Effective method for monitoring soil humidity by measuring neutron radiation

Abstract

The invention relates to the technical field of soil humidity monitoring, and particularly discloses an effective method for monitoring soil humidity by measuring neutron radiation, which comprises the steps of S1, region division, S2, equipment installation, S3, data processing, S4, humidity calculation and S5, data analysis and feedback. According to the method, based on the fact that fast neutrons collide with hydrogen nucleuses of water in soil to cause speed reduction or energy attenuation, as the cloud density of the slow neutrons around a probe changes along with the concentration of the hydrogen nucleuses of the water in the soil, the counting rate of the slow neutrons represents the water content of the soil, and the required volumetric water content of the soil can be converted through a correction curve; accumulated soil humidity data provides a scientific basis for agricultural management, helps farmers to make more reasonable planting and irrigation plans and establish a soil humidity database, is helpful for long-term research of interrelation between soil and crops, and guides future agricultural practice.

Description

Technical Field

[0001] The invention belongs to the technical field of soil moisture monitoring, and in particular relates to an effective method for monitoring soil moisture by measuring neutron radiation. Background Art

[0002] As agricultural precision management is increasingly valued, real-time monitoring of soil moisture has become a key link in optimizing irrigation strategies and improving crop yields and quality. Traditional soil moisture monitoring methods often have problems such as lag, lack of accuracy or complex operation, which makes it difficult to meet the needs of modern agriculture for precise management. In this context, neutron radiation monitoring technology based on the deceleration effect caused by the collision of fast neutrons with hydrogen nuclei in soil and water has emerged, providing an innovative solution for real-time and precise monitoring of soil moisture.

[0003] By optimizing the selection of neutron sources, the design of detectors, and the data processing algorithm, rapid and accurate monitoring of soil moisture is achieved. At the same time, by simplifying the equipment structure and reducing manufacturing costs, the technology is easier to promote and apply. In addition, in response to changes in soil properties, the present invention also provides a flexible calibration curve and data analysis model to ensure the accuracy and reliability of the measurement results. These advantages make this method have broad application prospects in agricultural management, and can provide strong technical support for achieving precision irrigation and improving crop yield and quality.

[0004] In this regard, the inventors propose an effective method for monitoring soil moisture by measuring neutron radiation to solve the above problems. Summary of the invention

[0005] The object of the present invention is to provide an effective method for monitoring soil moisture by measuring neutron radiation, so as to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An effective method for monitoring soil moisture by measuring neutron radiation includes the following steps:

[0008] S1. Regional division: Use the adaptive grid division method to divide the soil measurement area into uniform grids, measure at the center point of each grid to obtain systematic data, stratify the area according to land factors, and select representative points in each layer for measurement;

[0009] S2. Equipment installation: install the neutron source probe, the slow neutron counter BF3 detector connected to the neutron source and the measuring tube bracket in the grid measurement area, insert the probe into the soil to the set depth, start the equipment to emit fast neutrons, record the slow neutron count rate within the set time, and repeat the measurement multiple times to obtain more stable counting data;

[0010] S3, data processing, analyzing the collected slow neutron count rate and known humidity sample data, applying Kalman filtering to the slow neutron count data, smoothing the data, reducing the impact of random noise, and fitting the known humidity and the corresponding filtered slow neutron count rate data through a linear regression model;

[0011] S4, moisture calculation, using a linear regression model to calculate the current soil moisture, and quantify the model, evaluate the performance of the model, and convert the slow neutron counts into soil volume moisture content through a calibration curve;

[0012] S5. Data analysis and feedback: Generate a table of the relationship between humidity and count rate to facilitate analysis and identification of trends, record and report current soil moisture data, establish a database, record long-term measurement results, and conduct trend analysis.

