Fine particulate matter composition in American Indian vs. Non-American Indian communities.

BACKGROUND: Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US. METHODS: We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time. RESULTS: Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties. CONCLUSIONS: This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Can traffic-related air pollution trigger myocardial infarction within a few hours of exposure? Identifying hourly hazard periods.

INTRODUCTION: Traffic-related air pollution can trigger myocardial infarction (MI). However, the hourly hazard period of exposure to nitrogen dioxide (NO2), a common traffic tracer, for incident MI has not been fully evaluated. Thus, the current hourly US national air quality standard (100 ppb) is based on limited hourly-level effect estimates, which may not adequately protect cardiovascular health. OBJECTIVES: We characterized the hourly hazard period of NO2 exposure for MI in New York state (NYS), USA, from 2000 to 2015. METHODS: For nine cities in NYS, we obtained data on MI hospitalizations from the NYS Department of Health Statewide Planning and Research Cooperative System and hourly NO2 concentrations from the US Environmental Protection Agency's Air Quality System database. We used city-wide exposures and a case-crossover study design with distributed lag non-linear terms to assess the relationship between hourly NO2 concentrations over 24 h and MI, adjusting for hourly temperature and relative humidity. RESULTS: The mean NO2 concentration was 23.2 ppb (standard deviation: 12.6 ppb). In the six hours preceding MI, we found linearly increased risk with increasing NO2 concentrations. At lag hour 0, a 10 ppb increase in NO2 was associated with 0.2 % increased risk of MI (Rate Ratio [RR]: 1.002; 95 % Confidence Interval [CI]: 1.000, 1.004). We estimated a cumulative RR of 1.015 (95 % CI: 1.008, 1.021) for all 24 lag hours per 10 ppb increase in NO2. Lag hours 2-3 had consistently elevated risk ratios in sensitivity analyses. CONCLUSIONS: We found robust associations between hourly NO2 exposure and MI risk at concentrations far lower than current hourly NO2 national standards. Risk of MI was most elevated in the six hours after exposure, consistent with prior studies and experimental work evaluating physiologic responses after acute traffic exposure. Our findings suggest that current hourly standards may be insufficient to protect cardiovascular health.

Exposure to metals among Electronic Nicotine Delivery System (ENDS) users in the PATH study: A longitudinal analysis.

BACKGROUND: Few studies have evaluated Electronic Nicotine Delivery Systems (ENDS) in longitudinal studies, as a potential source of metals which may have carcinogenic, neurotoxic, and cardiotoxic effects. We evaluated metal body burden by ENDS use status in a longitudinal population-based national survey. METHODS: We used the Population Assessment of Tobacco and Health (PATH) Study wave 1 (2013-2014), wave 2 (2014-2015), and wave 3 (2015-2016) adult data to assess urinary concentrations of seven metals among (1) ENDS only users who never used any nonelectronic tobacco products (n = 50), (2) ENDS only users who were former users of any nonelectronic tobacco products (n = 123) and (3) Never users (n = 1501) of any tobacco product. RESULTS: Among ENDS only users who never used any nonelectronic tobacco products (n = 50), the geometric mean ratios (GMRs) of Cd and Pb were 1.25 (95%CI: 1.09-1.42) and 1.19 (95%CI: 1.05-1.34), respectively, compared to never users after adjustment for PATH Study wave, age, sex, race/ethnicity, education, region, secondhand smoke at home and work, and cannabis and other substance use. After the same adjustment, the corresponding GMRs were 1.48 (95%CI: 1.32-1.67) and 1.43 (95%CI: 1.28-1.60) for ENDS only users who were former users of any nonelectronic tobacco products (n = 123). No difference was observed in urinary concentrations of other metals comparing ENDS users to never users of any tobacco product. DISCUSSION: ENDS users show higher urinary levels of Cd and Pb, including lifetime exclusive ENDS users compared to never users of any tobacco product. These findings are limited by the small sample size and could be related to underreporting of past combustible tobacco use or other factors. Metals typical of ENDS such as nickel and chromium unfortunately are not available in PATH. Studies assessing metal exposure associated with long term lifetime exclusive ENDS use (≥5 years) with larger sample size are needed.

