The study was conducted to evaluate the physicochemical parameters of water and assess the public perception of the water quality status in the Tasik Chini watershed based on a community survey. The water sample was analyzed based on standard methods and categorized according to WQI (Water Quality Index). Multivariate statistical analysis was adopted to find spatial variations in water quality, determining the pollution level and sources of contamination. The study results were compared with NWQS (National Water Quality Standard for Malaysia). The results showed that the value of dissolved oxygen (DO) was low (4.68 mg.L-1), while the level of biological oxygen demand (BOD), chemical oxygen demand (COD), and total dissolved solids (TDS) was found to be high, 2.92 mg.L-1, 26.10 mg.L-1 and 22.93 mg.L-1 respectively. High turbidity was recorded in a mining area in the rainy season (35.76 NTU). The DOE-WQI value categorized the lake under class II and class III. The Principal Component Analysis (PCA) revealed that the major sources of contamination were due to anthropogenic activities, especially settlement, mining, agriculture, and illegal activities. Overall, Tasik Chini’s water quality status was classified as slightly polluted to highly polluted based on hierarchical cluster analysis (CA) results. The survey showed that 55% of the local community reported that the water quality was poor. The knowledge and attitude level of the local people was medium category, while community practice was low. The Pearson correlation coefficient test showed a strong significant relationship at 0.01 level between knowledge and attitude and knowledge and practices. The scientific findings with public perceptions might be useful for policymakers and the general public to improve the management system for a desirable future.

The research was carried out to determine the potential effect of microbiota, organic carbon, percentage of moisture content, and microbial biomass concentration as an evaluator of variation in basal soil respiration rate. Relative distribution and composition of the microbial population were estimated from six different chronosequence manganese mine spoil (MBO0, MBO2, MBO4, MBO6, MBO8, MBO10) and forest soil (FS). The variation was seen in moisture content (6.494±0.210-11.535±0.072)%, organic carbon (0.126±0.001- 3.469± 0.099)%, MB-C (5.519±1.371- 646.969± 11.428) μg.g-1 of soil. A positive correlation was shown between OC with MB-C (r = 0.938; p< 0.01) and moisture content (MC) (r = 0.962; p< 0.01). Variation in the basal soil respiration (BSR) and microbial metabolic quotients (MMQ) was shown to range between 0.352 ± 0.007- 0.958 ±0.014μg CO2-C.g-1 and 6.5× 10-3 - 1.481×10-3 μg CO2-C.g-1 microbial-C.h-1 with BSR: OC from (2.793-0.276)% respectively. This result shows that there is a gradual increase in OC, MC, MB-C, and BSR across seven different sites due to progressive enhancement in soil fertility that leads to the initialization of succession. Stepwise multiple regression analysis further confirms the degree of variability added by microbial biomass C, moisture content, organic carbon, and microbial population on basal soil respiration in microbes. Principal component analysis enables the differentiation of seven different soil profiles into independent clusters based on cumulative variance given by physico-chemical and microbial attributes that indicate the level of degradation of land and act as an index to restore soil fertility.

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.

The Indonesian government continues to accelerate the resolution of all problems related to the planning, infrastructure development, and arrangement of tourist visits, including the arrangement of parking spaces and commercial areas in the Borobudur temple area. The purpose of this study is to develop a waste management system in the parking and commercial areas of Kujon as an alternative to structuring the Borobudur temple area. The research method is a descriptive-qualitative observational approach. Surface water and groundwater examinations are carried out in laboratories and compared with quality criteria determined by the Indonesian government. Toxic and hazardous waste is stored in temporary facilities until it is collected by a company licensed by the Indonesian environmental ministry. The Shannon-Wiener Plankton and Benthos Diversity Index measures the diversity of organisms in a community. The study’s findings highlight the need to establish a waste processing facility based on the reduction, reuse, and recycling principles. Waste will be collected at a certain site and stored temporarily in line with the technical instructions for the Minister of Environment and Forestry’s Regulation. The findings of surface water and groundwater studies demonstrate that all measured parameters continue to meet the Indonesian government’s quality thresholds. Plankton Bioindicator Measurements: Plankton diversity index values range from 1.040 to 1.943, indicating moderate pollution, while benthos values range from 0.811 to 0.918, indicating weakly to moderately contaminated conditions. Sustainable environmental management is critical and should serve as a baseline for environmental quality in the activity area.

