Statistical approaches used to assess and redesign surface water-quality-monitoring networks

2009 ◽  
Vol 11 (11) ◽  
pp. 1915 ◽  
Author(s):  
B. Khalil ◽  
T. B. M. J. Ouarda
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Monitoring Networks ◽  
Statistical Approaches

scholarly journals Innovative Solutions in Surface Water Quality Monitoring

2014 ◽  
Vol 10 ◽  
pp. 26-30 ◽  
Author(s):  
Damian Absalon ◽  
Marek Ruman ◽  
Magdalena Matysik ◽  
Krystyna Kozioł ◽  
Żaneta Polkowska
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Innovative Solutions

scholarly journals Monitoring surface water quality using social media in the context of citizen science

2017 ◽  
Vol 21 (2) ◽  
pp. 949-961 ◽  
Author(s):  
Hang Zheng ◽  
Yang Hong ◽  
Di Long ◽  
Hua Jing
Keyword(s):  
Water Quality ◽  
Social Media ◽  
Surface Water ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Future Research ◽  
Water Quality Data ◽  
Essential Information

Abstract. Surface water quality monitoring (SWQM) provides essential information for water environmental protection. However, SWQM is costly and limited in terms of equipment and sites. The global popularity of social media and intelligent mobile devices with GPS and photography functions allows citizens to monitor surface water quality. This study aims to propose a method for SWQM using social media platforms. Specifically, a WeChat-based application platform is built to collect water quality reports from volunteers, which have been proven valuable for water quality monitoring. The methods for data screening and volunteer recruitment are discussed based on the collected reports. The proposed methods provide a framework for collecting water quality data from citizens and offer a primary foundation for big data analysis in future research.

Using a conductivity–alkalinity relationship as a tool to identify surface waters in reference condition across Canada

2018 ◽  
Vol 53 (4) ◽  
pp. 231-240
Author(s):  
C. L. Proulx ◽  
B. W. Kilgour ◽  
A. P. Francis ◽  
R. F. Bouwhuis ◽  
J. R. Hill
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Monitoring Network ◽  
Surface Water Quality ◽  
Water Quality Monitoring ◽  
Reference Condition ◽  
Quality Monitoring ◽  
Surveillance Program ◽  
Disturbed Condition ◽  
Time Period

Abstract The underlying natural relationship between conductivity and alkalinity was used to identify surface water quality monitoring sites that are in a ‘reference’ or minimally disturbed condition. Data from over 40,500 freshwater samples from 1,230 sites were combined for the time period of 2005–2015 from various federal, provincial, and joint federal–provincial/territorial freshwater monitoring programs (e.g., Freshwater Quality Monitoring and Surveillance Program, Ontario's Provincial Water Quality Monitoring Network). Of the samples, 30,347 provided conductivity and alkalinity data. Surface water samples with a measured conductivity that deviated (by more than 41 μS/cm) from the predicted conductivity calculated from the sample's alkalinity were deemed to be non-representative of a reference condition, while samples within 41 μS/cm of the predicted value were deemed representative of a reference condition. The 41 μS/cm cutoff value was determined using signal detection theory. The conductivity–alkalinity model was validated through a comparison with land cover data by demonstrating that samples identified as ‘reference’ were typically from catchments that had minimal anthropogenic disturbances. The proposed approach provides a rapid means of evaluating the reference condition of a watercourse, and of identifying data that provide an estimate of reference condition.

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