Ambient particle radioactivity (PR) predominantly originates from the attachment of short-lived (SL) radon radionuclides to accumulation mode ambient particles both indoors and outdoors. These radioactive particles can be inhaled and deposited in the lungs, where they continue to decay, resulting in internal exposures. Alpha-emitting radionuclides cause more biological damage internally than either beta- or gamma-emitting radionuclides due to their high linear energy transfer over short distances. Investigating the health effects of environmental exposures to PR, specifically particle alpha activity, is a relatively novel field of study. Recent epidemiological studies have shown associations between ambient PR exposures and a variety of human health effects; however, there is very little information available about ambient particle alpha activity exposures, especially since these are dominated by SL radionuclides. We have developed a particle alpha radioactivity sampler (PARS) which provides time-resolved/continuous measurement of SL particle gross alpha activity at environmentally relevant concentrations both indoors and outdoors. PARS measurements compare well with integrated gross alpha measurements. Collocated PARS have excellent precision during hourly measurement. PARS is suitable for use in exposure assessment or indoor/outdoor air chemistry studies and can be used to investigate the complex relationship between radon and ambient particle radioactivity.Copyright (c) 2025 American Association for Aerosol Research

Radon is a prevalent carcinogenic gas and the leading cause of lung cancer in the United States besides smoking. As the residential environment is the primary source of radon exposure, accessible and accurate measurements of radon in this environment are essential. However, no radon monitors have been evaluated that are inexpensive enough for regular household use. In this study, we examine two household-grade, continuous monitoring devices, the Ecosense RadonEye and EcoQube. We compare them to two research-grade instruments, the Durridge Company Rad7 and the Rad Elec Inc. E-PERM. In our study, the Ecosense household radon monitors performed accurately and can be used by homeowners and researchers alike as an affordable and reliable radon sensor.Implications: The ability of homeowners and renters to regularly monitor the radon levels inside their home is an important preventative health measure. However, low-cost instrumentation is needed that can provide accurate radon measurements. In this study, we show that the affordable Ecosense continuous monitors produce results that are in line with expensive research-grade instruments in a residential environment, over a range of concentrations. The Ecosense monitors may be suitable for home use, and they may provide a solution that can be used by policymakers and home-dwellers alike to improve regular radon monitoring in residences.

Recent studies conducted in the USA have shown that adverse health effects of fine particulate matter (PM 2.5 ) persist at levels below the national air quality standards. More recently, particle radioactivity has also been associated with adverse health effects. However, the importance of particle radioactivity at low PM 2.5 levels has not been thoroughly explored. The present paper investigates the temporal trends and the relationship of particle gross β-activity (PM-β) and PM 2.5 mass in the 48 states of the contiguous USA during the period of 2001–2017. With the implementation of stringent air pollution control policies, national ambient PM 2.5 concentrations decreased by 38.5% during this period. However, a smaller decrease of 9.4% was observed for PM-β, while the mean PM-β/PM 2.5 ratio increased by 49.1%. PM-β is mostly associated with radon emissions and its progeny, which do not change much with time. The largest PM 2.5 and PM-β reductions were observed in the Southeast, while the smallest were found in the West. When the aggregated PM-β to PM 2.5 ratio is stratified by PM 2.5 levels, the ratio was found to be highest when PM 2.5 is <3 μg/m 3 , with a median PM-β to PM 2.5 ratio of 0.77 (0.64–0.88; 25th–75th percentiles). Overall, when not stratified by PM 2.5 levels, the greatest state-wide overall PM-β /PM 2.5 ratios were found in Wyoming (0.69) and South Dakota (0.51), areas with higher radon, while the lowest (0.17) were in Delaware followed by New Jersey (0.18). These results indicate that the ratio of ambient particle radioactivity to particle mass concentration typically is higher at low PM 2.5 levels, and consequently, the toxicity per unit mass is expected to be higher.

