Somerville, MA, United States
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Martinez L.S.,Boston University | Reisner E.,Somerville Transportation Equity Partnership | Campbell M.,Tufts University | Brugge D.,Tufts University
International Journal of Environmental Research and Public Health | Year: 2017

Background: Conflicting interests, power imbalance and relationships characterized by distrust are just a few of the many challenges community–academic research partnerships face. In addition, the time it takes to build relationships is often overlooked, which further complicates matters and can leave well-intentioned individuals re-creating oppressive conditions through inauthentic partnerships. This paper presents a novel approach of using meeting minutes to explore partnership dynamics. The Community Assessment of Freeway Exposure and Health (CAFEH) partnership is used as an illustrative case study to identify how community academic partnerships overcome the challenges associated with community-based participatory research (CBPR). CAFEH is a study of ultrafine particle exposure (UFP) near highways in the Boston, MA area. Methods: Qualitative analysis was applied to meeting minutes and process evaluation reports from the first three years of the CAFEH study (n = 73 files). In addition, a group meeting was held with project partners in order to contextualize the findings from the document analysis. Results: The three most commonly referenced challenges included language barriers, the overall project structure and budgetary constraints. Meanwhile, a heavy emphasis on process and an approach steeped in participatory democracy facilitated CAFEH’s ability to overcome these challenges, as well as sustain and augment strong partnership ties. Conclusions: This experience suggests that leadership that incorporates an organizing approach and a transformational style facilitates CBPR processes and helps teams surmount challenges. © 2017 by the authors; licensee MDPI, Basel, Switzerland.

PubMed | Linnean Solutions., Somerville Transportation Equity Partnership., City of Somerville, Tufts University and Chinese Progressive Association.
Type: Journal Article | Journal: Environmental justice (Print) | Year: 2016

The literature consistently shows associations of adverse cardiovascular and pulmonary outcomes with residential proximity to highways and major roadways. Air monitoring shows that traffic-related pollutants (TRAP) are elevated within 200-400 m of these roads. Community-level tactics for reducing exposure include the following: 1) HEPA filtration; 2) Appropriate air-intake locations; 3) Sound proofing, insulation and other features; 4) Land-use buffers; 5) Vegetation or wall barriers; 6) Street-side trees, hedges and vegetation; 7) Decking over highways; 8) Urban design including placement of buildings; 9) Garden and park locations; and 10) Active travel locations, including bicycling and walking paths. A multidisciplinary design charrette was held to test the feasibility of incorporating these tactics into near-highway housing and school developments that were in the planning stages. The resulting designs successfully utilized many of the protective tactics and also led to engagement with the designers and developers of the sites. There is a need to increase awareness of TRAP in terms of building design and urban planning.

PubMed | Boston University, Somerville Transportation Equity Partnership, Yale University, Chinese Progressive Association and Tufts University
Type: | Journal: Environment international | Year: 2016

Long-term exposure to fine particulate matter has been linked to cardiovascular disease and systemic inflammatory responses; however, evidence is limited regarding the effects of long-term exposure to ultrafine particulate matter (UFP, <100nm). We used a cross-sectional study design to examine the association of long-term exposure to near-highway UFP with measures of systemic inflammation and coagulation.We analyzed blood samples from 408 individuals aged 40-91years living in three near-highway and three urban background areas in and near Boston, Massachusetts. We conducted mobile monitoring of particle number concentration (PNC) in each area, and used the data to develop and validate highly resolved spatiotemporal (hourly, 20m) PNC regression models. These models were linked with participant time-activity data to determine individual time-activity adjusted (TAA) annual average PNC exposures. Multivariable regression modeling and stratification were used to assess the association between TAA-PNC and single peripheral blood measures of high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), tumor-necrosis factor alpha receptor II (TNFRII) and fibrinogen.After adjusting for age, sex, education, body mass index, smoking and race/ethnicity, an interquartile-range (10,000particles/cm(3)) increase in TAA-PNC had a positive non-significant association with a 14.0% (95% CI: -4.6%, 36.2%) positive difference in hsCRP, an 8.9% (95% CI: -0.4%, 10.9%) positive difference in IL-6, and a 5.1% (95% CI: -0.4%, 10.9%) positive difference in TNFRII. Stratification by race/ethnicity revealed that TAA-PNC had larger effect estimates for all three inflammatory markers and was significantly associated with hsCRP and TNFRII in white non-Hispanic, but not East Asian participants. Fibrinogen had a negative non-significant association with TAA-PNC.Our findings suggest an association between annual average near-highway TAA-PNC and subclinical inflammatory markers of CVD risk.

