Toplu Taşıma Araçlarında Hava Kalitesi İçin Nesnelerin İnterneti Tabanlı Veri Toplama Modülü Tasarım
Abstract
Bu çalışmada, toplu taşıma araçlarında hava kalitesi analizi için Nesnelerin İnterneti ile kullanılarak ARM tabanlı bir veri toplama
modülü tasarlanmaktadır. Tasarlanan modül, araçtaki sürücü bilgisayarı ile haberleşmektedir. Sıcaklık ve nem verilerini toplamak için
TEMPerHUM USB Termometre Higrometre Sensörü ve PM2.5 ve PM10 sensörü olarak Dust Sensörü kullanılmaktadır. Bu
sensörlerden elde edilen veriler RS-485 portu ile mikroişlemciye gönderilmketedir. Microsoft Azure Hub, mikroişlemciden gelen tüm
verileri gerçek zamanlı olarak kaydetmek için kullanılmaktadır. Sıcaklık, nem ve PM verilerini oluşturan regresyon modellerini
değerlendirmek için makine öğrenme algoritmaları kullanılmaktadır. Regresyon modelleri Python dilinde üretilmektedir. Farklı
regresyon modelleri için R2 puanı ve RMSE sonuçları bulunmaktadır. Sonuçlar değerlendirilmekte ve temsil edilmektedir.
Supporting Institution
TÜBİTAK
Project Number
1139B412103093
References
- Bowdalo, D., Petetin, H., Jorba Casellas, O., Guevara, M., Soret, A., Bojovic, D., ... & Pérez García-Pando, C. (2022). Compliance with 2021 WHO air quality guidelines across Europe will require radical measures. Environmental Research Letters (ERL), 17(2).
- Pandey, P., Patel, D. K., Khan, A. H., Barman, S. C., Murthy, R. C., & Kisku, G. C. (2013). Temporal distribution of fine particulates (PM2. 5, PM10), potentially toxic metals, PAHs and Metal-bound carcinogenic risk in the population of Lucknow City, India. Journal of Environmental Science and Health, Part A, 48(7), 730-745.
- Mihăiţă, A. S., Dupont, L., Chery, O., Camargo, M., & Cai, C. (2019). Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling. Journal of cleaner production, 221, 398-418.
- Kingsy Grace, R., & Manju, S. (2019). A comprehensive review of wireless sensor networks based air pollution monitoring systems. Wireless Personal Communications, 108(4), 2499-2515.
- Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., ... & Britter, R. (2015). The rise of low-cost sensing for managing air pollution in cities. Environment international, 75, 199-205.
- Devarakonda, S., Sevusu, P., Liu, H., Liu, R., Iftode, L., & Nath, B. (2013, August). Real-time air quality monitoring through mobile sensing in metropolitan areas. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (pp. 1-8).
- Peci, A., Winter, A. L., Li, Y., Gnaneshan, S., Liu, J., Mubareka, S., & Gubbay, J. B. (2019). Effects of absolute humidity, relative humidity, temperature, and wind speed on influenza activity in Toronto, Ontario, Canada. Applied and environmental microbiology, 85(6), e02426-18. Budde, M., Schwarz, A. D., Müller, T., Laquai, B., Streibl, N., Schindler, G., ... & Beigl, M. (2018). Potential and limitations of the low cost SDS011 particle sensor for monitoring urban air quality. ProScience, 5, 6-12.
Internet of Things Based Data Acquisition Module Design for Air Quality in Public Transport Vehicles
Abstract
In this study, an ARM-based data acquisition module is designed with the Internet of Things in public transportation vehicles for air
quality analysis. The designed module communicates with the driver's computer in the vehicle. TEMPerHUM USB Thermometer
Hygrometer Sensor is used to collect temperature and humidity data and a dust sensor is used as PM2.5 and PM10 sensors. The data
obtained from these sensors are sent to the microprocessor with the RS-485 port. Microsoft Azure Hub is used to save all data from
the microprocessor in real-time. Machine learning algorithms are used to evaluate regression models constituting the temperature,
humidity, and PM data. Regression models are generated in the Python Language. Results of the R2
score and RMSE are found for the
different regression models. The results are assessed and represented.
Keywords
Air Quality Analysis Internet of Things Machine Learning. Air Quality Analysis, Internet of Things, Machine Learning
Project Number
1139B412103093
References
- Bowdalo, D., Petetin, H., Jorba Casellas, O., Guevara, M., Soret, A., Bojovic, D., ... & Pérez García-Pando, C. (2022). Compliance with 2021 WHO air quality guidelines across Europe will require radical measures. Environmental Research Letters (ERL), 17(2).
- Pandey, P., Patel, D. K., Khan, A. H., Barman, S. C., Murthy, R. C., & Kisku, G. C. (2013). Temporal distribution of fine particulates (PM2. 5, PM10), potentially toxic metals, PAHs and Metal-bound carcinogenic risk in the population of Lucknow City, India. Journal of Environmental Science and Health, Part A, 48(7), 730-745.
- Mihăiţă, A. S., Dupont, L., Chery, O., Camargo, M., & Cai, C. (2019). Evaluating air quality by combining stationary, smart mobile pollution monitoring and data-driven modelling. Journal of cleaner production, 221, 398-418.
- Kingsy Grace, R., & Manju, S. (2019). A comprehensive review of wireless sensor networks based air pollution monitoring systems. Wireless Personal Communications, 108(4), 2499-2515.
- Kumar, P., Morawska, L., Martani, C., Biskos, G., Neophytou, M., Di Sabatino, S., ... & Britter, R. (2015). The rise of low-cost sensing for managing air pollution in cities. Environment international, 75, 199-205.
- Devarakonda, S., Sevusu, P., Liu, H., Liu, R., Iftode, L., & Nath, B. (2013, August). Real-time air quality monitoring through mobile sensing in metropolitan areas. In Proceedings of the 2nd ACM SIGKDD international workshop on urban computing (pp. 1-8).
- Peci, A., Winter, A. L., Li, Y., Gnaneshan, S., Liu, J., Mubareka, S., & Gubbay, J. B. (2019). Effects of absolute humidity, relative humidity, temperature, and wind speed on influenza activity in Toronto, Ontario, Canada. Applied and environmental microbiology, 85(6), e02426-18. Budde, M., Schwarz, A. D., Müller, T., Laquai, B., Streibl, N., Schindler, G., ... & Beigl, M. (2018). Potential and limitations of the low cost SDS011 particle sensor for monitoring urban air quality. ProScience, 5, 6-12.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | 1139B412103093 |
| Early Pub Date | June 30, 2022 |
| Publication Date | July 15, 2022 |
| Published in Issue | Year 2022 Issue: 37 |
