JP2024031194A - Greenhouse gas concentration measurement method, greenhouse gas concentration measurement system program - Google Patents

Abstract

An object of the present invention is to provide a method for measuring a greenhouse gas concentration that can calculate a value with practical accuracy without performing calibration using a standard gas in outdoor measurements. [Solution] A method for measuring greenhouse gas concentration includes a standard station trend parameter extraction step S2 of extracting trend parameters of a standard station based on published data for a predetermined period at the standard station, and a step S2 for extracting trend parameters of a standard station based on data published for a predetermined period at a measurement point. The measurement point trend parameter calculation step S3 calculates the trend parameter of the measurement point, and the measurement value conversion step S4 converts the measured value of the measurement point so that the trend parameter of the measurement point matches the trend parameter of the reference station. [Selection diagram] Figure 2

Description

Application for application of Article 30, Paragraph 2 of the Patent Act has been filed. Published by uploading materials on the company website page from November 26, 2021 to July 25, 2020. http://www. isem. co. jp/Environment/CO2MonitoringSystem/CO2MonitoringSystemDetail. pdf

The present invention relates to a method for measuring greenhouse gas concentrations using a sensor at a measurement point that is different from a reference station, and a program for a system for measuring greenhouse gas concentrations.

Conventionally, CO2 concentration measuring means regularly measures the CO2 concentration, which is an example of a greenhouse gas, in a measurement target space, and a reference value storage means stores a predetermined value determined as the CO2 concentration of outside air as a reference value. Every time the CO2 concentration in the measurement target space is measured, the difference between the measured value of the measured CO2 concentration and the moving average value of the measured CO2 concentration is calculated, and the difference is determined in advance for a predetermined period of time. a CO2 concentration saturation determination means that determines that the measured value of the CO2 concentration is saturated if it continues to fall within the permissible range; A baseline correction device is known that includes a baseline correction unit that replaces this baseline with a reference value (for example, Patent Document 1).

Japanese Patent Application Publication No. 2013-120114

However, the conventional baseline correction device described above uses the inside of a building as the measurement target space, and determines whether or not the measured value of the CO2 concentration is saturated, and when it is determined that the measured value of the CO2 concentration is saturated, Since the configuration was based on the baseline, there was a problem in that it was difficult to measure CO2 concentration outdoors.
In other words, when measuring CO2 concentration outdoors, there is a problem in that it is difficult to measure with high accuracy.
Additionally, in Japan, the Japan Meteorological Agency, the National Institute for Environmental Studies, and Saitama Prefecture conduct fixed-point observations of atmospheric CO2 concentrations, and they have observed an increase of 2 to 3 ppm per year.
In such official observations, calibration is always performed using standard gases, resulting in extremely high accuracy (±0.02ppm), but the observation equipment and associated equipment required for this purpose are extremely high. Observations can only be made at limited locations because the facilities are expensive.
On the other hand, when using a relatively inexpensive sensor, the minimum value of the measured values for about a week is forced to match the specified value (for example, 400 ppm), and all measured values are corrected with the difference value. Some products have this function, but over the long term, the CO2 concentration in the atmosphere increases year by year, and the minimum value changes depending on the season, so if the specified value is set constant, there will be an error. There was a problem that the error became larger year by year and the error became larger depending on the season.

SUMMARY OF THE INVENTION Therefore, the present invention solves the problems of the prior art as described above, and the purpose of the present invention is to achieve practical accuracy in outdoor measurements without calibration using a standard gas. It is an object of the present invention to provide a method for measuring a greenhouse gas concentration and a program for a system for measuring a greenhouse gas concentration.

The invention according to claim 1 is a method for measuring greenhouse gas concentration using a sensor at a measurement point that is a different point from a reference station, wherein a trend parameter of the reference station is determined based on published data for a predetermined period at the reference station. a reference station trend parameter extraction step for extracting a reference station trend parameter; a measurement point trend parameter calculation step for calculating a trend parameter for a measurement point based on data for a predetermined period at the measurement point; and a measurement point trend parameter calculation step for calculating a trend parameter for the measurement point based on data for a predetermined period at the measurement point, and the trend parameter for the measurement point matches the trend parameter for the reference station. The above-mentioned problem is solved by including a measurement value conversion step of converting the measurement value at the measurement point.
Here, the term "reference station" refers to an observation station that constantly performs calibration using standard gas and publishes observation data that is observed with very high precision.
In addition, "trend parameter" refers to a parameter (variable) that typically indicates the trend of greenhouse gas concentration during a certain observation period (for example, about half a month) at a measurement point, and in the present invention, Moving average value, minimum value, average value, and median value are used.

The invention according to claim 2 provides, in addition to the configuration of the greenhouse gas concentration measuring method described in claim 1, a reference station is Let the published value S be the measured value C at the measurement point, the published value Sij of the reference station at j time on day i, the measured value Cijk at the measurement point of the measurement number k within the unit interval at j time on day i, and the trend of the reference station. In the parameter extraction step, the minimum value MinSi=Min(Sij)|j of the standard station's announced value Sij for the second domain, time j, is determined for each day i, which is the first domain, in the predetermined period, and then The minimum value MinS = Min(MinSi)|i of the published value Sij of the standard station for date i, which is the first variable range, of the minimum value MinSi = Min(Sij)|j is determined, and the measurement point trend parameter is calculated. In the step, the measurement points are calculated for each date i in the first domain, every time j in the second domain, and the number of measurements k within the unit interval of the second domain in the predetermined period. After calculating the average value AvCij=Average(Cijk)|k obtained by averaging the measured value Cijk over the number of measurements k in the third domain, the second domain of the obtained average value AvCij=Average(Cijk)|k Find the minimum value MinAvCi=Min(AvCij)|j for time j, and calculate the minimum value MinC=MinC= for date i which is the first domain of the obtained minimum value MinAvCi=Min(AvCij)|j. The above problem is further solved by finding MinMinAvC=Min(MinAvCi)|i and setting the pseudo calibration value C ₀ Cijk=Cijk+(MinS−MinC) in the measurement value conversion step.

The invention according to claim 3 provides, in addition to the configuration of the greenhouse gas concentration measuring method described in claim 1, a reference station is Let the published value S be the measured value C at the measurement point, the published value Sij of the reference station at j time on day i, the measured value Cijk at the measurement point of the measurement number k within the unit interval at j time on day i, and the trend of the reference station. In the parameter extraction step, the minimum value MinSi=Min(Sij)|j of the standard station's announced value Sij for the second domain, time j, is determined for each day i, which is the first domain, in the predetermined period, and then Furthermore, the minimum value MinS = Min (MinSi) | The median value MdS=Median(Sij)|i,j is calculated for day i, which is the first variable range, and time j, which is the second variable range, and in the measurement point trend parameter calculation step, the first change in the predetermined period is calculated. For every date i in the area, every time j in the second domain, and the number of measurements k within the unit interval in the second domain, the third domain, the measured value Cijk at the measurement point is calculated by After finding the average value AvCij = Average(Cijk)|k averaged over the number of measurements k, calculate the minimum value MinAvCi for time j which is the second domain of the found average value AvCij = Average(Cijk)|k. ＝Min(AvCij)|j is calculated, and the minimum value MinAvCi＝Min(AvCij)|j for the date i which is the first domain is the minimum value MinC＝MinMinAvC＝Min(MinAvCi)|i. At the same time, the median value MdC=Median(AvCij)|i,j of the average value AvCij of the measured value Cijk at the measurement point is calculated for date i which is the first domain and time j which is the second domain, and In the value conversion step, A and B obtained from MinS=A×MinC+B and MdS=A×MdC+B are used to set the pseudo calibration value C ₁ Cijk=A×Cijk+B, thereby further solving the above-mentioned problem. It is.

