CN114707119B - Method and system for quantifying atmospheric ozone pollution source based on domestic hyper-spectral satellite - Google Patents

Method and system for quantifying atmospheric ozone pollution source based on domestic hyper-spectral satellite Download PDF

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CN114707119B
CN114707119B CN202210429695.XA CN202210429695A CN114707119B CN 114707119 B CN114707119 B CN 114707119B CN 202210429695 A CN202210429695 A CN 202210429695A CN 114707119 B CN114707119 B CN 114707119B
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刘诚
张成歆
徐天怡
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University of Science and Technology of China USTC
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Abstract

The invention discloses a method and a system for quantifying an atmospheric ozone pollution source based on a domestic hyper-spectral satellite, which are used for analyzing the influence factors of atmospheric ozone pollution based on a generalized additive model, and can understand the influence process of different factors on the change of ozone concentration and quantify the relative contribution of meteorological conditions and control measures.

Description

Method and system for quantifying atmospheric ozone pollution source based on domestic hyper-spectral satellite
Technical Field
The invention relates to the technical field of data analysis, in particular to a method and a system for quantifying atmospheric ozone pollution sources based on domestic hyper-spectral satellites.
Background
Accurate quantification of factors affecting atmospheric pollutants is the basis for formulating pollution control measures. In the existing researches, although it is proposed that meteorological factors such as temperature and humidity and artificial emission all affect the change of pollutant concentration, the researches do not accurately analyze the change in the pollutant concentration in a quantitative mode.
The generalized additive model (GAM model) can well process the nonlinear relation between the dependent variable and a plurality of independent variables, and has the advantages of automation, regularization, interpretability and the like. Researches prove that the generalized additive model can avoid some complex algorithms and find the most reasonable way to explain the problems encountered in actual scientific research. However, at present, no relevant scheme for applying the generalized additive model to the analysis of the atmospheric pollutant influence factors exists.
Disclosure of Invention
The invention aims to provide a method and a system for quantifying an atmospheric ozone pollution source based on a domestic hyper-spectral satellite, which are used for quantifying the atmospheric ozone pollution source and are beneficial to making pollution control measures.
The purpose of the invention is realized by the following technical scheme:
a method for quantifying an atmospheric ozone pollution source based on a domestic hyper-spectral satellite comprises the following steps:
acquiring meteorological data and satellite observation data containing ozone concentration and other trace gas concentrations;
respectively inputting the ozone concentration serving as a dependent variable, the concentration of the trace gas of a single category and the data of a single meteorological factor in meteorological data serving as independent variables into a pre-constructed generalized addable model to obtain a plurality of single-factor models, and screening out the independent variables for constructing the multi-factor model by using the single-factor models;
integrating all screened independent variables into a comprehensive independent variable, and constructing a multi-factor model;
and dividing all the screened independent variables into meteorological factors and artificial factors, calculating the influence factors of the two factors on the ozone concentration by utilizing the output of the multi-factor model, and quantifying the relative contribution of the meteorological factors and the artificial factors.
A system for quantifying atmospheric ozone pollution sources based on domestic hyper-spectral satellites comprises:
the data acquisition unit is used for acquiring meteorological data and satellite observation data containing ozone concentration and other trace gas concentration;
the single-factor model construction and independent variable screening unit is used for taking the ozone concentration as a dependent variable, taking the concentration of the trace gas of a single category and the data of a single meteorological factor in meteorological data as independent variables, respectively inputting the independent variables into a pre-constructed generalized additive model to obtain a plurality of single-factor models, and screening the independent variables for constructing the multi-factor model by using the single-factor models;
a multi-factor model construction unit for integrating all screened independent variables into a comprehensive independent variable by using a principal component analysis method and constructing a multi-factor model;
and the quantization unit is used for dividing all screened independent variables into meteorological factors and artificial factors, calculating influence factors of the two factors on the ozone concentration, and quantizing the relative contributions of the meteorological factors and the artificial factors.
A processing device, comprising: one or more processors; a memory for storing one or more programs;
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the aforementioned methods.
