Coastal areas are where land interacts with the sea, with many complicated processes taking place, especially where mangroves exist. Governed by various drivers, sediment movement also plays an important role regarding hydrodynamics, the ecological environment, aquaculture, fisheries, etc. The suspended sediment concentration (SSC) is often considered to affect water quality, mangrove development and sometimes landscape attraction. To determine the SSC, direct measurements and/or sampling and laboratory tests are required. Therefore, it is costly to estimate the SSC continuously and in large areas.

In contrast, Remote Sensing (RS) inversion can possibly provide observations simultaneously at a large scale and within a specific period. As a result, the inversion of the SSC in the
surface layer of coastal areas using RS techniques has become more and more reliable and popular. The Moderate-Resolution Imaging Spectroradiometer (MODIS) is a satellite-based sensor that is used for measurements of the Earth and climate. This study singularly retrieves the red band of MODIS images to provide the values of reflectance, along with data derived from field measurements, for a coastal mangrove area of Bac Lieu province, part of the Mekong Delta. These two datasets are then analyzed to explore any relationship between them. As a result, a regression model is proposed to predict the values of the SSC at the same location for periods without field data.

This study investigates the temporal and seasonal trends of key climatic parameters within the Doon Valley, including maximum temperature (°C), minimum temperature (°C), precipitation (mm), and relative humidity (%). Utilizing data from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) Modern Era Retrospective-analysis for Research and Applications (MERRA-2), we analysed daily, monthly, and annual datasets spanning from 1981 to 2023 using R software version 4.4.1. The analysis covers four distinct seasons: Pre-Monsoon (March to May), Monsoon (June to September), Post-Monsoon (October to November), and Winter (December to February). Significant temporal variations (P < 0.05) were detected for all assessed parameters. Notably, maximum temperatures during the Monsoon season, particularly in June, exhibited a significant decline of -1.99°C (6.30%) from 1981 to 2023. In contrast, minimum temperatures increased significantly during the Post-Monsoon season, rising by 2.85°C (24.57%). Precipitation rates and occurrences saw a substantial increase during the Monsoon season, with a notable rise of 384.48 mm (42.65%) in July and August. Additionally, relative humidity showed a significant increase across all seasons, rising by 24.50%. These findings highlight the major climatic alterations in the Doon Valley during the last four decades. These findings provide insights into changes in temperature and precipitation trends, which could affect water resources and aggravate environmental hazards. This underscores the need for effective water resource management and climate adaptation strategies to mitigate these effects on the ecosystem and local communities.

Studies on wastewater treatments have improved the quality of effluents, minimizing risks to health and the environment. Among significant innovations, membrane bioreactors (MBRs) represents a reliable and efficient technology, which has become an alternative to traditional activated sludge processes.
However, membrane investment costs and membrane fouling, with its consequences for plant maintenance and energy consumption, limit the wide application of MBRs. For this reason, studies are still underway to control fouling and minimize costs.
Recent investigations have shown that the application of electrochemical processes to MBRs (eMBRs) represents a promising technology for fouling control.
In recent years, the scientific community has also focused its attention on the use of low-cost self-forming dynamic membranes (SFDM), whose distinction from traditional membranes is the high pore size (10-200 μm). Using these membranes leads to a significant cost reduction. However, the large pore size limits their application since effluents obtained at the start of the process are not of high quality.
In the present study, the performance of SFDM was investigated, integrating it in a conventional MBR and in an eMBR operating at different current densities. Both pollutant removal efficiencies and fouling control have been assessed as a function of the applied current density. This extremely innovative hybrid system is able to reach high effluent quality, allowing its reuse, reducing fouling significantly, and, at the same time, decreasing the high costs of traditional membranes.