[0013] Preferably, the adaptive grid division method in step S1 dynamically adjusts the grid size according to the gradient of soil moisture change, and the specific steps are:

[0014] S21, initial regular grid division, assuming that the length of the measurement area is L, the width is W, and it is divided into n×m uniform grids, and the grid side lengths Δx and Δy are Δx=L / n, Δy=W / m respectively;

[0015] S22, gradient calculation, based on the initial grid division, collect the humidity data of the initial point, and calculate the humidity gradient Gij of the adjacent grid center points;

[0016] S23, adaptive adjustment, adjusting the density of the grid according to the size of the humidity gradient Gij;

[0017] When Gij is large, smaller grids are divided to increase the sampling density of the area;

[0018] When Gij is small, the grid is enlarged and the density of measurement points is reduced.

[0019] Preferably, the calculation expression of the humidity gradient is:

[0020]

[0021] Where θ is the soil moisture and Gij is the moisture gradient at the (i, j) grid.

[0022] Preferably, the expression of the adaptive adjustment is:

[0023]

[0024] Where k is the adjustment coefficient, which is used to control the sensitivity of the grid density change.

[0025] Preferably, the radioactive material of the neutron source is beryllium or deuterium.

[0026] Preferably, the basic update equation of the Kalman filter in step S3 is:

[0027]

[0028] Kk=Pk|k-1H T (HPk|k-1+R) -1

[0029] Pk|k=(I-KkH)Pk|k-1

[0030] in Updated state estimate;

[0031] The state estimate at the previous time step;

[0032] Kk: Kalman gain, which determines the influence of new observation data on the estimation results;

[0033] zk: the observed value at the current moment, i.e., the slow neutron count;

[0034] H: measurement matrix, mapping state to observation space;

[0035] Pk|k-1: the estimation error covariance at the previous time;

[0036] R: observation noise covariance;

[0037] I: identity matrix;

[0038] Kalman filtering is used to update the estimate of slow neutron counts in real time, reduce the impact of noise, and provide more accurate humidity measurements. In the presence of measurement errors, Kalman filtering can effectively improve the reliability and stability of the data.

[0039] Preferably, the linear regression expression in step S3 is:

[0040] Y=aX+b

[0041] Where Y: soil moisture, volumetric water content;

[0042] X: slow neutron count rate cps;

[0043] a: regression coefficient, representing the slope of the relationship between slow neutron count rate and soil moisture;

[0044] b: intercept, indicating the theoretical value of soil moisture when the slow neutron count rate is 0;

[0045] By collecting slow neutron count rate and corresponding soil moisture data, a and b are obtained through linear regression analysis, and a mathematical model is constructed to predict unknown humidity. This model simplifies the relationship between soil moisture and neutron count and is used for real-time monitoring and prediction.

[0046] Preferably, the expression for quantifying the model in step S4 is:

[0047]

[0048] Among them, Yi: actual soil moisture value, that is, the observed value;

[0049] soil moisture values ​​predicted by the model;

[0050] n: sample size;

[0051] RMSE measures the deviation between the model prediction value and the actual value. The smaller the RMSE is, the better the prediction effect of the model is. It provides a quantitative indicator to evaluate the accuracy of the soil moisture measurement model and helps to optimize the model.

[0052] Preferably, the land factors include different soil types, vegetation cover and land use factors.

[0053] Preferably, the trend analysis in step S5 adopts linear trend analysis, and the humidity changes linearly with time or spatial position, and its expression is:

[0054] θ(t)=αt+β

[0055] θ(t): soil moisture, i.e., volumetric water content;

[0056] t: independent variable of time or spatial position;

[0057] α: the slope of the trend, which indicates the rate at which humidity changes with time or spatial location;

[0058] β: intercept, indicating the initial humidity value;

[0059] When α>0, humidity increases with time or spatial position;

[0060] When α < 0, humidity decreases with time or spatial position;

[0061] If the humidity monitoring data shows a decreasing trend, linear analysis helps estimate the rate at which humidity is decreasing.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] (1) The present invention is based on the deceleration or energy attenuation caused by the collision of fast neutrons with hydrogen nuclei of water in the soil. Since the density of the slow neutron "cloud" around the probe varies with the hydrogen nucleus concentration of soil moisture, the counting rate of slow neutrons represents the moisture content of the soil, which can be converted into the required soil volume moisture content through the calibration curve. The accumulated soil moisture data provides a scientific basis for agricultural management, helps farmers formulate more reasonable planting and irrigation plans, and establishes a soil moisture database, which is conducive to long-term research on the relationship between soil and crops and guides future agricultural practices.