Temporally resolved assessment of residential exposure to radon is essential for investigating radon's acute health effects. Recent studies have used large numbers of short-term radon measurements to model the spatiotemporal variations in radon concentrations. However, most short-term radon measurements in the northeastern and midwestern United States were conducted in the basements, which were less frequently occupied and had higher average radon concentrations than the upstairs spaces. Disproportionate usage of basement radon measurements in exposure assessment potentially introduces misclassifications. In an effort to mitigate the issue, we investigated the spatiotemporal gradients in ratios between the radon concentrations in the upstairs and basements (hereafter upstairs/basement ratio). Building-specific ratios were calculated on the basis of 10774 pairs of simultaneous short-term measurements and then aggregated by state and season. We found that upstairs/basement ratios of northeastern states are generally lower than those of Midwestern states, a pattern also found in 3508 pairs of simultaneous long-term radon measurements. Ratios in winter are higher than those in other seasons. Our results, in conjunction with behavior data, can improve the assessment of short-term residential exposure to radon and therefore facilitate future studies regarding the acute health effects of radon.

Traffic-related particulate matter (PM) plays an important role in urban air pollution. However, sources of urban pollution are difficult to distinguish. This study utilises a mobile particle concentrator platform and statistical tools to investigate factors affecting roadway ambient coarse particle (PM10-2.5) and fine particle (PM2.5-0.2) concentrations in greater Boston, USA. Positive matrix factorization (PMF) identified six PM10-2.5 sources (exhaust, road salt, brake wear, regional pollution, road dust resuspension and tyre-road abrasion) and seven fine particle sources. The seven PM2.5-0.2 sources include the six PM10-2.5 sources and a source rich in Cr and Ni. Non- exhaust traffic-related sources together accounted for 65.6% and 29.1% of the PM10-2.5 and PM2.5-0.2 mass, respectively. While the respective contributions of exhaust sources were 10.4% and 20.7%. The biggest non-exhaust contributor in the PM10-2.5 was road dust resuspension, accounting for 29.6%, while for the PM2.5-0.2, the biggest non-exhaust source was road-tyre abrasion, accounting for 12.3%. We used stepwise general additive models (sGAMs) and found statistically significant (p < 0.05) effects of temperature, number of vehicles and rush hour periods on exhaust, brake wear, road dust resuspension and road-tyre abrasion with relative importance between 19.1 and 62.2%, 12.5-42.1% and 4.4-42.2% of the sGAM model's explained variability. Speed limit and road type were also important factors for exhaust, road-tyre and brake wear sources. Meteorological variables of wind speed and relative humidity were significantly associated with both coarse and fine road dust resuspension and had a combined relative importance of 38% and 48%. The quantifying results of the factors that influence traffic-related sources can offer key insights to policies aiming to improve near-road air quality.

Little is known about whether exposure to unconventional oil and gas development is associated with higher mortality risks in the elderly and whether related air pollutants are exposure pathways. We studied a cohort of 15,198,496 Medicare beneficiaries (136,215,059 person-years) in all major U.S. unconventional exploration regions from 2001 to 2015. We gathered data from records of more than 2.5 million oil and gas wells. For each beneficiary's ZIP code of residence and year in the cohort, we calculated a proximity-based and a downwind-based pollutant exposure. We analyzed the data using two methods: Cox proportional hazards model and Difference-in-Differences. We found evidence of statistically significant higher mortality risk associated with living in proximity to and downwind of unconventional oil and gas wells. Our results suggest that primary air pollutants sourced from unconventional oil and gas exploration can be a major exposure pathway with adverse health effects in the elderly.

Exposure to particulate matter (PM) could increase both susceptibility to SARS-CoV-2 infection and severity of COVID-19 disease. Prior studies investigating associations between PM and COVID-19 morbidity have only considered PM2.5 or PM10, rather than PM1. We investigated the associations between daily-diagnosed COVID-19 morbidity and average exposures to ambient PM1 starting at 0 through 21 days before the day of diagnosis in 12 cities in China using a two-step analysis: a time-series quasi-Poisson analysis to analyze the associations in each city; and then a meta-analysis to estimate the overall association. Diagnosed morbidities and PM1 data were obtained from National Health Commission in China and China Meteorological Administration, respectively. We found association between short-term exposures to ambient PM1 with COVID-19 morbidity was significantly positive, and larger than the associations with PM2.5 and PM10. Percent increases in daily-diagnosed COVID-19 morbidity per IQR/10 PM1 for different moving averages ranged from 1.50% (-1.20%, 4.30%) to 241% (95%CI: 80.7%, 545%), with largest values for exposure windows starting at 17 days before diagnosis. Our results indicate that smaller particles are more highly associated with COVID-19 morbidity, and most of the effects from PM2.5 and PM10 on COVID-19 may be primarily due to the PM1. This study will be helpful for implementing measures and policies to control the spread of COVID-19.

