Please wait, while we are loading the content...
Please wait, while we are loading the content...
| Content Provider | Springer Nature Link |
|---|---|
| Author | Prakash, Om Datta, Bithin |
| Copyright Year | 2015 |
| Abstract | Often, when pollution is first detected in groundwater, very few spatiotemporal pollutant concentration measurements are available. The contaminant concentration measurement data initially available are generally sparse and insufficient for accurate source characterization. This requires development of a contaminant monitoring plan and its field implementation to collect more data. The location of scientifically chosen monitoring points and the number of measurements are important considerations in improving the source-characterization process, especially in a complex contamination scenario. In order to improve the efficiency of source characterization, a feedback-based methodology is implemented, integrating sequential-monitoring-network design and a source identification method. The simulated annealing (SA) optimization algorithm is used to solve the models for optimal source identification and the monitoring-network-design optimization. This sequence is repeated a few times to improve the accuracy of source characterization. The methodology is based on the premise that concentration measurements from a sequence of implemented monitoring networks provide feedback information on the actual concentration in the site. This additional information, obtained as feedback from monitoring networks designed and implemented based on intermediate source characterization, can result in sequential improvement in the resulting source characterization. The performance of this methodology is evaluated by application to a contaminated aquifer site in New South Wales, Australia, where source location, source-activity initiation time and source-flux (mass per unit time) release history are considered as unknown variables. The performance evaluation results demonstrate potential applicability of the proposed sequential methodology.Souvent, lorsque la pollution est d’abord détectée dans les eaux souterraines, très peu de mesures de concentrations réparties dans le temps et l’espace sont disponibles. Les données de mesure de concentration des contaminants initialement disponibles sont généralement éparses et insuffisantes pour une caractérisation précise de la source. Cela nécessite le développement d’un plan de surveillance des contaminants et de sa mise en œuvre sur le terrain pour recueillir davantage de données. L’emplacement des points de contrôle choisis scientifiquement et le nombre de mesures sont importants à prendre en considération pour améliorer le processus de caractérisation de la source, en particulier dans un scénario de contamination complexe. Afin d’améliorer l’efficacité de la caractérisation de la source, une méthodologie basée sur la rétroaction est mise en œuvre, intégrant la conception séquentielle d’un réseau de suivi et une méthode d’identification de la source. L’algorithme d’optimisation du recuit simulé (SA) est utilisé pour résoudre les modèles d’identification optimale de la source et l’optimisation de la conception du réseau de suivi. Cette séquence est répétée plusieurs fois afin d’améliorer la précision de la caractérisation de la source. La méthodologie est fondée sur la prémisse selon laquelle les mesures de concentration d’une séquence de réseaux de surveillance implantés fournissent des informations en retour sur la concentration réelle sur le site. Cette information additionnelle, obtenue comme retour d’information des réseaux de suivi conçus et mis en œuvre sur la base de la caractérisation intermédiaire de la source, peut avoir comme résultat une amélioration séquentielle dans la caractérisation de la source résultante. La performance de cette méthodologie est évaluée avec une application sur un aquifère contaminé en Nouvelles Galles du Sud, en Australie, où l’emplacement de la source, le temps d’initiation de l’activité de la source et l’historique d’émission du flux depuis la source (masse par unité de temps) sont considérés comme des variables inconnues. Les résultats de l’évaluation de la performance démontrent l’applicabilité potentielle de la méthodologie séquentielle proposée.Cuando se detecta la primera contaminación del agua subterránea, a menudo