The invention according to claim 4 provides that, in addition to the configuration of the method for measuring greenhouse gas concentration described in claim 1, the reference station is Let the published value S be the measured value C at the measurement point, the published value Sij of the reference station at j time on day i, the measured value Cijk at the measurement point of the measurement number k within the unit interval at j time on day i, and the trend of the reference station. In the parameter extraction step, the minimum value MinSi=Min(Sij)|j of the standard station's announced value Sij for the second domain, time j, is determined for each day i, which is the first domain, in the predetermined period, and then Furthermore, the minimum value MinS = Min (MinSi) | The average value AvS=Average(AvSi)|i for date i which is the first variable area is calculated, and in the measurement point trend parameter calculation step, the average value AvS=Average(AvSi)|i is calculated for each day i which is the first variable area in the predetermined period, and the second variable area is calculated. For each time j, and the number of measurements k within the unit interval of the second domain, which is the third domain, the average value AvCij is obtained by averaging the measured value Cijk at the measurement point by the number of measurements k in the third domain. After finding Average(Cijk)|k, find the minimum value MinAvCi=Min(AvCij)|j for time j which is the second domain of the found average value AvCij=Average(Cijk)|k, The minimum value MinAvCi=Min(AvCij)|j for date i, which is the first domain, is determined, and the minimum value MinC=MinMinAvC=Min(MinAvCi)|i is determined, and the average of the measured values Cijk at the measurement points is calculated. The average value AvC=AvAvAvC=Average(AvAvCi)|i for date i, which is the first range of the value AvCij, is calculated, and in the measurement value conversion step, A calculated from MinS=A×MinC+B, AvS=A×AvC+B , B, and by setting the pseudo calibration value C ₁ Cijk=A×Cijk+B, the above-mentioned problem is further solved.

The invention according to claim 5 provides, in addition to the configuration of the greenhouse gas concentration measuring method described in claim 1, a reference station is Let the published value S be the measured value C at the measurement point, the published value Sij of the reference station at j time on day i, the measured value Cijk at the measurement point of the measurement number k within the unit interval at j time on day i, and the trend of the reference station. In the parameter extraction step, the minimum value MinSi=Min(Sij)|j of the standard station's announced value Sij for the second domain, time j, is determined for each day i, which is the first domain, in the predetermined period, and then Furthermore, the minimum value MinS = Min (MinSi) | Median value MdS=Median(Sij)|i,j for date i which is the first domain and time j which is the second domain, or for date i which is the first domain of the published value Sij of the standard station. The average value AvS=Average(AvSi) | After calculating the average value AvCij=Average(Cijk)|k, which is obtained by averaging the measured value Cijk at the measurement point with the number of measurements k in the third domain for the number of measurements k within the unit interval of the second domain, which is the domain. , the obtained average value AvCij=Average(Cijk)|k, the minimum value MinAvCi=Min(AvCij)|j for time j which is the second domain, and the obtained minimum value MinAvCi=Min(AvCij) Furthermore, find the minimum value MinC=MinMinAvC=Min(MinAvCi)|i for date i which is the first domain of |j, and also find the date which is the first domain of the average value AvCij of the measured value Cijk at the measurement point. Median value MdC=Median(AvCij)|i,j for i and time j which is the second domain, or average value of the measured value Cijk at the measurement point AvCij for date i which is the first domain AvC=AvAvAvC=Average(AvAvCi)|i is calculated, and in the measurement value conversion step, as 0<w<1, C ₀ Cijk=Cijk+(MinS - MinC), C ₁ Cijk=A×Cijk+B as the weighted average of both. , the above-mentioned problem is further solved by setting the pseudo calibration value C _w Cijk=(1−w)×C ₀ Cijk+w×C ₁ Cijk.

The invention according to claim 6 is a program for a greenhouse gas concentration measurement system using a sensor at a measurement point that is a different point from a reference station, wherein a reference station trend parameter extraction step for extracting a trend parameter; a measurement point trend parameter calculation step for calculating a trend parameter at a measurement point based on data for a predetermined period at the measurement point; The above-mentioned problem is solved by including a measurement value conversion step of converting the measurement value at the measurement point so that it matches the measurement point.

The invention according to claim 7 is a program for a greenhouse gas concentration measurement system using a sensor at a measurement point that is a different point from a reference station, the program being based on published data for a first predetermined period at the reference station. a predicted value calculation step of calculating a predicted value for a prediction period of two years or more of the reference station after the first predetermined period; and a measurement point for a second predetermined period that is after the first predetermined period and within the prediction period. a measurement point trend parameter calculation step of calculating a trend parameter of the measurement point based on data at the measurement point; and a measurement step of converting the measured value of the measurement point so that the trend parameter of the measurement point matches a predicted value as a trend of a reference station. By providing a value conversion step, the above-mentioned problem is solved.

According to the method for measuring greenhouse gas concentration of the invention according to claim 1, a measurement point that has not been calibrated using a standard gas with respect to a trend parameter of data from a reference station that has been calibrated using a standard gas. Since the measured values at the measurement points are converted so that the trend parameters of the values match, it is possible to calculate values with practical accuracy without calibrating using standard gas.
In other words, instead of directly performing calibration using a standard gas, it is possible to indirectly calibrate data at a measurement point using data from a reference station that performs calibration using a standard gas.

According to the method for measuring greenhouse gas concentration of the invention according to claim 2, in addition to the effect achieved by the invention according to claim 1, the minimum value MinS of the reference station and the minimum value MinC of the measurement point coincide with each other. , since it is a one-point correction, it is possible to calculate a value with practical accuracy without performing calibration using a standard gas.
In particular, since the minimum value clearly shows the tendency of seasonal changes, the calculated value can have practical accuracy.

According to the method for measuring greenhouse gas concentration of the invention according to claim 3, in addition to the effects achieved by the invention according to claim 1, the minimum value MinS of the reference station and the minimum value MinC of the measurement point match, Since it is a so-called two-point correction in which the median value MdS of the reference station and the median value MdC of the measurement point match, it is possible to calculate values with practical accuracy without calibrating using standard gas. can.
In particular, since the minimum value and median value clearly show the tendency of seasonal changes, the calculated values can have practical accuracy.

According to the method for measuring greenhouse gas concentration of the invention according to claim 4, in addition to the effects achieved by the invention according to claim 1, the minimum value MinS of the reference station and the minimum value MinC of the measurement point match, Since it is a so-called two-point correction in which the average value AvS of the reference station and the average value AvC of the measurement point match, it is possible to calculate values with practical accuracy without calibrating using standard gas. can.
In particular, since the minimum value and average value clearly show the tendency of seasonal changes, the calculated values can have practical accuracy.

According to the method for measuring greenhouse gas concentration of the invention according to claim 5, in addition to the effect achieved by the invention according to claim 1, the minimum value MinS of the reference station and the minimum value MinC of the measurement point match, so-called , one-point correction, or in addition to this, the so-called 2 correction in which the median value MdS of the reference station and the median value MdC of the measurement point match, or the average value AvS of the reference station and the average value AvC of the measurement point match. Since it is intermediate between point corrections, it is possible to calculate values with practical accuracy without performing calibration using a standard gas.

According to the program of the greenhouse gas concentration measurement system of the invention according to claim 6, similar to the effect achieved by the invention according to claim 1, the trend parameter of the data of the reference station that is calibrated using the standard gas On the other hand, since the measured values at measurement points are converted to match the trend parameters of measurement points that have not been calibrated using standard gas, practical accuracy can be achieved even without calibration using standard gas. The value can be calculated.
In other words, instead of directly performing calibration using a standard gas, it is possible to indirectly calibrate data at a measurement point using data from a reference station that performs calibration using a standard gas.

According to the program of the greenhouse gas concentration measurement system of the invention according to claim 7, similar to the effects achieved by the inventions according to claims 1 and 6, data of a reference station performing calibration using a standard gas is obtained. The measured values at measurement points are converted so that the trend parameters at measurement points that have not been calibrated using standard gas match the predicted values based on It is possible to calculate values with practical accuracy.
In other words, instead of directly performing calibration using standard gas, the data at the measurement point is indirectly calibrated using predicted values based on data from a reference station that is calibrating using standard gas. can do.

