An Online Low-Cost System for Air Quality Monitoring, Prediction, and Warning

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11969)


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.


Air-quality Machine learning Warning 



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.


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Applied Cognitive Science LabIndian Institute of Technology MandiMandiIndia
  2. 2.Defence Terrain Research LaboratoryDeference Research and Development OrganizationNew DelhiIndia

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