The aim of this study was to investigate the effect of Moringa Seed Pod Powder (MSPP) as a biosorbent on the biosorption of Pb2+ and Cu2+ influenced by pH, contact duration, dosage, and extracting solution concentration. Soil samples were collected using a soil auger at depths ranging from 0 to 20 cm using a systematic (grid) sampling procedure. Moringa seed pod samples were air-dried in the shade, crushed with a stainless steel mortar and pestle, regrind with an electric grinder, and sieved (2-mm). The influence of pH on metal adsorption capacity was divided into three phases: slightly acidic, neutral, and alkaline. The adsorption of Cu2+ and Pb2+peaked in alkaline conditions, with values of 85.71% and 99.13%, respectively, and was lowest in neutral soil pH for both metals. The adsorption of Pb2+ gradually increases as the dose and shaking time change from 2 g for 2 h (97.20%) to 4 g for 4 h (98.81%), where it reaches a maximum, and then slightly drops as the dose and shaking time increase to 6 g and 6 h (98.14%). While Cu2+ exhibits a substantial drop in adsorption from 89.09% at (2 g 2 h) to 84.65% at (4 g 4 h), it rapidly increases to 89.99% (6 g 6 h), which represents the maximum adsorption. It is recommended to use moringa seed pod powder for the removal or remediation of Pb2+ and Cu2+ polluted soils.

Environmental issues primarily pollution of air, water and soil, overpopulation, deforestation, global warming, climate change, species extinction and waste disposal are critical issues which need to be handled and managed properly to preserve our planet. Environmental education is a tool to solve these issues as by this process, the learner would expand level of awareness and would learn solutions of environmental issues. The present case study was done to assess the level of environmental education in different age groups of school students of Bangla Nagar, Bikaner (Rajasthan). Seven schools were chosen for the survey. Questionnaires were prepared for different groups of classes such as group 1 of classes I to V, group 2 of classes VI to VIII, group 3 of classes IX to X, and group 4 of classes XI to XII. These forms were distributed among students to get their responses. Data were analysed, and results were set. It was found that students from class I to XII showed 63.73% positive response towards environmental education and awareness. The study revealed that we are making our future generations environmentally aware. However, we need more awareness among school students to make the planet earth sustainable as well as for human sustainability.

The current study aims to do rainfall analysis of Jhunjhunu district with reference to time and space for a period of 22 years by using quantum geographic information system (qGIS). The data collected were yearly rainfall and rainy days. These data were analysed in qGIS software. Inverse Distance Weighting (IDW) method of interpolation was adopted for the study. Thematic maps were generated. Rainfall maps displayed a growing tendency in rainfall amount while rainy days represented a slow increasing pattern. It was found that south, south-eastern and some part of north region of the Jhunjhunu get the utmost rainfall. However, north-eastern and western parts of the district receive the lowermost rainfall. It was observed that Khetri block received the highest rainfall during the average 20 years period. Though, Jhunjhunu block got the lowest rainfall during the whole period. Malsisar and Udaipurwati blocks got average rainfall. As ground water recharge rate is low in the study area, it is essential to collectively utilise surface water, available rainfall and groundwater for optimum irrigation and further agricultural management in the district. The rainfall analysis facilitated the understanding of the rainfall pattern which would be advantageous for strategic planning of efficient irrgiation and water availability in the study area.

The effects of climate change, including extreme weather conditions characterized by rising atmospheric CO2 levels, alterations in precipitation patterns, and elevated temperatures, significantly impact crop production and the prevalence of agricultural pests. This study aimed to unravel the nuanced responses of various insect pests to these dynamic climatic shifts by conducting an experiment within the agronomy field at BSMRAU. The objective was to investigate the incidence of insect pests on mungbean plants under varying temperature and CO2 level conditions. To achieve this, four open top chambers (OTCs) were deployed, each featuring distinct CO2 concentrations, OTC-1 (400 ppm), OTC-2 (450 ppm), OTC-3 (500 ppm), and OTC-4 (550 ppm). Additionally, electronic thermo-hygrometers were strategically placed within these chambers to monitor the relevant environmental parameters. The results of this comprehensive study unveiled the presence of five distinct insect species that were observed infesting mungbean crops. These included aphids, ladybird beetles, whiteflies, bean pod borers, and caterpillars. The findings provided valuable insights into the intricate interplay between CO2 levels, temperature, and the incidence of these insect pests. Notably, it was found that an atmospheric CO2 concentration of 550 ppm created an optimal environment for aphid infestations, with an ideal temperature range between 32-34°C. In contrast, a concentration CO2 of 450 ppm was identified as most conducive to the other five insect species. Bean pod borers, in particular, demonstrated a preference for temperatures ranging from 30.1-32.9°C, while the impact of temperature variations on the remaining insect species was deemed statistically insignificant. These findings shed light on the complex relationships between rising CO2 levels, temperature fluctuations, and insect pest dynamics within the context of mungbean cultivation. The knowledge gained from this research is indispensable for the development of effective strategies to manage and mitigate pest outbreaks in a rapidly changing climate, thereby safeguarding crop yields and ensuring food security. This study advances our comprehension of the intricate ecological interactions within agricultural ecosystems, offering a foundation for more informed decision-making in the face of ongoing climate change challenges.

