Modelling mitigation options to reduce diffuse nitrogen water pollution from agriculture

Artificial neural networks have proven to be an attractive mathematical tool to represent complex relationships in many branches of hydrology. Due to this attractive feature, neural networks are increasingly being applied in subsurface modeling where intricate physical processes and lack of detailed field data prevail. In this paper, a methodology using modular neural networks (MNN) is proposed to simulate the nitrate concentrations in an agriculture-dominated aquifer. The methodology relies on geographic information system (GIS) tools in the preparation and processing of the MNN input–output data. The basic premise followed in developing the MNN input–output response patterns is to designate the optimal radius of a specified circular-buffered zone centered by the nitrate receptor so that the input parameters at the upgradient areas correlate with nitrate concentrations in ground water. A three-step approach that integrates the on-ground nitrogen loadings, soil nitrogen dynamics, and fate and transport in ground water is described and the critical parameters to predict nitrate concentration using MNN are selected. The sensitivity of MNN performance to different MNN architecture is assessed. The applicability of MNN is considered for the Sumas-Blaine aquifer of Washington State using two scenarios corresponding to current land use practices and a proposed protection alternative. The results of MNN are further analyzed and compared to those obtained from a physically-based fate and transport model to evaluate the overall applicability of MNN.

/pdf/modular-neural-networks-to-predict-the-nitrate-distribution-4w7fw6zlue.pdf

Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data

A review of the impact of climate change on future nitrate concentrations in groundwater of the UK

[1] Four algorithms are outlined, each of which has interesting features for predicting contaminant levels in groundwater. Artificial neural networks (ANN), support vector machines (SVM), locally weighted projection regression (LWPR), and relevance vector machines (RVM) are utilized as surrogates for a relatively complex and time-consuming mathematical model to simulate nitrate concentration in groundwater at specified receptors. Nitrates in the application reported in this paper are due to on-ground nitrogen loadings from fertilizers and manures. The practicability of the four learning machines in this work is demonstrated for an agriculture-dominated watershed where nitrate contamination of groundwater resources exceeds the maximum allowable contaminant level at many locations. Cross-validation and bootstrapping techniques are used for both training and performance evaluation. Prediction results of the four learning machines are rigorously assessed using different efficiency measures to ensure their generalization ability. Prediction results show the ability of learning machines to build accurate models with strong predictive capabilities and hence constitute a valuable means for saving effort in groundwater contamination modeling and improving model performance.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2004WR003608

Applicability of statistical learning algorithms in groundwater quality modeling

A dynamic water quality model, HYPE, was applied to a large, data-sparse region to study whether reliable information on water quantity and water quality could be obtained for both gauged and ungauged waterbodies. The model (called S-HYPE) was set up for all of Sweden (∼450 000 km2), divided into sub-basins with an average area of 28 km2. Readily available national databases were used for physiographic data, emissions and agricultural practices, fixed values for representative years were used. Daily precipitation and temperature were used as the dynamic forcing of the model. Model evaluation was based on data from several hundred monitoring sites, of which approximately 90% had not been used in calibration on a daily scale. Results were evaluated using the Nash-Sutcliffe efficiency (NSE), correlation and relative errors: 92% of the spatial variation was explained for specific water discharge, and 88% and 59% for total nitrogen and total phosphorus concentrations, respectively. Day-to-day variatio...

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2011.637497

Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale

The aim of the calculations in this paper was i) to estimate the effect of measures to reduce nitrogen leaching from arable land and ii) to estimate what reduction in the gross load from southern Sweden could be expected. The measures taken were i) cultivation of a catch crop in spring cereals; ii) smaller applications of fertilizer-N to crops in cultivation systems without manure; iii) spring application of manure and a smaller amount of complementary fertilizer-N; and iv) a catch crop in combination with spring application of manure and a reduced rate of fertilizer-N. The models used were the mechanistic SOIL/SOILN models describing water, heat, and nitrogen flows in the soil profile. The largest reduction in leaching was obtained with the combination of catch crop, spring application of manure and reduced rate of fertilizer-N. Total gross load decreased with 16% in this scenario. The catch crop scenario reduced gross load by a mere 5% because it was only introduced in 30% of the acreage of spr...

