A water pollution monitoring and early warning system

Abstract

The application discloses a water pollution monitoring and early warning system, adopts an automatic water body collecting device, thereby removing the tediousness and time-consuming nature of manual sampling, and realizes real-time monitoring; water quality sensors are used to monitor multiple water quality indexes, and the accuracy and comprehensiveness of monitoring are improved; a central computer control system adopts an intelligent algorithm, can analyze and give early warning according to real-time data, and improves the monitoring efficiency; meanwhile, relevant personnel are informed through various ways, so that the pollution event can be responded in time, and the problems of high sampling cost, long time consumption, long monitoring time interval, and insufficient monitoring precision and real-time performance of the traditional water pollution monitoring method are solved.

Description

Technical Field

[0001] This invention relates to the technical field of environmental monitoring, and in particular to a water pollution monitoring and early warning system. Background Technology

[0002] Water pollution has become a significant aspect of global environmental issues, posing a serious threat to ecosystems and human health.

[0003] Water pollution monitoring systems typically rely on manual water sample collection, sending the samples to laboratories for analysis. This traditional method has certain limitations. First, manual sampling requires significant manpower and time, especially in large bodies of water and remote areas, making sampling extremely difficult and expensive. Second, due to the cyclical nature of sample collection and analysis, traditional periodic monitoring methods cannot promptly detect and warn of water pollution events. By the time water bodies are polluted, severe environmental and ecological damage may have already occurred, yet traditional monitoring systems fail to provide timely warning signals.

[0004] Furthermore, traditional water quality monitoring instruments often require calibration and maintenance, and are sensitive to environmental changes, making them susceptible to interference and affecting the accuracy of monitoring results. Additionally, traditional monitoring instruments are typically used for offline analysis, unable to monitor dynamic changes in water bodies in real time, thus limiting timely responses to changes in water quality.

[0005] In summary, traditional water pollution monitoring methods suffer from problems such as high sampling costs, long processing times, excessively long monitoring intervals, and insufficient monitoring accuracy and real-time performance. Therefore, there is an urgent need for a new type of water pollution monitoring and early warning system to overcome the shortcomings of traditional methods and achieve efficient, accurate, and real-time water quality monitoring and early warning. Summary of the Invention

[0006] The purpose of this section is to outline some aspects of embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0007] In view of the problems existing in the above-mentioned water pollution monitoring methods, the present invention is proposed.

[0008] Therefore, the technical problem solved by this invention is to address the issues of high sampling costs, long time consumption, excessively long monitoring intervals, and insufficient monitoring accuracy and real-time performance in traditional water pollution monitoring methods.

[0009] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a water pollution monitoring and early warning system, comprising the following components: a set of water sampling devices, uniformly arranged in various parts of a water body, which autonomously sample water at corresponding locations at preset time intervals; a set of sensor detection devices, each embedded in the corresponding water sampling device, which detects corresponding water quality indicators of the water body through corresponding sensor detection components; a data acquisition and transmission device, connected to the set of sensor detection devices, acquiring the water quality indicators of each set, and transmitting the water quality indicators of each set to a cloud central data processor through a preset wireless transmission network; a cloud central data processor, connected to the data acquisition and transmission device, receiving the water quality indicators of each set, synchronously completing water quality analysis, and transmitting the analysis results to a monitoring and early warning device; a monitoring and early warning device, connected to the cloud central data processor, receiving the analysis results, and transmitting the monitoring and early warning results to mobile user terminals currently connected to the same wireless local area network for alarm display through the connected wireless local area network; wherein, the sensor detection device specifically includes: acquiring water quality indicators through a configured turbidity sensor. The system includes three water quality indicators: turbidity, water temperature (obtained via a configured temperature sensor), pH (obtained via a configured pH sensor), and dissolved oxygen (obtained via a configured dissolved oxygen sensor). The cloud-based central data processor receives these water quality indicators and synchronously performs water quality analysis, specifically including: acquiring these indicators via a wireless network; establishing a general analysis model, sequentially inputting each set of water quality indicators, and sequentially acquiring and storing reference values ​​for each current water quality indicator at each location; when the error threshold between the reference values ​​at each location exceeds a set range, activating subsequent analysis for assistance, performing subsequent analysis at each location after autonomous sampling at preset time intervals, and acquiring a set of reference values ​​for different water bodies; establishing a water quality indicator variation curve for each location based on the reference values; acquiring reference values ​​for each variation based on the variation curves; establishing an early warning analysis model, inputting the reference values, acquiring early warning values ​​for each location, obtaining an early warning curve based on the early warning values, and determining whether to issue an early warning based on whether the maximum derivative of the early warning curve exceeds a threshold value of 1.