BACKGROUND:School classrooms, where students spend the majority of their time during the day, are the second most important indoor microenvironment for children. OBJECTIVE:We investigated factors influencing classroom exposures to fine particulate matter (PM2.5), black carbon (BC), and nitrogen dioxide (NO2) in urban schools in the northeast United States. METHODS:Over the period of 10 y (2008-2013; 2015-2019) measurements were conducted in 309 classrooms of 74 inner-city schools during fall, winter, and spring of the academic period. The data were analyzed using adaptive mixed-effects least absolute shrinkage and selection operator (LASSO) regression models. The LASSO variables included meteorological-, school-, and classroom-based covariates. RESULTS:LASSO identified 10, 10, and 11 significant factors (p<0.05) that were associated with indoor PM2.5, BC, and NO2 exposures, respectively. The overall variability explained by these models was R2=0.679, 0.687, and 0.621 for PM2.5, BC, and NO2, respectively. Of the model's explained variability, outdoor air pollution was the most important predictor, accounting for 53.9%, 63.4%, and 34.1% of the indoor PM2.5, BC, and NO2 concentrations. School-based predictors included furnace servicing, presence of a basement, annual income, building type, building year of construction, number of classrooms, number of students, and type of ventilation that, in combination, explained 18.6%, 26.1%, and 34.2% of PM2.5, BC, and NO2 levels, whereas classroom-based predictors included classroom floor level, classroom proximity to cafeteria, number of windows, frequency of cleaning, and windows facing the bus area and jointly explained 24.0%, 4.2%, and 29.3% of PM2.5, BC, and NO2 concentrations, respectively. DISCUSSION:The adaptive LASSO technique identified significant regional-, school-, and classroom-based factors influencing classroom air pollutant levels and provided robust estimates that could potentially inform targeted interventions aiming at improving children's health and well-being during their early years of development. https://doi.org/10.1289/EHP10007.

Road dust particles play an important role in atmospheric pollution and are associated with adverse human health effects. Traffic emissions are a major source of particles in road dust. However, there has been limited information about the relationship between distance from road and traffic-related elements levels in road dust. We investigated the relationships between proximity to the nearest major roadway and trace element mass fractions in PM10 and PM2.5 re-suspended from the road surface, based on measurements at three different distance ranges. We found that mass fractions of Ba, Cu, Zr, Zn, Cl, Co, Cr, Ca, Ti in PM10 road dust as well as Zr, Cu, Cl, Zn, Cr, Ti, Mn, Ca, Ni, and Fe in PM2.5 road dust, significantly decreased with distance from major road. Most of these elements are associated with road traffic emissions, including both tailpipe and non-tailpipe emissions. The decrease rates differed among elements due to differences in local traffic contributions. The decreases for elements which are mainly associated with non-tailpipe traffic emissions (e.g., Ba, Zr) were more dramatic. Our results indicate that traffic emissions, especially non-tailpipe emissions, contribute substantially to road dust, suggesting the need for control strategies for non-tailpipe emissions. Implications: We investigated the relationships between road proximity with trace element mass fractions in PM10 and PM2.5 re-suspended from the road surface. We observed significant decrease of traffic-related elements in PM10 and PM2.5 road dust with log distance from major road. We also found that the mass fractions for elements, which mainly come from traffic decrease more sharply compared to elements which come from both traffic and other sources. Our results indicate that traffic emissions contribute substantially to road dust, and imply that the distance to major road can be used as a proxy for ambient exposure.

