Monitoring Indoor Air Quality Using Low-Cost IoT
Bibliographic record
Abstract
Measuring air quality in some regions under non-ideal circumstances is still a challenge. In many third-world countries, acquiring expensive air quality testing equipment is beyond capacity. Monitoring non-healthy environments in such regions is vital, so we implemented a low-cost IoT indoor air quality tester. The system comprises attached field instrument sensors and a WiFi-to-cloud monitoring unit. The sensing unit includes Arduino UNO attached to MQ-7, CCS811, and MQ-137 sensors to measure carbon monoxide (CO), carbon dioxide (CO2), and the total volatile organic compounds (TVOCs), and NH3, respectively. The sensors also include the DHT11 to measure temperature (T) and relative humidity (RH). To collect data from distributed field sensing devices and monitor it on the ThingSpeak website, an NRF24l01+ wireless model is connected to each data logger and the central data collector ESP32. The proposed low-cost system was operated in one of the higher education buildings of the Middle Technical University, measuring the concentrations of the most common air quality factors (CO, CO2, NH3, TVOC, RH, and T).
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 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".