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

Health risk assessment from inhalation exposure to indoor formaldehyde: A systematic review and meta-analysis

This systematic review and meta-analysis investigated studies on formaldehyde (FA) inhalation exposure in indoor environments and related carcinogenic (CR) and non-carcinogenic (HQ) risk. Studies were obtained from Scopus, PubMed, Web of Science, Medline, and Embase databases without time limitation until November 21, 2023. Studies not meeting the criteria of Population, Exposure, Comparator, and Outcomes (PECO) were excluded. The 45 articles included belonged to the 5 types of sites: dwelling environments, educational centers, kindergartens, vehicle cabins, and other indoor environments. A meta-analysis determined the average effect size (ES) between indoor FA concentrations, CR, and HQ values in each type of indoor environment. FA concentrations ranged from 0.01 to 1620 μg/m3. The highest FA concentrations were stated in water pipe cafés and the lowest in residential environments. In more than 90% of the studies uncertain (1.00 ×10-6 1.00 ×10-4) due to FA inhalation exposure was reported and non-carcinogenic risk was stated acceptable. The meta-analysis revealed the highest CR values due to inhalation of indoor FA in high-income countries. As 90% of the time is spent indoors, it is crucial to adopt effective strategies to reduce FA concentrations, especially in kindergartens and schools, with regular monitoring of indoor air quality.

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods

OBJECTIVE

The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors.

METHOD

Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation.

RESULTS

Indoor particles were measured in a range of 0.8-370.7 μg/m3 and 1.9-465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m3 and 0.0-493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively.

CONCLUSIONS

Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Human exposure to formaldehyde and health risk assessment: a 46-year systematic literature review

After confirming that formaldehyde (FA) is carcinogenic, many studies were conducted in different countries to investigate this finding. Therefore, according to the dispersion of related studies, a bibliometric review of the current literature was performed with the aim of better understanding the exposure to FA and the resulting health risk, for the first time, using the Scopus database and the two open-source software packages, Bibliometrix R package. After screening the documents in Excel, the data was analyzed based on three aspects including performance analysis, conceptual structure, and intellectual structure, and the results were presented in tables and diagrams. A total of 468 documents were analyzed over period 1977-2023, in which 1956 authors from 56 countries participated. The number of scientific publications has grown significantly from 1977 (n = 1) to 2022 (n = 19). Zhang Y., from the Yale School of Public Health (USA), was identified as the most impactful author in this field. The Science of the Total Environment journal was identified as the main source of articles related to exposure to formaldehyde by publishing 25 studies. The United States and China were the most active countries with the most international collaboration. The main topics investigated during these 46 years included "formaldehyde" and "health risk assessment", which have taken new directions in recent years with the emergence of the keyword "asthma". The present study provides a comprehensive view of the growth and evolution of studies related to formaldehyde and the resulting health risks, which can provide a better understanding of existing research gaps and new and emerging issues.

The effects of urban green space and road proximity to indoor traffic-related PM2.5, NO2, and BC exposure in inner-city schools

BACKGROUND

Since there are known adverse health impacts of traffic-related air pollution, while at the same time there are potential health benefits from greenness, it is important to examine more closely the impacts of these factors on indoor air quality in urban schools.

OBJECTIVE

This study investigates the association of road proximity and urban greenness to indoor traffic-related fine particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in inner-city schools.

METHODS

PM2.5, NO2, and BC were measured indoors at 74 schools and outdoors at a central urban over a 10-year period. Seasonal urban greenness was estimated using the Normalized Difference Vegetation Index (NDVI) with 270 and 1230 m buffers. The associations between indoor traffic-related air pollution and road proximity and greenness were investigated with mixed-effects models.

RESULTS

The analysis showed linear decays of indoor traffic-related PM2.5, NO2, and BC by 60%, 35%, and 22%, respectively for schools located at a greater distance from major roads. The results further showed that surrounding school greenness at 270 m buffer was significantly associated (p < 0.05) with lower indoor traffic-related PM2.5: -0.068 (95% CI: -0.124, -0.013), NO2: -0.139 (95% CI: -0.185, -0.092), and BC: -0.060 (95% CI: -0.115, -0.005). These associations were stronger for surrounding greenness at a greater distance from the schools (buffer 1230 m) PM2.5: -0.101 (95% CI: -0.156, -0.046) NO2: -0.122 (95% CI: -0.169, -0.075) BC: -0.080 (95% CI: -0.136, -0.026). These inverse associations were stronger after fully adjusting for regional pollution and meteorological conditions.

IMPACT STATEMENT

More than 90% of children under the age of 15 worldwide are exposed to elevated air pollution levels exceeding the WHO's guidelines. The study investigates the impact that urban infrastructure and greenness, in particular green areas and road proximity, have on indoor exposures to traffic-related PM2.5, NO2, and BC in inner-city schools. By examining a 10-year period the study provides insights for air quality management, into how road proximity and greenness at different buffers from the school locations can affect indoor exposure.

In-vehicle exposure to NO2 and PM2.5: A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter.

