Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

Abstract

This paper formalizes the model of choosing a strategy for reducing air pollution in an urban environment. The model involves determining the optimal location of biotechnological systems – biotechnological filter systems or smart air purification devices based on solving the problem of discrete optimization, taking into consideration the forecast of the air quality index. Two subtasks have been formalized, which make it possible to form a strategy for reducing air pollution. To solve one of the subtasks, a combined selective model for predicting the time series of the Air Quality Index (CSM) was built. The combined model software suite consists of the EMD-ESM hybrid model (Empirical Mode Decomposition-Exponential Smoothing Model), the HWM additive model (Holt-Winters Model), and the adaptive TLM (Trigg-Lich Model). To verify the proposed combined selective model, the time series of air quality indices (AQI) for the city of Nur-Sultan (data from 2010‒2021, period 6 hours) were selected. As a result of verification, it was established that in the case of short-term forecasting of the air quality index time series, the EMD-ESM model has an advantage according to the criterion of a minimum root mean square error (RMSE), δ=0.11. For the case of medium-term forecasting of 3<τ≤5, the combined selective model (CSM) has the advantage. The results reported here are input data for the task of choosing strategies for reducing the volume of air pollution in the urban environment. The study’s results make it possible to increase the flexibility of the formation of strategies for reducing air pollution since they avoid restrictions on the location of cleaners in specific urban areas. The consequence is the improvement of the environmental situation in the city and the development of the region in general