• Can sustainable water monitoring be a reality?

      Ajmal, Tahmina; Guimares, Laura; Genthe, Bettina; Rivett, Ulrike; University of Bedfordshire; University of Porto; Water Resources - CSIR; University of Cape Town (Institute of Physics Publishing, 2020-05-13)
      In this paper, authors discuss the current methods used for surface water monitoring and the gaps left in monitoring in context of a low resourced area. Water quality monitoring [1] is a complex problem that can only be tackled through a systemic application of a transdisciplinary approach. This paper suggests use of a variety of innovative solutions adapted to the local conditions encouraging the prospect of sustainability. The approach relies on an emphasis on environmental and water quality for human life that will contribute to: 1) improved capacity building of local actors, including the role of women; 2) increased economic and social well-being at local and regional levels; and 3) protect natural capital in the region. This article reviews the state of water monitoring in low resourced area, example is taken here from Southern Arica and attempts to establish a sustainable water quality monitoring plan for application to cross-boundary water resources in the region. These are essential to diagnose and raise understanding on water quality problems in resources shared by countries with contrasting development levels. The innovative vision presented here proposes to resolve this multidimensional water quality problem by considering the broader system ranging from aquatic ecosystems providing this service to supply systems serving final consumers.
    • Design of a smart system for rapid bacterial test

      Patil, Rajshree; Levin, Saurabh; Rajkumar, Samuel; Ajmal, Tahmina; Institute of Chemical Technology (ICT), Mumbai; Foundation for Environmental Monitoring, Bangalore; University of Bedfordshire (MDPI, 2019-12-19)
      In this article, we present our initial findings to support the design of an advanced field test to detect bacterial contamination in water samples. The system combines the use of image processing and neural networks to detect an early presence of bacterial activity. We present here a proof of concept with some tests results. Our initial findings are very promising and indicate detection of viable bacterial cells within a period of 2 h. To the authors' knowledge this is the first attempt to quantify viable bacterial cells in a water sample using cell splitting. We also present a detailed design of the complete system that uses the time lapse images from a microscope to complete the design of a neural network based smart system.
    • Developing a novel water quality prediction model for a South African aquaculture farm

      Eze, Elias Chinedum; Halse, Sarah; Ajmal, Tahmina; University of Bedfordshire; Abagold Limited (MDPI, 2021-06-28)
      Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.
    • Evaluating urban surface water quality in Luton

      Ajmal, Tahmina; Anyachebelu, Tochukwu Kene; Conrad, Marc; Rawson, David M.; University of Bedfordshire (Springer, 2019-02-09)
      Using a single numerical value to indicate the quality of water, a so-called Water Quality Index (WQI) is a well-established way of rating the overall water quality status of a given water body. During the last few years, researchers in the water sector have developed different such indices to address their specific needs. In this study, we attempt to obtain a WQI formula suited for evaluating the water quality of the River Lea. We have selected four different sites on the River Lea and explore the possibility of monitoring using a minimum number of parameters only. The results obtained are very encouraging and provide a strong indication that only three parameters are enough to indicate water quality of a water body.
    • Modeling and prediction of surface water contamination using on-line sensor data

      Anyachebelu, Tochukwu Kene; Conrad, Marc; Ajmal, Tahmina (Exeley Inc., 2014-12-31)
      Water contamination is a great disadvantage to humans and aquatic life. Maintaining the aesthetics and quality of water bodies is a priority for environmental stake holders. The water quality sensor data can be analyzed over a period of time to give an indication of pollution incidents and could be a useful forecasting tool. Here we show our initial finding from statistical analysis on such sensor data from one of the lakes of the river Lea, south of Luton. Our initial work shows patterns which will form the basis for our forecasting model.
    • Nanoantenna arrays combining enhancement and beam control for fluorescence-based sensing applications

      Dorh, N.; Sarua, A; Ajmal, Tahmina; Okache, Julius; Rega, C.; Müller, G.; Cryan, M.; University of Bristol; University of Bedfordshire; ABB Ltd; et al. (OSA - The Optical Society, 2017-12-31)
      This paper presents measured fluorescence enhancement results for ~250 × 250 element aluminum nanoantenna arrays fabricated using electron beam lithography. The arrays have been designed to use diffractive coupling to enhance and control the direction of fluorescent emission. Highly directional emission is obtained at the designed angles with beam widths simulated to be in the range of 4–6°. Angle-resolved spectroscopy measurements of dye-coated nanoantenna arrays were in good agreement with finite difference time domain modeling. Critically, these results were obtained for near UV wavelengths (~360 nm), which is relevant to a number of biosensing applications.
    • Surface water quality prediction system for Luton Hoo lake: a statistical approach

      Anyachebelu, Tochukwu Kene; Conrad, Marc; Ajmal, Tahmina; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2014-10-20)
      Lake monitoring is a necessity for aquatic healthy living. Stakeholders are particularly interested not just in the aesthetics but also in the quality of water bodies. Our work tends to initially analyze historic data of the sensed water quality parameters at Luton Hoo lake to detect outliers. Dissolved oxygen has been predicted from available data since its one of the major surface water contaminants
    • Time series chlorophyll-A concentration data analysis: a novel forecasting model for aquaculture industry

      Eze, Elias Chinedum; Kirby, Sam; Attridge, John; Ajmal, Tahmina; University of Bedfordshire; Chelsea Technology Group (2021-06-29)
      Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.