This study used fine and coarse PM concentrator technology in a Mobile Particle Concentrator Platform (MPCP) designed and built to allow the collection of large amounts of ambient PM, enabling time-resolved speciation analysis, which would not be feasible using conventional methods. One hour of sampling yielded sufficient sample loading for trace elemental analysis using X-Ray Fluorescence (XRF). In addition, we developed a novel Road Dust Aerosolizer (RDA) sampler in order to collect PM2.5 and PM10 surface road dust in situ. This sampler aerosolizes dust from the road surface, simulating ambient road dust resuspension, resulting in measured PM composition and size more appropriately (and less labor-intensive) than those obtained from studies using bulk road dust sieved and re-suspended in the laboratory. Overall, our modified fine and coarse particle concentrators yielded good reproducibility between co-located samples and sufficient loading for trace elemental analysis. For particle mass concentration, we observed a relative error of 3% and 4% among pairs of filters for fine and coarse concentrators, respectively; confirming that the mass collected on an unweighted quartz filter in parallel with a Teflon filter will have the same PM mass as the weighed Teflon filter. For samples with elements that are well above the LOD, relative uncertainty values were between 5% and 10% for the fine and 3% and 10% for the coarse. Our results show that the RDA system has an excellent precision for mass and elements as well. The relative error for mass is 7% for PM10 and 3% for PM2.5 within pairs and ranged from 2 to10% for elements. In conclusion, we developed a method for collecting PM10 and PM2.5 near-road air and surface road dust for short durations, which allows investigation of the composition of direct (airborne) and indirect (re-suspended road dust) non-tailpipe vehicular emissions. Implications: The methods we developed in this study allow the collection of one-hour PM10-2.5 and PM2.5-0.2 samples from near-road ambient air at several distances from the same roadway in 1 day, and collection of road dust directly from the road surface, with sufficient loading for trace elemental analysis. This will allow investigation of the composition of direct (airborne) and indirect (re-suspended road dust) vehicular emissions.

Background: Rising temperatures due to climate change are expected to impact human adaptive response, including changes to home cooling and ventilation patterns. These changes may affect air pollution exposures via alteration in residential air exchange rates, affecting indoor infiltration of outdoor particles. We conducted a field study examining associations between particle infiltration and temperature to inform future studies of air pollution health effects. Methods: We measured indoor fine particulate matter (PM2.5) in Atlanta in 60 homes (810 sampling-days). Indoor-outdoor sulfur ratios were used to estimate particle infiltration, using central site outdoor sulfur concentrations. Linear and mixed-effects models were used to examine particle infiltration ratio-temperature relationships, based on which we incorporated projected meteorological values (Representative Concentration Pathways intermediate scenario RCP 4.5) to estimate particle infiltration ratios in 20-year future (2046?2065) and past (1981?2000) scenarios. Results: The mean particle infiltration ratio in Atlanta was 0.70 ? 0.30, with a 0.21 lower ratio in summer compared to transition seasons (spring, fall). Particle infiltration ratios were 0.19 lower in houses using heating, ventilation, and air conditioning (HVAC) systems compared to those not using HVAC. We observed significant associations between particle infiltration ratios and both linear and quadratic models of ambient temperature for homes using natural ventilation and those using HVAC. Future temperature was projected to increase by 2.1 ?C in Atlanta, which corresponds to an increase of 0.023 (3.9%) in particle infiltration ratios during cooler months and a decrease of 0.037 (6.2%) during warmer months. Discussion: We estimated notable changes in particle infiltration ratio in Atlanta for different 20-year periods, with differential seasonal patterns. Moreover, when stratified by HVAC usage, increases in future ambient temperature due to climate change were projected to enhance seasonal differences in PM2.5 infiltration in Atlanta. These analyses can help minimize exposure misclassification in epidemiologic studies of PM2.5, and provide a better understanding of the potential influence of climate change on PM2.5 health effects.

Dear Editor, Our research group has recently completed a study characterizing coarse and fine ambient PM and road dust near major roads in the Greater Boston area, with several papers included in a recent specialty issue of this journal (Huang et al. 2020; Martins et al. 2020; Silva et al. 2020). During this study, we investigated 27 roadway locations, selected based on parameters including traffic density and composition, slope and elevation, number of traffic lights nearby, type of structures/buildings present, condition and cleanness of the pavement, number of lanes and traffic directions, the width of the road, posted speed limit, nearby point sources (minimal), and having a safe and convenient place for sampling. At each site, we collected ambient PM and road dust samples at a series of three distances away from the road: <25 meters, 50–200 meters and >500 meters. Most road sites were sampled once, and a subset of seven roads was sampled multiple times (including different seasons). For this study, we developed (1) a mobile unit with ambient particle concentrators to measure hourly concentrations of elements as a function of distance to road, and (2) a road dust sampler to collect size-fractionated road dust particles from the road surface. These methods are described in detail elsewhere (Martins et al. 2020).