A readable storage medium, storing a computer program which, when executed by a processor, implements the aforementioned method.
According to the technical scheme provided by the invention, the influence factors of atmospheric ozone pollution are analyzed based on the generalized additive model, so that the influence process of different factors on the change of ozone concentration can be known, and the relative contributions of meteorological conditions and control measures can be quantified.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for quantifying atmospheric ozone pollution sources based on a domestic hyper-spectral satellite according to an embodiment of the present invention;
FIG. 2 is a diagram of residual error checking provided by an embodiment of the present invention;
FIG. 3 is a schematic illustration of the marginal effect of various factors on atmospheric ozone concentration provided by an embodiment of the present invention;
FIG. 4 is a graphical illustration of the relative contribution of meteorological and anthropogenic factors to atmospheric ozone concentration as provided by an embodiment of the present invention;
FIG. 5 is a schematic diagram of a system for quantifying atmospheric ozone pollution based on a domestic hyper-spectral satellite according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a processing apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The terms that may be used herein are first described as follows:
the terms "comprising," "including," "containing," "having," or other similar terms of meaning should be construed as non-exclusive inclusions. For example: including a feature (e.g., material, component, ingredient, carrier, formulation, material, dimension, part, component, mechanism, device, process, procedure, method, reaction condition, processing condition, parameter, algorithm, signal, data, product, or article of manufacture), is to be construed as including not only the particular feature explicitly listed but also other features not explicitly listed as such which are known in the art.
The method and system for quantifying the atmospheric ozone pollution source provided by the invention based on the domestic hyper-spectral satellite are described in detail below. Details which are not described in detail in the embodiments of the invention belong to the prior art which is known to the person skilled in the art. The examples of the present invention, in which specific conditions are not specified, were carried out according to the conventional conditions in the art or conditions suggested by the manufacturer. The instruments used in the examples of the present invention are not indicated by manufacturers, and are all conventional products that can be obtained by commercial purchase.
As shown in figure 1, the method for quantifying the atmospheric ozone pollution source based on the domestic hyper-spectral satellite mainly comprises the following steps:
1. acquiring meteorological data and satellite observation data containing ozone concentration and other trace gas concentration.
In an embodiment of the present invention, the satellite observation data includes: ozone (O) 3 ) Concentration, nitrogen dioxide (NO) 2 ) Concentration, carbon monoxide (CO) concentration and PM 2.5 Concentration; here, the nitrogen dioxide concentration, the carbon monoxide concentration and the PM 2.5 The concentrations are all the other trace gas concentrations. Because the ozone detector (OMI) has the advantages of stable spectral performance, high signal-to-noise ratio, long time coverage rate and the like, the troposphere O can be obtained from OMI primary data 3 、NO 2 CO and PM 2.5 The satellite observation data.Various satellite observation data can be acquired through domestic hyper-spectrum satellites.
In the embodiment of the present invention, the meteorological factors in the meteorological data mainly include: latitudinal wind (U), longitudinal wind (V), temperature (TEMP), pressure (PRES), relative Humidity (RH), boundary Layer Height (BLH), downlink short-wave solar radiation (DSR) and rainfall (PRECIP). Illustratively, it may be obtained from the fifth generation of the atmosphere re-analysis data set (ERA-5) of the mid-European weather forecast center.
2. And respectively inputting the ozone concentration serving as a dependent variable, the concentration of the trace gas of a single category and the data of a single meteorological factor in meteorological data serving as independent variables into a pre-constructed generalized addable model to obtain a plurality of single-factor models, and screening out the independent variables for constructing the multi-factor model by using the single-factor models.
In the embodiment of the invention, the generalized additive model establishes the relation between the mean value of the dependent variable and the smooth function of the independent variable through a link function. The concentration of the atmospheric ozone column is taken as a dependent variable, a log function is selected as a link function to construct a generalized additive model, and the generalized additive model is expressed as follows:
Figure BDA0003611273590000041
wherein C represents ozone concentration, using daily ozone concentration value; ε is the fitted residual, s (x) represents the smoothing function term for the ith argument, and N is the number of arguments.