[0010] The established general analytical model is specifically as follows:

[0011]

[0012] Where η is the reference value of water quality index, α is the turbidity index of water body, β is the temperature index of water body, λ is the dissolved oxygen index of water body, γ is the pH value index of water body, and 2.04, 1.1, -0.87, 1 or 1.08 are all normal function adjustment and correction parameters. The integral operation is a constant function integral and the integration constant is 0.

[0013] As a preferred embodiment of the water pollution monitoring and early warning system of the present invention, when the water body exhibits a strip-shaped flow pattern, a group of the water sampling devices are respectively set at the water source, several uniform locations in the middle section of the water body, and the inlet at the end of the water body.

[0014] In a preferred embodiment of the water pollution monitoring and early warning system of the present invention, the error threshold is obtained by the following formula:

[0015]

[0016] Where σ is the error threshold, η is the reference value of water quality index, i and j represent measurement points, and n represents the number of tests.

[0017] As a preferred embodiment of the water pollution monitoring and early warning system of the present invention, the defined range is specifically:

[0018]

[0019] As a preferred embodiment of the water pollution monitoring and early warning system of the present invention, when establishing the water quality index variation curve, a preset time interval is used as the horizontal axis, and the obtained water quality index reference value is used as the vertical axis. After obtaining the points in the two-dimensional coordinate system, different points are connected one after another with a smooth curve to obtain the water quality index variation curve.

[0020] As a preferred embodiment of the water pollution monitoring and early warning system of the present invention, the system obtains the derivative values ​​at different points on the curve after obtaining the water quality index change curve, and obtains the maximum derivative value, minimum derivative value and average derivative value on the current water quality index change curve. The three parameters are defined as the change reference values.

[0021] As a preferred embodiment of the water pollution monitoring and early warning system of the present invention, the early warning analysis model is specifically as follows:

[0022]

[0023] Where θ is the location warning value, w 最大 For the maximum derivative value, w 最小 For the minimum derivative value and w 均 The average derivative value is used, and the integration operation is a constant function integration with an integration constant of 0.

[0024] The beneficial effects of this invention are as follows: This invention provides a water pollution monitoring and early warning system that uses an automated water body sampling device, thereby eliminating the tediousness and time-consuming nature of manual sampling and achieving real-time monitoring; it utilizes water quality sensors to monitor multiple water quality indicators, improving the accuracy and comprehensiveness of monitoring; the central computer control system employs intelligent algorithms to analyze and issue early warnings based on real-time data, improving monitoring efficiency; simultaneously, early warning signals are communicated to relevant personnel through multiple methods, ensuring that pollution incidents can be responded to in a timely manner, thus solving the problems of high sampling costs, long time consumption, excessively long monitoring intervals, and insufficient monitoring accuracy and real-time performance of traditional water pollution monitoring methods. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0026] Figure 1 The overall system module diagram of the water pollution monitoring and early warning system provided by the present invention.

[0027] Figure 2 The flowchart shows the steps of receiving various water quality indicators and simultaneously completing water quality analysis using the cloud central data processor provided by this invention. Detailed Implementation

[0028] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0029] Traditional water pollution monitoring methods suffer from problems such as high sampling costs, long processing times, excessively long monitoring intervals, and insufficient monitoring accuracy and real-time performance.

[0030] Therefore, please refer to Figure 1 This invention provides a water pollution monitoring and early warning system, comprising the following components:

[0031] A set of water sampling devices are evenly distributed in various parts of the water body and automatically sample the water at the corresponding parts at preset time intervals.

