The relationship between personal exposure and ambient PM2.5 and black carbon in Beijing

Enhancing indoor PM2.5 predictions based on land use and indoor environmental factors by applying machine learning and spatial modeling approaches

The presence of fine particulate matter (PM2.5) indoors constitutes a significant component of overall PM2.5 exposure, as individuals spend 90% of their time indoors; however, personal monitoring for large cohorts is often impractical. In light of this, this study seeks to employ a novel geospatial artificial intelligence (Geo-AI) coupled with machine learning (ML) approaches to develop indoor PM2.5 models. Multiple predictor variables were collected from 102 residential households, including meteorological data; elevation; land use; indoor environmental factors including human activities, building characteristics, infiltration factors, and real-time measurements; and various other factors. Geo-AI, which integrates land use regression, inverse distance weighting, and ML algorithms, was utilized to construct outdoor PM2.5 and PM10 estimates for residential households. The most influential variables were identified via correlation analysis and stepwise regression. Three ML methods, namely support vector machine, multiple linear regression, and multilayer perceptron (MLP) were used to estimate indoor PM2.5 concentration. Then, MLP was employed to blend three ML algorithms. The resulting model demonstrated commendable performance, achieving a 10-fold cross-validation R2 of 0.92 and a root mean square error of 2.3 μg/m3 for indoor PM2.5 estimations. Notably, the combination of Geo-AI and ensembled ML models in this study outperformed all other individual models. In addition, the present study pointed out the most influential factors for indoor PM2.5 model were outdoor PM2.5, PM2.5/PM10 ratio, sampling month, infiltration factor, located near factory, cleaning frequency, number of door entrance linked with outdoor, and wall material. Further exploration of diverse ensemble model formats to integrate estimates from different models could enhance overall performance. Consequently, the potential applications of this model extend to estimating real individual exposure to PM2.5 for further epidemiological research. Moreover, the model offers valuable insights for efficient indoor air quality management and control strategies.

Exposure to diesel-related particulate matter, cortisol stress responsivity, and depressive symptoms in adolescents

Exposure to air pollution is associated with higher risk for psychopathology; however, the mechanisms underlying this association are not clear. Dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis in response to stress has been implicated in depression. Here, we estimated annual exposure to particulate matter (PM) from diesel emissions in 170 9- to 15-year-old adolescents (56 % female) using their residential addresses and data from nearby monitoring sites. We obtained salivary cortisol samples from participants while they completed a social stress task and calculated area under the curve with respect to ground (AUCg) and with respect to increase (AUCi) in order to assess cortisol responsivity during stress. Participants also reported on their depressive symptoms and sleep disturbances. Greater exposure to diesel PM was associated with lower cortisol output (AUCg) during stress, which was associated with higher depressive symptoms, particularly for adolescents with more sleep disturbances. Importantly, these effects were independent of household and neighborhood socioeconomic disadvantage and exposure to early adversity. Thus, HPA-axis dysfunction may be one mechanism through which environmental pollutants affect adolescents' mental health.

Characterization of airborne endotoxin in personal exposure to fine particulate matter (PM2.5) and bioreactivity for elderly residents in Hong Kong

The heavy metals and bioreactivity properties of endotoxin in personal exposure to fine particulate matter (PM2.5) were characterized in the analysis. The average personal exposure concentrations to PM2.5 were ranged from 6.8 to 96.6 μg/m3. The mean personal PM2.5 concentrations in spring, summer, autumn, and winter were 32.1±15.8, 22.4±11.8, 35.3±11.9, and 50.2±19.9 μg/m3, respectively. There were 85 % of study targets exceeded the World Health Organization (WHO) PM2.5 threshold (24 hours). The mean endotoxin concentrations ranged from 1.086 ± 0.384-1.912 ± 0.419 EU/m3, with a geometric mean (GM) varied from 1.034 to 1.869. The concentration of iron (Fe) (0.008-1.16 μg/m3) was one of the most abundant transition metals in the samples that could affect endotoxin toxicity under Toll-Like Receptor 4 (TLR4) stimulation. In summer, the interleukin 6 (IL-6) levels showed statistically significant differences compared to other seasons. Spearman correlation analysis showed endotoxin concentrations were positively correlated with chromium (Cr) and nickel (Ni), implying possible roles as nutrients and further transport via adhering to the surface of fine inorganic particles. Mixed-effects model analysis demonstrated that Tumor necrosis factor-α (TNF-α) production was positively associated with endotoxin concentration and Cr as a combined exposure factor. The Cr contained the highest combined effect (0.205-0.262), suggesting that Cr can potentially exacerbate the effect of endotoxin on inflammation and oxidative stress. The findings will be useful for practical policies for mitigating air pollution to protect the public health of the citizens.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

When and where to exercise: An assessment of personal exposure to urban tropical ambient airborne pollutants in Singapore

BACKGROUND

Physical activity is associated with health benefits and has been shown to reduce mortality risk. However, exposure to high levels of ambient fine particulate matter (PM2.5) during exercise can potentially reduce the health benefits of physical activity. This study aims to assess and compare the PM2.5 concentrations of different exercise venues in Singapore by their location attributes and time of day.

