Methods for the determination of metals and in organic chemicals in environmental samples. Environmental Monitoring Systems Laboratory (Cincinnati, OH), Noyes Publications, Westwood, NJ, (1996), 535 Pages [ISBN No.: 0-8155-1398-4], U.S. List Price: $64.00

Abstract The importance of removing the small amounts of residual organics is increasing as the sources of clean surface water decrease. Knowledge of the nature of these soluble residual organics will be needed in order to assess the type of treatment required for their removal. Residual organics in three different biological treatment plants were analyzed and compared. An attempt was made to characterize these organics by a molecular size distribution on a Sephadex column monitored by differential ultraviolet and refractive index detectors. The organic carbon and chemical oxygen demand of the fractions collected from the column was also determined. An investigation of some of the problems inherent in the monitoring systems was conducted.

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Using Machine Learning for Dependable Outlier Detection in Environmental Monitoring Systems

ACM Transactions on Cyber-Physical Systems ◽

10.1145/3445812 ◽

2021 ◽

Vol 5 (3) ◽

pp. 1-30

Author(s):

Gonçalo Jesus ◽

António Casimiro ◽

Anabela Oliveira

Keyword(s):

Machine Learning ◽

Environmental Monitoring ◽

Data Quality ◽

Outlier Detection ◽

Prediction Models ◽

Sensor Data ◽

Natural Phenomenon ◽

Monitoring Systems ◽

Data Errors ◽

Redundant Data

Sensor platforms used in environmental monitoring applications are often subject to harsh environmental conditions while monitoring complex phenomena. Therefore, designing dependable monitoring systems is challenging given the external disturbances affecting sensor measurements. Even the apparently simple task of outlier detection in sensor data becomes a hard problem, amplified by the difficulty in distinguishing true data errors due to sensor faults from deviations due to natural phenomenon, which look like data errors. Existing solutions for runtime outlier detection typically assume that the physical processes can be accurately modeled, or that outliers consist in large deviations that are easily detected and filtered by appropriate thresholds. Other solutions assume that it is possible to deploy multiple sensors providing redundant data to support voting-based techniques. In this article, we propose a new methodology for dependable runtime detection of outliers in environmental monitoring systems, aiming to increase data quality by treating them. We propose the use of machine learning techniques to model each sensor behavior, exploiting the existence of correlated data provided by other related sensors. Using these models, along with knowledge of processed past measurements, it is possible to obtain accurate estimations of the observed environment parameters and build failure detectors that use these estimations. When a failure is detected, these estimations also allow one to correct the erroneous measurements and hence improve the overall data quality. Our methodology not only allows one to distinguish truly abnormal measurements from deviations due to complex natural phenomena, but also allows the quantification of each measurement quality, which is relevant from a dependability perspective. We apply the methodology to real datasets from a complex aquatic monitoring system, measuring temperature and salinity parameters, through which we illustrate the process for building the machine learning prediction models using a technique based on Artificial Neural Networks, denoted ANNODE ( ANN Outlier Detection ). From this application, we also observe the effectiveness of our ANNODE approach for accurate outlier detection in harsh environments. Then we validate these positive results by comparing ANNODE with state-of-the-art solutions for outlier detection. The results show that ANNODE improves existing solutions regarding accuracy of outlier detection.

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Colorimetric Determination of Sulphur Dioxide and Sulphites in Various Environmental Samples

Journal of the Chinese Chemical Society ◽

10.1002/jccs.200300038 ◽

2003 ◽

Vol 50 (2) ◽

pp. 257-260 ◽

Cited By ~ 2

Author(s):

Sukumar Chatterjee ◽

Ajai K. Pillai ◽

V. K. Gupta

Keyword(s):

Sulphur Dioxide ◽

Environmental Samples ◽

Colorimetric Determination

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Field analysis Cr(VI) in water samples by a smartphone-based ultralong absorption path reflection colorimetric device

New Journal of Chemistry ◽

10.1039/d0nj05948j ◽

2021 ◽

Author(s):

Xiaolan Chen ◽

Cheng Ma ◽

Qi Kang ◽

Yu-Qin Chen ◽

Dazhong Shen

Keyword(s):

Hexavalent Chromium ◽

Water Samples ◽

Environmental Samples ◽

Field Analysis ◽

Absorption Path

Hexavalent chromium is highly toxic and regarded as one of the main pollutants in environmental samples. Sophisticated laboratory instruments are commercially available for in-field determination of chromium. It is highly...

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