CN117034660B - Street scale air quality forecasting method - Google Patents
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Abstract
The invention discloses a block scale air quality forecasting method, which comprises the following steps: preparing an initial basic data set; determining initial forecast information; determining a forecasting range, a forecasting grid size and a forecasting sub-grid size; updating the initial basic data set in a neighborhood scale air quality prediction model; obtaining the mesoscale weather forecast space-time characteristic information through a mesoscale weather numerical forecast model; inputting the mesoscale weather space-time characteristic information into a mesoscale air quality numerical forecasting model to obtain mesoscale air quality forecasting space-time characteristic information; and inputting the mesoscale air quality space-time characteristic information into a neighborhood scale air quality numerical forecasting model to obtain neighborhood scale air quality forecasting space-time characteristic information. The invention realizes the breakthrough of air quality simulation from regional-city scale to neighborhood scale.
Description
Technical Field
The invention belongs to the field of environmental quality prediction and early warning, relates to the technology of a prediction system, and particularly relates to a block scale air quality prediction method.
Background
The complex urban building structure and the large amount of motor vehicle exhaust emissions make the air quality inside the city unprecedented. The invention provides a city block scale air quality forecasting system and a method thereof, which are used for establishing a city block scale air quality forecasting system and a city block scale air quality forecasting method, wherein the air quality forecasting system adopted in the past city mainly simulates the concentration of atmospheric pollutants at a mesoscale, can not simulate the pollutant emission and flow conditions in a city block, but the flow of a wind field is more complex due to the diversity of a city structure (building form, roads, greenbelts and the like), the street-valley difference of pollutant distribution is obvious, the distribution of ozone precursors and particulate matters in the city block is clear for more accurate prevention and control of the city atmospheric pollution, the atmospheric pollution situation of the city block is mastered, and an action scheme for relieving the atmospheric pollution is more scientifically and more pertinently provided.
Disclosure of Invention
Aiming at the problem that the existing air quality prediction has insufficient local pollution simulation capability on the complicated underlying neighborhood in the city, the invention aims to provide a neighborhood-scale air quality prediction method, and the accuracy of air quality prediction is improved by pushing a business air quality prediction model from the regional-city scale to the neighborhood scale.
In order to solve the technical problems, according to one aspect of the present invention, the following technical solutions are provided:
a neighborhood-scale air quality forecasting method, comprising the steps of:
s1: preparing an initial basic data set, wherein the initial basic data set comprises initial weather forecast driving data, fine topography data, fine land utilization data, urban canopy data, mesoscale high-resolution emission list data and neighborhood scale extremely high-resolution emission list data;
s2: determining initial forecast information, wherein the initial forecast information comprises a forecast place and a forecast duration;
s3: determining a forecasting range, a forecasting grid size and a forecasting sub-grid size according to the forecasting places;
s4: updating the initial basic data set in a block-scale air quality forecasting model, wherein the block-scale air quality forecasting model comprises a mesoscale weather numerical forecasting model, a mesoscale air quality numerical forecasting model and a block-scale air quality numerical forecasting model;
s5: obtaining mesoscale weather forecast space-time characteristic information in the forecast duration and the forecast range through the mesoscale weather numerical forecast model; the resolution of the mesoscale weather numerical forecasting model is the forecasting grid size;
s6: obtaining mesoscale air quality forecasting space-time characteristic information in the forecasting duration and the forecasting range through the mesoscale air quality numerical forecasting model; the resolution of the mesoscale air quality numerical forecasting model is the forecasting grid size;
s7: and inputting the mesoscale air quality forecasting space-time characteristic information and the ultra-high resolution emission list data of the forecasting range in the neighborhood scale air quality numerical forecasting model to perform downscaling operation, so as to obtain the forecasting time length and the forecasting space-time characteristic information of the forecasting range in the neighborhood scale air quality forecasting.
Preferably, in the step S1, the initial weather forecast driving data uses GFS products; the fine topography data uses HWSD soil data sets and SRTM topography data; the fine land utilization data uses GLCs land utilization data; the medium-scale high-resolution emission inventory data uses a MEIC inventory; the neighborhood scale extremely high resolution emissions inventory data uses neighborhood scale emissions data within a forecast range, including neighborhood scale road movement source data, neighborhood scale population emissions data, neighborhood scale shipping emissions data, and neighborhood scale industrial source data.
Preferably, the fine land utilization data are manufactured into two sets of fine land utilization data with different classification systems according to classification settings of the mesoscale weather numerical forecasting model and the mesoscale air quality numerical forecasting model.
Preferably, in the step S3, the prediction range is set to be long in the east-west direction and wide in the north-south direction, with the prediction place as a center.
