CN114169179A - Air quality background concentration analysis method - Google Patents
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Abstract
The invention relates to the technical field of environmental monitoring, in particular to an air quality background concentration analysis method, which comprises the following steps of S100: establishing a WRF-CMAQ model according to a historical database; s200: acquiring infectious disease prevention and control data; s300: selecting meteorological field data and air quality data for analyzing a background pollution source emission list in a historical database according to infectious disease prevention and control data; s400: inverting a background pollution source emission list based on the WRF-CMAQ model and the EnKF data assimilation system according to the meteorological field data and the air quality data selected in the S300; s500: and analyzing the background air quality data of the year to be tested according to the background pollution source emission list and the meteorological field data of the year to be tested based on the WRF-CMAQ model. The background air quality data which is more accurate and accords with human life practice can be analyzed, and parameter data are provided for environmental pollution control.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to an air quality background concentration analysis method.
Background
Background concentration in the traditional concept refers to a natural situation which is not directly influenced by local conditions and human activities, and an environmental background value has relativity, regionality and timeliness, but is very difficult to be absolutely free from pollution and not influenced by human activities. The existing background concentration monitoring method is to establish an atmospheric background station or a background station to monitor the concentration of various pollutants, so that the concentration of the background pollutants is represented, but the existing background concentration monitoring method is not considered from human activity influence factors, cannot reflect the real background concentration in the living state of human beings, cannot provide a basis for controlling emission reduction, and is not suitable for actual management from an academic perspective. Therefore, in order to meet the requirements of deeper and more intensive and careful environmental management, a method capable of analyzing more accurate air quality background concentration is urgently needed, and background air quality data are provided for pollution control and serve as a reference.
Disclosure of Invention
The invention provides an air quality background concentration analysis method, which can analyze background air quality data which are more accurate and accord with human life practice, and provide parameter data for environmental pollution treatment.
The basic scheme provided by the invention is as follows:
an air quality background concentration analysis method comprises the following steps:
s100: establishing a WRF-CMAQ model according to a historical database, wherein the historical database comprises meteorological field data, MEIC list and air quality data;
s200: acquiring infectious disease prevention and control data, wherein the infectious disease prevention and control data comprise an infectious disease prevention and control mode and an infectious disease prevention and control time period;
s300: selecting meteorological field data and air quality data for analyzing a background pollution source emission list in a historical database according to infectious disease prevention and control data;
s400: inverting a background pollution source emission list based on the WRF-CMAQ model and the EnKF data assimilation system according to the meteorological field data and the air quality data selected in the S300;
s500: and analyzing the background air quality data of the year to be tested according to the background pollution source emission list and the meteorological field data of the year to be tested based on the WRF-CMAQ model.
The principle and the advantages of the invention are as follows: in the scheme, the WRF-CMAQ model is used for analyzing the pollution source emission list according to the meteorological field data and the air quality data, and the analysis is called as an inversion pollution source emission list.
Based on the above, according to the scheme, the infectious disease prevention and control data is obtained, the infectious disease prevention and control mode contained in the infectious disease prevention and control data can know the activity level of human beings in the historical infectious disease prevention and control time period, and the activity level of people is low to prevent further spread of infectious diseases because the activity level of people can be changed according to the infection properties of infectious diseases in the infectious disease prevention and control time period, especially in the large-scale infectious disease prevention and control period with strong infectivity. And the background air quality data which is more accurate and accords with the human life reality can be analyzed under different meteorological conditions and various time period conditions by combining the meteorological field data of the year to be detected, so that parameter data are provided for environmental pollution treatment, and the environmental pollution treatment is facilitated.
Further, in S300, the selected infectious disease prevention and control manner includes home epidemic prevention, and weather field data and air quality data of the corresponding infectious disease prevention and control time period.