METHODS

Personal PM2.5 exposures (μg/m3) at 24 common outdoor exercise venues in Singapore over 49 sampling days were collected using real-time personal sensors from September 2017 to January 2020. Wilcoxon rank-sum test and Kruskal-Wallis test were used to compare PM2.5 concentrations between different timings (peak (0700-0900; 1800-2000) vs. non-peak (0600-0700; 0900-1800; 2000-2300); weekend vs. weekday), and location attributes (near major roads (<50 m) vs. away from major roads (≥50 m)). Multivariable linear regression models were used to assess the associations between location attributes, timings and ambient PM2.5 with personal PM2.5 concentration, adjusting for potential confounders.

RESULTS

Compared with peak hours, exercising during non-peak hours was associated with a significantly lower PM2.5 exposure (median, 17.8 μg/m3 during peak vs. 14.5 μg/m3 during non-peak; P = 0.006). Exercise venues away from major roads have significantly lower PM2.5 concentrations as compared to those located next to major roads (median, 14.4 μg/m3 away from major roads vs. 18.5 μg/m3 next to major roads; P < 0.001). Individuals who exercised in parks experienced the highest PM2.5 exposure (median, 55.0 μg/m3) levels in the afternoon during 1400-1500. Furthermore, ambient PM2.5 concentration was significantly and positively associated with personal PM2.5 exposure (β = 0.85, P < 0.001).

CONCLUSIONS

Our findings suggest that exercising outdoors in the urban environment exposes individuals to differential levels of PM2.5 at different times of the day. Further research should investigate a wider variety of outdoor exercise venues, explore different types of air pollutants, and consider the varying activity patterns of individuals.

Personal PM2.5-bound PAH exposure and lung function in healthy office workers: A pilot study in Beijing and Baoding, China

The effect of short-term exposure to polycyclic aromatic hydrocarbons (PAHs) on the respiratory system among healthy residents is unclear. Beijing and Baoding are typical polluted cities in China, and there is little research on PAH exposure and its health effects at the individual level. Fourteen healthy female office workers were recruited in urban Beijing and Baoding, China, in 2019. The personal exposure to fine particulate matter (PM_2.5)-bound PAHs and lung function were seasonally monitored. The relationships between PAH exposure and lung function were determined by a generalized mixed linear model. Subjects were exposed to high levels of PAH, in which the benzo[a]pyrene (BaP) level (1.26 ng/m³) was over than Chinese national indoor standard (1 ng/m³). All PAHs concentration was higher in winter than that in summer and autumn. Only benz[a]anthracene (BaA) and chrysene (Chr) exposure showed weak relations with decreased lung function, i.e., a 0.58% and 0.73% decrease in peak expiratory flow at lag 2 day, respectively (p < 0.05). PAHs may not be suitable exposure indicators for short-term change in lung function. Our findings highlight the importance of reducing PAH pollution for public respiratory health protection in heavy-polluted cities of China. This pilot study also provides experience on personal PAH assessment such as estimation of the number of repeated measurements required, which is helpful to determine the relationship between PAH exposure and health effect.

Air Purifier Intervention to Remove Indoor PM2.5 in Urban China: A Cost-Effectiveness and Health Inequality Impact Study

Using air purifiers is an intervention to reduce exposure to fine particulate matter (PM_2.5) for health benefits. We performed a comprehensive simulation in urban China to estimate the cost-effectiveness of long-term use of air purifiers to remove indoor PM_2.5 from indoor and ambient air pollution in five intervention scenarios (S1-S5), where the indoor PM_2.5 targets were 35, 25, 15, 10, and 5 μg/m³, respectively. In scenarios S1 to S5, 5221 (95% uncertainty interval: 3886-6091), 6178 (4554-7242), 8599 (6255-10,109), 11,006 (7962-13,013), and 14,990 (10,888-17,610) thousand disability-adjusted-life-years (DALYs) can be avoided at the cost of 201 (199-204), 240 (238-243), 364 (360-369), 522 (515-530), and 921 (905-939) billion Chinese Yuan (CNY), respectively. A high disparity in per capita health benefits and costs was observed by city, which expanded with the decrease of the indoor PM_2.5 target. The net benefits of using purifiers in cities varied across scenarios. Cities with a lower ratio of annual average outdoor PM_2.5 concentration to gross domestic product (GDP) per capita tended to achieve higher net benefits in the scenario with a lower indoor PM_2.5 target. Controlling ambient PM_2.5 pollution and developing the economy can reduce the inequality in air purifier use across China.