The majority of flood assessment and warning systems primarily focus on the occurrence of floods caused by river overflow, taking into account factors such as intense precipitation. Improving flood resilience, on the other hand, requires a deeper understanding of how these factors affect each other and how specific local conditions can have an impact. This study offers impartial tools for estimating the severity of the effects brought on by heavy rainfall to facilitate the prompt communication of effective measures, such as the evacuation of livestock and human settlements and the provision of medical assistance. These tools take into account the cascading effects of various causative factors contributing to heavy rainfall. This article aims to assess the various factors that contribute to the impacts of heavy rainfall, including the timestamp (indicating soil saturation and moisture levels), river gauges (determining water congestion in canal systems), average aerial precipitation (indicating runoff), and the rainfall itself, taking into account both in situ and ex-situ impacts. Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbour (KNN), and Naive Bayes are some of the machine learning methods used in the study to find out how dynamically vulnerable affected districts are to flooding in different compound scenarios. This analysis is conducted by leveraging historical observed datasets. The results demonstrate the feasibility of mitigating the issue of excessive and insufficient flood warnings resulting from the cumulative effects of intense precipitation. By implementing a categorization system that divides the affected areas into various portions, or districts, according to the main factors contributing to flooding, namely rainfall, river discharge, and runoff, The suggested model presents novel insights into the sequential consequences of intense precipitation in the regularly inundated regions of North Bihar, India. Innovative tools can serve as valuable resources for flood forecasters and catastrophe managers to comprehend the extent of flooding and the consequential effects of intense precipitation.

This paper examines electronic waste and cycling in Lebanon. It describes the current situation regarding e-waste among government agencies and non-governmental organizations. It addresses two research questions: The first one asks if the Lebanese society and government are aware of the dangers posed by electronic waste and whether any action has been taken to prevent an environmental catastrophe. The second question asks about Lebanese attitudes toward e-waste and whether they are willing to fight against it. Interviews provided the first question’s responses. The authors have visited Organization A and NGO B. The first is worried about gathering waste in more prominent Beirut, while the second targets spreading attention to e-waste’s risks on legislative and social levels the same. Question two was discussed through surveys filled out by arbitrary people from Lebanese society. The answers to both research questions came in a manner that demonstrates the two hypotheses expected toward the start of the study, specifically that e-waste represents an incredible danger to the Lebanese climate. Hypothesis two, if climate neighborliness and proclivity to right e-garbage removal rely upon the instructive level of some random resident, has been confirmed while analyzing the answers in the survey.

The present study aims to characterize the water and sediment quality of the Colorado and Alajua rivers within Ecuador’s Ambato River watershed, with a specific focus on the presence of heavy metals. Measurements were conducted at five sampling points along the upper and lower zones of each river, where both physicochemical and microbiological parameters, as well as concentrations of heavy metals in water and sediments, were analyzed. Most parameters exhibited statistically significant differences, as determined by the analysis of variance (ANOVA), between the values observed in the upper and lower zones of the micro-watersheds. Water quality in the mentioned rivers was assessed using specific water quality indices, WQI, namely the NSF-WQI and Dinius WQI. Additionally, the impact of heavy metal presence in the water and sediments was evaluated using the Heavy Metal Evaluation Index (HEI). While most parameters met the Ecuadorian quality standards for water sources intended for human consumption, concerns emerged regarding elevated levels of total and fecal coliforms along both rivers, which could limit the suitability of these rivers as a water source for human use and consumption. At various sampling points, water quality criteria for the preservation of aquatic life were not met for several heavy metals. For example, the Colorado River exhibited elevated levels of zinc (59-76 μg.L-1), copper (12-47 μg.L-1), lead (1.2-3.9 μg.L-1 ), iron (0.33-0.37 mg.L-1 ), and manganese (0.37-0.47 mg.L-1), while the Alajua River showed excess copper (11 μg.L-1), iron (0.61-0.72 mg.L-1), and manganese (0.62-0.98 mg.L-1). Geological factors likely contribute to the concentration of heavy metals in the upper segments of the rivers, while agricultural runoff may contribute to concentrations in the lower segments. Sediments exhibited higher average values of the Heavy Metal Evaluation Index (HEI) (20.6-26.7) compared to water samples (13.9-15.4), indicating a potential accumulation of heavy metals in the river sediments. Overall, both rivers exhibited contamination levels ranging from regular to moderate, as indicated by the calculated average Water Quality Indices (WQI), with certain areas showing slight contamination or meeting acceptable standards. These results highlight the influence of anthropogenic activities on water quality, emphasizing the necessity of continuous monitoring to assess and control their impact.

