CN112132336A - Quarterly prediction method for PM2.5 concentration - Google Patents
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Abstract
The invention discloses a quarterly prediction method for PM2.5 concentration, and belongs to the technical field of air quality prediction. It comprises the following steps: s100: collecting data of the area and screening the data, wherein the data comprises meteorological data, pollution data and benchmark emission list data; s200: constructing a meteorological-air quality model of the region according to the screened data; s300: acquiring an inversion quarterly emission list of the region according to the screened data and the meteorological-air quality model; s400: collecting global weather forecast field data, and constructing a prediction model according to the global weather forecast field data; s500: the quarterly predicted concentration for zone PM2.5 is obtained from the inverted quarterly emission inventory and using a predictive model simulation. The invention overcomes the defect that the PM2.5 concentration prediction of a long time scale cannot be realized in the prior art, provides a PM2.5 concentration quarterly prediction method, can realize the PM2.5 concentration prediction of the long time scale, and thus can provide more control lead for fine treatment.
Description
Technical Field
The invention belongs to the technical field of air quality prediction, and particularly relates to a quarterly prediction method for PM2.5 concentration.
Background
With the rapid development of national economy and urbanization process in recent years, air pollution and dust haze events occur frequently, and air quality prediction increasingly becomes a focus of attention of governments and the public. Since 2013, with the general requirements of atmospheric ten items and national ecological environment management as the driving force, a plurality of provincial, municipal and regional environment monitoring units in China have the service capability of realizing short-term air quality prediction for 7-15 days in the future based on the third-generation air quality numerical prediction model WRF-CMAQ and other technologies, wherein the predicted pollutant concentration data comprise PM2.5, PM10, O3、NO2、SO2And CO, and the like.
PM2.5 is also called fine particles, which means particles with an aerodynamic equivalent diameter of less than or equal to 2.5 microns in ambient air. It can be suspended in air for a long time, and the higher the content concentration in the air, the more serious the air pollution is. Although PM2.5 is only a component of earth's atmospheric composition in small amounts, it has a significant effect on air quality and visibility, among other things. Compared with the thicker atmospheric particulate matters, the PM2.5 has small particle size, large area, strong activity, easy attachment of toxic and harmful substances (such as heavy metals, microorganisms and the like), long retention time in the atmosphere and long conveying distance, thereby having larger influence on human health and atmospheric environmental quality.
Dust haze pollution that autumn and winter PM2.5 lead to is frequent and pollutes the duration longer, from "a sword is cut" to the improvement that becomes more meticulous, and the requirement that air quality prediction and management and control were administered also constantly improves. The core technical tool WRF-CMAQ of numerical prediction is limited by the time step of global forecast field data and the hysteresis of an emission list, and accurate PM2.5 concentration prediction of more than 15 days cannot be realized.
In summary, how to realize the PM2.5 concentration prediction with a longer time scale is an urgent problem to be solved in the prior art.
Disclosure of Invention
1. Problems to be solved
The invention overcomes the defect that the PM2.5 concentration prediction of a long time scale cannot be realized in the prior art, provides a PM2.5 concentration quarterly prediction method, can realize the PM2.5 concentration prediction of the long time scale, and thus can provide more control lead for fine treatment.
2. Technical scheme
In order to solve the problems, the technical scheme adopted by the invention is as follows:
the invention discloses a quarterly prediction method for PM2.5 concentration, which comprises the following steps: s100: collecting data of the area and screening the data, wherein the data comprises meteorological data, pollution data and benchmark emission list data; s200: constructing a meteorological-air quality model of the region according to the screened data; s300: acquiring an inversion quarterly emission list of the region according to the screened data and the meteorological-air quality model; s400: collecting global weather forecast field data, and constructing a prediction model according to the global weather forecast field data; s500: the quarterly predicted concentration for zone PM2.5 is obtained from the inverted quarterly emission inventory and using a predictive model simulation.
Further, the specific process of constructing the weather-air quality model in step S200 is as follows: constructing a meteorological model of the region according to the mesoscale meteorological model, and driving the meteorological model according to meteorological data of the region to obtain meteorological simulation data of the region; and then, constructing an air quality model of the region according to the third generation air quality model, and driving the air quality model according to the reference emission list data and the meteorological simulation data of the region.
