Faculty Bibliography

BACKGROUND:Air pollution is a health risk in pregnant women and children. Despite the importance of refined exposure assessment, the characterisation of personalised air pollution exposure remains a challenge in paediatric and perinatal epidemiology. OBJECTIVE:We used portable personal air monitors to characterise personalised exposure to air pollutants in pregnant women. METHODS:), and volatile organic compounds (average use = 14 days). Data were stored in real-time on a secure database via synchronisation with a smartphone application. Of 497 women who agreed to use air monitors, 273 women (55%) were successful in using air monitors for longer than a day. For these participants, we identified daily patterns of exposure to air pollutants using functional principal component analysis (3827 days of air monitoring). RESULTS:had higher daily variations compared to PM. CONCLUSIONS:Small wearables are useful for the measurement of personalised air pollution exposure in birth cohorts and identify daily patterns that cannot be captured otherwise. Successful participation, however, depends on certain individual characteristics. Future studies should consider strategies in design and analysis to account for selective participation.

In this study we investigated the concentration and composition of particulate matter (PM2.5) in the New York City subway system. Realtime measurements, at a 1-s cadence, and gravimetric measurements were performed inside train cars along 300 km of nine subway lines, as well as on 333 platforms on 287 subway stations. The mean (±SD) PM2.5 concentration on the underground platforms was 142 ± 69 μg/m³ versus 29 ± 20 μg/m³ for aboveground stations. The average concentrations inside train cars were 88 ± 14 μg/m³ when traveling through underground tunnels and platforms and 29 ± 31 μg/m³ while on aboveground tracks. The particle composition analysis of filtered samples was done using X-ray fluorescence (XRF), revealing that iron made up approximately 43% of the total PM2.5 mass on station platforms, approximately 126 times higher than the outdoor ambient iron concentration. Other trace elements include silicon, sulfur, copper, nickel, aluminum, calcium, barium, and manganese. Considering the very high iron content, the comparative analysis of the measured concentration versus the standards set by the Environmental Protection Agency (US EPA) is not appropriate since those limits are largely based on particulate matter from fossil fuel combustion. Health impact analysis of inhalation of iron-based particles is needed to contextualize the results presented here.

The health and safety of using e-cigarette products (vaping) have been challenging to assess and further regulate due to their complexity. Inhaled e-cigarette aerosols contain chemicals with under-recognized toxicological profiles, which could influence endogenous processes once inhaled. We urgently need more understanding on the metabolic effects of e-cigarette exposure and how they compare to combustible cigarettes. To date, the metabolic landscape of inhaled e-cigarette aerosols, including chemicals originated from vaping and perturbed endogenous metabolites in vapers, is poorly characterized. To better understand the metabolic landscape and potential health consequences of vaping, we applied liquid chromatography-mass spectrometry (LC-MS) based nontargeted metabolomics to analyze compounds in the urine of vapers, cigarette smokers, and nonusers. Urine from vapers (n = 34), smokers (n = 38), and nonusers (n = 45) was collected for verified LC-HRMS nontargeted chemical analysis. The altered features (839, 396, and 426 when compared smoker and control, vaper and control, and smoker and vaper, respectively) among exposure groups were deciphered for their structural identities, chemical similarities, and biochemical relationships. Chemicals originating from e-cigarettes and altered endogenous metabolites were characterized. There were similar levels of nicotine biomarkers of exposure among vapers and smokers. Vapers had higher urinary levels of diethyl phthalate and flavoring agents (e.g., delta-decalactone). The metabolic profiles featured clusters of acylcarnitines and fatty acid derivatives. More consistent trends of elevated acylcarnitines and acylglycines in vapers were observed, which may suggest higher lipid peroxidation. Our approach in monitoring shifts of the urinary chemical landscape captured distinctive alterations resulting from vaping. Our results suggest similar nicotine metabolites in vapers and cigarette smokers. Acylcarnitines are biomarkers of inflammatory status and fatty acid oxidation, which were dysregulated in vapers. With higher lipid peroxidation, radical-forming flavoring, and higher level of specific nitrosamine, we observed a trend of elevated cancer-related biomarkers in vapers as well. Together, these data present a comprehensive profiling of urinary biochemicals that were dysregulated due to vaping.

