Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)

A major challenge in traffic-related air pollution exposure studies is the lack of information regarding pollutant exposure characterization. Air quality modeling can provide spatially and temporally varying exposure estimates for examining relationships between traffic-related air pollutants and adverse health outcomes. A hybrid air quality modeling approach was used to estimate exposure to traffic-related air pollutants in support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS) conducted in Detroit (Michigan, USA). Model-based exposure metrics, associated with local variations of emissions and meteorology, were estimated using a combination of the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) and Research LINE-source dispersion model for near-surface releases (RLINE) dispersion models, local emission source information from the National Emissions Inventory, detailed road network locations and traffic activity, and meteorological data from the Detroit City Airport. The regional background contribution was estimated using a combination of the Community Multi-scale Air Quality (CMAQ) and the Space-Time Ordinary Kriging (STOK) models. To capture the near-road pollutant gradients, refined “mini-grids” of model receptors were placed around participant homes. Exposure metrics for CO, NOx, PM2.5 and its components (elemental and organic carbon) were predicted at each home location for multiple time periods including daily and rush hours. The exposure metrics were evaluated for their ability to characterize the spatial and temporal variations of multiple ambient air pollutants compared to measurements across the study area

Within-Neighborhood Patterns and Sources of Particle Pollution: Mobile Monitoring and Geographic Information System Analysis in Four Communities in Accra, Ghana

BACKGROUND: Sources of air pollution in developing country cities include transportation and industrial pollution, biomass and coal fuel use, and resuspended dust from unpaved roads. OBJECTIVES: Our goal was to understand within-neighborhood spatial variability of particulate matter (PM) in communities of varying socioeconomic status (SES) in Accra, Ghana, and to quantify the effects of nearby sources on local PM concentration. METHODS: We conducted 1 week of morning and afternoon mobile and stationary air pollution measurements in four study neighborhoods. PM with aerodynamic diameters RESULTS: In our measurement campaign, the geometric means of PM2.5 and PM10 along the mobile monitoring path were 21 and 49 microg/m3, respectively, in the neighborhood with highest SES and 39 and 96 microg/m3, respectively, in the neighborhood with lowest SES and highest population density. PM2.5 and PM10 were as high as 200 and 400 microg/m3, respectively, in some segments of the path. After adjusting for other factors, the factors that had the largest effects on local PM pollution were nearby wood and charcoal stoves, congested and heavy traffic, loose dirt road surface, and trash burning. CONCLUSIONS: Biomass fuels, transportation, and unpaved roads may be important determinants of local PM variation in Accra neighborhoods. If confirmed by additional or supporting data, the results demonstrate the need for effective and equitable interventions and policies that reduce the impacts of traffic and biomass pollution

Exploring consumer exposure pathways and patterns of use for chemicals in the environment

AbstractHumans are exposed to thousands of chemicals in the workplace, home, and via air, water, food, and soil. A major challenge in estimating chemical exposures is to understand which chemicals are present in these media and microenvironments. Here we describe the Chemical/Product Categories Database (CPCat), a new, publically available (http://actor.epa.gov/cpcat) database of information on chemicals mapped to “use categories” describing the usage or function of the chemical. CPCat was created by combining multiple and diverse sources of data on consumer- and industrial-process based chemical uses from regulatory agencies, manufacturers, and retailers in various countries. The database uses a controlled vocabulary of 833 terms and a novel nomenclature to capture and streamline descriptors of chemical use for 43,596 chemicals from the various sources. Examples of potential applications of CPCat are provided, including identifying chemicals to which children may be exposed and to support prioritization of chemicals for toxicity screening. CPCat is expected to be a valuable resource for regulators, risk assessors, and exposure scientists to identify potential sources of human exposures and exposure pathways, particularly for use in high-throughput chemical exposure assessment

Ex-vivo three-dimensional assessment of carotid stenosis with ultrasound

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 71-74).Atherosclerosis causes heterogeneous remodeling of arterial structure and composition in the carotid vessel wall. It has been shown that the progression of the disease can be monitored by tracking changes in the carotid intima-media thickness (IMT). Non-invasive peripheral vascular ultrasound (U/S) of the carotid artery is a non-invasive, cost effective, accepted means of measuring IMT. Traditionally, evaluation of IMT in the carotid has been limited to 2D U/S scans. This method is disadvantageous as 2D scans are scan plane dependent, limiting the area over which one can evaluate the extent of the disease. Reproducing the identical scan plane on subsequent scans is also difficult. Evaluation of the carotid vessel wall in 3D will allow for a more complete and reproducible assessment of disease through IMT measurements. We have constructed a fully 3D image processing scheme for analyzing carotid U/S volumes to extract the inner and outer vessel wall boundaries. Sequences of 2D B-mode U/S cross sections of ex-vivo carotid specimens are collected and voxelized to create 3D U/S volumes. By applying a 3D directionally sensitive, edge preserving filter to the U/S volumes, we obtain 3D edge fields that are more distinct than traditional gradient edge fields. Initial point selection of the boundaries, together with these enhanced 3D edge fields, are used with a deformable surface to extract the final inner and outer vessel boundaries. Through intra- and inter-observer tests on IMT differences, we show that the 3D boundaries extracted using our automatic technique are more reproducible than boundaries extracted from manual point selection.by Kathie L. Dionisio.S.M