Further, the specific process of obtaining the inverse quarterly emission list of the region in step S300 is as follows: constructing a manifest inversion model according to the weather-air quality model; acquiring an inverted emission list every day according to the list inversion model; then obtaining a revised daily change inversion emission list from t-365-d to t-365+ d of the previous year and from t-d to t of the current year according to the emission list subjected to daily inversion and the list inversion model; wherein t is the date of the start of the forecast, d is the forecast duration, and d is less than or equal to 90; and then obtaining an inversion quarterly emission list from t to t + d days in the year by using an extrapolation method.
Further, the specific process of constructing the prediction model in step S400 is as follows: collecting global weather forecast field data with the time of starting to report t and the forecast duration d; then, constructing a seasonal weather forecast model of the region according to the mesoscale weather model; then, fusion processing is carried out on the global weather forecast field data, the fusion processing result is used as a weather initial field to drive a weather forecast model to carry out seasonal weather forecast, the starting time of the seasonal weather forecast is t, and the forecast duration is d; and then constructing an air quality forecasting model of the region according to the third generation air quality model.
Furthermore, the specific process of constructing the manifest inversion model is as follows: performing space-time distribution on the reference emission list according to the region, and driving a meteorological-air quality model to obtain a gridding reference emission list; generating n groups of different gridding discharge lists by using a random function according to the gridding reference discharge list, wherein n is more than 20; and then driving a meteorological-air quality model to simulate the air quality of pollutants by using meteorological simulation data and n groups of different gridding discharge lists respectively to generate n groups of air quality data in a simulation time period.
Furthermore, the specific process of obtaining the revised change-of-day inversion emission list is as follows: taking the inverted emission list every day as an initial emission field predicted next day, simultaneously correcting the pollution initial field in an assimilation time window by using a three-dimensional variation assimilation technology, and driving the list inversion model again to obtain the revised daily variation inversion emission list from the last year t-365-d to t-365+ d and from the last year t-d to t.
Furthermore, the specific process of obtaining the inverse quarterly emission list from t to t + d days in the present year by using the extrapolation method is as follows:
wherein the content of the first and second substances,
for the inverted quarterly emission schedule from day t to t + d,
the revised inversion emission list of the change of the day after the last year from t-365 to t-365+ d,
inverting the emission list for the revised change of day from t-d to t,
the emission list is a revised change inversion emission list from t-365-d to t-365 days in the last year, and H is an observation vector progressive coefficient.
Further, the specific process of obtaining the quarterly predicted concentration of the area PM2.5 in step S500 is as follows: and taking the inversion quarterly emission list as an emission field of the air quality forecasting model, taking the quarterly weather forecast as a weather driving field of the air quality forecasting model, and predicting by using the air quality forecasting model to obtain the quarterly predicted concentration of the PM2.5 in the area.
Further, the specific process of obtaining the inverted daily emission list is as follows: correcting the set emission list by using the pollution data and the n groups of air quality data to obtain an emission list of the region after daily inversion; wherein the set emission list is a set of n groups of different gridded emission lists.
Furthermore, the specific process of screening the data is as follows: missing values and outliers in the data were deleted.
3. Advantageous effects
Compared with the prior art, the invention has the beneficial effects that:
(1) according to the method for predicting the PM2.5 concentration in the quarterly, the inversion quarterly emission list is obtained by adopting an inversion and extrapolation mode, so that the latest emission list in a prediction time limit in an area is obtained, and the problem that long-time PM2.5 concentration prediction cannot be realized due to the hysteresis of the emission list is solved; PM2.5 concentration prediction in a long time scale can be realized by building a prediction model, and further more control lead can be provided for fine treatment.
(2) According to the quarter prediction method for the PM2.5 concentration, the quarter inversion emission list can be obtained through inversion and extrapolation, the defect that the latest emission situation cannot be truly reflected due to the time lag of the traditional emission list is overcome, the latest emission list in the time limit of the actual regional site data inversion prediction is realized, the simulation result is more in line with the actual situation, and the accuracy of PM2.5 concentration prediction is improved.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be 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 some embodiments of the present invention, but not all embodiments; moreover, the embodiments are not relatively independent, and can be combined with each other according to needs, so that a better effect is achieved. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For a further understanding of the invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples.