Assessing Neighborhood-scale Traffic from Crowd-sensed Traffic Data: Findings from an Environmental Justice Community in New York City.

Background: The waterfront in the South Bronx in New York City is used industrially and harbors the Harlem River Yards (HRY). The HRY borders an environmental justice area, which includes a mixed-use area that is separated from a densely populated residential area by interstates. Recently, development of the HRY has expanded including the 2018 opening of a large online store warehouse. Objective: The goal of this study was to evaluate trends in traffic congestion nearby the HRY between 2017 to 2019. Methods: We analyzed one-hourly time series of crowd-sensed traffic congestion maps, both at the neighborhood scale and the road stretch level. Traffic radar measurements at two locations did not indicate bias in the crowd-sensed data over the study period, i.e., changed mappings between vehicle speed and the reported congestion. Results: In the mixed-use areas, traffic congestion increased significantly during all hours of the day, with greatest increases at night and in the morning. Congestion increased close to the entrances of the HRY and along routes used by pedestrians and bicyclists to access a nearby recreational area. In the residential area, congestion increased significantly from midnight to morning and was unchanged for the remainder of the day. On the interstates, congestion decreased during the daytime but increased at night. Conclusions: Neighborhood-scale traffic congestion increased in mixed-use and residential areas in an environmental justice community. Our methods can be applied globally as long as crowd-sensed traffic data can be acquired. The data enable communities to advocate for mitigating measures.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018.

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Quantifying diurnal changes in NO2 due to COVID-19 stay-at-home orders in New York City.

Introduction: Policy responses to the COVID-19 pandemic, such as the NY on Pause stay-at-home order (March 22 - June 8, 2020), substantially reduced traffic and traffic-related air pollution (TRAP) in New York City (NYC). We evaluated the magnitude of TRAP decreases and examined the role of modifying factors such as weekend/weekday, road proximity, location, and time-of-day. Methods: Hourly nitrogen dioxide (NO2) concentrations from January 1, 2018 through June 8, 2020 were obtained from the Environmental Protection Agency's Air Quality System for all six hourly monitors in the NYC area. We used an interrupted time series design to determine the impact of NY on Pause on NO2 concentrations, using a mixed effects model with random intercepts for monitor location, adjusted for meteorology and long-term trends. We evaluated effect modification through stratification. Results: NO2 concentrations decreased during NY on Pause by 19% (-3.2 ppb, 95% confidence interval [CI]: -3.5, -3.0), on average, compared to pre-Pause time trends. We found no evidence for modification by weekend/weekday, but greater decreases in NO2 at non-roadside monitors and weak evidence for modification by location. For time-of-day, we found the largest decreases for 5 am (27%, -4.5 ppb, 95% CI: -5.7, -3.3) through 7 am (24%, -4.0 ppb, 95% CI: -5.2, -2.8), followed by 6 pm and 7 pm (22%, -3.7 ppb, 95% CI: -4.8, -2.6 and 22%, -4.8, -2.5, respectively), while the smallest decreases occurred at 11 pm and 1 am (both: 11%, -1.9 ppb, 95% CI: -3.1, -0.7). Conclusion: NY on Pause's impact on TRAP varied greatly diurnally. Decreases during early morning and evening time periods are likely due to decreases in traffic. Our results may be useful for planning traffic policies that vary by time of day, such as congestion tolling policies.

Effects of e-liquid flavor, nicotine content, and puff duration on metal emissions from electronic cigarettes.