1 is a diagram showing the concept of a carbon dioxide concentration measurement system according to a first embodiment of the present invention. 1 is a chart diagram showing an operation example (measuring method) of a carbon dioxide concentration measuring system according to a first embodiment of the present invention. 1 is a graph diagram showing an example of carbon dioxide concentration data at Kisai, which is a reference station according to a first embodiment of the present invention. 1 is a graph diagram showing examples of daily minimum values, maximum values, average values, and median values of carbon dioxide concentration data at Kisai, which is a reference station according to a first embodiment of the present invention; FIG. (A) and (B) are graphs showing changes over time in carbon dioxide concentrations at Ryori, Minamitorishima, and Yonagunijima, which are examples of standard stations, and graphs showing annual increases in carbon dioxide concentrations. A graph diagram showing carbon dioxide concentration data at Kisai, a reference station, and carbon dioxide concentration data at Nihonbashi, a measurement point (without pseudo calibration). FIG. 6 is a graph showing pseudo-calibration (one-point correction at the minimum value) by applying the carbon dioxide concentration measurement method according to the first embodiment of the present invention to the carbon dioxide concentration data at the measurement point, Nihonbashi. . In FIG. 6, the carbon dioxide concentration measuring method according to the first embodiment of the present invention is applied to the carbon dioxide concentration data at Nihonbashi, the measurement point, to perform pseudo calibration (two-point correction based on the average value, one-point correction A graph diagram corrected at an intermediate point between the two-point correction and the two-point correction. FIG. 7 is a chart diagram of a predicted value calculation step when calculating a predicted value according to a second embodiment of the present invention. FIG. 7 is a chart diagram of a measurement point trend parameter calculation step and a measurement value conversion step when calculating predicted values according to the second embodiment of the present invention.

The greenhouse gas concentration measurement method and measurement system program of the present invention includes a reference station trend parameter extraction step for extracting trend parameters of a reference station based on published data for a predetermined period at the reference station, and a step for extracting trend parameters of a reference station based on data published for a predetermined period at a measurement point. a measurement point trend parameter calculation step of calculating a trend parameter of the measurement point based on the measurement point; and a measurement value conversion step of converting the measured value of the measurement point so that the trend parameter of the measurement point matches the trend parameter of the reference station. By doing so, instead of directly performing calibration using standard gas, it is possible to indirectly calibrate data at measurement points by utilizing data from a reference station that performs calibration using standard gas. As long as it is, the specific embodiment thereof may be of any kind.
Furthermore, the program of the greenhouse gas concentration measurement method and measurement system which is an embodiment of the present invention is based on the data published by the reference station during the first predetermined period, and is based on the data of the reference station for two years or more after the first predetermined period. a predicted value calculation step of calculating a predicted value for a prediction period; and a measurement step of calculating a trend parameter of a measurement point based on data at the measurement point of a second predetermined period that is after the first predetermined period and within the prediction period. By comprising a point trend parameter calculation step and a measurement value conversion step of converting the measured value at the measurement point so that the trend parameter at the measurement point matches the predicted value as the trend of the reference station, it is possible to directly calculate the standard gas. If it is possible to indirectly calibrate the data at the measurement point CT in a pseudo manner by utilizing the data from the reference station ST which is calibrating using standard gas, instead of performing the calibration using standard gas, please let us know the details. Any specific embodiment may be used.

The reference station trend parameter extraction step and the measurement point trend parameter calculation step need only be included in the measurement method and program, and the order of the reference station trend parameter extraction step and the measurement point trend parameter calculation step does not matter whichever comes first.
Similarly, the predicted value calculation step and measurement point trend parameter calculation step only need to be included in the measurement method and program, and the order of the predicted value calculation step and measurement point trend parameter calculation step does not matter. good.
In addition, "greenhouse gas" refers to a gas that traps heat emitted by the sun in the earth and warms the earth's surface. "Greenhouse gas" includes carbon dioxide, methane, nitrous oxide, There are alternative fluorocarbons (hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, nitrogen trifluoride).

EMBODIMENT OF THE INVENTION Below, the measuring method of the carbon dioxide concentration as an example of the greenhouse gas concentration which is a 1st Example of this invention, the measuring system 100, and its program are demonstrated based on FIG. 1 thru|or FIG. 8.
Here, FIG. 1 is a diagram showing the concept of a carbon dioxide concentration measuring system 100 which is a first embodiment of the present invention, and FIG. 2 is a diagram showing the concept of a carbon dioxide concentration measuring system 100 which is a first embodiment of the present invention. FIG. 3 is a chart diagram showing an example of the operation (measurement method) of 100, and FIG. FIG. 5A is a graph showing an example of the daily minimum value, maximum value, average value, and median value of carbon dioxide concentration data at Kisai, which is the reference station ST according to the first embodiment of the present invention. is a graph showing the annual changes in the carbon dioxide concentration at Ryori, Minamitorishima, and Yonagunijima, which are examples of standard station ST. FIG. 6 is a graph showing the amount of increase in carbon dioxide concentration, and FIG. 6 shows carbon dioxide concentration data at Kisai, which is a reference station ST, and carbon dioxide concentration data (without pseudo calibration) at the measurement point CT, outdoors in Nihonbashi, Tokyo. 7 is a graph diagram, and FIG. 7 shows a simulated result obtained by applying the carbon dioxide concentration measurement method according to the first embodiment of the present invention to the carbon dioxide concentration data at the measurement point CT outdoors in Nihonbashi, Tokyo. FIG. 8 is a graph showing the carbon dioxide concentration measurement method according to the first embodiment of the present invention in FIG. 6 at the measurement point CT outdoors in Nihonbashi, Tokyo. It is a graph diagram in which pseudo calibration is applied to carbon concentration data (two-point correction using an average value, correction at an intermediate point between one-point correction and two-point correction).

In this example, measurement of the concentration of carbon dioxide as an example of "greenhouse gas" will be described.
As shown in FIG. 1, a carbon dioxide concentration measurement system 100, which is a first embodiment of the present invention, includes a sensor 110 at a measurement point CT, which is a different point from a reference station ST, and a cloud-type server 120, which is an example. and a user terminal 130.
The sensor 110 is connected to a microcomputer terminal 111, and measurement data obtained by the sensor 110 is transmitted to the server 120 via the microcomputer terminal 111.
Further, the server 120 acquires published data for a predetermined period at the standard station ST, either directly from the standard station ST or indirectly from another server that publishes the data of the standard station ST.
Here, the reference station ST has an observation instrument ST1 that is constantly calibrated using a standard gas, and performs observations with very high accuracy (±0.02 ppm).

Then, the server 120 extracts trend parameters of the reference station ST based on published data for a predetermined period at the reference station ST.
Furthermore, the trend parameters of the measurement point CT are calculated based on the received data for a predetermined period at the measurement point CT.
Furthermore, the measurement value at the measurement point CT is converted so that the trend parameter at the measurement point CT matches the trend parameter at the reference station ST.
The user terminal 130 is then configured to access the server 120 and display the converted measurement values.

As a result, the measured value of the measurement point CT is made to match the trend parameter of the data of the reference station ST, which is calibrated using the standard gas, with the trend parameter of the measurement point CT, which has not been calibrated using the standard gas. is converted.
As a result, values with practical accuracy can be calculated without performing calibration using standard gas.
In other words, instead of directly performing calibration using standard gas, it is possible to indirectly calibrate data at measurement point CT in a pseudo manner by utilizing data from reference station ST that performs calibration using standard gas. can.

Next, a method for measuring carbon dioxide concentration and an example of the operation of the measurement system 100 will be described in more detail.
As shown in FIG. 2, in step S1, as a data acquisition determination step, data for a predetermined period at the reference station ST is acquired, and it is determined whether data for a predetermined period at the measurement point CT has been acquired.
In the case of a program for the measurement system 100, the server 120 makes the determination.
The measurement method may be determined by the server 120 or by the user.
If it is determined that both have been acquired, the process proceeds to step S2, and on the other hand, if it is determined that both have not been acquired, step S1 is repeated.
For example, as shown in FIG. 3, hourly carbon dioxide concentration data including a predetermined period at Kisai, which serves as the reference station ST, is acquired.

