Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 16 (3)

Estimating the Underwater Diffuse Attenuation Coefficient With a Low-Cost Instrument: The KdUINO DIY Buoy

Affiliations

Estimating the Underwater Diffuse Attenuation Coefficient With a Low-Cost Instrument: The KdUINO DIY Buoy

Raul Bardaji et al. Sensors (Basel).

Abstract

A critical parameter to assess the environmental status of water bodies is the transparency of the water, as it is strongly affected by different water quality related components (such as the presence of phytoplankton, organic matter and sediment concentrations). One parameter to assess the water transparency is the diffuse attenuation coefficient. However, the number of subsurface irradiance measurements obtained with conventional instrumentation is relatively low, due to instrument costs and the logistic requirements to provide regular and autonomous observations. In recent years, the citizen science concept has increased the number of environmental observations, both in time and space. The recent technological advances in embedded systems and sensors also enable volunteers (citizens) to create their own devices (known as Do-It-Yourself or DIY technologies). In this paper, a DIY instrument to measure irradiance at different depths and automatically calculate the diffuse attenuation Kd coefficient is presented. The instrument, named KdUINO, is based on an encapsulated low-cost photonic sensor and Arduino (an open-hardware platform for the data acquisition). The whole instrument has been successfully operated and the data validated comparing the KdUINO measurements with the commercial instruments. Workshops have been organized with high school students to validate its feasibility.

Keywords: Arduino; KdUINO; buoy; citizen science; diffuse attenuation coefficient; do-it-yourself; light; low-cost sensor; oceanography.

Figures

Figure 1
Figure 1
Overview of the KdUINO’s design and computation of Kd as the slope of the linear regression of the measurements.
Figure 2
Figure 2
Basic electronic schema.
Figure 3
Figure 3
KdUINO components, (a) 9 V Battery button power plug for Arduino; (b) 100 nF Capacitor; (c) 8 pin SOIC to DIP8 Adapter; (d) SD memory card; (e) TSL230RP; (f) Polyester transparent box; (g) Data Logger Module Logging Recorder Shield V1.0; (h) Arduino MEGA 2560 R3; (i) Industrial cable, 3 cores; (j) Cable Gland Nylon 66; (k) Hermetic bottle.
Figure 4
Figure 4
Presentation of the KdUINO.
Figure 5
Figure 5
(a) Electrical schema of the light sensor; (b) Position of the sensor in transparent polyester box.
Figure 6
Figure 6
(a) Back of the sensor; (b) Front of the sensor.
Figure 7
Figure 7
Connection of sensors, the Data Logger Shield v1.0 and the Arduino MEGA.
Figure 8
Figure 8
Schema of the instrumentation in the black-room laboratory. (a) Horizontal quartz-halogen standard lamp; (b) Zeiss spectral monochromatic; (c) spherical lenses; (d) mechanical platform with focus variable angle; (e) the sensor (installed on a rotating platform).
Figure 9
Figure 9
Spectrum response of the TSL230RD with the Synolite capsule and comparison to the response without capsule.
Figure 10
Figure 10
Comparison of measurements obtained with three different TLS230RP sensors encapsulated in Synolite and the ideal response of a cosine collector.
Figure 11
Figure 11
Example of sensor calibration where the Calibration Factor (CF) of each sensor is calculated.
Figure 12
Figure 12
Lamp spectra of the experimental tank.
Figure 13
Figure 13
(a) Comparison of Kd results derived from commercial instruments: the hyper-spectral RAMSES radiometer and the PRR-800, and the KdUINO; (b) Same comparison after spectral response compensation of the original KdUINO measurements (see text).
Figure 14
Figure 14
Students from Sant Carles de la Rapita (a) and Mollet (b) building their own KdUINOs.
Figure 15
Figure 15
Results of the analysis of data in the Barcelona beach and the Alfacs Bay, data in square brackets correspond to the chlorophyll concentration ranges recorded in previous studies [23,24]. It can be seen that higher levels of chlorophyll concentration lead to higher attenuation coefficient.

Similar articles

See all similar articles

Cited by 7 articles

See all "Cited by" articles

References

    1. Mobley D. Light and Water: Radiative Transfer in Natural Waters. Academic Press; Cambridge, MA, USA: 1994. Optical Properties of Water.
    1. Sosik M. Characterizing Seawater Constituents from Optical Properties. In: Babin M., Roesler C.S., Cullen J.J., editors. Real-time Coastal Observing Systems for Ecosystem Dynamics and Harmful Algal Blooms. UNESCO; Paris, France: 2008. pp. 281–329.
    1. Mishra D.R., Narumalani S., Rundquist D., Lawson M. Characterizing the vertical diffuse attenuation coefficient for downwelling irradiance in coastal waters: Implications for water penetration by high resolution satellite data. ISPRS J. Photogramm. Remote Sens. 2005;60:48–64. doi: 10.1016/j.isprsjprs.2005.09.003. - DOI
    1. Shi K., Zhang Y., Liu X., Wang M., Qin B. Remote sensing of diffuse attenuation coefficient of photosynthetically active radiation in Lake Taihu using MERIS data. Remote Sens. Environ. 2014;140:365–377. doi: 10.1016/j.rse.2013.09.013. - DOI
    1. Zhang Y., Zhang B., Ma R., Feng S., Le C. Optically active substances and their contributions to the underwater light climate in Lake Taihu, a large shallow lake in China. Fundam. Appl. Limnol. 2007;170:11–19. doi: 10.1127/1863-9135/2007/0170-0011. - DOI

Publication types

Feedback