 ppm (intervention group), and the average proportion of teaching time at 400-1400 ppm CO₂ increased from 40% to 70%. The duration of ventilation was similar to spontaneous natural window ventilation (+5.8%). Stricter ventilation targets are possible. The concept of the intervention is suitable for immediate adoption in schools with natural window ventilation for a limited period, pending the installation of a mechanical ventilation system. The easy integration of this intervention into everyday school life promotes compliance, which is particularly important during the COVID-19 pandemic.

Seasonal Variations in the Chemical Composition of Indoor and Outdoor PM10 in University Classrooms

In the VIEPI project (Integrated evaluation of the exposure to indoor particulate matter) framework, we carried out a 1-year study of the concentration and chemical composition of particulate matter (PM) in a 5 story building in the Sapienza University of Rome (Italy). Each sampling had a duration of 1 month and was carried out indoors and outdoors in six classrooms. The chemical analyses were grouped to obtain information about the main PM sources. Micro-elements in their soluble and insoluble fractions were used to trace additional sources. Indoor PM composition was dominated by soil components and, to a lesser extent, by the organics, which substantially increased when people crowded the sites. The penetration of PM components was regulated by their chemical nature and by the dimensions of the particles in which they were contained. For the first time in crowded indoor environments, three different chemical assays aimed to determine PM redox properties complemented chemical composition measurements. These preliminary tests showed that substantially different redox properties characterised atmospheric particles in indoor and outdoor sites. The innovative characteristics of this study (time duration, number of considered environments) were essential to obtain relevant information about PM composition and sources in indoor academic environments and the occupants’ role.

A practical framework for predicting residential indoor PM2.5 concentration using land-use regression and machine learning methods

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM_2.5 concentration in indoor environments (e.g., residential households) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM_2.5 concentration in residential households. PM_2.5 concentration and related parameters (e.g., basic information about the households and ventilation settings) were collected in 116 households during the winter and summer seasons in Hong Kong. Outdoor PM_2.5 concentration at households was estimated using a land-use regression model. The random forest machine learning algorithm was then applied to develop indoor PM_2.5 prediction models. The results show that the random forest model achieved a promising predictive accuracy, with R² and cross-validation R² values of 0.93 and 0.65, respectively. Outdoor PM_2.5 concentration was the most important predictor variable, followed in descending order by the household marked number, outdoor temperature, outdoor relative humidity, average household area and air conditioning. The external validation result using an independent dataset confirmed the potential application of the random forest model, with an R² value of 0.47. Overall, this study shows the value of a combined land-use regression and machine learning approach in establishing indoor PM_2.5 prediction models that provide a relatively accurate assessment of exposure for use in epidemiological studies.

Identifying Volatile Chemical Product Tracer Compounds in U.S. Cities

With traffic emissions of volatile organic compounds (VOCs) decreasing rapidly over the last decades, the contributions of the emissions from other source categories, such as volatile chemical products (VCPs), have become more apparent in urban air. In this work, in situ measurements of various VOCs are reported for New York City, Pittsburgh, Chicago, and Denver. The magnitude of different emission sources relative to traffic is determined by measuring the urban enhancement of individual compounds relative to the enhancement of benzene, a known tracer of fossil fuel in the United States. The enhancement ratios of several VCP compounds to benzene correlate well with population density (R² ∼ 0.6-0.8). These observations are consistent with the expectation that some human activity should correlate better with the population density than transportation emissions, due to the lower per capita rate of driving in denser cities. Using these data, together with a bottom-up fuel-based inventory of vehicle emissions and volatile chemical products (FIVE-VCP) inventory, we identify tracer compounds for different VCP categories: decamethylcyclopentasiloxane (D5-siloxane) for personal care products, monoterpenes for fragrances, p-dichlorobenzene for insecticides, D4-siloxane for adhesives, para-chlorobenzotrifluoride (PCBTF) for solvent-based coatings, and Texanol for water-based coatings. Furthermore, several other compounds are identified (e.g., ethanol) that correlate with population density and originate from multiple VCP sources. Ethanol and fragrances are among the most abundant and reactive VOCs associated with VCP emissions.

