CleanCity IoT: A Vehicle-Mounted Platform for Real-Time Urban Air-Quality Monitoring and Forecasting in Resource-Constrained African Cities

Urban air pollution is a growing public-health challenge in African cities, yet traditional monitoring stations are sparse and expensive. The paper presents CleanCity IoT, a deployed, low-cost, vehicle-mounted air-quality platform that combines IoT sensors, GSM connectivity, cloud aggregation, and machine learning to produce near-real-time exposure maps and 2-hour forecasts for multiple pollutants. Each device integrates low-cost sensors for PM2.5, PM10, NO₂, O₃, SO₂, and CO₂, alongside temperature and humidity. Measurements are geotagged and transmitted over mobile networks form vehicles to a cloud backend, where data are validated, stored, and visualized through a user-friendly dashboard that also issues automated alerts and periodic reports. Using a dataset collected in Kigali and secondary cities via routine vehicular routes, the paper introduces the training of a multivariate time-series model to forecast short-horizon pollutant levels, supporting proactive health guidance and regulatory action. The system reports a performance in terms of latency, uptime, coverage, and data quality, and evaluate forecast accuracy using MAE/RMSE/MAPE and event-oriented metrics for spike prediction. Results indicate that CleanCity IoT provides reliable, scalable, and cost-effective urban air-quality intelligence, closing key gaps in spatiotemporal coverage while enabling citizen access, policy support, and social impact. The platform demonstrates a practical blueprint for African cities to operationalize air-quality intelligence using existing mobile infrastructure and locally developed technology.

@article{Nizeyimana2025,
  title     = {CleanCity IoT: A Vehicle-Mounted Platform for Real-Time Urban Air-Quality Monitoring and Forecasting in Resource-Constrained African Cities},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Eric Nizeyimana and Damien Hanyurwimfura and Gabriel Uwanyirigira and Bonaventure Karikumutima and Jimmy Nsenga and Irene Niyonambaza Mihigo},
  doi       = {10.14569/IJACSA.2025.01612100},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612100}
}