Vaping is the action of inhaling and exhaling aerosols from electronic cigarettes. The aerosols contain various amounts of toxic chemicals, including metals. The purpose of this study was to evaluate factors that can influence metal levels, including flavor and nicotine content in the e-liquid, and puff duration. Aerosols were collected from both closed-system (cartridge-based) and open-system e-cigarettes using e-liquids with different flavors (fruit, tobacco, and menthol), nicotine content (0, 6, 24, and 59 mg/mL), and different puff durations (1, 2, and 4 s). The concentrations of 14 metals in the collected aerosols were measured using inductively coupled plasma mass spectroscopy. Aerosol concentrations of As, Fe, and Mn varied significantly among fruit, tobacco, and menthol flavors in both closed-system and open-system devices. Concentrations of Al, Fe, Sn, and U were significantly higher in tobacco or menthol flavored aerosols compared to fruit flavors in closed-system devices. Aerosol W levels were significantly higher in tobacco flavored aerosols compared to fruit flavors in open-system devices. Concentrations of As, Fe, and Mn were higher in tobacco flavored aerosols compared to menthol flavors in both types of devices. The median Pb concentration decreased significantly from 15.8 to 0.88 µg/kg when nicotine content increased from 0 to 59 mg/mL, and median Ni concentration was 9.60 times higher in aerosols with nicotine of 59 mg/mL compared to 24 mg/mL (11.9 vs. 1.24 µg/kg) for closed-system devices. No significant differences were observed in aerosol metal concentrations for different puff durations. Aerosol metal concentrations varied widely between different flavors and nicotine content but not by puff duration. Flavor and nicotine content of the e-liquid could be potential factors in metal emissions. Some elements showed higher concentrations under certain conditions, highlighting the urgent need of developing strict product regulations, especially on e-liquid composition and nicotine content to inform e-cigarette users about metal exposure through vaping.

Identification of newly formed toxic chemicals in E-cigarette aerosols with Orbitrap mass spectrometry and implications on E-cigarette control.

The increasing use of electronic nicotine delivery systems (ENDS) is of concern due to multiple emerging adverse health effects. Most analyses of the harmful chemicals of ENDS have targeted metals or carbonyls generated by thermal decomposition of carrier liquids such as propylene glycol. However, new complex compounds not routinely identified and with unknown health consequences could be formed. ENDS aerosol samples were collected by the direct aerosol droplet deposition method. Untargeted analysis was performed using Orbitrap mass spectrometry with high mass accuracy. We identified more than 30 "features" in the aerosol characterized by pairs of the mass-to-charge ratio "m/z" of the compound and the retention time. We identified several compounds containing nicotine and propylene glycol (NIC-PG), whose abundance relative to nicotine increased along with vaping power used. On the basis of the prediction by the Environmental Protection Agency Toxicity Estimation Software Tool, these compounds exert developmental toxicity. In addition, a nitrogen-containing compound, likely tributylamine (a known lung irritant), was identified based on the molecular weight. This compound has not been previously identified in ENDS e-liquids and aerosols. ENDS produce not only small toxic compounds such as aldehydes, but also large complex toxic compounds such as NIC-PG. Predicted development toxicity for NIC-PG is concerning for fetal development in pregnant women who use ENDS, children exposed to secondhand or thirdhand ENDS aerosols, and teenage ENDS users whose brains are still developing. The strong positive association between NIC-PG levels and ENDS power output supports regulating high-powered ENDS.

Exposure to e-cigarette aerosol over two months induces accumulation of neurotoxic metals and alteration of essential metals in mouse brain.

Despite a recent increase in e-cigarette use, the adverse human health effects of exposure to e-cigarette aerosol, especially on the central nervous system (CNS), remain unclear. Multiple neurotoxic metals have been identified in e-cigarette aerosol. However, it is unknown whether those metals accumulate in the CNS at biologically meaningful levels. To answer this question, two groups of mice were whole-body exposed twice a day, 5 days a week, for two months, to either a dose of e-cigarette aerosol equivalent to human secondhand exposure, or a 5-fold higher dose. After the last exposure, the olfactory bulb, anterior and posterior frontal cortex, striatum, ventral midbrain, cerebellum, brainstem, remaining brain tissue and spinal cord were collected for metal quantification by inductively coupled plasma mass spectrometry and compared to tissues from unexposed control mice. The two-month exposure caused significant accumulation of several neurotoxic metals in various brain areas - for some metals even at the low exposure dose. The most striking increases were measured in the striatum. For several metals, including Cr, Cu, Fe, Mn, and Pb, similar accumulations are known to be neurotoxic in mice. Decreases in some essential metals were observed across the CNS. Our findings suggest that chronic exposure to e-cigarette aerosol could lead to CNS neurotoxic metal deposition and endogenous metal dyshomeostasis, including potential neurotoxicity. We conclude that e-cigarette-mediated metal neurotoxicity may pose long-term neurotoxic and neurodegenerative risks for e-cigarette users and bystanders.

