Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

Temporal trends, 2000-2017, of perfluoroalkyl acid (PFAA) concentrations in serum of Swedish adolescents

Per- and polyfluoroalkyl substances (PFAS) have been extensively used as surfactants because of their high stability and good water/oil-repellent properties. PFASs, especially perfluoroalkyl acids (PFAAs), have long biological half-lives, and exposure may cause adverse health effects in humans. We assessed temporal trends of concentrations of eight PFAAs in serum of Swedish adolescents (age 16-21 years) from the general population, and estimated the stability of PFAAs and serum samples after 6 years of storage. Repeated cross-sectional sampling was performed on five occasions (covering in total 1213 individuals, 83% males) in southern Sweden between 2000 and 2017. We analyzed serum for perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), perfluoroheptanoic acid (PFHpA), perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), and perfluorododecanoic acid (PFDoDA) using liquid chromatography-tandem mass spectrometry. We assessed time trends using linear regression, long-term stability was assessed by reanalyzing samples collected 2013, and the comparison was done using Pearson correlation and Bland-Altman plots. PFHxS, PFOS, and PFOA decreased by 6.7% (CI: -7.0, -6.3%), 12.6% (CI: -12.9, -12.3%), and 6.5% (CI: -6.8, -6.1%) per year, respectively, and year of sampling explained 48-81% of the variation in concentrations. PFNA and PFDA seemed to increase up to 2009 and decrease thereafter. The trends were consistent after sensitivity analyses excluding women. Strong correlations of 94-97% were observed for concentrations of all compounds, except PFHxS, after storage. The observed trends closely followed the timing of manufacturers' voluntary phase-out initiatives, and of regulatory measures governing the compounds implemented in the EU and USA. This indicates that these actions mitigated the population's exposure to PFHxS, PFOS, and PFOA and, in recent years, to PFNA and PFDA, in southern Sweden. Furthermore, the results suggest that PFAAs remain stable in serum samples after long-term storage.