scholarly journals Live-Cell Systems in Real-Time Biomonitoring of Water Pollution: Practical Considerations and Future Perspectives

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7028
Author(s):  
Donald Wlodkowic ◽  
Tomasz M. Karpiński
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Live Cell ◽  
Warning Systems ◽  
Water Contaminants ◽  
Sustainable Food ◽  
Recent Advances ◽  
Cell Microarrays

Continuous monitoring and early warning of potential water contamination with toxic chemicals is of paramount importance for human health and sustainable food production. During the last few decades there have been noteworthy advances in technologies for the automated sensing of physicochemical parameters of water. These do not translate well into online monitoring of chemical pollutants since most of them are either incapable of real-time detection or unable to detect impacts on biological organisms. As a result, biological early warning systems have been proposed to supplement conventional water quality test strategies. Such systems can continuously evaluate physiological parameters of suitable aquatic species and alert the user to the presence of toxicants. In this regard, single cellular organisms, such as bacteria, cyanobacteria, micro-algae and vertebrate cell lines, offer promising avenues for development of water biosensors. Historically, only a handful of systems utilising single-cell organisms have been deployed as established online water biomonitoring tools. Recent advances in recombinant microorganisms, cell immobilisation techniques, live-cell microarrays and microfluidic Lab-on-a-Chip technologies open new avenues to develop miniaturised systems capable of detecting a broad range of water contaminants. In experimental settings, they have been shown as sensitive and rapid biosensors with capabilities to detect traces of contaminants. In this work, we critically review the recent advances and practical prospects of biological early warning systems based on live-cell biosensors. We demonstrate historical deployment successes, technological innovations, as well as current challenges for the broader deployment of live-cell biosensors in the monitoring of water quality.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

scholarly journals Towards an operational database for real-time environmental monitoring and early warning systems

2017 ◽  
Vol 108 ◽  
pp. 2250-2259 ◽  
Author(s):  
Bartosz Balis ◽  
Marian Bubak ◽  
Daniel Harezlak ◽  
Piotr Nowakowski ◽  
Maciej Pawlik ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Monitoring And Early Warning

scholarly journals African Rainfall Climatology Version 2 for Famine Early Warning Systems

2013 ◽  
Vol 52 (3) ◽  
pp. 588-606 ◽  
Author(s):  
Nicholas S. Novella ◽  
Wassila M. Thiaw
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Correlation Coefficients ◽  
Global Precipitation Climatology Project ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Telecommunication System ◽  
African Rainfall ◽  
Global Telecommunication System ◽  
Rainfall Climatology

AbstractThis paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.

scholarly journals Early Warning Systems for Currency Crises with Real-Time Data

2019 ◽  
Vol 30 (4) ◽  
pp. 813-835 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Jan P. A. M. Jacobs ◽  
Gerard H. Kuper ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Currency Crises ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data

Using higher organisms in biological early warning systems for real-time toxicity detection

2001 ◽  
Vol 16 (7-8) ◽  
pp. 457-465 ◽  
Author(s):  
William H van der Schalie ◽  
Tommy R Shedd ◽  
Paul L Knechtges ◽  
Mark W Widder
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems

scholarly journals Early Warning Systems with Real-Time Data

2017 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Jan P. A. M. Jacobs ◽  
Gerard H. Kuper ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data

scholarly journals Early Warning Systems with Real-Time Data

2017 ◽  
Author(s):  
Tjeerd M. Boonman ◽  
Gerard H. Kuper ◽  
Jan P.A.M. Jacobs ◽  
Alberto Romero
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Time Data ◽  
Real Time Data

Recent Advances in Early Warning Systems for Landslide Forecasting

2021 ◽  
pp. 249-260
Author(s):  
Ram Wanare ◽  
Kannan K. R. Iyer ◽  
Prathyusha Jayanthi
Keyword(s):  
Early Warning ◽  
Early Warning Systems ◽  
Warning Systems ◽  
Recent Advances ◽  
Landslide Forecasting
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