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.

Neuroinflammation and Neurodegeneration of the Central Nervous System from Air Pollutants: A Scoping Review

In this scoping review, we provide a selective mapping of the global literature on the effects of air pollution on the life-span development of the central nervous system. Our synthesis first defines developmental neurotoxicants and the model effects of particulate matter. We then discuss air pollution as a test bench for neurotoxicants, including animal models, the framework of systemic inflammation in all affected organs of the body, and the cascade effects on the developing brain, with the most prevalent neurological structural and functional outcomes. Specifically, we focus on evidence on magnetic resonance imaging and neurodegenerative diseases, and the links between neuronal apoptosis and inflammation. There is evidence of a developmental continuity of outcomes and effects that can be observed from utero to aging due to severe or significant exposure to neurotoxicants. These substances alter the normal trajectory of neurological aging in a propulsive way towards a significantly higher rate of acceleration than what is expected if our atmosphere were less polluted. The major aggravating role of this neurodegenerative process is linked with the complex action of neuroinflammation. However, most recent evidence learned from research on the effects of COVID-19 lockdowns around the world suggests that a short-term drastic improvement in the air we breathe is still possible. Moreover, the study of mitohormesis and vitagenes is an emerging area of research interest in anti-inflammatory and antidegenerative therapeutics, which may have enormous promise in combatting the deleterious effects of air pollution through pharmacological and dietary interventions.

Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure

BACKGROUND

Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure.

OBJECTIVES

The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income.

METHODS

This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents).

RESULTS

From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 μg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources.

CONCLUSION

This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM_2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM_2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM_2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM_2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM_2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM_2.5, which vary by season.

Short-term exposure to traffic-related air pollution and STEMI events: Insights into STEMI onset and related cardiac impairment

AIMS

Evidence on the impacts of traffic-related air pollution (TRAP) on ST-segment elevation myocardial infarction (STEMI) events is limited. We aimed to assess the acute effects of TRAP exposure on the clinical onset of STEMI and related cardiac impairments.

METHODS AND RESULTS

We recruited patients who were admitted for STEMI and underwent primary percutaneous coronary intervention at Peking University Third Hospital between 2014 and 2020. Indicators relevant to cardiac impairments were measured. Concomitantly, hourly concentrations of traffic pollutants were monitored throughout the study period, including fine particulate matter, black carbon (BC), particles in size ranges of 5-560 nm, oxides of nitrogen (NO_X), nitrogen dioxide, and carbon monoxide. The mean (SD) age of participants was 62.4 (12.5) years. Daily average (range) concentrations of ambient BC and NO_X were 3.9 (0.1-25.0) μg/m³ and 90.8 (16.6-371.7) μg/m³. Significant increases in STEMI risks of 5.9% (95% CI: 0.1, 12.0) to 21.9% (95% CI: 6.0, 40.2) were associated with interquartile range increases in exposure to TRAP within a few hours. These changes were accompanied by significant elevations in cardiac troponin T levels of 6.9% (95% CI: 0.2, 14.1) to 41.7% (95% CI: 21.2, 65.6), as well as reductions in left ventricular ejection fraction of 1.5% (95% CI: 0.1, 2.9) to 3.7% (95% CI: 0.8, 6.4). Furthermore, the associations were attenuated in participants living in areas with higher residential greenness levels.

CONCLUSIONS

Our findings extend current understanding that short-term exposure to higher levels of traffic pollution was associated with increased STEMI risks and exacerbated cardiac impairments, and provide evidence on traffic pollution control priority for protecting vulnerable populations who are at greater risks of cardiovascular events.

