This study investigates the application of regression analysis as a tool for identifying patterns in atmospheric air quality monitoring data collected from IoT-based monitoring systems. Given the increasing importance of air pollution control and environmental safety, precise analytical methods are required to assess the relationships between pollutant concentrations and external factors such as meteorological conditions. The research focuses on employing the ordinary least squares (OLS) regression method to analyze the influence of key atmospheric parameters-including temperature, humidity, wind speed, and radiation-on air quality indices, specifically the Air Quality Index (AQI) and the Common Air Quality Index (CAQI). The study is based on data obtained from a network of IoT-enabled sensors deployed across various monitoring stations, which continuously measure air pollutants and meteorological parameters in real-time. A custom-built analytical module was developed to facilitate flexible data selection, enabling comparative assessments across diferent time frames, monitoring stations, and measurement parameters. Through statistical modeling, it was determined that radiation significantly influences both AQI and CAQI, while wind speed has a more pronounced efect during daytime hours. Furthermore, the study revealed that hourly aggregation of pollutant data is optimal for CAQI calculations, whereas daily averages better align with AQI assessments. The findings highlight the advantages of integrating IoT technology with regression-based analysis in modern air quality monitoring systems. By leveraging Python-based statistical tools such as StatsModels, this approach enables the identification of critical environmental factors afecting air pollution levels, thereby supporting more efective predictive modeling and decision-making in environmental policy and urban planning. Future research will explore the integration of nonlinear regression models and time series forecasting to further refine air quality assessments.
Atmospheric air is a critically important component of the ecosystem, providing oxygen to all living organisms and sustaining essential metabolic processes. Air pollution has a significant negative impact on human health, causing respiratory and cardiovascular diseases, weakening immune responses, and contributing to the development of chronic illnesses. At the same time, air pollution adversely afects animals and plants, compromising their health and vitality, which in turn negatively impacts ecosystems.
The state of atmospheric air quality is a determining factor for environmental safety and public health. In Ukraine, air quality monitoring involves the systematic collection, analysis, and processing of data on pollutant concentrations, serving as a foundation for developing state policies in environmental protection. Traditionally, monitoring has relied on centralized stations with limited spatial coverage. However, the advancement of IoT-based monitoring systems has enabled the deployment of low-cost, real-time air quality sensors, significantly enhancing data granularity and accessibility.
Modern air quality monitoring systems utilize IoT-enabled devices (or edge devices) to measure pollutant levels and meteorological parameters continuously. These IoT sensors form a distributed network, transmitting data to centralized platforms for processing and analysis. This approach allows for real-time air quality assessment, early warning systems, and better urban environmental management.
Recognizing these challenges, the development of efective strategies for air quality monitoring and analysis becomes particularly important. Such approaches enable timely responses to threats, forecasting the impacts of pollution, and implementing measures to mitigate its adverse efects. The integration of IoT technology with statistical modeling, such as regression analysis, ofers new opportunities for identifying hidden dependencies and trends in air pollution data.
In recent studies, significant attention has been given to integrating IoT technologies and machine
learning methods for monitoring and predicting air quality. Ángel Martín-Baos et al. [
Similarly, Banciu et al. [
Bobulski et al. [
Ravindra et al. [
Additionally, Dhanalakshmi and Radha [
These studies highlight the efectiveness of combining IoT technologies and regression analysis for air quality monitoring and prediction. The integration of modern machine learning methods and secure data transmission improves the accuracy and reliability of monitoring systems, which is crucial for making informed decisions in environmental protection and public health. While global studies focus on improving monitoring accuracy and data transmission security, national-level reports highlight the limitations of existing monitoring infrastructure, such as in Kyiv, Ukraine.
The 2023 report by the Kyiv City State Administration [
The objective of the study is to utilize regression analysis to identify patterns in atmospheric air quality monitoring data collected from IoT-based environmental monitoring systems. The research aims to develop a methodology for analyzing dependencies that will support efective decision-making to improve the state of atmospheric air.
