STRUCTURE OF A MULTISENSOR SYSTEM FOR MONITORING ATMOSPHERIC AIR POLLUTION NEAR ENERGY FACILITIES

Abstract

Continuous monitoring of atmospheric air quality in the vicinity of energy facilities ‒ thermal power plants (TPPs) and combined heat and power plants (CHPPs) ‒ is a critically important task for ensuring environmental safety and compliance with regulatory requirements. Such facilities are major stationary sources of pollutant emissions, including nitrogen oxides, sulfur dioxide, carbon monoxide, and fine particulate matter (PM2.5, PM10), whose concentrations directly affect public health. This paper addresses the development of a hybrid multisensor system architecture for atmospheric air pollution monitoring in the vicinity of energy facilities. A general review and comparative analysis of multisensor monitoring system architectures ‒ local, distributed (centralized and decentralized), and hybrid ‒ have been conducted. The characteristic features, advantages, and limitations of each system type are identified, and the optimal areas of their application are determined. Based on the review results, a hybrid monitoring system architecture has been proposed, combining the advantages of local and distributed approaches on the principles of distributed data acquisition with integrated centralized processing, ensuring scalability and effective air quality control within the zone of influence of energy infrastructure. The structure of a multisensor system module based on the ESP8266EX microcontroller has been developed, incorporating a comprehensive set of low-cost sensors for simultaneous measurement of PM2.5, PM10, CO, NO₂, NH₃, SO₂, O₃, CH₂O, and CO₂ concentrations, as well as temperature, relative humidity, and atmospheric pressure. The module includes dedicated functional units for geolocation, data storage and backup, status indication, and secure Wi-Fi communication. The proposed structure ensures energy efficiency, scalability, and the capability of integrating artificial intelligence algorithms for pollution dynamics forecasting. Keywords: multisensor monitoring system, atmospheric air pollution, energy facilities, hybrid architecture, IoT, low-cost sensors, ESP8266.