Please use this identifier to cite or link to this item: http://hdl.handle.net/10266/6470
Title: IoT Based Sodar Network for Estimation of Atmospheric Boundary Layer
Authors: Chourey, Parag
Supervisor: Agarwal, Ravinder
Singh, Nirbhow Jap
Soni, Kirti
Keywords: IoT;Sodar;Atmospheric Boundary Layer;ultra-low-noise amplifier;narrow band-pass filter;Amazon Web Services
Issue Date: 26-May-2023
Abstract: The advancement in connected and interactive networks such as the Internet of Things (IoT) has paved the way for the networking of various meteorological sensors to study and research in atmospheric science. Nowadays, it is important to consider regional meteorological parameters for planning and management the air quality of a defined area. The atmospheric boundary layer (ABL) is one of the essential components of meteorology and is utilized to estimate the emission and dispersion of air pollution. An integrated network of meteorological sensors using IoT expedites real-time monitoring of critical parameters. These networks facilitate regulatory authorities in enhancing their decision-making and planning in the fields of atmospheric science and air quality management. Remote sensing techniques have dominated the field of lower atmospheric parameter monitoring. Sonic Detection and Ranging (SODAR) is a robust technique to estimate the ABL structures. SODAR is a remote sensing technique that relies on acoustic backscattered signals to plot echogram structures. The ABL height is estimated by echogram structures which are interpreted as standard atmospheric conditions such as convection, inversion and etc. based on the echogram patterns. In the modern era of the Internet and informatics, the combination of ground-based and remote sensing meteorological sensors provides a new paradigm for ABL estimation. In this research work, an IoT-based SODAR network has been created for estimation and monitoring of the ABL height. Monostatic SODAR is used to measure the ABL height. The air quality of a region depends on the local pollutants and environmental conditions along with the pollutants transported through the region. The transport of hazardous gases and particulate matter has a significant impact on the quality of the air. The primary goal of this research is to develop an IoT-based SODAR network (IoT-SN) for measuring the ABL height at various locations in Northern India. The development process initiates with designing of an analog signal conditioning system for Monostatic SODAR followed by creation of an IoT-SN and integration of meteorological sensors with the IoT-SN. Furthermore, an attempt has been made to determine the relationship between the ABL height and other meteorological parameters such as temperature, relative humidity, wind speed, and wind direction. vi In the initial phase of research, an ultra-low-noise amplifier (ULNA) and dual-amplifier band-pass (DABP) filter have been designed for analog signal conditioning of SODAR. The designed analog signal conditioning system features improved gain and high signal-to-noise ratio (SNR) of 65 ๐‘‘๐ต and 92 ๐‘‘๐ต, respectively. The results have been then compared to the existing analog signal conditioning system. The existing analog signal conditioning stage consists of single low-noise amplifier (SLNA) and state variable (SV) narrow band-pass filter. It has been observed that the updated system has significant advantages in terms of gain and narrow band-pass response. The updated SODAR system has been deputed to capture the echogram structures. The resulting echogram structures demonstrate the efficiency in capturing the well-known standard structures of ABL. The second phase of research focuses on designing a digital signal processing and data processing system for SODAR in order to make it suitable for IoT networking. The updated SODAR system has been installed at the different locations of Northern India which includes Delhi, Roorkee, Aligarh, Alwar, Hisar, and Sangrur. The IoT-SN data has been validated against the local station data. The analysis yielded encouraging results, with a network accuracy of 99.74 % and ABL height estimation accuracy of 95.57 % from the IoT-SN. Subsequently, meteorological sensors namely wind sensor (MeteoWind-2), temperature, and relative humidity sensor (MeteoTemp + RH) have been integrated with the IoT-SN. The IoT network consists of SODAR and meteorological sensors has been designated as IoT-SMSN. To maintain the systemโ€™s reliability, calibration and testing of the sensors has been accomplished at the CSIR-National Physical Laboratory, New Delhi (National Measurement Institute, India). The data received from the IoT-SMSN have exhibited a promising level of accuracy, with a 95 % rate and an uncertainty range of 0.1 to 0.3. In the final phase of the research, correlation between SMSN parameters of all installation sites have been analyzed. In addition, key pollutant data has been collected from the Central Pollution Control Board (CPCB), India, and the relationships between SMSN parameters and air pollutants of each location have been investigated. It has been observed that the proposed system is reliable and accurate and offers distinct advantages in air quality management and planning of a region.
URI: http://hdl.handle.net/10266/6470
Appears in Collections:Doctoral Theses@EIED

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