Metal exposure and biomarker levels among e-cigarette users in Spain.

The use of electronic cigarettes (e-cigarettes) has increased due to the belief that they are healthier than tobacco cigarettes. E-cigarettes contain a metallic heating coil (composed of Ni, Cr, Al and other metals) to heat a solution (commonly called e-liquid) and convert it into an aerosol. This aerosol is inhaled (vaped) by the users who can be potentially exposed to a wide variety of metals. We investigated the possible transfer of metals from the coil to the e-liquid and the generated aerosol, and how the exposure to this aerosol can increase metal body burden in e-cigarette users. We recruited 75 e-cigarette users (50 who only vaped and 25 dual users who vaped and smoked) and 25 controls who neither vaped nor smoked. E-liquid samples before (dispenser e-liquid) and after (tank e-liquid) being added to their devices were collected. Aerosol samples were collected using a condensation method. All participants provided urine and hair samples. All samples were analyzed for metals by ICP-MS. We observed higher metal concentrations in the aerosol and tank e-liquid (in contact with the coil) compared to the dispenser e-liquid (before contact with the coil). The median concentrations for some of the metals with the most remarkable increases in aerosol and tank e-liquid vs. dispenser e-liquid were 36.90 and 62.73 vs. 18.29 µg/kg for Al; 6.71 and 28.97 vs. 0.98 µg/kg for Cr; 91.39 and 414.47 vs. 1.64 µg/kg for Ni; 738.99 and 744.24 vs. 16.56 µg/kg for Zn; and 10.17 and 22.31 vs. 0.88 µg/kg for Pb. We also found detectable and potentially high concentrations of other metals such as Mn, Cu, Sb and Sn. In urine, increases in the median levels (µg/g creatinine) in vapers/duals vs. controls were observed for some metals, including Cr (0.34/0.28 vs. 0.20), Cu (1.72/2.36 vs. 1.46), Sn (0.26/0.31 vs. 0.18) and Pb (0.39/0.44 vs. 0.22). In hair, there were no differences in metal concentrations among the three groups. In conclusion, e-cigarettes are likely a source of metals such as Cr, Cu, Ni, Pb or Sn. These metals come from the device, likely the heating resistance, as their concentrations were low in the dispenser e-liquid and higher in the aerosol and the e-liquid left in the tank. Although the exposure to e-cigarette aerosol can have an influence in the body burden of metals, aerosol metal levels were not clearly associated with metal levels in biological samples such as urine or hair in e-cigarette users in this study.

Benzene emissions from gas station clusters: a new framework for estimating lifetime cancer risk.

PURPOSE: During gas station operation, unburned fuel can be released to the environment through distribution, delivery, and storage. Due to the toxicity of fuel compounds, setback distances have been implemented to protect the general population. However, these distances treat gasoline sales volume as a categorical variable and only account for the presence of a single gas station and not clusters, which frequently occur. This paper introduces a framework for recommending setback distances for gas station clusters based on estimated lifetime cancer risk from benzene exposure. METHODS: Using the air quality dispersion model AERMOD, we simulated levels of benzene released to the atmosphere from single and clusters of generic gas stations and the associated lifetime cancer risk under meteorological conditions representative of Albany, New York. RESULTS: Cancer risk as a function of distance from gas station(s) and as a continuous function of total sales volume can be estimated from an equation we developed. We found that clusters of gas stations have increased cancer risk compared to a single station because of cumulative emissions from the individual gas stations. For instance, the cancer risk at 40 m for four gas stations each dispensing 1 million gal/year is 9.84 × 10-6 compared to 2.45 × 10-6 for one gas station. CONCLUSION: The framework we developed for estimating cancer risk from gas station(s) could be adopted by regulatory agencies to make setback distances a function of sales volume and the number of gas stations in a cluster, rather than on a sales volume category. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40201-020-00601-w.

Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data.

INTRODUCTION: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and result in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS: Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSION: Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

Social-distancing Fatigue: Evidence from Real-time Crowd-sourced Traffic Data.

INTRODUCTION: To mitigate the COVID-19 pandemic and prevent overwhelming the healthcare system, social-distancing policies such as school closure, stay-at-home orders, and indoor dining closure have been utilized worldwide. These policies function by reducing the rate of close contact within populations and results in decreased human mobility. Adherence to social distancing can substantially reduce disease spread. Thus, quantifying human mobility and social-distancing compliance, especially at high temporal resolution, can provide great insight into the impact of social distancing policies. METHODS: We used the movement of individuals around New York City (NYC), measured via traffic levels, as a proxy for human mobility and the impact of social-distancing policies (i.e., work from home policies, school closure, indoor dining closure etc.). By data mining Google traffic in real-time, and applying image processing, we derived high resolution time series of traffic in NYC. We used time series decomposition and generalized additive models to quantify changes in rush hour/non-rush hour, and weekday/weekend traffic, pre-pandemic and following the roll-out of multiple social distancing interventions. RESULTS: Mobility decreased sharply on March 14, 2020 following declaration of the pandemic. However, levels began rebounding by approximately April 13, almost 2 months before stay-at-home orders were lifted, indicating premature increase in mobility, which we term social-distancing fatigue. We also observed large impacts on diurnal traffic congestion, such that the pre-pandemic bi-modal weekday congestion representing morning and evening rush hour was dramatically altered. By September, traffic congestion rebounded to approximately 75% of pre-pandemic levels. CONCLUSION: Using crowd-sourced traffic congestion data, we described changes in mobility in Manhattan, NYC, during the COVID-19 pandemic. These data can be used to inform human mobility changes during the current pandemic, in planning for responses to future pandemics, and in understanding the potential impact of large-scale traffic interventions such as congestion pricing policies.

E-cigarette aerosol collection using converging and straight tubing Sections: Physical mechanisms.

HYPOTHESIS: Identification and quantification of harmful chemicals in e-cigarette aerosol requires collecting the aerosolized e-liquid for chemical analysis. In 2016, Olmedo at al. empirically developed a simple method for aerosol collection by directing the aerosol through a sequence of alternating straight and converging tubing sections, which drain the recovered e-liquid into a collection vial. The tubing system geometry and flow conditions promote inertial impaction of aerosolized e-liquid on tube walls, where it deposits and flows into the collection vial. EXPERIMENTS: We use high-speed optical imaging to visualize aerosol transport in proxies of the collection system. We also determined collection efficiencies of various configurations of the collection system. FINDINGS: A turbulent jet emerges from converging conical sections and impinges onto the wall of downstream tubing sections, resulting in inertial impaction and deposition of the aerosol. For inertial impaction to occur the tip radius of the converging section must be small enough for a jet to be formed and the sequence of tubing sections must be curved in a polygon-like manner such that the jet emerging from a converging section impinges on the downstream tube wall. The collection efficiency is significantly smaller without such curvature.

The effect of channel aspect ratio on air entrapment during imbibition in soil-on-a-chip micromodels with 2D and 2.5D pore structures.

We developed a low-cost method for fabricating "soil-on-a-chip" micromodels with 2D and 2.5D pore structures by stacking layers made with a conventional low-cost tabletop CNC router followed by tape bonding. The pore structure was extracted from an X-ray micro-computed tomography scanning image of a medium-grain sandstone sample. The imbibition experiments performed in the 2D and 2.5D micromodels showed the trends of the residual saturation versus capillary number (Ca). The channels showed opposing trends for low-aspect-ratio 2D and high-aspect-ratio 2.5D micromodels. As the channel aspect ratio increased, the location of air entrapment changed from dead-end pores to transport pores. The sizes of trapped air bubbles in the transport pores decreased as the injection flow rates increased. To show the relationship between the air trapped size and Ca, we derived equations that described the competition between the bulk menisci and the corner flow in the channels for different Ca based on the "supply principle." The relative contributions of the piston displacement and corner film flow, which were dependent on the cross-sectional shapes of the pores and Ca, determined the size and location of the air bubbles trapped in the 2.5D micromodel.

