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A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water and One Health".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 25760

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Prof. Dr. Harsha Ratnaweera

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Guest Editor

Faculty of Science and Technology, Norwegian University of Life Sciences, P.O. Box 5003-RealTek, 1432 Aas, Norway
Interests: water resources management; process surveillance and control; water and wastewater treatment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Scott A. Wells

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Guest Editor

Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Interests: surface water quality modelling; numerical methods; environmental fluid mechanics; solid-liquid separation processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid digitalization of the water sector is providing unparalleled opportunities for improved surveillance and modelling. Real-time water quality surveillance provides a unique insight into the aquatic environment and treatment processes, enabling better understanding and leading to environmental and socioeconomic benefits for various stakeholders. Innovative monitoring concepts, in combination with advanced data sciences, provide a whole new world of surveillance and modelling opportunities to water professionals. Lab-on-a-chip, noninvasive analysis, surrogate measurements, and various spectrometric tools are examples. They also make water quality monitoring more accurate and affordable.

Enhanced water quality monitoring provides a unique and affordable way for the early warning of anomalies—both natural and man-made. The broader availability and use of real-time monitoring, enabling the advanced automation of critically important processes, also make them more vulnerable to process failures as a result of instrumental errors due to both natural disturbances and human-made disasters.

In this context, it is important to share new knowledge in order to contribute to the development of the water sciences. The purpose of this Special Issue is to create a forum to share the latest innovations and research among the various stakeholders in the water industry.

Prof. Dr. Harsha Ratnaweera
Prof. Dr. Scott A. Wells
Guest Editors

Manuscript Submission Information

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Keywords

water quality
real-time monitoring
surveillance
process control
lab-on-a-chip
virtual sensors
noninvasive sensors

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Published Papers (6 papers)

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Research

25 pages, 6418 KiB

Open AccessArticle

Optimal Control Strategy of a Sewer Network

by Iulian Vasiliev, Laurentiu Luca, Marian Barbu, Ramon Vilanova and Sergiu Caraman

Water 2022, 14(7), 1062; https://doi.org/10.3390/w14071062 - 28 Mar 2022

Cited by 6 | Viewed by 2682

Abstract

This paper proposes a series of methods to increase the efficiency of the operating of a sewer network that serves a medium-sized city with a population of 250,000 inhabitants. The sewer network serves five areas of the city and consists of seven tanks that communicate with one another and with the treatment plant through pipes. The controls are applied to the process by valves and pumps. The main objective of this paper is to determine the optimal controls to minimize two performance criteria: volume of overflow, and overflow quality index. The sewer network was modeled in the BSMSewer environment. The optimization of the operating of the sewer network was carried out in the conditions of an influent computed in relation to the number of inhabitants and to the area served, using genetic algorithms as a method of optimization. Five optimization strategies were analyzed by numerical simulation. The analysis of the five strategies was done by comparison of their results with one another, as well as in relation to the case where all of the controls were set at maximum values of 100%. The simulations showed that the third strategy produced the best results in relation to each of the two criteria. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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25 pages, 2515 KiB

Open AccessArticle

Modeling Cyanobacteria Vertical Migration

by Corina Overman and Scott Wells

Water 2022, 14(6), 953; https://doi.org/10.3390/w14060953 - 18 Mar 2022

Cited by 8 | Viewed by 3173

Abstract

Cyanobacteria often cause harmful algal blooms and release toxic substances that can harm humans and animals. Accurately modeling these phytoplankton is a step towards predicting, preventing, and controlling such blooms. Certain cyanobacteria species are known to migrate vertically in the water column on a daily cycle. Capturing this behavior is one aspect of modeling their dynamics. Previous studies on modeling cyanobacterial vertical migration are reviewed and summarized. Several models of cyanobacteria vertical movement are tested using data from field studies. These models are applied using both continuum and particle-tracking frameworks. Models range in complexity from simple functions of time to more complicated calculations of cyanobacteria buoyancy. Simple models were often able to predict cyanobacteria migration at low values of vertical diffusion in both types of modeling frameworks. More complicated models of buoyancy change performed better in the particle-tracking framework than in the continuum framework. Analysis of the models developed and tested provides information on the applicability of these models in more complex hydrodynamic and water quality models. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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16 pages, 5504 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Estimating Phosphorus and COD Concentrations Using a Hybrid Soft Sensor: A Case Study in a Norwegian Municipal Wastewater Treatment Plant

by Abhilash Nair, Aleksander Hykkerud and Harsha Ratnaweera

Water 2022, 14(3), 332; https://doi.org/10.3390/w14030332 - 24 Jan 2022

Cited by 23 | Viewed by 5835

Abstract

Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are emerging as a viable alternative for real-time monitoring of parameters that either lack a reliable measuring principle or are measured using expensive online sensors. This paper presents the development, implementation, and validation of a hybrid soft sensor used to estimate Total Phosphorus (TP) and Chemical Oxygen Demand (COD) in the influent and effluent streams of a full-scale WWTP. A systematic method for cleaning and processing sensor data, identifying statistically significant correlations, and developing a mathematical model, is discussed. A non-intrusive Industrial Internet of Things (IIoT) infrastructure for soft-sensor deployment and a web-based GUI for data visualization are also presented in this work. The values of TP and COD estimated by the soft sensor are validated by comparing the estimated values to the daily average of their corresponding lab measurements. The data validation results demonstrate the potential of soft sensors in providing real-time values of essential wastewater quality parameters with an acceptable degree of accuracy. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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16 pages, 3168 KiB

Open AccessArticle

What Effect Does Rehabilitation of Wastewater Pipelines Have on the Share of Infiltration and Inflow Water (I/I-Water)?

