Heavy metals (iron, copper, and zinc) were quantified in purple crab (Platyxanthus orbignyi) tissues collected in winter (September 2021), spring (November 2021), and summer (March 2022) at three beaches (Tres Hermanas, Fundición, and El Diablo) in Ilo Harbour (Moquegua), South Peru. The rank order of heavy metal concentrations in purple crab tissues and sediments was similar; iron (Fe) was followed by Copper (Cu), and this last one was followed by Zinc (Zn). The heavy metal concentrations in tissue crabs from the three beaches differed from each other spatially and seasonally. In addition, Fundición Beach was the zone with the highest concentration of those three metals during the summer.

In this study, the AquaCrop model was employed to analyze the impact of projected future climate changes on the water usage and biomass production of potato crops in Babylon, Iraq, under varying irrigation methods. The irrigation techniques evaluated included sprinkler irrigation, surface drip irrigation, and subsurface drip irrigation at depths of 10 cm and 20 cm. The study involved simulating and forecasting conditions for the year 2050, comparing them to current conditions. The model measured and predicted the evapotranspiration (ETa) and actual biomass of potato crops for 2050 using the RCP 8.5 scenarios, which outline different trajectories for greenhouse gas emissions. The AquaCrop model was calibrated and validated using statistical measures such as the R2, RMSE, CV, EF, and D, achieving a 99% accuracy level in its performance. The findings suggest that using drip irrigation systems and applying the AquaCrop model significantly mitigates the adverse effects of environmental stress on desert soils and enhances sustainable agricultural practices in arid regions.

Diseases caused by bacteria, fungi, and viruses are a problem for many crops. Farmers have challenges when trying to evaluate their crops daily by manual inspection across all forms of agriculture. Also, it is difficult to assess the crops since they are affected by various environmental factors and predators. These challenges can be addressed by employing crop disease detection approaches using artificial intelligence-based machine learning and deep learning techniques. This paper provides a comprehensive survey of various techniques utilized for crop disease prediction based on machine learning and deep learning approaches. This literature review summarises the contributions of a wide range of research works to the field of crop disease prediction, highlighting their commonalities and differences, parameters, and performance indicators. Further, to evaluate, a case study has been presented on how the paradigm shift will lead us to the design of an efficient learning model for crop disease prediction. It also identifies the gaps in knowledge that are supposed to be addressed to forge a path forward in research. From the survey conducted, it is apparent that the deep learning technique shows high efficiency over the machine learning approaches, thereby preventing crop loss.

A high concentration of hydrogen sulfide (H2S) released from pig farming is one of the major environmental problems affecting surrounding communities. In modern pig farms, the bioscrubber is used to eliminate H2S, which is found to be driven mainly by the sulfur-oxidizing bacteria (SOB) community. Therefore, in this study, molecular biology techniques such as next-generation sequencing (NGS) and DNA microarray are proposed to study the linkage between enzyme activity and the abundance of the SOB community. The starting sludge (SFP1) and recirculating sludge (SFP2) samples were collected from the bioscrubber reactor in the pig farm. The abundance of microbial populations between the two sampling sites was considered together with the gene expression results of both soxABXYZ and fccAB. Based on the NGS analysis, the members of phylum Proteobacteria such as Halothiobacillus, Acidithiobacillus, Thiothrix, Novosphingobium, Sulfuricurvum, Sulfurovum, Sulfurimonas, Acinetobacter, Thiobacillus, Magnetospirillum, Arcobacter, and Paracoccus were predominantly found in SFP2. The presence of Cyanobacteria in SFP pig farms is associated with increased biogas yields. The microarray results showed that the expression of soxAXBYZ and fccAB genes involved in the oxidation of sulfide to sulfate was increased in Halothiobacillus, Paracoccus, Acidithiobacillus, Magnetospirillum, Sphingobium, Thiobacillus, Sulfuricurvum, Sulfuricurvum, Arcobacter, and Thiothrix. Both NGS and DNA microarray data supported the functional roles of SOB in odor elimination and the oxidation of H2S through the function of soxABXYZ and fccAB. The results also identified the key microbes for H2S odor treatment, which can be utilized to monitor the stability of biological treatment systems and the toxicity of sulfide minerals by oxidation.

The total soluble protein-mediated morphological traits in mustard treated with Thiourea and Salicylic acid were investigated. In addition, it tested the hypothesis that the growth regulator salicylic acid protects the photosynthetic apparatus by up-regulating morphological traits. Under natural environmental conditions, seeds were sown in the field, and seed emergence was recorded. For three days after the 15-day stage, plants in the area were treated with thiourea and salicylic acid and allowed to grow for 90 days. Plants were harvested to assess various morphological traits. A follow-up application of SA and Thiourea plants improved plant height, leaf area, internodal length, leaf number, and accelerated plant activity. The up-regulation of morphological traits may have occurred in SA and Thiourea-mediated plants. After treatments, the level of total soluble protein was estimated in the leaves at proposed day intervals.

Over time, predictive models tend to become more accurate but also more complex, thus achieving better predictive accuracy. When the data is improved by increasing its quantity and availability, the models are also better, which implies that the data must be processed to filter and adapt it for initial analysis and then modeling. This work aims to apply the Random Forest model to predict PM10 particles. For this purpose, data were obtained from environmental monitoring stations in Mexico City, which operates 29 stations of which 12 belong to the State of Mexico. The pollutants analyzed were CO carbon monoxide, NO nitrogen oxide, and PM10 particulate matter equal to or less than 10 μg.m-3, NOx nitrogen oxide, NO2 nitrogen dioxide, SO2 sulfur dioxide, O3 ozone, and PM2.5 particulate matter equal to or less than 2.5 μg.m-3. The result was that when calculating the certainty of our model, we have a value of 80.40% when calculating the deviation from the mean, using 15 reference variables.