están disponibles muy pocas mediciones espacio-temporales de la concentración de los contaminantes. Los datos de medición de concentración de contaminantes inicialmente disponibles son generalmente escasos e insuficientes para una caracterización precisa de la fuente. Esto requiere el desarrollo de un plan de monitoreo de contaminantes y su aplicación en el campo para recolectar más datos. La ubicación de los puntos de control científicamente seleccionados y el número de mediciones son consideraciones importantes para mejorar del proceso de la caracterización de la fuente, especialmente en un escenario complejo de contaminación. Con el fin de mejorar la eficiencia de la caracterización de la fuente, se implementa una metodología basada en la retroalimentación integrando el diseño de la red de monitoreo secuencial y un método de identificación de la fuente. El algoritmo de optimización de recocido simulado (SA) se utiliza para resolver los modelos de la identificación óptima de la fuente y de la optimización del diseño de la red de monitoreo. Esta secuencia se repite varias veces para mejorar la precisión de la caracterización de la fuente. La metodología se basa en la premisa que las mediciones de concentración de una secuencia en las redes de monitoreo implementadas proporcionan información de retroalimentación de la concentración real en el sitio. Esta información adicional, obtenida como la retroalimentación del monitoreo de las redes diseñadas e implementadas con base en una caracterización intermedia de la fuente, puede resultar en una mejora en la caracterización secuencial de la fuente resultante. El rendimiento de esta metodología se evalúa mediante la aplicación a un sitio en un acuífero contaminado en Nueva Gales del Sur, Australia, donde la ubicación de la fuente, la actividad de la fuente en el inicio y la fuente de flujo (masa por unidad de tiempo) en la historia de liberación se consideran como variables desconocidas. Los resultados de la evaluación de rendimiento demuestran el potencial de aplicabilidad de la metodología secuencial propuesta.通常,当地下水中首次检测到污染时,时空污染含量测量数据非常少。最初拥有的污染物含量测量数据一般很稀少和不足,难以对准确的源进行描述。这就需要制定污染物监测计划和室外实施方案,收集更多的数据。科学地选择出的监测点的位置和测量数量在改进源描述过程中是重要的考虑事项,特别是在复杂的污染情况下。为了提高源描述的效率,采用了基于反馈的方法,并综合连续监测网络设计和源识别方法。利用模拟退火最优化算法解决最优源识别模型和监测网络设计最优化。这个序列重复几次,以改进源描述的精度。方法基于这样的假设,即从一系列应用的监测网络获取的含量测量结果提供了测量点实际含量的反馈信息。这个基于中间的源描述从设计和应用的监测网络获取的额外信息可使源描述持续改进。通过应用到澳大利亚新南威尔士州一个污染的含水层,这个方法的性能得到了评估,在这个污染的含水层,其源位置、源活力起始时间和源通量(每单位时间的质量)释放历史被认为是未知变量。性能评估结果证明了所述连续方法的潜在适用性。Geralmente, quando a poluição é inicialmente detectada nas águas subterrâneas, muito poucas medidas espaço-temporais da concentração do poluente estão disponíveis. Os dados de medição da concentração do contaminante são geralmente esparsos e insuficientes para uma caracterização acurada da fonte. Isso requer o desenvolvimento de um plano de monitoramento do contaminante e sua implementação à campo para coletar mais dados. A locação de pontos de monitoramento especificamente escolhidos e o número de medições são considerações importantes na melhoria do processo de caracterização da fonte, especialmente em um cenário de contaminação complexo. Para melhorar a eficiência do processo da fonte, uma metodologia baseada em retroinformação é implementada, integrando desenho de uma rede de monitoramento sequencial integrada e um método de identificação de fonte. O algoritmo de otimização de arrefecimento simulado (AS) é utilizado para resolver os modelos para uma ótima identificação de fonte e para otimização do desenho da rede de monitoramento. Essa sequência é repetida algumas vezes para melhorar a acurácia da caracterização da fonte. A metodologia é baseada na premissa de que medidas de concentração para uma sequência de redes de monitoramento implementadas provem retroinformação da concentração atual no local. Essa informação adicional, obtida como retroinformação do desenho das redes de monitoramento e implementada baseada na caracterização intermediária da fonte, pode resultar em uma melhoria sequencial na caraterização de fonte resultante. O desempenho dessa metodologia é avaliado pela sua aplicação em um aquífero contaminado em Nova Gales do Sul, Austrália, onde local da fonte, tempo de início da atividade da fonte e histórico de lançamento do fluxo da fonte (massa por unidade de tempo) são consideradas variáveis desconhecidas. A avaliação do desempenho dos resultados demonstra uma aplicabilidade potencial da metodologia sequencial proposta. |