[0064] (2) The real-time monitoring of soil moisture in the present invention makes irrigation decisions more scientific and reduces unnecessary waste of water resources. Through precise water management, the irrigation frequency is reduced and water resources are saved, which is of great significance in arid areas. By maintaining appropriate soil moisture, the healthy growth of crop roots is promoted, and the crop yield and quality are improved. Appropriate soil moisture can reduce the occurrence of certain plant diseases and enhance crop resistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of an effective method for monitoring soil moisture by measuring neutron radiation according to the present invention;

[0066] Figure 2 It is a block diagram of the adaptive grid partitioning method of the present invention;

[0067] Figure 3 It is a block diagram of the neutron source composition of the present invention;

[0068] Figure 4 It is a block diagram of the data processing composition of the present invention;

[0069] Figure 5 It is a block diagram of the device installation composition of the present invention;

[0070] Figure 6 It is a block diagram of the land factor composition of the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Embodiment 1:

[0073] See also Figures 1 to 6 As shown, an effective method for monitoring soil moisture by measuring neutron radiation includes:

[0074] S1. Regional division: Use the adaptive grid division method to divide the soil measurement area into uniform grids, measure at the center point of each grid to obtain systematic data, stratify the area according to land factors, and select representative points in each layer for measurement;

[0075] The adaptive grid division method dynamically adjusts the grid size according to the gradient of soil moisture change. The specific steps are as follows:

[0076] S21, initial regular grid division, assuming that the length of the measurement area is L, the width is W, and it is divided into n×m uniform grids, and the grid side lengths Δx and Δy are Δx=L / n, Δy=W / m respectively;

[0077] S22, gradient calculation, based on the initial grid division, collect the humidity data of the initial point, and calculate the humidity gradient Gij of the adjacent grid center points;

[0078] S23, adaptive adjustment, adjusting the density of the grid according to the size of the humidity gradient Gij;

[0079] When Gij is large, smaller grids are divided to increase the sampling density of the area;

[0080] When Gij is small, the grid is enlarged and the density of measurement points is reduced.

[0081] Specifically, the calculation expression of the humidity gradient is:

[0082]

[0083] Where θ is the soil moisture and Gij is the moisture gradient at the (i, j) grid.

[0084] Specifically, the expression of the adaptive adjustment is:

[0085]

[0086] Among them, k is the adjustment coefficient, which is used to control the sensitivity of grid density changes;

[0087] S2. Equipment installation: install the neutron source probe, the slow neutron counter BF3 detector connected to the neutron source and the measuring tube bracket in the grid measurement area, insert the probe into the soil to the set depth, start the equipment to emit fast neutrons, the neutrons are deuterium, and record the slow neutron count rate within 5 minutes. Repeat the measurement multiple times to obtain more stable counting data;

[0088] S3, data processing, analyzing the collected slow neutron count rate and known humidity sample data, applying Kalman filtering to the slow neutron count data, smoothing the data, reducing the impact of random noise, and fitting the known humidity and the corresponding filtered slow neutron count rate data through a linear regression model;

[0089] S4, moisture calculation, using a linear regression model to calculate the current soil moisture, and quantify the model, evaluate the performance of the model, and convert the slow neutron counts into soil volume moisture content through a calibration curve;

[0090] S5. Data analysis and feedback: Generate a table of the relationship between humidity and count rate to facilitate analysis and identification of trends, record and report current soil moisture data, establish a database, record long-term measurement results, and conduct trend analysis.

