An integrated approach to service delivery in water, sanitation, and solid waste management (SWM) represents a paradigm shift from traditional isolated systems; it is more holistic and flexible, potentially optimising climate resilience and addressing public health challenges. This research aims to explore the political economy factors that facilitate an integrated approach to water, sanitation and SWM in urban Uganda. The secondary objective is to assess policy responsiveness, overlaps, and gaps in addressing climate change impacts across the three subsectors. The study employs a qualitative research design, incorporating key informant interviews (N = 30), literature reviews, and analysis of policy documents. This study finds that systems remain siloed for several reasons, particularly unclear and often non-legal provisions governing sectoral integration, poor infrastructure, uncoordinated planning, overlapping mandates, limited social cohesion, limited financing, and a tendency toward corruption. These issues render integration almost non-existent in urban Uganda, despite some legal provisions that provide for it. The study suggests a strategic, coherent review of the legal framework and the impact assessments of national laws, emphasising meaningful cooperation while prioritising residents’ needs. The study recommends a market-based system, with the potential to generate income to co-finance and maintain a complex yet efficient integrated system. Additionally, a market-based approach can pool domestic resources to counterbalance international financial threats. Incremental capacity building through training for decision-makers and scientists is recommended to facilitate the integrated administration, operation, and maintenance of WASH-SWM services. Such an approach has significant potential for further positive impacts, including improved public health and climate resilience. Not applicable. Integration refers to the explicit linking of sectors such as water, sanitation and solid waste to achieve more holistic and sustainable outcomes [34]. Political Economy refers to a framework by which governments undertake decisions that guide specific actions to achieve specific goals [29]. This research adopts a Nexus approach and examines political economy through the lens of barriers to, or enabling conditions for, integration.
The increasing presence of contaminants in rivers, particularly those that are not regularly monitored, requires a proactive exploration of innovative approaches to improve water monitoring in aquatic ecosystems. Through the use of artificial intelligence (AI) and remote sensing technologies, new ways of monitoring and protecting water quality have been developed. The combined technologies enable organizations to make more thoughtful decisions about managing their aquatic environments because of their ability to offer real-time data at lower costs. The objective of this review paper is to examine how AI and remote sensing technologies enhance water quality monitoring and management systems. Recent research has shown that emerging techniques like generative adversarial networks further enhance these capabilities by improving data quality through augmentation and super-resolution. A scoping review of 60 studies published in 2012–2026 (Scopus, Web of Science and Google Scholar; PRISMA flow in Fig. 2) shows that research effort within Sub-Saharan Africa is highly concentrated: 30 studies (50
Reference evapotranspiration (ET₀) is an important variable for irrigation scheduling, agricultural water management, and hydrological applications. This study examines a Physics–Regularized Autoencoder (PRAE): a neural network that compresses daily weather variables into a small set of learned features while being encouraged to keep air temperature, relative humidity, and dew point temperature consistent with one another. The learned features were passed to XGBoost to estimate ET₀. Hourly weather data for Delhi, India, from 1 January 2000 to 31 December 2025 were converted to 9,497 daily records, and daily ET₀ was calculated with the FAO–56 Penman–Monteith method. The records were split in time order into 70
The need for cost-effective and sustainable treatment methods has increased due to the growing build-up of heavy metals in wastewater. Agricultural wastes that have been pyrolyzed have become important biochar precursors, offering inexpensive adsorbents with large surface areas, porous structures, and lots of functional groups for the removal of heavy metals. The modeling and optimization techniques used to remove heavy metals from wastewater and leachate using pyrolyzed agricultural wastes are the main topic of this comprehensive review of the literature. After searching Scopus and Web of Science using the PRISMA framework, 47 pertinent studies were found out of 626 records (2016–2026). The ideal pyrolysis temperatures are usually between 400 and 800 degrees Celsius. Common feedstocks include rice husk, wheat straw, coffee grounds, corn cobs, and banana pseudostem. For process optimization, traditional statistical techniques are still the most popular; nonetheless, this study highlights the potential uses of machine learning models. Additionally, the paper proposes the potential application of deep learning architectures for precise adsorption efficiency prediction and mechanistic understanding. Hence, future research should prioritize interpretable, ensemble, deep learning, small language models, and multi-agents to guide biochar design and operational optimization for heavy metal removal from wastewater.
Abstract Inter‑basin water transfer (IBWT) is increasingly used to address water scarcity in arid and semi‑arid regions, yet these projects involve complex trade‑offs among economic, social, environmental and technical objectives. This study develops a comprehensive decision‑making framework for selecting the optimal discharge of a water transfer tunnel from the Doogh River to the Gorganrud River, upstream of the Bostan Dam in Golestan Province, Iran. A coupled simulation‑optimisation approach is employed, integrating the Water Evaluation and Planning (WEAP) model with three Multi‑Criteria Decision‑Making (MCDM) methods: AHP, TOPSIS and VIKOR. Six discharge scenarios (Q‑0.19, Q‑0.57, Q‑0.76, Q‑0.91, Q‑1.06 and Q‑2.00 m³/s) are evaluated against twelve criteria across four sustainability dimensions. The baseline scenario (without water transfer) reveals a substantial unmet water demand of 30.06 MCM/year, underscoring the need for the transfer. Results show that while the highest‑discharge scenario (Q‑2.00) performs best from a purely technical perspective, the intermediate scenario (Q‑0.91) provides an optimal balance, achieving 98% of environmental flow requirements with an estimated annual pumping cost of 0.417 billion Tomans (≈ 9,930 USD). This scenario exhibits strong robustness in sensitivity analyses under varying electricity prices and criteria weights. The proposed framework is transferable to similar water‑transfer projects in other arid regions.
