Review of IoT and Machine Learning Approaches for Air Quality Index (AQI) Prediction and Visualization
Keywords:
Air quality index (AQI), Internet of things (IoT), Machine learning, Multisensory kits, Prediction modelsAbstract
The Internet of Things (IoT) enables interrelated computing devices and sensors to exchange information autonomously for environmental monitoring. Air quality prediction is crucial due to its health impact and complex influencing factors. This study focuses on predicting Air Quality Index (AQI) in Neyveli, Tamil Nadu, using IoT-based multisensory kits integrated with machine learning. A multisensory kit called My Ambi AQI with low-cost sensors and wireless connectivity was developed and outperformed traditional costly monitoring methods. An Improved Long Short-Term Memory (ILSTM) model was employed for AQI prediction, showing improved accuracy over traditional models based on RMSE. ILSTM addresses non-linearity issues in pollutants by incorporating meteorological influences like temperature, humidity, and wind. Additionally, a Multivariate Recursive Linear Regressive Model (MRLRM) was developed to correlate pollutants and meteorological parameters for better AQI prediction. The proposed methods allow effective forecasting shared via cloud applications, promoting public awareness and health protection. The research demonstrates the integration of IoT and machine learning to provide precise, low-cost, and mobile air quality monitoring and prediction. Outcomes highlight the dominance of meteorological parameters in AQI prediction and the potential of cloud-based visualization for public welfare.
References
K.-J. Chuang, Y.-H. Yan, S.-Y. Chiu, and T.-J. Cheng, “Long-term air pollution exposure and risk factors for cardiovascular diseases among the elderly in Taiwan,” Occupational and Environmental Medicine, vol. 68, no. 1, pp. 64–68, Sep. 2010, doi: https://doi.org/10.1136/oem.2009.052704
J. A. Burke et al., “Participatory sensing,” Escholarship.org, May 05, 2006. Available: https://escholarship.org/uc/item/19h777qd#author
J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, Sep. 2013, doi: https://doi.org/10.1016/j.future.2013.01.010
G. Camprodon et al., “Smart citizen kit and station: An open environmental monitoring system for citizen participation and scientific experimentation,” HardwareX, vol. 6, p. e00070, Oct. 2019, doi: https://doi.org/10.1016/j.ohx.2019.e00070
Michaela, “hackAIR home sensors made easy for you,” Hackair.eu, 2018, Available: https://www.hackair.eu/hackair-home-sensors-made-easy-for-you/
S. Mahajan, C.-H. Luo, D.-Y. Wu, and L.-J. Chen, “From Do-It-Yourself (DIY) to Do-It-Together (DIT): Reflections on designing a citizen-driven air quality monitoring framework in Taiwan,” Sustainable Cities and Society, vol. 66, p. 102628, Mar. 2021, doi: https://doi.org/10.1016/j.scs.2020.102628
K. Zandberg, K. Schleiser, F. Acosta, H. Tschofenig and E. Baccelli, “Secure firmware updates for constrained IoT devices using open standards: A reality check,” in IEEE Access, vol. 7, pp. 71907–71920, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2919760
I. Romieu, “Epidemiological studies of health effects arising from motor vehicle air pollution,” in Urban Traffic Pollution, D. Schwela and O. Zali, Eds. 1st ed. London, U.K.: Taylor & Francis Group, 1999, Available: https://www.taylorfrancis.com/chapters/mono/10.1201/9781482272093-9/epidemiological-studies-health-effects-arising-motor-vehicle-air-pollution-dietrich-schwela-olivier-zali
Y. Zhang, D. H. Stedman, G. A. Bishop, P. L. Guenther, and S. P. Beaton, “Worldwide on-road vehicle exhaust emissions study by remote sensing,” Environmental Science & Technology, vol. 29, no. 9, pp. 2286–2294, Sep. 1995, doi: https://pubs.acs.org/doi/10.1021/es00009a020
L. Spinelle, M. Gerboles, M. G. Villani, M. Aleixandre, and F. Bonavitacola, “Field calibration of a cluster of low-cost available sensors for air quality monitoring. Part A: Ozone and nitrogen dioxide,” Sensors and Actuators B: Chemical, vol. 215, pp. 249–257, Aug. 2015, doi: https://doi.org/10.1016/j.snb.2015.03.031
C. Borrego et al., “Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise,” Atmospheric Environment, vol. 147, pp. 246–263, Dec. 2016, doi: https://doi.org/10.1016/j.atmosenv.2016.09.050
