Design and Evaluation of a Scalable Data Pipeline for AI-Driven Air Quality Monitoring in Low-Resource Settings

The increasing adoption of low-cost environmental sensors and AI-enabled applications has accelerated the demand for scalable and resilient data infrastructures, particularly in data-scarce and resource-constrained regions. This paper presents the design, implementation, and evaluation of the AirQo data pipeline: a modular, cloud-native Extract-Transform-Load (ETL) system engineered to support both real-time and batch processing of heterogeneous air quality data across urban deployments in Africa. It is Built using open-source technologies such as Apache Airflow, Apache Kafka, and Google BigQuery. The pipeline integrates diverse data streams from low-cost sensors, third-party weather APIs, and reference-grade monitors to enable automated calibration, forecasting, and accessible analytics. We demonstrate the pipeline's ability to ingest, transform, and distribute millions of air quality measurements monthly from over 400 monitoring devices while achieving low latency, high throughput, and robust data availability, even under constrained power and connectivity conditions. The paper details key architectural features, including workflow orchestration, decoupled ingestion layers, machine learning-driven sensor calibration, and observability frameworks. Performance is evaluated across operational metrics such as resource utilization, ingestion throughput, calibration accuracy, and data availability, offering practical insights into building sustainable environmental data platforms. By open-sourcing the platform and documenting deployment experiences, this work contributes a reusable blueprint for similar initiatives seeking to advance environmental intelligence through data engineering in low-resource settings.