Assessment on personal exposure to particulate compounds using an empirical exposure model in an elderly community in Tianjin, China

A systematic literature review on indoor PM2.5 concentrations and personal exposure in urban residential buildings

Particulate matter with an aerodynamic diameter less than 2.5μm (PM_2.5) is currently a major air pollutant that has been raising public attention. Studies have found that short/long-term exposure to PM_2.5 lead detrimental health effects. Since people in most region of the world spend a large proportion of time in dwellings, personal exposure to PM_2.5 in home microenvironment should be carefully investigated. The objective of this review is to investigate and summary studies in terms of personal exposure to indoor PM_2.5 pollutants from the literature between 2000 and 2021. Factors from both outdoor and indoor environment that have impact on indoor PM_2.5 levels were explicated. Exposure studies were verified relating to individual activity pattern and exposure models. It was found that abundant investigations in terms of personal exposure to indoor PM_2.5 is affected by factors including concentration level, exposure duration and personal diversity. Personal exposure models, including microenvironment model, mathematical model, stochastic model and other simulation models of particle deposition in different regions of human airway are reviewed. Further studies joining indoor measurement and simulation of PM_2.5 concentration and estimation of deposition in human respiratory tract are necessary for individual health protection.

Personal exposure to ambient PM2.5, PM10, O3, NO2, and SO2 for different populations in 31 Chinese provinces

Most epidemiological studies usually employ ambient air pollutant concentrations as a proxy of personal exposure to air pollutants originating outdoors, which could lead to a biased estimation of health effects. Herein, we modeled infiltration and exposure factors as the modifications of personal exposure to ambient PM_2.5, PM₁₀, O₃, NO₂, and SO₂ for all seasons, genders, and ages in 31 Chinese provinces. The annual average exposure factors of PM₁₀, PM_2.5, O₃, NO₂, and SO₂ were 0.42 ± 0.13 (arithmetic mean ± standard deviation), 0.68 ± 0.14, 0.34 ± 0.12, 0.50 ± 0.14, and 0.40 ± 0.13, respectively. We observed significant age, gender, seasonal, and geographical differences in infiltration and exposure factors for all studied ambient air pollutants. These factors were higher in southern China than in the north, and they were the highest in summer and the lowest in winter. The exposure factor of minors (age < 18 years) was significantly lower than that of adults (age ≥ 18 years, P < 0.01). Adult males had higher exposure factors than females (P < 0.01). Epidemiological studies utilizing outdoor concentrations of air pollutants could overestimate personal exposure to these pollutants. The present study could help in reducing the bias in the estimation of the health effects of air pollutants.

Determinants of personal exposure to fine particulate matter (PM2.5) in adult subjects in Hong Kong

Personal monitoring for fine particulate matter (PM_2.5) was conducted for adults (48 subjects, 18-63years of age) in Hong Kong during the summer and winter of 2014-2015. All filters were analyzed for PM_2.5 mass and constituents (including carbonaceous aerosols, water-soluble ions, and elements). We found that season (p=0.02) and occupation (p<0.001) were significant factors affecting the strength of the personal-ambient PM_2.5 associations. We applied mixed-effects models to investigate the determinants of personal exposure to PM_2.5 mass and constituents, along with within- and between-individual variance components. Ambient PM_2.5 was the dominant predictor of (R²=0.12-0.59, p<0.01) and the largest contributor (>37.3%) to personal exposures for PM_2.5 mass and most components. For all subjects, a one-unit (2.72μg/m³) increase in ambient PM_2.5 was associated with a 0.75μg/m³ (95% CI: 0.59-0.94μg/m³) increase in personal PM_2.5 exposure. The adjusted mixed-effects models included information extracted from individual's activity diaries as covariates. The results showed that season, occupation, time indoors at home, in transit, and cleaning were significant determinants for PM_2.5 components in personal exposure (R²_β=0.06-0.63, p<0.05), contributing to 3.0-70.4% of the variability. For one-hour extra time spent at home, in transit, and cleaning an average increase of 1.7-3.6% (ammonium, sulfate, nitrate, sulfur), 2.7-12.3% (elemental carbon, ammonium, titanium, iron), and 8.7-19.4% (ammonium, magnesium ions, vanadium) in components of personal PM_2.5 were observed, respectively. In this research, the within-individual variance component dominated the total variability for all investigated exposure data except PM_2.5 and EC. Results from this study indicate that performing long-term personal monitoring is needed for examining the associations of mass and constituents of personal PM_2.5 with health outcomes in epidemiological studies by describing the impacts of individual-specific data on personal exposures.

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

INTRODUCTION

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Human Exposure Assessment for Air Pollution

Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level. And since the great differences of geographical environment, source distribution, pollution characteristics, economic conditions, and living habits, there is a wide range of differences between indoor, outdoor, and individual air pollution exposure in different regions of China. In general, the indoor particles in most Chinese families comprise infiltrated outdoor particles, particles generated indoors, and a few secondary organic aerosol particles, and in most cases, outdoor particle pollution concentrations are a major contributor to indoor concentrations in China. Furthermore, since the time, energy, and expense are limited, it is difficult to measure the concentration of pollutants for each individual. In recent years, obtaining the concentration of air pollutants by using a variety of exposure assessment models is becoming a main method which could solve the problem of the increasing number of individuals in epidemiology studies.