The air quality monitoring system is based on IoT-enabled devices that continuously collect real-time data on key air parameters, including pollutant concentrations, temperature, humidity, and other environmental indicators.
The spatial distribution of these IoT sensors plays a crucial role in data reliability. Urban regions with high trafic density require a denser network of sensors compared to suburban or rural areas, where air circulation patterns difer significantly. In Kyiv, for example, the placement of sensors near industrial zones and major highways ensures a more accurate representation of pollution sources.
These IoT devices serve as edge computing units, transmitting collected data to cloud-based or on-premise servers for further analysis. The architecture of the monitoring system is illustrated in ifgure 1.
The collected data is integrated with other sources, such as municipal monitoring platforms (e.g., Kyiv City State Administration), community initiatives (SaveEcoBot), and MQTT servers. This multi-source system enables a comprehensive view of air quality and ensures data relevance. The data is transmitted to a centralized database built on PostgreSQL, providing reliable storage and accessibility for further processing.
This integration allows for cross-validation of sensor readings with oficially recognized monitoring stations, reducing the impact of sensor biases and ensuring higher data reliability. By leveraging municipal platforms, real-time data can be incorporated into existing environmental management systems, facilitating timely responses to pollution spikes.
An essential component of such systems is the organized storage and processing of datasets. In our architecture, these tasks are managed using the PostgreSQL database management system (DBMS). This open-source relational DBMS is well-suited for eficiently managing large datasets and executing advanced queries. The database’s logical structure is depicted in figure 1.
A vital element of the system is a set of tables designed to store data related to monitoring stations and their recorded measurements. The station table holds detailed information about each monitoring station, including unique identifiers, geographical coordinates, type of data source (e.g., stationary or mobile), and operational status.
Based on the accumulated data, two approaches are implemented for assessing air quality: the Air Quality Index (AQI, USA) and the Common Air Quality Index (CAQI, Europe). Both indices serve as key indicators of air pollution levels and reflect potential health risks, though there are notable diferences between them: • AQI (Air Quality Index): The AQI is calculated based on the concentrations of primary air pollutants, such as fine particulate matter (PM2.5, PM10), nitrogen dioxide (NO 2), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO). It is widely used to provide a clear assessment of health risks depending on pollution levels. • CAQI (Common Air Quality Index): The CAQI is a standard adopted in European cities, also based on pollutant concentrations, including PM2.5, PM10, NO2, O3, and SO2. This index allows for a unified evaluation of air quality, which is crucial for regional comparisons.
As part of the air quality monitoring system, a module was developed to enable flexible configuration
of parameters for data analysis using the OLS regression model, implemented via the Statsmodels
library [
The user can specify any time range for analysis, including both daily and hourly intervals. This lfexibility enables targeted investigations, such as focusing on peak activity periods or unusual meteorological conditions. Additionally, the system allows for selecting either a single station or multiple stations, which is particularly useful for comparative analysis across diferent areas or locations.
The regression analysis results yielded several key components that characterize the model’s quality and the relationships between variables, as shown in figure 4. The primary model metrics include the proportion of variance in the dependent variable explained by the independent variables (R-squared). The F-statistic was also calculated to evaluate the overall significance of the model, along with its p-value, confirming statistical relevance.
Furthermore, residual analysis provides deeper insights into the model’s fit to the data. For each independent variable, a regression coeficient table ( ) was generated, containing standard errors, t-values, and p-values. The importance of variables is assessed based on their statistical significance, helping to identify the most impactful factors.
The visualization of results includes a prediction plot that illustrates the relationship between the actual values of the dependent variable and those predicted by the model. This plot demonstrates the model’s accuracy, providing a visual assessment of its fit to the real data and its predictive performance. The graph highlights the degree to which the predicted values align with the actual observations, showcasing the efectiveness of the regression model in capturing key patterns and dependencies within the data.
