A modeling framework for characterizing near-road air pollutant concentration at community scales

Chang, Shih Ying; Vizuete, William; Valencia, Alejandro; Naess, Brian; Isakov, Vlad; Palma, Ted; Breen, Michael S.; Arunachalam, Saravanan

doi:10.1016/j.scitotenv.2015.06.139

Cited by 43 publications

(44 citation statements)

References 41 publications

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“…In continuous models, living farther from a major roadway (particularly more than 500 meters) increased the likelihood of pregnancy but we noted the lack of a dose-response at close distances where we may be underpowered to see an effect. We also recognize that dispersion of traffic-related pollutants will occur (30) and our straight-line exposure metric does not control for short-range local variation such as roadside vegetation (31). As with most other studies of roadway proximity, we also lack information on potential mediators of major roadway exposures, such as greenspace (32).…”

Section: 0 Discussionmentioning

confidence: 99%

Proximity to major roadways and prospectively-measured time-to-pregnancy and infertility

Mendola

Sundaram

Louis

et al. 2017

Science of The Total Environment

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We aimed to study the potential impact of proximity to major roadways on time-to-pregnancy and infertility in couples attempting pregnancy in the Longitudinal Investigation of Fertility and Environment (LIFE) study (2005-2009), a population-based, prospective cohort study. Couples attempting pregnancy (n=500) were enrolled and followed prospectively until pregnancy or 12 months of trying and 393 couples (78%) had complete data and full follow-up. Time-to-pregnancy was based on a standard protocol using fertility monitors, tracking estrone-3-glucuonide and luteinizing hormone, and pregnancy test kits to detect human chorionic gonadotropin (hCG). The fecundability odds ratio (FOR) and 95% confidence interval (CI) were estimated using proportional odds models. Infertility was defined as 12 months of trying to conceive without an hCG pregnancy and the relative risk (RR) and 95% CI were estimated with log-binomial regression. Final models were adjusted for age, parity, study site, and salivary alpha-amylase, a stress marker. Infertile couples (53/393; 14%) tended to live closer to major roadways on average than fertile couples (689 m vs. 843 m, respectively) but the difference was not statistically significant. The likelihood of pregnancy was increased 3% for every 200 meters further away the couples residence was from a major roadway (FOR=1.03; CI=1.01-1.06). Infertility also appeared elevated at moderate distances compared to 1000 meters or greater, but estimates lacked precision. Our findings suggest that proximity to major roadways may be related to reductions in fecundity. Prospective data from larger populations is warranted to corroborate these findings.

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Section: 0 Discussionmentioning

confidence: 99%

Proximity to major roadways and prospectively-measured time-to-pregnancy and infertility

Mendola

Sundaram

Louis

et al. 2017

Science of The Total Environment

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“…Approaches that have been developed to simulate the distribution of the concentration of air pollutants usually include geostatistical interpolation [30], land-use regression [31], dispersion [32], and hybrid [33] models. "Dispersion models use information on emissions, source characteristics, chemical and physical properties of the pollutants, topography, and meteorology to model the transport and transformation of gaseous or particulate pollutants through the atmosphere to predict, e.g., ground level concentrations" [34].…”

Section: Lur Modelmentioning

confidence: 99%

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

2017

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Abstract:With the increase in urbanization and energy consumption, PM 2.5 has become a major pollutant. This paper investigates the impact of road patterns on PM 2.5 pollution in Beijing, focusing on two questions: Do road patterns significantly affect PM 2.5 concentrations? How do road patterns affect PM 2.5 concentrations? A land-use regression model (LUR model) is used to quantify the associations between PM 2.5 concentrations, and road patterns, land-use patterns, and population density. Then, in the condition of excluding other factors closely correlated to PM 2.5 concentrations, based on the results of the regression model, further research is conducted to explore the relationship between PM 2.5 concentrations and the types, densities, and layouts of road networks, through the controlling variables method. The results are as follows: (1) the regression coefficient of road patterns is significantly higher than the water area, population density, and transport facilities, indicating that road patterns have an obvious influence on PM 2.5 concentrations; (2) under the same traffic carrying capacity, the layout of "a tight network of streets and small blocks" is superior to that of "a sparse network of streets and big blocks"; (3) the grade proportion of urban roads impacts the road patterns' rationality, and a high percentage of branch roads and secondary roads could decrease PM 2.5 concentrations. These findings could provide a reference for the improvement of the traffic structure and air quality of Beijing.

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“…We refined a previously developed novel hybrid approach that now combines dispersion modeling (R‐LINE), CMAQ, and space–time ordinary kriging (STOK) technique to model primary and secondary on‐road PM 2.5 as well as total PM 2.5 at Census block centroids (∼105,000 Census blocks). The detail of the R‐LINE and STOK hybrid framework is described elsewhere . We further improved the approach to capture secondary on‐road PM 2.5 by utilizing CMAQ predictions (see Section ).…”

Section: Methodsmentioning

confidence: 99%

“…The resultant simulated concentrations were then weighted and summed to yield the annual average concentration. A detailed description of METARE and the approach for developing the emissions inputs can be found in an earlier study …”

Section: Methodsmentioning

confidence: 99%

“…In a dispersion model, the emission source is allocated in a much finer spatial resolution more realistically retaining the shape and physical characteristics of the emitted plume. For example, dispersion models can model roadways as adjacent area sources or line sources and thus the sharp concentration gradients from roadways can be captured with a higher spatial resolution . Modeling TRAPs with dispersion models can still be challenging due to the requirement to characterize a temporally refined emission for each roadway.…”

Section: Introductionmentioning

confidence: 99%

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Finely Resolved On‐Road PM_2.5 and Estimated Premature Mortality in Central North Carolina

et al. 2017

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To quantify the on-road PM -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM -related premature mortality than CMAQ. The major difference is from the primary on-road PM where the hybrid approach estimated 2.5 times more primary on-road PM -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM and suggesting that previous studies may have underestimated premature mortality due to PM from traffic-related emissions.

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A modeling framework for characterizing near-road air pollutant concentration at community scales

Cited by 43 publications

References 41 publications

Proximity to major roadways and prospectively-measured time-to-pregnancy and infertility

Proximity to major roadways and prospectively-measured time-to-pregnancy and infertility

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

Finely Resolved On‐Road PM_2.5 and Estimated Premature Mortality in Central North Carolina

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A modeling framework for characterizing near-road air pollutant concentration at community scales

Cited by 43 publications

References 41 publications

Proximity to major roadways and prospectively-measured time-to-pregnancy and infertility

Proximity to major roadways and prospectively-measured time-to-pregnancy and infertility

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

Finely Resolved On‐Road PM2.5 and Estimated Premature Mortality in Central North Carolina

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Finely Resolved On‐Road PM_2.5 and Estimated Premature Mortality in Central North Carolina