Artificial neural network (ANN) is a mathematical model useful for forecasting on the any type of available data. This tool is not only useful in environment but also covers wide ranges of applicability. Utilizing this model, a study was carried out in an inland area of Nagpur for forecasting satellite-derived cloud parameters. Nine ANN architects are developed based on five pollutant parameter (aerosol optical depth, RSPM, SPM, SO₂, NO_x), meteorological and some cloud parameter. The models are used to simulate concentration of pollutants as well as the forecast and validation of cloud top temperature, cloud ice water path and cloud liquid water path during different seasons (winter, pre-monsoon and post-monsoon). Models based on back-propagation neural network were tested using the collected data of study area. The ANN models were trained using gradient descent algorithms to check the robustness and adaptability of the models. ANN models based on both satellite and ground-based data variables demonstrate the best performance and are skilled at resolving patterns of pollutant dispersion to the atmosphere during 2006–2013 for Nagpur city.

Unmanned Aerial Vehicle (UAV) platforms are emerging as an essential tool for various studies in environmental engineering. The quadcopters drones have immense potential for sensor interfacing and stable data acquisition. These UAVs can perform critical activities like volcanic eruption monitoring, stack emission monitoring, urban air quality monitoring, identification of pollution levels in 3D space, etc. Carbon dioxide (CO2) and the Discomfort Index (DI) are essential indicators of air quality and climate comfort. Hence, it is critical to monitor them with extreme accuracy. This study demonstrates a novel application of CO2 profiling using low-cost drones at varied altitudes. The drone-aided vertical CO2 profiling was carried out at 60 m AGL (Above Ground Level) during summer and winter, in Nagpur city of India. This study retrieved some exciting data on the DI. It was found that CO2 concentration in the range of 20-70 m AGL was lower than the surface level. The derived DI was maximum at the height range of 40-50 m. Inversion was observed in the range of 30-40 m. A positive correlation between CO2 and temperature was observed in both seasons. The lightweight commercial drones are capable of tethering sensor modules to get accurate results in less cost and effort. This type of novel tethered sensor technique could be applicable in weather forecasting, landfill surface monitoring, volcanic eruption monitoring, and other probable applications with few drone flight limits.