Estimation of on-road NO2 concentrations, NO2/NOX ratios, and related roadway gradients from near-road monitoring data

Abstract

This paper describes a new regression modeling approach to estimate on-road nitrogen dioxide (NO) and oxides of nitrogen (NO) concentrations and near-road spatial gradients using data from a near-road monitoring network. Field data were collected in Las Vegas, NV at three monitors sited 20, 100, and 300 m from Interstate-15 between December, 2008 and January, 2010. Measurements of NO and NO were integrated over 1-hour intervals and matched with meteorological data. Several mathematical transformations were tested for regressing pollutant concentrations against distance from the roadway. A logit-ln model was found to have the best fit (R = 94.7%) and also provided a physically realistic profile. The mathematical model used data from the near-road monitors to estimate on-road concentrations and the near-road gradient over which mobile source pollutants have concentrations elevated above background levels. Average and maximum on-road NO concentration estimates were 33 ppb and 105 ppb, respectively. Concentration gradients were steeper in the morning and late afternoon compared with overnight when stable conditions preclude mixing. Estimated on-road concentrations were also highest in the late afternoon. Median estimated on-road and gradient NO concentrations were lower during summer compared with winter, with a steeper gradient during the summer, when convective mixing occurs during a longer portion of the day On-road concentration estimates were higher for winds perpendicular to the road compared with parallel winds and for atmospheric stability with neutral-to-unstable atmospheric conditions. The concentration gradient with increasing distance from the road was estimated to be sharper for neutral-to-unstable conditions when compared with stable conditions and for parallel wind conditions compared with perpendicular winds. A regression of the NO/NO ratios yielded on-road ratios ranging from 0.25 to 0.35, substantially higher than the anticipated tail-pipe emissions ratios. The results from the ratios also showed that the diurnal cycle of the background NO/NO ratios were a driving factor in the on-road and downwind NO/NO ratios.

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

“…(27) One can deduce from the study that the referred production of NO 2 most likely took place on the streets. Another study of near-road monitoring data at Las Vegas, NV, indicated the R CS ranging from 0.25 to 0.35, substantially higher than the anticipated R TP ,(28) which we argue can potentially be explained by the on-road production of NO 2 . Even though the term “on-road” NO 2 /NO x ratio was used in the Las Vegas study,(28) a close examination of the reported methodology suggested that the term was referred to R CS according to our definition.…”

“…The near-road chemical transformation has been observed in several field studies. (22–25) In our previous study, we estimated R CS to be ~20–40% based on near-road monitoring data collected at several Texas roadways in the summer of 2007. (16) The roadways studied had low diesel traffic, and the penetration of diesel particulate filters was very low in 2007.…”

Mixing layer height as an indicator for urban air quality?

“…Rost et al (2009) found a strong anticorrelation between PM 10 and MLH (derived from radiosonde data) for Stuttgart, Germany, with a coefficient of determination of R 2 > 0.95. The awareness of the potential of active remote sensing started at the end of the last century, when the first generation of ceilometers was deployed.…”

“…The absolute values and the development during the day are, however, much more diverse than at the less polluted locations. One reason can be that roadside NO x concentrations depend strongly on the distance from the source (e.g., Bonn et al, 2016;Richmond-Bryant et al, 2017), which is similar as for PM 10 . Due to the spatial variability of the mean diurnal cycles it is clear that for the traffic sites the correlations must vary as well.…”

Fine-Scale Columnar and Surface NOx Concentrations over South Korea: Comparison of Surface Monitors, TROPOMI, CMAQ and CAPSS Inventory

“…In addition, the ratio between NO and NO 2 from emission sources is a major barrier to understanding the characteristics of NO x emissions [67][68][69][70][71][72][73]. The ambient NO 2 :NO x concentration ratio is often used as an important proxy for NO to NO 2 chemistry in air quality models, and most existing models use a simple assumption of the NO to NO 2 emission ratio without fully characterizing the important atmospheric chemical and mechanical processes.…”