# US 11,079,368 B2 · Optical-based monitoring and characterization of natural water

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- Number: US 11,079,368 B2
- Document ID: US11079368B2
- Status: Granted
- Kind: B2
- Application number: 15/633,656
- Filed: 2017-06-26
- Published: 2018-02-08
- Granted: 2021-08-03
- Earliest claimed: 2016-06-24
- Assignees: Integral Consulting Inc.
- Applicants: Grace Chang Spada; Craig A. Jones; Todd Martin
- Inventors: Grace Chang Spada; Craig A. Jones; Todd Martin
- Web page: https://patentgrep.com/patent/US11079368B2

## CPC

- G01N 33/18: Water
- G16C 10/00: Computational theoretical chemistry, i.e. ICT specially adapted for theoretical aspects of quantum chemistry, molecular mechanics, molecular dynamics or the like
- G16C 20/30: Prediction of properties of chemical compounds, compositions or mixtures
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- G16C: COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE

## Abstract

The invention is directed towards a method and system for optically-based monitoring of chemicals of potential concern (COPCs), including mercury (Hg), methyl mercury (MeHg), and polychlorinated biphenyls (PCBs). It employs a multi-parameter statistical analysis model, such as partial least square regression (PLS regression), to identify combinations of predictors (e.g., optical, water quality, and physical properties) that have large covariance with the response values. The predictive model is validated against analytic samples from laboratories. The steps in the method are repeated until predictors, or parameters are selected that avoid over-fitting and which yield physically-based results.

## Claims

### Claim 1

A computer-implemented method for monitoring natural waters, comprising:
receiving analytic data from a laboratory, which measures a concentration of a chemical of potential concern (COPC) over a designated period of time based on samples from a water column collected at a specific location, wherein the chemical of potential concern is a hydrophobic chemical;
receiving a time series of sensor data from each of a plurality of sensors, over the designated period of time, wherein each sensor measures a different characteristic of the water column, and wherein each sensor is submerged in the water column at the specific location, and wherein none of the sensors measure the concentration of the COPC;
determining parameters, based on the sensor data and the analytic data, that describe a primary variance of the (COPC);
developing a model, based on the determined parameters, that predicts a concentration of the COPC over the designated period of time;
receiving a time series of sensor data from the plurality of sensors, the time series of sensor data corresponding to a validation time period;
receiving analytic data that measures the concentration of the (COPC) at the specific location over the validation time period;
running the model using the time series of sensor data corresponding to the validation time period, to predict concentrations of the COPC at the specific location over the validation time period; and
determining whether the predicted concentrations are within a threshold value when compared to the received analytic data that measure concentrations over the validation time period.

### Claim 2 (depends on 1)

The computer-implemented method of claim 1 wherein the plurality of sensors comprise at least one sensor selected from the group consisting of an absorption-attenuation meter, a conductivity-temperature-depth sensor, a fluorometer, a backscatter sensor, a turbidity sensor, a spectrometer, a spectrophotometer, a current meter, and a nutrient sensor.

### Claim 3 (depends on 1)

The computer-implemented method of claim 1 wherein the chemical of potential concern is selected from the group comprising mercury (Hg), methyl mercury (MeHg), and polychlorinated biphenyls (PCBs).

### Claim 4 (depends on 1)

The computer-implemented method of claim 1 wherein the model that predicts the concentration of the chemical of potential concern in the water column is a multi-parameter statistical regression model.

### Claim 5 (depends on 1)

The computer-implemented method of claim 1 wherein the model that predicts the concentration of the chemical of potential concern is a partial least squares regression.

### Claim 6 (depends on 1)

The computer-implemented method of claim 1 wherein determining parameters that describe the primary variance comprises selecting a subset of parameters in order to minimize errors and avoid over-constraining the model.

