A Cost-Effective Air Quality Monitoring System for the Global South
Bibliographic record
Abstract
Air quality monitoring is crucial for mitigating the health risks associated with ambient air pollution, particularly in low- and middle-income countries, where resources for such systems are often limited. Traditional monitoring solutions are expensive, resulting in sparse data coverage and inequitable access to air quality information. This paper introduces the AI_r system, developed by the South African Consortium of Air Quality Monitoring (SACAQM), as a cost-effective air quality monitoring solution designed for the Global South. Leveraging Internet-of-Things (IoT) technology and low-cost sensors, the system establishes a dense network that provides high-resolution, real-time air quality data. The architecture includes clustered Wireless Sensor Networks (WSNs) Long-Term Evolution (LTE) communication, with data stored in a NoSQL database and accessed via an interactive dashboard. Calibration against established air quality systems, such as the South African Air Quality Information System (SAAQIS), ensures data accuracy and reliability. Additionally, the system integrates Artificial Intelligence (AI) techniques, including Graph Neural Networks (GNNs), to model and predict air quality trends. The system has been successfully piloted with sensor deployments in schools in Soweto, Johannesburg. The AI_r system aims to democratize access to critical air quality data, supporting public health initiatives and policy development to improve air quality, particularly for vulnerable populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".