Impacts of Regional Speed Control Strategy Based on Macroscopic Fundamental Diagram on Energy Consumption and Traffic Emissions: A Case Study of Beijing

Numerous studies shown that particulate matter in the ambient environment has a significant impact on the health of the respiratory system. To understand the interrelationships between urban built environment, transportation operations and health, this study proposes an innovative approach that uses real-world GPS datasets to calculate energy consumption and emissions from transportation. The experiment used the traffic operation state in the Fourth Ring Road of Beijing as the research object and tested the impact of using the Regional speed optimization (RSO) strategy based on Macroscopic Fundamental Diagram (MFD) on energy consumption and emissions during peak hours. The impact of traffic emission on the health of roadside pedestrians is also considered. Changes in PM2.5 concentrations around four different built-up areas were calculated and compared. The computational experiments indicate that the PM2.5 pollutants exhausted by the traffic on the Ring Road during peak hours can reach up to 250 μg/m3, while the traffic emission on general roads near residential areas is only 50 μg/m3. Adopting Regional speed optimization can reduce the energy consumption of the road network by up to 18.8%. For roadside runners, the PM2.5 inhalation caused by night running in commercial and recreational areas is about 1.3-2.6 times that of night running in residential areas. Compared with morning or night running, the risk of respiratory disease caused by PM2.5 inhalation was about 10.3% higher than commuter running behavior. The research results provide a useful reference for energy conservation and emission reduction control strategies for different road types in cities and help existing cities to establish a traveler health evaluation system caused by traffic operation.

Development and Performance Evaluation of a Low-Cost Portable PM2.5 Monitor for Mobile Deployment

The concentration of fine particulate matter (PM2.5) is known to vary spatially across a city landscape. Current networks of regulatory air quality monitoring are too sparse to capture these intra-city variations. In this study, we developed a low-cost (60 USD) portable PM2.5 monitor called Smart-P, for use on bicycles, with the goal of mapping street-level variations in PM2.5 concentration. The Smart-P is compact in size (85 × 85 × 42 mm) and light in weight (147 g). Data communication and geolocation are achieved with the cyclist’s smartphone with the help of a user-friendly app. Good agreement was observed between the Smart-P monitors and a regulatory-grade monitor (mean bias error: −3.0 to 1.5 μg m−3 for the four monitors tested) in ambient conditions with relative humidity ranging from 38 to 100%. Monitor performance decreased in humidity > 70% condition. The measurement precision, represented as coefficient of variation, was 6 to 9% in stationary mode and 6% in biking mode across the four tested monitors. Street tests in a city with low background PM2.5 concentrations (8 to 9 μg m−3) and in two cities with high background concentrations (41 to 74 μg m−3) showed that the Smart-P was capable of observing local emission hotspots and that its measurement was not sensitive to bicycle speed. The low-cost and user-friendly nature are two features that make the Smart-P a good choice for empowering citizen scientists to participate in local air quality monitoring.

Short-Term Cumulative Exposure to Ambient Traffic-Related Black Carbon and Blood Pressure: MMDA Traffic Enforcers' Health Study

Exposure to traffic-related air pollution is linked with acute alterations in blood pressure (BP). We examined the cumulative short-term effect of black carbon (BC) exposure on systolic (SBP) and diastolic (DBP) BP and assessed effect modification by participant characteristics. SBP and DBP were repeatedly measured on 152 traffic enforcers. Using a linear mixed-effects model with random intercepts, quadratic (QCDL) and cubic (CCDL) constrained distributed lag models were fitted to estimate the cumulative effect of BC concentration on SBP and DBP during the 10 hours (daily exposure) and 7 days (weekly exposure) before the BP measurement. Ambient BC was related to increased BP with QCDL models. An interquartile range change in BC cumulative during the 7 days before the BP measurement was associated with increased BP (1.2% change in mean SBP, 95% confidence interval (CI), 0.1 to 2.3; and 0.5% change in mean DBP, 95% CI, −0.8 to 1.7). Moreover, the association between the 10-h cumulative BC exposure and SBP was stronger for female (4.0% change, 95% CI: 2.1–5.9) versus male and for obese (2.9% change, 95% CI: 1.0–4.8) vs. non-obese traffic enforcers. Short-term cumulative exposure to ambient traffic-related BC could bring about cardiovascular diseases through mechanisms involving increased BP.

