Effect of precipitation and sediment concentration on the loss of nitrogen and phosphorus in the Pasikhan River

Natural and anthropogenic factors influence the entry of pollutants into surface waters and their accumulation in aquatic ecosystems. This study aimed to investigate precipitation and sediment concentration on the outflow of different forms of phosphorus (P) and nitrogen (N) in three primary land-use types along the Pasikhan River, the biggest river entering the Anzali Wetland in the Southern Caspian sea. Water sampling was performed on a monthly basis during the time bracket of 2017–2018. Different forms of P including total, soluble, particulate, total reactive, and dissolved reactive, and total Kjeldahl N, soluble N, particulate N, and were determined in the water samples. Total phosphorus and total Kjeldahl nitrogen contents lay within the range of 2.2–4.7 and from 0.14 to 0.33 mg l−1, respectively, downstream of the river. The highest monthly outflow of P from the watershed at the Agriculture station was recorded in October. Substantial conformity was found between the monthly trends of and and the trend of precipitation. The results indicated that sediment load intensified after an increase in the rainfall rate, leading to elevated N and P concentrations in the river water, mainly as particulate phosphorus and soluble nitrogen. It can also be inferred from the result that the concentration of N and P is directly related to the sediment concentration increase due to the rainfall. Increasing levels of nutrients such as N and P in the Pasikhan River can cause eutrophication in the Anzali Wetland, which needs conservative measures for reducing these elements' dynamic in the watershed.

Statistical approaches to explore the linkages between physicochemical parameters and BQEs, and set river nutrient threshold concentrations in Hungary

The objective of the Water Framework Directive (WFD) is to achieve good ecological status in surface waters by 2027. To make a proper evaluation of the ecological status of watercourses, it is necessary to harmonize class boundaries for chemical and biological quality elements (BQEs). This paper aims to explore the linkages between physicochemical parameters and BQEs and set river nutrient threshold concentrations that support good ecological status. Regression and mismatch methods were applied to find the relationship between phytoplankton (PP) and phytobenthos (PB) environmental quality ratio and mean total phosphorus (TP) and total nitrogen (TN) concentrations. Nutrient thresholds have been suggested for several water types, which are varied in the case of highland rivers 1.8–6.2 mg TN/l, 180–400 μg TP/l; in the case of lowland rivers 1.4–5.0 mg TN/l, and 100–350 μg TP/l. These values are similar to what other studies found, but the relationship between biology and nutrients was weaker. Besides nutrients, additional data of measured dissolved organic carbon, 5-day biochemical oxygen demand, chemical oxygen demand with potassium permanganate method, and information about hydromorphological features were involved in the analysis. The research demonstrates that random forest can be used as a nonlinear, multiparametric model for predicting biological class from five variables with 35–81% error for PP and with 18–47% error for PB.

Effects of organic carbon content on in situ remediation time using steam–air injection

Groundwater contamination by chlorinated hydrocarbons (CHC) is a common phenomenon that poses health risks to both humans and animals. These halogenated hydrocarbons infiltrate into the soil matrices and form pools at the bottoms of the aquifers thus contaminating the groundwater sources. Thermally enhanced soil vapour extraction (TSVE) using steam–air injection has gained popularity as an alternative technique to remediate the saturated and vadose source zones contaminated with CHC. This technique has been successfully applied in the remediation of contaminated sites (brownfields, industrial sites) and groundwater. However, the presence of organic carbon (OC) contents within the soil matrices has not been intensively studied. This paper, therefore, intends to contribute toward increasing the understanding of the effects of OC on the remediation time using TSVE. A 2-D flume experimental model was conducted in VEGAS laboratory using coarse sand, fine sand and silty soil with 0, 1 and 2% addition of the activated carbon as OC to investigate the desorption time of PCE and TCE as CHC during TSVE extraction using steam–air injection. 100 kg of soil mixed with the activated carbon was treated with 50 g TCE and 50 g PCE and then remediated using TSVE. The remediation times were recorded and recovered CHC was documented. It was discovered that the presence of OC enhanced the adsorption of the CHC onto the soil matrices thereby increasing the time required for the complete remediation of the contaminant from the soil. An increase of OC by 1% resulted in desorption time by a factor of 4–7.

