A Novel Conceptual Approach to Real-Time Air Quality Reporting Using Python Scripts and Relational Environmental Databases

Authors

  • Zamathula Sikhakhane Nwokediegwu  Independent Researcher, Durban, South Africa
  • Oluwapelumi Joseph Adebowale  Independent Researcher, USA

Keywords:

Real-time air quality, Python scripts, environmental databases, PM₂.₅, PostgreSQL, data visualization, IoT sensors, SQL, RESTful API, automated reporting.

Abstract

Real-time air quality monitoring has become increasingly critical in addressing public health concerns, regulatory compliance, and environmental sustainability in urban and industrial regions. This study proposes a novel conceptual approach to real-time air quality reporting through the integration of Python scripting and relational environmental databases. The framework leverages Python’s data processing capabilities, including libraries such as Pandas, SQLAlchemy, and Plotly, to automate data acquisition, transformation, visualization, and dissemination of air quality metrics. Environmental data, sourced from IoT-enabled sensors and standardized repositories, are ingested into structured relational databases such as PostgreSQL or MySQL designed to support rapid querying, indexing, and storage of spatiotemporal air pollution parameters including PM₂.₅, PM₁₀, CO, NO₂, and O₃. The model features dynamic data pipelines for continuous ingestion and real-time processing, allowing for the generation of interactive dashboards, automated alerts, and regulatory reporting. This approach introduces a conceptual architecture that integrates front-end user interfaces with back-end data infrastructure via RESTful APIs, enabling seamless user access to current and historical pollution data. Data integrity and latency challenges are addressed through validation scripts, caching mechanisms, and asynchronous task scheduling. By decoupling data logic from presentation layers, the proposed model enhances scalability, modularity, and system resilience. Use-case simulations demonstrate that the proposed Python-based architecture outperforms conventional static reporting systems in responsiveness, flexibility, and user customization. This conceptual framework is especially suitable for deployment by environmental agencies, research institutions, and smart city planners seeking cost-effective and scalable real-time air quality reporting tools. The model’s adaptability makes it a valuable asset for integrating predictive analytics, geospatial mapping, and public notification systems in future extensions. Ultimately, this novel approach contributes to democratizing air quality data, fostering public awareness, and empowering proactive decision-making for cleaner, healthier urban environments.

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2023-10-30

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Volume 9, Issue 5, September-October-2023

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Zamathula Sikhakhane Nwokediegwu, Oluwapelumi Joseph Adebowale, " A Novel Conceptual Approach to Real-Time Air Quality Reporting Using Python Scripts and Relational Environmental Databases" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 5, pp.621-653, September-October-2023.