Early Cardiovascular Risk in E-cigarette Users: the Potential Role of Metals.

PURPOSE OF REVIEW: Electronic cigarettes (e-cigs) are a source of metals. Epidemiologic and experimental evidence support that metals are toxic to the cardiovascular system. Little is known, however, about the role that e-cig metals may play as toxicants for the possible cardiovascular effects of e-cig use. The goal of this narrative review is to summarize the evidence on e-cig use and metal exposure and on e-cig use and cardiovascular toxicity and discuss the research needs. RECENT FINDINGS: In vitro studies show cytotoxicity and increased oxidative stress in myocardial cells and vascular endothelial cells exposed to e-liquids and e-cig aerosols, with effects partially reversed with antioxidant treatment. There is some evidence that the heating coil plays a role in cell toxicity. Mice exposed to e-cigs for several weeks showed higher levels of oxidative stress, inflammation, platelet activation, and thrombogenesis. Cross-over clinical experiments show e-cig use alters nitric oxide-mediated flow-mediated dilation, endothelial progenitor cells, and arterial stiffness. Cross-sectional evidence from large nationally representative samples in the USA support that e-cig use is associated with self-reported myocardial infarction. Smaller studies found associations of e-cig use with higher oxidized low-density protein and heart variability compared to healthy controls. Numerous studies have measured elevated levels of toxic metals in e-cig aerosols including lead, nickel, chromium, and manganese. Arsenic has been measured in some e-liquids. Several of these metals are well known to be cardiotoxic. Numerous studies show that e-cigs are a source of cardiotoxic metals. Experimental studies (in vitro, in vivo, and clinical studies) show acute toxicity of e-cigs to the vascular system. Studies of long-term toxicity in animals and humans are missing. Longitudinal studies with repeated measures of metal exposure and subclinical cardiovascular outcomes (e.g., coronary artery calcification) could contribute to determine the long-term cardiovascular effects of e-cigs and the potential role of metals in those effects.

Opening a Large Delivery Service Warehouse in the South Bronx: Impacts on Traffic, Air Pollution, and Noise.

Mott Haven, a low-income neighborhood in New York City, suffers from increased air pollution and accommodates several industrial facilities and interstates. In 2018, a large delivery service warehouse opened. Our objectives are to characterize black carbon (BC), fine particulate matter (PM2.5), and noise in the community; model changes in traffic due to the facility opening; and estimate associated BC and noise changes. BC, PM2.5, and noise were measured at eight sites pre-opening, and traffic counted continuously at two sites (June 2017-May 2019). An interrupted time series model was used to determine facility-related changes in traffic. Post-opening changes in traffic-related BC/noise were estimated from regressions of BC/noise with traffic flow. Mean (SD) pre-warehouse measures of BC and PM2.5 were 1.33 µg/m3 (0.41) and 7.88 µg/m3 (1.24), respectively. At four sites, equivalent sound levels exceeded the EPA's recommended 70 dBA limit. After the warehouse opening, traffic increased significantly, predominantly at night. At one site, the greatest change for trucks occurred 9PM-12AM: 31.7% (95%CI [23.4%, 40.6%]). Increased traffic translated into mean predicted increases of 0.003 µg/m3 (BC) and 0.06 dBA (noise). Though small, they negate the substantial decrease the community seeks. Our findings can help communities and policymakers better understand impacts of traffic-intensive facilities.

Gasoline Vapor Emissions During Vehicle Refueling Events in a Vehicle Fleet Saturated With Onboard Refueling Vapor Recovery Systems: Need for an Exposure Assessment.