Chemical Composition and Source Apportionment of PM10 in a Green-Roof Primary School Building

Research on air quality issues in recently refurbished educational buildings is relatively limited. However, it is an important topic as students are often exposed to high concentrations of air pollutants, especially in urban environments. This study presents the results of a 25-day experimental campaign that took place in a primary school located in a densely built-up area, which retains a green roof system (GRS). All measurements refer to mass concentrations and chemical analysis of PM10 (particulate matter less than 10 micrometers), and they were implemented simultaneously on the GRS and within the classroom (C3) below during different periods of the year. The results demonstrated relatively low levels of PM10 in both experimental points, with the highest mean value of 72.02 μg m−3 observed outdoors during the cold period. Elemental carbon (EC) was also found be higher in the ambient environment (with a mean value of 2.78 μg m−3), while organic carbon (OC) was relatively balanced between the two monitoring sites. Moreover, sulfate was found to be the most abundant water soluble anion (2.57 μg m−3), mainly originating from ambient primary SO2 and penetrating into the classroom from windows. Additionally, the crustal origin of particles was shown in trace metals, where Al and Fe prevailed (9.55% and 8.68%, respectively, of the total PM10). Nevertheless, infiltration of outdoor particles within the classroom was found to affect indoor sources of metals. Finally, source apportionment using a positive matrix factorization (PMF) receptor model demonstrated six main factors of emissions, the most important of which were vehicles and biomass burning (30.30% contribution), along with resuspension of PM10 within the classroom from human activities (29.89% contribution). Seasonal variations seem to play a key role in the results.

The impact of air filtration units on primary school students' indoor exposure to particulate matter in China

The air quality in classrooms is a critical factor that affects students' daily exposure. Although air filtration units (AFUs) are often used to reduce exposure to air pollution in China, the effectiveness of installing AFUs in Chinese classrooms have not been quantitatively studied. Hence, we carried out concurrent air quality experiments in two identical classrooms where one room was equipped with commercial AFUs and the other one relied on the natural introduction of outside air. Measurements were taken during regular school days in the winter at a primary school in Hangzhou, China. Three AFU ventilation modes, i.e., fresh air, mixed air and recirculation mode were evaluated, respectively. We found that classrooms equipped with AFU showed significant PM_2.5 reductions, but this was accompanied by CO₂ build-ups. In classroom with AFUs on internal recirculation, nearly 70% of the PM_2.5 concentration at the beginning of the class was reduced by the end of a 40-min class. However, the CO₂ concentration was observed to reach levels that were six times greater than that of the ambient due to inadequate air change rate (<1 h^-1). To mitigate the issue of excessive in-classroom CO₂ accumulation when using AFUs, filtered outside air must be brought in during their operation. In comparison with the internal recirculation mode, we demonstrated that the fresh air mode that draws the supply air from outdoor can considerably lower CO₂ build-ups in the classroom with a moderate increase in PM_2.5 concentration of 15 ± 10 μg/m³. The findings from this study helps policy makers especially in developing countries with serious air pollution issues to determine whether or not to install AFU in primary schools and what ventilation mode is effective in reducing negative health effects.

Using low cost open-face passive samplers to sample PM concentration and elemental composition in childcare facilities

Effective, low noise and low-cost samplers for airborne particulate matter (PM) in indoor environments are needed. In 15 northern California childcare facilities, we deployed open-faced polycarbonate substrate passive aerosol samplers and analyzed them using computer-controlled scanning electron microscopy to measure indoor PM particle size, number, and elemental concentration. Particle concentrations were compared to PM concentrations measured by filter-based active samplers. The PM mass concentrations measured after open-faced passive sampling were in good agreement with the PM concentrations obtained using co-located active samplers. The measured PM2.5 concentrations inside of the childcare facilities were in the range of 6-31 μg m-3, while the PM10 concentrations were in the range of 17-96 μg m-3. For PM10, 93.3% of the passive sampler results were within ±30% of those determined with the active sampler, and 67% of the PM2.5 results fell within ±30% of the active sampler measurements. The Pearson's correlation coefficient between PM mass concentrations estimated from the open-faced passive aerosol sampler (OPS) versus gravimetric (active sampler) methods was 0.77 for PM2.5 and 0.95 for PM10, respectively, indicating a strong correlation between the passive and active sampler results. PM and elemental composition varied by facility location and interior characteristics of the childcare facilities such as floor type, presence of combustion sources, occupant density, and the level of activity. The rich carbon content in the polycarbonate substrate produced positive bias in the carbon concentration analysis. Based on the carbon concentration, determined using a copper substrate, a correction factor was needed to determine the total carbon concentrations in the sampled PM for the polycarbonate substrate passive sampler. Overall, the results demonstrated that the OPS collected enough aerosol particles in one business day (6-8 hours) to assess PM levels.

