Figure 1 - uploaded by Janet M Burke
Content may be subject to copyright.
Map of modeled road network in study area, and locations of 218 homes of participants in NEXUS. Shaded area defines city of Detroit and population by Census Block group. Axis scales are Universal Traverse Mercator coordinates (m). AADT is annual average daily traffic (vehicles/day). Highlighted roads are National Functional Class 11, called high diesel/high traffic roads in NEXUS. Windsor, Canada (not shown), is immediately to the south-east.
Source publication
Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key informat...
Contexts in source publication
Context 1
... 139 children were recruited and participated in the study from September 2010 to December 2012. Because a number of children moved during the study, a total of 218 residence locations were considered ( Figure 1). The study population had approximately equal distribution across the three traffic categories. ...
Context 2
... differences between the GPS and automated geocoding coordinates exceeded 100 m, data were checked, plotted, and if needed, our technician was sent out to confirm GPS coordinates a second (and sometimes a third) time, at which point all GPS measurements agreed. Final home locations are plotted in Figure 1. On average, the automated geocoding estimates diverged from GPS measurements at the NEXUS homes by an average 30 ± 23 m, although much larger errors were not infrequent, e.g., 75th and 95th percentile errors were ~50 and 75 m [24]. ...
Context 3
... modeled road network used 9701 links (linear segments) to represent 3109 km of roads, which included all but the smaller and numerous local roads in the ~800 km 2 study area ( Figure 1). Major roads (e.g., freeways) were represented using separate links for each direction, large service roads, if any, and ramps. ...
Similar publications
Regional planning may require a better understanding of multijurisdictional planning and equity within intergovernmental context. This research explores how intergovernmental context and metropolitan planning organization (MPO) activities impact rail proposals for low-income and minority communities. In two case studies, Boston and Miami, other gov...
Citations
... To the best of our knowledge, such a model does not yet exist for Vienna, or is publicly available, and its development is beyond the scope of this paper. As the next best approximation for simulating concentration levels and exposure, we assume that PM10 emissions disperse normally in the 200-250 m range from emissions emerging from traffic on streets and decay exponentially beyond this range (Batterman et al., 2014;Fecht et al., 2016;Li and Managi, 2021). Emissions also tend to stay relatively close to the ground, diffusing approximately 20-30 m vertically. ...
... Likewise, the implementation of adequate noise mitigation measures may provide effective protection against emission of particle material, mainly PM 2.5, and various other pollutants as ozone, carbon dioxide, nitrogen dioxide and sulfur dioxide generated by motor vehicle traffic and roads. Long-term exposure to these contaminants has been linked to the occurrence of various diseases such as heart disease, lung malfunction, leukemia, asthma and lung cancer [42][43][44][45]. But not every noise reduction correlates positively with air pollution reduction [46]. ...
The most important source of environmental noise is generated by vehicular traffic on roads and highways. Several organisms have reported human health and various social problems related to noise. The Agua Negra international road seeks to improve the physical and commercial connectivity of the zones located between the ports of Porto Alegre in Brazil and Coquimbo in Chile. The Chilean sector includes the expansion and improvement of the Route 41-CH. The objective of this work is establishing a methodology to determine the cities or places with tourist, commercial and recreational interest on the route that may be affected by noise and which noise mitigation measures could be used to reduce the impacts. It was concluded that it will have an impact on the communities located along the route. We present mitigation measures to reduce the impact if they are considered from the design phase of the project.
... However, limited work to date has specifically examined the potential additional influence of traffic congestion ( Fig. 1), largely due to the challenges of measuring congestion accurately for large geographic areas (12). Across a wide variety of countries and settings, living near a major road during pregnancy, as well as exposure to specific traffic air pollutants, has consistently been associated with decreased birth weight and increased risk of preterm birth (5,10,11,(13)(14)(15)(16). Most of the exposure assessments used in these studies were based on proximity to major roads or models of specific traffic pollutants (e.g., NO 2 ), with very little of this evidence incorporating traffic congestion in their exposure measures (17). No studies have specifically examined the added impact of congestion, in addition to traffic volume and "normal" background traffic air pollution levels, on adverse birth outcomes. ...
