Determinants of personal exposure to fine particulate matter in the retired adults - Results of a panel study in two megacities, China

Understanding the time-activity pattern to improve the measurement of personal exposure: An exploratory and experimental research

Although ambient fine particulate matter (PM2.5) concentrations and their components are commonly used as proxies for personal exposure monitoring, developing an accurate and cost-effective method to use these proxies for personal exposure measurement continues to pose a significant challenge. Herein, we propose a scenario-based exposure model to precisely estimate personal exposure level of heavy metal(loid)s (HMs) using scenario HMs concentrations and time-activity patterns. Personal exposure levels and ambient pollution levels for PM2.5 and HMs differed significantly with corresponding personal/ambient ratios of approximately 2, and exposure scenarios could narrow the assessment error gap by 26.1-45.4%. Using a scenario-based exposure model, we assessed the associated health risks of a large sample population and identified that the carcinogenic risk of As exceeded 1 × 10-6, while we observed non-carcinogenic risks from As, Cd, Ni, and Mn in personal exposure to PM2.5. We conclude that the scenario-based exposure model is a preferential alternative for monitoring personal exposure compared to ambient concentrations. This method ensures the feasibility of personal exposure monitoring and health risk assessments in large-scale studies.

Mapping the personal PM2.5 exposure of China's population using random forest

Ambient monitoring may cause estimation errors, and wearable monitoring is expensive and labor-intensive when assessing PM_2.5 personal exposure. Estimation errors have limited the development of exposure science and environmental epidemiology. Thus, we developed a scenario-based exposure (SBE) model that covered 8 outdoor exposure scenarios and 1 indoor scenario with corresponding time-activity patterns in Baoding City. The linear regression analysis of the SBE yielded an R² value of 0.913 with satisfactory accuracy and reliability. To apply the SBE model to large-scale studies, we predicted time-activity patterns with the random forest model and atmosphere-to-scenario ratios with the linear regression model to obtain the essential parameters of the SBE model; their R² was 0.65-0.93. The developed model would economize the study expenditure of field sampling for personal PM_2.5 and deepen the understanding of the influences of indoor and outdoor factors on personal PM_2.5. Using this method, we found that the personal PM_2.5 exposure of Chinese residents was 10.50-347.02 μg/m³ in 2020, higher than the atmospheric PM_2.5 concentration. Residents in North and Central China, especially the Beijing-Tianjin-Hebei region and the Fen-Wei Plains, had higher personal PM_2.5 exposure than those in other areas.

Personal exposure to PM2.5 in different microenvironments and activities for retired adults in two megacities, China

Microenvironmental concentrations and time-activity patterns influence personal exposure to fine particulate matter (PM_2.5). However, the variations and contributions of PM_2.5 exposures from various microenvironments (MEs) and activities remain unclear. In this study, gravimetrically corrected real-time personal PM_2.5 measurements were collected during routine activities in different MEs from 66 non-smoking retired adults. Exposure data were collected for five consecutive days over two seasons in Nanjing (NJ) and Beijing (BJ), China. Measured PM_2.5 concentrations varied substantially both between and within different MEs and activities. The highest average concentrations were observed in restaurants (NJ: mean 192 μg/m³, SD 242 μg/m³; BJ: mean 91 μg/m³, SD 79 μg/m³) and were associated with sources such as passive smoking and cooking emissions. Overall, PM_2.5 concentrations in different MEs and activities were moderately to highly correlated with outdoor PM_2.5 concentrations (Spearman's r = 0.51-0.97) except in restaurants and during passive smoking. The at-home ME contributed approximately 85 % of the total PM_2.5 exposure, corresponding to the participants spending about 87 % of their time there. The majority of household exposures occurred during sleeping, cooking, and other home-based activities. Transportation accounted for <5 % of total exposure. Our results indicate that improving indoor air quality, especially residential indoors, is important to reduce personal exposure to PM_2.5.

Characteristics and determinants of personal exposure to typical air pollutants: A pilot study in Beijing and Baoding, China

Personal exposure to fine particulate matter (PM_2.5), nitrogen oxides (NO_x, NO₂ and NO), ozone (O₃) and sulfur dioxide (SO₂) was repeatedly measured among fourteen office workers in Beijing and Baoding, China in summer, autumn and winter of 2019. Time-activity patterns were simultaneously recorded. Determinants of personal air pollution exposure were investigated for each pollutant via a linear mixed effect model. The personal concentrations of PM_2.5, NO₂, NO and O₃ were higher in autumn and winter than those in summer. A decreasing trend was found in the personal PM_2.5 level for a typical indoor population in Beijing, indicating that particulate pollution was effectively controlled in Beijing and its surrounding area. The personal levels of PM_2.5, NO₂, and O₃ were weakly correlated with those monitored at ambient stations and were lower than the respective ambient levels except for PM_2.5 in summer and NO₂ in winter. This pilot study showed that the indoor air environment, ambient pollution, traffic-related variables and temperature were significant exposure sources for office workers. Our study highlighted the significance of controlling traffic emissions and improving the workplace air quality to protect the health of office workers. More importantly, we demonstrated the feasibility of model development for personal air pollution exposure prediction.

