Abstract
Background
Water transparency is one indicator of water quality. High water transparency is an indication of clean water. A common method for measuring water transparency is Secchi depth. In this paper, we present an approach to water quality (Secchi depth and turbidity) monitoring using mobile phones and a small device designed for water quality measurements.
Results
The water quality parameters were analysed automatically from the images taken using mobile phone cameras. During the summer of 2012, we conducted a field trial in which 100 test users gathered 1,146 observations using the system. The results of the automatic Secchi3000 depth analysis were compared against reference measurements, and they indicate that our approach can be used for quantitative water quality measurements.
Conclusions
Results show that overall the system performs well. Both Secchi depth and turbidity are estimated with excellent or good accuracy when the measurements are taken with care.
Water transparency is one indicator of water quality. High water transparency is an indication of clean water. A common method for measuring water transparency is Secchi depth. In this paper, we present an approach to water quality (Secchi depth and turbidity) monitoring using mobile phones and a small device designed for water quality measurements.
Results
The water quality parameters were analysed automatically from the images taken using mobile phone cameras. During the summer of 2012, we conducted a field trial in which 100 test users gathered 1,146 observations using the system. The results of the automatic Secchi3000 depth analysis were compared against reference measurements, and they indicate that our approach can be used for quantitative water quality measurements.
Conclusions
Results show that overall the system performs well. Both Secchi depth and turbidity are estimated with excellent or good accuracy when the measurements are taken with care.
Original language | English |
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Article number | 9 |
Number of pages | 6 |
Journal | Environmental Systems Research |
Volume | 2 |
Issue number | 9 |
DOIs | |
Publication status | Published - 2013 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Mobile application
- Participatory sensing
- Secchi depth
- Water quality