IN-SITU IONIC CHEMICAL ANALYSIS OF FRESH WATER VIA A NOVEL COMBINED MULTI-SENSOR / SIGNAL PROCESSING ARCHITECTURE
Abstract
The capability for comprehensive, real-time, in-situ characterization of the chemical constituents of natural waters is a powerful tool for the advancement of the ecological and geochemical sciences, e.g. by facilitating rapid high-resolution adaptive sampling campaigns and avoiding the potential errors and high costs related to traditional grab sample collection, transportation and analysis. Portable field-ready instrumentation also promotes the goals of large-scale monitoring networks, such as CUASHI and WATERS, without the financial and human resources overhead required for traditional sampling at this scale. Problems of environmental remediation and monitoring of industrial waste waters would additionally benefit from such instrumental capacity. In-situ measurement of all major ions contributing to the charge makeup of natural fresh water is thus pursued via a combined multi-sensor/multivariate signal processing architecture. The instrument is based primarily on commercial electrochemical sensors, e.g. ion selective electrodes (ISEs) and ion selective field-effect transistors (ISFETs), to promote low cost as well as easy maintenance and reproduction,. The system employs a novel architecture of multivariate signal processing to extract accurate information from in-situ data streams via an "unmixing" process that accounts for sensor non-linearities at low concentrations, as well as sensor cross-reactivities. Conductivity, charge neutrality and temperature are applied as additional mathematical constraints on the chemical state of the system. Including such non-ionic information assists in obtaining accurate and useful calibrations even in the non-linear portion of the sensor response curves, and measurements can be made without the traditionally-required standard additions or ionic strength adjustment. Initial work demonstrates the effectiveness of this methodology at predicting inorganic cations (Na+, NH4+, H+, Ca2+, and K+) in a simplified system containing only a single anion (Cl-) in addition to hydroxide, thus allowing charge neutrality to be easily and explicitly invoked. Calibration of every probe relative to each of the five cations present is undertaken, and resulting curves are used to create a representative environmental data set based on USGS data for New England waters. Signal processing methodologies, specifically artificial neural networks (ANNs), are extended to use a feedback architecture based on conductivity measurements and charge neutrality calculations. The algorithms are then tuned to optimize performance of the algorithm at predicting actual concentrations from these simulated signals. Results are compared to use of component probes as stand-alone sensors. Future extension of this instrument for multiple anions (including carbonate and bicarbonate, nitrate, and sulfate) will ultimately provide rapid, accurate field measurements of the entire charge balance of natural waters at high resolution, improving sampling abilities while reducing costs and errors related to transport and analysis of grab samples.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2009
- Bibcode:
- 2009AGUFM.H53D0948M
- Keywords:
-
- 0470 BIOGEOSCIENCES / Nutrients and nutrient cycling;
- 0496 BIOGEOSCIENCES / Water quality;
- 1094 GEOCHEMISTRY / Instruments and techniques