Association of ambient air pollution exposure and its variability with subjective sleep quality in China: A multilevel modeling analysis

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM_2.5) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002-1.006) and 0.3% (95% CI: 1.001-1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM_2.5 and PM₁₀ were 2.2% (95% CI: 1.012-1.032) and 2.2% (95% CI: 1.012-1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Validity of using ambient concentrations as surrogate exposures at the individual level for fine particle and black carbon: A systematic review and meta-analysis

Exposure measurement error is an important source of bias in epidemiological studies. We assessed the validity of employing ambient (outdoor) measurements as proxies of personal exposures at individual levels focusing on fine particles (PM_2.5) and black carbon (BC)/elemental carbon (EC) on a global scale. We conducted a systematic review and meta-analysis and searched databases (ISI Web of Science, Scopus, PubMed, Ovid MEDLINE®, Ovid Embase, and Ovid BIOSIS) to retrieve observational studies in English language published from 1 January 2006 until 5 May 2021. Correlation coefficients (r) between paired ambient (outdoor) concentration and personal exposure for PM_2.5 or BC/EC were standardized as effect size. We used random-effects meta-analyses to pool the correlation coefficients and investigated the causes of heterogeneity and publication bias. Furthermore, we employed subgroup and meta-regression analyses to evaluate the modification of pooled estimates by potential mediators. This systematic review identified thirty-two observational studies involving 1744 subjects from ten countries, with 28 studies for PM_2.5 and 11 studies for BC/EC. Personal PM_2.5 exposure is more strongly correlated with ambient (outdoor) concentrations (0.63, 95% confidence interval [CI]: 0.57-0.68) than personal BC/EC exposure (0.49, 95% CI: 0.38-0.59), with significant differences in ṝ (0.14, 95% CI: 0.03-0.25; p < 0.05). The results demonstrated that the health status of participants was a significant modifier of pooled correlations. In addition, the personal to ambient (P/A) ratio for PM_2.5 and average ambient BC/EC levels were potential effect moderators of the pooled ṝ. The funnel plots and Egger's regression test indicated inevident publication bias. The pooled estimates were robust through sensitivity analyses. The results support the growing consensus that the validity coefficient of proxy measures should be addressed when interpreting results from epidemiological studies to better understand how strong health outcomes are affected by different levels of PM_2.5 and their components.

Reliability and accuracy analysis of time-weighted average exposure to heavy metals based on personal exposure

Time-weighted average (TWA) exposure has been used as a surrogate for personal air exposure in some large-scale studies. However, the uncertainties of TWA exposure remain to be determined, although its boundedness has been widely recognized. This study aims to evaluate the reliability and accuracy of TWA exposure based on personal exposure. A total of 180 combined indoor-outdoor-personal air samples were collected of six cities during the non-heating and heating periods. The personal exposure levels of Hg, As, Cd, and Pb were 0.16, 21.20, 0.74, and 34.47 ng/m³ in the non-heating period, respectively, but were 0.20, 34.53, 3.45, and 18.59 ng/m³ in the heating period, respectively. The ratios of TWA and personal exposure of heavy metal(loid)s ranged from 0.91 to 1.53. Indoor pollution was the most significant factor of TWA exposure, accounting for 78.3-97.6% and 88.4-98.6% in the heating and non-heating period, respectively. Based on the results of redundancy analysis and risk assessment by TWA exposure, we concluded that TWA exposure could be used for qualitative investigation, as a substitute for personal exposure, but it may result in large bias when used for quantitative investigation. Larger sample size and more exposure scenarios can reduce the estimation error of TWA.

Inter- and Intra-Individual Variability of Personal Health Risk of Combined Particle and Gaseous Pollutants across Selected Urban Microenvironments

Exposure surrogates, such as air quality measured at a fixed-site monitor (FSM) or residence, are typically used for health estimates. However, people spend various amounts of time in different microenvironments, including the home, office, outdoors and in transit, where they are exposed to different magnitudes of particle and gaseous air pollutants. Health risks caused by air pollution exposure differ among individuals due to differences in activity, microenvironmental concentration, as well as the toxicity of pollutants. We evaluated individual and combined added health risks (AR) of exposure to PM_2.5, NO_2, and O₃ for 21 participants in their daily life based on real-world personal exposure measurements. Exposure errors from using surrogates were quantified. Inter- and intra-individual variability in health risks and key contributors in variations were investigated using linear mixed-effects models and correlation analysis, respectively. Substantial errors were found between personal exposure concentrations and ambient concentrations when using air quality measurements at either FSM or the residence location. The mean exposure errors based on the measurements taken at either the FSM or residence as exposure surrogates was higher for NO₂ than PM_2.5, because of the larger spatial variability in NO₂ concentrations in urban areas. The daily time-integrated AR for the combined PM_2.5, NO₂, and O₃ (TIAR_combine) ranged by a factor of 2.5 among participants and by a factor up to 2.5 for a given person across measured days. Inter- and intra-individual variability in TIAR_combine is almost equally important. Several factors were identified to be significantly correlated with daily TIAR_combine, with the top five factors, including PM_2.5, NO₂ and O₃ concentrations at ‘home indoor’, O₃ concentrations at ‘office indoor’ and ambient PM_2.5 concentrations. The results on the contributors of variability in the daily TIAR_combine could help in targeting interventions to reduce daily health damage related to air pollutants.