Investigation of the PM2.5, NO2 and O3 I/O ratios for office and school microenvironments

Seasonal analysis of indoor and outdoor ratios of PM2.5 and PM10 in Bangkok and Chiang Mai: A comparative study of haze and non-haze episodes

Particulate matter (PM) air pollution has been identified as one cause of human health impact with the estimated global 8.8 million attributable deaths. Thailand experiences haze episode every year, which can lead to high ambient concentrations of particulate matter in ambient air. This study aims to investigate the relationship of indoor and outdoor air quality in both haze and non-haze period in two cities in Thailand: namely Bangkok and Chiang Mai. We conducted the air quality sampling in various styles of house, with 17 houses in both urban and rural areas, between April to October 2019. The results indicated that the concentration of PM2.5 in indoor air in Bangkok were 19.85 and 11.40 μg/m3 for haze and non-haze period, respectively, whereas the PM10 concentrations were 32.124 and 17.49 μg/m3 for haze and non-haze period, respectively. The corresponding average of outdoor air concentrations were 26.26 and 16.68 μg/m3 for haze and non-haze, respectively. While the PM10 concentrations were 46.36 and 23.86 μg/m3 for haze and non-haze period, respectively. In Chiang Mai, it was observed that the mean concentration of PM2.5 in indoor was 106.80 μg/m3 and 5.52 μg/m3 for haze and non-haze periods, respectively. Regarding PM10, it was observed that the mean concentration in indoor was 118.54 μg/m3 and 9.74 μg/m3 for haze and non-haze periods, respectively. Indoor/Outdoor (I/O) ratios of PM2.5 varied in Bangkok average was 0.76 for haze and 0.68 for non-haze period. The I/O ratio in Chiang Mai was 0.91 and 1.16 for haze and non-haze episode, respectively. Indoor/Outdoor (I/O) ratios of PM10 varied in Bangkok average was 0.70 for haze and 0.73 for non-haze period. The I/O ratio in Chiang Mai was 0.92 and 0.96 for haze and non-haze episode, respectively Our findings indicated the influences of outdoor air quality on indoor air quality during both haze and non-haze episode. The intrusion of outdoor air in Chiang Mai to the houses caused a higher I/O ratio than Bangkok due to the characteristics of house and culture. The indoor air quality in terms of particulate matter were dominated by outdoor air quality. Thus, people should close doors/windows during the haze as well as non-haze episode to avoiding the pollutant accumulation.

Towards healthy sleep environments: Ambient, indoor, and personal exposure to PM2.5 and its implications in children's sleep health

The growing impact of climate change and escalating wildfire seasons has led to heightened ambient air pollution, potentially affecting children's sleep health. However, current epidemiological research often relies on outdoor weather data to model the environmental impacts on sleep health, potentially mischaracterizing the actual bedroom environment. To address these challenges, we conducted experiments to investigate the relationships among ambient, indoor, and personal exposure to PM2.5 concentrations and obstructive sleep apnea (OSA) in children. We employed computational fluid dynamics (CFD) simulations to assess how personal exposures are influenced by factors such as air distribution design, supply air temperature (Tsa), body shape, and sleep position. Our statistical analysis revealed notable associations between OSA severity as measured by obstructive apnea-hypopnea index (OAHI) and indoor PM2.5 concentrations (β: 11.52; 95% CI: 5.07 to 17.96; p < 0.01) and personal PM2.5 exposures (β: 18.92; 95% CI: 9.80 to 28.04; p < 0.001), with personal exposure demonstrating a stronger relationship. Our findings highlighted the critical role of Tsa and body shape in exacerbating personal exposure, as they could modify the bedding microenvironment around children's breathing zone during sleep. We assessed the effect of air filtration interventions on mitigating personal PM2.5 exposure and modulating OSA severity in children. Higher air filter efficiencies such as MERV14 or above can modulate severe OSA for more than 80% of the year. However, during wildfire episodes, because air filtration interventions alone may be insufficient, comprehensive strategies, including the potential use of air cleaners and personal protective equipment (PPE), are necessary to ensure children's health. Our research demonstrated that quantifying personal exposure is a more informative predictor than solely relying on ambient or indoor measures for estimating OSA in children.