Background: Gasoline contains large proportions of harmful chemicals, which can be released during vehicle refueling. Onboard Refueling Vapor Recovery (ORVR) can reduce these emissions, but there is limited research on the system's efficacy over time in an actual vehicle fleet. The aims of this study are: (1) determine the feasibility of using an infrared camera to view vapor emissions from refueling; (2) examine the magnitude of refueling-related emissions in an ORVR-saturated fleet, to determine need for an exposure-assessment. Methods: Using an infrared camera optimized for optical gas imaging of volatile organic chemicals, refueling was recorded for 16 vehicles at six gas stations. Pumps were inspected for damage, refueling shut-off valve functioning, and presence of Stage II Vapor Recovery. Vehicle make/model and age were recorded or estimated. Results: Vapor emissions were observed for 14 of 16 vehicles at each station, with severity varying substantially by vehicle make/model and age. Use of an infrared camera allowed for identification of vapor sources and timing of release, and for visualizing vapor trajectories. Discussion: Notably emissions occurred not only at the beginning and end of refueling but also throughout, in contrast to a prior study which did not detect increases in atmospheric hydrocarbon levels mid-refueling. Future studies are vitally needed to determine the risk to individuals during typical refueling in an ORVR saturated vehicle fleet. We recommend comprehensive exposure-assessment including real-time monitoring of emitted volatile organic compounds paired with infrared gas-imaging and measurement of internal dose and health effects of gas station customers.

Metal/Metalloid Levels in Electronic Cigarette Liquids, Aerosols, and Human Biosamples: A Systematic Review.

BACKGROUND: Electronic cigarettes (e-cigarettes) have become popular, in part because they are perceived as a safer alternative to tobacco cigarettes. An increasing number of studies, however, have found toxic metals/metalloids in e-cigarette emissions. OBJECTIVE: We summarized the evidence on metal/metalloid levels in e-cigarette liquid (e-liquid), aerosols, and biosamples of e-cigarette users across e-cigarette device systems to evaluate metal/metalloid exposure levels for e-cigarette users and the potential implications on health outcomes. METHODS: We searched PubMed/TOXLINE, Embase®, and Web of Science for studies on metals/metalloids in e-liquid, e-cigarette aerosols, and biosamples of e-cigarette users. For metal/metalloid levels in e-liquid and aerosol samples, we collected the mean and standard deviation (SD) if these values were reported, derived mean and SD by using automated software to infer them if data were reported in a figure, or calculated the overall mean (mean ± SD) if data were reported only for separate groups. Metal/metalloid levels in e-liquids and aerosols were converted and reported in micrograms per kilogram and nanograms per puff, respectively, for easy comparison. RESULTS: We identified 24 studies on metals/metalloids in e-liquid, e-cigarette aerosols, and human biosamples of e-cigarette users. Metal/metalloid levels, including aluminum, antimony, arsenic, cadmium, cobalt, chromium, copper, iron, lead, manganese, nickel, selenium, tin, and zinc, were present in e-cigarette samples in the studies reviewed. Twelve studies reported metal/metalloid levels in e-liquids (bottles, cartridges, open wick, and tank), 12 studies reported metal/metalloid levels in e-cigarette aerosols (from cig-a-like and tank devices), and 4 studies reported metal/metalloid levels in human biosamples (urine, saliva, serum, and blood) of e-cigarette users. Metal/metalloid levels showed substantial heterogeneity depending on sample type, source of e-liquid, and device type. Metal/metalloid levels in e-liquid from cartridges or tank/open wicks were higher than those from bottles, possibly due to coil contact. Most metal/metalloid levels found in biosamples of e-cigarette users were similar or higher than levels found in biosamples of conventional cigarette users, and even higher than those found in biosamples of cigar users. CONCLUSION: E-cigarettes are a potential source of exposure to metals/metalloids. Differences in collection methods and puffing regimes likely contribute to the variability in metal/metalloid levels across studies, making comparison across studies difficult. Standardized protocols for the quantification of metal/metalloid levels from e-cigarette samples are needed. https://doi.org/10.1289/EHP5686.