The analysis of results obtained through the developed module revealed important patterns in the formation of air quality indices (AQI and CAQI) and the nuances of their application. The radiation factor emerged as a significant variable, substantially influencing the calculation of both indices.
When comparing the approaches to index calculation, it was found that hourly averages are more efective for CAQI, while daily averages are more appropriate for AQI (figure 5). This diference reflects the distinct algorithms used for each index and their impact on the final results. Such findings allow for more precise adaptation of analysis to specific tasks and regional characteristics.
An intriguing observation concerns the influence of wind on CAQI. During nighttime, wind has minimal impact on the air quality index, while during daytime or over an entire day, its influence becomes noticeable. This suggests a reduction in wind intensity at night, diminishing its capacity to disperse pollutants and, consequently, afecting the modeling outcomes.
These findings highlight the importance of tailoring air quality analyses to account for specific temporal and environmental factors, ensuring more accurate and actionable insights. Despite the advantages of using IoT-based monitoring systems and regression analysis, several limitations exist. IoT sensors are prone to measurement drift over time, requiring regular calibration to maintain accuracy. Additionally, regression models assume linear relationships between variables, which may not fully capture complex atmospheric interactions. Incorporating nonlinear models or hybrid approaches with machine learning could help address these challenges.
This study confirms the efectiveness of applying regression analysis to identify patterns in atmospheric air quality monitoring data, particularly in combination with IoT-based monitoring systems. The integration of real-time sensor networks allows for more accurate and dynamic assessment of pollutant levels and meteorological factors, improving the overall reliability of air quality analysis. Unlike traditional methods that rely on static datasets from reference stations, this research introduces a lfexible approach based on Ordinary Least Squares (OLS) regression, which enables the identification of statistically significant dependencies. One of the key findings is the impact of radiation levels on air quality indices, a factor often overlooked in previous studies. The results also highlight fundamental diferences in data aggregation strategies, demonstrating that hourly averaging is more suitable for CAQI, whereas daily averaging better aligns with AQI calculations. These insights allow for a more precise selection of analytical techniques depending on the index used, enhancing predictive accuracy in air quality assessments.
A significant contribution of this work lies in the development of a custom analytical module that facilitates flexible configuration of regression parameters, allowing users to explore pollutant dependencies across diferent periods, stations, and environmental conditions. This tool supports decision-making processes in environmental management by enabling targeted analysis of pollution dynamics. The use of Python-based statistical libraries, particularly StatsModels, proves to be a costefective alternative to more complex machine learning models while maintaining high interpretability.
The study not only advances the methodological approach to air quality assessment but also sets the foundation for future research. Expanding the analytical framework with nonlinear regression models and time series forecasting could improve predictive capabilities, while further integration of data mining techniques may reveal hidden dependencies within air pollution trends. The findings underscore the importance of real-time monitoring systems and data-driven approaches for environmental policymaking, providing a technological basis for more adaptive and responsive air quality management strategies.
The developed analytical framework can be utilized by policymakers to design data-driven air pollution control strategies. By identifying critical pollution sources and peak contamination periods, urban planners can implement targeted interventions such as emission restrictions in high-risk areas, trafic rerouting, or increased green zones to mitigate pollution levels.
Author Contributions: Conceptualization, Dmytro V. Shevchenko and Bella L. Holub; methodology, Dmytro V. Shevchenko; software, Dmytro V. Shevchenko; writing – original draft, Dmytro V. Shevchenko; writing – review and editing, Bella L. Holub. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: No new data were created or analysed during this study. Data sharing is not applicable. Conflicts of Interest: The authors declare no conflict of interest.
Declaration on Generative AI: During the preparation of this work, the authors used GPT-4o in order to: Drafting content, Generate literature review, Paraphrase and reword, Improve writing style, Abstract drafting, Grammar and spelling check, Content enhancement. After using this service, the authors reviewed and edited the content as needed and takes full responsibility for the publication’s content.