Browsing by Author "Turner, Andrew"

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    A generic approach for live prediction of the risk of agricultural field runoff and delivery to watercourses: linking parsimonious soil-water-connectivity models with live weather data APIs in decision tools
    (Frontiers, 2019-06-04) Comber, Alexis; Collins, Adrian L.; Haro Monteagudo, David; Hess, Tim; Zhang, Yusheng; Smith, Andrew; Turner, Andrew
    This paper describes the development and application of a novel and generic framework for parsimonious soil-water interaction models to predict the risk of agro-chemical runoff. The underpinning models represent two scales to predict runoff risk in fields and the delivery of mobilized pesticides to river channel networks. Parsimonious field and landscape scale runoff risk models were constructed using a number of pre-computed parameters in combination with live rainfall data. The precomputed parameters included spatially-distributed historical rainfall data to determine long term average soil water content and the sensitivity of land use and soil type combinations to runoff. These were combined with real-time live rainfall data, freely available through open data portals and APIs, to determine runoff risk using SCS Curve Numbers. The rainfall data was stored to provide antecedent, current and future rainfall inputs. For the landscape scale model, the delivery risk of mobilized pesticides to the river network included intrinsic landscape factors. The application of the framework is illustrated for two case studies at field and catchment scales, covering acid herbicide at field scale and metaldehyde at landscape scale. Web tools were developed and the outputs provide spatially and temporally explicit predictions of runoff and pesticide delivery risk at 1 km2 resolution. The model parsimony reflects the driving nature of rainfall and soil saturation for runoff risk and the critical influence of both surface and drain flow connectivity for the risk of mobilized pesticide being delivered to watercourses. The novelty of this research lies in the coupling of live spatially-distributed weather data with precomputed runoff and delivery risk parameters for crop and soil types and historical rainfall trends. The generic nature of the framework supports the ability to model the runoff and field-to-channel delivery risk associated with any in-field agricultural application assuming application rate data are available.
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    Predicting the impact of underwater skimming on dissolved oxygen consumption in slow sand filters for potable water treatment
    (Elsevier, 2024-12-01) Elemo, Tolulope; Chipps, Michael; Graham, Nigel; Turner, Andrew; Jefferson, Bruce; Hassard, Francis
    In a well-functioning slow sand filter (SSF), dissolved oxygen (DO) is crucial for enabling aerobic processes and microbiota growth. Given that DO supply is predominantly via the feed water, flow pauses (e.g., during cleaning) may trigger anoxic/anaerobic conditions in the stagnant filter bed. Underwater skimming (UWS) is an advanced cleaning technique that employs a skimmer with a shrouded blade, mounted on a mobile platform, to remove the fouling layer composed of sand and particles in order to improve the efficiency of slow sand filtration. As UWS results in changes to the flow pattern of the SSF, a mathematical model was developed to predict DO utilization after a flow perturbation associated with UWS operation. The model was based on a depth resolved measurement of specific oxygen utilization derived from a full scale SSF. Pilot plant experiments monitored DO in the feed and filtrate of SSFs cleaned using underwater and conventional dry skimming techniques. The highest oxygen utilization was in the Schmutzdecke layer, with additional demand imposed by the presence of a granular activated carbon (GAC) sandwich layer. It was observed that pseudo-steady state conditions occurred following filter ripening, where DO utilization, driven by biological activity, remained relatively constant regardless of filter cleaning technique. For flow pauses between three and 24 h, the pause duration's importance decreased, while the hydraulic loading rate became the critical factor for DO recovery in the filter. Additionally, introducing a 'sweetening flow' during UWS ensured a continuous DO supply, facilitating quicker DO replenishment post-cleaning. The model reliably predicted filtrate DO within ±0.6 mg/L, demonstrating its operational utility, especially in the optimisation of UWS methodology. As such, UWS can be applied to clean SSFs with the methodology modified to prevent any detrimental effects to DO management within the filter. This study predicted DO dynamics in SSFs, advancing UWS techniques and could be applied for enhancing water treatment strategies by filtration.
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    ItemOpen Access
    Underwater remote skimming of slow sand filters for sustainable water production
    (American Chemical Society, 2022-08-16) Hassard, Francis; Elemo, Tolulope; Chipps, Michael; Turner, Andrew; Jefferson, Bruce; Graham, Nigel
    Slow sand filters (SSF) are a simple water treatment technology providing an important alternative to conventional drinking water treatment. SSF are extensive in terms of carbon cost and chemical use but require a large land area and are complex to operate, as periodic cleaning is required to prevent filter clogging. Therefore, redundant SSF beds are required to enable water production to occur during long cleaning downtimes. Underwater skimming (UWS) is a cleaning innovation where the foulant layer (containing sand and particles) is removed using a skimmer consisting of a shrouded blade mounted on a vehicle platform. Sand, particles, and biofilm are skimmed prior to ex situ washing of the recovered sand. In this Viewpoint, we posit that the introduction of an in situ underwater skimmer operated remotely can substantially help to offset the aforementioned challenge of downtime, with its associated loss of production, enabling the technology to operate more efficiently and remain a pertinent and advantageous process option within modern water treatment facilities or possibly resource constrained settings. Otherwise, this resilient biotechnological process could be replaced by chemical and energy-intensive processes which increase the entropy of water treatment more than SSF. The anticipated benefits and challenges of UWS of SSF are discussed.