Example 1
Referring to fig. 1, a method for predicting PM2.5 concentration in a quarterly environment according to the present invention includes the following steps:
s100: collecting data of a region and screening the data, wherein the data of the collected region comprises meteorological data, pollution data and reference emission list data, and it is noted that the reference emission list data is the latest reference emission list data of the region. In addition, the start date of the meteorological data and the pollution data is t, the forecast duration is d, and d is less than or equal to 90, so that the meteorological data and the pollution data are respectively time-by-time meteorological element data and corresponding time-by-time pollutant concentration data of each site in the region from t-d to t days and from t-365-d to t-365+ d days in the previous year.
Further, the specific process of screening the data in the invention is as follows: missing values and outliers in the data were deleted.
S200: the meteorological-air quality model of the region is constructed according to the screened data, and it is worth explaining that the meteorological-air quality model comprises a meteorological model and an air quality model, and the construction process of the meteorological-air quality model is as follows:
s210: constructing a meteorological model of the region according to the mesoscale meteorological model, and driving the meteorological model according to meteorological data of the region to obtain meteorological simulation data of the region;
s220: and then, constructing an air quality model of the region according to the third generation air quality model, and driving the air quality model according to the reference emission list data and the meteorological simulation data of the region, so that the air quality data of the region can be simulated.
S300: acquiring an inversion quaternary emission list of an area according to the screened data and the meteorological-air quality model, wherein the inversion quaternary emission list is used for quaternary prediction simulation of particulate matter PM2.5 concentration and is used as emission source input of a used prediction model; specifically, the process of obtaining the inverted quarterly emission list is as follows:
s310: the method comprises the following steps of constructing a manifest inversion model according to a meteorological-air quality model, specifically:
s311: performing space-time distribution on the reference emission list according to the region, and driving a meteorological-air quality model to obtain a gridding reference emission list;
s312: generating n groups of different gridding discharge lists by using a random function according to the gridding reference discharge list, wherein n is more than 20; it is worth to be noted that the value of each grid in the grid standard emission list is randomly modified by a random function, and n groups of randomly modified grid emission lists are obtained after n times of modification.
S313: and respectively driving a meteorological-air quality model to simulate the air quality of pollutants by using meteorological simulation data and n groups of different gridding emission lists, and generating n groups of air quality data in a simulation time period.
S320: acquiring a daily inverted emission list according to the list inversion model, specifically, correcting the set emission list by using pollution data and n groups of air quality data to obtain a daily inverted emission list of the region; the integrated emission list is a set of n groups of different gridding emission lists; furthermore, the time resolution of the daily inverted emission list was 1 hour. It is worth mentioning that the corresponding relation among the pollution data, the simulated n groups of air quality data and the collection emission list is constructed, and the collection emission list is corrected according to the corresponding relation.
S330: obtaining a revised daily change inversion emission list from t-365-d to t-365+ d of the previous year and from t-d to t of the current year according to the emission list subjected to daily inversion and the list inversion model; the specific process is as follows: taking the inverted emission list every day as an initial emission field predicted next day, simultaneously correcting the pollution initial field in an assimilation time window by using a three-dimensional variation assimilation technology, and driving the list inversion model again to obtain the revised daily variation inversion emission list from the last year t-365-d to t-365+ d and from the last year t-d to t. Wherein t is the date of the forecast, d is the forecast duration, and d is less than or equal to 90.
S340: and obtaining an inversion quarterly emission list from t to t + d days in the year by using an extrapolation method, specifically:
wherein the content of the first and second substances,
inversion quarterly emissions for days t to t + dThe list of the number of the messages is,
the revised inversion emission list of the change of the day after the last year from t-365 to t-365+ d,
inverting the emission list for the revised change of day from t-d to t,
inverting an emission list for the change of the day after the revision from the last year t-365-d to the last year t-365 day, wherein H is an observation vector progressive coefficient; it should be noted that the revised daily change inverted emission list may reflect the characteristics of the daily emission change characteristics of the region.
To regional SO2、NOXAnd (5) carrying out inversion on the PM2.5 secondary component precursor to obtain a quarterly inversion emission list with forecast duration d days and time resolution of 1 hour. It is worth to be noted that the traditional emission list has time lag of more than one year, the latest emission situation cannot be truly reflected, the quarterly inverted emission list is obtained through inversion and extrapolation, the latest emission list in an inversion prediction time limit according to actual regional site data is realized, a simulation result is more in line with the actual situation, and the accuracy of PM2.5 concentration prediction is improved.