Note that FIG. 3 shows hourly carbon dioxide concentration data from May 15, 2021 to July 4, 2022, as an example in Kisai.
Of these, data for a predetermined period may be extracted.
In addition, Figure 4 shows the daily minimum value, maximum value, average value, and median value of hourly carbon dioxide concentration data from May 15, 2021 to July 4, 2022 as an example in Kisai. It is.
From FIG. 4, it can be seen that the fluctuation range of the maximum value is relatively large.
In other words, it can be observed that the maximum value varies greatly from day to day and does not represent the trend of this season of the year very well, whereas the minimum value can be observed that it well represents the trend of this season of the year. can do. That is, it can be used as a trend parameter described in the above-mentioned "Means for solving the problem".
Furthermore, compared to the average value, the influence of the maximum value itself is reduced, so it can be observed that the median value represents the trend of this season next to the minimum value.
Therefore, as a trend for this season, we use time-series data of daily minimum, average, and median values, as well as a moving average of 15 days (half a month, about two weeks).

Furthermore, as shown in Figures 5(A) and 5(B), if we look at the long-term changes in carbon dioxide concentration at Ryori, Minamitorishima, and Yonagunijima, which are examples of standard station ST, we can see that the carbon dioxide concentration changes by about 1 to 3 ppm every year. It can be seen that the carbon dioxide concentration is increasing.
Furthermore, in the summer, the daylight hours are relatively long, and the carbon dioxide concentration decreases due to photosynthesis of plants.In the winter, the daylight hours are relatively short, and the amount of leaves is reduced due to factors such as falling leaves, resulting in less carbon dioxide. This is thought to be a trend of increasing carbon concentration.
From FIG. 5(A) and FIG. 5(B), it can be seen that the tendency of change in carbon dioxide concentration is the same even if the locations are different.

In step S2, as a reference station trend parameter extraction step, trend parameters of the reference station ST are extracted based on published data for a predetermined period at the reference station ST.
In the case of a program for the measurement system 100, the server 120 extracts it.
Regarding the measurement method, the server 120 may extract it, or the user may extract it using the user terminal 130.
For example, as shown in Figure 6, based on the published data for two weeks from July 13, 2022 to July 16, 2022 as a predetermined period in Kisai as the reference station ST, as an example of the trend parameter of the reference station ST. The minimum value, average value, or median value for each day is determined, and the minimum value, average value, or median value for each day in a predetermined period is determined.

This will be explained more specifically.
Data from the reference station ST is released every hour in real time with a delay of about 1 minute.
The announcement from the standard station ST will be explained as being made every hour.
i is the i-th day (i=0,1,2,…,13) of the given period
The published value of the reference station ST is represented by S, where j is the jth time of the day (j=0, 1, 2,..., 23).
Since the CO2 concentration of standard station ST is announced every hour,
Let Sij be the published value on day i and hour j (the letter S means Standard).
(i=0,1,2,…,13) (j=0,1,2,…,23)

Here, when the daily minimum value for time is taken as the trend parameter of the reference station ST, as shown in Equation 1, the daily minimum value MinSi for day i is determined by determining the minimum value for j.
(Number 1)
MinSi＝Min(Sij)|j
This means finding the minimum value of Sij for domain j.

Furthermore, when the daily average value for time is used as the trend parameter of the reference station ST, as shown in Equation 2, the daily average value AvSi on day i is obtained by averaging Sij for j.
(Number 2)
AvSi＝Average(Sij)|j
This means finding the average value of Sij for domain j.

Further, when the daily median value for time is used as the trend parameter of the reference station ST, as shown in Equation 3, the daily median value MdSi for day i is Sij, and the median value of Sij is calculated for j.
(Number 3)
MdSi＝Median(Sij)|j
This means finding the median value for domain j.

Furthermore, the minimum value MinS in a predetermined period is determined by Equation 4.
(Number 4)
MinS=Min(MinSi)|i
This means finding the minimum value of MinSi for domain i.
Similarly, the average value AvS for a predetermined period is calculated using Equation 5.
(Number 5)
AvS＝Average(AvSi)|i
This means finding the average value of AvSi for domain i.
Furthermore, similarly, the median value MdS for a predetermined period is calculated using Equation 6.
(Number 6)
MdS＝Median(Sij)|i,j
This means finding the median value of Sij for the variables i and j.

In step S3, as a measurement point trend parameter calculation step, trend parameters of the measurement point CT are calculated based on data for a predetermined period at the measurement point CT.
In the case of the program of the measurement system 100, the server 120 calculates.
Regarding the measurement method, the server 120 may calculate it, or the user may use the user terminal 130 to calculate it.
For example, as shown in FIG. 6, based on measurement data for two weeks from July 13, 2022 to July 16, 2022, which is a predetermined period outside Nihonbashi, Tokyo, as a measurement point CT, the trend parameter of the measurement point CT is As an example, one of the minimum value, average value, and median value for each hour of each day is determined, and further, any one of the minimum value, average value, and median value for each day in a predetermined period is determined.

This will be explained more specifically.
The measurement at the measurement point CT will be explained assuming that the measurement is performed every 30 seconds and the predetermined period is 14 days.
As in the example of the reference station ST, let i be the i-th day (i=0,1,2,…,13) of the predetermined period.
Let j be the jth time of the day (j=0,1,2,…,23)
The measured value at the measurement point CT is expressed as C, where k is the kth 1/2 minute of one hour (k=0,1,...,119) 1/2 minute.
The CO ₂ concentration at measurement point CT is measured every 30 seconds, the predetermined period is 14 days, and the measured value on day i, hour j, minute k1/2 is Cijk (the letter C means the CO ₂ measurement value). ).
(i=0,1,2,…,13) days, (j=0,1,2,…,23) hours, (k=0,1,…,119) 1/2 minutes

Here, in order to use the measured value Cijk of the measurement point CT for comparison and contrast with the data Sij of the reference station ST, as shown in Equation 7, the hourly average value AvCij of day i and hour j is calculated by averaging Cijk with k. become
(Number 7)
AvCij＝Average(Cijk)|k
This means finding the average value of Cijk for domain k.
In other words, this AvCij becomes hourly data of Cijk.

Then, when the daily minimum value for time is taken as the trend parameter of the measurement point CT, as in Equation 8, the daily minimum value MinAvCi on the i day is determined by finding the minimum value for AvCij with respect to j.
(Number 8)
MinAvCi＝Min(AvCij)|j
This means finding the minimum value of AvCij for domain j.

Furthermore, when the daily average value for time is used as the trend parameter of the measurement point CT, as shown in Equation 9, the daily average value AvAvCi on day i is obtained by averaging AvCij with respect to j.
(Number 9)
AvAvCi＝Average(AvCij)|j
This means finding the average value of AvCij for domain j.

Furthermore, when the daily median value for time is used as the trend parameter of the measurement point CT, as in Equation 10, the daily median value MdAvCi on day i is determined by calculating the median value for j of AvCij.
(Number 10)
MdAvCi＝Median(AvCij)|j
This means finding the median value of AvCij for domain j.

Furthermore, the minimum value MinC for a predetermined period is obtained using Equation 11.
(Number 11)
MinC＝MinMinAvC＝Min(MinAvCi)|i
This means finding the minimum value of MinAvCi for domain i.
Similarly, the average value AvC for a predetermined period is calculated using Equation 12.
(Number 12)
AvC＝AvAvAvC＝Average(AvAvCi)|i
This means finding the average value of AvAvCi for domain i.
Furthermore, similarly, the median value MdC for a predetermined period is calculated using Equation 13.
(Number 13)
MdC＝Median(AvCij)|i,j
This means finding the median value of AvCij for the variables i and j.

