Near roadway air pollution across a spatially extensive road and cycling network

Farrell, William J.; Weichenthal, Scott; Goldberg, Mark S.; Valois, Marie‐France; Shekarrizfard, Maryam; Hatzopoulou, Marianne

doi:10.1016/j.envpol.2016.02.041

Cited by 37 publications

(39 citation statements)

References 33 publications

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“…Commercial areas have also been shown to have a positive relationship with PM levels [10,[18][19][20]. For instance, in a study on air pollution around roads, the number of commercial facilities (e.g., restaurants) had a greater influence on PM 2.5 than the general presence of commercial land use [23]. Meanwhile, most studies have found high PM 10 and PM 2.5 concentrations in industrial areas [12,13,[16][17][18]20,21,[23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, in a study on air pollution around roads, the number of commercial facilities (e.g., restaurants) had a greater influence on PM 2.5 than the general presence of commercial land use [23]. Meanwhile, most studies have found high PM 10 and PM 2.5 concentrations in industrial areas [12,13,[16][17][18]20,21,[23][24][25]. Finally, many studies have found lower PM 10 and PM 2.5 levels in green areas [10,19,26,27].…”

Section: Introductionmentioning

confidence: 99%

“…However, McCarty and Kaza [28] found high PM 10 levels in green areas and Weichenthal et al [20] showed the same result for PM 2.5 . Other studies have assessed the association of PM with other land use variables (e.g., river area [11,20,23,24] and urbanization area [25]). These findings showed that PM 2.5 and PM 10 have a dependency on the typology of measurement site but not the metal content [29].…”

Section: Introductionmentioning

confidence: 99%

“…Built environment attributes related to transportation are also associated with PM concentration. Traffic volume on roads was found to have a significant effect on PM [16,23,27,30]. Regarding vehicle type, truck density and truck route length were found to be positively correlated with PM 2.5 [20,31,32].…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea

Park

2018

Sustainability

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Air pollution has a major impact on human health and quality of life; therefore, its determinants should be studied to promote effective management and reduction. Here, we examined the influence of the built environment on air pollution by analyzing the relationship between the built environment and particulate matter (i.e., PM2.5 and PM10). Air pollution data collected in Seoul in 2014 were spatially mapped using geographic information system tools, and PM2.5 and PM10 concentrations were determined in individual neighborhoods using an interpolation method. PM2.5 and PM10 failed to show spatial autocorrelation; therefore, we analyzed the associations between PM fractions and built environment characteristics using an ordinary least squares regression model. PM2.5 and PM10 exhibited some differences in spatial distributions, suggesting that the built environment has different effects on these fractions. For instance, high PM10 concentrations were associated with neighborhoods with more bus routes, bus stops, and river areas. Meanwhile, both PM2.5 and PM10 were more likely to be high in areas with more commercial areas and multi-family housing, but low in areas with more main roads, more single-family housing, and high average gross commercial floor area. This study is expected to contribute to establishing policies and strategies to promote sustainability in Seoul, Korea.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea

Park

2018

Sustainability

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“…Emissions from motor vehicles have been linked to air quality degradation (e.g., WHO, 2005;Han and Naeher, 2006;Zhang and Batterman, 2013;Farrell et al, 2016;Zimmerman et al, 2016;Gentner et al, 2017;Wang et al, 2017) and greenhouse gas pollution (e.g., Sims et al, 2014;Zimmerman et al, 2016;Boulton, 2016;US EPA, 2002) globally. In North America, vehicle emission controls have gradually reduced emissions of many pollutants and made vehicles more fuel efficient (U.S. Environmental Protection Agency, 2012;Reid and Aherne, 2016), with transportation policies being closely aligned in Canada and the United States (ECCC, 2019b).…”

Section: Introductionmentioning

confidence: 99%

How much does traffic contribute to benzene and polycyclic aromatic hydrocarbon air pollution? Results from a high-resolution North American air quality model centred on Toronto, Canada

et al. 2020

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Benzene and polycyclic aromatic hydrocarbons (PAHs) are toxic air pollutants that have long been associated with motor vehicle emissions, though the importance of such emissions has never been quantified over an extended domain using a chemical transport model. Herein we present the first application of such a model (GEM-MACH-PAH) to examine the contribution of motor vehicles to benzene and PAHs in ambient air. We have applied the model over a region that is centred on Toronto, Canada, and includes much of southern Ontario and the northeastern United States. The resolution (2.5 km) was the highest ever employed by a model for these compounds in North America, and the model domain was the largest at this resolution in the world to date. Using paired model simulations that were run with vehicle emissions turned on and off (while all other emissions were left on), we estimated the absolute and relative contributions of motor vehicles to ambient pollutant concentrations. Our results provide estimates of motor vehicle contributions that are realistic as a result of the inclusion of atmospheric processing, whereas assessing changes in benzene and PAH emissions alone would neglect effects caused by shifts in atmospheric oxidation and particle-gas partitioning. A secondary benefit of our scenario approach is in its utility in representing a fleet of zero-emission vehicles (ZEVs), whose adoption is being encouraged in a variety of jurisdictions. Our simulations predicted domain-average on-road vehicle contributions to benzene and PAH concentrations of 4 %-21 % and 14 %-24 % in the spring-summer and fall-winter periods, respectively, depending on the aromatic compound. Contributions to PAH concentrations up to 50 % were predicted for the Greater Toronto Area, and the domain maximum was simulated to be 91 %. Such contributions are substantially higher than those reported at the national level in Canadian emissions inventories, and they also differ from inventory estimates at the subnational scale in the US. Our model has been run at a finer spatial scale than reported in those inventories, and furthermore includes physico-chemical processing that alters pollutant concentrations after their release. The removal of onroad vehicle emissions generally led to decreases in benzene and PAH concentrations during both periods that were studied, though atmospheric processing (such as chemical reactions and changes to particle-gas partitioning) contributed to non-linear behaviour at some locations or times of year. Such results demonstrate the added value associated with regional air quality modelling relative to examinations of emissions inventories alone. We also found that removing on-road vehicle emissions reduced spring-summertime surface O 3 volume mixing ratios and fall-wintertime PM 10 concentrations each by ∼ 10 % in the model domain, providing further air quality benefits. Toxic equivalents contributed by vehicle emissions of PAHs were found to be substantial (20 %-60 % depending on location), and this finding is particularl...

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Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt

Talaat

Hatzopoulou

et al. 2021

Environ Monit Assess

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Near roadway air pollution across a spatially extensive road and cycling network

Cited by 37 publications

References 33 publications

Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea

Investigating the Effects of the Built Environment on PM2.5 and PM10: A Case Study of Seoul Metropolitan City, South Korea

How much does traffic contribute to benzene and polycyclic aromatic hydrocarbon air pollution? Results from a high-resolution North American air quality model centred on Toronto, Canada

Mobile monitoring and spatial prediction of black carbon in Cairo, Egypt

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