S400: collecting global weather forecast field data, and constructing a prediction model according to the global weather forecast field data; the specific process of constructing the prediction model is as follows:
s410: collecting global weather forecast field data with the time of starting to report t and the forecast duration d; the global weather forecast field data in this embodiment is weather forecast data of various global places where the network is open.
S420: constructing a seasonal weather forecast model of the region according to the mesoscale weather model;
s430: fusing the data of the global weather forecast field, wherein the fused result is used as a weather initial field to drive a weather forecast model to carry out seasonal weather forecast, the starting time of the seasonal weather forecast is t, and the forecast duration is d; the specific process of the fusion treatment is as follows: and performing data fusion on the collected different global meteorological forecast field data (different space-time resolutions, different meteorological elements and the like) according to the regional grid setting of the seasonal meteorological forecast model and the simulation parameters required for driving the meteorological forecast model to obtain fused meteorological data which has uniform spatial resolution and is continuous in time and can drive the seasonal meteorological forecast model in the S420.
S440: and constructing an air quality forecasting model of the region according to the third-generation air quality model. It is worth to be noted that, in the invention, the meteorological model, the meteorological forecast model, the air quality model and the air quality forecast model are constructed according to the mesoscale meteorological model and the third-generation air quality model in the prior art, and the corresponding areas of the constructed models are the same.
S500: obtaining the quarterly predicted concentration of the area PM2.5 according to the inversion quarterly emission list and by utilizing a prediction model simulation, wherein the specific process of obtaining the quarterly predicted concentration of the area PM2.5 in the step S500 is as follows:
and taking the inversion quarterly emission list as an emission field of the air quality forecasting model, taking the quarterly weather forecast as a weather driving field of the air quality forecasting model, and predicting by using the air quality forecasting model to obtain the quarterly predicted concentration of the PM2.5 in the area. It is worth noting that the present invention can achieve a quarterly prediction of PM2.5 concentration for 90 days.
According to the method for predicting the PM2.5 concentration in the quarterly, the inversion quarterly emission list is obtained by adopting an inversion and extrapolation mode, so that the latest emission list in a prediction time limit in an area is obtained, and the problem that long-time PM2.5 concentration prediction cannot be realized due to the hysteresis of the emission list is solved; PM2.5 concentration prediction in a long time scale can be realized by building a prediction model, and further more control lead can be provided for fine treatment.
The invention has been described in detail hereinabove with reference to specific exemplary embodiments thereof. It will, however, be understood that various modifications and changes may be made without departing from the scope of the invention as defined in the appended claims. The detailed description and drawings are to be regarded as illustrative rather than restrictive, and any such modifications and variations are intended to be included within the scope of the present invention as described herein. Furthermore, the background is intended to be illustrative of the state of the art as developed and the meaning of the present technology and is not intended to limit the scope of the invention or the application and field of application of the invention.
Claims (10)
1. A method for predicting the concentration of PM2.5 in a quarterly manner is characterized by comprising the following steps:
s100: collecting data of the area and screening the data, wherein the data comprises meteorological data, pollution data and benchmark emission list data;
s200: constructing a meteorological-air quality model of the region according to the screened data;
s300: acquiring an inversion quarterly emission list of the region according to the screened data and the meteorological-air quality model;
s400: collecting global weather forecast field data, and constructing a prediction model according to the global weather forecast field data;
s500: the quarterly predicted concentration for zone PM2.5 is obtained from the inverted quarterly emission inventory and using a predictive model simulation.
2. The method for predicting the PM2.5 concentration in the quarterly according to claim 1, wherein the concrete process for constructing the weather-air quality model in the step S200 is as follows:
constructing a meteorological model of the region according to the mesoscale meteorological model, and driving the meteorological model according to meteorological data of the region to obtain meteorological simulation data of the region;
and constructing an air quality model of the region according to the third generation air quality model, and driving the air quality model according to the reference emission list data and the meteorological simulation data of the region.
3. The method according to claim 2, wherein the specific process of obtaining the inverse quaternary emission list of the region in step S300 is as follows:
constructing a manifest inversion model according to the weather-air quality model;
acquiring an inverted emission list every day according to the list inversion model;
obtaining a revised daily change inversion emission list from t-365-d to t-365+ d of the previous year and from t-d to t of the current year according to the emission list subjected to daily inversion and the list inversion model; wherein t is the date of the start of the forecast, d is the forecast duration, and d is less than or equal to 90;
and obtaining an inversion quarterly emission list from t to t + d days in the current year by using an extrapolation method.