In step S4, as a measurement value conversion step, the measurement value at the measurement point CT is converted (pseudo calibration) so that the trend parameter at the measurement point CT matches the trend parameter at the reference station ST.
In the case of the measurement system 100 program, the server 120 converts the measurement values.
Regarding the measurement method, the server 120 may convert the measured value, or the user may use the user terminal 130 to convert the measured value.

As a result, for example, as shown in FIG. 6, before conversion, the measured values deviated by about 30 ppm compared to the data from the standard station ST, but as shown in FIGS. The measurement value at the measurement point CT is converted so that the trend parameter of the measurement point CT, which has not been calibrated using the standard gas, matches the trend parameter of the data from the reference station ST, which has been calibrated using the standard gas.
In other words, the level and amount of change are pseudo-calibrated.
As a result, values with practical accuracy can be calculated without performing calibration using standard gas.
In other words, instead of directly performing calibration using standard gas, it is possible to indirectly calibrate data at measurement point CT in a pseudo manner by utilizing data from reference station ST that performs calibration using standard gas. can.

This will be explained more specifically.
<0th order correction (1 point correction)>
Equation 4 for the minimum value MinS of the reference station ST and Equation 11 for the minimum value MinC of the measured values are made to match.
Therefore, the measured value is corrected using Equation 14. Let the correction value (pseudo calibration value) be C ₀ Cijk.
(Number 14)
C ₀ Cijk＝Cijk＋(MinS－MinC)

That is, first, in the standard station trend parameter extraction step S2, according to Formula 1, the minimum value MinSi of the announced value Sij of the standard station ST for each day i, which is the first variable range, and the time j, which is the second variable range, in a predetermined period = Find Min(Sij)|j.
Next, using Equation 4, the minimum value MinS = Min(Sij) | )|i.
Subsequently, in measurement point trend parameter calculation step S3, Equation 7 is used to calculate the values for each day i, which is the first range, every time j, which is the second range, and the second range, which is the third range, in the predetermined period. For the number of measurements k within the unit interval of the region, the average value AvCij=Average(Cijk)|k is obtained by averaging the measured value Cijk at the measurement point CT by the number of measurements k in the third region.

Then, using Equation 8, the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, of the average value AvCij=Average(Cijk)|k obtained using Equation 7 is calculated.
Next, using Equation 11, calculate the minimum value MinC=MinMinAvC=Min(MinAvCi)|i for date i, which is the first domain, of the minimum value MinAvCi=Min(AvCij)|j obtained using Equation 8. .
Then, in the measurement value conversion step S4, the pseudo calibration value C ₀ Cijk=Cijk+(MinS−MinC) is set.

This results in a so-called one-point correction in which the minimum value MinS of the reference station ST and the minimum value MinC of the measurement point CT match, as shown in FIG.
As a result, values with practical accuracy can be calculated without performing calibration using standard gas.
In particular, since the minimum value clearly shows the tendency of seasonal changes, the calculated value can have practical accuracy.
In addition, there are some places where relatively large mountains in the Kisai graph and relatively large mountains in the graph outside Nihonbashi, Tokyo are shifted in the time axis direction, but this is because Kisai's carbon dioxide Due to the time difference from the date and time when the carbon concentration was high, the carbon dioxide concentration increases outdoors in Nihonbashi, Tokyo due to the influence of northerly winds.Conversely, from the date and time when the carbon dioxide concentration was high outdoors in Nihonbashi, Tokyo, due to the influence of northerly winds, the carbon dioxide concentration increases. It can be considered that the carbon dioxide concentration increased in Kisai due to the time difference due to this influence.

<Primary correction using median value (two-point correction)>
Equation 4 for the minimum value MinS of the reference station ST, Equation 11 for the minimum value MinC of the measured values, Equation 6 for the median value MdS of the reference station ST, and Equation 13 for the median value MdC of the measured values match in two points. Convert Cijk using the linear equation as follows.
Then, the conversion coefficients A and B are determined by two-dimensional simultaneous linear equations of Equations 15 and 16.
(Number 15)
MinS=A×MinC+B
(Number 16)
MdS=A×MdC+B
Using A and B obtained in this way, correction is made using Equation 17. Let the correction value (pseudo calibration value) be C ₁ Cijk.
(Number 17)
C ₁ Cijk＝A×Cijk＋B

That is, first, in the standard station trend parameter extraction step S2, according to Formula 1, the minimum value MinSi of the announced value Sij of the standard station ST for each day i, which is the first variable range, and the time j, which is the second variable range, in a predetermined period = Find Min(Sij)|j.
Next, using Equation 4, the minimum value MinS = Min(Sij) | )|Find i.
At the same time, using Equation 6, the median value MdS=Median(Sij)|i,j for the first domain, day i, and the second domain, time j, of the published value Sij of the reference station ST is determined.
Subsequently, in measurement point trend parameter calculation step S3, Equation 7 is used to calculate the values for each day i, which is the first range, every time j, which is the second range, and the second range, which is the third range, in the predetermined period. For the number of measurements k within the unit interval of the region, the average value AvCij=Average(Cijk)|k is obtained by averaging the measured value Cijk at the measurement point CT by the number of measurements k in the third region.
Next, using Equation 8, the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, of the average value AvCij=Average(Cijk)|k obtained using Equation 7 is calculated.

Then, using Equation 11, of the minimum value MinAvCi=Min(AvCij)|j obtained using Equation 8, the minimum value MinC=MinMinAvC=Min(MinAvCi)|i for date i, which is the first domain, is determined.
Along with this, according to Equation 13, the median value MdC=Median(AvCij)|i,j of the average value AvCij of the measured value Cijk at the measurement point CT for date i which is the first domain and time j which is the second domain seek.
Then, in the measurement value conversion step S4,
MinS=A×MinC+B of formula 15,
MdS of formula 16=A×MdC+B
Using A and B obtained from , set the pseudo calibration value C ₁ Cijk = A × Cijk + B.

As a result, the minimum value MinS of the reference station ST and the minimum value MinC of the measurement point CT match, and the median value MdS of the reference station ST and the median value MdC of the measurement point CT match, resulting in a so-called two-point correction.
As a result, values with practical accuracy can be calculated without performing calibration using standard gas.
In particular, since the minimum value and median value clearly show the tendency of seasonal changes, the calculated values can have practical accuracy.

<Primary correction using average value (two-point correction)>
Equation 4 of the minimum value MinS of the reference station ST, Equation 11 of the minimum value MinC of the measured values, Equation 5 of the average value AvS of the reference station ST, and Equation 12 of the average value AvC of the measured values match in two points. Convert Cijk using the linear equation as follows.
Then, the conversion coefficients A and B are determined by the two-dimensional simultaneous linear equations of Equations 18 and 19.
(Number 18)
MinS=A×MinC+B
(Number 19)
AvS＝A×AvC＋B
Using A and B obtained in this way, correction is made using Equation 20. Let the correction value (pseudo calibration value) be C ₁ Cijk.
(Number 20)
C ₁ Cijk＝A×Cijk＋B

That is, first, in the standard station trend parameter extraction step S2, according to Formula 1, the minimum value MinSi of the announced value Sij of the standard station ST for each day i, which is the first variable range, and the time j, which is the second variable range, in a predetermined period = Find Min(Sij)|j.
Next, using Equation 4, the minimum value MinS = Min(Sij) | )|i.
At the same time, using Equation 5, the average value AvS=Average(AvSi)|i for date i, which is the first domain, of the published value Sij of the reference station ST is calculated.

Subsequently, in measurement point trend parameter calculation step S3, Equation 7 is used to calculate the values for each day i, which is the first range, every time j, which is the second range, and the second range, which is the third range, in the predetermined period. For the number of measurements k within the unit interval of the region, the average value AvCij=Average(Cijk)|k is obtained by averaging the measured value Cijk at the measurement point CT by the number of measurements k in the third region.
Next, using Equation 8, the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, of the average value AvCij=Average(Cijk)|k obtained using Equation 7 is calculated.

