A watershed water quality prediction model based on attention mechanism and Bi-LSTM

Environmental Science and Pollution Research - Accurate prediction of water quality contributes to the intelligent management and control of watershed ecology. Water Quality data has time series...     

An efficient supply management in water flow network using graph spectral techniques

An efficient way of distributing water in urban cities is one of the major global challenges of our time. A Water Distribution Network (WDN) is a vital infrastructure, intended to provide fresh water to households within a city or designated boundary. Given the WDN’s complexity, effective numerical techniques are required to aid in the development of an ideal monitoring system. Flow meters and gate valves should be placed in low-connected areas of the WDN with water flow reaching several regions of the network. This study proposes a general strategy for assisting water utilities in making decisions for effective water supply. The aim of the research is to use weighted spectral clustering algorithms to outline water districts while addressing hydraulic restrictions via weighted adjacency and Laplacian matrices of the weighted network. This work aims to identify influenced nodes in the network based on Eigen centrality and effectively distribute water across those nodes. This project also looks at how to measure the network’s connection strength to avoid water leaks. The best clusters are found using Eigenvalues and Eigenvectors of weighted adjacency matrices and Laplacian matrices of the water network in the proposed graph spectral framework. In order to establish the optimum water network division approach, topological and graph metrics were used to compare multiple spectral clustering techniques. The proposed graph spectral approach is tested using a genuine water distribution network serving an urban area of Coimbatore city in India and offering a method for partitioning complex networks that employ the spectral graph partitioning algorithm.

     

Concrete slump prediction modeling with a fine-tuned convolutional neural network: hybridizing sea lion and dragonfly algorithms

Environmental Science and Pollution Research - High-strength concrete (HSC) is defined as concrete that meets a special combination of uniformity and performance requirements, which cannot be...     

A convolutional neural network approach for detection of E. coli bacteria in water

Environmental Science and Pollution Research - The detection of Escherichia coli bacteria is essential to prevent health diseases. According to the laboratory-based methods, 12–48 h is...     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework     

Evaluation of the Swat River,Northern Pakistan,water quality using multivariate statistical techniques and water quality index (WQI) model

Environmental Science and Pollution Research - This study evaluates the characteristics of water along the Swat River, Northern Pakistan. For this purpose, water samples (n = 30) were...     

苏南某市河流水质参数时空变异性研究

以苏南某市区320km2内的河流为研究对象,基于对高锰酸盐指数、NH3-N、TP3个主要水质参数的监测,应用地质统计学的变差函数球状模型和Kriging插值法,对河流有机污染指标、富营养化指标进行了空间插值,用以揭示其时空分布特征及变化趋势,并绘制了时空分布等值线图。结果表明,受不同区域污染物来源的差异、不同河道自身条件的差异和不同水期水生植物、入流水量、河水流动性的差异等因素的影响,研究区河流水质参数呈现出不同的时空变异特征;各水质参数污染均相当严重,尤以富营养化指标氮磷最为显著。     

Comparison of artificial neural network and regression models in the prediction of urban stormwater quality.

Urban stormwater quality is influenced by many interrelated processes. However, the site-specific nature of these complex processes makes stormwater quality difficult to predict using physically based process models. This has resulted in the need for more empirical techniques. In this study, artificial neural networks (ANN) were used to model urban stormwater quality. A total of 5 different constituents were analyzed-chemical oxygen demand, lead, suspended solids, total Kjeldahl nitrogen, and total phosphorus. Input variables were selected using stepwise linear regression models, calibrated on logarithmically transformed data. Artificial neural networks models were then developed and compared with the regression models. The results from the analyses indicate that multiple linear regression models were more applicable for predicting urban stormwater quality than ANN models.     

Neuro-fuzzy and neural network systems for air quality control

In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system.     

Ditch network maintenance in peat-dominated boreal forests: Review and analysis of water quality management options

The objective of this study was to evaluate the potential of different water management options to mitigate sediment and nutrient exports from ditch network maintenance (DNM) areas in boreal peatland forests. Available literature was reviewed, past data reanalyzed, effects of drainage intensity modeled, and major research gaps identified. The results indicate that excess downstream loads may be difficult to prevent. Water protection structures constructed to capture eroded matter are either inefficient (sedimentation ponds) or difficult to apply (wetland buffers). It may be more efficient to decrease erosion, either by limiting peak water velocity (dam structures) or by adjusting ditch depth and spacing to enable satisfactory drainage without exposing the mineral soil below peat. Future research should be directed towards the effects of ditch breaks and adjusted ditch depth and spacing in managing water quality in DNM areas.     

A multivariate statistical approach for the evaluation of spatial and temporal dynamics of surface water quality from the small reservoir located in the drought-prone area of South-West India: a case study of Tiru reservoir (India)

Environmental Science and Pollution Research - With the use of different multivariate statistical analysis methods, spatio-temporal fluctuations in the water parameters of Tiru reservoir located at...     