[0091] From the above, we can see that the fast neutrons collide with the hydrogen nuclei of water in the soil, resulting in deceleration or energy attenuation. Since the density of the slow neutron "cloud" around the probe varies with the hydrogen nucleus concentration of soil moisture, the counting rate of the slow neutrons represents the moisture content of the soil, which can be converted into the required soil volume moisture content through the correction curve. The accumulated soil moisture data provides a scientific basis for agricultural management, helps farmers develop more reasonable planting and irrigation plans, and establishes a soil moisture database, which is conducive to long-term research on the relationship between soil and crops and guides future agricultural practices.

[0092] Embodiment 2:

[0093] refer to Figures 1 to 6 As shown, the radioactive material of the neutron source is beryllium or deuterium.

[0094] Specifically, the basic update equation of Kalman filtering is:

[0095]

[0096] Kk=Pkk-1H T (HPkk-1+R) -1

[0097] Pkk=(I-KkH)Pkk-1

[0098] in Updated state estimate;

[0099] The state estimate at the previous time step;

[0100] Kk: Kalman gain, which determines the influence of new observation data on the estimation results;

[0101] zk: the observed value at the current moment, i.e., the slow neutron count;

[0102] H: measurement matrix, mapping state to observation space;

[0103] Pk|k-1: the estimation error covariance at the previous time;

[0104] R: observation noise covariance;

[0105] I: identity matrix;

[0106] Kalman filtering is used to update the estimate of slow neutron counts in real time, reduce the impact of noise, and provide more accurate humidity measurements. In the presence of measurement errors, Kalman filtering can effectively improve the reliability and stability of the data.

[0107] Specifically, the linear regression expression is:

[0108] Y=aX+b

[0109] Where Y: soil moisture, volumetric water content;

[0110] X: slow neutron count rate cps;

[0111] a: regression coefficient, representing the slope of the relationship between slow neutron count rate and soil moisture;

[0112] b: intercept, indicating the theoretical value of soil moisture when the slow neutron count rate is 0;

[0113] By collecting slow neutron count rate and corresponding soil moisture data, a and b are obtained through linear regression analysis, and a mathematical model is constructed to predict unknown humidity. This model simplifies the relationship between soil moisture and neutron count and is used for real-time monitoring and prediction.

[0114] Specifically, the expression for quantifying the model is:

[0115]

[0116] Among them, Yi: actual soil moisture value, that is, the observed value;

[0117] soil moisture values ​​predicted by the model;

[0118] n: sample size;

[0119] RMSE measures the deviation between the model prediction value and the actual value. The smaller the RMSE is, the better the prediction effect of the model is. It provides a quantitative indicator to evaluate the accuracy of the soil moisture measurement model and helps to optimize the model.

[0120] Specifically, the land factors include different soil types, vegetation coverage and land use factors.

[0121] Specifically, the trend analysis adopts linear trend analysis, and the humidity changes linearly with time or spatial position, and its expression is:

[0122] θ(t)=αt+β

[0123] θ(t): soil moisture, i.e., volumetric water content;

[0124] t: independent variable of time or spatial position;

[0125] α: the slope of the trend, which indicates the rate at which humidity changes with time or spatial location;

[0126] β: intercept, indicating the initial humidity value;

[0127] When α>0, humidity increases with time or spatial position;

[0128] When α < 0, humidity decreases with time or spatial position;

[0129] If the humidity monitoring data shows a decreasing trend, linear analysis helps estimate the rate at which humidity is decreasing.

[0130] From the above, we can see that real-time monitoring of soil moisture makes irrigation decisions more scientific, reduces unnecessary waste of water resources, reduces irrigation frequency and saves water resources through precise water management. This is especially important in arid areas. By maintaining appropriate soil moisture, it promotes the healthy growth of crop roots and increases crop yield and quality. Moderate soil moisture can reduce the occurrence of certain plant diseases and enhance crop resistance.