Environmental pollution is escalating due to inadequate waste management, with the open burning of agricultural waste being a significant contributor. This process releases various harmful gases into the environment. This study introduces an innovative approach to creating sound absorption materials using agricultural by-products, specifically paddy straw and coconut coir, along with newspaper by-products. The research was conducted in two phases: first, the production of sound absorption panels with different densities and adhesive quantities, and second, the evaluation of these panels’ sound absorption capabilities through laboratory experiments. The impedance tube test was used to determine the sound absorption coefficient (SAC). The results showed effective sound absorption, especially at lower frequencies ranging from 125 Hz to 6300 Hz. Notably, paddy straw and coconut coir exhibited significant sound absorption values at 1,000 Hz (0.59 and 0.52, respectively). This study highlights the potential of paddy straw and coconut coir as sustainable, cost-effective materials for sound absorption panels. These natural materials demonstrate excellent sound-absorbing properties, making them suitable for various applications such as classrooms, sound recording rooms, auditoriums, and theaters at low to medium frequencies.

Maize is one of the staple food crops after wheat and rice crops. There is a reduction in the yield of maize due to biotic and abiotic factors. Due to more spacing in maize weeds are highly infested in the field which leads to reduced fertility of soil and sustainability. To maintain the fertility of soil and reduce the wastage of resources intercropping is the best option. By growing crops in between the rows of maize crops we can increase production and can achieve zero hunger. A field experiment was conducted at Lovely Professional University (Kharif 2022) to check the effect of black gram and French bean as intercrop in maize on weed flora, rhizospheric bacterial count, and yield parameters of maize. The experiment comprised 9 treatments i.e. Sole maize, Sole French bean and Sole black gram, Maize + French bean (1:1, 1:2, 1:3), Maize + black gram (1:1, 1:2, 1:3). Weed density and biomass recorded by quadrant 1 m2 method at 30 and 60 DAS (Days after sowing). Results of the study showed that minimum weed count of grasses (3.44, 3.26), sedges (3.13, 2.73), and BLW (Broad leaf weed) (3.26, 4.58) at 30 and 60 DAS recorded in those plots where intercropping of maize and black gram practiced in 1:3 proportion. Rhizospheric bacterial count viz. THB (total heterotrophic bacteria) (232.82), NRB (nitrate-reducing bacteria) (41.89), and NB (nitrifying bacteria) (161.86) were recorded highest in Maize + French bean 1:3 at 30 DAS. Whereas THB, NRB, and PSB (phosphate solubilizing bacteria) highest count recorded in Maize + Black gram 1:3 at 90 DAS. In the case of maize yield attributes maize + Black gram 1:2 gave the best result. Land Equivalent ratio and Maize Equivalent yield (2.23, 11671.03 kg.ha-1) were recorded maximum in those plots where Maize + Black gram 1:2 proportion was practiced. Intercropping can be used as an eco-friendly alternative to herbicides to reduce the weed population and infestation, which leads to maintaining soil fertility and enhancing sustainability.

India’s fast industrialization and population expansion have resulted in heavy metal accumulation from many operations, which has caused massive waste generation and poisoning of soils. Therefore, it is necessary to design reclamation to improve th T.Ne soil. Phytoremediation presents itself as a viable, economical, and environmentally sustainable solution to this problem. This study was carried out by using plants namely, aloe-vera (Aloe-Barbadensis), tulsi (Ocimum Tenuiflorium), and vetiver (Chrysopogon Zizanoides) plants which were planted in a simulated soil of Cd, Zn and Pb, for 4 weeks. The sample of plant and soil were taken in 9 different pots, (15 cm diameter and 25 cm height) among 9 potted soils one will be tested as a controlled sample. An aqueous solution of lead, cadmium and zinc were added separately to the dry soil samples. The moisture level of the soil was maintained to near field water capacity (35.6%) and equilibrated for two weeks. The saplings of vetiver grass, aloe vera and tulsi were selected and pruned (the shoots were originally 20 cm high and the roots 8 cm long), and then transplanted into the pots. The AAS test was conducted after 4 weeks of growing in simulated soil. Tulsi demonstrated the highest efficacy in reducing Zn concentrations from 300 mg/kg to 188.3 mg/kg, followed by vetiver (179.3 mg/kg) and Aloe vera (158.3 mg/kg). Similarly, for Pb, tulsi exhibited the most substantial reduction (from 600 mg/kg to 188.3 mg/kg), followed by vetiver (164.3 mg/kg) and Aloe vera (179.6 mg/kg). Regarding Cd, tulsi reduced concentrations from 80 mg/kg to 18.62 mg/kg, while vetiver achieved a 17.62 mg/kg reduction. The result highlights Tulsi’s superior remediation potential, attributed to its efficient heavy metal uptake and translocation mechanisms. Thus, using these plants in the phytoremediation process, the heavy metals are extracted more economically than other plants. This technique highlights the innate ability of hyper-accumulator plant species, which flourish in situations high in heavy metals, to extract contaminants from contaminated soil.