Refining Children's exposure assessment to NO2, SO2, and O3: Incorporating indoor-to-outdoor concentration ratios and individual daily routine

Exposure to air pollutants like sulfur dioxide (SO2), nitrogen oxides (NOx), and ozone (O3) is associated with adverse health effects, particularly with exacerbations of asthma symptoms and new asthma cases in both children and adults. While fixed-site monitoring (FSM) stations are commonly used in air pollutant exposure studies, they may not fully capture personal exposures due to limitations such as inadequate consideration of daily routines and indoor/outdoor concentration variations. In this study, to enhance the accuracy of personal exposure calculated by using FSM data, individual's daily activity routine, encompassing both indoor and outdoor environments, were incorporated by using indoor-to-outdoor concentration ratios. Three methodologies were compared to assess the accuracy of exposure calculations: (i) direct exposure determination employing passive samplers (PS), (ii) personal exposure calculated using FSM data alone, and (iii) personal exposure calculated using FSM data refined by integrating local average individual daily activity routines and indoor-to-outdoor ratios. The results demonstrate that the refined method (iii) yields substantial improvements in estimated exposure levels, reducing the average error from 1.4% to 0.4% for NO2, from 72.1% to 12.7% for SO2, and from 323.4% to 24.9% for O3.

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

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

Systematically quantifying the dynamic characteristics of PM2.5 in multiple indoor environments in a plateau city: Implication for internal contribution

People generally spend most of their time indoors, making a comprehensive evaluation of air pollution characteristics in various indoor microenvironments of great significance for accurate exposure estimation. In this study, field measurements were conducted in Kunming City, Southwest China, using real-time PM2.5 sensors to characterize indoor PM2.5 in ten different microenvironments including three restaurants, four public places, and three household settings. Results showed that the daily average PM2.5 concentrations in restaurants, public spaces, and households were 78.4 ± 24.3, 20.1 ± 6.6, and 18.0 ± 4.3 µg/m3, respectively. The highest levels of indoor PM2.5 in restaurants were owing to strong internal emissions from cooking activities. Dynamic changes showed that indoor PM2.5 levels increased during business time in restaurants and public places, and cooking time in residential kitchens. Compared with public places, restaurants generally exhibit more rapid increases in indoor PM2.5 due to cooking activities, which can elevate indoor PM2.5 to high levels (5.1 times higher than the baseline) in a short time. Furthermore, indoor PM2.5 in restaurants were dominated by internal emissions, while outdoor penetration contributed mostly to indoor PM2.5 in public places and household settings. Results from this study revealed large variations in indoor PM2.5 in different microenvironments, and suggested site-specific measures for indoor PM2.5 pollution alleviation.

The underpinning factors affecting the classroom air quality, thermal comfort and ventilation in 30 classrooms of primary schools in London

The health and academic performance of children are significantly impacted by air quality in classrooms. However, there is a lack of understanding of the relationship between classroom air pollutants and contextual factors such as physical characteristics of the classroom, ventilation and occupancy. We monitored concentrations of particulate matter (PM), CO2 and thermal comfort (relative humidity and temperature) across five schools in London. Results were compared between occupied and unoccupied hours to assess the impact of occupants and their activities, different floor coverings and the locations of the classrooms. In-classroom CO2 concentrations varied between 500 and 1500 ppm during occupancy; average CO2 (955 ± 365 ppm) during occupancy was ∼150% higher than non-occupancy. Average PM10 (23 ± 15 μgm-3), PM2.5 (10 ± 4 μgm-3) and PM1 (6 ± 3 μg m-3) during the occupancy were 230, 125 and 120% higher than non-occupancy. Average RH (29 ± 6%) was below the 40-60% comfort range in all classrooms. Average temperature (24 ± 2 °C) was >23 °C in 60% of classrooms. Reduction in PM10 concentration (50%) by dual ventilation (mechanical + natural) was higher than for PM2.5 (40%) and PM1 (33%) compared with natural ventilation (door + window). PM10 was higher in classrooms with wooden (33 ± 19 μg m-3) and vinyl (25 ± 20 μgm-3) floors compared with carpet (17 ± 12 μgm-3). Air change rate (ACH) and CO2 did not vary appreciably between the different floor levels and types. PM2.5/PM10 was influenced by different occupancy periods; highest value (∼0.87) was during non-occupancy compared with occupancy (∼0.56). Classrooms located on the ground floor had PM2.5/PM10 > 0.5, indicating an outdoor PM2.5 ingress compared with those located on the first and third floors (<0.5). The large-volume (>300 m3) classroom showed ∼33% lower ACH compared with small-volume (100-200 m3). These findings provide guidance for taking appropriate measures to improve classroom air quality.

