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Surface water quality database for five watersheds in the Arequipa region of Southern Peru.
Rymes, Molly Noel; Rasmussen, Rebecca; Garcia-Chevesich, Pablo A; Burgert, Katie; Long, Jean; McBride, Elsie; Martínez, Gisella; Martínez, Kattia; Tejada, Teresa; Murray, Kyle E; Vanzin, Gary; Sharp, Jonathan O; McCray, John E.
Afiliación
  • Rymes MN; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • Rasmussen R; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • Garcia-Chevesich PA; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • Burgert K; Intergobernmental Hydrological Programme, United Nations Educational, Scientific, and Cultural Organization (UNESCO), Montevideo, Uruguay.
  • Long J; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • McBride E; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • Martínez G; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • Martínez K; Universidad Nacional de San Agustín de Arequipa, Santa Catalina 117, Arequipa, Perú.
  • Tejada T; Universidad Nacional de San Agustín de Arequipa, Santa Catalina 117, Arequipa, Perú.
  • Murray KE; Universidad Nacional de San Agustín de Arequipa, Santa Catalina 117, Arequipa, Perú.
  • Vanzin G; Murray GeoConsulting LLC, PO Box 150458, Denver, CO 80215, USA.
  • Sharp JO; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
  • McCray JE; Colorado School of Mines, 1500 Illinois St, Golden, CO 80401, USA.
Data Brief ; 56: 110770, 2024 Oct.
Article en En | MEDLINE | ID: mdl-39211484
ABSTRACT
Though surface water quality has been monitored in southern Peru over the past and current century, it has been implemented by multiple organizations. The data lacks a centralized repository and access requires logistical and temporal hurdles associated with official requests. A substantial portion of the data has not been quality assured and is in difficult-to-access formats such as scanned PDF documents. These obstacles collectively make it challenging to maximize the impact of these monitoring efforts such as efficiently evaluating long-term water quality trends. To address this opportunity, we gathered available surface water quality information from five watersheds in the Arequipa Region of southern Peru Camaná, Chili, Ocoña, Tambo, and Yauca. The effort required entry of more than 130,000 records of water quality properties across 274 monitoring stations with data including the concentration of select nutrients, metals, organic compounds, and biological taxa. The water quality records in the Chili watershed go back as far as 1905, while data for the other watersheds was largely confined to the years 2012-2021. This document describes how the surface water quality information was assimilated with quality control and provides a centralized Excel database so that the data can be efficiently used for research and decision making purposes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE País/Región como asunto: America do sul / Peru Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE País/Región como asunto: America do sul / Peru Idioma: En Revista: Data Brief Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Países Bajos