### Claim 7 (depends on 1)

The computer-implemented method of claim 1 further comprising:
testing whether the predicted concentrations correspond to physically-based data, and wherein a test for physically-based data includes at least one member from the group consisting of testing for a negative concentration of the chemical of potential concern, testing if a dissolved or particulate concentration is higher than a total unfiltered concentration, testing if a concentration during a tidal phase jumps up and down with high variance, and testing if a concentration grossly increases during a physically quiescent period.

### Claim 8 (depends on 1)

The computer-implemented method of claim 1 further comprising:
responsive to a determination that the predicted concentrations exceed a threshold value:
determining new parameters that describe the primary variance;
developing a revised model to predict concentrations of the COPC; and
testing the model for an additional validation time period.

### Claim 9

A system for monitoring and characterizing natural waters, comprising:
a device for characterizing natural waters, comprising:
a waterproof housing;
a processor enclosed by the waterproof housing; and
a plurality of sensors, enclosed by the waterproof housing, wherein each sensor provides a time series of sensor data over a designated period of time, wherein each sensor measures a different characteristic of a water column, and wherein each sensor is submerged in the water column at a specific location;
a processing device for predicting concentrations of a chemical of potential concern, comprising:
a data storage for (1) storing the time series of sensor data received from each of the plurality of sensors, and (2) storing analytic data from a laboratory that measures a concentration of a chemical of potential concern (COPC) over the designated period of time based on samples from the water column collected at the specific location, wherein the (COPC) is a hydrophobic chemical and wherein none of the sensors in the plurality of sensors measures the concentration of the COPC;
a processor
a nontransitory memory, in communication with the processor, for storing instructions, which when executed by the processor, cause the device:
to determine parameters, based on the sensor data and the analytic data, that describe a primary variance of the (COPC);
to develop a model, based on the determined parameters, that predicts the concentration of the COPC over the designated period of time;
to receive a time series of sensor data from the plurality of sensors, the time series of sensor data corresponding to a validation time period;
to receive analytic data that measures the concentration of the COPC at the specific location over the validation time period;
to run the model using the time series of sensor data corresponding to the validation time period, to predict concentrations of the COPC at the specific location over the validation time period; and
to determine whether the predicted concentrations are within a threshold value when compared to the received analytic data that measure concentrations over the validation time period.

### Claim 10 (depends on 9)

The system of claim 9 wherein the plurality of sensors comprise at least one sensor selected from the group consisting of an absorption-attenuation meter, a conductivity-temperature-depth sensor, a fluorometer, a backscatter sensor, a turbidity sensor, a spectrometer, a spectrophotometer, a current meter, and a nutrient sensor.

### Claim 11 (depends on 9)

The system of claim 9 wherein the chemical of potential concern is selected from the group comprising mercury (Hg), methyl mercury (MeHg), and polychlorinated biphenyls (PCBs).

### Claim 12 (depends on 9)

The system of claim 9 wherein the model that predicts the concentration the chemical of potential concern in the water column is a multi-parameter statistical regression model.

### Claim 13 (depends on 9)

The system of claim 9 wherein the model that predicts the concentration of the chemical of potential concern is a partial least squares regression.

### Claim 14 (depends on 9)

The system of claim 9 wherein determining parameters that describe the primary variance comprises selecting a subset of parameters in order to minimize errors and avoid over-constraining the model.

### Claim 15 (depends on 9)

The system of claim 9 wherein the instructions, when executed by the processor, further cause the device:
to test whether the predicted concentrations correspond to physically-based data, and wherein a test for physically-based data includes at least one member from the group consisting of testing for a negative concentration of the chemical of potential concern, testing if a dissolved or particulate concentration is higher than a total unfiltered concentrations, testing if a concentration during a tidal phase jumps up and down with high variance, and testing if a concentration grossly increases during a physically quiescent period.

### Claim 16 (depends on 9)

The system of claim 9 wherein the instructions, when executed by the processor, further cause the device, responsive to a determination that the predicted concentrations exceed a threshold value:
to determine new parameters that describe the primary variance;
to develop a revised model to predict concentrations of the COPC; and
to test the model for an additional validation time period.