Air quality monitoring datasets are inherently temporal, multi-pollutant, and long-horizon in character, making them well suited to scalable distributed computing frameworks that can handle the volume, velocity, and variety of multiyear daily observations while supporting transparent, SQL accessible analytical workflows. This paper presents a reproducible PySpark–SparkSQL analytical pipeline applied to four years (2021–2024) of daily urban air quality monitoring data, addressing five structured research questions about seasonal and temporal variability, pollutant drivers of the Air Quality Index (AQI), human-activity effects on ambient concentrations, and compliance with the revised WHO 2021 Air Quality Guidelines. Our findings document: consistently higher AQI values in winter months, with November peaking at a mean AQI of 342.13 across the four-year period; PM2.5 and PM10 as the dominant AQI drivers with Pearson correlations of 0.87 and 0.91 respectively; marginal but consistent reductions in pollutant levels during holiday and weekend periods, with the strongest effects for trafficrelated NO2; and a chronic, near-daily exceedance of WHO 24-hour mean thresholds for PM2.5 (343–364 exceedance days per year) and PM10 (347–359 days). A previously unreported SO2 exceedance spike in 2024—110 exceedance days compared to fewer than 30 in each prior year—is identified as an anomaly of policy significance indicating an emerging industrial emission source. The pipeline, implemented in Python (PySpark, Pandas, Matplotlib, Seaborn) on Google Colab, provides a reproducible, computationally scalable framework adaptable to other urban air quality monitoring contexts.
Prince Nweke Onyeka, Chinoso Job., Onwe, Festus Chijioke· International journal of re...· 0 citations
This paper documents the architecture, security design, and database evolution of a two-tier system built to support an environmental-telemetry and micro-desalination business scenario, comprising a Flask REST API for Internet of Things (IoT) buoy telemetry ingestion and a Django eCommerce platform for product sales and subscription management. We describe the system’s modular separation of concerns (a stateless, JWT-secured ingestion API decoupled from a session-oriented commerce platform), the specific security controls applied at each layer (JWT-based stateless authentication for the API; Django’s built-in Cross-Site Request Forgery and SQL-injection middleware for the platform), and the database migration from SQLite to MySQL undertaken specifically in response to a measured concurrency limitation under bulk telemetry ingestion. We report the integration mechanism connecting the two otherwise loosely coupled services (token-based cross-service authentication and shared schema conventions) and the specific coordination overhead this integration introduced. We position the architecture against established microservice design literature and REST architectural-style principles, and we provide a candid account of which design decisions are well supported by that literature and which represent pragmatic compromises specific to a timeand resource-constrained development context, together with the concrete changes (CI/CD pipeline integration, containerisation, formal load testing) that would be required before the architecture could be considered production-ready rather than a validated prototype.
Nwokpuru Samuel Abafu, Chinonso Job, Onwe, Festus Chijioke· International Journal of Lat...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