Efficient irrigation management is a critical factor for enhancing agricultural productivity and conserving water resources, particularly in semi-arid regions such as the Lower Chenab Canal (LCC) region of Punjab, Pakistan. This study explores the integration of remote sensing technology and machine learning algorithms to develop a sophisticated irrigation management system aimed at optimizing water use and improving crop yields. This research involved collecting high-resolution satellite imagery, climatic data, and field observations to monitor crop health, soil moisture levels, and evapotranspiration rates across different cropping seasons. These data were utilized to train machine learning models capable of predicting crop water requirements with high accuracy. The core of our methodology lies in the application of various machine learning algorithms, including Random Forests, Support Vector Machines, and Neural Networks, to analyze the complex interactions between climatic variables, soil properties, and crop phenology. The predictive model developed was used to generate dynamic irrigation schedules tailored to the specific needs of different crops and growth stages. Field trials were conducted across multiple farms in the LCC region, comparing the performance of our technology-driven irrigation management system with the traditional irrigation practices. The results demonstrated a significant reduction in water usage, with up to 30% savings achieved without compromising crop yields. Additionally, the optimized irrigation schedules contributed to improved soil health and reduced incidences of waterlogging and salinity. This study highlights the potential of remote sensing and machine learning technologies to transform irrigation management, offering a scalable and cost-effective solution for farmers in water-scarce regions. By providing real-time insights and actionable recommendations, our approach empowers farmers to make informed decisions, promoting sustainable agricultural practices and ensuring long-term water resource sustainability.

Introduction

Water contamination is a pressing challenge at the global scale. Along with other neighbours, Pakistan is not an exception among other countries, where protecting water resources and battling pollution are critical issues. Significantly, urbanization and industrialization due to the exponential growth of the population is intensifying the situation. So, public understanding of pollution and its implications is necessary. This is why this study sought to evaluate the public's understanding of water quality, contamination sources, and related obligations in Islamabad, Pakistan.

Materials and Methods

An investigation of the various sociodemographic elements influencing the perception of water pollution (POWP) was carried out through a questionnaire survey. These variables were used in conjunction with the statistical analysis programs SPSS and R to examine the effects of these variables on the community's opinion of the quality of the drinking water and how it affects human health.

Results

The results show that between 60 and 70 percent of the people in the research region were aware of the quality of the water and the risks that are linked with it. Notably, 76 percent of them agreed that there is a connection between water pollution and health problems. Higher income and educational levels were associated with a noticeably better comprehension of the health effects of water pollution. Worrisomely, diarrhoea turned out to be the most common waterborne illness.

Conclusion

This study highlights the potential use of public perception for policymakers in developing interventions for water management and behavioural change strategies, emphasising social and technological drivers, in addition to tackling the urgent issue of water pollution and scarcity.

The increasing demand for water resources and sustainable water supply planning have sparked interest in reusing produced water, offering economic, social, and environmental benefits, especially in water-scarce regions. Genome editing technologies are revolutionizing wastewater treatment, removing contaminants, heavy metals, and hydrocarbons, benefiting water environmental fields and attracting increased interest in the oil and gas industry. Genome editing techniques may lead to the development of microbial bioremediation technologies, crucial for controlling environmental pollution in aquatic environments, despite challenges posed by variable and stressed environments. Genomic engineering techniques optimize microbial metabolic pathways and enhance enzymatic activities, but rely on selectable markers like herbicide, anti-metabolite, and antibiotic resistance genes (ARGs), posing environmental risks . This paper explores the use of TALEN, ZFNs, and especially CRISPR Cas9 as gene editing tools for wastewater bioremediation, focusing on specific microbes. The CRISPR technique enhances genome editing efficacy, versatilities, and orthogonalities by complementing targeted sequences with gRNA and associated protein’s endonuclease activities. CRISPR techniques are not yet widely used in the field of water environment microbiology due to its complexity, heterogeneity, and variability. CRISPR technologies may bring breakthroughs in environmental microbial technologies, but future efforts must minimize off-target cleavage, expand PAM recognition, and mitigate gene release risks. This review highlights the practical application of CRISPR techniques in water environmental microbiological processes, including microbial community balance engineering, wastewater treatment system optimization, and selective pollutant-to-chemical bioconversion.

Abstract:

Introduction: Flash floods are among the most devastating natural disasters, impacting both rural and urban areas by causing extensive damage to communities, infrastructure, and livelihoods. Effective flash flood mapping is essential for assessing risks in flood-prone regions, involving a comprehensive decision-making framework that considers multiple criteria. Globally, flash floods result in significant casualties and damage, underscoring the need for enhanced mapping and damage assessment to mitigate their impacts. In Pakistan, the Dera Ismail Khan (DI Khan) district is particularly vulnerable to flash floods, with the 2022 event marking the most severe flood in the district's history.

Materials and Methods: To address this, we employed Remote Sensing (RS) and Geographic Information System (GIS) techniques to map the spatiotemporal dynamics of the flood and assess the resulting damage. Landsat 9 data were utilized for flood mapping and damage assessment. We developed flood maps for pre-flood, during-flood, and post-flood scenarios using the Modified Normalized Difference Water Index (MNDWI). Land use and land cover were classified using the Supervised Maximum Likelihood technique, with spatial training and validation samples sourced from Google Earth Pro.

