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Air pollution is an important issue, especially in megacities across the world. There are emission sources within and also in the regions around these cities, which cause fluctuations in air quality based on prevailing meteorological conditions. Short term air quality forecasting is used not to just possibly mitigate forthcoming high air pollution episodes, but also to plan for reduced exposures of residents. In this study, a model using Artificial Neural Networks (ANN) has been developed to forecast pollutant concentration of PM 10 , PM 2.5 , NO 2 , and O 3 for the current day and subsequent 4 days in a highly polluted region (32 different locations in Delhi). The model has been trained using meteorological parameters and hourly pollution concentration data for the year 2018 and then used for generating air quality forecasts in real-time. It has also been equipped with Real Time Correction (RTC), to improve the quality of the forecasts by dynamically adjusting the forecasts based on the model performance during the past few days. The model without RTC performs decently, but with RTC the errors are further reduced in forecasted values. The utility of the model has been demonstrated in real-time and model validations were performed for the whole year of 2018 and also independently for 2019. The model shows very good performance for all the pollutants on several evaluation metrics. Coefficient of correlations for various pollutants varies from 0.79-0.88 to 0.49-0.68 between the Day0 to Day4 forecasts. Lowest deterioration of performance was observed for ozone over the four days of forecasts. Use of RTC further improves the model performance for all pollutants., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Effects of vegetation on pollutant dispersion receive increased attention in attempts to reduce air pollutant concentration levels in the urban environment. In this study, we examine the influence of vegetation on the concentrations of traffic pollutants in urban street canyons using numerical simulations with the CFD code OpenFOAM. This CFD approach is validated against literature wind tunnel data of traffic pollutant dispersion in street canyons. The impact of trees is simulated for a variety of vegetation types and the full range of approaching wind directions at 15° interval. All these results are combined using meteo statistics, including effects of seasonal leaf loss, to determine the annual average effect of trees in street canyons. This analysis is performed for two pollutants, elemental carbon (EC) and PM10, using background concentrations and emission strengths for the city of Antwerp, Belgium. The results show that due to the presence of trees the annual average pollutant concentrations increase with about 8% (range of 1% to 13%) for EC and with about 1.4% (range of 0.2 to 2.6%) for PM10. The study indicates that this annual effect is considerably smaller than earlier estimates which are generally based on a specific set of governing conditions (1 wind direction, full leafed trees and peak hour traffic emissions)., (Copyright © 2015 Elsevier B.V. All rights reserved.)