It is well-documented that subway stations exhibit high fine particulate matter (PM2.5) concentrations. Little is known about the potential of river-tunnels to increase PM2.5 concentrations in subways. We hypothesized a "river-tunnel" effect exists: Stations adjacent to poorly ventilated tunnels that travel beneath rivers exhibit higher PM2.5 concentrations than more distant stations. Accordingly, the PM2.5 concentrations were monitored at stations adjacent to and two- and three-stations distant from the river-tunnel. Multivariate linear regression analysis was conducted to disentangle how proximity to a river-tunnel and other factors (e.g., depth) influence concentrations. Stations adjacent to a river-tunnel had 80"“130% higher PM2.5 concentrations than more distant stations. Moreover, distance from a river-tunnel was the strongest PM2.5-influencing factor This distance effect was not observed at underground stations adjacent to a river-bridge. The "river-tunnel" effect explains some of the inter-station variability in subway PM2.5 concentrations. These results support the need for improving ventilation systems in subways.

INTRODUCTION/BACKGROUND:In July 2018, the U.S. Department of Housing and Urban Development passed a rule requiring public housing authorities to implement smoke-free housing (SFH) policies. We measured secondhand smoke (SHS) exposure immediately before, and repeatedly up to 36 months post-SFH policy implementation in a purposeful sample of 21 New York City (NYC) high-rise buildings (>15 floors): 10 NYC Housing Authority (NYCHA) buildings subject to the policy and 11 privately managed buildings in which most residents received housing vouchers (herein 'Section 8'). METHODS:We invited participants from non-smoking households (NYCHA n=157, Section 8 n=118) to enroll into a longitudinal air monitoring study, measuring (1) nicotine concentration with passive, bi-sulfate-coated filters, and (2) particulate matter (PM2.5) with low-cost particle sensors. We also measured nicotine concentrations and counted cigarette butts in common areas (n=91 stairwells and hallways). We repeated air monitoring sessions in households and common areas every 6 months, totaling six post-policy sessions. RESULTS:After three years, we observed larger declines in nicotine concentration in NYCHA hallways than in Section 8, [difference-in-difference (DID) = -1.92 µg/m 3 (95% CI -2.98, -0.87), p=0.001]. In stairwells, nicotine concentration declines were larger in NYCHA buildings, but the differences were not statistically significant [DID= -1.10 µg/m 3 (95% CI -2.40, 0.18), p=0.089]. In households, there was no differential change in nicotine concentration (p=0.093) or in PM2.5 levels (p=0.385). CONCLUSIONS:Nicotine concentration reductions in NYCHA common areas over three years may be attributable to the SFH policy, reflecting its gradual implementation over this time. IMPLICATIONS/CONCLUSIONS:Continued air monitoring over multiple years has demonstrated that SHS exposure may be declining more rapidly in NYCHA common areas as a result of SFH policy adherence. This may have positive implications for improved health outcomes among those living in public housing, but additional tracking of air quality and studies of health outcomes are needed. Ongoing efforts by NYCHA to integrate the SFH policy into wider healthier-homes initiatives may increase policy compliance.

INTRODUCTION/BACKGROUND:A major site of secondhand smoke exposure for children and adults is the home. Few studies have evaluated the impact of e-cigarette or hookah use on home air quality, despite evidence finding toxic chemicals in secondhand e-cigarette aerosols and hookah smoke. We assessed the effect of e-cigarette and hookah use on home air quality and compared it with air quality in homes where cigarettes were smoked and where no smoking or e-cigarette use occurred. METHODS:), black carbon and carbon monoxide (CO) were measured during a smoking or vaping session, both in a 'primary' smoking room and in an adjacent 'secondary' room where no smoking or vaping occurred. Log transformed data were compared with postanalysis of variance Tukey simultaneous tests. RESULTS:levels only in primary rooms. Additionally, in-home use of hookah resulted in greater CO concentrations than the use of cigarettes in primary rooms. CONCLUSIONS:penetrated even into rooms adjacent to where smoking occurs. Extending smoke-free rules inside homes to include e-cigarette and hookah products is needed to protect household members and visitors from passive exposure to harmful aerosols and gases.

Previous studies have explored using calibrated low-cost particulate matter (PM) sensors, but important research gaps remain regarding long-term performance and reliability. Evaluate longitudinal performance of low-cost particle sensors by measuring sensor performance changes over 2 years of use. 51 low-cost particle sensors (Airbeam 1 N = 29; Airbeam 2 N = 22) were calibrated four times over a 2-year timeframe between 2019 and 2021. Cigarette smoke-specific calibration curves for Airbeam 1 and 2 PM sensors were created by directly comparing simultaneous 1-min readings of a Thermo Scientific Personal DataRAM PDR-1500 unit with a 2.5 µm inlet. Inter-sensor variability in calibration coefficient was high, particularly in Airbeam 1 sensors at study initiation. Calibration coefficients for both sensor types trended downwards over time to < 1 at final calibration timepoint [Airbeam 1 Mean (SD) = 0.87 (0.20); Airbeam 2 Mean (SD) = 0.96 (0.27)]. We lost more Airbeam 1 sensors (N = 27 out of 56, failure rate 48.2%) than Airbeam 2 (N = 2 out of 24, failure rate 8.3%) due to electronics, battery, or data output issues. Evidence suggests degradation over time might depend more on particle sensor type, rather than individual usage. Repeated calibrations of low-cost particle sensors may increase confidence in reported PM levels in longitudinal indoor air pollution studies.

