<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents methodology of accurate mobile measurements of water quality parameters such as temperature, dissolved oxygen, chlorophyll “a” concentration, ammonium ion concentration, conductivity, pH and blue-green algae content in water. The measurements (probe EXO 2, YSI, USA) were made on various depths of probe immersion (1.5, 2.5 and 3.5 m) and at different towing speeds of the probe (approx. 5.4 and 9.0 km/h). Static measurements carried out on the same route provided reference values for the measurements in motion to compare the repeatability of static and mobile methods. The tests were also evaluated by observation of probe behavior in motion, e.g. water disturbance intensity, access of light (sun rays) to the sensors. Statistical tests confirmed that the mean values of water quality parameters from mobile measurements with the speed of 5.4 km/h at the depth 1.5 m does not differ from the stationary measurements. Results of statistical analysis prove that water quality parameters can be measured accurately keeping established speed of towing the probe at the fixed depth. Methodology of mobile measurements elaborated in the frame of this work allows to collect vast number of data which can be used to obtain GIS point maps of water quality parameters in large water bodies.</p></abstract>

The paper presents methodology of accurate mobile measurements of water quality parameters such as temperature, dissolved oxygen, chlorophyll “a” concentration, ammonium ion concentration, conductivity, pH and blue-green algae content in water. The measurements (probe EXO 2, YSI, USA) were made on various depths of probe immersion (1.5, 2.5 and 3.5 m) and at different towing speeds of the probe (approx. 5.4 and 9.0 km/h). Static measurements carried out on the same route provided reference values for the measurements in motion to compare the repeatability of static and mobile methods. The tests were also evaluated by observation of probe behavior in motion, e.g. water disturbance intensity, access of light (sun rays) to the sensors. Statistical tests confirmed that the mean values of water quality parameters from mobile measurements with the speed of 5.4 km/h at the depth 1.5 m does not differ from the stationary measurements. Results of statistical analysis prove that water quality parameters can be measured accurately keeping established speed of towing the probe at the fixed depth. Methodology of mobile measurements elaborated in the frame of this work allows to collect vast number of data which can be used to obtain GIS point maps of water quality parameters in large water bodies.

Online Measurement Method of Water Quality in the Sulejow Reservoir

Faculty of Process and Environmental Engineering

Ecological Chemistry and Engineering S

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Online Measurement Method of Water Quality in the Sulejow Reservoir"}

The paper presents methodology of accurate mobile measurements of water quality parameters such as temperature, dissolved oxygen, chlorophyll “a”...

Online Measurement Method of Water Quality in the Sulejow Reservoir

Publié en ligne: 24 avr. 2018

Pages: 89 - 100

DOI: https://doi.org/10.1515/eces-2018-0006

Mots clés
surface water monitoring, mobile measurements, statistical analysis, GIS, MONSUL project

© 2018 Aleksandra Ziemińska-Stolarska et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Online Measurement Method of Water Quality in the Sulejow Reservoir

Publié en ligne: 24 avr. 2018

Pages: 89 - 100

DOI: https://doi.org/10.1515/eces-2018-0006

Mots cléssurface water monitoring, mobile measurements, statistical analysis, GIS, MONSUL project

© 2018 Aleksandra Ziemińska-Stolarska et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
surface water monitoring, mobile measurements, statistical analysis, GIS, MONSUL project