Has the advantages that: during the household epidemic prevention period, the activity level of human is low, the meteorological field data and the air quality data at the moment are selected to analyze the background pollution source emission list, the essence of the background concentration is better met, and the more real background concentration in the living state of the human can be reflected better. Specifically, compared with the traditional background station background concentration monitoring mode, the meteorological field data and the air quality data selected by the scheme are based on the data meeting the condition of the minimum living standard of human beings, are more in line with the actual situation, and are more significant for researching the background pollutant emission list in human life.
Further, S100 includes the steps of:
s101: setting model parameters and establishing a WRF-CMAQ model;
s102: simulating air quality data according to meteorological field data and MEIC lists in a historical database based on a WRF-CMAQ model;
s103: and adjusting and optimizing the WRF-CMAQ model according to the simulated air quality data and the actually measured air quality data.
Has the advantages that: and adjusting and optimizing the WRF-CMAQ model according to the simulated air quality data and the actually measured air quality data, so that the scientificity and the accuracy of the WRF-CMAQ model are improved.
Further, in S103, a normalized mean deviation NMB and a normalized mean error NME of each parameter in the simulated air quality data and the measured air quality data are calculated, and the WRF-CMAQ model is adjusted and optimized according to the calculation result.
Has the advantages that: the normalized Mean deviation nmb (normalized Mean bias) is the arithmetic Mean of the absolute values of the deviations of the values in the series from their arithmetic Mean. The mean deviation is a measure of the degree of deviation of the mean of the values in the series, reflecting the average difference between the values of the markers and the arithmetic mean. The normalized Mean error nme (normalized Mean error) is the arithmetic Mean of the differences between the simulated and measured values of the global feature, and in terms of mathematical relationship, since the Mean of the means of all samples is equal to the Mean of the population, the normalized Mean error is the standard deviation of a series of means, reflecting the degree of Mean error between the Mean and the global Mean. In addition, the normalized average error NME of each parameter reflects the average absolute degree of each analog value deviating from the measured value, and the two indexes are dimensionless, and the closer to 0, the closer to the measured value, the more the analog value is closer to the measured value. Therefore, in the scheme, the WRF-CMAQ model is adjusted and optimized according to the calculation result of the normalized mean deviation NMB and the normalized mean error NME, so that the WRF-CMAQ model can be adjusted to a simulation value simulated by the WRF-CMAQ model to be closer to an actual measurement value, and the accuracy of the WRF-CMAQ model is improved.
Further, in S103, when the normalized mean deviation NMB is less than the NMB threshold and the normalized mean error NME is less than the NME threshold, the WRF-CMAQ model is not tuned; and when the normalized mean deviation NMB is not less than the NMB threshold or the normalized mean error NME is not less than the NME threshold, tuning the WRF-CMAQ model.
Has the advantages that: the normalized mean deviation NMB and the normalized mean error NME are both guaranteed to be within their corresponding threshold ranges, thereby reducing model errors.
Further, in S103, adjusting model parameters to complete tuning of the WRF-CMAQ model, where the model parameters include one or more of micro-physics, cloud convection, long wave radiation, short wave radiation, viscous layer, surface layer, boundary layer, chemical scheme, land use cover type, aerosol scheme, horizontal transport and vertical transport.
Has the advantages that: and by combining various model parameters, the WRF-CMAQ model is more comprehensively established and optimized.
Further, in S101, an area location where air quality data needs to be analyzed is obtained, and a WRF-CMAQ model is established by setting a central location, nested grids, and meteorological field parameters according to the area location.
Has the advantages that: and setting corresponding central position, nested grids and meteorological field parameters according to specific analysis requirements, thereby obtaining a WRF-CMAQ model which is more in line with the regional position to be analyzed. Therefore, by adopting the scheme, the background air quality data which is more accurate and accords with the human life reality can be analyzed under different meteorological conditions, various regional conditions and various time period conditions.
Drawings
Fig. 1 is a flow chart of an air quality background concentration analysis method according to an embodiment of the present invention.
