Radon sampling methodologies: A case for accurate, accessible measurements using household instruments.

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.

Quantifying factors affecting contributions of roadway exhaust and non-exhaust emissions to ambient PM10-2.5 and PM2.5-0.2 particles.

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.

Factors Influencing Classroom Exposures to Fine Particles, Black Carbon, and Nitrogen Dioxide in Inner-City Schools and Their Implications for Indoor Air Quality.

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.

Exposure to Unconventional Oil and Gas Development and All-cause Mortality in Medicare Beneficiaries.

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.

The association between daily-diagnosed COVID-19 morbidity and short-term exposure to PM1 is larger than associations with PM2.5 and PM10.

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.

Predictors of indoor radon levels in the Midwest United States.

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/m3) in Minnesota, and 0.67 (RMSE = 52.5 Bq/m3) 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.

A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguous United States.

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.

Estimating climate change-related impacts on outdoor air pollution infiltration.

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.

Trace element concentrations in ambient air as a function of distance from road.

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.

Trace element mass fractions in road dust as a function of distance from road.

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.

Development and evaluation of a mobile laboratory for collecting short-duration near-road fine and coarse ambient particle and road dust samples.

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.

Measurement of the gross alpha activity of the fine fractions of road dust and near-roadway ambient particle matter.

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.

Cardiac Autonomic Effects of Secondhand Exposure to Nicotine from Electronic Cigarettes: An Exploratory Study.

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 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 one hour of EC exposure session. RESULTS: Nicotine from EC emissions was associated with a 7.8% decrease in SDNN (95% CI, -11.2% to -4.3%), 7.7% decrease in ASDNN (95% CI, -11.0% to -4.2%) and 3.8 msec decrease in QTc (95% CI, -5.8 to -1.9). Compared with a short exposure time period (<15min), 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 15-30 min 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.

Secondary particles formed from the exhaust of vehicles using ethanol-gasoline blends increase the production of pulmonary and cardiac reactive oxygen species and induce pulmonary inflammation.

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 µg/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 (E0), 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.

Road proximity influences indoor exposures to ambient fine particle mass and components.

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.

Inhaled ambient-level traffic-derived particulates decrease cardiac vagal influence and baroreflexes and increase arrhythmia in a rat model of metabolic syndrome.

BACKGROUND: Epidemiological studies have linked exposures to ambient fine particulate matter (PM2.5) and traffic with autonomic nervous system imbalance (ANS) and cardiac pathophysiology, especially in individuals with preexisting disease. It is unclear whether metabolic syndrome (MetS) increases susceptibility to the effects of PM2.5. We hypothesized that exposure to traffic-derived primary and secondary organic aerosols (P + SOA) at ambient levels would cause autonomic and cardiovascular dysfunction in rats exhibiting features of MetS. Male Sprague Dawley (SD) rats were fed a high-fructose diet (HFrD) to induce MetS, and exposed to P + SOA (20.4 ± 0.9 µg/m3) for 12 days with time-matched comparison to filtered-air (FA) exposed MetS rats; normal diet (ND) SD rats were separately exposed to FA or P + SOA (56.3 ± 1.2 µg/m3). RESULTS: In MetS rats, P + SOA exposure decreased HRV, QTc, PR, and expiratory time overall (mean effect across the entirety of exposure), increased breathing rate overall, decreased baroreflex sensitivity (BRS) on three exposure days, and increased spontaneous atrioventricular (AV) block Mobitz Type II arrhythmia on exposure day 4 relative to FA-exposed animals receiving the same diet. Among ND rats, P + SOA decreased HRV only on day 1 and did not significantly alter BRS despite overall hypertensive responses relative to FA. Correlations between HRV, ECG, BRS, and breathing parameters suggested a role for autonomic imbalance in the pathophysiologic effects of P + SOA among MetS rats. Autonomic cardiovascular responses to P + SOA at ambient PM2.5 levels were pronounced among MetS rats and indicated blunted vagal influence over cardiovascular physiology. CONCLUSIONS: Results support epidemiologic findings that MetS increases susceptibility to the adverse cardiac effects of ambient-level PM2.5, potentially through ANS imbalance.

Interlab comparison of elemental analysis for low ambient urban PM2.5 levels.

There is growing concern about the accuracy of trace elemental analysis of ambient particulate matter (PM) samples. This has become important because ambient PM concentrations have decreased over the years, and the lower filter loadings result in difficulties in accurate analysis. The performance of energy-dispersive X-ray reflectance spectrometry was evaluated at Harvard School of Public Health using several methodologies, including intercomparison between two other laboratories. In reanalysis of standard films as unknown samples following calibration, the HSPH ED XRF measurements represented good performance: 2% errors in precision and 4% errors in accuracy. Replicate analysis of ambient air filters with low PM2.5 levels indicated that S, K, Fe, and Ca showed excellent reproducibility, most other quantifiable elements were below 15% error, and the elements with larger percent of flagged measurements had less in precision. Results from the interlaboratory comparison demonstrated that most quantifiable elements, except Na and Al, were quite comparable for the three laboratories. Na performance could be validated from the stoichiometry of Na to Cl of indoor PM2.5 filter samples.

Effects of fresh and aged vehicular exhaust emissions on breathing pattern and cellular responses--pilot single vehicle study.

The study presented here is a laboratory pilot study using diluted car exhaust from a single vehicle to assess differences in toxicological response between primary emissions and secondary products resulting from atmospheric photochemical reactions of gas phase compounds with O3, OH and other radicals. Sprague Dawley rats were exposed for 5 h to either filtered room air (sham) or one of two different atmospheres: (i) diluted car exhaust (P)+Mt. Saint Helens Ash (MSHA); (ii) P+MSHA+secondary organic aerosol (SOA, formed during simulated photochemical aging of diluted exhaust). Primary and secondary gases were removed using a nonselective diffusion denuder. Continuous respiratory data was collected during the exposure, and bronchoalveolar lavage (BAL) and complete blood counts (CBC) were performed 24 h after exposure. ANOVA models were used to assess the exposure effect and to compare those effects across different exposure types. Total average exposures were 363 ± 66 µg/m³ P+MSHA and 212 ± 95 µg/m³ P+MSHA+SOA. For both exposures, we observed decreases in breathing rate, tidal and minute volumes (TV, MV) and peak and median flows (PIF, PEF and EF50) along with increases in breathing cycle times (Ti, Te) compared to sham. These results indicate that the animals are changing their breathing pattern with these test atmospheres. Exposure to P+MSHA+SOA produced significant increases in total cells, macrophages and neutrophils in the BAL and in vivo chemiluminescence of the lung. There were no significant differences in CBC parameters. Our data suggest that simulated atmospheric photochemistry, producing SOA in the P+MSHA+SOA exposures, enhanced the toxicity of vehicular emissions.

Development and evaluation of a countercurrent parallel-plate membrane diffusion denuder for the removal of gas-phase compounds from vehicular emissions.

Diffusion denuders have been commonly used to remove trace gases from an aerosol (mixture of gases and particles), while allowing the particles to remain suspended in air. We present the design and evaluation of a high-flow (16.7 L min⁻¹) countercurrent parallel-plate membrane diffusion denuder that has high removal efficiencies for both non-reactive gases such as carbon monoxide (89%), as well as volatile organic compounds (80-85%) from an automobile exhaust. Particle losses were approximately 15% for particles around 100 nm in diameter. This denuder is suitable for toxicological tests involving both human and animal exposures to combustion aerosols. The denuder may also be used for other applications, for example, to reduce the effect of gas-phase sampling artifacts on particle composition.