Results: Our results indicate that the flood inundated an area of approximately 2,876 km², lasting around one and a half months, and caused substantial damage to agricultural lands and built-up areas.

Conclusion: This study provides valuable insights into flood management in DI Khan, offering strategies to improve flood management practices. Furthermore, this approach can be applied to analyze flash floods in other regions globally, enhancing our ability to manage and mitigate the impacts of such events.

Successful hydraulic infrastructure design necessitates information about future precipitation conditions, i.e., the return period for a specific event magnitude, which is why we need to treat rainfall as a random variable controlled by a distribution law. Our research addresses the sampling uncertainty associated with parameter estimation of the GEV distribution, providing a comparison between at-site and regional approaches. Regional frequency analysis was performed in the region of Thessaly, Greece, using annual maximum rainfall data from 55 rain gauges. To identify statistically homogeneous regions, four spatial covariates (rain gauge elevation, mean annual precipitation, latitude, and longitude) were used as inputs to the principal component analysis followed by the k-means clustering algorithm. We then calculated the parameters and their confidence intervals using two distinct methods. For the regional estimates, we used Bayesian MCMC sampling using the metropolis algorithm with non-informative priors, while for the at-site estimates, we used a typical L-moments procedure along with bootstrap resampling. Lastly, we compared the results to identify relationships with the aforementioned covariates. The results indicate that the gauge elevation and the mean annual precipitation are the best covariates. The location and scale parameters show a strong correlation with these covariates, while the shape parameter does not. The study procedure demonstrates the sampling uncertainty of the regional frequency analysis method for precipitation maxima. The results will be useful for hydrologic studies in the area.

Agriculture contributes 24% of Pakistan's GDP, serving as the backbone of the country's economy. In 2022, agriculture contributed around 22.5% to the GDP of Pakistan. This sector provides food and raw materials for the people of Pakistan, but it faces many problems despite its importance. In traditional agriculture, soil serves as the growing medium, providing the necessary nutrients and minerals for optimal plant growth. However, fertile land is decreasing rapidly due to soil erosion, land pollution, and urbanization, which reduces agricultural productivity. As fertile land becomes infertile and the world population continues to grow, reaching an estimated 9.7 billion by the end of 2050, alternative solutions are necessary to meet the food demand. This necessity leads us to the hydroponic system, a promising method that relies on a nutrient solution for plant growth. This final year project is an important step in promoting sustainable agriculture and effective food production. In hydroponics, growing media such as perlite, rockwool, and coconut coir are used. The roots are submerged in a nutrient-rich solution, where plants absorb the necessary nutrients. Soil-less farming practices have shown very promising results globally, especially in areas with low fertility, due to improved space and water-conserving techniques that minimize environmental effects. The primary focus of this final year project on hydroponic systems for kitchen gardening is to provide access to fresh, 100% organic homemade vegetables and fruits, regardless of the user's agricultural knowledge. Plants can achieve 20-25% higher yields compared to the traditional soil-based method, with a productivity range of 2-5 times greater.

The city of Terre Haute, Indiana, faces significant challenges with its combined sewer system (CSS), which frequently results in combined sewer overflows (CSOs) during heavy rainfall events. These overflows lead to the discharge of untreated wastewater into local water bodies, posing environmental and public health risks. This study focuses on the application of the Storm Water Management Model (SWMM) to analyze and propose solutions for the CSO problem in Terre Haute. The SWMM was employed to simulate various hydrologic and hydraulic conditions within the CSS, assessing the impact of different storm events and potential mitigation strategies. Key parameters such as rainfall intensity, sewer capacity, and land use were incorporated into the model to ensure accurate representation of the system’s performance. Over the course of the study, we created a model in SWMM consisting of 9 subcatchments, 42 conduits, 33 junctions, and 11 outfalls for the city of Terre Haute. The simulation results identified critical areas prone to overflow and evaluated the effectiveness of various control measures, including green infrastructure, storage tanks, and sewer separation. The findings suggest that a combination of these measures can significantly reduce CSO occurrences, improving water quality and compliance with environmental regulations. Future work will focus on optimizing the model and aligning the model with the new plans based on city of Terre Haute’s CSO Long-Term Control Plan.