by Kristin Jenssen Sola, Jarle Tommy Bjerkholt, Oddvar Georg Lindholm and Harsha Ratnaweera

Water 2021, 13(14), 1934; https://doi.org/10.3390/w13141934 - 13 Jul 2021

Cited by 3 | Viewed by 3119

Abstract

Infiltration and inflow water (I/I-water) is a big challenge in sewage systems in many countries. I/I-water above an acceptable level indicates that the sewage system is not functioning properly. I/I-water leads to increased pumping costs and increased sewage overflow, leading to increased pollution of the receiving waters. Many rehabilitation projects are driven by the need to reduce the share of I/I-water and common measures are to replace pipes and manholes. The share of I/I-water is predominantly driven by rainfall. This makes it difficult to document the efficiency of mitigating measures. One way to address this issue is to compare data from rehabilitation areas to areas where no measures have been implemented. Three rehabilitation areas in Asker Municipality, Norway, were successfully assessed by applying this approach. Asker has a 100% separate system. The strategy to reduce I/I-water in Asker Municipality was to rehabilitate sewage mains, either by full replacement or lining the old pipes, and replacement of manholes. The assessment shows that rehabilitation of selected municipal pipes, pipes proven to be in bad condition through closed circuit TV inspection, reduced the share of I/I-water only to a limited extent. Since the rehabilitation done was not a complete replacement of all pipes and manholes, the limited effects are assumed to be caused by the water finding other ways into the system. In separate systems other measures than renovations of pipes should be considered when aiming to reduce I/I-water. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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29 pages, 6110 KiB

Open AccessArticle

Study of the Water Quality Index and Polycyclic Aromatic Hydrocarbon for a River Receiving Treated Landfill Leachate

by Brenda Tan Pei Jian, Muhammad Raza Ul Mustafa, Mohamed Hasnain Isa, Asim Yaqub and Yeek Chia Yeek Ho

Water 2020, 12(10), 2877; https://doi.org/10.3390/w12102877 - 16 Oct 2020

Cited by 12 | Viewed by 4612

Abstract

Rising solid waste production has caused high levels of environmental pollution. Population growth, economic patterns, and lifestyle patterns are major factors that have led to the alarming rate of solid waste production. Generally, solid wastes such as paper, wood, and plastic are disposed into landfills due to its low operation and maintenance costs. However, leachate discharged from landfills could be a problem in surfaces and groundwater if not adequately treated. This study investigated the patterns of the water quality index (WQI) and polycyclic aromatic hydrocarbons (PAH) along Johan River in Perak, Malaysia, which received treated leachate from a nearby landfill. An artificial neural network (ANN) was also applied to predict WQI and PAH concentration of the river. Seven sampling stations were chosen along the river. The stations represented the upstream of leachate discharge, point of leachate discharge, and five locations downstream of the landfill. Sampling was conducted for one year starting July 2018. Physicochemical parameters, namely pH, biological oxygen demand, chemical oxygen demand, ammoniacal nitrogen, total suspended solids, and dissolved oxygen, were used to compute the water quality index (WQI). PAH concentrations were determined by liquid–liquid extraction of water samples followed by an analysis using gas chromatography. Results showed that WQI of Johan River was under Class III where intensive treatment was required to make it suitable for drinking purposes. The highest recorded PAH concentrations were fluoranthene (333.4 ppb) in the dry season and benzo(a) pyrene (93.5 ppb) in the wet season. A correlation coefficient (Rp) for a model prediction based on WQI-ANN and TEC-ANN (toxicity equivalent concentration) in the wet and dry seasons was 0.9915, 0.9431, 0.9999, and 0.9999, respectively. ANN results showed good model performance with Rp ≈ 0.9. This study suggested that ANN is a useful tool for water quality studies. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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16 pages, 4823 KiB

Open AccessArticle

Simulation of the Surface Energy Flux and Thermal Stratification of Lake Taihu with Three 1-D Models

by Yongwei Wang, Qian Ma, Yaqi Gao, Xiaolong Hao and Shoudong Liu

Water 2019, 11(5), 1026; https://doi.org/10.3390/w11051026 - 16 May 2019

Cited by 6 | Viewed by 5429

Abstract

The accurate simulation of lake-air exchanges can improve weather and climate predictions, quantify the lake water cycle and provide evidence for water demand management and decision making. This paper analyzes the thermal stratification and surface flux of eastern Lake Taihu and evaluates three common surface models: CLM4-LISSS, E-ε and LAKE. The results show that the thermal stratification and lake-air exchanges are greatly affected by the weather conditions and have obvious diurnal variations in the Lake Taihu. The eddy exchange coefficient (EEC) in the thermodynamic equation varies greatly with the weather conditions and the water depth too, and an accurate parameterization scheme is important for the temperature simulations. The lake surface temperature simulation results of the CLM4-LISSS model have the highest accuracy due to the more accurate EEC simulation, with a correlation coefficient (CC) of 0.94 and a root mean square error (RMSE) of 0.85 °C, and latent flux simulation with a CC of 0.78 and a RMSE of 55.32 W m⁻². Moreover, the submerged plants in shallow water have obvious influences on the radiation, thermal transferring and eddy motion. The E–ε model can accurately simulate the surface temperature with submerged plants consideration, though a better scheme to deal with surface flux and turbulence dissipation in the areas of submerged plants is still need to be developed. The physical process in the LAKE model is comprehensive, while when it is used to simulate Lake Taihu and other shallow lakes, the EEC is large and needs to be adjusted. Full article

(This article belongs to the Special Issue Water Quality Monitoring and Modeling Research)

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