Lane K.J.,Boston University | Kangsen Scammell M.,Boston University | Levy J.I.,Boston University | Fuller C.H.,Georgia State University | And 4 more authors.
Environmental Health: A Global Access Science Source | Year: 2013

Background: The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways. Methods. We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups. Results: Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0-50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0-50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups. Conclusions: These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings. © 2013 Lane et al.; licensee BioMed Central Ltd.

Patton A.P.,Tufts University | Perkins J.,Tufts University | Zamore W.,Somerville Transportation Equity Partnership | Levy J.I.,Boston University | And 2 more authors.
Atmospheric Environment | Year: 2014

Relatively few studies have characterized differences in intra- and inter-neighborhood traffic-related air pollutant (TRAP) concentrations and distance-decay gradients in neighborhoods along an urban highway for the purposes of exposure assessment. The goal of this work was to determine the extent to which intra- and inter-neighborhood differences in TRAP concentrations can be explained by traffic and meteorology in three pairs of neighborhoods along Interstate 93 (I-93) in the metropolitan Boston area (USA). We measured distance-decay gradients of seven TRAPs (PNC, pPAH, NO, NOX, BC, CO, PM2.5) in near-highway (<400m) and background areas (>1km) in Somerville, Dorchester/South Boston, Chinatown and Malden to determine whether (1) spatial patterns in concentrations and inter-pollutant correlations differ between neighborhoods, and (2) variation within and between neighborhoods can be explained by traffic and meteorology. The neighborhoods ranged in area from 0.5 to 2.3km2. Mobile monitoring was performed over the course of one year in each pair of neighborhoods (one pair of neighborhoods per year in three successive years; 35-47 days of monitoring in each neighborhood). Pollutant levels generally increased with highway proximity, consistent with I-93 being a major source of TRAP; however, the slope and extent of the distance-decay gradients varied by neighborhood as well as by pollutant, season and time of day. Spearman correlations among pollutants differed between neighborhoods (e.g., ρ=0.35-0.80 between PNC and NOX and ρ=0.11-0.60 between PNC and BC) and were generally lower in Dorchester/South Boston than in the other neighborhoods. We found that the generalizability of near-road gradients and near-highway/urban background contrasts was limited for near-highway neighborhoods in a metropolitan area with substantial local street traffic. Our findings illustrate the importance of measuring gradients of multiple pollutants under different ambient conditions in individual near-highway neighborhoods for health studies involving inter-neighborhood comparisons. © 2014 Elsevier Ltd.

Patton A.P.,Tufts University | Collins C.,Tufts University | Naumova E.N.,Tufts University | Zamore W.,Somerville Transportation Equity Partnership | And 2 more authors.
Environmental Science and Technology | Year: 2014

Estimating ultrafine particle number concentrations (PNC) near highways for exposure assessment in chronic health studies requires models capable of capturing PNC spatial and temporal variations over the course of a full year. The objectives of this work were to describe the relationship between near-highway PNC and potential predictors, and to build and validate hourly log-linear regression models. PNC was measured near Interstate 93 (I-93) in Somerville, MA using a mobile monitoring platform driven for 234 h on 43 days between August 2009 and September 2010. Compared to urban background, PNC levels were consistently elevated within 100-200 m of I-93, with gradients impacted by meteorological and traffic conditions. Temporal and spatial variables including wind speed and direction, temperature, highway traffic, and distance to I-93 and major roads contributed significantly to the full regression model. Cross-validated model R2 values ranged from 0.38 to 0.47, with higher values achieved (0.43 to 0.53) when short-duration PNC spikes were removed. The model predicts highest PNC near major roads and on cold days with low wind speeds. The model allows estimation of hourly ambient PNC at 20-m resolution in a near-highway neighborhood. © 2014 American Chemical Society.

Padro-Martinez L.T.,Tufts University | Patton A.P.,Tufts University | Trull J.B.,Tufts University | Zamore W.,Somerville Transportation Equity Partnership | And 2 more authors.
Atmospheric Environment | Year: 2012