Then, using Equation 11, of the minimum value MinAvCi=Min(AvCij)|j obtained using Equation 8, the minimum value MinC=MinMinAvC=Min(MinAvCi)|i for date i, which is the first domain, is determined.
At the same time, using Equation 12, the average value AvC=AvAvAvC=Average(AvAvCi)|i for date i, which is the first range of the average value AvCij of the measured value Cijk at the measurement point CT, is calculated.
Then, in the measurement value conversion step S4,
MinS=A×MinC+B of formula 18,
AvS=A×AvC+B in formula 19
Using A and B obtained from , set the pseudo calibration value C ₁ Cijk = A × Cijk + B.

As a result, the minimum value MinS of the reference station ST and the minimum value MinC of the measurement point CT match, and the average value AvS of the reference station ST and the average value AvC of the measurement point CT match, resulting in a so-called two-point correction.
As a result, as shown in FIG. 8, values with practical accuracy can be calculated without performing calibration using standard gas.
In particular, since the minimum value and average value clearly show the tendency of seasonal changes, the calculated values can have practical accuracy.

<W-order correction (intermediate between 0-order correction and 1-order correction)>
As an intermediate between the above-mentioned zero-order correction and first-order correction, w (0<w<1)-order correction is defined as a weighted average of C ₀ Cijk and C ₁ Cijk as shown in Equation 21. Let the correction value (pseudo calibration value) be C _w Cijk.
(Number 21)
C _w Cijk＝(1－w)×C ₀ Cijk＋w×C ₁ Cijk

That is, first, in the standard station trend parameter extraction step S2, according to Formula 1, the minimum value MinSi of the announced value Sij of the standard station ST for each day i, which is the first variable range, and the time j, which is the second variable range, in a predetermined period = Find Min(Sij)|j.
Next, using Equation 4, the minimum value MinS = Min(Sij) | )|i.
At the same time, calculate the median value MdS=Median(Sij)|i,j for the first domain, day i, and the second domain, time j, of the published value Sij of the reference station ST using Formula 6, or , the average value AvS=Average(AvSi)|i for date i, which is the first domain, of the announced value Sij of the reference station ST is determined by Equation 5.

Subsequently, in measurement point trend parameter calculation step S3, Equation 7 is used to calculate the values for each day i, which is the first range, every time j, which is the second range, and the second range, which is the third range, in the predetermined period. For the number of measurements k within the unit interval of the region, the average value AvCij=Average(Cijk)|k is obtained by averaging the measured value Cijk at the measurement point CT by the number of measurements k in the third region.
Next, using Equation 8, the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, of the average value AvCij=Average(Cijk)|k obtained using Equation 7 is calculated.

Then, using Equation 11, of the minimum value MinAvCi=Min(AvCij)|j obtained using Equation 8, the minimum value MinC=MinMinAvC=Min(MinAvCi)|i for date i, which is the first domain, is determined.
Along with this, according to Equation 13, the median value MdC=Median(AvCij)|i,j of the average value AvCij of the measured value Cijk at the measurement point CT for date i which is the first domain and time j which is the second domain Alternatively, the average value AvC=AvAvAvC=Average(AvAvCi)|i for day i, which is the first domain of the average value AvCij of the measured value Cijk at the measurement point CT, is calculated using Equation 12.
Then, in the measurement value conversion step S4,
As 0＜w＜1,
C ₀ Cijk=Cijk+(MinS-MinC) of Formula 14,
Formula 17 or Formula 20 C ₁ Cijk=A×Cijk+B
As the weighted average of both, the pseudo calibration value C _w Cijk = (1 - w) × C ₀ Cijk + w × C ₁ Cijk.

This results in a so-called one-point correction in which the minimum value MinS of the reference station ST and the minimum value MinC of the measurement point CT match, or in addition to this, the median value MdS of the reference station ST and the median value MdC of the measurement point CT or the average value AvS of the reference station ST and the average value AvC of the measurement point CT are in the middle of so-called two-point correction.
As a result, as shown in FIG. 8, values with practical accuracy can be calculated without performing calibration using standard gas.

In this embodiment, correction of the measured value of the concentration of carbon dioxide (pseudo calibration) has been described as an example of "greenhouse gas"; however, the present invention is not limited to this.
In other words, similar effects can be obtained with correction (pseudo calibration) of the measured concentrations of methane, dinitrogen monoxide, and CFC substitutes (hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, and nitrogen trifluoride). can.

The method of measuring greenhouse gas concentration or the program of the system 100 for measuring greenhouse gas concentration, which is the first embodiment of the present invention thus obtained, is based on the data published at the standard station ST for a predetermined period. A reference station trend parameter extraction step S2 for extracting a trend parameter of ST, a measurement point trend parameter calculation step S3 for calculating a trend parameter for a measurement point CT based on data for a predetermined period at a measurement point CT, and a trend parameter for a measurement point CT. By comprising a measurement value conversion step S4 of converting the measurement value at the measurement point CT so that the measurement value matches the trend parameter of the reference station ST, the calibration method using the standard gas can be performed instead of directly performing calibration using the standard gas. The effect is enormous, such as being able to indirectly calibrate the data of the measurement point CT in a pseudo manner by utilizing the data of the reference station ST that is performing the calibration.

Next, FIGS. 5(A), 5(B), and FIGS. The explanation will be based on 10.
Here, FIG. 9 is a chart diagram of the predicted value calculation step when calculating the predicted value according to the second embodiment of the present invention, and FIG. 10 is a chart diagram of the predicted value calculation step according to the second embodiment of the present invention. It is a chart figure about the measurement point tendency parameter calculation step and the measurement value conversion step in the case of calculation.
The method for measuring carbon dioxide concentration, the measuring system 100, and the program of the second embodiment are based on the data for a predetermined period at the measurement point CT of the method of measuring carbon dioxide concentration, the measuring system 100, and the program of the first embodiment. , when the data for a predetermined period at the reference station ST is unavailable or not used, and instead, the predicted value for the prediction period at the reference station ST corresponding to the second predetermined period (described later) at the measurement point CT is used for the prediction period earlier than the prediction period. The calculation is based on the data for the first predetermined period, and many elements are the same as the carbon dioxide concentration measurement method, measurement system 100, and its program in the first embodiment, so the common items will be explained below. Detailed explanation will be omitted.

<When data for a predetermined period at the reference station ST cannot be used or is not used>
As mentioned above, Figures 5(A) and 5(B) show monthly secular changes in carbon dioxide at observation points of the Japan Meteorological Agency (Ryari, Yonaguni Island, Minamitorishima), which are examples of standard station ST. .
From this graph, it is a curve that is superimposed on a function that monotonically increases almost linearly year by year, and a curve that undulates each season. By adding the monotonically increasing amount of , and also taking into account seasonal fluctuations (curves that wave in each season due to the effects of photosynthesis in plants, etc.), the predicted value for a certain month in a certain year can be calculated. Obtainable.
In order to minimize the increase in carbon dioxide emissions caused by human activities, the three observation points mentioned above are located in remote locations. It is possible to understand this intention and interpret this data as representing the minimum value, or to interpret it as including human activities, that is, as an average value.

<How to interpret it as the minimum value>
As mentioned above, data from Kisai as a reference station ST is obtained hourly, whereas Japan Meteorological Agency data is obtained monthly, so the predicted data for a certain day in a month is the same as the value at the beginning of the month and the following month. It is possible to calculate the value of day i from the starting value by interpolation.
Let Mi be the data on the i day of the carbon dioxide concentration prediction data predicted in this way (the letter M stands for Japan Meteorological Agency. ).
The minimum value MinM=Min(Mi)|i in the comparison period (observation period, 14 days in the example) is determined from the predicted carbon dioxide concentration data predicted in this way.
A correction method (zero-order correction (one-point correction)) is used in which the value of MinAvCi in Equation 8 is matched to this MinM. Let the correction value be C ₀ Cijk.