Relationship between the actual fine dust concentration and media exposure that influenced the changes in outdoor activity behavior in South Korea

The one reason of the decrease of walking time for adults in South Korea among various factors is the sense of fear about fine dust sparked by media reports, which has created a negative perception of fine dust. This study aimed to assess the change in concentration of fine dust, as well as individuals' walking time and health status, in South Korea, and to investigate the relationship between the media reports on fine dust. Using the national government statistics data, we analyzed the relationship between walking time, concentration of fine dust, and amount of media reports on fine dust. From 2008 to 2017, the average walking time and PM10 levels decreased from 76.17 to 49.47 min and 52 to 45 μg/m³; whereas PM10 media frequency increased from 349 to 9,234. No positive correlation existed between walking time in South Korea and exposure to fine dust. However, media reports on fine dust increased steadily from 2012 and peaked in 2015. The decrease in average walking time in South Korea was due to the negative perception created by the increase in media reports on fine dust, rather than the increase in the actual concentration of fine dust.

Indoor air quality at school and students' performance: Recommendations of the UNESCO Chair on Health Education and Sustainable Development & the Italian Society of Environmental Medicine (SIMA)

The issue of indoor air quality (IAQ) concerns 64 million students across Europe, but it is still a neglected topic, although it impacts both their health and learning outcomes. Classroommicroclimate is the first key factor determining a healthy or unhealthy school environment, and it is influenced by ventilation, temperature and humidity rate. Classrooms are usually crowded, overheated and poorly ventilated, thus resulting in possible increases of carbon dioxide (CO₂), that can cause several problems when its concentrations exceed the value of 0.15 percentage volume of CO₂ (1500 ppm) or even at lower levels (1000 ppm). CO₂ can also arise from outside the school, being widely produced by the combustion of fossils or road traffic. Anthropogenic activities are responsible for the emission of nitrogen dioxide (NO₂) and polycyclic aromatic hydrocarbons(PAH) too, which represent other possible external contaminants potentially impairing IAQ. Furtherdangerous exposures for students' health are those related to natural emission of gas Radon, which typically accumulates in poorly ventilated classrooms, and volatile organic compounds (VOCs, released by building materials, paints, furnishings, detergents), while chemicals substances (i.e.cyanoacrylate, lead, cadmium, nickel) might be contained in school materials. Finally, particulate matter (PM2.5 and PM10) originating from road traffic, domestic heating or industrial activities represent additional possible contaminants impacting schools' air quality. Poor IAQ might result in mild adverse events (i.e. headaches, nausea etc.) or cause respiratory problems. More frequently, IAQ affects students' attention and their school performances, as widely documented by many studies. Standardized tests administered to pupils exposed to poor IAQ (to assess reading and mathematical abilities) systematically result in worse outcomes compared to students staying in healthy classroom environments. In this paper, we present recommendations of UNESCO Chair on Health Education and Sustainable Development and Italian Society of Environmental Medicine(SIMA) to ensure an optimal IAQ at school, including some post-COVID-19 issues.

Assessment of Air Quality in School Environments in Hanoi, Vietnam: A Focus on Mass-Size Distribution and Elemental Composition of Indoor-Outdoor Ultrafine/Fine/Coarse Particles

Indoor and outdoor ultrafine, accumulation mode, and coarse fractions collected at two preschools (S1 and S2) in Hanoi capital, Vietnam were characterized in terms of mass-size distribution and elemental composition to identify major emission sources. The sampling campaigns were performed simultaneously indoors and outdoors over four consecutive weeks at each school. Indoor average concentrations of CO2 and CO at both schools were below the limit values recommended by American Society of Heating, Refrigerating and Air-Conditioning Engineers (1000 ppm for CO2) and World Health Organization (7 mg/m3 for CO). Indoor concentrations of PM2.5 and PM10 at S1 and S2 were strongly influenced by the presence of children and their activities indoors. The indoor average concentrations of PM2.5 and PM10 were 49.4 µg/m3 and 59.7 µg/m3 at S1, while those values at S2 were 7.9 and 10.8 µg/m3, respectively. Mass-size distribution of indoor and outdoor particles presented similar patterns, in which ultrafine particles accounted for around 15–20% wt/wt while fine particles (PM2.5) made up almost 80% wt/wt of PM10. PM2.5–10 did not display regular shapes while smaller factions tended to aggregate to form clusters with fine structures. Oxygen (O) was the most abundant element in all fractions, followed by carbon (C) for indoor and outdoor particles. O accounted for 36.2% (PM0.5–1) to 42.4% wt/wt (PM0.1) of indoor particles, while those figures for C were in the range of 14.5% (for PM0.1) to 18.1% (for PM1–2.5). Apart from O and C, mass proportion of other major and minor elements (Al, Ca, Cr, Fe , K, Mg, Si, Ti) could make up to 50%, whereas trace elements (As, Bi, Cd, Co, Cr, Cu, La, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sn, Sr, and Zn) accounted for less than 0.5% of indoor and outdoor airborne particles. There were no significant indoor emission sources of trace and minor elements. Traffic significantly contributed to major and trace elements at S1 and S2.