Particulate radioactivity, a characteristic of particulate matter, is primarily determined by the abundance of radionuclides that are bound to airborne particulates. Exposure to high levels of particulate radioactivity has been associated with negative health outcomes. However, there are currently no spatially and temporally resolved particulate radioactivity data for exposure assessment purposes. We estimated the monthly distributions of gross beta particulate radioactivity across the contiguous United States from 2001 to 2017 with a spatial resolution of 32 km, via a multi-stage ensemble-based model. Particulate radioactivity was measured at 129 RadNet monitors across the contiguous U.S. In stage one, we built 264 base learning models using six methods, then selected nine base models that provide different predictions. In stage two, we used a non-negative geographically and temporally weighted regression method to aggregate the selected base learner predictions based on their local performance. The results of block cross-validation analysis suggested that the non-negative geographically and temporally weighted regression ensemble learning model outperformed all base learning model with the smallest rooted mean square error (0.094 mBq/m3). Our model provided an accurate estimation of particulate radioactivity, thus can be used in future health studies.

Radon (Rn) is a natural and toxic radioactive gas that accumulates indoors, mainly in low-ventilated underground floors and basements. Several factors make prediction of indoor Rn exposure in enclosed spaces challenging. In this study, we investigated the influence of soil, geology, topography, atmospheric variables, radiation, urbanization, community economic well-being, and monthly and yearly variations on indoor Rn concentrations. We analyzed 7,515 monthly indoor Rn measurements in 623 zip codes from two U.S. States (Michigan and Minnesota) during 2005-2018 using a random forest model. Using Shapley Additive exPlanations (SHAP) values we investigated the contribution of each factor using variable importance and partial dependence plots. Factors that predict indoor Rn differed between states, with topographical, geological and soil composition being most influential. Cross-validated Pearson correlation between predictions and measurements was 0.68 (RMSE = 47.8 Bq/m(3)) in Minnesota, and 0.67 (RMSE = 52.5 Bq/m(3)) in Michigan. Our results underline the importance of soil structure for radon exposure, presumably due to strapped Rn in soil. The differences across states also suggest that Rn studies performing model development should consider geographical variables, along with other factors. As indoor Rn levels are multifactorial, an understanding of the factors that influence its emanation and build up indoors will help better assess spatial and temporal variations, which will be useful to improve prevention and mitigation control strategies. Implications: Radon exposure has become a year-round problem as people spend most of their time indoors. In North America, radon exposure is increasing over time and awareness related to its health effects remains low in the general population. Several factors make prediction of indoor radon exposure in enclosed spaces challenging. In this study, we used random forest to investigate the influence of factors on indoor radon in the Midwest United States. We found that topography, geology, and soil composition were the most influential factors on indoor radon levels. These results will help better assess spatial and temporal variations, which will further help better prevention and mitigation control strategies.

Traffic-related air pollution is associated with various adverse health effects. In the absence of more complicated exposure assessment techniques, many environmental health studies have used the natural logarithm of distance to road as a proxy for traffic-related exposures. However, research validating this proxy and further explaining the spatial patterns and elemental composition of traffic-related particulate matter air pollution remains limited. In this study, we collected air samples using a mobile particle concentrator that allowed for high sample loading from major roadways in the Greater Boston Area. We found that concentrations of Cl, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, Sr, Zr, Sn, Ba, and Pb were significantly associated with the natural logarithm of distance to road in coarse particulate matter, and total fine particulate mass concentrations of Al, Ca, Ti, Cr, Mn, Fe, Cu, and Zn were significantly associated with natural logarithm of distance to road in fine particulate matter. Road type (A1 or A2 [primary roads or highways] versus A3 [secondary and connecting roads]) was not a significant predictor of any traffic-related elements in particulate matter air pollution. Our results help identify traffic-related elements in particulate matter air pollution and support the use of logarithm of distance to road as a proxy for traffic-related particulate matter air pollution exposure assessment in epidemiological studies.

