ABSTRACT
Personal care products can contain phthalates, parabens and other endocrine-disrupting chemicals. However, information on perception of risks from personal care product use and how use varies by race and ethnicity is limited. We evaluated differences in personal care product use and risk perception in a diverse sample of participants recruited from a US college campus and online. A self-administered questionnaire captured information on sociodemographic factors, personal care product use trends and perception of risk associated with them. Pearson's chi-square and Fisher's exact tests were used to determine differences in personal care product use and risk perception by race and ethnicity. Ordered logistic regressions were performed to measure associations between personal care product use frequency across racial/ethnic categories. Participant (n = 770) mean age was 22.8 years [standard deviation ± 6.0]. Daily use of make-up (eye = 29.3%; other = 38.0%; all = 33.7%) and skincare products (55%) was most frequently reported among Middle Eastern and North African participants. Non-Hispanic Black participants reported the highest daily use of hairstyling products (52%) and lotion (78%). Daily make-up use was more frequently reported among females (41%) than males (24.6%). Levels of agreement were similar across racial and ethnic groups, that personal care product manufacturers should be required to list all ingredients (≥87%). There were significant associations between the frequency of use of some personal care products and racial/ethnic categories when the use frequencies of participants from other racial/ethnic categories were compared to the use frequency of non-Hispanic White participants. There were significant differences in daily use frequency, levels of trust, perception of safety and health risks associated with personal care products by race and ethnicity, underscoring that there may be different sources of exposure to chemicals in personal care products by race and ethnicity.
ABSTRACT
Traffic-related fine particulate matter air pollution (tr-PM2.5) has been associated with adverse health outcomes such as cardiopulmonary morbidity and mortality, with in-vehicle tr-PM2.5 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 PM2.5 exposures. We hypothesized that some commuter characteristics, such as whether commuters travel primarily during rush hour, would also be associated with increased tr-PM2.5 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 PM2.5 over 48 h was estimated using Positive Matrix Factorization. Linear regression was used to estimate differences in source-specific PM2.5 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 PM2.5, 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 PM2.5 and road salt PM2.5 were smaller. Further research may elucidate whether commuter characteristics are an efficient way to identify individuals with highest tr-PM2.5 exposures associated with commuting and to develop effective mitigation strategies.