In order to investigate the pollution situation of the relocation site of an oil depot in Guangzhou, based on the health risk assessment theory, the investigation and monitoring of soil and groundwater organic pollutants in the site was conducted, and the health risk assessment was carried out combined with the monitoring data.24 types of pollution factors, including 1,3,5-trimethyl benzene, were detected and the maximum concentration accounted for 67% of the screening value; toluene and extractable petroleum hydrocarbon (C10-C40) were detected in the plot, and the maximum concentration accounted for 1%~2% of the screening value. The results show that the maximum risk area of the legacy site is the oil depot area, with soil and groundwater organic matter; the maximum concentration of organic pollutants in the site (≤10-6)Or non-carcinogenic hazard entropy (≤1), and the future exposure receptor is not affected by the health risk of the soil and groundwater organic pollutants in the site.

Agricultural machinery is an essential tool for improving agricultural production per unit area. It helps farmers to grow more crops in less time and with greater efficiency to meet food security for exponentially growing population. Tunnel farming is an agriculture technique in which the crop is grown in a long row, covered with plastic sheets to maintain favorable environment for crop growth while minimizing environmental and biological hazards. Tunnel farms are divided into low height tunnel, high tunnel, and walk-in tunnel. It is important to monitor tunnels for temperature and relative humidity because these two factors promote disease and pest attack. In low height tunnels, it becomes difficult to physically monitor temperature variations, humidity variation and pest or disease attack. Therefore, it is required to develop suitable solution to this problem which can be provided in the form of small, unmanned ground vehicle (UGV) equipped with necessary sensing devices. In this study, a small UGV having camera, temperature and humidity sensors was developed and evaluated at four different sites. These sites included lemon orchard, research farms, main boulevard and field farm. The temperature and humidity reading were recorded at 8:00, 11:00, 14:00 and 16:00 a day on 10, 20, 30, 40, and 50 ft. length from field boundary. In lemon orchard temperature is minimum (240C) and humidity is maximum (69%) at 8:00 AM. In the evening at 2:00 PM temperature was maximum (390C) and humidity was minimum (45%). In research farm temperature was minimum (280C) and humidity was maximum (65%) at 8:00AM. In the evening at 2:00PM temperature was maximum (430C) and humidity was minimum (40%). In Kfueit boulevard temperature was minimum (250C) and humidity was maximum (69%) at 8:00AM. In the evening at 2:00PM temperature was maximum (400C) and humidity was minimum (46%). In Progressive farmer farm temperature was minimum (300C) and humidity was maximum (69%) at 8:00AM. In the evening at 2:00PM temperature was maximum (430C) and humidity was minimum (46%).

As agriculture becomes increasingly important in ensuring food security for the world’s growing population, there has been a rise in the development of smart agricultural robots to optimize crop yield. One critical area where these robots can make a significant impact is in weed detection and health monitoring, which can have a significant impact on crop yield and quality. This review paper aims to examine the latest research in smart weed detection and health monitoring agrobots. The paper discusses several studies on autonomous agricultural robots that detect and remove weeds from fields using image processing, deep learning, and fuzzy logic-based classification techniques. In addition to weed detection and removal, the review paper also examines research on agrobots that monitor the health of crops. Moreover, the paper also discusses various techniques for path planning and control for autonomous agricultural vehicles. Finally, the review paper analyzes the role of single-board computers such as Raspberry Pi in agriculture. Overall, this review paper presents a comprehensive analysis of the latest research in smart weed detection and health monitoring agrobots. By examining the various techniques, methodologies, and algorithms employed by researchers, this paper offers valuable insights for future research and development in this field.