4. The method for predicting the PM2.5 concentration in the quarterly environment according to claim 1, wherein the specific process of constructing the prediction model in step S400 is as follows:
collecting global weather forecast field data with the time of starting to report t and the forecast duration d;
constructing a seasonal weather forecast model of the region according to the mesoscale weather model;
fusing the data of the global weather forecast field, wherein the fused result is used as a weather initial field to drive a weather forecast model to carry out seasonal weather forecast, the starting time of the seasonal weather forecast is t, and the forecast duration is d;
and constructing an air quality forecasting model of the region according to the third-generation air quality model.
5. The method for predicting the quarter of the concentration of PM2.5 according to claim 3, wherein the specific process for constructing the manifest inversion model is as follows:
performing space-time distribution on the reference emission list according to the region, and driving a meteorological-air quality model to obtain a gridding reference emission list;
generating n groups of different gridding discharge lists by using a random function according to the gridding reference discharge list, wherein n is more than 20;
and respectively driving a meteorological-air quality model to simulate the air quality of pollutants by using meteorological simulation data and n groups of different gridding emission lists, and generating n groups of air quality data in a simulation time period.
6. The method of claim 3, wherein the specific process of obtaining the revised day-to-day variation inverted emission list is as follows:
taking the inverted emission list every day as an initial emission field predicted next day, simultaneously correcting the pollution initial field in an assimilation time window by using a three-dimensional variation assimilation technology, and driving the list inversion model again to obtain the revised daily variation inversion emission list from the last year t-365-d to t-365+ d and from the last year t-d to t.
7. The method of claim 3, wherein the specific process of obtaining the inverted quarterly emission list from t to t + d days of the year by extrapolation is as follows:
wherein the content of the first and second substances,
for the inverted quarterly emission schedule from day t to t + d,
the revised inversion emission list of the change of the day after the last year from t-365 to t-365+ d,
inverting the emission list for the revised change of day from t-d to t,
the emission list is a revised change inversion emission list from t-365-d to t-365 days in the last year, and H is an observation vector progressive coefficient.
8. The method according to claim 4, wherein the specific process of obtaining the quarterly predicted concentration of PM2.5 in the area S500 is as follows:
and taking the inversion quarterly emission list as an emission field of the air quality forecasting model, taking the quarterly weather forecast as a weather driving field of the air quality forecasting model, and predicting by using the air quality forecasting model to obtain the quarterly predicted concentration of the PM2.5 in the area.
9. The method of claim 5, wherein the specific process of obtaining the daily inverted emission list is as follows:
correcting the set emission list by using the pollution data and the n groups of air quality data to obtain an emission list of the region after daily inversion; wherein the set emission list is a set of n groups of different gridded emission lists.
10. The method for predicting the PM2.5 concentration in the quarterly according to any one of claims 1 to 9, wherein the data is screened by: missing values and outliers in the data were deleted.
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|---|---|---|---|---|
| CN113627529A (en) * | 2021-08-11 | 2021-11-09 | 成都佳华物链云科技有限公司 | Air quality prediction method, device, electronic equipment and storage medium |
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| CN109858686A (en) * | 2019-01-14 | 2019-06-07 | 南京大学 | A kind of ground emission inventories inverting optimization method based on EnKF |
| CN111353634B (en) * | 2020-02-12 | 2022-09-20 | 中国人民解放军国防科技大学 | Sulfur dioxide emission source optimization method based on mode initial field assimilation |
| CN111523717B (en) * | 2020-04-15 | 2023-05-16 | 北京工业大学 | Inversion estimation method of atmospheric pollutant emission list |
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| CN113627529A (en) * | 2021-08-11 | 2021-11-09 | 成都佳华物链云科技有限公司 | Air quality prediction method, device, electronic equipment and storage medium |
| CN114324780A (en) * | 2022-03-03 | 2022-04-12 | 阿里巴巴达摩院(杭州)科技有限公司 | Atmospheric pollutant emission flux processing method, storage medium and computer terminal |
| CN114324780B (en) * | 2022-03-03 | 2022-08-02 | 阿里巴巴达摩院(杭州)科技有限公司 | Atmospheric pollutant emission flux processing method, storage medium and computer terminal |
| CN115079308A (en) * | 2022-07-04 | 2022-09-20 | 湖南省生态环境监测中心 | Air quality ensemble forecasting system and method thereof |
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