[0131] Embodiment three:

[0132] This design is specifically applied in the agricultural field, especially for agricultural development in arid and semi-arid areas. In these areas where water resources are scarce, accurate control of soil moisture is crucial to ensure crop growth and maintain ecological balance. Monitoring soil moisture through neutron radiation can accurately guide irrigation operations, ensuring the water required for crop growth while avoiding problems such as soil salinization caused by over-irrigation, opening up a new path for sustainable agricultural development in arid areas.

[0133] Further, farmland soil moisture monitoring embodiment:

[0134] In order to optimize irrigation management and improve water resource utilization efficiency, a farm decided to implement a neutron radiation soil moisture monitoring system in a 1-hectare corn field.

[0135] 2. Measurement area selection and division

[0136] Area division: The farmland area is L = 100 meters, W = 100 meters. It is initially divided into 5×5 grids, and the side length of each grid is Δx = Δy = 20 meters.

[0137] Initial data collection

[0138] Humidity measurements were taken at each grid center point to obtain preliminary data, as shown in Table 1 below:

[0139] Grid Central point humidity Adjacent grid humidity gradient G (1,1) 0.15 0.03 (1,2) 0.18 0.05 (1,5) 0.16 0.04 ... ... ... (5,5) 0.30 0.12

[0140] Table 1

[0141] Adaptive Adjustment:

[0142] Through the calculation of the gradient G, it is found that the area with drastic changes in humidity is in the middle part. The grid density is adjusted to make the grid in this area denser and the surrounding grids remain larger.

[0143] The adjustment coefficient k is set to 5. In the area with larger gradient, the grid side length is reduced to 60% of the original grid side length, that is, Δxij≈12 meters, Δyij≈12 meters.

[0144] In areas with smaller gradients, the grid density was maintained or slightly increased, and the side length was increased to 24 m.

[0145] Feature selection: Choose an area with plenty of sunlight and moderate slope, and avoid shade and waterlogged areas.

[0146] Measurement process:

[0147] Equipment preparation: Use a neutron soil moisture meter, including a fast neutron source, a BF3 detector, and a data recorder.

[0148] Measuring point setting: Select 3 measuring points in each sub-area, a total of 75 measuring points.

[0149] Data recording: At each measurement point, record the slow neutron count rate for 5 minutes. Repeat the measurement 3 times and take the average value.

[0150] Measurement data:

[0151] Table 2 below shows the recorded measurement data:

[0152]

[0153]

[0154] Table 2

[0155] Data processing:

[0156] Linear regression analysis: Statistical software was used to perform linear regression analysis on the collected data and to establish a relationship model between the slow neutron count rate and soil moisture.

[0157] Regression equation: Y = 0.0035X + 0.08

[0158] Where Y is soil moisture and X is the slow neutron count rate.

[0159] Humidity Estimation and Reporting:

[0160] Real-time monitoring: The real-time measured slow neutron count rate is input into the regression equation to estimate the current soil moisture and generate a monitoring report.

[0161] Data visualization: Generate moisture distribution to show soil moisture conditions in different sub-areas to help farm irrigation decisions.

[0162] From the above, we can see that through the above content, we can obtain the effectiveness and practical application value of neutron radiation monitoring of soil moisture. This method not only provides a scientific basis for agricultural management, but also realizes the efficient use of resources.