Association between microenvironment air quality and cardiovascular health outcomes

Exposure to fine particulate matter (PM_2.5) is associated with cardiovascular disease risk. To date, there are few studies on short-term PM_2.5 exposure in different microenvironments and its impact on immediate health effects, particularly in the Southeast Asia region. This study assessed PM_2.5 concentrations in different microenvironments in a densely populated city in the tropics using low-cost personal PM_2.5 sensors as well as their associations with acute cardiovascular health outcomes. A total of 49 adult participants affiliated with the National University of Singapore (NUS) community were recruited. Personal low-cost sensors were used to measure PM_2.5 concentrations between September 2017 and March 2019. Demographic information and time-activity patterns were collected using questionnaires. Wilcoxon pairwise comparisons were used to determine statistical differences between PM_2.5 exposures at 18 different microenvironments. Generalized Estimating Equations (GEE) models were used to assess the association between PM_2.5 exposure and blood pressure as well as heart rate. All models were adjusted for age, sex, body mass index, physical activity, temperature, duration of exposure, and baseline cardiovascular parameters. Significant differences in PM_2.5 concentrations were observed across different microenvironments. Air-conditioned offices and tertiary teaching spaces had the lowest (median = 13.1 μg/m³) and hawker centres had the highest (median = 32.0 μg/m³) PM_2.5 concentrations. Significant positive associations between PM_2.5 exposure and heart rate (β = 0.40, p = 4.6 × 10^-5) as well as diastolic blood pressure (β = 0.16, p = 0.0077) were also observed. Short-term exposure to PM_2.5 was significantly associated with higher heart rate and blood pressure. Further work is needed to investigate the variations within each type of microenvironment and expand the study to other sub-populations such as the elderly and children.

Relationship between indoor and outdoor size-fractionated particulate matter in urban microenvironments: Levels, chemical composition and sources

Exposure to particulate matter (PM) has been associated with adverse health outcomes, particularly in susceptible population groups such as children. This study aims to characterise children's exposure to PM and its chemical constituents. Size-segregated aerosol samples (PM0.25, PM0.25-0.5, PM0.5-1.0, PM1.0-2.5 and PM2.5-10) were collected in the indoor and outdoor of homes and schools located in Lisbon (Portugal). Organic and elemental carbon (OC and EC) were determined by a thermo-optical method, whereas major and trace elements were analysed by X-Ray Fluorescence. In school, the children were exposed to higher PM concentrations than in home, which might be associated not only to the elevated human occupancy but also to outdoor infiltration. The pattern of PM mass size distribution was dependent on the location (home vs. school and indoor vs. outdoor). The presence of EC in PM0.25 and OC in PM0.25-0.5 was linked to traffic exhaust emissions. OC and EC in PM2.5-10 may be explained by their adhesion to the surface of coarser particles. Generally, the concentrations of mineral and marine elements increased with increasing PM size, while for anthropogenic elements happened the opposite. In schools, the concentrations of mineral matter, anthropogenic elements and marine aerosol were higher than in homes. High mineral matter concentrations found in schools were related to the close proximity to busy roads and elevated human occupancy. Overall, the results suggest that exposure to PM is relevant and highlights the need for strategies that provide healthier indoor environments, principally in schools.

The Effects of Risk Perceptions Related to Particulate Matter on Outdoor Activity Satisfaction in South Korea

In recent years, the Korean public has become aware of the form of air pollution known as particulate matter, with a consequent growth of public anxiety causing a negative risk perception about outdoor activity. This study aims at determining the causal relationship between risk perceptions about particulate matter and outdoor activity satisfaction in South Korea. An Internet survey was conducted with 412 people, and a structural equation model was used to perform confirmatory factor analysis. The statistically significant results show that the perceived risk of particulate matter is higher when people do not show interest in or trust public opinion or policy on the subject. This increases people’s perceptions of health risks, which in turn lowers their satisfaction with outdoor activity. Although trust levels in public opinion or policy had a positive impact on outdoor activity satisfaction, this was not statistically significant. These results are expected to contribute to risk communication guidelines in public opinion reporting and to the direction of environmental health policies in developing countries with high levels of air pollution, such as particulate matter.