[0032] It should be noted that when the water body exhibits a strip-shaped flow pattern, a set of water sampling devices is set up at the water source, several uniform locations in the middle section of the water body, and at the confluence point at the end of the water body; when the water body exhibits a fan-shaped pattern, a set of water sampling devices is set up uniformly around it.

[0033] Specifically, the water sampling devices are set at uniform intervals.

[0034] A set of sensor detection devices are all embedded in the corresponding water sampling device, and the corresponding water quality indicators of the water body are detected through the corresponding sensing detection components;

[0035] Specifically, the sensor detection device includes:

[0036] The turbidity index of the water body is obtained by configuring a turbidity sensor. The turbidity sensor assesses the transparency of water by measuring the concentration of suspended particulate matter in the water. It uses the principle of light scattering, which determines the turbidity of the water body by sending a light beam and measuring the intensity of the scattered light. This can provide key information about the clarity of the water, especially when a pollution event occurs.

[0037] The water temperature index is obtained by the configured temperature sensor. The temperature sensor is used to monitor the temperature change of the water body. Water temperature has an important impact on the ecosystem and water quality in the water body. Therefore, timely monitoring of temperature changes is crucial to maintaining the ecological balance of the water body.

[0038] The pH value of the water body is obtained by configuring a pH sensor. The pH sensor is used to measure the acidity and alkalinity of the water body. Changes in the pH value of the water body can affect the chemical reactions and biological activities of dissolved substances in the water. Therefore, it is of great significance for water quality monitoring and pollution detection.

[0039] The dissolved oxygen level in the water is obtained by configuring a dissolved oxygen sensor. The dissolved oxygen sensor is used to measure the dissolved oxygen content in the water. Dissolved oxygen is one of the essential conditions for the survival of organisms in the water. Therefore, monitoring the dissolved oxygen level can provide key information about the health of the water body and the status of the ecosystem.

[0040] The data acquisition and transmission device is connected to a set of sensor detection devices to acquire various water quality indicators and transmits these indicators to the cloud central data processor via a preset wireless transmission network.

[0041] It should be noted that the preset wireless transmission network is set up and data network transmission is performed using existing technology to achieve wireless transmission.

[0042] The cloud-based central data processor connects to the data acquisition and transmission device, receives various water quality indicators, performs water quality analysis synchronously, and transmits the analysis results to the monitoring and early warning device.

[0043] For further details, please refer toFigure 2 The cloud-based central data processor receives various water quality indicators and synchronously completes water quality analysis, specifically including:

[0044] S1: The wireless network acquires water quality indicators for each group;

[0045] S2: Establish a general analysis model, input each group of water quality indicators in sequence, obtain and store the reference values ​​of water quality indicators at each current location in sequence;

[0046] Specifically, the general analytical model established is as follows:

[0047]

[0048] Where η is the reference value of water quality index, α is the turbidity index of water body, β is the temperature index of water body, λ is the dissolved oxygen index of water body, γ is the pH value index of water body, and 2.04, 1.1, -0.87, 1 or 1.08 are all normal function adjustment and correction parameters. The integral operation is a constant function integral and the integration constant is 0.

[0049] The design principles behind this step are explained below: First, it's important to understand that after the sampling device collects water samples at each location, it can be analyzed. The analysis of each location yields a set of water quality indicators for that location. Then, an analytical model is established to derive reference values ​​for the water quality indicators at the current location. Since subsequent monitoring of the location's condition will require referencing these current values, this value must be calculated first, and all subsequent steps will use this value as a reference.

[0050] Then, the index values ​​at the current location are calculated: First, those skilled in the art know that the index of a water body at a location mainly consists of the following four aspects: turbidity index, which reflects the concentration of suspended particulate matter in the water; temperature index, which reflects the temperature of the water body; pH ​​index, which reflects the pH value of the water body; and dissolved oxygen index, which reflects the dissolved oxygen concentration of the water body. By obtaining these four indexes, the index values ​​of the water body can be reflected from various perspectives.