Tropical rainforests, characterized by their remarkable biodiversity and critical role in climate regulation, face unprecedented threats from deforestation. This research seeks to comprehensively explore the ecological consequences of deforestation in tropical rainforests by synthesizing existing literature and empirical studies. Our objectives encompass assessing the impacts on biodiversity, climate, and ecosystem services, while also examining conservation efforts and policy recommendations. The analysis of biodiversity impacts reveals that deforestation disrupts complex ecosystems, leading to species extinctions, altered ecological interactions, and genetic diversity loss. These effects resonate across taxonomic groups, affecting both well-known and lesser-known species. Deforestation’s relationship with climate change is a central concern. We find that tropical rainforests act as vital carbon sinks, and their degradation exacerbates global warming. Deforestation-induced changes in precipitation patterns and greenhouse gas emissions further highlight the interconnectedness of these ecosystems with climate dynamics. Ecosystem services, including water purification, pollination, and cultural values, are compromised by deforestation, impacting local communities and global society. Effective conservation strategies, such as protected areas and reforestation initiatives, offer hope, but face challenges of scale and implementation. Drawing on case studies from diverse tropical rainforest regions, we illustrate the variation in ecological consequences, emphasizing the need for context-specific solutions. Overall, It examines the causes and drivers of deforestation, the ecological functions of rainforests, and the impacts of deforestation on biodiversity, carbon cycling, climate, and local communities. The paper also discusses conservation efforts and policy implications for mitigating these consequences, this research underscores the urgent need for collective action to combat deforestation in tropical rainforests. The implications of this study inform policy recommendations, emphasizing the importance of international agreements and multi-stakeholder collaboration. Our findings highlight the imperative to protect these irreplaceable ecosystems to safeguard biodiversity, mitigate climate change, and preserve the ecosystem services they provide for present and future generations.

Coastal sediments and soils along Qua Iboe Terminal shoreline, Ibeno have suffered high potential of being contaminated by heavy metals leading to health risk. it is expedient to evaluate their levels in terms of contamination or /and pollution on the soils and sediments. The aim of this research was to determine the extent of contamination caused by heavy metals in the study area so as to assess its eco-toxicological risk and geochemical distribution pattern. Sediment and soil samples from Qua Iboe Terminal shoreline and its environs were collected and analyzed for Six heavy metals [Copper (Cu), Lead (Pb), Molybdenum (Mo), Zinc (Zn), Chromium (Cr), and Iron (Fe)] using Inductively Coupled Plasma- Mass Spectrometry (ICP-MS). The physicochemical parameters of sediments and soils were determined using standard methods. The results of all the physicochemical parameters determined were within maximum tolerable limits. The average heavy metals concentration in soil and sediment followed the order of Zn > Cr > Pb > Cu > Mo with corresponding values for soil and sediment of 55.60, 48.30, 21.80, 11.9, 3.4 mg/kg and 47.80, 35.00, 17.90, 11.74 and 5.4 mg/kg respectively. Statistical analysis revealed that Pearson Correlation were significant at r >0.500 between some metals. The anthropogenic influence on soil indicated low contamination by Cu, Cr, and Zn and moderate contamination by Pb and Mo, for both sediments and soils. The geo-accumulation index for most metals was of class 0 (uncontaminated) except for elements like Pb and Mo. The enrichment factor had values less than 1.5 for both soil and sediments showing deficiency to minimal enrichment. The pollution load index (PLI) for sediment were < 1 indicating low pollution status and > 1 for soil at locations 4, 6, 8, 9, 13, 14 and 15 indicating moderate pollution status in some sites. Evaluated mean contamination factor revealed moderate contamination for Pb and Mo for both soil (1.71 & 3.43) and sediment(1.09 &1.14). On the basis of the analytical data available, areas in close proximity to the offshore installations (Utana/Iwokpom/Opulum Creek) have the greatest impact on sediment and soil in the study area.

Climate change could alter forest disturbances, such as insect outbreaks, invasive species, wildfires, storms, and disease. In the present study, the influence of climate change on the structure and decline of Persian oak in the Zagros forest stands is scrutinized. A regular random sampling with fixed sample plots through two sampling periods was applied in 1998 and 2018. Further, the precipitation, temperature, frost, and relative humidity climatic elements pattern was studied from 1988 to 2018. The cross-sectional of standard-form oak was decreased from 19.2, 23.3, and 24 m3.ha in the first sampling stage to 12.3, 16.8, 17.8 m3.ha in the second sampling in Vizhenan, Chelleh, Ghalajeh, respectively. Levene’s test for equality of variances and mean comparison findings via an independent t-test showed a significant difference between the compared groups in 1998 and 2018. The Mann-Kendall analysis results of climatic elements showed that the precipitation, frost, and relative humidity decreased, and the temperature increased during the study period. Noticeable stability was perceived in the studied indices in the first decade and partially in the second decade, but there were many fluctuations in the third decade. According to the findings, it can be assumed that climate change and the occurrence of severe drought stresses cause oak trees to suffer from physiological vulnerability.