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.

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.

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).

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.

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.

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.

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 concentrations in electronic cigarette aerosol: Effect of open-system and closed-system devices and power settings.

BACKGROUND: Electronic cigarettes (E-cigarettes) generate aerosol containing metal contaminants. Our goals were to quantify aerosol metal concentrations and to compare the effects of power setting and device type (closed-system vs. open-system) on metal release. METHODS: Aerosol samples were collected from two closed-system devices (a cigalike and pod) and two open-system devices (mods). Each open-system device was operated at three different power settings to examine the effect of device power on metal release. Concentrations of 14 metals in e-cigarette aerosol collected via droplet deposition were measured using inductively coupled plasma mass spectroscopy. Aerosol metal concentrations were reported as mass fractions (µg/kg) in the e-liquid. RESULTS: For open-system device 1 (OD1), median arsenic (As), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), antimony (Sb), tin (Sn), and zinc (Zn) concentrations increased 14, 54, 17, 30, 41, 96, 14, 81, 631, and 7-fold when the device power was increased from low (20 W) to intermediate (40 W) setting. When the power was further increased from intermediate (40 W) to high (80 W) setting, concentrations of As, Cr, Cu, Mn, Ni, and Sb did not change significantly. For open-system device 2 (OD2), Cr and Mn concentrations increased significantly when device power was increased from low (40 W) to intermediate (120 W) setting, and then decreased significantly when power was further increased from intermediate (120 W) to high (200 W) setting. Among the four devices, aerosol metal concentrations were higher for the open-system than the closed-system devices, except for aluminum (Al) and uranium (U). For Cr, median (interquartile range) concentrations (µg/kg) from the open-system devices were 2.51 (1.55, 4.23) and 15.6 (7.88, 54.5) vs. 0.39 (0.05, 0.72) and 0.41 (0.34, 0.57) for the closed-system devices. For Ni, concentrations (µg/kg) from the open-system devices were 793 (508, 1169) and 2148 (851, 3397) vs. 1.32 (0.39, 3.35) and 11.9 (10.7, 22.7) from the closed-system devices. Inhalation of 0% and 100% of samples from OD1, 7.4% and 88.9% from OD2 by typical e-cigarette users would exceed chronic minimum risk levels (MRL) of Mn and Ni, respectively. No MRL exceedance was predicted for the closed-system devices. A large fraction of users of OD1 (100%) and OD2 (77.8%) would be exposed to Ni levels higher than those from reference tobacco cigarette 3R4F. CONCLUSIONS: Our findings suggest that power setting and device type affect metal release from devices to aerosol which would subsequently be inhaled by users. Metal concentrations from open-system devices first increased with device power, and then leveled off for most metals. Open-system devices generate aerosol with higher metal concentrations than closed-system devices. These findings inform tobacco regulatory science, policy makers and health professionals on potential metal health risks associated with e-cigarette use, design and manufacturing.

A custom-built low-cost chamber for exposing rodents to e-cigarette aerosol: practical considerations.

Objectives: To (1) design and build a low-cost exposure chamber system for whole-body exposure of rodents to electronic cigarette aerosol, (2) provide detailed instructions with particular focus on automated e-cigarette activation, and (3) develop a simple mathematical model for aerosol levels in the exposure chamber.Methods: We built the system with standard laboratory equipment and an open-source electronics platform (Arduino) for e-cigarette activation. Arduino is used to control a solenoid, which pushes the activation button of so-called "Mod" e-cigarettes, and a pump to move the aerosol from the mouthpiece of the e-cigarette into the chamber. For "Pods" and "Cigalikes," the solenoid is not used as they are activated by the vacuum created by the pump. Aerosol concentrations were measured with a light-scattering laser photometer.Results: The system allows varying the air exchange rate, monitoring aerosol levels, and programing arbitrary puff topography. Aerosol concentrations observed for different chamber operating conditions (puff time and period, e-cigarette power output, air exchange rate) were consistent with the mathematical model.Conclusions: Our low-cost exposure chamber can be used in animal experimental studies of the health effects of e-cigarettes. Our model allows estimating design parameters such as chamber volume, air exchange rate, and puff period.