However, since the predicted value Mi is the predicted average value for one day, the minimum value on the i day is smaller than that value, so it is corrected with a statistically obtained value Δm (m is the month). Δm is obtained by averaging the difference between the daily average value of a certain period (for example, a month) and the minimum value of the day from the data of a standard station ST, such as Kisai, from which hourly data is obtained. Calculations are made from past data from the reference station ST and used repeatedly (data from the reference station ST is not used in real time for this correction calculation itself).
For example, regarding the correction of m months (Equation 22)
Δm=(Average(AvSi−MinSi)|i)|m=AvS−MinS|m
(Number 23)
AvSi=Average(Sij)|Average value on ji day (Equation 24)
MinSi=Min(Sij)|Minimum value of day ji In the above calculation, Δ was set to Δm corresponding to month m, but by interpolation calculation, the minimum value was detected as Δmd corresponding to month m and day d in a certain period. Δmd can also be used according to the calendar date.
Furthermore, although the above calculation was based on past data from the Japan Meteorological Agency, calculations can also be made using past data from organizations other than the Japan Meteorological Agency that conduct high-precision observations (Kisai, Saitama Prefecture, National Institute for Environmental Studies, etc.).
(Number 25)
C ₀ Cijk＝Cijk＋(Mi－Δm－MinAvCi)

<How to interpret it as an average value>
As mentioned above, data from Kisai as a reference station ST is obtained hourly, whereas Japan Meteorological Agency data is obtained monthly, so the predicted data for a certain day in a month is the same as the value at the beginning of the month and the following month. It is possible to calculate the value of day i from the starting value by interpolation.
The carbon dioxide concentration prediction data predicted in this manner is subjected to the above-described zero-order correction, first-order correction, and intermediate (w) correction. Let Mi be the Japan Meteorological Agency data for the i day of the comparison period (14 days in the example) (the letter M stands for Japan Meteorological Agency).
A correction method (zero-order correction (one-point correction)) of matching the value of AvAvCi in Formula 9 to this Mi may be used. Let the correction value be CCijk.
In the case of primary correction, the minimum value, the average value, or the median value is used for correction (primary correction (two-point correction)).
(Number 23)
CCijk＝Cijk＋(Mi－AvAvCi)

Since the Japan Meteorological Agency publishes actual data every year, the predicted values should be calculated for at least two years.
For example, performance data from January to December 2021 will be published at the end of March 2022. Even if we make predictions for one year from this data, the predicted values for January to March 2023 will not be ready in time for the next announcement at the end of March 2023, so we will need to make predictions for at least two years from 2022 to 2023. It is necessary to predict the future (over two years).

As shown in FIG. 9, in step S11, as a predicted value calculation step, predictions for two years including the current year are performed at the time of data publication once a year.
Specifically, based on the published carbon dioxide concentration data for the first predetermined period (for example, from January 1, 1987 to December 31, 2021) at the reference station ST, the reference station ST after the first predetermined period is determined. Calculate the predicted value of carbon dioxide concentration for a prediction period of two years or more (January 1, 2022 to December 31, 2023).
In step S12, as a predicted value calculation step, since the prediction in step S11 is a monthly average value, a prediction of data for a certain day is obtained by interpolation calculation from data at the beginning and end of the month.

In step S13, as a predicted value calculation step, since the prediction in step S12 is a daily average, instead of automatically calibrating to a constant value (e.g. 400 ppm) as described in the above "Problem to be Solved by the Invention", If we decide to use the minimum value of (processing period), it is necessary to find the minimum value for that day. For example, the minimum value can be calculated by calculating the difference between the daily average value and the minimum value using Kisai's hourly data, and averaging it over a month. You can find the difference from the minimum value.
In step S14, as a predicted value calculation step, the values in step S13 are also the values at the beginning and end of the month, and these are calculated by interpolation for each day.
In step S15, as a predicted value calculation step, the minimum value MINmd of month m and day d is obtained.
Steps S11 to S15 are calculated in advance and updated once a year.
In addition, as a technical concept, it is sufficient to calculate the predicted value using data from at least one of Ryori, Yonagunijima, and Minamitorishima as reference stations ST, and the use of Kisai data in steps S13 to S15 is not essential. do not have.
The technical significance of using Kisai's data is to find the difference between the average value and the minimum value, and to obtain a statistical value about how much the average value and the minimum value deviate from each other, not the average value. This is to obtain a more accurate minimum value and further improve the accuracy of the converted value of the measured value at the measurement point CT.

Then, as shown in FIG. 10, in step S21, as a measurement point trend parameter calculation step, as in step S3 described above, after a first predetermined period (for example, from January 1, 1987 to December 31, 2021), Based on the data at the measurement point CT during the second predetermined period (July 13, 2022 to July 26, 2022), which is within the prediction period (January 1, 2022 to December 31, 2023). The trend parameters of the measurement point CT are calculated.
In step S22, as a measurement value conversion step, the measurement value at the measurement point CT is converted so that the trend parameter at the measurement point CT matches the predicted value as the trend of the reference station ST, as in step S4 described above.

As a result, the trend parameters of the measurement point CT that have not been calibrated using the standard gas are made to match the predicted values based on the data of the reference station ST that has been calibrated using the standard gas. Measured values are converted.
As a result, values with practical accuracy can be calculated without performing calibration using standard gas.
In other words, it is possible to calculate a value with practical accuracy comparable to that obtained by the carbon dioxide concentration measuring method in steps S1 to S4 described above or the program of the carbon dioxide concentration measuring system 100.

Assuming that there is no connection to the network, the microcomputer terminal 111 and server 120 at the measurement point CT are equipped with an RTC (real-time clock), and the microcomputer terminal 111 and server 120 at the measurement point CT are equipped with clocks for the product lifespan of approximately You can also predict and incorporate 10 years' worth.
In this way, by automatically calibrating to a constant value (e.g. 400 ppm) mentioned in the above "Problem to be Solved by the Invention", the drawback of not being able to follow the yearly increase in carbon dioxide concentration and seasonal changes can be solved. , this drawback can be overcome by automatic calibration to values in line with long-term trends and seasonal changes within Japan.

The method of measuring greenhouse gas concentration or the program of the measuring system 100, which is the second embodiment of the present invention obtained in this way, is used for a first predetermined period (for example, from January 1, 1987 to December 2021) at the reference station ST. Calculate the predicted value for a forecast period of two years or more (January 1, 2022 to December 31, 2023) of the reference station ST after the first predetermined period based on the published data of the month (January 1, 2022 to December 31, 2023). Based on the predicted value calculation steps S11 to S15 and the data at the measurement point CT during the second predetermined period (July 13, 2022 to July 26, 2022) that is after the first predetermined period and within the prediction period. measurement point trend parameter calculation step S21 in which the trend parameters of the measurement point CT are calculated, and a measurement step in which the measured value of the measurement point CT is converted so that the trend parameter of the measurement point CT matches the predicted value as the trend of the reference station ST. By providing the value conversion step S22, instead of directly performing calibration using the standard gas, the predicted value based on the data of the reference station ST that is performing the calibration using the standard gas is used to indirectly perform the calibration. The effects are enormous, such as being able to pseudo-calibrate the data at the measurement point CT.

