- -

Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study

RiuNet: Institutional repository of the Polithecnic University of Valencia

Share/Send to

Cited by

Statistics

Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study

Show simple item record

Files in this item

dc.contributor.author García-Díaz, J. Carlos es_ES
dc.date.accessioned 2016-02-22T12:28:47Z
dc.date.available 2016-02-22T12:28:47Z
dc.date.issued 2011-03
dc.identifier.issn 1436-3240
dc.identifier.uri http://hdl.handle.net/10251/61058
dc.description.abstract Contaminated water resources have important implications on health and the environment. Nitrate contamination of the groundwater is a serious problem in the European Union. A method based on the statistical process control (SPC) and time series analysis is developed to monitoring and to predict the concentration evolution of nitrate (NO 3 -) in groundwater. In many pumping wells the NO 3 -concentration ([NO 3 -]) increases and approaches or even passes the European Community standard of 50 mg l -1. The objective of this paper is to show the application of statistical process control as a monitoring tool for groundwater pollution from agricultural practices. We propose the autoregressive integrated moving average (ARIMA) model as a management tool to monitoring and reduction of the intrusion of nitrate into the groundwater. This tool should help in setting up useful guidelines for evaluating actual environmental performance against the firm's environmental objectives and targets and regulatory requirements. We concluded that the statistical process control method may be a potentially important way of monitoring groundwater quality that also permits rapid response to serious increases in pollutants concentrations. In doing so, the paper fills an important gap in the water pollution standards and emerging polices (Water Framework directives). © 2010 Springer-Verlag. es_ES
dc.description.sponsorship The author is grateful to the anonymous referees and the editor for several constructive comments that have improved this paper. The author acknowledge the financial support of Programa de Apoyo a la Investigacion y Desarrollo (PAID-06-08) of the Universidad Politecnica de Valencia. en_EN
dc.language Inglés es_ES
dc.publisher Springer Verlag es_ES
dc.relation Universidad Politecnica de Valencia [PAID-06-08] es_ES
dc.relation.ispartof Stochastic Environmental Research and Risk Assessment es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject ARIMA model es_ES
dc.subject Environmental pollution es_ES
dc.subject Groundwater es_ES
dc.subject Nitrate es_ES
dc.subject Statistical process control es_ES
dc.subject Agricultural practices es_ES
dc.subject ARIMA models es_ES
dc.subject Autoregressive integrated moving average models es_ES
dc.subject Contaminated water es_ES
dc.subject Environmental objectives es_ES
dc.subject Environmental performance es_ES
dc.subject European community es_ES
dc.subject European Union es_ES
dc.subject Groundwater quality es_ES
dc.subject Management tool es_ES
dc.subject Monitoring tools es_ES
dc.subject Nitrate concentration es_ES
dc.subject Nitrate contamination es_ES
dc.subject Pollution standards es_ES
dc.subject Pumping well es_ES
dc.subject Rapid response es_ES
dc.subject Regulatory requirements es_ES
dc.subject Statistical process es_ES
dc.subject Water Framework Directives es_ES
dc.subject Concentration (process) es_ES
dc.subject Environmental management es_ES
dc.subject Groundwater pollution es_ES
dc.subject Nitrates es_ES
dc.subject Pollution control es_ES
dc.subject Quality control es_ES
dc.subject Time series es_ES
dc.subject Time series analysis es_ES
dc.subject Water quality es_ES
dc.subject Water resources es_ES
dc.subject Agricultural practice es_ES
dc.subject Concentration (composition) es_ES
dc.subject Environmental monitoring es_ES
dc.subject Forecasting method es_ES
dc.subject Numerical model es_ES
dc.subject Europe es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00477-010-0371-6
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Centro de Gestión de la Calidad y del Cambio - Centre de Gestió de la Qualitat i del Canvi es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation García-Díaz, JC. (2011). Monitoring and forecasting nitrate concentration in the groundwater using statistical process control and time series analysis: a case study. Stochastic Environmental Research and Risk Assessment. 25(3):331-339. doi:10.1007/s00477-010-0371-6 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1007/s00477-010-0371-6 es_ES
dc.description.upvformatpinicio 331 es_ES