J. M. Cordero, R. Borge, and A. Narros, “Using statistical methods to carry out in field calibrations of low cost air quality sensors,” Sensors and Actuators B: Chemical, vol. 267, pp. 245–254, Aug. 2018, doi: https://doi.org/10.1016/j.snb.2018.04.021
C. Lin, J. Gillespie, M. D. Schuder, W. Duberstein, I. J. Beverland, and M. R. Heal, “Evaluation and calibration of Aeroqual series 500 portable gas sensors for accurate measurement of ambient ozone and nitrogen dioxide,” Atmospheric Environment, vol. 100, pp. 111–116, Jan. 2015, doi: https://doi.org/10.1016/j.atmosenv.2014.11.002
S. De Vito, E. Massera, M. Piga, L. Martinotto, and G. Di Francia, “On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario,” Sensors and Actuators B: Chemical, vol. 129, no. 2, pp. 750–757, Feb. 2008, doi: https://doi.org/10.1016/j.snb.2007.09.060
S. Kim, J. M. Lee, J. Lee, and J. Seo, “Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM,” 8th International Workshop on Climate Informatics, 2018, Available: https://arxiv.org/pdf/1901.10106
D. Qin, J. Yu, G. Zou, R. Yong, Q. Zhao and B. Zhang, “A novel combined prediction scheme based on CNN and LSTM for urban PM2.5 concentration,” in IEEE Access, vol. 7, pp. 20050–20059, 2019, doi: https://doi.org/10.1109/ACCESS.2019.2897028
M. Aurangojeb, “Relationship between PM10, NO2 and particle number concentration: validity of air quality controls,” Procedia Environmental Sciences, vol. 6, pp. 60–69, 2011, doi: https://doi.org/10.1016/j.proenv.2011.05.007
K. Zhang and S. Batterman, “Air pollution and health risks due to vehicle traffic,” Science of The Total Environment, vol. 450–451, no. PMC4243514, pp. 307–316, Apr. 2013, doi: https://doi.org/10.1016/j.scitotenv.2013.01.074
T. Banerjee, S. C. Barman, and R. K. Srivastava, “Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate Pantnagar,” Environmental Pollution, vol. 159, no. 4, pp. 865–875, Apr. 2011, doi: https://doi.org/10.1016/j.envpol.2010.12.026
T. Banerjee and R. K. Srivastava, “Evaluation of environmental impacts of integrated industrial estate Pantnagar through application of air and water quality indices,” Environmental Monitoring and Assessment, vol. 172, no. 1–4, pp. 547–560, Feb. 2010, Available: https://link.springer.com/article/10.1007/s10661-010-1353-3
K. Siwek and S. Osowski, “Data mining methods for prediction of air pollution,” International Journal of Applied Mathematics and Computer Science, vol. 26, no. 2, pp. 467–478, Jun. 2016, Available: https://reference-global.com/article/10.1515/amcs-2016-0033
M. H. Lee, N. H. Abd. Rahman, Suhartono, M. T. Latif, M. E. Nor, and N. A. B. Kamisan, “Seasonal ARIMA for forecasting air pollution index: A case study,” American Journal of Applied Sciences, vol. 9, no. 4, pp. 570–578, Apr. 2012, doi: https://doi.org/10.3844/ajassp.2012.570.578
Y. Sun, M. Ji, F. Jin, and H. Wang, “Public responses to air pollution in Shandong Province using the online complaint data,” ISPRS International Journal of Geo-Information, vol. 10, no. 3, p. 126, Mar. 2021, doi: https://doi.org/10.3390/ijgi10030126
Y. Dou, N. D. Le, and J. V. Zidek, “Temporal forecasting with a Bayesian spatial predictor: application to ozone,” Advances in Meteorology, vol. 2012, no. 1, pp. 1–13, 2012, doi: https://doi.org/10.1155/2012/191575
G. Corani, “Air quality prediction in Milan: Feed-forward neural networks, pruned neural networks and lazy learning,” Ecological Modelling, vol. 185, no. 2–4, pp. 513–529, Jul. 2005, doi: https://doi.org/10.1016/j.ecolmodel.2005.01.008
G. Andrienko and N. Andrienko, “A general framework for using aggregation in visual exploration of movement data,” The Cartographic Journal, vol. 47, no. 1, pp. 22–40, Jan. 2010, doi: https://geoanalytics.net/and/papers/caj10.pdf
M.-J. Kraak, “Geovisualization illustrated,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 57, no. 5–6, pp. 390–399, Apr. 2003, Available: https://research.utwente.nl/en/publications/geovisualization-illustrated
J. J. van Wijk, “The value of visualization,” VIS 05. IEEE Visualization, 2005., Minneapolis, MN, USA, 2005, pp. 79–86, doi: https://doi.org/10.1109/VISUAL.2005.1532781
