JP2001027634A - Water quality-monitoring system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To restrict water quality sensors to a lowest limit and reduce operation costs by estimating by a multiple regression analysis with using meteorological data from a meteorological sensor as a descriptive variable and plankton data from the water quality sensors as a target variable. SOLUTION: A water quality measurement point 1 for monitoring a kind and a quantity of plankton is set to a water source 2, a water purification plant 3 or the like. Meteorological measurement points 4 such as a meteorological observatory or the like are present everywhere. Each of measured data is collected by a communication means 5 to a central water quality- monitoring center or the water purification plant 3 and processed. For the processing, a multiple regression analysis is carried out in which the water quality measurement point 1 and near meteorological measurement point 4 are paired, data of the kind and quantity of plankton from the water quality measurement point 1 is used as a target variable, and meteorological data such as a quantity of sunshine, a quantity of rainfall, a wind velocity, a temperature and the like from the meteorological measurement point 4 is used as a descriptive variable. The plankton data shows a positive correlation to the quantity of sunshine, wind velocity, temperature and the like, and shows a negative correlation to the quantity of rainfall and the like. According to the method, a concentration of plankton can be analyzed and estimated through a multiple regression from previous meteorological data.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a water quality monitoring system for monitoring the quality of water at a water source, and more particularly to a device for measuring the type and amount of particles such as plankton contained in water and a device for measuring the amount of solar radiation, precipitation, and wind speed. The present invention relates to a water quality monitoring system suitable for predicting future water quality based on data obtained from a device for measuring weather data.

[0002]

2. Description of the Related Art As the water quality of tap water sources such as lakes and rivers deteriorates, water blooms such as phytoplankton are generated, and not only the operation of purification facilities at water source sites but also advanced treatment such as ozone injection at water purification plants. Is becoming necessary. In addition, the efficiency of operation has been required due to the frequency of backwashing of filtration ponds, which are existing facilities of water purification plants, and the increase in the amount of coagulant injected into sedimentation basins. In order to improve the efficiency, it is necessary to directly monitor plankton, which is a problem, and feed it back to a purification facility such as a water purification plant, as disclosed in JP-A-5-172728, JP-A-5-332915, and JP-A-5-172728. , JP-A-6-27014
Water quality and meteorological instruments are installed in the water area to be targeted, as described in JP-A-6-304556, JP-A-7-20199, etc., and at the same time as grasping the growth state of plankton and water quality values in the water area, and simulating Simulates the convection, diffusion, biological and chemical reaction states of water in the aquatic area, grasps the water quality distribution, and based on the results, purifies it with a purification device installed in the aquatic area, and operates the water purification plant. Controlling.

With regard to plankton, a camera is immersed in water, water is pumped up and flown into a flow cell, an enlarged image of plankton is captured by the camera, and image processing is performed to determine the type and amount of plankton. In these conventional examples, for example, information on inflow / outflow of water into a water area, water quality information for each monitoring block, weather information, topographic information of a water area from a terrain database are read, and the flow velocity, water temperature, Calculate the convection of the whole water area based on the inflow / outflow information. From this convection calculation value and water temperature,
Model the condition where water quality information is diffused in the monitoring block. Next, DO, turbidity Tu, phosphorus P, nitrogen TN, chemical oxygen demand COD, and pH after a certain period of time are calculated using the current water quality information as initial values, and then image information and nutrients, water temperature, With DO and COD as initial values, plankton generation is calculated using a plankton breeding model to predict future water quality fluctuations and take measures.

[0004]

In order to achieve these, a huge amount of geographic information and the accumulation of past measurement databases,
A large amount of measuring equipment is required, and a complicated system needs to be built for a long time.

[0005]

In order to solve such a problem, the type and amount of plankton obtained from the means for measuring the amount of plankton generated are predicted based on less geographic information, water quality and weather data. This is very important.
Usually, it is necessary to submerge the sensor in order to collect water quality data. For DO and pH, for electrodes and the like, maintenance such as calibration and replacement of the electrodes is required. Also,
As for the measurement of COD, phosphorus, nitrogen, etc., each item requires a dedicated reagent and sequence, and the equipment becomes expensive.

On the other hand, weather sensors for solar radiation, precipitation, wind speed, etc. have a simple configuration and are easy to maintain, and existing measuring instruments are widely distributed. It is becoming available at.

[0007] Therefore, if a plankton amount in the future can be predicted from a plankton measuring device and a weather sensor that can automatically measure plankton on site, a low-cost system can be constructed. Therefore, we attempted to predict the plankton data obtained by the plankton measurement device as an objective variable by multiple regression analysis using the weather data obtained from the weather sensor as an explanatory variable. It turned out that it can be predicted by the multiple regression equation.