BACKGROUND:Cigarette smoking, secondhand cigarette smoke (SHS) exposure, and e-cigarette use ("vaping") are each associated with increased rates of depressive symptoms and other internalizing mental health disorders. The prevalence of vaping has increased greatly, yet the mental health correlates of secondhand exposure to e-cigarette emissions are as yet to be investigated. This study examined the potential adverse mental health outcomes associated with different tobacco exposures (direct and passive), with a particular focus on the mental health correlates of secondhand exposure to e-cigarette emissions. METHODS:The Population Assessment of Tobacco and Health Study data collected from a sample of 16,173 Wave 4 adults were used to test the hypothesis that secondhand e-cigarette emissions exposure is associated with increased odds of internalizing mental health disorders. Individuals were categorized as exclusive cigarette smokers, exclusive e-cigarette users, cigarette and e-cigarette dual users, exclusive noncombustible tobacco users, secondhand smoke exposed non-users, secondhand e-cigarette emissions exposed non-users, and non-users with no current SHS/secondhand e-cigarette aerosol exposure. Adjusted weighted logistic regression analysis was used to investigate the association between exposure type and internalizing problems as assessed by scores on the Global Appraisal of Individual Needs-Short Screener (GAIN-SS), a widely used instrument for assessing mental health problems. RESULTS:Cigarette smokers (AOR = 2.53, 95% CI: 2.19-2.92), e-cigarette users (AOR = 3.14, 2.41-4.09), dual users (AOR = 3.37, 2.85-4.00), noncombustible tobacco users (AOR = 1.48, 1.01-2.17), SHS exposed non-users (AOR = 1.63, 1.37-1.94), and secondhand e-cigarette emissions exposed non-users (AOR = 1.43, 1.03-1.99) were each associated with increased odds of moderate to severe internalizing mental health problems as compared to unexposed non-users. Odds of internalizing problems among SHS and secondhand e-cigarette emissions exposed non-users did not differ (p = 0.46). CONCLUSIONS:This is the first study, to our knowledge, to identify an association between recent secondhand exposure to e-cigarette emissions and mental health problems, and the risk is comparable to that of SHS. Corroboration of this relationship needs further research to explicate directionality and mechanisms underlying this association.

Fine particulate matter air pollution (PM2.5) is a major contributor to cardiovascular morbidity and mortality, potentially via increased inflammation. PM2.5 exposure increases inflammatory biomarkers linked to cardiovascular disease, including CRP, IL-6 and TNFα. Portable air cleaners (PACs) reduce individual PM2.5 exposure but evidence is limited regarding whether PACs also reduce inflammatory biomarkers. We performed a systematic review and meta-analysis of trials evaluating the use of PACs to reduce PM2.5 exposure and inflammatory biomarker concentrations. We identified English-language articles of randomized sham-controlled trials evaluating high efficiency particulate air filters in non-smoking, residential settings measuring serum CRP, IL-6 and TNFα before and after active versus sham filtration, and performed meta-analysis on the extracted modeled percent change in biomarker concentration across studies. Of 487 articles identified, we analyzed 14 studies enrolling 778 participants that met inclusion criteria. These studies showed PACs reduced PM2.5 by 61.5 % on average. Of the 14 included studies, 10 reported CRP concentrations in 570 participants; these showed active PAC use was associated with 7 % lower CRP (95 % CI: −14 % to 0.0 %, p = 0.05). Nine studies of IL-6, with 379 participants, showed active PAC use was associated with 13 % lower IL-6 (95 % CI: [−23 %, −3 %], p = 0.009). Six studies, with 269 participants, reported TNF-α and demonstrated no statistical evidence of difference between active and sham PAC use. Portable air cleaners that reduce PM2.5 exposure can decrease concentrations of inflammatory biomarkers associated with cardiovascular disease. Additional studies are needed to evaluate clinical outcomes and other biomarkers.