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

Reducing Outdoor Air Pollutants through a Moss-Based Biotechnological Purification Filter in Kazakhstan

“…Consequently, it has become crucial to judiciously employ these filters, situating them solely in areas where their use is warranted. In [7], a model was constructed to identify the optimal placement of biotechnological filter systems for air purification by addressing the discrete optimization problem. This model provided forecasts of the air quality index for a specific region, ensuring the efficient deployment of these biotechnological systems to improve air quality.…”

“…Figure 4 shows that the universal programmable logic controller (1) had input and output channels connected to intermediate relays. In turn, the light sensor (2), air temperature and humidity sensor (3), and sawdust meter PM 2.5-10 (4) were connected to the input channels of controller 1, the output channels of which were connected through intermediate relays to the cooling system/air conditioner (5), heat generator/heating system (6), air humidifier (7), LED lamp 8, air pumping fan (9) and drip irrigation pump (10). A universal controller based on a Cortex A57 was used as a programmable log controller (1), a GY-30 (BH1750FVI, I2C) as a light sensor (2), a Temperature and Humidity Transmitter FGHGF as a temperature and humidity sensor (3), a PMS5003 high-precision laser sensor pm2.5 as a sawdust meter (4), a Midea MPPDA09CRN7 as a cooling/conditioning system (5), a Ballu B heating system IH-LW-1.2 as a heat generator (6), a Deerma DEM-F500 as a humidifier (7), FitoLED 20 bicolor equipment as lighting lamps (8), a channel fan with three speeds as an air pumping fan (9), and a Magnetta 1AWZB550 as a drip irrigation pump (10).…”

“…After interrogating and receiving data from the sensors, the controller compared the results with the programmed values of the settings and values of the parameters required to support the necessary microclimate inside the biotechnological filter. When the polled value from the sensors was different than the set value of the parameter, controller (1) gave a command to the output channels through the intermediate relays to turn on or off the connected cooling/air conditioning system (5), heat generator/heating system (6), air humidifier (7), and LED lamp (8). After an interval, it polled the data from the sensors again and, if necessary, exerted a control effect through intermediate relays.…”

“…Considering the collected data from air quality monitoring in the Republic of Kazakhstan, the construction of the model was carried out for the city, about which the maximum data are known, namely, for the city of Almaty. The task of the optimal placement of filters was described in [7].…”