More than 11 million Americans reside within 150 meters of a highway, an area of high air pollution exposure. Traffic congestion further contributes to environmental pollution (e.g., air and noise), but its unique importance for population health is unclear. We hypothesized that degraded environmental quality specifically from traffic congestion has harmful impacts on fetal growth. Using a population-based cohort of births in Texas (2015-2016), we leveraged connected vehicle data to calculate traffic congestion metrics around each maternal address at delivery. Among 579,122 births, we found consistent adverse associations between traffic congestion and reduced term birth weight (8.9 grams), even after accounting for sociodemographic characteristics, typical traffic volume, and diverse environmental coexposures. We estimated that up to 1.2 million pregnancies annually may be exposed to traffic congestion (27% of births in the United States), with ~256,000 in the highest congestion zones. Therefore, improvements to traffic congestion may yield positive cobenefits for infant health.
... Despite declining regional levels of pollution as measured by central monitors, increase in traffic exposures has led to increase in contribution of traffic as a source of air pollution and an increase in the population exposed to such pollution beyond the metropolitan areas [10]. Key components of emissions from traffic sources such as PM 2.5 , volatile organic compounds (VOCs), nitrogen oxides (NO x ), carbon monoxide (CO), sulfur dioxide (SO 2 ), and ammonia (NH 3 ), and other ultrafine and nanoparticles, along with other stressors such as noise, may contribute to multiple health outcomes including asthma [11][12][13][14][15]. Measures of proximity to traffic, specifically those accounting for traffic density and truck traffic, may therefore be a cumulative measure of exposure comprising of multiple risk factors of asthma exacerbations than single pollutant measures [9,[16][17][18][19][20]. ...
Background
Environmental exposures such as traffic may contribute to asthma morbidity including recurrent emergency department (ED) visits. However, these associations are often confounded by socioeconomic status and health care access.
Objective
This study aims to assess the association between traffic density and recurrence of asthma ED visits in the primarily low income Medicaid population in New York State (NYS) between 2005 and 2015.
Methods
The primary outcome of interest was a recurrent asthma ED visit within 1-year of index visit. Traffic densities (weighted for truck traffic) were spatially linked based on home addresses. Bivariate and multivariate logistic regression analyses were conducted to identify factors predicting recurrent asthma ED visits.
Results
In a multivariate model, Medicaid recipients living within 300-m of a high traffic density area were at a statistically significant risk of a recurrent asthma ED visit compared to those in a low traffic density area (OR = 1.31; 95% CI:1.24,1.38). Additionally, we evaluated effect measure modification for risk of recurrent asthma visits associated with traffic exposure by socio-demographic factors. The highest risk was found for those exposed to high traffic and being male (OR = 1.87; 95% CI:1.46,2.39), receiving cash assistance (OR = 2.11; 95% CI:1.65,2.72), receiving supplemental security income (OR = 2.21; 95% CI:1.66,2.96) and being in the 18.44 age group (OR = 1.59;95% CI 1.48,1.70) was associated with the highest risk of recurrent asthma ED visit. Black non-Hispanics (OR = 2.35; 95% CI:1.70,3.24), Hispanics (OR = 2.13; 95% CI:1.49,3.04) and those with race listed as “Other” (OR = 1.89 95% CI:1.13,3.16) in high traffic areas had higher risk of recurrent asthma ED visits as compared to White non-Hispanics in low traffic areas.
Conclusion
We observed significant persistent disparities in asthma morbidity related to traffic exposure and race/ethnicity in a low-income population. Our findings suggest that even within a primarily low-income study population, socioeconomic differences persist. These differences in susceptibility in the extremely low-income group may not be apparent in health studies that use Medicaid enrollment as a proxy for low SES.
... On roads with higher density of traffic, greater levels of PM 2.5 have been noted in cities such as Detroit. 91 The highest concentration of these pollutants is usually found nearer to the major roadways, and residence in these locations is exposed to elevated levels of these pollutants. Carbon dioxide (CO 2 ) emissions from road travel were found to decrease as the concentration of Whites increases in a county, whereas counties with higher Black and African American populations were found to emit more CO 2 . ...
