Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States – Part II: Source apportionment and cancer risk assessment

Zhang, Jie; Wang, Peng; Li, Jingyi; Mendola, Pauline; Sherman, Seth; Ying, Qi

doi:10.1016/j.envint.2016.08.024

Cited by 43 publications

(27 citation statements)

References 43 publications

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“…Other PAH CTMs include the following: GEOS-Chem (Friedman and Selin, 2012;Thackray et al, 2015), which is a global model; CMAQ, which was run on the Europe continental domain in Aulinger et al (2007) and in North America in Zhang et al (2016Zhang et al ( , 2017; and FARM (Flexible Air quality Regional Model) (Gariazzo et al, 2007), which is a regional model, applied for the Rome region in Italy. The most relevant of these model studies to our own is that by Zhang et al (2016Zhang et al ( , 2017, whereby they ran CMAQ with 16 PAH species added, at 36 km resolution in a mainly US domain (that included parts of Canada and Mexico), evaluated their model results against NATTS measurements, and used their results to determine the cancer risk to the US human populations from various sources.…”

Section: Introductionmentioning

confidence: 99%

GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene

et al. 2018

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Abstract. Environment and Climate Change Canada's online air quality forecasting model, GEM-MACH, was extended to simulate atmospheric concentrations of benzene and seven polycyclic aromatic hydrocarbons (PAHs): phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, and benzo(a)pyrene. In the expanded model, benzene and PAHs are emitted from major point, area, and mobile sources, with emissions based on recent emission factors. Modelled PAHs undergo gas–particle partitioning (whereas benzene is only in the gas phase), atmospheric transport, oxidation, cloud processing, and dry and wet deposition. To represent PAH gas–particle partitioning, the Dachs–Eisenreich scheme was used, and we have improved gas–particle partitioning parameters based on an empirical analysis to get significantly better gas–particle partitioning results than the previous North American PAH model, AURAMS-PAH. Added process parametrizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapour sorption to the snow surface. The resulting GEM-MACH-PAH model was used to generate the first online model simulations of PAH emissions, transport, chemical transformation, and deposition for a high-resolution domain (2.5 km grid cell spacing) in North America, centred on the PAH data-rich region of southern Ontario, Canada and the northeastern US. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the US Average spring–summertime model results were found to be statistically unbiased from measurements of benzene and all seven PAHs. The same was true for the fall–winter seasonal mean, except for benzo(a)pyrene, which had a statistically significant positive bias. We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to particulate matter and oxidant processes and transport. Our analysis focused on four key components to the prediction of atmospheric PAH levels: spatial variability, sensitivity to mobile emissions, gas–particle partitioning, and wet deposition. Spatial variability of PAHs ∕ PM2.5 at a 2.5 km resolution was found to be comparable to measurements. Predicted ambient surface concentrations of benzene and the PAHs were found to be critically dependent on mobile emission factors, indicating the mobile emissions sector has a significant influence on ambient PAH levels in the study region. PAH wet deposition was overestimated due to additive precipitation biases in the model and the measurements. Our overall performance evaluation suggests that GEM-MACH-PAH can provide seasonal estimates for benzene and PAHs and is suitable for emissions scenario simulations.

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Section: Introductionmentioning

confidence: 99%

GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene

et al. 2018

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“…There was a high emission area located in Kansas and northern Alabama in April, which was caused by open burning activities in Flint Hill, the largest contiguous area of tallgrass prairie remaining today, where burn regimes are implemented during spring and fall (Wilgers and Horne, 2006). More details of the source of PAH emissions are discussed in a companion paper on the source apportionment of PAHs (Zhang et al, 2016). …”

Section: Model Applicationmentioning

confidence: 99%

Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States – Part I: Model development and evaluation

Zhang

Wang

et al. 2017

Environment International

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PAHs (polycyclic aromatic hydrocarbons) in the environment are of significant concern due to their negative impact on human health. PAH measurements at the air toxics monitoring network stations alone are not sufficient to provide a complete picture of ambient PAH levels or to allow accurate assessment of public exposure in the United States. In this study, speciation profiles for PAHs were prepared using data assembled from existing emission profile data bases, and the Sparse Matrix Operator Kernel Emissions (SMOKE) model was used to generate the gridded national emissions of 16 priority PAHs in the US. The estimated emissions were applied to simulate ambient concentration of PAHs for January, April, July and October 2011, using a modified Community Multiscale Air Quality (CMAQ) model (v5.0.1) that treats the gas and particle phase partitioning of PAHs and their reactions in the gas phase and on particle surface. Predicted daily PAH concentrations at 61 air toxics monitoring sites generally agreed with observations, and averaging the predictions over a month reduced the overall error. The best model performance was obtained at rural sites, with an average mean fractional bias (MFB) of −0.03 and mean fractional error (MFE) of 0.70. Concentrations at suburban and urban sites were underestimated with overall MFB = −0.57 and MFE = 0.89. Predicted PAH concentrations were highest in January with better model performance (MFB = 0.12, MFE = 0.69; including all sites), and lowest in July with worse model performance (MFB = −0.90, MFE = 1.08). Including heterogeneous reactions of several PAHs with O3 on particle surface reduced the over-prediction bias in winter, although significant uncertainties were expected due to relative simple treatment of the heterogeneous reactions in the current model.

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“…These simulations provide consistent information about the spatial distribution of concentrations and on-road vehicle contributions for benzene and PAHs. While other PAH chemical transport models exist (Aulinger et al, 2007; and Selin, 2012;San José et al, 2013;Gariazzo et al, 2014;Gariazzo et al, 2015;Thackray et al, 2015;Zhang et al, 2016Zhang et al, , 2017, this is the first study to use such a model to evaluate traffic contributions to ambient air and assess the change in resulting airborne toxicity. Our simulations also have the highest resolution employed to date in a North American domain, and the largest high-resolution domain compared to other PAH modelling studies anywhere in the world.…”

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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Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States – Part II: Source apportionment and cancer risk assessment

Cited by 43 publications

References 43 publications

GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene

GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene

Estimating population exposure to ambient polycyclic aromatic hydrocarbon in the United States – Part I: Model development and evaluation

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

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