Meteorologically normalized spatial and temporal variations investigation using a machine learning-random forest model in criteria pollutants across Tehran, Iran

Abstract

Investigating road traffic contribution to urban air pollution is helpful to determine traffic management approaches and environmental intervention policies that focus on better air quality. To this end, hourly average values of air pollutants collected from 2015 to 2021 from 20 monitoring sites including 10 traffic (TS) and 10 urban background (UB) sites in Tehran, Iran were retrieved. A machine learning-random forest (RF) model was applied to decouple the meteorology effects of the observed air pollutant values. The meteorologically normalized concentrations of air quality data were used for calculating the traffic increment and trend analysis. The seasonal component showed the greatest importance for predicting PM2.5 and PM10 and boundary layer heights (BLH) was the dominant explanatory variable for the predictions of CO, NO, NO2, and O3 concentrations. The overall annual average value of traffic increment during study period was found as PM10 (10.5 μg m−3), PM2.5 (0.19 μg m−3), NO (9.24 ppb), NO2 (3.67 ppb), NOx (15.6 ppb), and SO2 (0.33 ppb). Ozone indicates a traffic decrement in concentration (−0.70 ppb). The Theil-Sen estimated slopes showed meteorological normalized negative trends for PM2.5, PM10, CO, NO, NOx, and SO2, while meteorological normalized positive trends were observed for NO2 and O3. The PM10, CO, NO, NOx, and SO2 concentrations trends at the traffic sites that experience more anthropogenic emissions revealed greater reductions when emission interventions or control policies are used. The higher increase of NO2 at the UB sites than the traffic sites can be due to the weaker control policies, high traffic volume, higher number of diesel-fueled vehicles, and a higher NO to NO2 conversion because of the relatively high levels of temperature and O3. This quantitative summary provides useful information for decision-makers for evaluating the intervention policies related to road traffic pollutants.