Crosscutting of the pollutants and building ventilation systems: a literature review

Considering the time spent in enclosed environments, it is essential to study the relationship between pollutants and building ventilation systems to find whether the types and levels of pollutants and greenhouse gasses, which are expected to be exhaled through ventilation systems into the atmosphere, have been adequately evaluated. We propose the hypothesis that the exhaled air from residential buildings contains pollutants that may become another source of contamination affecting urban air quality and potentially contributing to climate drivers. Thus, the main goal of this article is to present a cross-review of the identification of pollutants expected to be exhaled through ventilation systems in residential buildings. This approach has created the concept of "exhalation of buildings" a new concept enclosed within the research project in which this article is included. We analyze the studies related to the most significant pollutants found in buildings and the studies about the relation of buildings' ventilation systems with such pollutants. Our results show that, on the one hand, the increase in the use of mechanical ventilation systems in residential buildings has been demonstrated to enhance the ventilation rate and generally improve the indoor air quality conditions. But no knowledge could be extracted about the corresponding environmental cost of this improvement, as no systematic data were found about the total mass of contaminants exhaled by those ventilation systems. At the same time, no projects were found that showed a quantitative study on exhalation from buildings, contrary to the existence of studies on pollutants in indoor air.

Pesticides in the Indoor Environment of Residential Houses: A Case Study in Strasbourg, France

Indoor environmental exposure to pesticides has become one of the major concerns that might adversely affect human health and development. People spend most of their lifetime in enclosed indoor environments where they might inhale harmful toxic chemicals, such as pesticides, dispersed either in particulate or in a gas phase. In this study, an assessment of pesticide contamination in indoor environments was conducted. The study covered nine houses during one year, starting from February 2016 and ending in February 2017, in which both air and dust samples were assessed for their potential contamination with 50 pesticides. The results showed that all the assessed houses were contaminated by several pesticides, especially with the allethrin pesticide (detection frequency (DF) = 100%). The highest pesticide contamination was detected in the spring/summer season when it reached an average of around 185 ng g^-1 and 186.4 ng sampler^-1 in the collected dust and air samples, respectively. The potential contamination of pyrethroid insecticides within all the targeted samples revealed by this study stresses the importance of minimizing the use of such indoor treatments as part of the efficient prevention and control of human exposure to pesticides.

Barking up the Right Tree: Using Tree Bark to Track Airborne Particles in School Environment and Link Science to Society

Children's exposure to air pollution affects both their health and learning skills. Fine and ultrafine particulate matter (PM2.5, PM1), notably issued from traffic sources in urban centers, belong to the most potential harmful health hazards. However their monitoring and the society's awareness on their dangers need to be consolidated. In this study, raising teacher and pupil involvement for air quality improvement in their schools environment is reached through developing a passive monitoring technique (bio-sensors made of tree bark). The experiment was implemented in two urban elementary schools situated close to a main traffic road of the city of Toulouse (South of France). Magnetic properties, carbonaceous fraction measurements, and scanning electronic microscopy (SEM-EDX) investigations were realized both on passive bio-sensors and filters issued from active sampling. We find that traffic is the main PM1 source for both outdoors and indoors at schools. Higher levels of outdoor PM in the school's environments compared to urban background are reached especially in the cold period. The schools proximity to a main traffic source and lack of ventilation are the main causes for observed PM1 accumulation in classrooms. The co-working experiment with educational teams and pupils shows that the use of bio-sensors is a driver for children empowerment to air pollution and therefore represents a potential key tool for the teachers though limiting eco-anxiety. As PM accumulation is observed in many scholar environments across Europe, the proposed methodology is a step toward a better assessment of PM impact on pupil's health and learning skills.

