Using near-road observations of CO, NOy, and CO2 to investigate emissions from vehicles: Evidence for an impact of ambient temperature and specific humidity

Hall, Dolly; Anderson, Daniel C.; Martin, Cory; Ren, X.; Salawitch, R. J.; He, Hao; Canty, T. P.; Hains, J. C.; Dickerson, Russell R.

doi:10.1016/j.atmosenv.2020.117558

Cited by 23 publications

(29 citation statements)

References 43 publications

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“…This variation in ΔNO X /ΔCO is consistent with other studies in the mid-Atlantic region . While no breakdown of the traffic patterns of different vehicle types is available near the Drexel site, at other sites on I-95, diesel trucks comprise a larger fraction of total vehicles before 7 am than in the afternoon when spark ignition (gasoline) vehicles account for almost 95% of total traffic . This difference in fleet composition would lead to the observed decrease in ΔNO X /ΔCO.…”

Section: Resultssupporting

confidence: 88%

Urban Emissions of Nitrogen Oxides, Carbon Monoxide, and Methane Determined from Ground-Based Measurements in Philadelphia

Anderson

Lindsay

DeCarlo

et al. 2021

Environ. Sci. Technol.

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Nitrogen oxides (NO X ) and methane impact air quality through the promotion of ozone formation, and methane is also a strong greenhouse gas. Despite the importance of these pollutants, emissions in urban areas are poorly quantified. We present measurements of NO X , CH4, CO, and CO2 made at Drexel University in Philadelphia along with NO X and CO observations at two roadside monitors. Because CO2 concentrations in the winter result almost entirely from combustion with negligible influence from photosynthesis and respiration, we are able to infer fleet-averaged fuel-based emission factors (EFs) for NO X and CO, similar in some ways to how EFs are determined from tunnel studies. Comparison of the inferred NO X and CO fuel-based EF to the National Emissions Inventory (NEI) suggests errors in NEI emissions of either NO X , CO, or both. From the measurements of CH4 and CO2, which are not emitted by the same sources, we infer the ratio of CH4 emissions (from leaks in the natural gas infrastructure) to CO2 emissions (from fossil fuel combustion) in Philadelphia. Comparison of the CH4/CO2 emission ratios to emission inventories from the Environmental Protection Agency suggests underestimates in CH4 emissions by almost a factor of 4. These results demonstrate the need for the addition of long-term observations of CH4 and CO2 to existing monitoring networks in urban areas to better constrain emissions and complement existing measurements of NO X and CO.

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Section: Resultssupporting

confidence: 88%

Urban Emissions of Nitrogen Oxides, Carbon Monoxide, and Methane Determined from Ground-Based Measurements in Philadelphia

Anderson

Lindsay

DeCarlo

et al. 2021

Environ. Sci. Technol.

Self Cite

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“…The concentration of NO 2 , which is a short-lived species, closely follows that of the traffic emission patterns. For example, NO 2 concentration is negatively correlated to wind speed due to the dilution effect and slightly increased as temperature decreases because of lower SCR efficiency at low temperatures (30). In contrast to NO 2 , ozone variations are largely regulated by meteorological conditions.…”

Section: Resultsmentioning

confidence: 99%

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

Yang

Wen

Wang

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

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“…While the MOVES emissions ratios are not statistically different from the cross-gradient and OLS estimates at 25 m, the MOVES model does not capture the highest observed values or the temporal variability in ∆CO:∆NO x (see Figure 2), indicating that the inputs used to run MOVES may represent typical conditions but did not fully capture hour-specific variability in the types of vehicles, age of vehicles and traffic conditions for this section of I-15. [33,55]. These plots do not show any discernable seasonal bias pattern in observed ∆CO:∆NO x or MOVES emitted ratios.…”

Section: ∆Co:∆nomentioning

confidence: 66%

“…Figures S5 through S8 split out comparisons from Figure 2 by season. Recent literature suggested that MOVES NO x emissions estimates are unbiased in the winter but overpredicted in summer [[ 33 ],[ 54 ]]. These plots do not show any discernable seasonal bias pattern in observed ΔCO:ΔNO x or MOVES emitted ratios.…”

Section: Resultsmentioning

confidence: 99%

Variability in Observation-Based Onroad Emission Constraints from a Near-Road Environment

et al. 2020

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This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NOx) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NOx ratios (∆CO:∆NOx) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NOx ratios are used to infer NOx emission adjustments for a priori emissions estimates from EPA’s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NOx scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25 m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ∆CO:∆NOx from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115 m and 300 m) increases OLS and orthogonal regression estimates of ∆CO:∆NOx but not cross-road gradient ∆CO:∆NOx. The inferred NOx emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NOx emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NOx estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

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Using near-road observations of CO, NOy, and CO2 to investigate emissions from vehicles: Evidence for an impact of ambient temperature and specific humidity

Cited by 23 publications

References 43 publications

Urban Emissions of Nitrogen Oxides, Carbon Monoxide, and Methane Determined from Ground-Based Measurements in Philadelphia

Urban Emissions of Nitrogen Oxides, Carbon Monoxide, and Methane Determined from Ground-Based Measurements in Philadelphia

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

Variability in Observation-Based Onroad Emission Constraints from a Near-Road Environment

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