Odisha University of Agriculture and Technology

Ozone (O₃) is a short-lived climate pollutant of growing concern due to its dual role as a greenhouse gas and a secondary air pollutant adversely affecting human health, ecosystems, and agricultural productivity. This study investigated the vertical dynamics of atmospheric O₃ over 28 Indian states during 2003–2024 using Atmospheric Infra-red Sounder (AIRS)-derived O₃, methane (CH₄), and air temperature, land surface temperature (LST), and solar radiation data. Results revealed a pronounced vertical and regional heterogeneity. Tropospheric O₃ exhibited strong seasonality, with monsoon minima and pre-monsoon maxima, while stratospheric layers showed a coherent bimodal pattern. Significant long-term increases were detected in both upper-stratospheric (1–20 hPa) and lower-tropospheric (300–1000 hPa) layers, whereas mid-stratospheric levels (30–150 hPa) displayed widespread declines, particularly during monsoon months. Correlation analyses highlighted that CH₄ and O₃ were negatively associated in the upper atmosphere but strongly positively associated near the surface, whereas LST and solar radiation showed consistent positive relationships with O₃ across states. Threat assessment indicated that several northern and eastern states frequently exceeded the 40 ppbv crop-damage threshold, posing substantial agricultural risk. Multi- Layer Perceptron (MLP) - based neural network models resulted in high accuracy as indicated by the Root Mean Square Error values. Overall, the study provides the first vertically resolved, multi-decadal ozone assessment for India, offering critical insights into climate–chemistry interactions, agricultural vulnerability, and the utility of machine learning for atmospheric prediction. The findings underscore the need for integrated mitigation strategies targeting O₃ precursors under a rapidly warming climate.

Over recent decades, global energy demand has risen sharply, driven by rapid economic growth, increasing population, and accelerated industrial development, particularly in emerging economies. The aim of the present investigation is to develop SnS2/rGO/Bi2S3 nanocomposite using hydrothermal process for the electrodes of smart supercapacitors (SC). The characterization processes have been conducted through XRD, SEM, EDS, RAMAN, and FTIR. The electrochemical measurement of SnS2/rGO/Bi2S3 nanocomposite has been successfully carried out through CV, GCD, and EIS. In the present investigation, the synthesized nanocomposite exhibits a combined spherical and rod-like morphology. The material shows a reduced optical band gap accompanied by an increased density of free charge carriers, suggesting improved electrical conductivity and efficient electron transport. As a result, the nanocomposite delivers a high specific capacitance (Cs) of 1312 F g-1 at CD of 1 A g-1, which is prominently higher than that of the corresponding SnS2/rGO and Bi2S3/rGO electrodes. Furthermore, the electrode achieves an energy density (ED) of 48.78 Wh kg-1 and power density (PD) of 862 W kg-1 at the same current density (CD), demonstrating its excellent electrochemical performance.

High-performance thermal management systems in solar thermal energy harvesting and electronic cooling technologies have presented their advantages of hybrid nanofluids due to the increasing demand for those containing magnetic and metallic nanoparticles. From all these nanofluids, the Fe3O4-Ag/water hybrid nanofluid shows superior thermal conductivity, enhanced radiative absorption, and so forth, making it ideal for advanced heat transfer applications. The current investigation aims to present a 3D movement of a hybrid nanofluid of Fe3O4-Ag/water over a permeable expanding surface mounted via a porous medium. In particular, the combined effect of dissipative heat associated with both Joule and Darcy and the interpretation of thermal radiation, and heat source/sink enriches the flow process. The proposed flow problem's mathematical model is reformulated into a standard dimensionless form to enable the application of similarity rules and reveal the effect of different factors. The computation of various profiles with the variation of these factors is deployed graphically using standard numerical techniques with proper validation. The implication of distinct factors on the flow profiles is depicted and deliberated briefly. Moreover, the important outcomes of the study are reported as the enhanced concentration of the nanoparticles encourages the fluid temperature and the role of dissipative heat conducted by the Eckert number and the thermal radiation favors in enhancing the fluid temperature.

Crop yield modeling and forecasting have been an essential step in determining agricultural and economic policy decisions in India. Planning and policy decisions on distribution, price, Aexport-import, storage, and other issues are critically dependent on it. This study aimed to develop a trustworthy crop yield prediction system for rice yield by analyzing the relationship between crop yield and several weather variables. Weather indices, an assimilation of the weekly weather effects on crop yield, were used to study the impact of weather factors on rice yield. ASeveral statistical and neural network models have been developed based on the linearity and non-linearity pattern of the data. The results of statistical models demonstrated that both linear and non-linear patterns were present in the data and the effects of rainfall, minimum temperature, and time variable t were significant. The neural network models outperformed statistical models in terms of accuracy, and the study found hybrid models with three and four hidden nodes were the best-fit models in the districts of Birbhum and Burdwan, Arespectively. A reliable rice yield estimate can be obtained six to eight weeks before harvest by using the best-fit models for various policy decisions.

Accurate estimation of evapotranspiration is crucial for enhancing real-time irrigation scheduling and decision making in water resource planning. Traditionally, empirical methods are used to calculate reference evapotranspiration (ET0) using available meteorological data. However, in many areas, such data are limited or unavailable for ET0 estimation. Hence, this study aims to explore data-driven models such as machine learning (ML) and deep learning (DL) for estimating ET0 with minimal meteorological data. In this study, five ML models, including linear regression (LR), random forest (RF), support vector regression (SVR), XGBoost, KNN regression, and two deep learning methods, such as feedforward neural networks and long-term short-term memory (LSTM), were used to estimate the reference evapotranspiration (ET0) over the Phulnakhara canal command area, Odisha, India using various combinations of meteorological variables. The results of these models were compared with the Penman‒Monteith-based ET0. The Penman-Monteith based ET0 is significantly (p < 0.01) positively correlated with sunshine hour and maximum temperature, with correlation coefficients of 0.8 and 0.6, respectively, whereas maximum relative humidity and minimum humidity are negatively correlated. The findings revealed that when all climate data (maximum temperature (Tmax), minimum temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), wind speed and sunshine hour) are available, the coefficient of determination (R2) increases to 0.98. However, when data are limited, it decreases to 0.78. The SVR model outperformed the other ML models with all the input combinations. However, KNN emerged as the most reliable model for estimating ET0 with input data of maximum and minimum temperature. The results revealed that using only three variables (temperature, wind speed, and relative humidity) or even two-parameter combinations (temperature with either relative humidity or wind speed) yielded R² values in the range of 0.78–0.79. These findings indicate that ML and DL can effectively estimate ET0 even under sparse meteorological conditions within the canal command area.The findings of this study offer valuable insights for estimating ET0 in regions with limited climate data, which is crucial for effective agricultural water management.