Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

Luhtala, Hanna; Tolvanen, Harri

doi:10.1016/j.seares.2016.03.004

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…In addition, in-situ sampling may provide inaccurate measurements within its surrounding on different days (Erkkilä and Kalliola, 2004). Even though measurements may be highly accurate for a specific spot, the data may not correspond well with a sample taken at a nearby location (Luhtala and Tolvanen, 2016). Additionally, sampling location is an important factor for effectiveness of water quality monitoring.…”

Section: Limitations Of Water Samplingmentioning

confidence: 99%

Unmanned Aerial Vehicle (UAV) assisted water sampling

Koparan

2016

2016 ASABE International Meeting

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Water quality assessment programs require the collection of water samples for physical, chemical, and bacteriological analysis. Lack of personnel, accessibility of water bodies, and time constraints for water sampling, especially after natural disasters and emergencies, are some of the challenges of water sampling. To overcome these challenges, a water collection mechanism was developed and mounted on a multirotor unmanned aerial vehicle (UAV) for autonomous water sampling from water bodies. The payload capacity and endurance of the UAV (hexacopter) were verified using an indoor test station. The hexacopter was equipped with floating foam, and the electronic components were coated against water damage in case of landing on water due to emergencies or water sampling. The system was able to collect water samples 48 times out of 73 autonomous flight missions from a pond. The unsuccessful missions were mainly due to the malfunctions of the servo motor used in water sampler's triggering mechanism. The servo motor for the mechanism was replaced to prevent the future malfunctions. UAV-assisted autonomous water sampling is a promising method for collection of water from water bodies. The system would be useful for collection of water samples from large lakes or difficult to access water sources. The details of the developed water sampling mechanism and the multirotor UAV, and experiment results are reported in this thesis.iii ACKNOWLEDGMENTS

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Section: Limitations Of Water Samplingmentioning

confidence: 99%

Unmanned Aerial Vehicle (UAV) assisted water sampling

Koparan

2016

2016 ASABE International Meeting

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“…The environmental conditions vary due to the presence of islands and shallow topography, causing the seasonal developments (i.e., timing and magnitude) of, e.g., algal growth, to differ within small areas-numerous previous studies, which have utilised in situ and remotely sensed data and spatial modelling, highlight these features in the SW Finnish archipelago in terms of spatial [11,24,25], temporal [9,12,26], and spatio-temporal variability [10,27]. Many studies conclude that the variation is difficult to capture, and it is advisable to use combined methods to complement in situ sampling (e.g., [9][10][11][12]26]). However, to our knowledge, the four-dimensional (temporal, vertical and horizontal) patterns of water quality have not yet been statistically quantified.…”

Section: Introductionmentioning

confidence: 99%

Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast

2021

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Our paper aims at advancing global change management in marine archipelago environments. Water properties vary along temporal and vertical gradients, and studies indicate that these patterns may be site-specific, i.e., they may vary at local or regional scales. Understanding these complex processes is crucial for designing environmental monitoring campaigns or assessing the scalability of their results. To our knowledge, the four-dimensional (temporal, vertical and horizontal) patterns of water quality have not been statistically quantified. In this paper, we partition the variation in four key water property variables into temporal, vertical and horizontal dimensions, by utilising a unique pre-existing high-density dataset and multilevel regression modelling. The dataset comprised measurements of temperature, salinity, pH and chlorophyll-a concentration, sampled eight times from April to October on the SW Finnish archipelago coast. All variables were sampled along the depth gradient and at local (102 m) and regional scales (104 m) at 20 sites. All measured variables varied significantly along the temporal and vertical gradients, and the overall levels, temporal patterns and vertical gradients of these variables were significantly site-dependent. Our study confirms that many water properties, especially chlorophyll-a concentration, show high four-dimensional variability in the complex archipelago environment. Thus, studies on the regional dynamics of archipelago water properties call for a high sampling density in time, along the vertical gradient, and in space.

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“…Standards and guidelines have been published for the in situ determination of river water parameters or for the collection and preservation of samples that can be analyzed in the laboratory. − Some authors also studied the impact of river composition heterogeneity in the characterization of a sampling point. − However, when trends of river water composition need to be monitored, it is necessary to characterize a large river area because it is more robust to river’s heterogeneity and representative of its status. This area would be characterized by the parameter value that would be obtained after mixing and analyzing the water of the river area that is equivalent to the mean of the parameter values determined in the area.…”

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confidence: 99%

Optimization of River Sampling: Application to Nutrients Distribution in Tagus River Estuary

2019

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The assessment of river water pollution trends is affected by the seasonal variation of river conditions, the variability of pollution sources, the heterogeneity of pollutants distribution, the representativeness/uncertainty of sampling, and the uncertainty of sample analysis. This work presents a methodology to model the uncertainty of river water sampling based on available information about the spatial distribution of the studied parameter in the river. The uncertainty from “single sampling” (SS) or by production of a composite sample by mixing m subsamples collected randomly (RS) or in a line that crosses the sampling circle (LS) was studied. This methodology was applied to the determination of nutrients (NO x , NO2, PO4, and SiO2) in an area of the Tagus River estuary with a range of about 350 m. This methodology can be applied to the determination of the mean value of other parameters in other river areas requiring a previous study of system heterogeneity. The spatial distribution of nutrients in the studied river area was characterized from the analysis of 10 samples collected at known geographical coordinates. The system heterogeneity was described by a three-dimensional (x, y, z) surface with x and y variables for samples positions and z variable representing the measured nutrient levels. The randomization of this surface for the uncertainty of coordinates and repeatability of nutrient concentration measurement, using Monte Carlo simulations, allowed estimation of the uncertainty of the three sampling strategies: SS, RS, and LS. The uncertainty from RS and LS is equivalent and significantly smaller than that from SS when at least three subsamples are mixed in the composite sample. The sampling relative standard uncertainty ranged from 0.31% to 4.4%, producing nutrient concentration estimates in the river area with a relative expanded uncertainty from 5.9% to 10% with approximately 95% confidence level (coverage factor of 2). The used spreadsheet is available as Supporting Information.

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Spatio-temporal representativeness of euphotic depth in situ sampling in transitional coastal waters

Cited by 5 publications

References 30 publications

Unmanned Aerial Vehicle (UAV) assisted water sampling

Unmanned Aerial Vehicle (UAV) assisted water sampling

Scalability of Water Property Measurements in Space and Time on a Brackish Archipelago Coast

Optimization of River Sampling: Application to Nutrients Distribution in Tagus River Estuary

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