Traffic-related air pollution, including direct exhaust emissions and road dust (RD), impacts individuals living near busy roads. We recently conducted a study to investigate the sources and composition of tailpipe and non-tailpipe traffic emissions, where we collected and analyzed samples of ambient air fine particulate matter (PM2.5) and fine RD (RD2.5) at different distances from major roadways. We analyzed a subset of the samples, including those collected at the roadside and local background, for their alpha activity level. Subsequently, we investigated whether there is a distance-related decay in the alpha activity in RD2.5 or PM2.5 similar to those observed for traffic-related species in PM2.5 and RD2.5. We found that the alpha activity of ambient air PM2.5 (Bq/mg) was more than an order of magnitude higher than the activity level of the corresponding RD2.5 sample, suggesting that PM2.5 may be more toxic than RD2.5. Using mixed-effects regression models, we found that ambient PM2.5 alpha activity was significantly higher during the cold months than during warm months, and that the background was higher than the roadside (though not significantly). In contrast, the RD2.5 alpha activity was significantly higher at the background site compared to the roadside but was not significantly affected by season. In addition to sampling position, both Zn and elemental carbon (EC) were significant predictors of RD2.5 alpha activity. In addition, the roadside RD2.5 activity levels were found to be higher at highways as compared to secondary roads. While traffic-related emissions do not appear to be significant sources of either ambient PM2.5 or RD2.5 alpha activity, the RD2.5 results suggest that traffic-related particles may contribute to RD2.5 alpha-activity. Implications: Many studies have reported the effects of traffic-related particulate matter (PM) on human health, and there is growing interest in the health effects of exposure to environmental PM alpha activity. This is the first study to report on the alpha activity of road dust (RD) or near-roadway ambient PM. We found that the alpha activity of ambient PM is twenty times higher than RD, suggesting that ambient PM may be more toxic. In PM and RD, the alpha activities were higher at background sites than at the roadside, indicating that traffic-related emissions are not a significant source of particulate radioactivity.

Background: Ethanol vehicles release exhaust gases that contribute to the formation of secondary organic aerosols (SOA). Objective: To determine in vivo toxicity resulting from exposure to SOA derived from vehicles using different ethanol-gasoline blends (E0, E10, E22, E85W, E85S, E100). Methods: Exhaust emissions from vehicles using ethanol blends were delivered to a photochemical chamber and reacted to produce SOA. The aerosol samples were collected on filters, extracted, and dispersed in an aqueous solutions and intratracheally instilled into Sprague Dawley rats in doses of 700 mu g/0.2 ml. After 45 min and 4 h pulmonary and cardiac chemiluminescence (CL) was measured to estimate the amount of reactive oxygen species (ROS) produced in the lungs and heart. Inflammation was measured by differential cell count in bronchoalveolar lavages (BAL). Results: Statistically and biologically significant differences in response to secondary particles from the different fuel formulations were detected. Compared to the control group, animals exposed to SOA from gasoline (E0) showed a significantly higher average CL in the lungs at 45 min. The highest CL averages in the heart were observed in the groups exposed to SOA from E10 and pure ethanol (E100) at 45 min. BAL of animals exposed to SOA from E0 and E85S had a significant increased number of macrophages at 45 min. BAL neutrophil count was increased in the groups exposed to E85S (45 min) and E0 (4 h). Animals exposed to E0 and E85W had increased BAL lymphocyte count compared to the control and the other exposed groups. Discussion: Our results suggest that SOA generated by gasoline (al), followed by ethanol blends E85S and E85W, substantially induce oxidative stress measured by ROS generation and pulmonary inflammation measured by the recruitment of white blood cells in BAL.

Background: The rapid increase in prevalence of e-cigarette (EC) use may lead to widespread exposure to secondhand emissions among nonsmokers, but evidence on the potential cardiovascular health risks is limited. We aimed to investigate the effect of short-term secondhand exposure to nicotine from e-cigarette (EC) emissions on cardiac autonomic function using heart rate variability (HRV). Methods: A randomized, repeated measures crossover study of healthy nonsmoking volunteers was conducted. Standard deviation of NN intervals (SDNN), average of the standard deviation of NN intervals (ASDNN), root mean square of successive differences (rMSSD), and heart rate–corrected QT interval (QTc) were calculated during 1 hour of EC exposure session. Results: Nicotine from EC emissions was associated with a 7.8% decrease in SDNN (95% CI [confidence interval]: −11.2%, −4.3%), 7.7% decrease in ASDNN (95% CI: −11.0%, −4.2%), and 3.8 milliseconds decrease in QTc (95% CI: −5.8, −1.9). Compared with a short exposure time period (<15 minutes), greater nicotine associated with reductions in ASDNN (P for interaction = 0.076) with longer exposure time periods. For QTc, greater nicotine associated with reductions were found during 15- to 30-minute exposure time period (P for interaction = 0.04). Conclusion: We present the first evidence of cardiac autonomic effects of short-term secondhand exposure to nicotine from EC emissions among healthy nonsmokers. Further comprehensive research on EC exposure extending to more subjects and flavor compounds is warranted.