FIG. 2 is a technical route diagram of WRF-CMAQ and EnKF combination in an air quality background concentration analysis method according to an embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example 1:
example 1 is substantially as shown in figure 1:
as shown in fig. 1, an air quality background concentration analysis method includes the following steps.
S100: and establishing a WRF-CMAQ model according to a historical database, wherein the historical database comprises meteorological field data, MEIC list and air quality data.
S100 comprises the following specific steps:
s101: setting model parameters, acquiring the regional position of air quality data to be analyzed, setting central position, nested grids and meteorological field parameters according to the regional position, and establishing a WRF-CMAQ model. The model parameters include micro-physics, cumulus convection, long wave radiation, short wave radiation, viscous layer, surface layer, boundary layer, chemical scheme, land use coverage type, aerosol scheme, horizontal transport and vertical transport.
In the embodiment, the central position is 32-degree N and 108-degree E, four layers of nested grids are arranged in the WRF-CMAQ, and the grid resolution is 27km, 9km, 3km and 1km respectively. The first layer of grids covers most regions of east Asia by taking Chongqing as a center, and the second layer of grids mainly comprises most regions of the middle and west parts of China; the third layer of grids mainly comprises Chongqing cities and surrounding areas; the innermost grid comprises a main city area of Chongqing so as to meet the requirement of fine research. The WRF simulation in the vertical direction was divided into 28 layers with a mode ceiling of 50 hPa. In the construction of a simulation system in Chongqing city, a boundary layer scheme is planned to be set locally, and a scheme with the optimal overall effect is selected through a sensitivity experiment on different boundary layer parameterization schemes as shown in table 1.
TABLE 1 model parameter configuration Table
S102: and simulating air quality data according to the meteorological field data and the MEIC list in the historical database based on the WRF-CMAQ model.
In the present embodiment, the reanalysis data for driving the WRF weather mode uses the american weather environment forecasting center NCEP FNL (Final) Operational Global Analysis data weather reanalysis data (FNL data) of 2017 as weather field data. The temporal resolution is 6 hours, the spatial resolution is 1.0 ° × 1.0 °, the range covers 180 ° W-180 ° E, 90 ° S-90 ° N. The data set contains 26 standard isobars (1000 mb-10 mb), boundary layer and troposphere ceiling element information. The parameters include ground air pressure, sea level air pressure, potential height, air temperature, sea surface temperature, soil temperature and humidity, ice cover, relative humidity, wind, vertical motion, vorticity, ozone and the like. The man-made source Emission list driving the air quality mode comes from a Chinese Multi-scale Emission list model (MEIC for China) developed by Qinghua university, which comprises 10 main atmospheric pollutants and greenhouse gases including SO2, NOx, CO, NMVOC (volatile organic compounds), NH3, CO2, PM2.5, PM10, BC, OC and more than 700 man-made Emission sources, the 2017 version pollution source Emission list used in the research is a pollution source Emission list of 1.0, the types of data in the pollution source Emission list comprise five departments of electric power, industry, civil use, transportation and agriculture, the spatial resolution is 0.25 degrees multiplied by 0.25 degrees, and the time resolution is 1 month.
S103: in the simulated air quality data and the measured air quality data, the normalized mean deviation NMB and the normalized mean error NME of each parameter are calculated, the two indexes are dimensionless, and the closer to 0, the closer to the measured value, the closer to the simulated value is, the closer to the measured value is. The specific calculation formulas are respectively as follows:
wherein i is time, and N represents the total time; mi and Oi are respectively a simulation value and an observed value of the station at the ith moment.