Considering the short-term time persistence and the autocorrelation of the control time sequence residual error, the independent variables not only comprise meteorological factors (belonging to meteorological data) such as latitude wind, longitude wind, water-vapor mixing ratio, downlink short-wave solar radiation, precipitation, temperature and the like, and NO 2 、CO、PM 2.5 Column concentration (pertaining to trace gas concentration) and a time variable of days (daynum) are included to account for seasonal, daily variations in air quality.
In the embodiment of the invention, the respective variables (response variables) are respectively and independently input into the generalized additive model, and a plurality of single-factor models are constructed. Then, the influence degree of each independent variable on the atmospheric ozone concentration change is checked through a summery () function, a significance probability P ' is obtained (a reference basis is provided for later screening of the independent variables for constructing the multi-factor model), and the response variable is discarded when P ' is larger than a set threshold (for example, P ' > 0.05). The sum () function is mainly used to obtain the fitting objects generated by the model and generate various useful information from the fitting objects, and finally, the arguments used for constructing the multi-factor model are screened out.
3. And integrating all screened independent variables into a comprehensive independent variable by using a principal component analysis method, and constructing a multi-factor model.
In the embodiment of the invention, in order to eliminate multiple collinearity possibly existing among all response variables, the performance of a generalized additive model is influenced; therefore, based on the idea of dimension reduction, principal Component Analysis (PCA) is used to integrate the screened independent variables (i.e. the screened meteorological factors and other trace gas concentrations) into a plurality of integrated independent variables. The new synthetic independent variable is represented by a linear combination of several variables, and they are independent and independent of each other.
In the embodiment of the present invention, when constructing the multi-factor model, the ozone concentration may be used as a dependent variable by referring to the model formula provided in the step 2, a log function is selected as a link function by combining the comprehensive independent variable, and the multi-factor model is constructed based on the gam () function in the R language mgcv package.
4. And carrying out autocorrelation detection on the residual error of the generalized additive model by using the data set and evaluating the performance of the model.
In the embodiment of the invention, the data set is used for carrying out autocorrelation detection on the residual error of the generalized additive model, and whether the tailing phenomenon exists is checked. The observation data is divided into a training set and a test set, and the generalized additive model prediction value and the actual observation value are compared based on comprehensive evaluation indexes to evaluate the reproducibility and the forecast performance of the generalized additive model; the overall evaluation index is expressed as:
Figure BDA0003611273590000051
wherein, Y represents the comprehensive evaluation index, P is Pearson's R, B is Mean deviation (Mean Bias), R is root Mean square error (root Mean square error); the closer the value of Y is to 1, the better the reproducibility and predictive performance of the generalized additive model.
The step is mainly used for verifying the model, and no direct sequence relation exists in the subsequent quantization process. As will be understood by those skilled in the art, the training of the model is to select a part of the data set as the input of the multi-factor model that has been constructed, predict the corresponding time period of the test set, and compare the result with the actual data, and the purpose of the process is to detect the performance of the model.
5. And judging whether a nonlinear relation exists between the screened single independent variable and the ozone concentration by utilizing the value of the degree of freedom of the output of the multi-factor model.
The input of the multi-factor model is the concentration of ozone in the region in the research time range and the value of each screened independent variable, the output of the multi-factor model, including F value, edf and the like, can be obtained through a summary () function, and the fitting value calculated by the model and the like are used for drawing to perform more intuitive analysis.
In the embodiment of the invention, 100% × (exp (s (x) -1)) is used for explaining the marginal effect of each factor in the GAM model on the atmospheric ozone concentration, the relative percentage contribution of a single independent variable to the ozone concentration is quantified, and the change trend of the ozone concentration along with a single factor is analyzed.
Meanwhile, whether a complex nonlinear relationship (which is expressed when edf > 1) exists between each independent variable and the ozone concentration can be judged based on the value of the degree of freedom, and the judgment result is an independent partial result; where edf represents the value of the degree of freedom of the output of the multi-factor model, and edf can be directly obtained by a sum () function.