Background: Place is a social determinant of health, as recently evidenced by COVID-19. Previous literature surrounding health disparities in the United States often fails to acknowledge the role of structural racism on place-based health disparities for historically marginalized communities (i.e., Black and African American communities, Hispanic/Latinx communities, Indigenous communities [i.e., First Nations, Native American, Alaskan Native, and Native Hawaiian], and Pacific Islanders). This narrative review summarizes the intersection between structural racism and place as contributors to COVID-19 health disparities.
Methods: This narrative review accounts for the unique place-based health care experiences influenced by structural racism, including health systems and services and physical environment. We searched online databases for peer-reviewed and governmental sources, published in English between 2000 and 2021, related to place-based U.S. health inequities in historically marginalized communities. We then narrate the link between the historical trajectory of structural racism and current COVID-19 health outcomes for historically marginalized communities.
Results: Structural racism has infrequently been named as a contributor to place as a social determinant of health. This narrative review details how place is intricately intertwined with the results of structural racism, focusing on one's access to health systems and services and physical environment, including the outdoor air and drinking water. The role of place, health disparities, and structural racism has been starkly displayed during the COVID-19 pandemic, where historically marginalized communities have been subject to greater rates of COVID-19 incidence and mortality.
Conclusion: As COVID-19 becomes endemic, it is crucial to understand how place-based inequities and structural racism contributed to the COVID-19 racial disparities in incidence and mortality. Addressing structurally racist place-based health inequities through anti-racist policy strategies is one way to move the United States toward achieving health equity.
... (CO) (Batterman et al., 2014a;Delfino et al., 2014;Pennington et al., 2018), sulfur dioxide (SO 2 ) (Koiwanit et al., 2016;Milando et al., 2016;Pascal et al., 2013) and volatile organic compounds (VOC) (Charpin et al., 2009;Spadaro and Rabl, 1999). An even more limited number of studies explored the contribution of different sources (e.g., Batterman et al. (2014a), Galvis et al. (2015), Ganguly et al. (2015), Guttikunda et al. (2015), Guttikunda and Goel (2013), Khafaie et al. (2017) and Milando et al. (2016)). ...
... (CO) (Batterman et al., 2014a;Delfino et al., 2014;Pennington et al., 2018), sulfur dioxide (SO 2 ) (Koiwanit et al., 2016;Milando et al., 2016;Pascal et al., 2013) and volatile organic compounds (VOC) (Charpin et al., 2009;Spadaro and Rabl, 1999). An even more limited number of studies explored the contribution of different sources (e.g., Batterman et al. (2014a), Galvis et al. (2015), Ganguly et al. (2015), Guttikunda et al. (2015), Guttikunda and Goel (2013), Khafaie et al. (2017) and Milando et al. (2016)). ...
... EPISODE, ADMS and AirViro) (Gruzieva et al., 2017(Gruzieva et al., , 2013Idavain et al., 2019;Melén et al., 2008;Modig and Forsberg, 2007;Nordling et al., 2008;Oftedal et al., 2009;Schultz et al., 2017;Sommar et al., 2014). More than half of the studies investigated Traffic-Related Air Pollution (TRAP) that may require dispersion models such as the CALINE4 and RLINE (Batterman et al., 2014a;Delfino et al., 2014Delfino et al., , 2009Franklin and Fruin, 2017;Ganguly et al., 2015;Gauderman et al., 2005;McConnell et al., 2010;Pennington et al., 2018Pennington et al., , 2017Pershagen et al., 1995;Shankardass et al., 2009). ...
There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.
... We observed a statistically significant interaction by race for the relationship of AL score with living less than 400 m from major roadways with heavy traffic. As an exposure metric, residential distance to the nearest roadway is a surrogate measure that represents the effects of multiple traffic-related air pollutants, such as particulates, nitrogen oxides and carbon monoxide, as well as other potentially important components such as traffic noise (Stansfeld, 2015;Batterman et al., 2014). Though caution is warranted for over-interpretation of our findings, they support further investigation. ...
Objective
We investigated the effects of chronic exposures to particulate and traffic-related air pollution on allostatic load (AL) score, a marker of cumulative biological risk, among youth with type 1 diabetes.