[0051] Then, calculations are performed using four indicators. This scheme uses an integral form of sum-product to express the results. Those skilled in the art will know that under normal operating conditions, there are two basic expressions that can reflect the current water quality indicators: α*β*γ*λ and α+β+γ+λ. Both of these can achieve the interconnection of the four influencing factors.

[0052] Regarding the first part of the formula: α 2.04 β 1.1 γ -0.87 λ 1或1.08The exponential factors for different parameters are derived through mathematical calculations as follows: Taking α as an example, firstly, the influence of the other three factors is eliminated, and only one factor, α, is used to express the result. In the formula generation stage, n known samples are tested to obtain the relevant parameters in the samples. An additional set of comparison water samples is obtained. This set of comparison water samples generally uses regularly placed purified water as a reference to obtain the corresponding indicators in the purified water. The variance values ​​of the corresponding parameters of each known sample compared with the standard indicators are compared to obtain the average value of each variance value. The integral of the constant function of the average value is calculated with 0-1. The integral function is to show the error in the most detailed way. The upper limit of 1 is to reflect the specific difference with purified water (represented by 1).

[0053] In summary, the given method can be used to sequentially obtain each correction constant. Of course, without considering cost, a simulation data simulator can be used for specific simulations. The method is the same, and the obtained correction function is more accurate.

[0054] In the latter half of the formula, αdx, βdx, λdx, and γdx actually represent definite integrals over the four constants mentioned earlier. α, β, λ, and γ are four basic indicators, whose values ​​can be clearly calculated based on the corresponding sensors; these values ​​are constants. The analytical model established by this method is obtained through two expressions, and the purpose of the latter half of the formula is essentially to further refine the expression of the former half.

[0055] To illustrate, consider this example: it's easy to understand that α + β + γ + λ can reflect the four major influencing factors. The proportion of α relative to these four factors (all values ​​are in SI units) represents the degree of influence of α. On a function line, the only complete functional expression representing the distance 0 - α is the integral.

[0056] The second half of the formula: To illustrate, it's easy to understand that α + β + γ + λ can reflect the four major influencing factors. The proportion of α to the four major influencing factors (all values ​​are in SI units) is equivalent to the degree of influence of α. When applied to a function, only the integral can fully demonstrate the function. Therefore, this section obtains the degree of influence of each factor to correct the rough calculation in the first half of the term, and finally forms the whole formula.

[0057] Please refer to Table 1 below for the ratio of water quality index reference values ​​output by the data simulator when λ is 1 and 1.08:

[0058] Table 1: Reference Values ​​and Quantities of Water Quality Indicators

[0059] Water quality index reference value λ = 1.08 12.3046 λ = 1 12.2653 Difference 0.0393 Amount ratio 0.32％

[0060] S3: When the error threshold between the reference values ​​of water quality indicators at each point exceeds the set range, subsequent analysis is activated for assistance. After automatic sampling at a preset time interval, subsequent analysis is performed at each point, and a set of reference values ​​of water quality indicators is obtained for different water bodies.

[0061] It should be noted that if the error threshold between the reference values ​​of water quality indicators at each point does not exceed the set range, no further action will be taken, no warning will be issued, and the water quality will be normal.

[0062] Furthermore, the error threshold is obtained using the following formula:

[0063]

[0064] Where σ is the error threshold, η is the reference value of water quality index, i and j represent measurement points, and n represents the number of tests.

[0065] The basic model of this formula does not require much detailed theoretical explanation; it simply reflects the maximum and minimum values ​​of error. The denominator and numerator of the formula reflect the difference between two points and the degree of their mutual influence.

[0066] Specifically, the defined scope is as follows:

[0067]

[0068] S4: Establish water quality index variation curves for each location based on the reference values ​​of water quality indicators at each location.

[0069] Specifically, when establishing the water quality index variation curve, a preset time interval is used as the horizontal axis, and the obtained water quality index reference value is used as the vertical axis. After obtaining the points in the two-dimensional coordinate system, different points are connected one after another with a smooth curve to obtain the water quality index variation curve.