https://bioone.org/journals/ambio-a-journal-of-the-human-environment/volume-29/issue-2/0044-7447-29.2.67/Nitrogen-Leaching-from-Agricultural-Land-in-Sweden/10.1579/0044-7447-29.2.67.pdf

Nitrogen Leaching from Agricultural Land in Sweden

Deicing performance of common deicing agents for winter maintenance with and without corrosion-inhibiting substances

Carbapenemase-producing Gram-negative bacteria in hospital wastewater, wastewater treatment plants and surface waters in a metropolitan area in Germany, 2020.

Som en del av ett uppdrag att utreda hur langt Sverige natt med att halvera kvavetillforseln till omgivande hav har berakningar gjorts av kvaveutlakningen fran Sveriges akermark 1985 och 1994. Berakningen, som utforts med hjalp av en matematisk modell, omfattar akerareal i tillrinningsomradet till Ostersjon och Vasterhavet, dvs ungefar upp till Dalalven. Omradet har dock delats upp i nio stycken utlakningsregioner, vilka karakteriseras av olika klimat, produktionsinriktning, godslings- och produktionsnivaer. For varje region har s k normalutlakningar beraknats for ett antal typsituationer bestaende av en kombination av nio olika grodor, tre olika jordarter och tva godslingsregimer. Normalutlakningarna representerar utlakningen for ett ar med normalklimat och motsvarande normala skord (s k normalskord) och har utforts med hjalp av langre tidsperioder av vaderdata i kombination med statistik om bl a normalskordar och godsling. Det som kvantifieras i berakningarna ar rotzonsutlakning av kvave, dvs det kvave som passerat rotzonen och ej langre ar tillgangligt for vaxterna eller paverkbart med olika odlingsatgarder. Rotzonsutlakningen kan betraktas som akermarkens bruttobelastning fore retentionsprocesser i grundvatten och vattendrag. De framraknade normalutlakningarna har anvants for att ta fram medelutlakning och bruttoutlakning av kvave fran de olika utlakningsregionerna. Som underlag for detta har statistik om grodarealer och andel handels- och stallgodslad areal anvants. Med hjalp av GIS har en uppskattning av uppdelningen av jordarter i de olika omradena gjorts. For hela omradet beraknades medelutlakningen av kvave minska fran ca 30 kg N /ha 1985 till ca 22 kg N/ha 1994 vilket motsvarar ca 25 %. Skillnaden i medelutlakning emllan de olika regionerna var stor och varierade mellan 14 och 48 kg/ha for 1994. Lagsta utlakningen fanns i skogsbygderna och i regionerna med lagsta avrinningen. Bruttobelastningen av kvave for hela omradet minskade med ca 29 % fran 77 000 ton 1985 till ca 55 000 ton 1994. Det finns tre anledningar till att bruttobelasningen av kvave minskat mellan 1985 och 1994: i) akerarealen odlas med annan sammansattning av grodor foretradesvis mer vall och mindre spannmalsodling, ii) utlakningen for de enskilda grodorna (utlakningskoefficienterna) har minskat till foljd av battre kvaveutnyttjande, iii) arealen aker i landet har minskat. Storsta anledningen till den minskade arealen spannmal och okade arealen vall star att finna i "Omstallning-90" som syftade just till att minska spannmalsodlingen. Okat kvaveutnyttjande har uppnatts genom att normskordarna stadigt okat samtidigt som kvavegodslingen inte okat i samma takt. Dvs, mer kvave an tidigare tas ut som skord i forhallande till tillfort kvave med godsling. Ca halften av bruttobelastningsminskningen beror pa forandrad grodareal, ca 40 % pa battre kvaveutnyttjande och 10 % pa minskad akerareal i landet.

Normalutlakning av kväve från svensk åkermark 1985 och 1994

Gram-Negative Bacteria With 3MRGN Resistance Phenotype in Hospital Wastewater, Receiving Water Channels, and Sewage Plant Outlets in the Leipzig Metropolitan Area.

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