Bioethanol for use in vehicles is becoming a substantial part of global energy infrastructure because it is renewable and some emissions are reduced. Carbon monoxide (CO) emissions and total hydrocarbons (THC) are reduced, but there is still controversy regarding emissions of nitrogen oxides (NOx), aldehydes, and ethanol; this may be a concern because all these compounds are precursors of ozone and secondary organic aerosol (SOA). The amount of emissions depends on the ethanol content, but it also may depend on the engine quality and ethanol origin. Thus, a photochemical chamber was used to study secondary gas and aerosol formation from two flex-fueled vehicles using different ethanol blends in gasoline. One vehicle and the fuel used were made in the United States, and the others were made in Brazil. Primary emissions of THC, CO, carbon dioxide (CO2), and nonmethane hydrocarbons (NMHC) from both vehicles decreased as the amount of ethanol in gasoline increased. NOx emissions in the U.S. and Brazilian cars decreased with ethanol content. However, emissions of THC, CO, and NOx from the Brazilian car were markedly higher than those from the U.S. car, showing high variability between vehicle technologies. In the Brazilian car, formation of secondary nitrogen dioxide (NO2) and ozone (O3) was lower for higher ethanol content in the fuel. In the U.S. car, NO2 and O3 had a small increase. Secondary particle (particulate matter [PM]) formation in the chamber decreased for both vehicles as the fraction of ethanol in fuel increased, consistent with previous studies. Secondary to primary PM ratios for pure gasoline is 11, also consistent with previous studies. In addition, the time required to form secondary PM is longer for higher ethanol blends. These results indicate that using higher ethanol blends may have a positive impact on air quality.IMPLICATIONS:The use of bioethanol can significantly reduce petroleum use and greenhouse gas emissions worldwide. Given the extent of its use, it is important to understand its effect on urban pollution. There is a controversy on whether there is a reduction or increase in PM emission when using ethanol blends. Primary emissions of THC, CO, CO2, NOx, and NMHC for both cars decreased as the fraction of ethanol in gasoline increased. Using a photochemical chamber, the authors have found a decrease in the formation of secondary particles and the time required to form secondary PM is longer when using higher ethanol blends.

Exposure to traffic-related PM2.5 mass and its components can affect human health. Meanwhile, indoor concentrations are better exposure predictors as compared to outdoor concentrations because individuals spend the majority of their time indoors. We estimated the impact of traffic emissions on indoor PM2.5 mass and its species as a function of road proximity in Massachusetts. A linear regression model was built using 662 indoor samples and 580 ambient samples. Analysis shows that indoor exposures to traffic-related particles increased dramatically with road proximity. We defined relative concentration decrease, R(α), as the ratio of the indoor concentration at perpendicular distance α in meters from the closest major road to the indoor concentration at 1800 m from the major road. R(13) values for PM2.5 mass and Black Carbon (BC) were 1.3 (95%CI: 1.4, 1.6) and 2.1 (95%CI: 1.3, 2.8) for A12 roads, and 1.3 (95%CI: 1.2, 1.4) and 1.2 (95%CI: 1.1, 1.3) for A3 roads. R(α) values were also estimated for Fe, Mn, Mo, Sr and Ti for A12 roads, and Ca, Cu, Fe, Mn, Mo, Ni, Si, Sr, V and Zn for A3 roads. R(α) values for species associated mainly with brakes, tires or road dust (e.g., Mn, Mo and Sr) were higher than others. For A12 roads, R(13) values for Mn and Mo were 10.9 (95%CI: 0.9, 20.9) and 6.5 (95%CI: 1.4, 11.5), and ranged from 1.3 to 2.1 for other species; for A3 roads, R(13) values for Mn, Mo and Sr were 1.9 (95%CI: 1.1, 2.9), 1.8 (95%CI: 1.1, 2.4), and 8.5 (95%CI: 5.9, 10.9), and ranged from 1.2 to 1.6 for others. Our results indicate a significant impact of local traffic emissions on indoor air, which depends on road proximity. Thus road proximity which has been used in many epidemiological studies is a reasonable exposure metric.