title: Optimal design of monitoring networks for multiple groundwater quality parameters using a Kalman filter: application to the Irapuato-Valle aquifer.
abstract: A new method for the optimal design of groundwater quality monitoring networks is introduced in this paper. Various indicator parameters were considered simultaneously and tested for the Irapuato-Valle aquifer in Mexico. The steps followed in the design were (1) establishment of the monitoring network objectives, (2) definition of a groundwater quality conceptual model for the study area, (3) selection of the parameters to be sampled, and (4) selection of a monitoring network by choosing the well positions that minimize the estimate error variance of the selected indicator parameters. Equal weight for each parameter was given to most of the aquifer positions and a higher weight to priority zones. The objective for the monitoring network in the specific application was to obtain a general reconnaissance of the water quality, including water types, water origin, and first indications of contamination. Water quality indicator parameters were chosen in accordance with this objective, and for the selection of the optimal monitoring sites, it was sought to obtain a low-uncertainty estimate of these parameters for the entire aquifer and with more certainty in priority zones. The optimal monitoring network was selected using a combination of geostatistical methods, a Kalman filter and a heuristic optimization method. Results show that when monitoring the 69 locations with higher priority order (the optimal monitoring network), the joint average standard error in the study area for all the groundwater quality parameters was approximately 90 % of the obtained with the 140 available sampling locations (the set of pilot wells). This demonstrates that an optimal design can help to reduce monitoring costs, by avoiding redundancy in data acquisition.
authors.affiliation: Maestría en Ingeniería Aplicada, Universidad Autónoma de Zacatecas, Av. Ramón López Velarde No. 801, Carretera a la Bufa, C.P. 98010, Zacatecas, Mexico. hejunez@hotmail.com.
authors.forename: H E
authors.initials: HE
authors.lastname: Júnez-Ferreira
authors.affiliation: Instituto de Geofísica, Universidad Nacional Autónoma de México, Ciudad Universitaria, Del. Coyoacán, CP 04510, Mexico City, Mexico.
authors.forename: G S
authors.initials: GS
authors.lastname: Herrera
authors.affiliation: Subcoordinación de Hidrología Subterránea, Instituto Mexicano de Tecnología del Agua, Paseo Cuauhnáhuac 8532, Col. Progreso, C.P. 62550, Jiutepec, Morocco, Mexico.
authors.forename: L
authors.initials: L
authors.lastname: González-Hita
authors.affiliation: Área Ciencias de la Tierra, Facultad de Ingeniería, Universidad Autónoma de San Luis Potosí, Dr. Manuel Nava No 8, Zona Universitaria, C.P. 78290, San Luis Potosí, S.L.P., Mexico.
authors.forename: A
authors.initials: A
authors.lastname: Cardona
authors.affiliation: Departamento de Ingeniería Geomática e Hidráulica, Universidad de Guanajuato, Av. Juárez, 77. Col. Centro, C.P. 36000, Guanajuato, Mexico.
authors.forename: J
authors.initials: J
authors.lastname: Mora-Rodríguez
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D060587:Groundwater / Q000737:chemistry*; D008800:Mexico; D035501:Uncertainty; D014873:Water Pollutants / Q000592:standards*; D060753:Water Quality
chemical_list: D014873:Water Pollutants
keywords: Geostatistics; Groundwater quality; Kalman filter; Optimal monitoring network; Priority zones