Evaluation of particle penetration factors based on indoor PM2.5 removal by an air cleaner

The particle penetration factor is an important parameter to determine the concentration of indoor particles. In this paper, a mathematical model for calculating this parameter was established by combining with the decay of the indoor PM_2.5 and CO₂ concentrations measured in a bedroom with an air cleaner. The convergence of the penetration factors was analyzed under different working conditions. The results show that the particle penetration factors converge to stable values within the range of 0.69 to 0.84 close to the value from the empirical formula when the indoor PM_2.5 concentration decays to stable values. When the role of particle deposition is ignored, the penetration factors at the low and middle airflow modes are 0.78 and 0.69, respectively. The particle penetration factors are mainly determined by the clean air delivery rate (CADR) of the air cleaner, clearance airflow, and I/O ratio during the balanced phase when the roles of indoor particle deposition and exfiltration can be ignored. This work can provide a convenient method for the calculation of the particle penetration factor.

Assessment of the spatio-temporal pattern of PM2.5 and its driving factors using a land use regression model in Beijing, China

With the acceleration of urbanization and industrialization, atmospheric pollution has become a major issue, restricting the sustainable development of the urban environment. Since 2013, Beijing has been among China's most seriously affected regions in terms of haze pollution. Atmospheric pollution is closely linked to land use, particularly the spatial patterns of green and urban land. Therefore, the quantification of the relationship between fine particulate matter (PM_2.5) concentration and its driving factors in Beijing is of considerable significance for environmental management and spatial epidemiological studies. A land use regression (LUR) model was constructed to simulate the spatio-temporal distribution of PM_2.5 concentration. In this study, the independent variables (driving factors) included land use, meteorological factors, population, roads, the digital elevation model, and the normalized difference vegetation index. The five models had adjusted R² of 0.887, 0.770, 0.742, 0.877, and 0.798, respectively. Land use and meteorological factors were the main factors affecting PM_2.5 concentration. The driving factors of land use on a large scale and roads on a small scale had a significant impact on PM_2.5 emissions. Beijing's PM_2.5 concentrations in 2015 showed clear spatio-temporal characteristics. The highest (lowest) average PM_2.5 concentration was recorded in winter (summer). In terms of spatial distribution, PM_2.5 concentrations showed a "low in the northwest and high in the southeast" trend. The most polluted areas were mainly distributed in the central city and the southeastern and southwestern regions. The PM_2.5 concentration boundary was essentially consistent with the boundary of land use type. Different land use types promoted or inhibited PM_2.5 concentrations, with a difference of more than 20 μg/m³ PM_2.5 between the two land use categories. Thus, PM_2.5 concentrations should be controlled by optimizing the spatial and temporal patterns of land use.

Effects of air cleaners and school characteristics on classroom concentrations of particulate matter in 34 elementary schools in Korea

Exposure to particulate matter (PM) in school environments has been associated with respiratory illnesses among children. Although using air cleaners was reported to reduce PM exposure and improve residents' health in homes, their effects in classrooms are not well understood. We examined how the use of air cleaners in classrooms and school/classroom characteristics affect the levels of indoor PM. Our environmental study included 102 classrooms from 34 elementary schools located on the mainland peninsula and an island in Korea. Indoor and outdoor PM were monitored simultaneously with portable aerosol spectrometers, and indoor gravimetric PM levels were measured with low volume, size-selective samplers during the class hours. Correlations among PM measurements were computed and final multiple regression models for indoor PM were constructed with a model building procedure. Correlation between indoor and outdoor PM_2.5 (PM < 2.5 μm in aerodynamic diameter) was higher (r = 0.78, p < 0.01) than that of PM₁₀ (PM < 10 μm) (r = 0.49, p < 0.01). School location, classroom occupant density, and ambient PM levels significantly (p-values<0.05) affected classroom PM concentrations. The adjusted PM levels in classrooms using air cleaners were significantly (p-values<0.01) lower by approximately 35% than in classrooms not using them. However, air cleaners appeared to remove PM_2.5 more effectively than PM₁₀, perhaps because coarse particles settle more rapidly than fine particles on surfaces, or their resuspension and generation rate by occupants exceeds the removal rate by air cleaners. Our study suggests that routine cleaning to remove surface dust along with the use of air cleaners might be required to effectively reduce occupants' exposure in classrooms.