| Starting Page | 1089 |
| Ending Page | 1107 |
| Page Count | 19 |
| File Format | |
| ISSN | 14312174 |
| Journal | Hydrogeology Journal |
| Volume Number | 23 |
| Issue Number | 6 |
| e-ISSN | 14350157 |
| Language | Portuguese |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2015-07-28 |
| Publisher Institution | International Association of Hydrogeologists |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Groundwater-monitoring-network design Contamination Integrated sequential source identification Optimization Australia Hydrogeology Hydrology/Water Resources Geology Water Quality/Water Pollution Geophysics/Geodesy Waste Water Technology Water Pollution Control Water Management Aquatic Pollution |
| Content Type | Text |
| Resource Type | Article |
| Subject | Earth and Planetary Sciences Water Science and Technology |
National Digital Library of India (NDLI) is a virtual repository of learning resources which is not just a repository with search/browse facilities but provides a host of services for the learner community. It is sponsored and mentored by Ministry of Education, Government of India, through its National Mission on Education through Information and Communication Technology (NMEICT). Filtered and federated searching is employed to facilitate focused searching so that learners can find the right resource with least effort and in minimum time. NDLI provides user group-specific services such as Examination Preparatory for School and College students and job aspirants. Services for Researchers and general learners are also provided. NDLI is designed to hold content of any language and provides interface support for 10 most widely used Indian languages. It is built to provide support for all academic levels including researchers and life-long learners, all disciplines, all popular forms of access devices and differently-abled learners. It is designed to enable people to learn and prepare from best practices from all over the world and to facilitate researchers to perform inter-linked exploration from multiple sources. It is developed, operated and maintained from Indian Institute of Technology Kharagpur.
Learn more about this project from here.
NDLI is a conglomeration of freely available or institutionally contributed or donated or publisher managed contents. Almost all these contents are hosted and accessed from respective sources. The responsibility for authenticity, relevance, completeness, accuracy, reliability and suitability of these contents rests with the respective organization and NDLI has no responsibility or liability for these. Every effort is made to keep the NDLI portal up and running smoothly unless there are some unavoidable technical issues.
Ministry of Education, through its National Mission on Education through Information and Communication Technology (NMEICT), has sponsored and funded the National Digital Library of India (NDLI) project.
| Sl. | Authority | Responsibilities | Communication Details |
|---|---|---|---|
| 1 | Ministry of Education (GoI), Department of Higher Education |
Sanctioning Authority | https://www.education.gov.in/ict-initiatives |
| 2 | Indian Institute of Technology Kharagpur | Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project | https://www.iitkgp.ac.in |
| 3 | National Digital Library of India Office, Indian Institute of Technology Kharagpur | The administrative and infrastructural headquarters of the project | Dr. B. Sutradhar bsutra@ndl.gov.in |
| 4 | Project PI / Joint PI | Principal Investigator and Joint Principal Investigators of the project |
Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon |
| 5 | Website/Portal (Helpdesk) | Queries regarding NDLI and its services | support@ndl.gov.in |
| 6 | Contents and Copyright Issues | Queries related to content curation and copyright issues | content@ndl.gov.in |
| 7 | National Digital Library of India Club (NDLI Club) | Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach | clubsupport@ndl.gov.in |
| 8 | Digital Preservation Centre (DPC) | Assistance with digitizing and archiving copyright-free printed books | dpc@ndl.gov.in |
| 9 | IDR Setup or Support | Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops | idr@ndl.gov.in |
|
Loading...
|