When the normalized mean deviation NMB is less than an NMB threshold and the normalized mean error NME is less than an NME threshold, not tuning the WRF-CMAQ model; and when the normalized mean deviation NMB is not less than the NMB threshold or the normalized mean error NME is not less than the NME threshold, tuning the WRF-CMAQ model. The specific tuning method is to repeatedly adjust model parameters (such as micro-physics). In the embodiment, the meteorological field data and the MEIC list in 2017 are used as model driving data, the WRF-CMAQ model is used for simulating the concentration of atmospheric pollutants in main urban areas of Chongqing city in 12 months in 2017, the difference between a simulation value and an actual measurement value is compared, when the normalized mean deviation NMB or the normalized mean error NME exceeds 20%, model tuning is performed, and when the normalized mean deviation NMB and the normalized mean error NME do not exceed 20%, model tuning is not performed any more.
As shown in table 2, the results of checking the NMB and NME of the simulated values and the measured values of the parameters are shown. Wherein the CO unit is mg/m3The unit of the other factors is mu g/m3(ii) a Δ represents a deviation; NMB is the normalized mean deviation; NME is normalized mean error.
TABLE 2NMB, NME verification results table
The results show that the variation trend of the simulated values of the parameters is consistent with that of the measured values, the NMB and NME values are both less than 20%, and the simulation effect is within an acceptable range. The model can better capture the variation trend of the concentration of the atmospheric pollution in the Chongqing, and the WRF-CMAQ regional air quality model which is adjusted and optimized is feasible for simulating the background concentration of the main pollutants in the main town of Chongqing city.
S200: and acquiring infectious disease prevention and control data, wherein the infectious disease prevention and control data comprise an infectious disease prevention and control mode and an infectious disease prevention and control time period.
S300: according to the data for preventing and controlling the infectious diseases, meteorological field data and air quality data used for analyzing a background pollution source emission list in a historical database are selected. In embodiments of the present application, infectious disease prevention and control may also be selected to include data that reduces periods of people movement.
S400: and inverting the background pollution source emission list based on the WRF-CMAQ model and the EnKF data assimilation system according to the meteorological field data and the air quality data selected in the S300. In this example, a background pollution source emission list is inverted according to meteorological field data and air quality data in winter and spring in 2020, as shown in table 3.
TABLE 3 background pollution sources emission List
Based on an autonomously built ensemble Kalman filtering (EnKF) data assimilation system, air quality monitoring data of 17 national control monitoring stations in a main urban area, which are provided by a Chongqing city ecological environment big data application center, are utilized to invert emission source data of an MEIC list, so that the uncertainty of the emission list is reduced, and the MEIC list is closer to real emission data in an epidemic situation period.
In the embodiment, inversion is realized by adopting an ensemble Kalman filtering (EnKF) algorithm and combining a WRF-CMAQ model, and a background emission list in winter and spring in main cities of Chongqing cities is inverted by constructing a regional emission inversion assimilation system, and the technical route is shown in FIG. 2.
The inversion process in the project is divided into two steps, wherein the first step is based on prior emission (Xb) and an initial field, the hourly PM2.5 concentration is obtained through CMAQ simulation by taking simulation of PM2.5 concentration as an example, and the optimized PM2.5 emission (Xa) is obtained by further combining observation data and an EnKF algorithm; the emissions optimized in the second step are re-input into the CMAQ model to generate the initial field for the next synchronization window. The prior emissions for the first window are from the original emissions list, and the prior emissions for each subsequent window are from the optimized emissions for the previous window. In this embodiment, the time window is set to 1 day, and the time average is performed on the emissions optimized in all the windows to obtain the final optimized posterior emissions.