[0008]

FIG. 1 shows a system configuration. A water quality measurement point 1 including a plankton measurement device that monitors the type and quantity of plankton is used for inflow and
It will be installed near the outflow point, near the entrance of the water treatment plant, and inside the water treatment plant 3. In addition, weather measurement points 4 such as weather stations exist everywhere. Close meteorological measurement point 4 that can represent the weather near water quality measurement point 1
If there is no, a weather sensor may be provided at the water quality measurement point 1. The data of each point is collected and processed by a central water quality monitoring center and a water purification plant (not shown) through communication means 5 such as a network.

In the processing, a set of water quality measurement points and meteorological measurement points near the water quality measurement points is used as a set, and the plankton type / quantity data (plankton data) obtained at the water quality measurement points is used as a target variable, and is obtained at the weather measurement points. Multiple regression analysis is performed using weather data such as solar radiation, precipitation, wind speed, and temperature as explanatory variables. In the correlation with plankton data, a group having a positive correlation and a group having a negative correlation are separated. In a short time range of several days, pairs having a positive correlation include solar radiation, wind speed, and temperature. Precipitation has a negative correlation.

At this time, the time of each weather data is independently shifted with respect to each plankton data, and a correlation is obtained between the plankton data and the weather data.

FIG. 2 shows an example of measurements taken in a summer at a retarding pond.

FIG. 2 shows the daily average of the linear plankton density. The plankton density was determined by continuously flowing sample water through the flow cell using an apparatus described in the 32nd Annual Meeting of the Society of Water Environment, p43, processing plankton images captured by a camera, and classifying and counting according to the external shape. Plankton density varies from day to day in a period of about 10 days, 200-1,000 particles / m
The range of L is increased or decreased.

FIGS. 3 and 4 show data of a monthly weather report released by the Japan Meteorological Agency. These are daily values of solar radiation (global solar radiation), precipitation, temperature, and wind speed.
No meteorological data alone can explain the change in plankton density when compared to the change in plankton density.

FIG. 5 shows the correlation between the density of elongated linear plankton such as diatoms contained in the sample water and each weather data. As is clear from the figure, the positive correlation between the amount of solar radiation two days before the plankton measurement day and the linear plankton density is the highest, and the negative correlation between the precipitation two days before the plankton measurement day and the linear plankton density is the highest. To avoid the multicollinearity problem, only the most correlated solar radiation data among the positively correlated variables is used.

FIG. 6 shows the result of multiple regression analysis with the time axis of the weather data shifted by two days using the two weather data as explanatory variables and the linear plankton density as the objective variable. As is clear from the figure, the predicted result of the linear plankton density and the measured value are in good agreement (at this time, the multiple correlation coefficient is 0.9).
8).

According to this embodiment, the plankton density can be predicted by multiple regression analysis based on past weather data, so that it can be used for water quality management in water sources and water purification plants.

In this embodiment, two values of the amount of solar radiation and the amount of precipitation
The linear plankton density was predicted by combining two meteorological data. It is needless to say that the weather data may be narrowed down and analyzed based on the accumulation of the past data as described above, and the weather data used for the prediction may be different depending on other plankton, season, and terrain.

[0018]

(1) Since the water quality sensor can be minimized, the installation cost and running cost of the sensor are greatly reduced.

(2) Since the database is small and there is no need to model a biological system, the time required to construct a water quality monitoring system can be reduced.

(3) Since the system is configured at low cost, the applicable range can be expanded.

[Brief description of the drawings]

FIG. 1 is a configuration diagram of a water quality monitoring system according to an embodiment of the present invention.

FIG. 2 is a characteristic diagram showing plankton density in a retarding pond according to the present invention.

FIG. 3 is a characteristic diagram showing actual measurement data of a weather report according to the present invention.

FIG. 4 is a characteristic diagram showing a correlation between data of a weather monthly report of the present invention.

FIG. 5 is a characteristic diagram showing a correlation between plankton density and each meteorological data of the present invention.

FIG. 6 is a characteristic diagram showing a relationship between linear plankton density and weather data according to the present invention.

[Explanation of symbols]

1 ... water quality measurement point, 2 ... water source, 3 ... water purification plant, 4 ... weather measurement point, 5 ... communication means.

Claims (1)

[Claims] 1. A monitoring system comprising means for measuring water quality data and means for measuring weather conditions, wherein a statistical analysis is performed on the basis of weather data obtained by means for measuring water quality data and means for measuring weather conditions. And a means for explaining water quality data obtained from a means for measuring water quality data.