A new exposure metric for traffic-related air pollution? An analysis of determinants of hopanes in settled indoor house dust

Increased contribution to PM2.5 from traffic-influenced road dust in Shanghai over recent years and predictable future

Traffic contributes to fine particulate matter (PM_2.5) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM_2.5 emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM_2.5 over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM_2.5, increasing at 6.1% year^-1, more than twice that of EC (2.9% year^-1). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

An Investigation into Which Methods Best Explain Children’s Exposure to Traffic-Related Air Pollution

There have been several methods employed to quantify individual-level exposure to ambient traffic-related air pollutants (TRAP). These include an individual’s residential proximity to roads, measurement of individual pollutants as surrogates or markers, as well as dispersion and land use regression (LUR) models. Hopanes are organic compounds still commonly found on ambient particulate matter and are specific markers of combustion engine primary emissions, but they have not been previously used in personal exposure studies. In this paper, children’s personal exposures to TRAP were evaluated using hopanes determined from weekly integrated filters collected as part of a personal exposure study in Windsor, Canada. These hopane measurements were used to evaluate how well other commonly used proxies of exposure to TRAP performed. Several of the LUR exposure estimates for a range of air pollutants were associated with the children’s summer personal hopane exposures (r = 0.41–0.74). However, all personal hopane exposures in summer were more strongly associated with the length of major roadways within 500 m of their homes. In contrast, metrics of major roadways and LUR estimates were poorly correlated with any winter personal hopanes. Our findings suggest that available TRAP exposure indicators have the potential for exposure misclassification in winter vs. summer and more so for LUR than for metrics of major road density. As such, limitations are evident when using traditional proxy methods for assigning traffic exposures and these may be especially important when attempting to assign exposures for children’s key growth and developmental windows. If long-term chronic exposures are being estimated, our data suggest that measures of major road lengths in proximity to homes are a more-specific approach for assigning personal TRAP exposures.

The Canadian Healthy Infant Longitudinal Development (CHILD) birth cohort study: assessment of environmental exposures

The Canadian Healthy Infant Longitudinal Development birth cohort was designed to elucidate interactions between environment and genetics underlying development of asthma and allergy. Over 3600 pregnant mothers were recruited from the general population in four provinces with diverse environments. The child is followed to age 5 years, with prospective characterization of diverse exposures during this critical period. Key exposure domains include indoor and outdoor air pollutants, inhalation, ingestion and dermal uptake of chemicals, mold, dampness, biological allergens, pets and pests, housing structure, and living behavior, together with infections, nutrition, psychosocial environment, and medications. Assessments of early life exposures are focused on those linked to inflammatory responses driven by the acquired and innate immune systems. Mothers complete extensive environmental questionnaires including time-activity behavior at recruitment and when the child is 3, 6, 12, 24, 30, 36, 48, and 60 months old. House dust collected during a thorough home assessment at 3-4 months, and biological specimens obtained for multiple exposure-related measurements, are archived for analyses. Geo-locations of homes and daycares and land-use regression for estimating traffic-related air pollution complement time-activity-behavior data to provide comprehensive individual exposure profiles. Several analytical frameworks are proposed to address the many interacting exposure variables and potential issues of co-linearity in this complex data set.

A comparison between self-reported and GIS-based proxies of residential exposure to environmental pollution in a case-control study on lung cancer

In epidemiological studies both questionnaire results and GIS modeling have been used to assess exposure to environmental risk factors. Nevertheless, few studies have used both these techniques to evaluate the degree of agreement between different exposure assessment methodologies. As part of a case-control study on lung cancer, we present a comparison between self-reported and GIS-derived proxies of residential exposure to environmental pollution. 649 subjects were asked to fill out a questionnaire and give information about residential history and perceived exposure. Using GIS, for each residence we evaluated land use patterns, proximity to major roads and exposure to industrial pollution. We then compared the GIS exposure-index values among groups created on the basis of questionnaire responses. Our results showed a relatively high agreement between the two methods. Although none of these methods is the "exposure gold standard", understanding similarities, weaknesses and strengths of each method is essential to strengthen epidemiological evidence.