EnKF carries out random disturbance according to uncertainty of model state variable or parameter to obtain background set XbTo represent the error statistics and simulate the forward propagation along with the set of the dynamic model, thereby obtaining the background error PbAnd the response relation of the simulated concentration to the emission not only solves the problem of P faced by Kalman filtering in a high-dimensional mode systembThe problem of excessive matrix storage and computation cost also makes PbThe matrix can evolve along with the update of the power mode, can be applied to a highly nonlinear mode system, does not need to write a concomitant mode, and is easy to implement and apply. And generating set disturbance by adopting a Monte Carlo method for the emission set sample:
wherein b represents the background state, i is the identification of the disturbed sample, and the sample disturbed randomly
Adding the original list
Obtaining a collective sample of emissions
The pre-inversion set sample averages. In this study, PM2.5、SO2、NO2Is/are as follows
Mean 0, standard deviation 40%, 25% gaussian distribution of a priori emissions, respectively. To better balance computational efficiency and assimilation, the number of sets was set to 40. In addition, PM is considered in this study2.5Influence of precursors on SO2、NOXThe emissions of (a) are inversely corrected, and since it is difficult to directly obtain the correlation of each state variable, the state variables are assumed to be independent of each other. The study adopted the EnSRF algorithm (a variant of EnKF) to invert emissions, and optimized pollutant emissions by combining the observed data y and the observed error R
The update is obtained by the following formula:
h is an observation operator, R is an observation error covariance matrix, model state variables are interpolated from a model space to an observation space, and K is a gain matrix, so that the weights of the background field and observation are determined. In thatIn each of the analysis steps, the analysis step,
i.e. the optimized emission list, is the optimal estimate.
In order to avoid the influence of the model simulation daily variation error on inversion, the daily average concentration is adopted for inversion, and the assimilation window is set to be 1 day. In order to reduce the calculation cost and the influence of representative errors, the observed values are subjected to 'super-observation' processing based on the optimal estimation theory, namely m observed values y positioned at the same grid pointiAnalog values corresponding to n members
And forming a super observed value and a corresponding analog value. Assuming that the observation errors at different time points of different sites are independent of each other, yiCorresponding standard deviation of observation riThen newly construct observation ynewObserved standard deviation rnewAnd corresponding simulation
The following relationships are satisfied:
in order to avoid storing and converting larger matrixes during analysis, and considering mutual independence between observed data, the research adopts a sequential assimilation mode to assimilate single sites in turn, namely assimilate an analysis field updated after observation asThe background field of the next assimilation continues to assimilate. In order to reduce the influence of false increments generated by a limited set, a localization scheme is adopted to limit the analysis in a grid with a certain range of observation, and the method has the advantage of conveniently eliminating the weak influence generated by long-distance observation. The optimal localization scale is related to the choice of the assimilation window, the power system and the residence time of the chemical species in the atmosphere, and is set to a distance of 2 grids from the observation site, while the PM is set to a distance of 2 grids, considering the NOx survival cycle in the atmosphere around one day2.5And SO2Set to a distance of 3 grids. Another serious problem of EnKF in the assimilation process is the filter divergence, which manifests as too small an aggregate dispersion to ignore the observed information. One approach is to expand the background error covariance, but this approach is limited in its effectiveness due to the lack of a physical basis and the introduction of false linear increases in background error in regions far from observation, and we use the same perturbation on the source discharge in each step of the inversion. In addition, the covariance of the sampling error in the inversion process is set to 0, which is negative (indicating that the correlation between the analog concentration and the source emission is negative). In order to prevent unreasonable correction values, the emission adjustment factor of each inversion is controlled to be 0.2-5 times of the original emission.
And (3) inverse list downscaling processing: due to the limitation of observation data, when the list of the Chongqing city is inverted, the list of 36km in China is inverted, and the list can reflect the emission change condition under a large background. When simulating the background concentration with higher resolution, the following treatment is carried out: and (4) distributing the emission in each grid of the inverted 36 km-36 km list according to the original space distribution proportion of the reference list. Wherein the main urban area part in the reference list is replaced by a main urban area list result of an air pollution gridding analysis service item of the main urban area in Chongqing city in 2020.
