Variability of NO₂/NO_x Ratios in Multiple Microenvironments from On-Road and Near-Roadway Measurements

Abstract

An understanding of the spatial distribution of nitrogen dioxide (NO2) in cities is required to improve health impact assessments for NO2 exposure. In this study, 32 702 5 s averaged on-road measurements and 125 hourly near-roadway measurements were used to evaluate the variability of NO2 and nitrogen oxide (NO x ) concentrations in multiple microenvironments during two sampling campaigns (nonholiday and holiday). Microenvironments impacted by heavy-duty diesel vehicles (HDDVs) had large variations in NO2/NO x ratios due to the holiday effect. There was a strong linear relationship between NO2/NO x ratios and ambient background O3 in on-road and near-roadway microenvironments. The slopes of the regression lines (Δ­(NO2/NOx)/ΔO3) provide estimations of the change rate of NO2/NO x ratios with respect to ambient background O3. Variable slopes occurred in on-road but not for near-roadway microenvironments. This is evidence that the change rate of on-road NO2/NO x ratios is strongly affected by vehicle fleet composition (i.e., HDDV%), while the near-roadway NO2/NO x ratios are more influenced by the atmospheric turbulence. The findings indicate that HDDVs could be a valid indicator of variability in NO2/NO x ratios concerning ambient background O3 and the effect of HDDVs should be embedded into dispersion models for better prediction of NO2 concentrations in traffic-related environments.

Evaluation of the Relationship between Meteorological Variables and NOx Emission Factors Based on Plume-Chasing Measurements

“…For example, the length of wake could be equivalent to only one vehicle height (generally 1.4–3.5 m depending on the vehicle types) when ψ = 90° (i.e., v ≫ u ). In real-world conditions, chasing measurements are usually conducted with a distance of 10–20 m behind the tested vehicles for the sake of safety. , When the vehicle wake length is significantly smaller than the distance between chasing measurements and tested vehicles, it is reasonable to expect errors in the chasing measurements (erroneous measurements). Thus, it is critical to consider the effect of the yaw angle when evaluating the chasing measurements.…”

Evaluation of City-Scale Disparities in PM2.5 Exposure Using Hyper-Localized Taxi-Based Mobile Monitoring

“…Additionally, only PM 2.5 exposure was evaluated by the taxi-based measurements in this study. Researchers have found that the concentrations of traffic-related air pollutants (e.g., NO 2 ) on roadways with a large proportion of diesel trucks were significantly higher than those dominated by gasoline cars . However, PM 2.5 generally showed no rapid variations that can be related to the sub-km level microenvironments (i.e., types of roadways). , Thus, aggravated exposure disparities in urban and rural regions that are related to traffic fleet composition would be disclosed if taxi-based measurements are applied to other traffic-related air pollutants (e.g., NO 2 ).…”

From CO2 emissions to atmospheric NO2 mixing ratios: simulating chemical processes and their impacts on TROPOMI retrievals over the Middle East

“…CC BY 4.0 License 2017;Visser et al, 2019),. while the high fraction of NO2 in the no-chemistry run (90 %) has been used in recent studies to compensate for the absence of short-term conversion of NO-to-NO2 (e. g.Kuhn et al, 2023;Xiang et al, 2022).…”