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.

The relationship between indoor and outdoor levels of PM10 and its chemical composition at schools in a coastal region in Spain

PM10 levels and its chemical composition were studied inside and outdoor of seven primary schools (3 in urban environment, 3 in industrial environment, 1 in rural environment) located in the Mediterranean coast in an area with an important industrial nucleus dedicated to the treatment of raw mineral materials. The main objective of this work is a comparison between these levels obtained inside and outside schools and also asses the influence of various natural and anthropogenic emission sources on particles concentrations found inside. The indoor airborne samples were collected using RespiCon TM. In the three outdoor sampling stations was used a minivol air sampler type 3.1 LVS of Derenda. PM10 Chemical composition was obtained by ICP-MS (elements) and ion chromatography The ratio I/O (indoor/outdoor) has been calculated taking into account only the samples taken in the same conditions. In all schools the ratio I/O for PM10 was greater than unity (between 1.3 and 7.8), indicating that existed significant indoor sources of these particles. In the three schools located in the industrial environment were collected PM10 samples inside and outside in non-teaching periods. Comparing the values of I/O when the classrooms were unoccupied with respect to the average value of these same schools when the classrooms are occupied, the behaviour is different depending on the location. On the other hand, a sample in an industrial school was obtained when some infrastructure works were being carried out outside of school. This caused a significant increase in the concentration of particles in the interior (I/O = 19.9). From the levels of As, Ni, Cd, Pb, Al, B, Zn, Mg, Sb, F⁻, ClO^2-, NO^3- and SO₄^2- in PM10 inside and outside of each school, also the ratios I/O were calculated. These chemical ratios I/O were higher than unity in all cases and generally higher than those recorded in the case of PM10. Finally, Pearson correlation coefficients (r) between the elements and anions and the PM10, and between the different elements and anions were calculated for the purpose of establishing the existence of common emission sources.

Indoor Sources of Air Pollutants

People spend an average of 90% of their time in indoor environments. There is a long list of indoor sources that can contribute to increased pollutant concentrations, some of them related to human activities (e.g. people's movement, cooking, cleaning, smoking), but also to surface chemistry reactions with human skin and building and furniture surfaces. The result of all these emissions is a heterogeneous cocktail of pollutants with varying degrees of toxicity, which makes indoor air quality a complex system. Good characterization of the sources that affect indoor air pollution levels is of major importance for quantifying (and reducing) the associated health risks. This chapter reviews some of the more significant indoor sources that can be found in the most common non-occupational indoor environments.

Evaluation of PM1, PM2.5, and PM10 exposure and the resultant health risk of preschool children and their caregivers

Preschool children have a higher respiratory rate per unit body weight than adults, and their respiratory systems are not mature. Hence, children may have more health risks associated with particulate matter (PM) exposure. In this study, we assessed the exposure of preschool children and their caregivers to PM and the resulting health risks. The PM concentrations at heights of 60-80 cm (preschool children) and 150 cm (adults) were measured at ten indoor and eight outdoor sites in the Taipei metropolitan area from March 2015 to February 2017. Four PM_2.5 and seven PM₁₀ indoor measurements exceeded the indoor air quality standard of Taiwan, whereas only two PM_2.5 outdoor measurements exceeded the ambient air quality standard. The outdoor PM concentrations were related to traffic emissions, whereas the indoor PM concentrations were associated with ventilation rate and occupant density. The chronic daily PM₁, PM_2.5, and PM₁₀ intakes of preschool children were notably higher than those of adults. In addition, the hazard quotient resulting from PM_2.5 exposure indicated a significant health risk for preschool children (93.74% greater than 1). Consequently, reducing the exposure of preschool children to PM_2.5 is an emerging issue in the Taipei metropolitan area.

Assessing the seasonality of occupancy number-associated CO2 level in a Taiwan hospital