Research Design and Methods
Participants were drawn from five clinical sites of the SEARCH for Diabetes in Youth (SEARCH) study (n=2,338). Baseline questionnaires, anthropometric measures, and a fasting blood test were taken at a clinic visit between 2001 and 2005. AL was operationalized using 10 biomarkers reflecting cardiovascular, metabolic, and inflammatory risk. Annual residential exposures to PM2.5 and proximity to heavily-trafficked major roadways were estimated for each participant. Poisson regression models adjusted for sociodemographic and lifestyle factors were conducted for each exposure.
Results
No significant associations were observed between exposures to PM2.5 or proximity to traffic and AL score, however analyses were suggestive of effect modification by race for residential distance to heavily-trafficked major roadways (p=0.02). In stratified analyses, residing <100, 100-<200 and 200-<400 m compared to 400 m or more from heavily-trafficked major roadways was associated with 11%, 26% and 14% increases in AL score, respectively (95% CIs: -4, 29; 9, 45.0; -1, 30) for non-white participants compared to 6%, -2%, and -2% changes (95% CIs: -2, 15; -10, 7; -8, 6) for white participants.
Conclusions
Among this population of youth with type 1 diabetes, we did not observe consistent relationships between chronic exposures to particulate and traffic-related air pollution and changes in AL score, however associations for traffic-related pollution exposures may differ by race/ethnicity and warrant further examination.
... The results of PM 10 and PM 2.5 emission levels of the current study (Addis Ababa) were compared with other cities in developing countries ( Table 1 ). In developing countries, particularly cities in Africa air pollution has been severely affected by vehicles in urban areas due to old vehicle's age ( Zachariadis et al., 2001 ), high daily mileage travelled ( Batterman et al., 2014 ), fuel quality and fleet composition ( Sternbeck et al., 2002 ). Moreover, scarce of relevant transport-related data, air pollution controlling and monitoring activity has been given less attention by the higher official and sometimes even used as a political tool to motivate their people and celebrate once a year. ...
Artificial neural network PM prediction Roadside emission a b s t r a c t Currently, vehicle-related particulate matter is the main determinant air pollution in the urban environment. This study was designed to investigate the level of fine (PM 2.5) and coarse particle (PM 10) concentration of roadside vehicles in Addis Ababa, the capital city of Ethiopia using artificial neural network model. To train, test and validate the model, the traffic volume , weather data and particulate matter concentrations were collected from 15 different sites in the city. The experimental results showed that the city average 24-hr PM 2.5 concentration is 13%-144% and 58%-241% higher than air quality index (AQI) and world health organization (WHO) standards, respectively. The PM 10 results also exceeded the AQI (54%-65%) and WHO (8%-395%) standards. The model runs using the Levenberg-Marquardt (Trainlm) and the Scaled Conjugate Gradient (Trainscg) and comparison were performed, to identify the minimum fractional error between the observed and the predicted value. The two models were determined using the correlation coefficient and other statistical parameters. The Trainscg model, the average concentration of PM 2.5 and PM 10 exhaust emission correlation coefficient were predicted to be (R 2 = 0.775) and (R 2 = 0.92), respectively. The Trainlm model has also well predicted the exhaust emission of PM 2.5 (R 2 = 0.943) and PM 10 (R 2 = 0.959). The overall results showed that a better correlation coefficient obtained in the Trainlm model, could be considered as optional methods to predict transport-related particulate matter concentration emission using traffic volume and weather data for Ethiopia cities and other countries that have similar geographical and development settings.
... In their study, they examined the difference in mortality risk between neighborhoods in the city of Rotterdam and found that the mortality risks between neighborhoods had a difference of up to 7%. By utilizing land use regression techniques and air quality models, several studies have managed to demonstrate that an increased spatial resolution of the exposure concentration could lead to significantly different exposure or health burden estimates [15][16][17][18]. Similarly, Punger and West [19] assessed the effect of spatial resolution to population-weighted PM 2.5 concentrations in the U.S. by utilizing the Community Multiscale Air Quality (CMAQ) model. ...
... Res. Public Health 2020,17, 1099 ...
