Sediment and Nutrient Retention in Ponds on an Agricultural Stream: Evaluating Effectiveness for Diffuse Pollution Mitigation

Integrated water quality dynamics in Wadi Hanifah: Physical, chemical, and biological perspectives

The Wadi Hanifah, a crucial aquatic ecosystem, has unfavorable consequences from natural occurrences and human activities. Recognizing the critical need for sustainable water management, this study provides an in-depth evaluation of wadi water quality. A comprehensive assessment was conducted, analyzing physical properties (temperature, pH, electrical conductivity, turbidity, color, and odor), chemical constituents (nitrogen compounds, ion concentrations, heavy metals), and bacterial diversity. The study found significant temperature fluctuations, particularly in sun-exposed or stagnant water areas. The water exhibited slight alkalinity and variable electrical conductivity and turbidity, indicating differing pollution levels. High ammonia and heavy metal concentrations suggested organic and industrial contamination, respectively. In addition, the prevalent fecal-indicator bacteria pointed to possible sewage or agricultural runoff. The research highlights the complex interplay of natural and anthropogenic factors affecting Wadi Hanifah's water quality. It emphasizes the need for location-specific environmental management strategies focusing on pollution control and conservation to safeguard the wadi's ecological health. This study provides vital insights for effective water resource management in Wadi Hanifah, serving as a model for similar ecosystems.

Evaluation of pollutant removal efficiency of urban stormwater wet ponds and the application of machine learning algorithms

Wet ponds have been extensively used for controlling stormwater pollutants, such as sediment and nutrients, in urban watersheds. The removal of pollutants relies on a combination of physical, chemical, and biological processes. It is crucial to assess the performance of wet ponds in terms of removal efficiency and develop an effective modeling scheme for removal efficiency prediction to optimize water quality management. To achieve this, a two-year field program was conducted at two wet ponds in Calgary, Alberta, Canada to evaluate the wet ponds' performance. Additionally, machine learning (ML) algorithms have been shown to provide promising predictions in datasets with intricate interactions between variables. In this study, the generalized linear model (GLM), partial least squares (PLS) regression, support vector machine (SVM), random forest (RF), and K-nearest neighbors (KNN) were applied to predict the outflow concentrations of three key pollutants: total suspended solids (TSS), total nitrogen (TN), and total phosphorus (TP). Generally, the concentrations of inflow pollutants in the two study ponds are highly variable, and a wide range of removal efficiencies are observed. The results indicate that the concentrations of TSS, TN, and TP decrease significantly from the inlet to outlet of the ponds. Meanwhile, inflow concentration, rainfall characteristics, and wind are important indicators of pond removal efficiency. In addition, ML algorithms can be an effective approach for predicting outflow water quality: PLS, GLM, and SVM have shown strong potential to capture the dynamic interactions in wet ponds and predict the outflow concentration. This study highlights the complexity of pollutant removal dynamics in wet ponds and demonstrates the potential of data-driven outflow water quality prediction.

Low water level drives high nitrous oxide emissions from treatment wetland

Wetlands that are restored for carbon sequestration or created for water treatment are an important sources of greenhouse gases, especially methane. The emission of nitrous oxide (N₂O) from these systems is often considered negligible due to the inundation and anerobic conditions that support complete denitrification. We used closed chamber method to analyze N₂O fluxes over a long-term period across heterogeneous wetland ecosystem constructed for treating nitrate-rich agricultural runoff. Our results showed that the water depth and temperature were most important factors affecting high N₂O emissions. The shallow areas where water depth was less than 9 cm created N₂O hot spots that emitted 48.8% of the total wetlands annual emission while only covering 6% of the total area. The annual emission from shallow-water hot spots with dense helophytic vegetation was 4.85 ± 0.5 g N₂O-N m^-2 y^-1 while it was only 0.37 ± 0.01 g N₂O-N m^-2 y^-1 in deeper zones. While the water depth was the main factor for high N₂O emissions, the temperatures increased the magnitude of the flux and therefore summer droughts and water drawdown created even larger hot spots. These results also suggest that IPCC benchmarks could underestimate N₂O emission from shallow waterbodies. Thus, it is important that the shallow zones and water level drawdown in the created or restored wetlands is avoided to minimize the N₂O flux.

Elements’ Content in Stream Sediment and Wildfire Ash of Suburban Areas in West Attica (Greece)

The composition of sediments and other materials occurring in streams, geochemical processes within the drainage basin, and various land uses are among the main factors influencing stream water composition. Stream sediment and wildfire ash samples were gathered from the area studied. The applied methodology consists of aqua regia and Diethylene-Triamine-Penta-Acetic acid (DTPA) chemical extraction; Cation Exchange Capacity (CEC), pH, and soil organic matter (OM) determination; a Geographic Information System (GIS) database; factor analysis; and determination of the contamination factor (CF) for the assessment of contamination degree. This study aimed to evaluate the elements’ content in stream sediments of Kineta and Nea Peramos areas (West Attica, Greece) and investigate any relationship between elements (aqua regia and DTPA extracted) in stream sediment and ash in wildfire and flood-impacted areas. The stream sediments’ properties, the bioavailable forms of copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn), and the total content of other potentially toxic elements in wildfire ash samples, are discussed. This research estimated moderate contamination for FeDTPA, MnDTPA and ZnDTPA in stream sediments of the study area. Contamination for CuDTPA and moderate contamination for ZnDTPA in the stream sediments of the Nea Peramos area was recorded. Factor analysis results suggested that the contents of FeDTPA, MnDTPA, CuDTPA and ZnDTPA in the study area’s stream sediments may affect the chemistry of stream water.