Impact of Climate Change and Air Pollution Forecasting Using Machine Learning Techniques in Bishkek - Erkin Isaev; Boobek Ajikeev; Urmatbek Shamyrkanov; Kenjebek-uulu Kalnur; Karimov Maisalbek; Roy C. Sidle (Aerosol and Air Quality Research)
Abstract During recent years, severe air-pollution problems have garnered worldwide attention due to their effects on human health and the environment. Air pollution in Bishkek, Kyrgyz Republic, is an ever-increasing problem with little research conducted on the impact of air pollutants on public health. We evaluate the performance of several machine learning algorithms applied to air quality and meteorology datasets and compare prediction accuracies of Bishkek air quality given its significant public importance. Data on 16 synoptic atmospheric process were collected by Kyrgyzhydromet from 2016 to 2018 and used to train and build a forecasting model. The model was then tested using data collected in 2020. Climate change in Bishkek and the impact on air pollution was assessed via the frequency of days characterized by daytime temperature inversions and air stagnation. Atmospheric stability increased from 2015 to 2020 with ongoing climate change leading to more temperature inversions. About 80%–90% of days with temperature inversions are associated with winter heating seasons and these numbers increased two-fold during the past 5 years. The impact of lockdown during COVID-19 (22 March–11 May 2020) on air quality in Bishkek is also shown. During the lockdown period, CO, NO, NO2, SO2, and PM2.5 decreased by 64%, 1.5%, 75%, 24%, and 54%, respectively, compared to concentrations of these pollutants in 2019. Where identified, emissions from vehicles make up a significant part of the air pollution.