Design characteristics of showering aeration system

The showering aeration system (SAS) was designed and its performance was evaluated by conducting the aeration experiments in a tank of dimension 2 × 4 × 1.5 m. Initially, the aeration experiments were conducted to optimize the radius of curvature of the SAS with different values, such as = 0, 5, 10, 15, and 20 mm, and maintain other geometric parameters, i.e. number of holes in the shower (); height of water fall (H); diameter of the shower hole (d); volume of water under aeration (V) and water flow rate (Q) as constants. The optimum radius of curvature () was found to be 10 mm. The aeration experiments were further conducted with four different non-dimensional geometric parameters such as the number of holes , the ratio of the height of water fall to the length of shower arm the ratio of the diameter of the hole to the length of shower arm and the ratio of the volume of water to the cube of the length of shower arm The Response Surface Methodology and Box–Behnken Design were used to optimize the non-dimensional geometric parameters of the SAS to maximize the Non-Dimensional Standard Aeration Efficiency. The result indicates that the maximum NDSAE of 16.98 × 106 was obtained from the SAS performance at = 80; = 2; = 4 and = 48. HIGHLIGHT The optimized non-dimensional geometric parameters (H/l; d/l; V/l3; n) for the showering aeration system were experimentally validated, and the final NDSAE value was found to be 16.98 × 106 against the predicted NDSAE value of 17.70 × 106.

Data-driven and model-based framework for smart water grid anomaly detection and localization

With increasing adoption of advanced meter infrastructure, smart sensors together with SCADA systems, it is imperative to develop novel data analytics and couple the results with hydraulic modeling to improve the quality and efficiency of water services. One important task is to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. In this paper, a comprehensive solution framework has been developed for anomaly detection and localization by formulating and integrating data-driven analytics with hydraulic model calibration. Data analysis for anomaly detection proceeds in multiple steps including the following: (1) data pre-processing to eliminate and correct erroneous data records, (2) outlier detection by statistical process control methods and deep machine learning, and (3) system anomaly classification by correlation analysis of multiple sensor events. Classified system anomaly events are subsequently localized via hydraulic model calibration. The integrated solution framework is developed as a user-friendly and effective software tool, tested, and validated on the selected target areas in Singapore.

Integration of anammox into the MBR process for main stream sewage treatment

The integration of Anaerobic ammonia oxidation (anammox) into the membrane bioreactor (MBR) process (AX-MBR) is proposed in this study to reduce operating costs. The temperature was not controlled during the study. Anammox, denitrification, and nitrification were studied in the AX-MBR for 210 days. The reactor was fed with mainstream sewage from Guilin City, China. The results showed that AX-MBR could run with reduced dissolved oxygen (DO) concentration, and COD, NH4+-N, and total nitrogen removal were maintained or improved. The microbial analysis results demonstrated that the added anammox sludge could survive in the AX-MBR, but the sludge microbial diversity decreased. Nitrospira, Candidatus Kuenenia, and Nitrosomonas dominated the anammox sludge. In a word, the AX-MBR developed in this study could treat mainstream sewage with the appropriate management, and the operation costs are expected to reduce by decreasing the amount of aeration.

