Traffic-related air pollution near roadways: discerning local impacts from background

Abstract

Abstract. Adverse health outcomes related to exposure to air pollution have gained much attention in recent years, with a particular emphasis on traffic-related pollutants near roadways, where concentrations tend to be most severe. As such, many projects around the world are being initiated to routinely monitor pollution near major roads. Understanding the extent to which local on-road traffic directly affects these measurements, however, is a challenging problem, and a more thorough comprehension of it is necessary to properly assess its impact on near-road air quality. In this study, a set of commonly measured air pollutants (black carbon; carbon dioxide; carbon monoxide; fine particulate matter, PM2.5; nitrogen oxides; ozone; and ultrafine particle concentrations) were monitored continuously between 1 June 2015 and 31 March 2017 at six stations in Canada: two near-road and two urban background stations in Toronto, Ontario, and one near-road and one urban background station in Vancouver, British Columbia. Three methods of differentiating between local and background concentrations at near-road locations were tested: (1) differences in average pollutant concentrations between near-road and urban background station pairs, (2) differences in downwind and upwind pollutant averages, and (3) interpolation of rolling minima to infer background concentrations. The last two methods use near-road data only, and were compared with method 1, where an explicit difference was measured, to assess accuracy and robustness. It was found that method 2 produced average local concentrations that were biased high by a factor of between 1.4 and 1.7 when compared with method 1 and was not universally feasible, whereas method 3 produced concentrations that were in good agreement with method 1 for all pollutants except ozone and PM2.5, which are generally secondary and regional in nature. The results of this comparison are intended to aid researchers in the analysis of data procured in future near-road monitoring studies. Lastly, upon determining these local pollutant concentrations as a function of time, their variability with respect to wind speed (WS) and wind direction (WD) was assessed relative to the mean values measured at the specific sites. This normalization allowed generalization across the pollutants and made the values from different sites more comparable. With the exception of ozone and PM2.5, local pollutant concentrations at these near-road locations were enhanced by a factor of 2 relative to their mean in the case of stagnant winds and were shown to be proportional to WS−0.6. Downwind conditions enhanced local concentrations by a factor of ∼2 relative to their mean, while upwind conditions suppressed them by a factor of ∼4. Site-specific factors such as distance from roadway and local meteorology should be taken into consideration when generalizing these factors. The methods used to determine these local concentrations, however, have been shown to be applicable across pollutants and different near-road monitoring environments.

Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

“…The mobile platform keeps sampling in the urban road network which carries a strong signal from traffic sources. By contrast, stationary stations are often located far away from major roads to represent a regional background air pollution level (Hilker et al, 2019). Therefore, the contribution from traffic-related emissions can be obtained by differencing the mobile 130 measurements and the stationary ones, following Bossche et al (2015):…”

Impact of the COVID-19 lockdown on the chemical composition and sources of urban PM2.5

“…In addition to the measurements of air pollutants, vehicle counts, speeds, and lengths (i.e., C2: 1.0–7.6 m, C3: 7.6–15 m, C4: 15–36.5 m) were measured by a radar detector (SmartSensor HD, Wavetronix). Routine calibration and maintenance including reference and background checks of the gas and particle monitors were conducted as discussed elsewhere (e.g., Hilker et al, 2019 ; Evans et al, 2019 ). Meteorological parameters including ambient temperature, relative humidity (RH), wind speed (WS), and wind direction (WD) were measured using a weather sensor (Vaisala WXT520) at the SOCAAR site.…”

Measuring the effect of fireworks on air quality in Minneapolis, Minnesota

“…Meteorological conditions are an important factor influencing local pollutant concentrations [37,38]. Low wind speed (< 4 km h −1 ) is frequently associated with high levels of air pollution because it reduces dispersion [39,40]. Temperature inversion is a layer in the atmosphere across which the temperature increases with height [41].…”