Indoor air quality in the primary school of China-results from CIEHS 2018 study

Indoor air quality ((IAQ) in classrooms was associated with the daily exposure of school-age children who are particularly vulnerable to air pollutants exposure, while few data exist to evaluate classroom indoor air quality nationwide in China. The subsample of the CIEHS 2018 study was performed in 66 classrooms of 22 primary schools nationwide in China. Temperature, relative humidity, PM_2.5, PM₁₀, CO₂, CO, formaldehyde concentrations, bacteria and fungi were detected in all classrooms by using the instruments that meet the specified accuracy. The ratios of indoor to outdoor (I/O) of PM_2.5 were calculated in each classroom to identify whether the indoor environment the pollutants comes from outdoors. The indoor PM_2.5, PM₁₀, CO, HCHO, bacteria and fungi GM concentration are 47.40 μg/m³, 72.91 μg/m³, 0.37 mg/m³, 0.02 mg/m³, 347.51 CFU/m³ and 362.76 CFU/m³, respectively. We observed that there were 66.5%, 52.6%, 22.4%, 1.8%, and 9.6% of the classrooms that exceeded the guideline values of PM_2.5, PM₁₀, CO₂, HCHO, and bacteria, respectively. It should be attention that all of the classroom's PM_2.5 concentrations in Shijiazhuang and Nanning, PM₁₀ concentrations in Nanning, CO₂ concentration in Lanzhou were exceeded the suggested values. Bacteria contamination in Shijiazhuang's classrooms is also serious. All classroom CO concentrations meet the requirement. The results indicated that classroom indoor PM_2.5 was significantly positively correlated with indoor PM₁₀ and CO₂, while was negative correlated with temperature, CO, and fungi. Our results suggest that indoor air pollution in classrooms was a severe problem in Chinese primary schools. It is necessary to strengthen ventilation in the classroom to improve indoor air quality. What's more, a healthy learning environment should be created for primary school students.

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.

Estimates of emission strengths of 43 VOCs in wintertime residential indoor environments, Beijing

There are many sources of volatile organic compounds (VOCs) in indoor environments, leading to much higher total indoor VOC concentrations than outdoor counterparts. Given the potential health hazards associated with VOC exposure, it is necessary to estimate the indoor VOC emission strengths. In this study, the indoor and outdoor concentrations of 43 VOCs were concurrently measured in 8 urban residences, Beijing. The indoor/outdoor concentration ratio was used to screen out 36 species having significant indoor sources. A one-compartment steady-state model was developed to estimate the indoor emission strengths of these VOCs, in which ventilation and reaction with ozone were included as sink routes. The order of VOCs in terms of indoor emission strength was d-limonene (a median value of 1.05 g/h), α-pinene (82.50 mg/h), styrene (24.12 mg/h), ß-pinene (9.70 mg/h), formaldehyde (1.97 mg/h), n-dodecane (1.82 mg/h), n-pentadecane (1.66 mg/h), n-hexadecane (1.62 mg/h), n-undecane (1.20 mg/h), acetaldehyde (1.05 mg/h) and 1, 4-dichlorobenzene (0.80 mg/h). The sum of estimates of those VOCs accounted for >95% of total emission strength. Specific indoor sources of those VOCs in the tested homes were identified. Air exchange rate, indoor temperature and air humidity were found to pose significant impacts to the indoor emission strengths of VOCs.