Design and assessment of water source alternatives for Mariout 2 water treatment plant extension

Factors such as population increase and industrialization, coupled with the establishment of touristic villages, have necessitated an upgradation of water treatment plants (WTPs) in Egypt. In this study, three different water source alternatives were designed and compared with a simple decision matrix to select the most appropriate one for upgrading and extending the Mariout 2 WTP. The first two alternatives are located on the k-40 Alex-Cairo desert road and k-77 EL Nasr canal, respectively, where the water source is obtained from the Nile River. The third alternative is located at the k-51 Alex-Matrouh coastal road and a non-conventional seawater source is used. The design results showed that the required energy power of the first, second, and third alternatives were 0.31, 0.066, and 0.72 kw/purified m3, respectively. The operational costs of the first, second, and third alternatives were 0.665, 0.426, and 6.621 EGP/m3, respectively. The cost of the intake pipes was found to be the lowest for the third alternative, whereas it was found to be the highest for the first one. Based on the results obtained from the decision matrix, the third alternative was found to be the most appropriate alternative followed by the second one. This study may assist in making decisions regarding the water source selection and treatment methods for the extension of the fourth stage of the Mariout 2 WTP.

Minimum transport-driven algorithm for water distribution network partitioning

This paper presents a novel algorithm driven by the minimization of the transport function for the partitioning of water distribution networks (WDNs) into district metered areas (DMAs). The algorithm is based on the linear programming (LP) embedded inside a multi-objective genetic algorithm, which enables engineering criteria, such as the minimization of the boundary pipes and the maximization of the uniformity of DMAs, to be considered in the partitioning. Furthermore, the application of the algorithm on the dual network topology based on segments and valves guarantees that configurations of DMAs that respect the real positions of isolation valves for WDN partitioning are obtained. After being described on a small WDN, it is successfully validated on a large size WDN, proving better performance than other algorithms in the scientific literature for the generation of engineeringly appealing DMA configurations, with almost identical hydraulic performance to the unpartitioned WDN.

Rainfall prediction optimization model in ten-day time step based on sliding window mechanism and zero sum game

Rainfall is a precious water resource, especially for Shenzhen with scarce local water resources. Therefore, an effective rainfall prediction model is essential for improvement of water supply efficiency and water resources planning in Shenzhen. In this study, a deep learning model based on zero sum game (ZSG) was proposed to predict ten-day rainfall, the regular models were constructed for comparison, and the cross-validation was performed to further compare the generalization ability of the models. Meanwhile, the sliding window mechanism, differential evolution genetic algorithm, and discrete wavelet transform were developed to solve the problem of data non-stationarity, local optimal solutions, and noise filtration, respectively. The k-means clustering algorithm was used to discover the potential laws of the dataset to provide reference for sliding window. Mean square error (MSE), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute error (MAE) were applied for model evaluation. The results indicated that ZSG could better optimize the parameter adjustment process of models, and improved generalization ability of models. The generalization ability of the bidirectional model was superior to that of the unidirectional model. The ZSG-based models showed stronger superiority compared with regular models, and provided the lowest MSE (1.29%), NSE (21.75%), and MAE (7.5%) in the ten-day rainfall prediction.

Modeling the quality of sewage during the leaking of stormwater surface runoff to the sanitary sewer system using SWMM: a case study

This paper describes the application of the storm water management model (SWMM) for predicting the sewage quality in the sanitary sewer system of the study area resulting from the leaking of stormwater surface runoff to the system during rainfall events at different return periods. The concentrations of major pollutants were assessed in the sanitary sewer system at different rainfall intensities. Then, a solution to mitigate the problem was proposed using low impact development (LID) technology. The results of sensitivity analysis indicated that maximum build-up possible was the most sensitive parameter for model calibration. The model was calibrated using actual rainfall events, and statistical validation coefficients of R (0.81–0.82) and NMSE (0.0173–0.022) proved that the model is valid. The sewage quality assessment results showed that pollutants concentration increased to its maximum level at 20 min and gradually decreased to a slightly constant minimum value after 2 h. The proposed solution of LID reduced the pollutants concentrations by 82–88, 75–77, 52–55, and 7–10% for all pollutants at return periods of 2, 5, 10, and 25 years, respectively. To conclude, SWMM simulation successfully predicted the concentration of the pollutants, and leaking of stormwater surface runoff has changed the sewage quality.