dc.description.upvformatpfin 339 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 25 es_ES
dc.description.issue 3 es_ES
dc.relation.senia 202144 es_ES
dc.relation.references Alwan LC, Roberts HV (1989) Time series modelling for statistical process control. In: Keats JB, Hubele NF (eds) Automated manufacturing. Marcel Dekker Inc., New York, pp 87–95 es_ES
dc.relation.references Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden Day, San Francisco es_ES
dc.relation.references Canter LW (1997) Nitrates in groundwater. CRC Press, Boca Raton, pp 73–143 es_ES
dc.relation.references CEC (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Off J Eur Union L 327:1–73 es_ES
dc.relation.references CEC (2006) Directive 2006/118/EC of the European Parliament and of the Council of 12 December 2006 on the protection of groundwater against pollution and deterioration. Off J Eur Union L 372:19–31 es_ES
dc.relation.references Chistensen OF, Cassiani G, Diggle PJ, Ribero P, Andreotti G (2004) Statistical estimation of the relative efficiency of natural attenuation mechanisms in contaminated aquifers. Stoch Environ Res Risk Assess 18:339–350 es_ES
dc.relation.references Cook DF, Zobel CW, Wolfe ML (2006) Environmental statistical process control using an augmented neural network classification approach. Eur J Oper Res 174:1631–1642 es_ES
dc.relation.references De Paz JM, Ramos C (2004) Simulation of nitrate leaching for different nitrogen fertilization rates in a region of Valencia (Spain) using a GIS–GLEAMS system. Agric Ecosyst Environ 103(1):59–73 es_ES
dc.relation.references European Commission (1998) The Implementation of Council Directive 91/676/EEC concerning the protection of waters against pollution caused by nitrates from agricultural sources. Office for Official Publications of the European Communities, Luxembourg es_ES
dc.relation.references Generalitat Valenciana (2000) Decreto 13/2000 del 25-enero de 2000, del gobierno valenciano por el que se designan, en el ámbito de la Comunidad Valenciana, determinados municipios como zonas vulnerables a la contaminación de las aguas por nitratos procedentes de fuentes agrarias. DOGV (Diario Oficial de la Generalitat Valenciana) No. 3677. Valencia, Spain, pp 1511–1515 es_ES
dc.relation.references Harris TJ, Ross WH (1991) Statistical process control procedures for correlated observations. Can J Chem Eng 69:48–57 es_ES
dc.relation.references ITGME (1998) Mapa de contenido en nitrato de las aguas subterráneas en España. Instituto Tecnológico GeoMinero de España. Ministerio de Medio Ambiente, Madrid es_ES
dc.relation.references Li N, Liang X, Li X, Wang C, Wu DD (2009) Network environment and financial risk using machine learning and sentiment analysis. Hum Ecol Risk Assess 15(2):227–252 es_ES
dc.relation.references Militino AF, Ugarte MD, Ibáñez B (2008) Longitudinal analysis of spatially correlated data. Stoch Environ Res Risk Assess 22:49–57 es_ES
dc.relation.references Montgomery DC, Mastrangelo CM (1991) Some statistical process control methods for autocorrelated data. J Qual Technol 23:179–268 es_ES
dc.relation.references Ramos C, Agut A, Lidón A (2002) Nitrate leaching in important crops of the Valencian Community region (Spain). Environ Pollut 118:215–223 es_ES
dc.relation.references Sanchis EJ (1991) Estudio de la contaminación por nitratos de las aguas subterráneas de la Provincia de Valencia. Origen, balance y evolución espacial y temporal. Graficuatre SL, Valencia es_ES
dc.relation.references USEPA (Unites States Environmental Protection Agency) (1990) The drinking water criteria document on nitrate/nitrite. National Technical Information Service document no. PB91-142836 es_ES
dc.relation.references Vigil J, Warburton S, Haynes WS, Kaiser LR (1965) Nitrates in municipal water supply cause metahemoglobinemia in infant. Public Health Rep 80:12 es_ES
dc.relation.references Wardell DG, Moskowitz H, Plante RD (1992) Control charts in the presence of data correlation. Manage Sci 38(8):1084–1105 es_ES
dc.relation.references Wardell DG, Moskowitz H, Plante RD (1994) Run length distributions of special-cause control charts for correlated processes. Technometrics 36:3–17 es_ES
dc.relation.references WHO (World Health Organization) (1993) Guidelines for drinking water quality, 2nd edn (vol 1: Recommendations). WHO, Geneva es_ES
dc.relation.references Worrall F, Burt TP (1999) A univariate model of river water nitrate time series. J Hydrol 214:74–90 es_ES
dc.relation.references Xu J, Li X, Wu DD (2009) Optimizing circular economy planning and risk analysis using system dynamics. Hum Ecol Risk Assess 15(2):316–331 es_ES


This item appears in the following Collection(s)

Show simple item record