The Influence of Outdoor Particulate Matter PM2.5 on Indoor Air Quality: The Implementation of a New Assessment Method

Epidemiological research has shown that there is a positive correlation between the incidence of disease and mortality in humans and the mass concentration of particulate matter. An average 1 g of suspended dust emitted in a room results in the same exposure as 1 kg emitted to the outside air. In this study, the authors described the state of knowledge on dust pollution inside and outside buildings (I/O ratios), and methods of testing the PM infiltration process parameters. According to the law of indoor–outdoor particle mass balance and the physical basis of aerosol penetration theory, a relatively simple but new method for estimating the penetration factor P was tested. On the basis of the curve of dynamic changes of internal dust concentration in the process of particle concentration decay and next of the followed curve of dynamic rebound of particle concentration, authors measured penetration factor of ambient PM2.5 through building envelope. Authors modification of the method is to be used for determining the value of the particle deposition rate k not from the course of the characteristics in the transient state (the so-called particle concentration decay curves) but from the concentration rebound course, stimulated by natural particle infiltration process. Recognition measurements of the mass concentration of suspended PM2.5 and PM10 particles inside the rooms were carried out. In this study, the choice of the prediction particle penetration factor P calculation method was supported by the exemplary calculation of the p value for a room polluted by PM2.5. The preliminary results of the penetration factors determined by this method P = 0.61 are consistent with the P factor values from the literature obtained so far for this dimensional group of dusts.

Analysis of spatial factors, time-activity and infiltration on outdoor generated PM2.5 exposures of school children in five European cities

Atmospheric particles are a major environmental health risk. Assessments of air pollution related health burden are often based on outdoor concentrations estimated at residential locations, ignoring spatial mobility, time-activity patterns, and indoor exposures. The aim of this work is to quantify impacts of these factors on outdoor-originated fine particle exposures of school children. We apply nested WRF-CAMx modelling of PM_2.5 concentrations, gridded population, and school location data. Infiltration and enrichment factors were collected and applied to Athens, Kuopio, Lisbon, Porto, and Treviso. Exposures of school children were calculated for residential and school outdoor and indoor, other indoor, and traffic microenvironments. Combined with time-activity patterns six exposure models were created. Model complexity was increased incrementally starting from residential and school outdoor exposures. Even though levels in traffic and outdoors were considerably higher, 80-84% of the exposure to outdoor particles occurred in indoor environments. The simplest and also commonly used approach of using residential outdoor concentrations as population exposure descriptor (model 1), led on average to 26% higher estimates (15.7 μg/m³) compared with the most complex model (# 6) including home and school outdoor and indoor, other indoor and traffic microenvironments (12.5 μg/m³). These results emphasize the importance of including spatial mobility, time-activity and infiltration to reduce bias in exposure estimates.

Indoor Air Quality and Human Health Risk Assessment in the Open-Air Classroom

Indoor air quality is associated with academic performance and harmful health effects on students and teachers who participate in the classroom. Outdoor sources always contribute to classroom air quality. This study aims to estimate the amounts of indoor and outdoor pollutants and the influence of outdoor sources on open-air classrooms in a school located in the city. A health risk assessment was applied to assess the non-carcinogenic risk to students and teachers from exposure to the pollutants in the classroom. The concentrations of indoor NO2 ranged between 46.40 and 77.83 µg/m3, which is about 0.8 times that of outdoor NO2. A strong correlation and a high indoor/outdoor (I/O) ratio (>0.5) without a source, indicated that indoor NO2 is significantly influenced by outdoor sources. The range of indoor PM2.5 concentrations was 1.66 to 31.52 µg/m3 which was influenced by meteorological conditions. The indoor PM2.5 concentrations were affected by both indoor and outdoor sources. Although the level of indoor air pollutants met the official standard, the young children were exposed to indoor air pollutants which were above the recommended limits to human health with regard to the hazard index (HI) of 1.12. Instant measures such as regularly cleaning the classrooms, zoning the students, and installation of solid and vegetation barriers are recommended to reduce the daily dose of pollutants affecting students in open-air classrooms.

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.