Abstract. In recent years, the problem of air pollution has become more and more acute, especially for industrial regions. The constant growth of environmental monitoring data requires not only their accumulation, but also effective intelligent processing. One of the key tasks is the timely detection of abnormal values that can indicate both real emissions of pollutants and errors in measuring systems.
In this paper, an algorithm for detecting anomalies in the atmospheric air monitoring system is proposed, based on a combination of statistical methods and machine learning algorithms. This approach allows you to take into account both simple emissions and more complex, hidden patterns in the data. For primary filtration, the methods of Z-score and interquartile range (IQR) were used, and for a more in-depth analysis, the Isolation Forest algorithm was used, which is able to effectively work with multidimensional ecological time series. The novelty of the study lies in the hybrid decision procedure that combines statistical filtering, unsupervised anomaly detection and meteorological-context interpretation for industrial air pollution monitoring data.
Particular attention is paid to the construction of the system architecture, which is implemented using cloud technologies. This provides the ability to process large amounts of data coming from monitoring sensors, as well as analyze them in near real time. The algorithm was tested on data from the city of Ust-Kamenogorsk, including indicators of the concentration of the main pollutants and meteorological parameters. The results showed that the proposed hybrid approach achieved higher performance than individual methods, reaching Precision = 0.94, Recall = 0.91 and F1-score = 0.92. At the same time, the system is able to automatically record sharp deviations associated with industrial emissions, weather conditions or technical failures.
The practical significance of the work lies in the possibility of introducing the proposed algorithm into environmental information systems and smart city solutions. Its application makes it possible to improve the quality of monitoring, the efficiency of response and the validity of management decisions in the field of environmental protection.

Keywords: atmospheric air monitoring, data anomalies, machine learning, isolation forest, environmental monitoring, cloud technologies.