Ambient NO2 Air Pollution and Public Schools in the United States: Relationships with Urbanicity, Race-Ethnicity, and Income

Schools may have important impacts on children's exposure to ambient air pollution, yet ambient air quality at schools is not consistently tracked. We characterize ambient air quality at home and school locations in the United States using satellite-based empirical model (i.e., land use regression) estimates of outdoor annual nitrogen dioxide (NO2). We report disparities by race-ethnicity and impoverishment status, and investigate differences by level of urbanicity. Average NO2 levels at home and school for racial-ethnic minoritized students are 18-22% higher than average (and 37-39% higher than for non-Hispanic, white students). Minoritized students are less likely than their white peers to live (0.55 times) and attend school (0.58 times) in areas below the World Health Organization's NO2 guideline. Predominantly minoritized schools (i.e., >50% minoritized students) are less likely than predominantly white schools (0.43 times) to be in locations below the guideline. Income and race-ethnicity impacts are intertwined, yet in large cities, racial disparities persist after controlling for income.

Breathing zone pollutant levels are associated with asthma exacerbations in high-risk children

Background

Indoor and outdoor air pollution levels are associated with poor asthma outcomes in children. However, few studies have evaluated whether breathing zone pollutant levels associate with asthma outcomes.

Objective

Determine breathing zone exposure levels of NO 2 , O 3 , total PM 10 and PM 10 constituents among children with exacerbation-prone asthma, and examine correspondence with in-home and community measurements and associations with outcomes.

Methods

We assessed children's personal breathing zone exposures using wearable monitors. Personal exposures were compared to in-home and community measurements and tested for association with lung function, asthma control, and asthma exacerbations.

Results

81 children completed 219 monitoring sessions. Correlations between personal and community levels of PM 10 , NO 2 , and O 3 were poor, whereas personal PM 10 and NO 2 levels correlated with in-home measurements. However, in-home monitoring underdetected brown carbon (Personal:79%, Home:36.8%) and ETS (Personal:83.7%, Home:4.1%) personal exposures, and detected black carbon in participants without these personal exposures (Personal: 26.5%, Home: 96%). Personal exposures were not associated with lung function or asthma control. Children experiencing an asthma exacerbation within 60 days of personal exposure monitoring had 1.98, 2.21 and 2.04 times higher brown carbon (p<0.001), ETS (p=0.007), and endotoxin (p=0.012), respectively. These outcomes were not associated with community or in-home exposure levels.

Conclusions

Monitoring pollutant levels in the breathing zone is essential to understand how exposures influence asthma outcomes, as agreement between personal and in-home monitors is limited. Inhaled exposure to PM 10 constituents modifies asthma exacerbation risk, suggesting efforts to limit these exposures among high-risk children may decrease their asthma burden.

CLINICAL IMPLICATIONS

In-home and community monitoring of environmental pollutants may underestimate personal exposures. Levels of inhaled exposure to PM 10 constituents appear to strongly influence asthma exacerbation risk. Therefore, efforts should be made to mitigate these exposures.

CAPSULE SUMMARY

Leveraging wearable, breathing-zone monitors, we show exposures to inhaled pollutants are poorly proxied by in-home and community monitors, among children with exacerbation-prone asthma. Inhaled exposure to multiple PM 10 constituents is associated with asthma exacerbation risk.

Construction and evaluation of hourly average indoor PM2.5 concentration prediction models based on multiple types of places

Background

People usually spend most of their time indoors, so indoor fine particulate matter (PM2.5) concentrations are crucial for refining individual PM2.5 exposure evaluation. The development of indoor PM2.5 concentration prediction models is essential for the health risk assessment of PM2.5 in epidemiological studies involving large populations.

Methods

In this study, based on the monitoring data of multiple types of places, the classical multiple linear regression (MLR) method and random forest regression (RFR) algorithm of machine learning were used to develop hourly average indoor PM2.5 concentration prediction models. Indoor PM2.5 concentration data, which included 11,712 records from five types of places, were obtained by on-site monitoring. Moreover, the potential predictor variable data were derived from outdoor monitoring stations and meteorological databases. A ten-fold cross-validation was conducted to examine the performance of all proposed models.

Results

The final predictor variables incorporated in the MLR model were outdoor PM2.5 concentration, type of place, season, wind direction, surface wind speed, hour, precipitation, air pressure, and relative humidity. The ten-fold cross-validation results indicated that both models constructed had good predictive performance, with the determination coefficients (R2) of RFR and MLR were 72.20 and 60.35%, respectively. Generally, the RFR model had better predictive performance than the MLR model (RFR model developed using the same predictor variables as the MLR model, R2 = 71.86%). In terms of predictors, the importance results of predictor variables for both types of models suggested that outdoor PM2.5 concentration, type of place, season, hour, wind direction, and surface wind speed were the most important predictor variables.

Conclusion

In this research, hourly average indoor PM2.5 concentration prediction models based on multiple types of places were developed for the first time. Both the MLR and RFR models based on easily accessible indicators displayed promising predictive performance, in which the machine learning domain RFR model outperformed the classical MLR model, and this result suggests the potential application of RFR algorithms for indoor air pollutant concentration prediction.

Air pollution and childhood asthma

PURPOSE OF REVIEW

Asthma is the most common chronic disease of childhood. Environmental exposures, such as allergens and pollutants, are ubiquitous factors associated with asthma development and asthma morbidity. In this review, we highlight the most recent studies relevant to childhood asthma risk, onset, and exacerbation related to air pollution exposure.

RECENT FINDINGS

In this article, we review current research that has been published between 2021 and 2022, demonstrating the effects of early-life exposure to key air pollutants (e.g., particulate matter (PM), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ) and ground-level ozone (O 3 ), environmental tobacco smoke, radon, and volatile organic compounds (VOC) on respiratory health.

SUMMARY

Air pollution continues to be a global burden with serious consequences related to respiratory health. Interventions aimed at reducing air pollution in the environment must be achieved in an effort to improve asthma outcomes and pediatric health.