Improving data driven decision making through integration of environmental sensing technologies

Coastal and estuarine zones contain vital and increasingly exploited resources. Traditional uses in these areas (transport, fishing, tourism) now sit alongside more recent activities (mineral extraction, wind farms). However, protecting the resource base upon which these marine-related economic and social activities depend requires access to reliable and timely data. This requires both acquisition of background (baseline) data and monitoring impacts of resource exploitation on aquatic processes and the environment. Management decisions must be based on analysis of collected data to reduce negative impacts while supporting resource-efficient, environmentally sustainable uses. Multi-modal sensing and data fusion offer attractive possibilities for providing such data in a resource efficient and robust manner. In this paper, we report the results of integrating multiple sensing technologies, including autonomous multi-parameter aquatic sensors with visual sensing systems. By focussing on salinity measurements, water level and freshwater influx into an estuarine system; we demonstrate the potential of modelling and data mining techniques in allowing deployment of fewer sensors, with greater network robustness. Using the estuary of the River Liffey in Dublin, Ireland, as an example, we present the outputs and benefits resulting from fusion of multi-modal sensing technologies to predict and understand freshwater input into estuarine systems and discuss the potential of multi-modal datasets for informed management decisions

Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope

Data assimilation has recently been the focus of much attention for integrated surface–subsurface hydrological models, whereby joint assimilation of water table, soil moisture, and river discharge measurements with the ensemble Kalman filter (EnKF) has been extensively applied. Although the EnKF has been specifically developed to deal with nonlinear models, integrated hydrological models based on the Richards equation still represent a challenge, due to strong nonlinearities that may significantly affect the filter performance. Thus, more studies are needed to investigate the capabilities of the EnKF to correct the system state and identify parameters in cases where the unsaturated zone dynamics are dominant, as well as to quantify possible tradeoffs associated with assimilation of multi-source data. Here, the CATHY (CATchment HYdrology) model is applied to reproduce the hydrological dynamics observed in an experimental two-layered hillslope, equipped with tensiometers, water content reflectometer probes, and tipping bucket flow gages to monitor the hillslope response to a series of artificial rainfall events. Pressure head, soil moisture, and subsurface outflow are assimilated with the EnKF in a number of scenarios and the challenges and issues arising from the assimilation of multi-source data in this real-world test case are discussed. Our results demonstrate that the EnKF is able to effectively correct states and parameters even in a real application characterized by strong nonlinearities. However, multi-source data assimilation may lead to significant tradeoffs: the assimilation of additional variables can lead to degradation of model predictions for other variables that are otherwise well reproduced. Furthermore, we show that integrated observations such as outflow discharge cannot compensate for the lack of well-distributed data in heterogeneous hillslopes.</p

Technology transfer potential of an automated water monitoring system

The nature and characteristics of the potential economic need (markets) for a highly integrated water quality monitoring system were investigated. The technological, institutional and marketing factors that would influence the transfer and adoption of an automated system were studied for application to public and private water supply, public and private wastewater treatment and environmental monitoring of rivers and lakes

The impact of agricultural activities on water quality: a case for collaborative catchment-scale management using integrated wireless sensor networks

The challenge of improving water quality is a growing global concern, typified by the European Commission Water Framework Directive and the United States Clean Water Act. The main drivers of poor water quality are economics, poor water management, agricultural practices and urban development. This paper reviews the extensive role of non-point sources, in particular the outdated agricultural practices, with respect to nutrient and contaminant contributions. Water quality monitoring (WQM) is currently undertaken through a number of data acquisition methods from grab sampling to satellite based remote sensing of water bodies. Based on the surveyed sampling methods and their numerous limitations, it is proposed that wireless sensor networks (WSNs), despite their own limitations, are still very attractive and effective for real-time spatio-temporal data collection for WQM applications. WSNs have been employed for WQM of surface and ground water and catchments, and have been fundamental in advancing the knowledge of contaminants trends through their high resolution observations. However, these applications have yet to explore the implementation and impact of this technology for management and control decisions, to minimize and prevent individual stakeholder’s contributions, in an autonomous and dynamic manner. Here, the potential of WSN-controlled agricultural activities and different environmental compartments for integrated water quality management is presented and limitations of WSN in agriculture and WQM are identified. Finally, a case for collaborative networks at catchment scale is proposed for enabling cooperation among individually networked activities/stakeholders (farming activities, water bodies) for integrated water quality monitoring, control and management

Harmonization of space-borne infra-red sensors measuring sea surface temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing

Development of miniature all-solid-state potentiometric sensing system

A procedure for the development of a pen-like, multi-electrode potentiometric sensing platform is described. The platform comprises a seven-in-one electrode incorporating all-solid-state ion-selective and reference electrodes based on the conductive polymer (poly(3,4-ethylenedioxythiophene) (PEDOT)) as an intermediate layer between the contacts and ion-selective membranes. The ion-selective electrodes are based on traditional, ionophore-based membranes, while the reference electrode is based on a polymer membrane doped with the lipophilic salt tetrabutyl ammonium tetrabutyl borate (TBA-TBB). The electrodes, controlled with a multichannel detector system, were used for simultaneous determination of the concentration of Pb2+ and pH in environmental water samples. The results obtained using pH-selective electrodes were compared with data obtained using a conventional pH meter and the average percent difference was 0.3%. Furthermore, the sensing system was successfully used for lead-speciation analysis in environmental water samples

Development and deployment of a microfluidic platform for water quality monitoring

There is an increasing demand for autonomous sensor devices which can provide reliable data on key water quality parameters at a higher temporal and geographical resolution than is achievable using current approaches to sampling and monitoring. Microfluidic technology, in combination with rapid and on-going developments in the area of wireless communications, has significant potential to address this demand due to a number of advantageous features which allow the development of compact, low-cost and low-powered analytical devices. Here we report on the development of a microfluidic platform for water quality monitoring. This system has been successfully applied to in-situ monitoring of phosphate in environmental and wastewater monitoring applications. We describe a number of the technical and practical issues encountered and addressed during these deployments and summarise the current status of the technology

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain