AI_r: Transforming Air Quality Monitoring through Cost-Effective AI Solutions
Bibliographic record
Abstract
Air quality monitoring is vital for public health, particularly in resource-limited areas. However, traditional monitoring systems are often costly, leading to inadequate data and unequal access to air quality information. To address this issue, the South African Consortium of Air Quality Monitoring (SACAQM) has developed a more affordable air quality monitoring system tailored to resource-constrained regions. This system uses IoT technology and cost-effective sensors to create a wide network that provides real-time data on air pollution. The network consists of grouped Wireless Sensor Networks (WSNs) that communicate using LoRa and LTE technologies. The data is stored in a NoSQL database and is easily accessible through a user-friendly dashboard. Calibration against existing air quality systems ensures the data’s accuracy and reliability. Additionally, the system employs Artificial Intelligence (AI), particularly Graph Neural Networks (GNNs), to enhance air quality modeling and prediction capabilities. After a successful pilot deployment at schools in Soweto, Johannesburg, the system is now being expanded to hospitals and other community hubs. This expansion underscores the system’s potential to democratize access to critical air quality data, aiding public health strategies and improving air quality in vulnerable communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".