[0070] S5: Obtain reference values ​​for each change based on the change curves of each water quality indicator;

[0071] Specifically, after obtaining the water quality index change curve, the derivative values ​​at different points on the curve are obtained. The maximum, minimum, and average derivative values ​​on the current water quality index change curve are obtained, and these three parameters are defined as reference values ​​for change.

[0072] S6: Establish an early warning analysis model, input various change reference quantities, obtain early warning values ​​for locations, obtain early warning curves based on the early warning values ​​for locations, and determine whether to issue an early warning based on whether the maximum derivative value of the early warning curve exceeds the threshold 1.

[0073] It should be noted that a threshold of 1 is introduced here. This refers to the degree of change in the warning curve. Those skilled in the art can understand this using conventional methods. The threshold of 1 is not a constant value; users can set it according to their specific circumstances. We have already obtained a curve composed of derivative points. This curve reflects the degree of change over different times. After obtaining the warning curve, we can take its derivative. The derivative reflects the shift in the warning curve. By pre-setting a threshold, a warning can be triggered when this value is reached. Of course, this threshold can be set manually; a smaller threshold is set for higher sensitivity, and a larger threshold is set for lower sensitivity.

[0074] Furthermore, the early warning analysis model is specifically as follows:

[0075]

[0076] Where θ is the location warning value, w 最大 For the maximum derivative value, w 最小 For the minimum derivative value and w 均 The average derivative value is used, and the integration operation is a constant function integration with an integration constant of 0.

[0077] It's easy to understand that after obtaining the water quality index change curve, it's necessary to study the water quality change curve. The degree of water quality change is only reflected on the curve by the derivative value. The derivative value expresses the degree of change of a later point compared to a previous point. In practical terms, it means the degree of change of subsequent index detections compared to previous index detections.

[0078] In the context of a specific curve graph, a curve can yield derivative values ​​at countless points, with countless possible variations. The most comprehensive reflection of its overall nature is undoubtedly the average value. Therefore, the average of all derivative values ​​is calculated here to reflect this. The maximum and minimum derivative values ​​are self-explanatory, reflecting the maximum and minimum degrees of variation, respectively.

[0079] The purpose of integrating here is to determine the degree of variation. We know that all derivatives lie between their maximum and minimum values. Arranging countless derivatives yields a curve representing the derivative. The influence of this curve is then determined by the area formed by it, which is the meaning of integration. Therefore, integration is introduced here. The choice of the average value for constant function integration is for computational convenience. The average value already reflects the general shape of the derivative curve. Integrating the average value between its maximum and minimum values ​​allows for a quick calculation of the derivative's responsiveness, i.e., the extent of its variation.

[0080] It should be noted that when obtaining different point warning values, refer to the above method, with the location interval as the horizontal axis and the point warning value as the vertical axis. A smooth curve is used to connect all points in the two-dimensional coordinate system to form a warning curve. Obtain the maximum derivative value of the warning curve. When the maximum derivative value exceeds the threshold of 1, a warning is issued. When the maximum derivative value does not exceed the threshold of 1, no warning is issued.

[0081] The monitoring and early warning device is connected to the cloud central data processor. After receiving and analyzing the results, it transmits the monitoring and early warning results to the mobile user terminals currently connected to the same wireless local area network for alarm display.

[0082] To verify the practicality of this invention, tests were conducted on two closed water pipes. The pollution source in both pipes was simultaneously increased 10 times. The test results were reviewed within 24 hours. Multiple tests were performed, and the results are shown in Table 2 below:

[0083] Table 2: Comparison of Test Results

[0084] Detection pollution times (times) Accuracy rate (%) The present application 9 91.88 Conventional technology 4 49.73

[0085] This invention provides a water pollution monitoring and early warning system. It employs an automated water sampling device, eliminating the tediousness and time-consuming nature of manual sampling and enabling real-time monitoring. Water quality sensors monitor multiple water quality indicators, improving the accuracy and comprehensiveness of the monitoring. The central computer control system uses intelligent algorithms to analyze and issue early warnings based on real-time data, enhancing monitoring efficiency. Simultaneously, early warning signals are communicated to relevant personnel through various means, ensuring timely response to pollution incidents. This system solves the problems of high sampling costs, long sampling times, excessively long monitoring intervals, and insufficient monitoring accuracy and real-time performance inherent in traditional water pollution monitoring methods.

