Turbidity stands as a crucial indicator for assessing water quality, and while turbidity sensors exist, their high cost prohibits their extensive use. In this paper, we introduce an innovative turbidity sensor, and it is the first low-cost turbidity sensor that is designed specifically for long-term stormwater in-field monitoring. Its low cost (USD 23.50) enables the implementation of high spatial resolution monitoring schemes. The sensor design is available under open hardware and open-source licences, and the 3D-printed sensor housing is free to modify based on different monitoring purposes and ambient conditions. The sensor was tested both in the laboratory and in the field. By testing the sensor in the lab with standard turbidity solutions, the proposed low-cost turbidity sensor demonstrated a strong linear correlation between a low-cost sensor and a commercial hand-held turbidimeter. In the field, the low-cost sensor measurements were statistically significantly correlated to a standard high-cost commercial turbidity sensor. Biofouling and drifting issues were also analysed after the sensors were deployed in the field for more than 6 months, showing that both biofouling and drift occur during monitoring. Nonetheless, in terms of maintenance requirements, the low-cost sensor exhibited similar needs compared to the GreenSpan sensor.
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http://dx.doi.org/10.3390/s24123926 | DOI Listing |
PLoS One
December 2024
School of Geography, Archaeology and Environmental Studies, University of the Witwatersrand, Johannesburg, South Africa.
Atmospheric correction plays an important role in satellite monitoring of lake water quality. However, different atmospheric correction algorithms yield significantly different accuracy for inland lake waters beset by shallowness and turbidity. Finding a suitable algorithm for a specific lake is critical for quantitative satellite water-environmental monitoring.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Faculty of Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan.
Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability.
View Article and Find Full Text PDFInt J Mol Sci
November 2024
Institute of Chemical Biology and Fundamental Medicine (ICBFM), Siberian Branch of the Russian Academy of Sciences (SB RAS), Novosibirsk 630090, Russia.
Fused in sarcoma (FUS) is involved in the formation of nuclear biomolecular condensates associated with poly(ADP-ribose) [PAR] synthesis catalyzed by a DNA damage sensor such as PARP1. Here, we studied FUS microphase separation induced by poly(ADP-ribosyl)ated PARP1 [PAR-PARP1] or its catalytic variants PARP1 and PARP1, respectively, synthesizing (short PAR)-PARP1 or (short hyperbranched PAR)-PARP1 using dynamic light scattering, fluorescence microscopy, turbidity assays, and atomic force microscopy. We observed that biologically relevant cations such as Mg, Ca, or Mn or polyamines (spermine or spermidine) were essential for the assembly of FUS with PAR-PARP1 and FUS with PAR-PARP1 in vitro.
View Article and Find Full Text PDFEnviron Sci Pollut Res Int
December 2024
Department of Civil Engineering, Indian Institute of Technology (BHU), Varanasi, 221005, India.
Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral data cube for turbidity prediction in inland water. Nine machine learning regressors Cat Boost, Decision Trees, Extra Trees, Gradient Boost, Light Gradient Boost (LightGBM), Recursive Feature Elimination (RFE), Random Forest, Support Vector Regressor (SVR), and Xtreme Gradient Boost (XGBoost) have been used to compute the feature importance of the hyperspectral bands for predicting turbidity.
View Article and Find Full Text PDFAdverse weather conditions present a primary challenge for ground-based LiDAR imaging systems in outdoor applications. The use of polarization has been proposed as an effective filtering mechanism. However, the number of potential situations is large, complex and difficult to parameterize with accuracy.
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