Water Quality Sampling Frequency Analysis of Surface Freshwater: A Case Study on Bristol Floating Harbour

Coraggio, Elisa; Han, Dawei; Gronow, Claire; Tryfonas, Theo

doi:10.3389/frsc.2021.791595

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…The peaks in the frequency spectrum are the frequencies that characterise the signal; therefore, they are the frequencies that should be preserved. These frequencies, when transformed back in time intervals, are, according to Coraggio et al (2022) significant sampling frequencies for water quality parameters and can be used as windows for the SG filter.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the physics of the variable is taken into consideration to determine what can be defined as an acceptable significant change in the variable to establish the type of filter and cut-off frequency that is most suitable for each variable. (Coraggio et al, 2022)…”

Section: Evaluation Criteriamentioning

confidence: 99%

“…The data used in this study is part of the dataset collected using EXO2 water quality sondes. (Coraggio et al, 2022) These are multiparameter sondes equipped with up to seven sensors capable of measuring a wide range of variables (YSI, 2019), found to be largely successful in the field by other researchers (Snazelle, 2015;Snyder et al, 2018). For this study, turbidity, fDOM (fluorescent dissolved organic matter, a surrogate for dissolved organic content), conductivity, water temperature, and dissolved oxygen (DO) were considered sufficient to capture changes in the area of study, Bristol floating harbour (UK) due to weather, pollutant, and tidal stimuli.…”

Section: Figure 3-bristol City Council Sampling Sites (Bristol City C...mentioning

confidence: 99%

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Low-pass filter application for noise removal in water quality data with high temporal resolution

Coraggio,

Gronow,

Tryfonas

et al. 2023

Preprint

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Large datasets with high temporal resolution are becoming widely available through the use of wireless sensors and other low-effort, automated data collection techniques. The higher the sampling frequency is, the more obvious and significant the noise will be due to the highly unrealistic oscillations in the observations. Machine learning techniques work well with large amounts of data, but it is essential to ensure that the data collected is as clean as possible from noise; otherwise, the machine learning algorithm will struggle to predict the actual data and instead attempt to reproduce the noise. This study explores the use of four low pass filters: Butterworth, Chebyshev I, Chebyshev II and Savitzky-Golay filter for removing noise from water quality dataset with high temporal resolution. This study describes how the filters are implemented and gives advice on how to evaluate the filters’ capability to reduce noise and preserve signal features. The method is applied to five water quality parameters based on a water quality dataset with a 5 minutes resolution collected in an urban surface water body in Bristol, United Kingdom. Based on the results of this study, it has been found that for the analysed water quality parameters (conductivity, water temperature, dissolved oxygen, and fDOM) Butterworth filter with a cut-off frequency between 2.33E-05 Hz~12 hours and 4.5E-04 Hz~ 6 hours is the filter that allows the best compromise between noise removal and signal preservation

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Section: Discussionmentioning

confidence: 99%

Section: Evaluation Criteriamentioning

confidence: 99%

Section: Figure 3-bristol City Council Sampling Sites (Bristol City C...mentioning

confidence: 99%

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Low-pass filter application for noise removal in water quality data with high temporal resolution

Coraggio,

Gronow,

Tryfonas

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…From a water quality management perspective, it is evident that continuous environmental monitoring at certain points of the rivers is essential to reduce uncertainty in the results. High frequency samplings with the adoption of modern sensors can provide high frequency data [34] and substantially larger datasets to reduce uncertainty of pollution load estimates, particularly in urban rivers with multiple stressors, such as the Kifisos river [35].…”

Section: Discussionmentioning

confidence: 99%

Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River

2023

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Recreational use of urban rivers is becoming popular since rivers may act as amenity corridors with the potential for exhibition, recreation, leisure, relaxation and retreat. However, several point and non-point pollution sources contribute to the degradation of urban rivers’ water quality, thereby impeding their beneficial uses and amenities. The physicochemical and microbiological quality of a Greek urban river (Kifisos–Athens) was analyzed over a period of 12 months. A sampling campaign was implemented, collecting monthly data from five sites. Spearman’s analysis showed significant correlation of the Hellenic Water Quality Index with specific nutrients. The total physicochemical status of all sampling stations was characterized as poor or bad. The annual average concentration of Escherichia coli (E. coli) was extremely high in four sites out of five, ranging from 16,822 to 26,780 cfu/100 mL. Bacteriological quality was unacceptable, as the study demonstrated the widespread occurrence of E. coli and low-quality physiochemical conditions. The spatiotemporal distribution of pollution levels revealed hotspots to be monitored further via automatic monitoring stations. A series of management and restoration measures, including tracing the exact pollution routes, should be initiated to minimize pollution pressures and establish the good ecological status of an important Mediterranean river.

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“…Coraggio et al (2022) used the power spectral density (PSD) function to determine the frequencies with the highest density in the water quality measurements with high-frequency sensors [40]. This helped them determine how representative a particular frequency is of the overall situation.…”

Section: Optimal Predictor Subset and Sampling Freuqnecymentioning

confidence: 99%

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

Arhab,

Huang

2023

Sensors

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Despite advancements in sensor technology, monitoring nutrients in situ and in real-time is still challenging and expensive. Soft sensors, based on data-driven models, offer an alternative to direct nutrient measurements. However, the high demand for data required for their development poses logistical issues with data handling. To address this, the study aimed to determine the optimal subset of predictors and the sampling frequency for developing nutrient soft sensors using random forest. The study used water quality data at 15-min intervals from 2 automatic stations on the Main River, Germany, and included dissolved oxygen, temperature, conductivity, pH, streamflow, and cyclical time features as predictors. The optimal subset of predictors was identified using forward subset selection, and the models fitted with the optimal predictors produced R2 values above 0.95 for nitrate, orthophosphate, and ammonium for both stations. The study then trained the models on 40 sampling frequencies, ranging from monthly to 15-min intervals. The results showed that as the sampling frequency increased, the model’s performance, measured by RMSE, improved. The optimal balance between sampling frequency and model performance was identified using a knee-point determination algorithm. The optimal sampling frequency for nitrate was 3.6 and 2.8 h for the 2 stations, respectively. For orthophosphate, it was 2.4 and 1.8 h. For ammonium, it was 2.2 h for 1 station. The study highlights the utility of surrogate models for monitoring nutrient levels and demonstrates that nutrient soft sensors can function with fewer predictors at lower frequencies without significantly decreasing performance.

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Water Quality Sampling Frequency Analysis of Surface Freshwater: A Case Study on Bristol Floating Harbour

Cited by 14 publications

References 29 publications

Low-pass filter application for noise removal in water quality data with high temporal resolution

Low-pass filter application for noise removal in water quality data with high temporal resolution

Preliminary Analysis of the Water Quality Status in an Urban Mediterranean River

Determination of Optimal Predictors and Sampling Frequency to Develop Nutrient Soft Sensors Using Random Forest

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