An approach for assessment of water quality using semipermeable membrane devices (SPMDs) and bioindicator tests

As an integral part of our continued development of water quality assessment approaches, we combined integrative sampling, instrumental analysis of widely occurring anthropogenic contaminants, and the application of a suite of bioindicator tests as a specific part of a broader survey of ecological conditions, species diversity, and habitat quality in the Santa Cruz River in Arizona, USA. Lipid-containing semipermeable membrane devices (SPMDs) were employed to sequester waterborne hydrophobic chemicals. Instrumental analysis and a suite of bioindicator tests were used to determine the presence and potential toxicological relevance of mixtures of bioavailable chemicals in two major water sources of the Santa Cruz River. The SPMDs were deployed at two sites; the effluent weir of the International Wastewater Treatment Plant (IWWTP) and the Nogales Wash. Both of these systems empty into the Santa Cruz River and the IWWTP effluent is a potential source of water for a constructed wetland complex. Analysis of the SPMD sample extracts revealed the presence of organochlorine pesticides (OCs), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs). The bioindicator tests demonstrated increased liver enzyme activity, perturbation of neurotransmitter systems and potential endocrine disrupting effects (vitellogenin induction) in fish exposed to the extracts. With increasing global demands on limited water resources, the approach described herein provides an assessment paradigm applicable to determining the quality of water in a broad range of aquatic systems.     

Chronic toxicity tests with Daphnia magna for examination of river water quality

Chronic toxicity tests with Daphnia magna were applied for examination of river water quality. Water was sampled from the Maioka River in Yokohama City on May 14, 20, and 27, 1999, and used for the test after solid-phase extraction. The chronic test was carried out according to the OECD method. The duration was 21 days and the total number of live offspring produced per parent animal was counted. The results of the tests showed, survival rates of 100% using river water sampled on May 14 and 20 and the total numbers of live offspring produced per parent animal did not differ from the control. However, the survival rate of the sample collected on May 27 was 0% and the pesticides, fenitrothion, and thiobencarb were detected in the water. In addition to the river water samples, reconstituted water (Elendt M7) with additions of fenitrothion and thiobencarb was prepared to investigate mortality. When the reconstituted water with thiobencarb was applied to the test, the total number of live offspring produced per parent animal did not differ from the control. In contrast, when reconstituted water with fenitrothion was applied to the test, most parents were alive, but the total number of live offspring produced per parent animal was apparently different. The results of the above tests indicate that D. magna was affected not only by fenitrothion in the river water collected on May 27, but also by other factors that were not clarified in this study.     

Stationary and non-stationary autoregressive processes with external inputs for predicting trends in water quality

An autoregressive approach for the prediction of water quality trends in systems subject to varying meteorological conditions and short observation periods is discussed. Under these conditions, the dynamics of the system can be reliably forecast, provided their internal processes are understood and characterized independently of the external inputs. A methodology based on stationary and non-stationary autoregressive processes with external inputs (ARX) is proposed to assess and predict trends in hydrosystems which are at risk of contamination by organic and inorganic pollutants, such as pesticides or nutrients. The procedures are exemplified for the transport of atrazine and its main metabolite deethylatrazine in a small agricultural catchment in France. The approach is expected to be of particular value to assess current and future trends in water quality as part of the European Water Framework Directive and Groundwater Directives.     

Amending greenroof soil with biochar to affect runoff water quantity and quality

Numbers of greenroofs in urban areas continue to grow internationally; so designing greenroof soil to reduce the amount of nutrients in the stormwater runoff from these roofs is becoming essential. This study evaluated changes in extensive greenroof water discharge quality and quantity after adding biochar, a soil amendment promoted for its ability to retain nutrients in soils and increase soil fertility. Prototype greenroof trays with and without biochar were planted with sedum or ryegrass, with barren soil trays used as controls. The greenroof trays were subjected to two sequential 7.4cm/h rainfall events using a rain simulator. Runoff from the rain events was collected and evaluated. Trays containing 7% biochar showed increased water retention and significant decreases in discharge of total nitrogen, total phosphorus, nitrate, phosphate, and organic carbon. The addition of biochar to greenroof soil improves both runoff water quality and retention.     

An improved UV/Fe3+ process by combination with electrocatalysis for p-nitrophenol degradation

p-nitrophenol (PNP) was investigated as a model pollutant under the improved UV/Fe3+ process by combination with electrocatalysis. In the individual UV/Fe3+ process, PNP degradation rate was dependent on Fe(III) concentration and decreased during degradation due to the depletion of ferric ion and thus it was very difficult to reach the quick mineralization of organics. These drawbacks could be significantly overcome in the modified UV/Fe3+ process, and synergetic effects for PNP and COD removal were observed at two investigated Fe(III) concentrations. The enhancements on the degree of conversion for PNP and COD in presence of 0.5 mM Fe(III) were 184% and 242%, respectively, and PNP of initial concentration of 1.0 mM could be completely removed within 1 h. Thus such a process would be very attractive to the rapid mineralization of the biorefractory compounds for wastewater treatment. The possible reasons for the synergetic effects were the electrochemical regeneration of ferric ion and the role of the oxygen that formed on the anode. Based on degradation intermediates identification and synergetic effect probe, a general reaction pathway for PNP degradation in the improved process was proposed.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.