Design and Implementation of Air Pollution Monitoring System in Mosul City
Bibliographic record
Abstract
This paper aims to solve the problem of air pollution resulting from the operation of electrical generators and car exhausts by monitoring and evaluating the air quality within the University of Mosul.Researchers encountered challenges in sending data across long ranges.This study suggests using LoRa-WAN wireless technology to gather information on air quality using sensor nodes fixed in eight sites inside Mosul University, and then communicate with the cloud via the LoRa gateway to transfer data to the monitoring center.The primary advantage of the proposed technique is the transfer of data with low cost, long-range communication, low energy consumption, low latency, and ease of uploading information to the IoT Cloud platform.This system employs sensor nodes to measure (temperature, CO2, PM1.0, PM2.5, PM10, humidity, and Air Quality Index (AQI)).The sensor nodes are continually powered by a battery.This paper presents two essential contributions: an environmental contribution by presenting findings on air pollution levels, and a technological contribution to monitor and transfer air quality data inside Mosul University to a monitoring center using the LoRa network in the OMNET++ simulation program.The typical CO2 concentrations in the air are about 400 ppm (parts per million), with temperature at 21℃, and humidity at 50%.Standard limits for PM2.5 and PM10 have been set by the World Health Organization (WHO) with a daily average of 15 μg/m 3 and 45 μg/m 3 respectively.The findings conclude that medium CO2 concentrations above 518 ppm and PM2.5 concentrations above 11 μg/m 3 in site 8 were due to car exhausts.The data transmission efficiency between sensor nodes and the LoRa gateway was investigated via the Received Signal Strength Indicator (RSSI) and total received packets.Simulation results in all scenarios demonstrate the possibility of covering the distance between the sensor nodes fixed in the eight sites inside Mosul University and its wireless communication with the LoRa gateway, as well as improved LoRa performance by increasing the total received packets at the network server from 104 to 309 when decreasing the transmission time interval of packets.Our technology is capable of accurately monitoring air quality indicators and effectively transmitting the data synchronously to the cloud to monitor parameters and reduce the impact of air pollution.
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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".