The specific calculation process takes the case that the 36km × 36km grid is reduced to 9km × 9km grid, and the calculation formula is as follows:
C＝A；
B＝B1+B2+…+Bn；
C＝C1+C2+…+Cn。
in the formula, A is the total discharge amount of a certain grid in the inversion list, B is the total discharge amount in the corresponding range of the reference list and the inversion list in the model, Bi is the total discharge amount in the ith 9km x 9km grid of the reference list, C is the total discharge amount of the corresponding area of the small-scale list after inversion optimization, and Ci is the total discharge amount in the ith 9km x 9km grid of the small-scale list after inversion optimization.
S500: acquiring meteorological field data of the year to be tested based on a WRF-CMAQ model, and analyzing the background air quality data of the year to be tested according to the background pollution source emission list and the meteorological field data of the year to be tested. In this embodiment, based on the emission list of the background pollution sources obtained in S400, the background air quality data of the national control site in 2021 year is analyzed in combination with the weather field data of 2021 year in winter and spring, as shown in table 4, where the CO unit is: mg/m3And the other factor units are as follows: mu g/m3。
Table 42021 year background air quality data of national control station
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (7)
1. An air quality background concentration analysis method is characterized in that: the method comprises the following steps:
s100: establishing a WRF-CMAQ model according to a historical database, wherein the historical database comprises meteorological field data, MEIC list and air quality data;
s200: acquiring infectious disease prevention and control data, wherein the infectious disease prevention and control data comprise an infectious disease prevention and control mode and an infectious disease prevention and control time period;
s300: selecting meteorological field data and air quality data for analyzing a background pollution source emission list in a historical database according to infectious disease prevention and control data;
s400: inverting a background pollution source emission list based on the WRF-CMAQ model and the EnKF data assimilation system according to the meteorological field data and the air quality data selected in the S300;
s500: and analyzing the background air quality data of the year to be tested according to the background pollution source emission list and the meteorological field data of the year to be tested based on the WRF-CMAQ model.
2. The air quality background concentration analysis method according to claim 1, characterized in that: in S300, selecting an infectious disease prevention and control mode including home epidemic prevention and weather field data and air quality data of a corresponding infectious disease prevention and control time period.
3. The air quality background concentration analysis method according to claim 1, characterized in that: s100 includes the steps of:
s101: setting model parameters and establishing a WRF-CMAQ model;
s102: simulating air quality data according to meteorological field data and MEIC lists in a historical database based on a WRF-CMAQ model;
s103: and adjusting and optimizing the WRF-CMAQ model according to the simulated air quality data and the actually measured air quality data.
4. The air quality background concentration analysis method according to claim 3, characterized in that: in S103, the normalized mean deviation NMB and the normalized mean error NME of each parameter in the simulated air quality data and the actually measured air quality data are calculated, and the WRF-CMAQ model is optimized according to the calculation result.
5. The air quality background concentration analysis method of claim 4, wherein: in S103, when the normalized mean deviation NMB is smaller than an NMB threshold and the normalized mean error NME is smaller than an NME threshold, the WRF-CMAQ model is not adjusted and optimized; and when the normalized mean deviation NMB is not less than the NMB threshold or the normalized mean error NME is not less than the NME threshold, tuning the WRF-CMAQ model.
6. The air quality background concentration analysis method of claim 5, wherein: and S103, adjusting model parameters to finish adjusting and optimizing the WRF-CMAQ model, wherein the model parameters comprise one or more of micro-physics, cumulus convection, long-wave radiation, short-wave radiation, a viscous layer, a surface layer, a boundary layer, a chemical scheme, a land use coverage type, an aerosol scheme, horizontal conveying and vertical conveying.
7. The air quality background concentration analysis method according to claim 3, characterized in that: and S101, acquiring the regional position of the air quality data to be analyzed, setting the central position, the nested grids and the meteorological field parameters according to the regional position, and establishing a WRF-CMAQ model.
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| CN115270013A (en) * | 2022-09-22 | 2022-11-01 | 中科三清科技有限公司 | Method and device for evaluating emission reduction measures during activities and electronic equipment |
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