Additional file 1: of A simulation study to quantify the impacts of exposure measurement error on air pollution health risk estimates in copollutant time-series models

Supplemental materials including supplemental text, figures, and tables, are available as an additional file. (PDF 392 kb

Measuring the exposure of infants and children to indoor air pollution from biomass fuels in the Gambia

Background/Aims: Smoke from biomass fuels contains multiple gaseous and particulate pollutants, and is a risk factor for pneumonia, the leading cause of child death worldwide. Few studies have assessed the exposure-response relationship for biomass smoke and pneumonia, primarily because measuring children's exposure is difficult, especially for particulate matter (PM). We report on a study which utilized multiple methods to evaluate children's exposure to PM and carbon monoxide (CO) from biomass fuels. Methods: Seventy-two hours personal CO exposure was measured for 1200 children, with 12%–15% receiving an additional 3–4 CO measurements. In the same subset of children, 72-hours integrated and continuous PM and 72-hours CO were measured in the cookhouse. Personal PM exposure was measured for 20–25 of these children. Usual personal exposure to CO is estimated using a random effects model including child- and household-level covariates. Personal exposure of children to PM is estimated using 3 different approaches, as well as a combined model: (1) combining time-location-activity budget with microenvironment PM; (2) estimating PM exposure using CO exposure combined with stationary CO-PM relationship; and (3) direct measurement of personal PM. Results: Preliminary results show a mean 72-hours CO exposure on children of 1.0 ± 1.3 ppm, with an age distribution of 0.8 ± 0.9 ppm (0–11 months) and 1.1 ± 1.5 ppm (12–59 months). Regression analysis adjusting for child's age, study site, and sex indicates a seasonal pattern for CO exposure, with significantly higher exposure in July, August, and September. Using the CO-PM relationship from a pilot study, estimated average personal PM2.5 exposure for these children would be 189 μg/m3. Conclusion: Children's exposure to PM2.5 in The Gambia is well above WHO Air Quality Guidelines. The results of multiple measurement methods can be combined to estimate children's personal PM2.5 exposure

Household fuel use and biomarkers of inflammation and respiratory illness among rural South African women

Though literature suggests a positive association between use of biomass fuel for cooking and inflammation, few studies among women in rural South Africa exist. We included 415 women from the South African Study of Women and Babies (SOWB), recruited from 2010 to 2011. We obtained demographics, general medical history and usual source of cooking fuel (wood, electricity) via baseline questionnaire. A nurse obtained height, weight, blood pressure, and blood samples. We measured plasma concentrations of a suite of inflammatory markers (e.g., interleukins, tumor necrosis factor-α, C-reactive protein). We assessed associations between cooking fuel and biomarkers of inflammation and respiratory symptoms/illness using crude and adjusted linear and logistic regression models. We found little evidence of an association between fuel-use and biomarkers of inflammation, pre-hypertension/hypertension, or respiratory illnesses. Though imprecise, we found 41% (95% confidence interval (CI) = 0.72–2.77) higher odds of self-reported wheezing/chest tightness among wood-users compared with electricity-users. Though studies among other populations report positive findings between biomass fuel use and inflammation, it is possible that women in the present study experience lower exposures to household air pollution given the cleaner burning nature of wood compared with other biomass fuels (e.g., coal, dung).The Intramural Research Program of the National Institutes of Health (NIH), National Institute of Environmental Health Sciences (NIEHS).http://www.elsevier.com/locate/envres2019-10-01hj2019Urolog

The Residential Population Generator (RPGen): Parameterization of Residential, Demographic, and Physiological Data to Model Intraindividual Exposure, Dose, and Risk

Exposure to chemicals is influenced by associations between the individual’s location and activities as well as demographic and physiological characteristics. Currently, many exposure models simulate individuals by drawing distributions from population-level data or use exposure factors for single individuals. The Residential Population Generator (RPGen) binds US surveys of individuals and households and combines the population with physiological characteristics to create a synthetic population. In general, the model must be supported by internal consistency; i.e., values that could have come from a single individual. In addition, intraindividual variation must be representative of the variation present in the modeled population. This is performed by linking individuals and similar households across income, location, family type, and house type. Physiological data are generated by linking census data to National Health and Nutrition Examination Survey data with a model of interindividual variation of parameters used in toxicokinetic modeling. The final modeled population data parameters include characteristics of the individual’s community (region, state, urban or rural), residence (size of property, size of home, number of rooms), demographics (age, ethnicity, income, gender), and physiology (body weight, skin surface area, breathing rate, cardiac output, blood volume, and volumes for body compartments and organs). RPGen output is used to support user-developed chemical exposure models that estimate intraindividual exposure in a desired population. By creating profiles and characteristics that determine exposure, synthetic populations produced by RPGen increases the ability of modelers to identify subgroups potentially vulnerable to chemical exposures. To demonstrate application, RPGen is used to estimate exposure to Toluene in an exposure modeling case example