Authors : Angel. B. John

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/536vucrc

Scribd : https://tinyurl.com/3hpfn5db

DOI : https://doi.org/10.5281/zenodo.14899185

Abstract : Air pollution has emerged as a critical global challenge with significant implications for human health, environmental sustainability, and economic productivity. The presence of harmful pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO), carbon monoxide (CO), and ozone (O) in the atmosphere contributes to severe health issues, ecosystem degradation, and climate change. Addressing air pollution requires advanced data-driven approaches to analyze, predict, and mitigate its effects effectively. This project, “Comprehensive Air Quality Analysis using R Programming,” aims to develop a robust analytical framework that integrates data preprocessing, visualization, modeling, and prediction to provide actionable insights into air quality trends and dynamics. The project utilizes real-world air quality datasets and begins by addressing the common challenge of missing and inconsistent data. Imputation techniques are employed to handle missing values, ensuring that the datasets are complete and reliable for further analysis. Exploratory data analysis (EDA) is conducted to uncover temporal and spatial trends in pollutant levels, providing a foundation for more advanced modeling. Relationships between key environmental variables such as ozone, temperature, wind speed, and solar radiation are explored through correlation analysis, offering insights into the factors driving air pollution. Time series analysis forms a critical component of the framework, with decomposition techniques used to identify trends, seasonality, and residual variations in pollutant concentrations. Predictive models, including ARIMA and regression models, are developed to forecast future pollutant levels, enabling proactive decision-making. Additionally, clustering techniquessuch as Kmeans are applied to segment air quality data, revealing distinct patterns and aiding in the identification of pollution hotspots or region-specific trends. The project leverages R programming’s extensive libraries for statistical computing, machine learning, and data visualization, including ggplot2, forecast, and corrplot, to ensure a comprehensive and user-friendly analysis. Visualizations such as heatmaps, scatter plots, and cluster diagrams are created to communicate findings effectively to diverse stakeholders, including policymakers, researchers, and environmentalists. The ultimate goal of this project is to provide a scalable and adaptable framework for air quality analysis that can inform evidence-based strategies to mitigate pollution and promote sustainability. By combining advanced computational techniques with environmental science, this project underscores the transformative potential of data science in addressing one of the most pressing environmental challenges of our time.

Keywords : R Programming for Data Analysis, Real-Time Air Quality Data, Time Series Analysis, Data Interpretation and Reporting, Machine Learning for Air Quality, Air Quality Monitoring, Statistical Analysis in R.

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