Abstract
Air-quality is degrading in developing countries and there is an urgent need to monitor and predict air-quality online in real-time. Although offline air-quality monitoring using hand-held devices is common, online air-quality monitoring is still expensive and uncommon, especially in developing countries. The primary objective of this paper is to propose an online low-cost air-quality monitoring, prediction, and warning system (AQMPWS) which monitors, predicts, and warns about air-quality in real-time. The AQMPWS monitors and predict seven pollutants, namely, PM1.0, PM2.5, PM10, Carbon Monoxide, Nitrogen Dioxide, Ozone and Sulphur Dioxide. In addition, the AQMPWS monitors and predicts five weather variables, namely, Temperature, Pressure, Relative Humidity, Wind Speed, and Wind Direction. The AQMPWS has its sensors connected to two microcontrollers in a Master-Slave configuration. The slave sends the data to the API in the cloud through an HTTP GET request via a GSM Module. A python-based web-application interacts with the API for visualization, prediction, and warning. Results show that the AQMPWS monitor different pollutants and weather variables within range specified by pollution control board. In addition, the AQMPWS predict the value of the pollutants and weather variables for the next 30-min given the current values of these pollutants and weather variables using an ensemble model containing a multilayer-perceptron and long short-term memory model. The AQMPWS is also able to warn stakeholders when any of the seven pollutants breach pre-defined thresholds. We discuss the implications of using AQMPWS for air-quality monitoring in the real-world.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ayele, T., Mehta, R.: Air pollution monitoring and prediction using IoT. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 1741–1745 (2018)
Brauer, M., et al.: The Global Burden of Disease Study 2017 (2017)
Brienza, S., et al.: A low-cost sensing system for cooperative air quality monitoring in urban areas (2015)
European Environment Agency: Air Quality in Europe (2016)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning, 2nd edn. MIT Press, Cambridge (2016)
Gowen, A.: As China cleans up its act, Indias cities named the world’s most polluted (2019). https://wapo.st/2KFabc3
Griffiths, J.: 22 of the top 30 most polluted cities in the world are in India (2019). https://cnn.it/2TdTUQY
How air pollution is destroying our health (2019). https://bit.ly/2ryRjRH
I2C Bus, Interface and Protocol. https://i2c.info/
Ikram, J., et al.: View: implementing low cost air quality monitoring solution for urban areas. Environ. Syst. Res. 1(1), 10 (2012)
Irfan, U.: Why India’s air pollution is so horrendous (2019). https://bit.ly/2CU7ShD
Kiruthika, R., et al.: Low cost pollution control and air quality monitoring system using Raspberry Pi for Internet of Things. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 2319–2326 (2017)
Lightweight Low-Power Arduino Library. https://bit.ly/2Zd72UG
Martnez-Espaa, R., et al.: Air-pollution prediction in smart cities through machine learning methods: a case of study in Murcia, Spain. J. Univ. Comput. Sci. 24(3), 261–276 (2018)
National Ambient Air Quality Standards (2009). https://bit.ly/2ZbYVId
Nandi, J.: Study hints at bias in India’s air pollution monitoring stations (2018). https://bit.ly/2O2c4BL
NDTV Report: Centre To Install 1,500 Manual Air Quality Monitoring Systems By 2024 (2019). https://bit.ly/2P1mZxC
Parmar, G., et al.: An IoT based low cost air pollution monitoring system. In: 2017 International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), pp. 524–528 (2017)
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)
Raschka, S.: About feature scaling and normalization and the effect of standardization for machine learning algorithms (2014). https://bit.ly/2U0DJlG
Rückerl, R., Schneider, A., Breitner, S., Cyrys, J., Peters, A.: Health effects of particulate air pollution: a review of epidemiological evidence (2011)
Sharma, N., et al.: Forecasting air pollution load in Delhi using data analysis tools. Procedia Comput. Sci. 132, 1077–1085 (2018)
Subramanian, V., et al.: Data analysis for predicting air pollutant concentration in Smart city Uppsala (2016)
Tiele, A., et al.: Design and development of a low-cost, portable monitoring device for indoor environment quality (2018). Article 5353816
WHO Ambient Air Quality Facts (2019). https://bit.ly/2FlSZpw
World Health Organization: WHO air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide (2005)
Yan, S.: Understanding LSTM and its diagrams (2016). https://bit.ly/2z5kdir. Accessed 18 Aug 2019
Zhang, J., et al.: Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong (2017)
Zhao, C., et al.: Application of data mining to the analysis of meteorological data for air quality prediction: a case study in Shenyang (2017)
Acknowledgement
We are thankful for the generous funding from Department of Environment, Science & Technology, Government of Himachal Pradesh for the project IITM/DST-HP/VD/240 to Varun Dutt and Pratik Chaturvedi. We appreciate the help of Khyati Agrawal in developing the machine learning models. We thank Jhalak Choudhary and Roshan Sharma for helping in designing and developing the circuit and helping in programming microcontroller. Moreover, the experiment for ventilation was conducted with the help of Aman Raj and Amit Chauhan who also helped in designing the external housing.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sharma, R. et al. (2020). An Online Low-Cost System for Air Quality Monitoring, Prediction, and Warning. In: Hung, D., D´Souza, M. (eds) Distributed Computing and Internet Technology. ICDCIT 2020. Lecture Notes in Computer Science(), vol 11969. Springer, Cham. https://doi.org/10.1007/978-3-030-36987-3_20
Download citation
DOI: https://doi.org/10.1007/978-3-030-36987-3_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36986-6
Online ISBN: 978-3-030-36987-3
eBook Packages: Computer ScienceComputer Science (R0)