This study enabled the assessment of indoor CO₂ levels and evaluated the relationship between occupancy numbers with CO₂ levels in a Taiwan hospital. The measurements were conducted over four seasons for five working days (Monday to Friday), with sampling conducted simultaneously from 09:00 am to 5:00 pm and across six locations (for spatial variability): hall (H), registration and cashier (RC), waiting area (WA), occupational therapy room (OT), physical therapy room (PT), and outdoors (O). Based on the analysis, three of the five indoor sampling sites showed significant differences in seasonal CO₂ concentrations (p < 0.0001). Based on our result, the physical therapy room had the highest level of CO₂ concentration that exceeded the IAQ standard in Taiwan Environmental Protection Agency (EPA) in all seasons, in that the number of occupants contributing to nearly 40% of the variation in CO₂ measured. Our results also showed that the indoor/outdoor (I/O) ratios of CO₂ concentration for all locations and seasons exceeded 1 in ~ 100% of those locations. The median I/O ratio at sites WA and OT was 2.37 and 2.08 during four seasons, respectively. The highest median I/O ratio was found at site PT, with a calculated range of 2.69 in spring to 3.90 in fall. The highest correlation of occupancy number and CO₂ concentration also occurred in PT which correlation coefficients were estimated at 0.47, 0.65, 0.63, and 0.40 in spring, summer, fall, and winter. The findings of the present study can be used to understand occupancy number and its effect on CO₂ levels in a hospital environment, as well as the effect of time of day (Monday to Friday) on the number of patients admitted.

Indoor air quality in inner-city schools and its associations with building characteristics and environmental factors

Indoor concentrations of fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and carbon monoxide (CO) were measured across 16 urban public schools in three different seasons. Exceedance of the WHO guidelines for indoor air was observed, mainly for the hourly average NO₂ concentrations. Seasonal variability was statistically significant for indoor NO₂ and CO concentrations, with higher exposures in fall and winter. An extensive list of potential factors at the outdoor environment, school, and room level that may explain the variability in indoor exposure was examined. Factors with significant contributions to indoor exposure were mostly related to the outdoor pollution sources. This is evidenced by the strong associations between indoor concentration of CO and NO₂ and factors including outdoor PM_2.5 and NO₂ concentrations, including length of the nearby roads and the number of nearby industrial facilities. Additionally, we found that poor conditions of the buildings (a prevalent phenomenon in the studied urban area), including physical defects and lack of proper ventilation, contributed to poor air quality in schools. The results suggest that improving building conditions and facilities as well as a consideration of the school surroundings may improve indoor air quality in schools.

Indoor air quality in schools of a highly polluted south Mediterranean area

This study aimed at surveying lower secondary schools in southern Italy, in a highly polluted area. A community close to an industrial area and three villages in rural areas was investigated. Indoor temperature, relative humidity (RH), gaseous pollutants (CO₂ and NO₂ ), selected biological pollutants in indoor dust, and the indoor/outdoor mass concentration and elemental composition of PM_2.5 were ascertained. Temperature and RH were within, or close to, the comfort range, while CO₂ frequently exceeded the threshold of 1000 ppm, indicating inadequate air exchange rate. In all the classrooms, median NO₂ levels were above the WHO threshold value. Dermatophagoides p. allergen concentration was below the sensitizing threshold, while high endotoxin levels were detected in the classrooms, suggesting schools may produce significant risks of endotoxin exposure. Concentration and solubility of PM_2.5 elements were used to identify the sources of indoor particles. Indoor concentration of most elements was higher than outdoors. Resuspension was responsible for the indoor increase in soil components. For elements from industrial emission (Cd, Co, Ni, Pb, Sb, Tl, V), the indoor concentration depended on penetration from the outside. For these elements, differences in rural vs industrial concentrations were found, suggesting industrial sources may influence indoor air quality nearby schools.

Smog, Cognition and Real-World Decision-Making

Cognitive functioning is critical as in our daily life a host of real-world complex decisions in high-stakes markets have to be made. The decision-making process can be vulnerable to environmental stressors. Summarizing the growing economic and epidemiologic evidence linking air pollution, cognition performance and real-world decision-making, we first illustrate key physiological and psychological pathways between air pollution and cognition. We then document the main patterns of air pollution affecting cognitive test performance by type of cognitive tests, gender, window of exposure, age profile, and educational attainment. We further extend to a review of real-world decision-making that has been found to be affected by air pollution and the resulting cognitive impairments. Finally, rich implications on environmental health policies are drawn based on existing evaluations of social costs of air pollution.

Indoor nanoscale particulate matter-induced coagulation abnormality based on a human 3D microvascular model on a microfluidic chip

BACKGROUND

A growing body of evidence shows that indoor concentrations of airborne particles are often higher than is typically encountered outdoors. Since exposure to indoor PM2.5 is thought to be associated with cardiovascular disease, the health impacts of indoor air pollution need to be explored. Based on animal models, ambient particulate matter has been proved to promote coagulation which is very likely involved in the pathogenic development of cardiovascular disease. However, animal models are insufficient to predict what will happen with any certainty in humans. For this reason, the precise pathogenic mechanisms behind the development of cardiovascular disease in humans have not yet been determined.