[0086] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A water pollution monitoring and early warning system, characterized in that, Includes the following components: A set of water sampling devices are evenly distributed in various parts of the water body and automatically sample the water at the corresponding parts at preset time intervals. A set of sensor detection devices are all embedded in the corresponding water body sampling device, and the corresponding water quality indicators of the water body are detected through the corresponding sensor detection components; The data acquisition and transmission device is connected to a set of sensor detection devices to acquire the water quality indicators of each set, and transmits the water quality indicators of each set to the cloud central data processor through a preset wireless transmission network. The cloud central data processor is connected to the data acquisition and transmission device, receives the water quality indicators from each group, performs water quality analysis synchronously, and transmits the analysis results to the monitoring and early warning device. The monitoring and early warning device is connected to the cloud central data processor. After receiving the analysis results, it transmits the monitoring and early warning results to the mobile user terminal currently connected to the same wireless local area network for alarm display through the connected wireless local area network. The sensor detection device specifically includes: acquiring water turbidity index through a configured turbidity sensor, acquiring water temperature index through a configured temperature sensor, acquiring water pH index through a configured pH value sensor, and acquiring water dissolved oxygen index through a configured dissolved oxygen sensor. Specifically, the cloud central data processor receives the water quality indicators from each group and synchronously completes the water quality analysis, including: The wireless network acquires the water quality indicators described in each group; Establish a general analysis model, input the water quality indicators of each group in sequence, and obtain and store the reference values ​​of the water quality indicators of each current point in sequence. When the error threshold between the reference values ​​of water quality indicators at each point exceeds the set range, subsequent analysis is activated for assistance. After automatic sampling at a preset time interval, subsequent analysis is performed at each point, and a set of reference values ​​of water quality indicators is obtained for different water bodies. Based on the reference values ​​of water quality indicators at each location, establish the water quality indicator variation curves corresponding to each location. Obtain the derivative values ​​at different points on the curve, and obtain the maximum, minimum, and average derivative values ​​on the current water quality index variation curve. These three parameters are defined as variation reference values. Establish an early warning analysis model, input the variation reference values, obtain early warning values ​​at the points, obtain an early warning curve based on the early warning values ​​at the points, and determine whether to issue an early warning based on whether the maximum derivative value of the early warning curve exceeds a threshold of 1. The early warning analysis model is specifically as follows: ； Where θ is the location warning value, w 最大 For the maximum derivative value, w 最小 For the minimum derivative value and w 均 The average derivative value is used, and the integration operation is a constant function integration with an integration constant of 0. The established general analytical model is specifically as follows: ； Where η is the reference value of water quality index, α is the turbidity index of water body, β is the temperature index of water body, λ is the dissolved oxygen index of water body, γ is the pH value index of water body, and 2.04, 1.1, -0.87, 1 or 1.08 are all normal function adjustment and correction parameters. The integral operation is a constant function integral and the integration constant is 0.

2. The water pollution monitoring and early warning system according to claim 1, characterized in that: When the water body exhibits a strip-shaped flow pattern, a set of the water sampling devices are respectively set at the water source, several uniform locations in the middle section of the water body, and the inlet at the end of the water body.

3. The water pollution monitoring and early warning system according to claim 2, characterized in that, The error threshold is obtained using the following formula: ； Where σ is the error threshold, η is the reference value of water quality index, i and j represent measurement points, and n represents the number of tests.

4. The water pollution monitoring and early warning system according to claim 3, characterized in that, The specified range is as follows: 。 5. The water pollution monitoring and early warning system according to claim 4, characterized in that: When establishing the water quality index variation curve, a preset time interval is used as the horizontal axis, and the obtained water quality index reference value is used as the vertical axis. After obtaining the points in the two-dimensional coordinate system, different points are connected one after another with a smooth curve to obtain the water quality index variation curve.

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