On-Road Chemical Transformation as an Important Mechanism of NO2 Formation

Yang, Bo; Zhang, K. Max; Xu, Wenxi; Zhang, Shaojun; Batterman, Stuart; Baldauf, Richard; Deshmukh, Parikshit; Snow, Richard; Wu, Ye; Zhang, Qiang; Li, Zhenhua; Wu, Xian

doi:10.1021/acs.est.7b05648

Cited by 29 publications

(20 citation statements)

References 38 publications

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“…On the aggregate level, the biases of rush hour speeds between estimated results and reported data were within ±5 % for all districts. It is noted that link-level speeds for public buses were corrected due to their frequent stops for discharging and picking up passengers (Zhang et al, 2014a). We further used congestion-map-informed road speeds to improve the temporal resolution of traffic volumes, which were originally investigated on an annual basis (BJTU and Beijing EPB, 2014).…”

Section: Generating Dynamic Traffic Profiles Based On Real-time Congementioning

confidence: 99%

High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

et al. 2019

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Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet), based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that nonlocal HDTs could account for 29 % and 38 % of estimated total on-road emissions of NOx and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 µg m−3 of NOx in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

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Section: Generating Dynamic Traffic Profiles Based On Real-time Congementioning

confidence: 99%

High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

et al. 2019

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“…Additionally, according to [72], it appears that on-road oxidation of NO by ambient O 3 is a significant, but so far ignored, contributor to curbside and near-road NO 2 . This is in agreement with on-road NO 2 /NO x ratios in U.S. being reported to be 25-35% and substantially higher than anticipated tailpipe emissions rates [73].…”

Section: Effects Of Travel Restrictions On Ambient No and No 2 Concentrationsmentioning

confidence: 99%

High NO2 cOncentrations Measured by Passive Samplers in Czech Cities: Unresolved Aftermath of Dieselgate?

Vojtíšek-Lom¹,

Sikorová²,

Srám³

2021

Preprint

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This work examines the effects of two problematic trends in diesel passenger car emissions – increasing NO2/NOx ratio by conversion of NO into NO2 in catalysts and a disparity between the emission limit and the actual emissions in everyday driving – on ambient air quality in Prague. NO2 concentrations were measured by 104 membrane-closed Palmes passive samplers at 65 locations in Prague in March-April and September-October of 2019. NO2 concentrations measured by city stations during those periods were comparable with the 2016-2019. The average measured NO2 concentrations at the selected locations, after correcting for the 18.5% positive bias of samplers co-located with a monitoring station, were 36 µg/m3 (range 16-69 µg/m3, median 35.3 µg/m3), with the EU annual limit of 40 µg/m3 exceeded at 32% of locations. The NO2 concentrations have correlated well (R2=0.76) with the 2019 average daily vehicle counts, corrected for additional emissions due to uphill travel and intersections. In addition to expected “hot-spots” at busy intersections in the city center, new ones were identified, i.e. along a six-lane road V Hole&scaron;ovičkách. Comparison of data from six monitoring stations during March 15-April 30, 2020 travel restrictions with the same period in 2016-2019 revealed an overall reduction of NO2 and even a larger reduction of NO. The spatial analysis of data from passive samplers and time analysis of data during the travel restrictions both demonstrate a consistent positive correlation between traffic intensity and NO2 concentrations along/near the travel path. The slow pace of NO2 reductions in Prague suggests that stricter vehicle NOx emission limits, introduced in the last decade or two, have so far failed to sufficiently reduce the ambient NO2 concentrations, and there is no clear sign of remedy of Dieselgate NOx excess emissions.

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“…In China, the environmental protection authorities only report the NO2 concentrations measured at the official air quality 245 monitoring sites, which do not include NO concentrations. We referred to the approximate photostationary state (i.e., chemical equilibrium between the NO2 photolysis and the O3 depletion) to estimate total NO concentrations for the official sites (Yang et al, 2018). In this study, we only used the tropospheric NOX chemistry to estimate NO concentrations during the daytime (approximately 6:00 to 18:00 GMT+8 as the annual average) and derived the total NOX as the sum of observed NO2 and estimated NO (see the Supplement, Part III).…”

Section: High-resolution Simulation Of Vehicular Nox Concentrationsmentioning

confidence: 99%

High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

Yang¹,

Zhang²,

Niu³

et al. 2019

Preprint

Self Cite

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Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link, based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16&#8201;400&#8201;km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOX), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that non-local HDTs could for 29&#8201;% and 38&#8201;% of estimated total on-road emissions of NOX and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir&#174;, to simulate vehicular NOX concentrations at fine resolutions (10&#8201;m&#8201;&#215;&#8201;10&#8201;m in the entire municipality and 1&#8201;m&#8201;&#215;&#8201;1&#8201;m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10&#8201;&#956;g&#8201;m&#8722;3 of NOX in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

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On-Road Chemical Transformation as an Important Mechanism of NO₂ Formation

Cited by 29 publications

References 38 publications

High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

High NO2 cOncentrations Measured by Passive Samplers in Czech Cities: Unresolved Aftermath of Dieselgate?

High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

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