Abstract
Bu çalışma, Nesnelerin İnterneti kullanılarak şehirlerdeki PM2.5 ve PM10 parçacıklarının etkisini gerçek zamanlı olarak analiz etmek
için toplu taşıma araçlarına yerleştirilen ARM tabanlı bir hava kalitesi modülünü önermektedir. STM32 mikrodenetleyicisi, PM'den ve
nem, sıcaklık sensörlerinden veri elde etmek için kullanılır. Sensörlerden toplanan veriler, ethernet modülü ile internet portalına
bağlanan i.MX6UL’ya, RS-485 kullanılarak iletilir. i.MX6UL, verileri çevrimiçi olarak Microsoft Azure Hub'a gönderir ve bilgisayar
aracılığıyla da kaydedilir. Elde edilen veriler hava kalitesi-meteorolojik değişkenler için analiz edilmiş ve regresyon modelleri makine
öğrenme algoritmaları ile uygulanmıştır. PM2.5, PM10, nem ve sıcaklık verileri R2 testi ve regresyon modelleri için ortalama karekök hatası ile değerlendirilmiştir. Rastgele orman algoritması, kullanılan diğer regresyon modelleri arasında en iyi sonucu göstermiştir.
Keywords
hava kalitesi analizi gerçek zamanlı izleme makine öğrenimi nesnelerin internet arm tabanlı mikroişlemci.
Project Number
1139B412000704
References
- Ahmad, T., Chen, H., Huang, R., Yabin, G., Wang, J., Shair, J., Azeem Akram, H. M., Hassnain Mohsan, S. A., & Kazim, M. (2018). Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy, 158, 17–32.https://doi.org/10.1016/j.energy.2018.05.169
- Air quality in Europe, 2018 Report, European Environment Agency https://www.eea.europa.eu/publications/air-quality-ineurope2018/download
- Brokamp, C., Jandarov, R., Rao, M. B., LeMasters, G., & Ryan, P. (2017). Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment, 151, 1-11.
- 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
- El Houssaini, D., Khriji, S., Besbes, K., & Kanoun, O. Real Time Temperature Measurement for Industrial Environment.
- Giannadaki, D., Lelieveld, J., & Pozzer, A. (2016). Implementing the US air quality standard for PM 2.5 worldwide can prevent millions of premature deaths per year. Environmental Health, 15(1), 1-11.
- Hu, K., Rahman, A., Bhrugubanda, H., & Sivaraman, V. (2017). HazeEst: Machine learning based metropolitan air pollution estimation from fixed and mobile sensors. IEEE Sensors Journal, 17(11), 3517-3525.
- Kamińska, J. A. (2018). The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: a case study in Wrocław. Journal of environmental management, 217, 164-174.
- Liu, X., Li, B., Jiang, A., Qi, S., Xiang, C., & Xu, N. (2015, June). A bicycle-borne sensor for monitoring air pollution near roadways. In 2015 IEEE International Conference on Consumer Electronics-Taiwan (pp. 166-167). IEEE.
- Saukh, O., Hasenfratz, D., Noori, A., Ulrich, T., & Thiele, L. (2012, February). Demo Abstract: Route Selection of Mobile Sensors for Air Quality Monitoring. In 9th European Conference on Wireless Sensor Networks (EWSN 2012 (pp. 10-11).
- Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S., ... & Preuss, P. W. (2013). The changing paradigm of air pollution monitoring. Environmental science & technology, 47(20), 11369-11377.
- Yaacoub, E., Kadri, A., Mushtaha, M., & Abu-Dayya, A. (2013, July). Air quality monitoring and analysis in Qatar using a wireless sensor network deployment. In 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 596-601). IEEE.
Abstract
This study proposes an ARM based air quality module placed to public transport vehicles for analyzing the effect of PM2.5 and PM10
particles in the cities in real-time using Internet of Things. The STM32 microcontroller is used for obtaining the data from the PM,
humidity, and temperature sensors. The data collected from the sensors are sent to the i.MX6UL microprocessor using RS-485
connected to the internet portal with an Ethernet module. The microprocessor sends the data to the Microsoft Azure Hub in-on-line,
and it is also recorded via the computer. The obtained data is analyzed for air quality-meteorological variables and the regression
models are implemented via machine learning algorithms. PM2.5, PM10, humidity and temperature data are evaluated with R2
test and root mean square error for regression models. The Random Forest algorithm shows better results among other used regression
models.
Keywords
air quality analysis real-time monitoring machine learning internet of things arm-based microprocessor
Supporting Institution
Scientific and Technical Research Council of Turkey (TUBITAK)
Project Number
1139B412000704
References
- Ahmad, T., Chen, H., Huang, R., Yabin, G., Wang, J., Shair, J., Azeem Akram, H. M., Hassnain Mohsan, S. A., & Kazim, M. (2018). Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment. Energy, 158, 17–32.https://doi.org/10.1016/j.energy.2018.05.169
- Air quality in Europe, 2018 Report, European Environment Agency https://www.eea.europa.eu/publications/air-quality-ineurope2018/download
- Brokamp, C., Jandarov, R., Rao, M. B., LeMasters, G., & Ryan, P. (2017). Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches. Atmospheric Environment, 151, 1-11.
- 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
- El Houssaini, D., Khriji, S., Besbes, K., & Kanoun, O. Real Time Temperature Measurement for Industrial Environment.
- Giannadaki, D., Lelieveld, J., & Pozzer, A. (2016). Implementing the US air quality standard for PM 2.5 worldwide can prevent millions of premature deaths per year. Environmental Health, 15(1), 1-11.
- Hu, K., Rahman, A., Bhrugubanda, H., & Sivaraman, V. (2017). HazeEst: Machine learning based metropolitan air pollution estimation from fixed and mobile sensors. IEEE Sensors Journal, 17(11), 3517-3525.
- Kamińska, J. A. (2018). The use of random forests in modelling short-term air pollution effects based on traffic and meteorological conditions: a case study in Wrocław. Journal of environmental management, 217, 164-174.
- Liu, X., Li, B., Jiang, A., Qi, S., Xiang, C., & Xu, N. (2015, June). A bicycle-borne sensor for monitoring air pollution near roadways. In 2015 IEEE International Conference on Consumer Electronics-Taiwan (pp. 166-167). IEEE.
- Saukh, O., Hasenfratz, D., Noori, A., Ulrich, T., & Thiele, L. (2012, February). Demo Abstract: Route Selection of Mobile Sensors for Air Quality Monitoring. In 9th European Conference on Wireless Sensor Networks (EWSN 2012 (pp. 10-11).
- Snyder, E. G., Watkins, T. H., Solomon, P. A., Thoma, E. D., Williams, R. W., Hagler, G. S., ... & Preuss, P. W. (2013). The changing paradigm of air pollution monitoring. Environmental science & technology, 47(20), 11369-11377.
- Yaacoub, E., Kadri, A., Mushtaha, M., & Abu-Dayya, A. (2013, July). Air quality monitoring and analysis in Qatar using a wireless sensor network deployment. In 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC) (pp. 596-601). IEEE.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Project Number | 1139B412000704 |
| Publication Date | July 31, 2021 |
| Published in Issue | Year 2021 Issue: 26 - Ejosat Special Issue 2021 (HORA) |