Personal exposure to PM2.5 in five commuting modes under hazy and non-hazy conditions

Effective reducing exposure to fine particulate matter (PM_2.5) during commuting can help lower the risk of adverse health effects therefrom; however, few studies have examined the influence of different background levels of air pollution-particularly in China where PM_2.5 concentrations are high globally. In this study, personal sampling was conducted to measure individual exposure during five different modes of commuting (bus, metro, car, bicycle and walking) in Shanghai, China. A total of 125 measurements were conducted for five days under haze and non-haze conditions, following which the corresponding doses of PM_2.5 inhaled were estimated. The mean concentrations (±standard deviation, SD, 1-min averaging) of background PM_2.5 were 155.9 (±98.7) μg/m³ during haze and 36.3 (±17.6) μg/m³ under the non-haze conditions. Under both conditions, active commuters were exposed to higher PM_2.5 concentrations than those using motorized commuting modes (Wilcoxon test, p < 0.01). Moreover, driving with closed windows and air conditioning effectively reduces the PM_2.5 concentrations in cars by 35 %-57 %. Cyclists inhaled the highest doses (539.8 ± 313.2 and 134.8 ± 71.3 μg/h under haze and non-haze conditions, respectively), whereas car drivers inhaled the lowest doses (28.8 ± 21.2 and 3.7 ± 2.6 μg/h under haze and non-haze conditions, respectively). Individual exposure to PM_2.5 during commuting varied with the modes; the discrepancy between the latter depended largely on the ambient concentration. Our findings provided evidence that traffic-related air pollution contributed to daily pollutant exposure and highlighted the importance of taking personal protective measures while commuting, particularly during haze.

Commuter types identified using clustering and their associations with source-specific PM2.5

Traffic-related fine particulate matter air pollution (tr-PM_2.5) has been associated with adverse health outcomes such as cardiopulmonary morbidity and mortality, with in-vehicle tr-PM_2.5 exposure contributing to total personal pollution exposure. Trip characteristics, including time of day, day of the week, and traffic congestion, are associated with in-vehicle PM_2.5 exposures. We hypothesized that some commuter characteristics, such as whether commuters travel primarily during rush hour, would also be associated with increased tr-PM_2.5 exposures. The commute data consisted of unscripted personal vehicle trips of 46 commuters in the Washington, D.C. metro area over 48-h, with a total of 320 trips. We identified commuter types using sparse K-means clustering, which identifies the hours throughout the day important for clustering commuters. Source-specific PM_2.5 over 48 h was estimated using Positive Matrix Factorization. Linear regression was used to estimate differences in source-specific PM_2.5 by commuter cluster. Two commuter clusters were identified using the clustering approach: rush hour commuters, who primarily travelled during rush hour, and sporadic commuters, who travelled throughout the day. The hours given the largest weights by sparse K-means were 7-8 a.m. and 6-7 p.m., corresponding to peak travel times. Integrated black carbon (BC) was higher for rush hour commuters (median = 3.1 μg/m³ (IQR = 1.5)) compared to sporadic commuters (2.0 μg/m³ (IQR = 1.9)). Mobile PM_2.5, consisting primarily of tailpipe emissions and brake/tire wear, was also higher for rush hour commuters (2.9 μg/m³ (IQR = 1.6)) compared to sporadic commuters (2.1 μg/m³ (IQR = 2.4)), though this difference was not statistically significant in regression models. Estimated differences between commuter types for secondary/mixed PM_2.5 and road salt PM_2.5 were smaller. Further research may elucidate whether commuter characteristics are an efficient way to identify individuals with highest tr-PM_2.5 exposures associated with commuting and to develop effective mitigation strategies.

Characterising professional drivers' exposure to traffic-related air pollution: Evidence for reduction strategies from in-vehicle personal exposure monitoring

Professional drivers working in congested urban areas are required to work near harmful traffic related pollutants for extended periods, representing a significant, but understudied occupational risk. This study collected personal black carbon (BC) exposures for 141 drivers across seven sectors in London. The aim of the study was to assess the magnitude and the primary determinants of their exposure, leading to the formulation of targeted exposure reduction strategies for the occupation. Each participant's personal BC exposures were continuously measured using real-time monitors for 96 h, incorporating four shifts per participant. 'At work' BC exposures (3.1 ± 3.5 µg/m³) were 2.6 times higher compared to when 'not at work' (1.2 ± 0.7 µg/m³). Workers spent 19% of their time 'at work driving', however this activity contributed 36% of total BC exposure, highlighting the disproportionate effect driving had on their daily exposure. Taxi drivers experienced the highest BC exposures due to the time they spent working in congested central London, while emergency services had the lowest. Spikes in exposure were observed while driving and were at times greater than 100 µg/m³. The most significant determinants of drivers' exposures were driving in tunnels, congestion, location, day of week and time of shift. Driving with closed windows significantly reduced exposures and is a simple behaviour change drivers could implement. Our results highlight strategies by which employers and local policy makers can reduce professional drivers' exposure to traffic-related air pollution.