Abstract
In recent years, air quality has become a significant environmental and health related issue due to rapid urbanization and industrialization. As a consequence, real-time monitoring and precise prediction of air quality gained increased importance. In this paper, we present a complete solution to this problem by using NB-IoT (Narrowband-Internet-of-Things) sensors and machine learning techniques. This solution includes our own compiled cheap micro-sensor devices that are planned to be deployed at stationary locations as well as on the moving vehicles to provide a comprehensive overview of air quality in the city. We developed our own IoT data and analysis platform to support the gathering of air quality data as well as weather and traffic data from external sources. We applied seven machine learning methods to predict air quality in the next 48-h, which showed promising results. Finally, we developed a mobile application named Lufta, which is now available in Google play for testing purposes.
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Notes
- 1.
- 2.
The modem is based on Nordic Semiconductor technology, see https://www.nordicsemi.com/Products/Low-power-cellular-IoT/nRF9160 and had an indicative price of USD 45, see https://shop.exploratory.engineering/.
- 3.
Honeywell HPM Series, Particle Sensor, 32322550, Issue E, using a light scattering method. The price of this sensor measuring both \(\text {PM}_{2.5}\) and PM10 was less than 40 USD as of November 2018.
References
Horde IoT Backend Service. https://blog.exploratory.engineering/post/horde-iot-backend/. Accessed 13 Aug 2019
Lufta app. https://play.google.com/store/apps/details?id=com.luftaapp.lufta. Accessed 13 Aug 2019
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., Baklanov, A.: Real-time air quality forecasting, part I: history, techniques, and current status. Atmos. Environ. 60, 632–655 (2012a)
Yi, X., et al.: Deep distributed fusion network for air quality prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2018), pp. 965–973. ACM, New York. https://doi.org/10.1145/3219819.3219822
Chen, L., et al.: Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning. In: International Joint Conference on Pervasive and Ubiquitous Computing, pp. 1076–1087. ACM (2016)
Wang, J., Song, G.: A deep spatial-temporal ensemble model for air quality prediction. Neuro Comput. 314, 198–206 (2018)
Qi, Z., et al.: Deep air learning: interpolation, prediction, and feature analysis of fine-grained air quality. IEEE Trans. Knowl. Data Eng. 30(12), 2285–2297 (2018)
Lin, Y., et al.: Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 359–368 (2018)
Kök, İ., et al.: A deep learning model for air quality prediction in smart cities. In: IEEE International Conference on Big Data, pp. 1983–1990 (2017)
Bougoudis, I., et al.: HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in athens. Neural Comput. Appl. 27(5), 1191–1206 (2016)
Tamas, W., et al.: Hybridization of air quality forecasting models using machine learning and clustering: an original approach to detect pollutant peaks. Aerosol Air Qual. Res. 16, 405–416 (2016)
Athira, V., et al.: DeepairNet: applying recurrent networks for air quality prediction. Procedia Comput. Sci. 132, 1394–1403 (2018)
Li, X., et al.: Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ. Pollut. 231, 997–1004 (2017)
Zhan, Y., et al.: Satellite-based estimates of daily NO2 exposure in China using hybrid random forest and spatiotemporal Kriging model. Environ. Sci. Technol. 52(7), 4180–4189 (2018)
Chen, G., et al.: A machine learning method to estimate \(\text{PM}_{2.5}\) concentrations across China with remote sensing, meteorological and land use information. Sci. Total Environ. 636, 52–60 (2018)
Zheng, Y., et al.: Forecasting fine-grained air quality based on big data. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2267–2276. ACM (2015)
Ghoneim, O.A., Manjunatha, B., et al.: Forecasting of ozone concentration in smart city using deep learning. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp. 1320–1326 (2017)
Denby, B.R., et al.: The norwegian air quality service: model forecasting (2018). https://www.met.no/
Tørseth, K., et al.: Introduction to the European monitoring and evaluation programme (EMEP) and observed atmospheric composition change during 1972–2009. Atmos. Chem. Phys. 12(12), 5447–5481 (2012)
Fishbain, B., et al.: An evaluation tool kit of air quality micro-sensing units. Sci. Total Environ. 575(1), 639–648 (2017). https://doi.org/10.1016/j.scitotenv.2016.09.061
Kiran, M., Murphy, P., Monga, I., Dugan, J., Baveja, S.S.: Lambda architecture for cost-effective batch and speed big data processing. In: 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, pp. 2785–2792 (2015)
Singh, M., Rajan, M.A., Shivraj, V.L., Balamuralidhar, P.: Secure MQTT for Internet of Things (IoT). In: 2015 Fifth International Conference on Communication Systems and Network Technologies, Gwalior, pp. 746–751 (2015)
Kauling, D., Mahmoud, Q.H.: Sensorian Hub: an IFTTT-based platform for collecting and processing sensor data. In: 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV 2017, pp. 504–509 (2017)
Norwegian Institute for Air Research. https://www.nilu.no/en/
Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30, pp. 3146–3154. Curran Associates Inc. (2017)
Acknowledgements
This work is part of the AI4EU project (https://www.ai4eu.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation program, under the Grant Agreement No 825619.
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Lepperød, A., Nguyen, H.T., Akselsen, S., Wienhofen, L., Øzturk, P., Zhang, W. (2020). Air Quality Monitor and Forecast in Norway Using NB-IoT and Machine Learning. In: Santos, H., Pereira, G., Budde, M., Lopes, S., Nikolic, P. (eds) Science and Technologies for Smart Cities. SmartCity 360 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-030-51005-3_7
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