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

• Published in: Stochastic environmental research and risk assessment Vol. 25; no. 3; pp. 331 - 339
• Main Author: García-Díaz, J. Carlos
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.03.2011; Springer Nature B.V
• Subjects: Aquatic Pollution; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Earth and Environmental Science; Earth Sciences; Environment; Environmental monitoring; Groundwater; Groundwater pollution; Math. Appl. in Environmental Science; Mathematical models; Monitoring; Nitrate content; Nitrates; Original Paper; Physics; Probability Theory and Stochastic Processes; Statistical methods; Statistical process control; Statistics for Engineering; Time series analysis; Waste Water Technology; Water Management; Water Pollution Control; Statistical process control; Nitrate; Environmental pollution; Groundwater; ARIMA model
• ISSN: 1436-3240; 1436-3259
• DOI: 10.1007/s00477-010-0371-6

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).