6. All screened independent variables are divided into meteorological factors (s (metos)) and artificial factors (s (non _ metos)), and the influence factors of the two factors on the ozone concentration are calculated by utilizing the output of the multi-factor model, and the relative contributions of the meteorological factors and the artificial factors are quantified.
In the embodiment of the present invention, the artificial factors correspond to the aforementioned trace gas concentrations of each category, and of course, only the category screened in step 2 is considered here, that is, the artificial factors are only other trace gas concentrations screened; similarly, the meteorological factors include only the screened categories.
The relevant expressions here are:
Figure BDA0003611273590000061
/>
Figure BDA0003611273590000062
wherein R is m 、R n Respectively representing the influence factors of all the screened meteorological factors and artificial factors on the atmospheric ozone concentration, C m 、C n Respectively representing the quantitative results of the relative contributions of all the screened meteorological factors and artificial factors to the ozone concentration, s mi 、s ni Respectively represents the annual average value of the change of the ozone explained by the meteorological factor and the artificial factor of the ith year,
Figure BDA0003611273590000063
each represents all of s mi 、s ni Mean value of (c), m i Represents the annual mean value of the meteorological factor of the ith year>
Figure BDA0003611273590000064
Represents all m i N denotes the total number of years of the study time frame, s mi 、s ni And m i Are the outputs of the multi-factor model; s m0 Year mean, m, representing the meteorological factor of the initial year 0 The annual average value representing the initial annual ozone concentration.
Furthermore, the residual error of the multi-factor model can be calculated based on the calculation result so as to verify the performance of the multi-factor model.
Figure BDA0003611273590000065
Figure BDA0003611273590000066
Wherein m represents the mean value of the ozone concentration over the study time range,
Figure BDA0003611273590000067
representing the relative change in atmospheric ozone concentration, R is the residual error.
FIGS. 2, 3 and 4 provide related schematic diagrams of the above-described scheme; wherein, fig. 2 is a residual error test chart, and the upper and lower are an acf chart and a pacf chart, respectively. FIG. 3 is a schematic diagram of the marginal effect of each factor on the atmospheric ozone concentration, and each line graph in FIG. 3 represents the marginal effect of each influencing variable, i.e., the nonlinear relationship between the influencing variable and the change of the ozone concentration when no influence is generated by other variables, the solid line is the marginal effect, and the two dotted lines are 95% confidence intervals; FIG. 4 is a schematic diagram of the relative contribution of meteorological factors and human factors to atmospheric ozone concentration, wherein a part is the comparison of an actual observed value of ozone and a GAM model fitting value, a black point is a daily actual observed value of ozone, and a line is formed by the GAM model fitting value; the part b is the comparison of the relative contribution of meteorological factors to the ozone concentration and the actual observation value, the part c is the comparison of the relative contribution of artificial factors to the ozone concentration and the actual observation value, the upper part and the lower part of the part bc correspond to a positive example and a negative example respectively, a black line represents a moving average line of a window of 15 days, and R represents the correlation of the two parts.
Another embodiment of the present invention further provides a system for quantifying an atmospheric ozone pollution source based on a domestic hyper-spectral satellite, which is mainly used for implementing the method provided in the foregoing embodiment, as shown in fig. 5, the system mainly includes:
the data acquisition unit is used for acquiring meteorological data and satellite observation data containing ozone concentration and other trace gas concentrations;
the single-factor model construction and independent variable screening unit is used for taking the ozone concentration as a dependent variable, taking the concentration of the trace gas of a single category and the data of a single meteorological factor in meteorological data as independent variables, respectively inputting the independent variables into a pre-constructed generalized additive model to obtain a plurality of single-factor models, and screening the independent variables for constructing the multi-factor model by using the single-factor models;
a multi-factor model construction unit for integrating all screened independent variables into a comprehensive independent variable by using a principal component analysis method and constructing a multi-factor model;
the detection unit is used for carrying out autocorrelation detection on the residual error of the generalized addable model by utilizing the data set;
a judging unit for judging whether a nonlinear relationship exists between the screened single independent variable and the ozone concentration by using the value of the degree of freedom of the output of the multi-factor model;
and the quantization unit is used for dividing all screened independent variables into meteorological factors and artificial factors, calculating influence factors of the two factors on the ozone concentration, and quantizing the relative contributions of the meteorological factors and the artificial factors.