Accurate quantification of exposures to traffic-related air pollution in near-highway neighborhoods is challenging due to the high degree of spatial and temporal variation of pollutant levels. The objective of this study was to measure air pollutant levels in a near-highway urban area over a wide range of traffic and meteorological conditions using a mobile monitoring platform. The study was performed in a 2.3-km 2 area in Somerville, Massachusetts (USA), near Interstate 93 (I-93), a highway that carries 150,000 vehicles per day. The mobile platform was equipped with rapid-response instruments and was driven repeatedly along a 15.4-km route on 55 days between September 2009 and August 2010. Monitoring was performed in 4-6-h shifts in the morning, afternoon, and evening on both weekdays and weekends in winter, spring, summer, and fall. Measurements were made of particle number concentration (PNC; 4-3000 nm), particle size distribution, fine particle mass (PM 2.5), particle-bound polycyclic aromatic hydrocarbons (pPAH), black carbon (BC), carbon monoxide (CO), and nitrogen oxides (NO and NO x). The highest pollutant concentrations were measured within 0-50 m of I-93 with distance-decay gradients varying depending on traffic and meteorology. The most pronounced variations were observed for PNC. Annual median PNC 0-50 m from I-93 was two-fold higher compared to the background area (>1 km from I-93). In general, PNC levels were highest in winter and lowest in summer and fall, higher on weekdays and Saturdays compared to Sundays, and higher during morning rush hour compared to later in the day. Similar spatial and temporal trends were observed for NO, CO and BC, but not for PM 2.5. Spatial variations in PNC distance-decay gradients were non-uniform largely due to contributions from local street traffic. Hour-to-hour, day-to-day and season-to-season variations in PNC were of the same magnitude as spatial variations. Datasets containing fine-scale temporal and spatial variation of air pollution levels near highways may help to inform exposure assessment efforts. © 2012 Elsevier Ltd.

PubMed | Somerville Transportation Equity Partnership and Tufts University
Type: Journal Article | Journal: Environmental science & technology | Year: 2016

Ultrafine particles are emitted at high rates by jet aircraft. To determine the possible impacts of aviation activities on ambient ultrafine particle number concentrations (PNCs), we analyzed PNCs measured from 3 months to 3.67 years at three sites within 7.3 km of Logan International Airport (Boston, MA). At sites 4.0 and 7.3 km from the airport, average PNCs were 2- and 1.33-fold higher, respectively, when winds were from the direction of the airport compared to other directions, indicating that aviation impacts on PNC extend many kilometers downwind of Logan airport. Furthermore, PNCs were positively correlated with flight activity after taking meteorology, time of day and week, and traffic volume into account. Also, when winds were from the direction of the airport, PNCs increased with increasing wind speed, suggesting that buoyant aircraft exhaust plumes were the likely source. Concentrations of other pollutants [CO, black carbon (BC), NO, NO2, NOx, SO2, and fine particulate matter (PM2.5)] decreased with increasing wind speed when winds were from the direction of the airport, indicating a different dominant source (likely roadway traffic emissions). Except for oxides of nitrogen, other pollutants were not correlated with flight activity. Our findings point to the need for PNC exposure assessment studies to take aircraft emissions into consideration, particularly in populated areas near airports.

PubMed | University of Massachusetts Boston, Somerville Transportation Equity Partnership and Tufts University
Type: Journal Article | Journal: Environmental science & technology | Year: 2015

Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal LUR models of hourly particle number concentration (PNC) for Boston-area (MA, U.S.A.) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34-46 days/neighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to the other neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both the transferability and generalizability of models were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R(2) = 0.24-0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability was poor (R(2) < 0.17). Locally-calibrated transferred models (R(2) = 0.19-0.40) and the Boston-area model (adjusted-R(2) = 0.26, range: 0.13-0.30) performed similarly to neighborhood-specific models; however, some coefficients of locally calibrated transferred models were uninterpretable. Our results show that transferability of neighborhood-specific LUR models of hourly PNC was limited, but that a general model performed acceptably in multiple areas when calibrated with local data.

PubMed | Boston University, Somerville Transportation Equity Partnership and Tufts University
Type: Journal Article | Journal: Journal of exposure science & environmental epidemiology | Year: 2015

Exposures to ultrafine particles (<100nm, estimated as particle number concentration, PNC) differ from ambient concentrations because of the spatial and temporal variability of both PNC and people. Our goal was to evaluate the influence of time-activity adjustment on exposure assignment and associations with blood biomarkers for a near-highway population. A regression model based on mobile monitoring and spatial and temporal variables was used to generate hourly ambient residential PNC for a full year for a subset of participants (n=140) in the Community Assessment of Freeway Exposure and Health study. We modified the ambient estimates for each hour using personal estimates of hourly time spent in five micro-environments (inside home, outside home, at work, commuting, other) as well as particle infiltration. Time-activity adjusted (TAA)-PNC values differed from residential ambient annual average (RAA)-PNC, with lower exposures predicted for participants who spent more time away from home. Employment status and distance to highway had a differential effect on TAA-PNC. We found associations of RAA-PNC with high sensitivity C-reactive protein and Interleukin-6, although exposure-response functions were non-monotonic. TAA-PNC associations had larger effect estimates and linear exposure-response functions. Our findings suggest that time-activity adjustment improves exposure assessment for air pollutants that vary greatly in space and time.

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