Steelmaking is a water-intensive process. The mean water intake against each ton of steel manufactured is ascertained as between 2 and 20 m³. Primarily, the stated requirement is in the form of make-up water to compensate for evaporation and mechanical losses and does not contribute to wastewater generation. Conversely, unit operations, such as rolling, continuous casting, pickling, etc., generate highly complex wastewater rich in polycyclic aromatic hydrocarbons (PAH), cyanide, ammonia, non-consumed acids, benzene, toluene, xylene, oil, grease, etc. Further, the conjugative wastewater contains a high concentration of metallic oxides, toxic elements, oil, nitrogen, and heavy metals such as zinc, nickel, chromium, etc. These contaminants are generally treated and neutralized using physicochemical and membrane-based systems. This also yields hazardous sludge, which is landfilled, thereby incurring an ancillary financial burden. However, sludge can be a frugal source of extracting multi-dimensional benefits. The present review investigated and identified the most water-intensive and wastewater/sludge-contributing unit operations and proposed a preferential combination of treatments to balance efficacy and economy. Further, the various global practices for sludge recycling and management documented in the existing literature are summarized and ranked with the help of the analytic hierarchy process (AHP). The findings revealed concrete making and nutrient recovery as the most- and least-preferred recycling alternatives.

The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.

Potassium (K) is a critical nutrient for crops, as it is a major constituent in fertilizer formulations. With increasing concentrations of K in agricultural soil, it is necessary to understand its movement and retention in the soil. Sub-surface modeling is an alternative method to overcome the exhausting and uneconomical methods to study and determine the actual concentration of K in soil. HYDRUS-1D is considered an effective finite-element model which is suitable for sub-surface modeling. This model requires the input of ground-station meteorological (GM) data taken at a daily timestep for the simulation period. It can be a limiting factor in the absence of ground stations. The study compares K predictions in surface and sub-surface soil layers under Boro rice cultivation obtained with the usage of different meteorological datasets. Thus, the main hypothesis of the study was to validate that, in the absence of GM data, satellite-based meteorological data could be utilized for simulating the K concentration in soil. The two meteorological datasets that are considered in the study included the GM and satellite-derived NASA-Power (NP) meteorological datasets. The usage of a satellite meteorological product at a field scale may help in applying the method to other regions where GM data is not available. The numerical model results were validated with field experiments from four experimental fields which included varied K doses. The concentration in soil was assessed at the regular depths (0–5, 5–10, 10–15, 15–30, 30–45 and 45–60 cm), and at various stages of crop growth, from bare soil and sowing, to the tillering stages. The concentration of K was measured in the laboratory and also simulated through the optimized model. The modeled values were compared with measured values statistically using relative root mean square error (RMSER) and Nash–Sutcliffe modeling efficiency (E) for simulating K concentration in the soil for the Boro rice cropping pattern with both GM data and NP data. The model was found most suitable for the 0–30 cm depth on all days and for all treatment variations.

Taxol drugs can be extracted from various species of the taxaceae family. It is an alkaloid (metabolic product) used for the treatment of various types of cancer. Since taxol is a metabolic product, multiple aspects such as edaphic, biochemical, topographic factors need to be assessed in determining the variation in Taxol Content (TC). In this study, both sensor-based hyperspectral reflectance data and absorption-based indices were tested together for the development of an advanced statistical unfolding approach to understand the influencing factors for TC in high altitude Himalayan region. Seriation analysis based on permutation matrix was applied with complete linkage and a multi-fragment heuristic scaling rule along with the common techniques such as Principal Component Analysis (PCA) and correlation to understand the relationship of TC with various factors. This study also tested the newly developed taxol indices to rule out the possibility of overlapping of TC determining bands with the foliar pigment’s wavelengths in the visible region. The result implies that T. wallichiana with a high TC is found more in its natural habitat of deep forest, relating it indirectly to elevation in the case of the montane ecosystem. Taxol is the most varying parameter among the measured variables, followed by hyperspectral Taxol content (TC) indices such as TC 2, TC 5, and carotenoids, which suggests that the indices are well versed to capture variations in TC with elevation.

Global Navigation Satellite Systems (GNSS) have revolutionized precision agriculture, enhancing productivity and sustainability. This chapter delves into the integration of GNSS in modern farming practices, highlighting its pivotal role in precision agriculture (PA). GNSS technology provides precise location data, enabling accurate soil sampling, variable rate technology (VRT), precision planting, and yield mapping. These applications optimize resource use, minimize environmental impact, and increase crop yields. The chapter also explores the vulnerabilities of GNSS, including signal interference and economic barriers, and discusses potential solutions. Furthermore, it examines the integration of GNSS with emerging technologies such as Geographic Information Systems (GIS), unmanned aerial vehicles (UAVs), the Internet of Things (IoT), and advanced sensor technologies. Case studies demonstrate the effectiveness of GNSS in diverse agricultural contexts from automated irrigation systems to robotic harvesting. Despite challenges, the future of GNSS-enabled precision agriculture looks promising, with advancements in signal accuracy, satellite constellations, and receiver sensitivity. This integration is set to transform agriculture, ensuring efficient and sustainable food production. By leveraging GNSS, farmers can make data-driven decisions, enhancing productivity and reducing waste. This chapter underscores the critical role of GNSS in achieving global food security and sustainable agricultural practices. <!----> <!----> <!----> <!---->