Over 30 million people rely on Lake Victoria for survival in Northeast African countries, including Ethiopia, Eritrea, Somalia, and Djibout. The lake faces significant challenges due to changes in land use and climate. This study used multi-source remote sensing data in the Google Earth Engine (GEE) platform to create Land Use and Land Cover (LULC), land surface temperature (LST), and Normalized Difference Water Index (NDWI) layers in the period 2000–2023 to understand the impact of LULC and climate change on Lake Victoria Basin. The land use/land cover trends before 2020 indicated an increase in the urban areas from 0.13% in 2000 to 0.16% in 2020. Croplands increased from 6.51% in 2000 to 7.88% in 2020. The water surface area averaged 61,559 square km, which has increased since 2000 with an average rate of 1.3%. The “Permanent Wetland” size change from 2000 to 2020 varied from 1.70% to 1.83%. Cropland/Natural Vegetation Mosaics rose from 12.77% to 15.01%, through 2000 to 2020. However, more than 29,000 residents were displaced in mid-2020 as the water increased by 1.21 m from the fall of 2019 to the middle of 2020. Furthermore, land-surface temperature averaged 23.98 degrees in 2000 and 23.49 in 2024.

The increasing presence of pollutants, including pharmaceuticals and pesticides, in water resources necessitates the development of effective remediation technologies. Zeolites are promising agents for pollutant removal due to their high surface area, ion-exchange capacity, natural abundance, and diverse tailorable porous structures. This review focuses on the efficient application of modified zeolites and mesoporous materials as photocatalysts and adsorbents for removing contaminants from water bodies. The adsorption and photodegradation of pesticides and selected non-steroidal anti-inflammatory drugs and antibiotics on various zeolites reveal optimal adsorption and degradation conditions for each pollutant. In most reported studies, higher SiO₂/Al₂O₃ ratio zeolites exhibited improved adsorption, and thus photodegradation activities, due to increased hydrophobicity and lower negative charge. For example, SBA-15 demonstrated high efficiency in removing diclofenac, ibuprofen, and ketoprofen from water in acidic conditions. Metal doped into the zeolite framework was found to be a very active catalyst for the photodegradation of organic pollutants, including pesticides, pharmaceuticals, and industrial wastes. It is shown that the photocatalytic activity depends on the zeolite-type, metal dopant, metal content, zeolite pore structure, and the energy of the irradiation source. Faujasite-type Y zeolites combined with ozone achieved up to 95% micropollutant degradation. Bentonite modified with cellulosic biopolymers effectively removed pesticides such as atrazine and chlorpyrifos, while titanium and/or silver-doped zeolites showed strong catalytic activity in degrading carbamates, highlighting their environmental application potential.

San Francisco Bay’s sediment is currently monitored for a variety of contaminants; however, data regarding the microplastics (MPs) in the area are still scarce. MPs’ occurrence in sediment samples has gained recognition as a reservoir for MP accumulation. Moreover, Bay sediment is also an important matrix for monitoring because sediment tends to accumulate certain contaminants and act as a source of contaminants in the Bay food web. This study analyzed MPs ranging from 25 µm to 5 mm in surface sediment grab samples (n = 8) and two sediment core samples (n = 2 cores analyzed with 11 samples from different depths). Our findings provide an evaluation of MP levels in different regions of the bay. The MP levels detected in Bay surface grab samples ranged from 2.1 to 11.9 MPs/g dry weight (n = 8), with a mean value of 6.2 MPs/g. The most abundant morphology was fibers, followed by fragments and films.

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region.

This study investigated the concentrations of metals in stormwater runoff collected during two extreme flooding events on the American University of Sharjah (AUS) campus in the United Arab Emirates (UAE). Given the increasing frequency of intense rainfall in arid regions, stormwater contamination represents a growing environmental and public health concern. Stormwater samples were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) to quantify metal concentrations. The results showed that iron (0.049–2.080 mg/L), aluminum (0.097–2.020 mg/L), and potassium (0.614–3.860 mg/L) were the most abundant metals detected. Lower concentrations were observed for manganese (0.000–0.058 mg/L), barium (0.000–0.073 mg/L), chromium (0.000–0.013 mg/L), nickel (0.000–0.038 mg/L), and vanadium (0.000–0.004 mg/L). These findings underscore the critical need for effective stormwater management in arid regions, where climate change is expected to increase the frequency and intensity of extreme weather events. Improved drainage systems and long-term monitoring are essential to mitigate the environmental and public health risks posed by stormwater contamination in rapidly urbanizing areas.