A binomial modeling approach for upscaling colloid transport under unfavorable attachment conditions: Emergent prediction of non-monotonic retention profiles.

We used a recently developed simple mathematical network model to upscale pore-scale colloid transport information determined under unfavorable attachment conditions. Classical log-linear and non-monotonic retention profiles, both well-reported under favorable and unfavorable attachment conditions, respectively, emerged from our upscaling. The primary attribute of the network is colloid transfer between bulk pore fluid, the near surface fluid domain (NSFD), and attachment (treated as irreversible). The network model accounts for colloid transfer to the NSFD of down-gradient grains and for reentrainment to bulk pore fluid via diffusion or via expulsion at rear flow stagnation zones (RFSZs). The model describes colloid transport by a sequence of random trials in a 1D network of Happel cells, which contain a grain and a pore. Using combinatorial analysis that capitalizes on the binomial coefficient, we derived from the pore-scale information the theoretical residence time distribution of colloids in the network. The transition from log-linear to non-monotonic retention profiles occurs when the conditions underlying classical filtration theory are not fulfilled, i.e., when a NSFD colloid population is maintained. Then, nonmonotonic retention profiles result, potentially both for attached and NSFD colloids. The concentration maxima shift downgradient depending on specific parameter choice. The concentration maxima were also shown to shift downgradient temporally (with continued elution) under conditions where attachment is negligible, explaining experimentally-observed down-gradient transport of retained concentration maxima of adhesion-deficient bacteria. For the case of zero reentrainment, we develop closed form, analytical expressions for the shape and the maximum of the colloid retention profile.

A multiple-relaxation-time lattice-boltzmann model for bacterial chemotaxis: effects of initial concentration, diffusion, and hydrodynamic dispersion on traveling bacterial bands.

Bacterial chemotaxis can enhance the bioremediation of contaminants in aqueous and subsurface environments if the contaminant is a chemoattractant that the bacteria degrade. The process can be promoted by traveling bands of chemotactic bacteria that form due to metabolism-generated gradients in chemoattractant concentration. We developed a multiple-relaxation-time (MRT) lattice-Boltzmann method (LBM) to model chemotaxis, because LBMs are well suited to model reactive transport in the complex geometries that are typical for subsurface porous media. This MRT-LBM can attain a better numerical stability than its corresponding single-relaxation-time LBM. We performed simulations to investigate the effects of substrate diffusion, initial bacterial concentration, and hydrodynamic dispersion on the formation, shape, and propagation of bacterial bands. Band formation requires a sufficiently high initial number of bacteria and a small substrate diffusion coefficient. Uniform flow does not affect the bands while shear flow does. Bacterial bands can move both upstream and downstream when the flow velocity is small. However, the bands disappear once the velocity becomes too large due to hydrodynamic dispersion. Generally bands can only be observed if the dimensionless ratio between the chemotactic sensitivity coefficient and the effective diffusion coefficient of the bacteria exceeds a critical value, that is, when the biased movement due to chemotaxis overcomes the diffusion-like movement due to the random motility and hydrodynamic dispersion.

Identification of Tet45, a tetracycline efflux pump, from a poultry-litter-exposed soil isolate and persistence of tet(45) in the soil.

OBJECTIVES: To characterize a tetracycline resistance (Tc(R)) determinant, closely related to the TetL efflux pump, in a Bhargavaea cecembensis strain previously isolated from a poultry-litter-impacted soil. METHODS: Genomic DNA of B. cecembensis DMV42A was shotgun cloned and expressed in Escherichia coli. Antimicrobial susceptibility testing and a [(3)H]tetracycline uptake assay were used to confirm the function of the target gene. Transferability of the gene was examined using filter matings and confirmed by PCR and sequencing. Real-time quantitative PCR was performed on soil metagenomic DNA to evaluate the prevalence of the gene in the soil from which B. cecembensis DMV42A was isolated and in more pristine local soils. RESULTS: The Tc(R) determinant from B. cecembensis DMV42A, designated Tet45, was identified as a tetracycline efflux pump sharing 78% amino acid identity with certain TetL proteins. In B. cecembensis DMV42A, tet(45) was adjacent to truncated and non-functional arsenic resistance genes with high sequence similarities to genes from staphylococcal plasmids. After filter matings, the tet(45) gene could be found in E. coli transconjugants, although the transfer mechanism was unknown. Tet45 homologues are also present in the genomes of several Bacillus cereus strains and a Bacillus thuringiensis strain. tet(45) was detected in the poultry-litter-impacted soil, and persisted at a similar level 2 years after removal of the chicken waste, although it was not detected in several more pristine soils. CONCLUSIONS: Tet45 is a tetracycline efflux pump closely related to TetL. Horizontal gene transfer may have contributed to the dissemination and persistence of tet(45) in a poultry-litter-impacted soil.

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.

Detection of a common and persistent tet(L)-carrying plasmid in chicken-waste-impacted farm soil.

The connection between farm-generated animal waste and the dissemination of antibiotic resistance in soil microbial communities, via mobile genetic elements, remains obscure. In this study, electromagnetic induction (EMI) surveying of a broiler chicken farm assisted soil sampling from a chicken-waste-impacted site and a marginally affected site. Consistent with the EMI survey, a disparity existed between the two sites with regard to soil pH, tetracycline resistance (Tc(r)) levels among culturable soil bacteria, and the incidence and prevalence of several tet and erm genes in the soils. No significant difference was observed in these aspects between the marginally affected site and several sites in a relatively pristine regional forest. When the farm was in operation, tet(L), tet(M), tet(O), erm(A), erm(B), and erm(C) genes were detected in the waste-affected soil. Two years after all waste was removed from the farm, tet(L), tet(M), tet(O), and erm(C) genes were still detected. The abundances of tet(L), tet(O), and erm(B) were measured using quantitative PCR, and the copy numbers of each were normalized to eubacterial 16S rRNA gene copy numbers. tet(L) was the most prevalent gene, whereas tet(O) was the most persistent, although all declined over the 2-year period. A mobilizable plasmid carrying tet(L) was identified in seven of 14 Tc(r) soil isolates. The plasmid's hosts were identified as species of Bhargavaea, Sporosarcina, and Bacillus. The plasmid's mobilization (mob) gene was quantified to estimate its prevalence in the soil, and the ratio of tet(L) to mob was shown to have changed from 34:1 to 1:1 over the 2-year sampling period.

Transport of Cryptosporidium parvum oocysts in a silicon micromodel.

Effective removal of Cryptosporidium parvum oocysts by granular filtration requires the knowledge of oocyst transport and deposition mechanisms, which can be obtained based on real time microscopic observation of oocyst transport in porous media. Attachment of oocysts to silica surface in a radial stagnation point flow cell and in a micromodel, which has 2-dimensional (2-D) microscopic pore structures consisting of an array of cylindrical collectors, was studied and compared. Real time transport of oocysts in the micromodel was recorded to determine the attached oocyst distributions in transversal and longitudinal directions. In the micromodel, oocysts attached to the forward portion of clean collectors, where the flow velocity was lowest. After initial attachment, oocysts attached onto already attached oocysts. As a result, the collectors ripened and the region available for flow was reduced. Results of attachment and detachment experiments suggest that surface charge heterogeneity allowed for oocyst attachment. In addition to experiments, Lattice-Boltzmann simulations helped understanding the slightly nonuniform flow field and explained differences in the removal efficiency in the transversal direction. However, the hydrodynamic modeling could not explain differences in attachment in the longitudinal direction.

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.

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 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.

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.

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.