100 ... Measurement system (of greenhouse gas concentration) 110 ... Sensor 111 (at the measurement point) ... Microcomputer terminal 120 (at the measurement point) ... Server 130 ... User terminal CT ... Measurement Point ST... Reference station ST1... Observation equipment (of the reference station)

Claims (7)

A method for measuring greenhouse gas concentration using a sensor at a measurement point that is different from a reference station,
a reference station trend parameter extraction step of extracting a trend parameter of the reference station based on published data of the reference station for a predetermined period;
a measurement point trend parameter calculation step of calculating a trend parameter of the measurement point based on data for a predetermined period at the measurement point;
A method for measuring greenhouse gas concentration, comprising: converting a measured value at a measurement point so that a trend parameter at the measurement point matches a trend parameter at a reference station. As the i-th day and j-th time of the day in the predetermined period, the published value S of the standard station and the measured value C of the measurement point are the published value Sij of the standard station at j time on day i, and j time on day i. Let Cijk be the measurement value at the measurement point k for the number of measurements within the unit interval of ,
In the reference station trend parameter extraction step, the minimum value MinSi=Min(Sij)|j of the published value Sij of the reference station for each day i, which is the first variable range, and time j, which is the second variable range, in the predetermined period is calculated. Then, find the minimum value MinS = Min(MinSi)|i for date i, which is the first domain, of the minimum value MinSi = Min(Sij)|j of the published value Sij of the standard station,
In the measurement point trend parameter calculation step, measurements are performed at each day i in the first range, at each time j in the second range, and within a unit interval of the second range in the third range in the predetermined period. After calculating the average value AvCij=Average(Cijk)|k obtained by averaging the measured value Cijk at the measurement point with the number of measurements k in the third domain for the number of times k, the obtained average value AvCij=Average(Cijk)|k is calculated. Find the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, and then calculate the date i, which is the first domain of the obtained minimum value MinAvCi=Min(AvCij)|j. Find the minimum value MinC=MinMinAvC=Min(MinAvCi)|i,
2. The method for measuring greenhouse gas concentration according to claim 1, wherein in the measurement value conversion step, the pseudo calibration value C ₀ Cijk=Cijk+(MinS−MinC). As the i-th day and j-th time of the day in the predetermined period, the published value S of the standard station and the measured value C of the measurement point are the published value Sij of the standard station at j time on day i, and j time on day i. Let Cijk be the measurement value at the measurement point k for the number of measurements within the unit interval of ,
In the reference station trend parameter extraction step, the minimum value MinSi=Min(Sij)|j of the published value Sij of the reference station for each day i, which is the first variable range, and time j, which is the second variable range, in the predetermined period is calculated. From this, the minimum value MinS = Min (MinSi) | Find the median value MdS=Median(Sij)|i,j for date i, which is the first domain, and time j, which is the second domain, of the published value Sij,
In the measurement point trend parameter calculation step, measurements are performed at each day i in the first range, at each time j in the second range, and within a unit interval of the second range in the third range in the predetermined period. After calculating the average value AvCij=Average(Cijk)|k obtained by averaging the measured value Cijk at the measurement point with the number of measurements k in the third domain for the number of times k, the obtained average value AvCij=Average(Cijk)|k is calculated. Find the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, and then calculate the date i, which is the first domain of the obtained minimum value MinAvCi=Min(AvCij)|j. Find the minimum value MinC=MinMinAvC=Min(MinAvCi)|i, and calculate the median value of the average value AvCij of the measured value Cijk at the measurement point for day i, which is the first domain, and time j, which is the second domain. Find MdC＝Median(AvCij)|i,j,
In the measurement value conversion step,
MinS=A×MinC+B,
MdS=A×MdC+B
The method for measuring greenhouse gas concentration according to claim 1, characterized in that the pseudo calibration value C ₁ Cijk=A×Cijk+B is set using A and B determined from . As the i-th day and j-th time of the day in the predetermined period, the published value S of the standard station and the measured value C of the measurement point are the published value Sij of the standard station at j time on day i, and j time on day i. Let Cijk be the measurement value at the measurement point k for the number of measurements within the unit interval of ,
In the reference station trend parameter extraction step, the minimum value MinSi=Min(Sij)|j of the published value Sij of the reference station for each day i, which is the first variable range, and time j, which is the second variable range, in the predetermined period is calculated. From the above, the minimum value MinS = Min (MinSi) | Find the average value AvS=Average(AvSi)|i for date i, which is the first domain of the published value Sij,
In the measurement point trend parameter calculation step, measurements are performed at each day i in the first range, at each time j in the second range, and within a unit interval of the second range in the third range in the predetermined period. After calculating the average value AvCij=Average(Cijk)|k obtained by averaging the measured value Cijk at the measurement point with the number of measurements k in the third domain for the number of times k, the obtained average value AvCij=Average(Cijk)|k is calculated. Find the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, and calculate the minimum value MinAvCi=Min(AvCij)|j, which is the date i, which is the first domain. In addition to finding the minimum value MinC=MinMinAvC=Min(MinAvCi)|i, the average value AvC=AvAvAvC=Average(AvAvCi)| Find i,
In the measurement value conversion step,
MinS=A×MinC+B,
AvS＝A×AvC＋B
The method for measuring greenhouse gas concentration according to claim 1, characterized in that the pseudo calibration value C ₁ Cijk=A×Cijk+B is set using A and B determined from . As the i-th day and j-th time of the day in the predetermined period, the published value S of the standard station and the measured value C of the measurement point are the published value Sij of the standard station at j time on day i, and j time on day i. Let Cijk be the measurement value at the measurement point k for the number of measurements within the unit interval of ,
In the reference station trend parameter extraction step, the minimum value MinSi=Min(Sij)|j of the published value Sij of the reference station for each day i, which is the first variable range, and time j, which is the second variable range, in the predetermined period is calculated. From the minimum value MinSi = Min(Sij)|j of the published value Sij of the reference station, the minimum value MinS = Min(MinSi)|i for date i, which is the first domain, is determined.
Median value MdS=Median(Sij)|i,j for date i, which is the first domain, and time j, which is the second domain, of the published value Sij of the standard station,
Alternatively, find the average value AvS=Average(AvSi)|i for date i, which is the first domain of the published value Sij of the standard station,
In the measurement point trend parameter calculation step, measurements are performed at each day i in the first range, at each time j in the second range, and within a unit interval of the second range in the third range in the predetermined period. After calculating the average value AvCij=Average(Cijk)|k obtained by averaging the measured value Cijk at the measurement point with the number of measurements k in the third domain for the number of times k, the obtained average value AvCij=Average(Cijk)|k is calculated. Find the minimum value MinAvCi=Min(AvCij)|j for time j, which is the second domain, and then calculate the date i, which is the first domain of the obtained minimum value MinAvCi=Min(AvCij)|j. Find the minimum value MinC=MinMinAvC=Min(MinAvCi)|i, and
Median value MdC=Median(AvCij)|i,j for date i which is the first domain and time j which is the second domain of the average value AvCij of the measured value Cijk at the measurement point,
Alternatively, find the average value AvC=AvAvAvC=Average(AvAvCi)|i for date i which is the first domain of the average value AvCij of the measured value Cijk at the measurement point,
In the measurement value conversion step,
As 0＜w＜1,
C ₀ Cijk＝Cijk＋(MinS－MinC),
C ₁ Cijk＝A×Cijk＋B
The method for measuring greenhouse gas concentration according to claim 1, characterized in that the pseudo calibration value C _w Cijk = (1 - w) x C ₀ Cijk + w x C ₁ Cijk is set as a weighted average of both. A program for a greenhouse gas concentration measurement system using a sensor at a measurement point that is different from a reference station,
a reference station trend parameter extraction step of extracting a trend parameter of the reference station based on published data of the reference station for a predetermined period;
a measurement point trend parameter calculation step of calculating a trend parameter of the measurement point based on data for a predetermined period at the measurement point;
A program for a greenhouse gas concentration measurement system, comprising: a measurement value conversion step of converting a measurement value at a measurement point so that a trend parameter at the measurement point matches a trend parameter at a reference station. A program for a greenhouse gas concentration measurement system using a sensor at a measurement point that is different from a reference station,
a predicted value calculation step of calculating a predicted value for a prediction period of two years or more for the reference station after the first predetermined period based on the published data for the first predetermined period at the reference station;
a measurement point trend parameter calculation step of calculating a trend parameter of the measurement point based on data at the measurement point during a second predetermined period that is after the first predetermined period and within the prediction period;
A program for a greenhouse gas concentration measurement system, comprising: a measurement value conversion step of converting a measurement value at a measurement point so that a trend parameter at the measurement point matches a predicted value as a trend of a reference station.