Exposure to PM2.5 has been associated with increased mortality in urban areas. Hence, reducing the uncertainty in human exposure assessments is essential for more accurate health burden estimates. Here, we quantified the misclassification that occurred when using different exposure approaches to predict the mortality burden of a population using London as a case study. We developed a framework for quantifying the misclassification of the total mortality burden attributable to exposure to fine particulate matter (PM2.5) in four major microenvironments (MEs) (dwellings, aboveground transportation, London Underground (LU) and outdoors) in the Greater London Area (GLA), in 2017. We demonstrated that differences exist between five different exposure Tier-models with incrementally increasing complexity, moving from static to more dynamic approaches. BenMap-CE, the open source software developed by the U.S. Environmental Protection Agency, was used as a tool to achieve spatial distribution of the ambient concentration by interpolating the monitoring data to the unmonitored areas and ultimately estimating the change in mortality on a fine resolution. Indoor exposure to PM2.5 is the largest contributor to total population exposure concentration, accounting for 83% of total predicted population exposure, followed by the London Underground, which contributes approximately 15%, despite the average time spent there by Londoners being only 0.4%. After incorporating housing stock and time-activity data, moving from static to most dynamic metric, Inner London showed the highest reduction in exposure concentration (i.e., approximately 37%) and as a result the largest change in mortality (i.e., health burden/mortality misclassification) was observed in central GLA. Overall, our findings showed that using outdoor concentration as a surrogate for total population exposure but ignoring different exposure concentration that occur indoors and time spent in transit, led to a misclassification of 1174–1541 mean predicted mortalities in GLA. We generally confirm that increasing the complexity and incorporating important microenvironments, such as the highly polluted LU, could significantly reduce the misclassification of health burden assessments.
... 7 Several other recently developed modeling schemes that incorporate multiple methods in one framework were not yet vastly seen in population health studies. 8 Although individual estimates produced by the different modeling approaches are expected to concur, several studies have shown that these estimates often disagree, 7,[9][10][11] implying a substantial exposure misclassification among study subjects. We therefore formulated a paradigm, leveraging models' disagreement by creating an ensemble of exposure assessments to identify individuals with a higher certainty regarding their exposure status. ...
... In the second stage, we integrated estimates from the two models to identify subjects for whom exposure prediction has a higher level of certainty (i.e., subjects who are more likely to be truly exposed or truly unexposed). Since both ODM and LUR are established methods for assessing exposure, with neither having demonstrated superiority, but with clear differences seen in their assigned individual estimates, 7,[9][10][11] we assumed that each model-based estimate captures some aspects of the true exposure along with some noise. In this sense, the probability of subjects ...
Background: Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. This induces exposure misclassification for a substantial fraction of subjects.
Aim: We used an ensemble of well-established modeling approaches to increase certainty of exposure classification and reevaluated the association with cancers previously linked to TRAP (lung, breast and prostate), other cancers, and all-cause mortality in a cohort of coronary patients.
Methods: Patients undergoing percutaneous coronary interventions in a major Israeli medical center from 2004 to 2014 (n=10,627) were followed for cancer (through 2015) and mortality (through 2017) via national registries. Residential exposure to nitrogen oxides (NOx) –a proxy for TRAP– was estimated by optimized dispersion model (ODM) and land use regression (LUR) (rPearson=0.50). Mutually exclusive groups of subjects classified as exposed by none of the methods (high-certainty low-exposed), ODM alone, LUR alone, or both methods (high-certainty high-exposed) were created. Associations were examined using Cox regression models.
Results: During follow-up, 741 incident cancer cases were diagnosed and 3051 deaths occurred. Using a ≥25 ppb cutoff, compared with high-certainty low exposed, the multivariable-adjusted hazard ratios (95% confidence intervals) for lung, breast and prostate cancer were 1.56 (1.13–2.15) in high-certainty exposed, 1.27 (0.86–1.86) in LUR-exposed alone, and 1.13 (0.77–1.65) in ODM-exposed alone. The association of the former category was strengthened using more extreme NOx cutoffs. A similar pattern, albeit less strong, was observed for mortality, whereas no association was shown for cancers not previously linked to TRAP.
Conclusions: Use of an ensemble of TRAP exposure estimates may improve classification, resulting in a stronger association with outcomes.
