Smart Air Quality Monitoring for Automotive Workshop Environments
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866914965466644480 |
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| author | Mariano, Kauan Divino Pouso Almada, Fabrycio Leite Nakano Dutra, Maykon Adriell |
| author_facet | Mariano, Kauan Divino Pouso Almada, Fabrycio Leite Nakano Dutra, Maykon Adriell |
| contents | Air quality monitoring in automotive workshops is crucial for occupational health and regulatory compliance. This study presents the development of an environmental monitoring system based on Internet of Things (IoT) and Artificial Intelligence (AI) technologies. DHT-11 and MQ-135 sensors were employed to measure temperature, humidity, and toxic gas concentrations, with real-time data transmission to the ThingSpeak platform via the MQTT protocol. Machine learning algorithms, including Linear Regression, Decision Trees, and SVM, were applied to analyze the data and compute an air salubrity index based on Gaussian functions. The system proved effective in detecting pollutant peaks and issuing automatic alerts, significantly improving worker health and safety. Workshops that implemented the system reported greater regulatory compliance and reduced occupational risks. The study concludes that the combination of IoT and AI provides an efficient and replicable solution for environmental monitoring in industrial settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Smart Air Quality Monitoring for Automotive Workshop Environments Mariano, Kauan Divino Pouso Almada, Fabrycio Leite Nakano Dutra, Maykon Adriell Systems and Control Air quality monitoring in automotive workshops is crucial for occupational health and regulatory compliance. This study presents the development of an environmental monitoring system based on Internet of Things (IoT) and Artificial Intelligence (AI) technologies. DHT-11 and MQ-135 sensors were employed to measure temperature, humidity, and toxic gas concentrations, with real-time data transmission to the ThingSpeak platform via the MQTT protocol. Machine learning algorithms, including Linear Regression, Decision Trees, and SVM, were applied to analyze the data and compute an air salubrity index based on Gaussian functions. The system proved effective in detecting pollutant peaks and issuing automatic alerts, significantly improving worker health and safety. Workshops that implemented the system reported greater regulatory compliance and reduced occupational risks. The study concludes that the combination of IoT and AI provides an efficient and replicable solution for environmental monitoring in industrial settings. |
| title | Smart Air Quality Monitoring for Automotive Workshop Environments |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2410.03986 |