Another embodiment of the present invention further provides a processing apparatus, as shown in fig. 6, which mainly includes: one or more processors; a memory for storing one or more programs; wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods provided by the foregoing embodiments.
Further, the processing device further comprises at least one input device and at least one output device; in the processing device, a processor, a memory, an input device and an output device are connected through a bus.
In the embodiment of the present invention, the specific types of the memory, the input device, and the output device are not limited; for example:
the input device can be a touch screen, an image acquisition device, a physical key or a mouse and the like;
the output device may be a display terminal;
the Memory may be a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as a disk Memory.
Another embodiment of the present invention further provides a readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the method provided by the foregoing embodiment.
The readable storage medium in the embodiment of the present invention may be provided in the foregoing processing device as a computer readable storage medium, for example, as a memory in the processing device. The readable storage medium may be various media that can store program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims (9)

1. A method for quantifying an atmospheric ozone pollution source based on a domestic hyper-spectral satellite is characterized by comprising the following steps:
acquiring meteorological data and satellite observation data containing ozone concentration and other trace gas concentrations;
respectively inputting the ozone concentration serving as a dependent variable, the concentration of the trace gas of a single category and the data of a single meteorological factor in meteorological data serving as independent variables into a pre-constructed generalized addable model to obtain a plurality of single-factor models, and screening out the independent variables for constructing the multi-factor model by using the single-factor models;
integrating all screened independent variables into a comprehensive independent variable, and constructing a multi-factor model;
dividing all screened independent variables into meteorological factors and artificial factors, calculating influence factors of the two factors on ozone concentration by utilizing the output of a multi-factor model, and quantifying the relative contribution of the meteorological factors and the artificial factors, wherein the method comprises the following steps:
the artificial factors are the concentrations of other screened trace gases, and the meteorological factors are various screened meteorological factors;
calculating the influence factors of the two factors on the ozone concentration, and quantifying the relative contribution of the meteorological factor and the artificial factor, wherein the relative contribution is expressed as follows:
Figure FDA0003921492790000011
Figure FDA0003921492790000012
wherein R is m 、R n Respectively representing the influence factors of all the screened meteorological factors and artificial factors on the atmospheric ozone concentration, C m 、C n Respectively representing the quantitative results of the relative contributions of all the screened meteorological factors and artificial factors to the ozone concentration, s mi 、s ni Respectively represents the annual average value of the change of the ozone explained by the meteorological factor and the artificial factor of the ith year,
Figure FDA0003921492790000013
each represents all of s mi 、s ni Mean value of (1), m i Represents the annual mean value of the meteorological factor of the ith year>
Figure FDA0003921492790000014
Represents all m i N represents the total number of years in the study time frame, s mi 、s ni And m i All are the outputs of the multi-factor model; s is m0 Mean of years, m, representing the meteorological factors of the initial year 0 Represents the annual average value of the initial annual ozone concentration.
2. The method for quantifying the atmospheric ozone pollution source based on the domestic hyper-spectral satellite according to claim 1,
meteorological factors in the meteorological data include: latitude wind, longitude wind, temperature, pressure, relative humidity boundary layer height, downlink short-wave solar radiation amount and rainfall amount;
the satellite observation data includes: ozone concentration, nitrogen dioxide concentration, carbon monoxide concentration and PM 2.5 Concentration; wherein the nitrogen dioxide concentration, the carbon monoxide concentration and the PM 2.5 The concentrations are all the other trace gas concentrations.
