Publication

Abstract : Air pollution is a major concern worldwide due to its significant impacts on the global environment and human health. The conventional instruments used by the air quality monitoring stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, these stations cannot provide high spatial and temporal resolution in real-time. Although energy-efficient, wireless sensor network with the high spatio-temporal resolution is one of the potential solutions, real-time remote monitoring of all significant air quality parameters with low power consumption is challenging. To address this challenge, we propose internet of things-enabled low power environment monitoring system for real-time monitoring of ten significant air quality parameters. Moreover, the proposed system enables remote monitoring and storage of data for future analysis. Unlike earlier research work, further expansion of the proposed system is easily possible, as the proposed Wireless Sensor Node (WSN) can interface a higher number of sensors with the same number of interfacing pins. We did an in-depth analysis through calibration, experiments, and deployment which confirms the power efficiency, flexibility, reliability and accuracy of the proposed system. Results illustrate the low power consumption of 25.67mW, data transmission reliability of 97.4%, and battery life of approximately 31 months for a sampling time of 60 min. The study of the correlation between Particulate Matter 2.5 (PM2.5) and other pollutants is performed using Central Pollution Control Board data of 41 months. The initial study related to correlation is performed for the future work of developing a prediction model of PM2.5 using highly correlated pollutants. The future approach for developing a prediction model in the form of analytical equations with the help of artificial neural network is demonstrated. This approach can be implemented using the proposed WSN or low-cost processing tool for evaluating PM2.5 from precursor gases. Therefore, this approach can be one of the promising approaches in the future for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.