The project aims to remove reactive blue dye from the effluent of textile industries by utilizing coal-associated soil as an adsorbent, as it possesses effective physical properties and distinguishing characteristics. In comparison to other separation techniques, the adsorption method is the most effective, cost-effective, and straightforward. A batch adsorption investigation was carried out to examine the various adsorption-influencing factors, including solution pH, adsorbent dosage, contact time, temperature, and dye concentration. Contact time of 30 min, an adsorbent dosage of 10g.100 mL-1, a solution pH of 7, a temperature of 30°C, and an initial dye concentration of 100 mg.L-1 were found to be optimal for dye adsorption. Using two distinct kinetic models, the evaluation of kinetic studies revealed that the pseudo-second-order provided the greatest fit, with a higher R2 value than the pseudo-first-order. The thermodynamic parameters Gibbs free energy (ΔG°), entropy (ΔS°), and enthalpy (ΔH°) indicated that the current adsorption system was exothermic and spontaneous. Further study of the adsorption isotherm revealed that the Langmuir isotherm model provided the best fit, with an R2 value of 0.977%.

This research investigates the potential of two Plant Growth-Promoting Bacteria (PGPB) strains, EP1-AS and EP1-BM, isolated from the halophyte Euphorbia prostrata, to enhance plant growth and provide abiotic stress resilience. The study addresses the urgent need for sustainable agricultural practices in the face of challenges like soil salinization and heavy metal contamination. The investigation comprehensively analyzes the heavy metal and salt tolerance of the PGPB strains, revealing their potential applications in promoting plant growth under adverse environmental conditions. The research further explores the impact of these PGPB strains on wheat plants subjected to varying concentrations of heavy metals and salts. Results indicate that both PGPB strains, especially EP1-BM, exhibit significant tolerance to heavy metals and salt stress. EP1-BM demonstrates remarkable resilience even under high concentrations of these stressors. The study extends its findings to in vitro testing on wheat plants, revealing the positive influence of PGPB strains on germination, shoot length, and root length in the presence of salt and heavy metals. This research underscores the significance of understanding plant-microbe interactions, particularly in the context of promoting sustainable agriculture in challenging environments. The identified resilience of PGPB strains, especially EP1-BM, suggests their potential application as bio-remediators and plant growth promoters in soils affected by salinity and heavy metal stress. The promising results observed will be followed-up field trials. They will highlight the translational potential of these PGPB strains, offering a novel avenue for developing biofertilizer formulations with a cautious approach to safety concerns. Overall, this study contributes valuable insights into harnessing the untapped potential of resilient plants and their associated microbial communities for sustainable agriculture. It addresses key global challenges outlined by the United Nations Sustainable Development Goals.

The growing population and rapid urbanization are significant challenges for Indian cities. Pune City generates nearly 2,258 tonnes of waste per day. Pune’s informal waste sector has demonstrated remarkable efficiency, cost-effectiveness, and self-sustainability. Moreover, it contributes to favorable economic and social outcomes for the city. With the support of the self-help group SWaCH Seva Sahakari Sanstha Maryadit, Pune, the municipal solid waste management model has successfully achieved a remarkable 95 percent segregation rate. Implementing the Pune municipal solid waste management model showcases the active and efficient engagement of informal waste workers in the collection and resource utilization process. This underscores the possibility of favorable economic, social, and environmental results stemming from collaborations between municipalities and waste pickers. This paper looks at the role of SWaCH in line with Pune Municipal Corporation towards the present waste management system. Primarily reliant on labor, this model accomplishes recycling tasks at a notably lower cost compared to conventional mechanized and centralized waste management approaches. It can also accomplish high recycling levels and relatively considerable plastic waste segregation. Promoting the retrieval of valuable materials, especially plastics, for local and global recycling enterprises actively contributes to the advancement of a circular urban waste management approach. The objective of this research is to explore and provide a realistic understanding of Pune’s current status of waste generation, collection, transportation, and disposal. Apart from the SwaCH-PMC model, the paper also focuses on plastic waste recycling, the Red Dot Campaign towards sanitary waste, and household e-waste management in Pune.

Access to affordable and reliable energy sources can substantially enhance the lives of marginalized communities in rural areas. Unfortunately, numerous households in these communities rely upon unclean sources of energy such as kerosene to light the house even during daylight. To address this issue, solar off-grid technology - Micro Solar Dome (MSD) was implemented in various states across India, specifically benefiting the scheduled caste and scheduled tribe communities. The study, across the eight selected states, highlights the advantages of adopting off-grid technologies and their roles in promoting awareness of renewable energy solutions. The survey used purposive sampling to collect community members’ perceptions of the product’s benefits and their awareness of renewable technologies. The results indicated that the utilization of the product not only enhanced illumination levels within households but also contributed to improved safety, increased study hours for children, and facilitated economic activities during the evening hours. Furthermore, the study revealed that education plays a crucial role in adopting solar energy. However, interventions such as awareness programs and hands-on experiences with the products can also greatly enhance awareness and promote adoption in rural areas. Overall, the study provided compelling evidence of the significant and positive impact that small-scale initiatives like the MSD can have on the lives of marginalized communities. It also emphasized the potential of such solutions to empower these communities and improve their overall well-being.