RESULTS

We generated a 3D functional human microvascular network in a microfluidic device. This model enables human vascular endothelial cells to form tissue-like microvessels that behave very similarly to human blood vessels. The perfusable microvasculature allows the delivery of particles introduced into these generated human-like microvessels to follow the fluid flow. This exposure path effectively simulates the dynamic movement of airborne nanoscale particles (ANPs) within human vessels. In this study, we first identified the existence of ANPs in indoor air pollution. We then showed that ANPs could activate endothelial cells via ROS induced inflammation, and further resulted in abnormal expression of the coagulation factors (TF, TM and t-PA) involved in coagulation cascades. In addition, we found that a protein could cover ANPs, and this biointeraction could interfere with heparan sulfate (HS). Human organotypic 3D microvessel models provide a bridge for how research outcomes can translate to humans.

CONCLUSIONS

The 3D human microvessel model was used to determine the physiological responses of human vessels to ANP stimulation. Based on the obtained data, we concluded that ANPs not only disrupts normal coagulation functions, but also act directly on anticoagulant factors in human vessels. These experimental observations provide a potential biological explanation for the epidemiologically established link between ANPs and coagulation abnormality. This organ-on-chip model may provide a bridge from in vitro results to human responses.

The influence of relative humidity and ground material on indoor walking-induced particle resuspension

The resuspension of indoor particulate matters caused by people indoor walking could affect indoor air quality and human health. Therefore, it is particularly important to study the resuspension rules of the particulate matters in different indoor environments. The influence of the ground material and the relative humidity on resuspension of the particulate matters were investigated under three kinds of ground materials and three different relative humidity. Results showed that different relative humidity and different ground materials had different effects on the mass concentration of the particulate matters. In addition, different particle sizes had diverse influence on the mass concentration. Compared with low-level loop pile carpet and shaggy carpet, hardwood floor was more conductive to human health which was less likely to cause the resuspension of the particulate matters. At the same time, relative humidity had a great influence on the resuspension of the particulate matters. With the increase of relative humidity, the resuspension rate of fine particulate matters decreased.

Design and application of a web-based real-time personal PM2.5 exposure monitoring system

Growing demand from public health research for conduct large-scale epidemiological studies to explore health effect of PM_2.5 was well-documented. To address this need, we design a web-based real-time personal PM_2.5 exposure monitoring system (RPPM2.5 system) which can help researcher to get big data of personal PM_2.5 exposure with low-cost, low labor requirement, and low operating technical requirements. RPPM2.5 system can provide relative accurate real-time personal exposure data for individuals, researches, and decision maker. And this system has been used in a survey of PM_2.5 personal exposure level conducted in 5 cities of China and has provided mass of valuable data for epidemiological research.

Lung Function of Grain Millers Exposed to Grain Dust and Diesel Exhaust in Two Food Markets in Ibadan Metropolis, Nigeria

Background

Despite growing concern over occupational exposure to particulate matter (PM) such as grain dust and diesel exhaust, information about the exposure level and health implications among workers in small-scale milling enterprises in developing countries like Nigeria has not been adequately documented. The purpose of this study was to assess the level of exposure to grain dust and diesel exhaust and effect on lung function among grain millers in food markets in Ibadan metropolis, Nigeria.

Methods

The study adopted descriptive cross-sectional design with a comparative approach. Sixteen grain milling shops each were randomly selected from two major food markets in Ibadan metropolis for indoor PM₁₀ and PM_2.5 monitoring. Seventy-two respondents each were proportionately selected from grain millers and shop owners for forced expiratory volume in one second and peak expiratory flow rate tests.

Results

The PM_2.5 concentrations for both market locations ranged between 1,269.3 and 651.7 μg/m³, while PM₁₀ concentrations were between 1,048.2 and 818.1 μg/m³. The recorded concentrations exceeded the World Health Organization guideline limit of 50 μg/m³ and 25 μg/m³ for PM_2.5 and PM₁₀, respectively. As compared with control group (2.1 L), significantly lower forced expiratory volume in one second value (1.61 L) was observed among the exposed group (p < 0.05). Likewise, significantly lower peak expiratory flow rate value (186.7 L/min) was recorded among the exposed group than the control group (269.51 L/min) (p < 0.05).

Conclusion

Exposure to grain dust and diesel exhaust accentuated respiratory disorders with declines in lung functions amongst grain millers. Improved milling practices and engaging cleaner milling facilities should be adopted to minimize exposure and related hazards.