ABSTRACT Conceptual diagram of three input streams: satellite remote sensing, IoT sensors, and citizen science. These inputs are processed using data fusion and a machine learning layer to provide near real-time water quality outputs and alerts. In semiarid and tropical regions, freshwater ecosystems are facing an increasing pressure from agricultural run-off, urban effluents, hydrologic alterations, and climate variability; yet, effective monitoring is hindered by sparse, irregular observations and limited institutional capacity. A total of 44 peer-reviewed studies from 2005 to 2025 (based on PRISMA 2020 guidelines) were collected for this systematic review, and the thematic synthesis of the results focuses on remote sensing (RS), machine learning (ML), Internet of Things (IoT) sensors, big data (BD) platforms, and citizen science to create near real-time monitoring capabilities for surface water quality. All studies originated from Sub-Saharan Africa (47%), Asia (32%), South America (14%), and other parts of the world (7%). RS had the highest percentage of use as a technology at 38%, ML and predictive modeling had a 25% use rate, IoT was at 14%, BD platforms accounted for 11%, citizen science made up 7%, and empirical methods at the watershed level made up the remaining 5%. RS-based models obtained estimates for chlorophyll a and turbidity using the Sentinel-2 and Landsat 8 satellites with R2 values between 0.72 and 0.94, while ML models (specifically random forest and support vector machine) maintained consistency in achieving root mean square error values below 10%. IoT and citizen science have the capacity to collect valuable local data but have not been adequately incorporated into satellite analytics or local calibrations with ML models. The main challenges to integrating these data sources into a coherent monitoring framework include (1) the absence of consistent time series; (2) the low resolution of satellite images; (3) very few ML models can be transferred between study areas; and (4) there is no interoperable platform to integrate and analyze multiple sources of information that can be used for surface water quality monitoring. Transitioning to a modular RS–ML–IoT framework that includes standardized citizen science methodologies and local calibrations of the ML models would enable scalable, context-specific early warning systems across all data-scarce regions, such as the East African Rift Valley.
ABSTRACT Mathematical models provide a powerful tool for estimating emerging contaminants (ECs) concentrations and enabling continuous observation of contamination levels in aquatic environments, serving as a practical tool for studying the transport, transformation, and fate of ECs within water systems. A systematic review integrating urban drainage systems and receiving water bodies is lacking. Furthermore, existing research on EC modelling is predominantly focused on rivers or watersheds, representing a relatively narrow scope of application. This paper reviews EC modelling studies across different water systems – including urban water systems (water supply networks, sewer networks, wastewater treatment plants) and surface water bodies (rivers, reservoirs, lakes, floodplains, wetlands, and tidal zones)—from the perspective of modelling methodologies, namely mechanistic models, data-driven models, and hybrid models. It provides insights on the aspect of model structural simplification, parameter uncertainty, dynamic variability representation, multi-pollutant interactions and current case study abundance. It also identifies emerging research hotpots in EC simulation and provides information on relevant EC databases. This study aims to identify gaps in current research and provide guidance for users in selecting appropriate study areas and models for practical water quality assessment and management.
ABSTRACT A comprehensive analysis of new technologies, challenges and trends in wastewater treatment plant modeling and control Wastewater treatment is a critical process for protecting water resources and ensuring environmental sustainability. The modeling of key parameters such as dissolved oxygen (DO), nitrogen (in various forms like ammonia, nitrite, and nitrate), plays a fundamental role in understanding and optimizing the performance of wastewater treatment plants (WWTPs). The present document offers a thorough review of modeling strategies for oxygen and nitrogen compounds, emphasizing the importance of predictive accuracy and process control. A comprehensive review of the extant literature reveals the current state of the field, while also analyzing recent research findings to identify gaps and limitations in existing models. This review employed a systematic approach to analyze 64 peer-reviewed studies (2021–2025) using PRISMA criteria to identify emerging trends in AI-driven and hybrid modeling. The paper's conclusions offer a series of recommendations for the refinement of the model and its subsequent implementation in real-time monitoring systems. The review underscores recent advancements in data-driven, classical, and hybrid modeling strategies, with a particular emphasis on the increasing integration of artificial intelligence and machine learning techniques into conventional process models. Key trends, limitations, and research gaps are identified.
ABSTRACT Schematic illustration of transport and transformation pathways of pharmaceuticals, endocrine-disrupting compounds, and PFAS in aquatic systems, showing their release into surface water, movement through wastewater and groundwater compartments, interaction with sediments and biofilms, partial degradation or persistence under natural and engineered processes, and eventual accumulation or transport to downstream ecosystems, highlighting key fate processes including adsorption, dilution, biodegradation, photolysis, and long-range environmental dispersion. Pharmaceuticals, endocrine-disrupting compounds (EDCs), and per- and polyfluoroalkyl substances (PFAS) are widely detected contaminants of emerging concern that pose significant risks to aquatic ecosystems and human health due to their persistence, bioaccumulation potential, and resistance to conventional treatment processes. Originating from anthropogenic sources including municipal wastewater effluents, industrial discharges, and agricultural runoff, these contaminants are increasingly reported in surface water, groundwater, and wastewater systems worldwide. Their physicochemical properties, such as hydrophobicity, molecular structure, ionization state, and environmental stability, govern their mobility, sorption behavior, degradation pathways, and transformation processes. Beyond environmental persistence, these contaminants exert ecotoxicological effects, including endocrine disruption, altered reproductive function, behavioral changes in aquatic organisms, antimicrobial resistance development, and trophic transfer within food webs. Chronic human exposure through drinking water and dietary intake has been associated with endocrine disorders, immunotoxicity, liver dysfunction, reproductive impairment, and elevated cancer risk. This review provides a comparative synthesis of the environmental fate, transport mechanisms, transformation pathways, and bioaccumulation dynamics of pharmaceuticals, EDCs, and PFAS across aquatic matrices. Advances and limitations in analytical detection methods and environmental fate modeling are critically evaluated. The review also discusses regulatory challenges, risk assessment frameworks, and emerging mitigation strategies.
Groundwater is the primary drinking water source for peri-urban communities surrounding Islamabad, yet its quality remains insufficiently characterized. This study assessed the physicochemical and microbiological quality of groundwater from Mera Abadi, Saidpur Village, and Kalajar Village between March and June 2023. A total of 30 samples were analyzed using standard laboratory protocols for physical, chemical, and bacteriological parameters. Mean electrical conductivity exceeded the WHO guideline of 400 mu S/cm at all sites, reaching 402.9 +/- 30.7 mu S/cm (378-480) in Mera Abadi, 399.0 +/- 52.0 mu S/cm (336-499) in Saidpur, and 513.7 +/- 170.7 mu S/cm (368-884) in Kalajar. Total dissolved solids ranged from 262 to 630 mg/L, while measured total salinity (as dissolved salts) ranged from 173 to 427 mg/L. Alkalinity averaged 147.8-185.6 mg/L but reached 300 mg/L in some samples, exceeding recommended limits. Microbiological analysis revealed total bacterial counts ranging from 5 to >300 CFU/mL, with several samples, particularly from Kalajar, exceeding the acceptable limit of 100 CFU/mL; limited coliform presence was detected in selected samples. These findings indicate potential risks of waterborne diseases such as diarrhea, typhoid, and gastrointestinal infections. This study provides a comparative assessment of groundwater quality across three peri-urban settlements and identifies spatial variability in contamination patterns. The results highlight the need for household-level filtration, improved sanitation management, community awareness programs, and routine seasonal monitoring to reduce health risks and protect drinking water safety.