3. The method for quantifying the atmospheric ozone pollution source based on the domestic hyper-spectral satellite according to claim 1, wherein the step of inputting the ozone concentration as a dependent variable, the concentration of the trace gas of a single category and the data of a single meteorological factor in meteorological data as independent variables into a pre-constructed generalized additive model respectively to obtain a plurality of single-factor models, and the step of screening out the independent variables for constructing the multi-factor model by using the single-factor models comprises the steps of:
the concentration of an atmospheric ozone column is taken as a dependent variable, a log function is selected as a link function to construct a generalized additive model, and the generalized additive model is expressed as follows:
Figure FDA0003921492790000021
wherein C represents ozone concentration, and daily ozone concentration value is used; epsilon is the fitting residual, s (x) represents the smoothing function term of the ith independent variable, and N is the number of the independent variables;
and (3) checking the influence degree of each independent variable on the change of the atmospheric ozone concentration through a summary () function to obtain a significance probability P ', abandoning the corresponding independent variable method when the significance probability P' is greater than a set threshold value, and finally screening out the independent variables for constructing the multi-factor model.
4. The method for quantifying the atmospheric ozone pollution source based on the domestic hyper-spectral satellite according to claim 1, wherein the integrating all the screened independent variables into a plurality of comprehensive independent variables by using a principal component analysis method and constructing a multi-factor model comprises:
integrating the screened independent variables into a comprehensive independent variable by utilizing a principal component analysis method;
when a multi-factor model is constructed, the ozone concentration is used as a dependent variable, a log function is selected as a link function by combining a comprehensive independent variable, and the multi-factor model is constructed based on a gam () function in an R language mgcv package.
5. The method for quantifying the atmospheric ozone pollution source based on the domestic hyper-spectral satellite according to claim 1, which is characterized by further comprising the following steps: calculating residual errors of the multi-factor model, and verifying the performance of the multi-factor model through the residual errors; the formula for calculating the residual error of the multifactor model is:
Figure FDA0003921492790000022
Figure FDA0003921492790000023
wherein m represents the mean value of the ozone concentration over the study time range,
Figure FDA0003921492790000024
representing the relative change in atmospheric ozone concentration, R is the residual error.
6. The method for quantifying the atmospheric ozone pollution source based on the domestic hyper-spectral satellite according to claim 1, which is characterized by further comprising the following steps:
and judging whether a nonlinear relation exists between the screened single independent variable and the ozone concentration by utilizing the value edf of the degree of freedom of the output of the multi-factor model.
7. A system for quantifying atmospheric ozone pollution sources based on domestic hyper-spectral satellites, which is used for implementing the method of any one of claims 1 to 6, and comprises:
the data acquisition unit is used for acquiring meteorological data and satellite observation data containing ozone concentration and other trace gas concentration;
the single-factor model construction and independent variable screening unit is used for inputting the ozone concentration serving as a dependent variable, the concentration of the trace gas in a single category and the data of a single meteorological factor in meteorological data serving as independent variables into a pre-constructed generalized addable model respectively to obtain a plurality of single-factor models, and screening the independent variables for constructing the multi-factor model by using the single-factor models;
a multi-factor model construction unit for integrating all screened independent variables into a comprehensive independent variable by using a principal component analysis method and constructing a multi-factor model;
and the quantization unit is used for dividing all screened independent variables into meteorological factors and artificial factors, calculating influence factors of the two factors on the ozone concentration, and quantizing the relative contributions of the meteorological factors and the artificial factors.
8. A processing device, comprising: one or more processors; a memory for storing one or more programs;
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-6.
9. A readable storage medium, storing a computer program, characterized in that the computer program, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9216390B2 (en) * 2010-07-15 2015-12-22 Ohio State Innovation Foundation Systems, compositions, and methods for fluid purification
CN105320843A (en) * 2015-10-30 2016-02-10 中山大学 Atmospheric pollutant concentration influence factor quantitative analysis method
US11144835B2 (en) * 2016-07-15 2021-10-12 University Of Connecticut Systems and methods for outage prediction
KR20210149619A (en) * 2020-06-02 2021-12-09 삼성전자주식회사 Solid electrolyte, preparation method thereof, and electrochemical device including the solid electrolyte
CN113570163A (en) * 2021-09-02 2021-10-29 河北科技大学 Atmospheric ozone concentration prediction method, system and device based on mathematical model

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