Accurate, timely air quality index (AQI) forecasting helps industries in selecting the most suitable air pollution control measures and the public in reducing harmful exposure to pollution. This article proposes a comprehensive method to forecast AQIs. Initially, the work focused on predicting hourly ambient concentrations of PM_2.5 and PM₁₀ using artificial neural networks. Once the method was developed, the work was extended to the prediction of other criteria pollutants, i.e., O_3, SO₂, NO₂, and CO, which fed into the process of estimating AQI. The prediction of the AQI not only requires the selection of a robust forecasting model, it also heavily relies on a sequence of pre-processing steps to select predictors and handle different issues in data, including gaps. The presented method dealt with this by imputing missing entries using missForest, a machine learning-based imputation technique which employed the random forest (RF) algorithm. Unlike the usual practice of using RF at the final forecasting stage, we utilized RF at the data pre-processing stage, i.e., missing data imputation and feature selection, and we obtained promising results. The effectiveness of this imputation method was examined against a linear imputation method for the six criteria pollutants and the AQI. The proposed approach was validated against ambient air quality observations for Al-Jahra, a major city in Kuwait. Results obtained showed that models trained using missForest-imputed data could generalize AQI forecasting and with a prediction accuracy of 92.41% when tested on new unseen data, which is better than earlier findings.

This study deals with the vegetation change in Kuwait from 1974 compared to recent vegetation map and their capabilities on trapping mobile sand, dust and carbon dioxides (Co₂). The average cost of one cubic meter removal of encroached sand around infrastructures in Kuwait is 1.32 USD. The capability of trapping sand is much higher for Haloxylon sp. than Stipagrostis sp. by 100%. The areas that were covered by Haloxylon in 1974 lose recently 4385 km² for the benefits of Stipagrostis causing the formation of a new mobile sand corridor. The total estimated annual cost for the vegetation change is 35,429,379 USD obtained from the costs of sand encroachment, CO₂ consumption loss and solar energy efficiency loss. Rehabilitation for areas that were occupied with large size canopy vegetation will surely cause a decrease in the aeolian activities and air temperature, lower the albedo and increase the precipitation and solar energy production.

Inhalation Exposure to Volatile Organic Compounds in the Printing Industry. Journal of the Air & Waste Management Association. Accepted 22 May 2019.