[0163] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0164] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An effective method for monitoring soil moisture by measuring neutron radiation, characterized in that: The following steps are involved: S1. Regional division: Use the adaptive grid division method to divide the soil measurement area into uniform grids, measure at the center point of each grid to obtain systematic data, stratify the area according to land factors, and select representative points in each layer for measurement; S2. Equipment installation: install the neutron source probe, the slow neutron counter BF3 detector connected to the neutron source and the measuring tube bracket in the grid measurement area, insert the probe into the soil to the set depth, start the equipment to emit fast neutrons, record the slow neutron count rate within the set time, and repeat the measurement multiple times to obtain more stable counting data; S3, data processing, analyzing the collected slow neutron count rate and known humidity sample data, applying Kalman filtering to the slow neutron count data, smoothing the data, reducing the impact of random noise, and fitting the known humidity and the corresponding filtered slow neutron count rate data through a linear regression model; S4, moisture calculation, using a linear regression model to calculate the current soil moisture, and quantify the model, evaluate the performance of the model, and convert the slow neutron counts into soil volume moisture content through a calibration curve; S5. Data analysis and feedback: Generate a table of the relationship between humidity and count rate to facilitate analysis and identification of trends, record and report current soil moisture data, establish a database, record long-term measurement results, and conduct trend analysis.

2. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The adaptive grid division method in step S1 dynamically adjusts the grid size according to the gradient of soil moisture change, and the specific steps are as follows: S21, initial regular grid division, assuming that the length of the measurement area is L, the width is W, and it is divided into n×m uniform grids, and the grid side lengths Δx and Δy are Δx=L / n, Δy=W / m respectively; S22, gradient calculation, based on the initial grid division, collect the humidity data of the initial point, and calculate the humidity gradient Gij of the adjacent grid center points; S23, adaptive adjustment, adjusting the density of the grid according to the size of the humidity gradient Gij; When Gij is large, smaller grids are divided to increase the sampling density of the area; When Gij is small, the grid is enlarged and the density of measurement points is reduced.

3. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 2, characterized in that: The calculation expression of the humidity gradient is: Where θ is the soil moisture and Gij is the moisture gradient at the (i, j) grid.

4. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 2, characterized in that: The expression of the adaptive adjustment is: Where k is the adjustment coefficient, which is used to control the sensitivity of the grid density change.

5. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The radioactive material of the neutron source is beryllium or deuterium.

6. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The basic update equation of the Kalman filter in step S3 is: Kk=Pkk-1H T (HPkk-1+R) -1 Pkk=(I-KkH)Pkk-1 in Updated state estimate; The state estimate at the previous time step; Kk: Kalman gain, which determines the influence of new observation data on the estimation results; zk: the observed value at the current moment, i.e., the slow neutron count; H: measurement matrix, mapping state to observation space; Pk|k-1: the estimation error covariance at the previous time; R: observation noise covariance; I: identity matrix; Kalman filtering is used to update the estimate of slow neutron counts in real time to reduce the impact of noise.

7. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The expression of linear regression in step S3 is: Y=aX+b Where Y: soil moisture, volumetric water content; X: slow neutron count rate cps; a: regression coefficient, representing the slope of the relationship between slow neutron count rate and soil moisture; b: intercept, indicating the theoretical value of soil moisture when the slow neutron count rate is 0; Through the collected slow neutron counting rate and the corresponding soil moisture data, a and b are obtained through linear regression analysis, and a mathematical model is constructed to predict the unknown humidity.

8. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The expression for quantizing the model in step S4 is: Among them, Yi: actual soil moisture value, that is, the observed value; soil moisture values ​​predicted by the model; n: sample size; RMSE measures the deviation between the model's predicted value and the actual value. The smaller the RMSE, the better the model's prediction effect.

9. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The land factors include different soil types, vegetation cover and land use factors.

10. An effective method for monitoring soil moisture by measuring neutron radiation according to claim 1, characterized in that: The trend analysis in step S5 adopts linear trend analysis, and the humidity changes linearly with time or spatial position, and its expression is: θ(t)=αt+β θ(t): soil moisture, i.e., volumetric water content; t: independent variable of time or spatial position; α: the slope of the trend, which indicates the rate at which humidity changes with time or spatial location; β: intercept, indicating the initial humidity value; When α>0, humidity increases with time or spatial position; When α<0, humidity decreases with time or spatial position.

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