Preferably, in the step S3, the forecast grid size is set based on the forecast range, and the forecast sub-grid size is set based on a forecast location.
Preferably, in the step S4, the mesoscale weather numerical prediction model is a weather model, and the mesoscale air quality numerical prediction model is a regional model.
Preferably, in the step S5, initial weather forecast driving data, fine topography data and city canopy data are input into the mesoscale weather numerical forecast model.
Preferably, in the step S6, mesoscale weather forecast spatiotemporal characteristic information, fine land utilization data, mesoscale high resolution emission inventory data are input into the mesoscale air quality numerical forecast model.
Preferably, the method for performing downscaling operation on the neighborhood-scale air quality numerical prediction model in S7 is an emission redistribution method, which specifically includes the following steps:
dividing the forecast grid concentration in the mesoscale air quality numerical forecast model into local emission and non-local emission;
and redistributing the neighborhood scale emission data of the prediction range to the prediction subgrid in the neighborhood scale air quality numerical prediction model, recalculating the concentration of the prediction subgrid by using a Gaussian diffusion formula, deleting the contribution of the middle scale air quality numerical prediction model on the prediction subgrid, and reserving non-local conveying capacity to obtain the concentration of the prediction subgrid.
Preferably, the neighborhood-scale air quality forecast spatiotemporal characteristic information includes a concentration of one or more of PM2.5, PM10, NO2, NOx, O3 in the air.
Compared with the prior art, the invention has the following beneficial effects:
(1) According to the method, various aspects affecting the concentration of the pollutants are considered, including regional weather elements, regional urban canopy structure conditions, the mutual influence of atmospheric diffusion and chemical reaction between adjacent regions, local pollutant accumulation conditions and the mutual influence of local atmospheric diffusion and chemical reaction, so that the prediction accuracy is remarkably improved compared with a common prediction model;
(2) The invention realizes the construction of the block-scale air quality forecasting system based on the forecasting range mesoscale high-resolution emission list data and the forecasting range block-scale extremely-high-resolution emission list data, effectively simulates the pollutant emission and flow conditions in the city block, solves the problem of insufficient local pollution simulation capability of air quality forecasting, and provides a more effective city air quality management policy.
Drawings
FIG. 1 is a flow chart of a block-scale air quality prediction method of the present invention;
FIG. 2 is a block-scale air quality prediction method according to an embodimentA specific flow chart of the market as an example.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings.
It should be noted that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that in the flowchart. The terms and the like in the description and in the claims, and in the above-described drawings, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
In the description of the present invention, unless explicitly defined otherwise, terms such as arrangement, mounting, connection, etc. should be construed broadly and the specific meaning of the terms in this application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical solution.
The invention provides a block-scale air quality forecasting method, which considers the influence of diversity of urban structures (building forms, roads, greenbelts and the like) on local pollutant diffusion, considers the mutual influence of atmospheric diffusion and chemical reaction between regions/countries, comprehensively considers factors such as operation timeliness, calculation accuracy and the like, solves the problem of insufficient local pollution simulation capability of a complex underlying block in a city, and improves the targeting treatment capability of local atmospheric pollution.
FIG. 1 is a flow chart of a technical route of a block-scale air quality forecasting method of the present invention, and FIG. 2 is a block-scale air quality forecasting method of the present inventionTo (1)The city is exemplified by a specific flow chart, which is described below in connection with FIGS. 1-2 and by +.>The city is an example for describing the block scale air quality forecasting method in detail.
S1: preparing and manufacturing fine topography data covering a preset area, fine land utilization data covering the preset area,Urban canopy data, 2017 MEIC emission list data covering a preset area +.>The local city block scale extremely high resolution emission inventory data is downloaded for GFS forecasting products of time resolution 6 hours (18 total) from 2023, 5, month, 18, to 2023, 5, month, 20 (3 total days).
The fine topography data covering the preset area uses HWSD soil data set and SRTM topography data, and the fine land utilization data covering the preset area uses GLCs land utilization data.
It should be noted that in addition to this,the local neighborhood scale extremely high resolution emissions inventory data includes neighborhood scale road movement source data, neighborhood scale population emissions data, neighborhood scale shipping emissions data, and neighborhood scale industrial source data. The block scale road movement source data is obtained by using OpenStreetMap road network information and using localized road weights; the neighborhood-scale population emission data uses Global Human Settlement Layer m gridded population data sets; neighborhood-scale shipping emission data usage +.>Localized shipping emission data; neighborhood scale industrial source numberAccording to use +.>Industrial point source data. />-/>Is 18 different preset sites.
S2: a mesoscale weather numerical forecasting model (hereinafter referred to as a weather model) and a mesoscale air quality numerical forecasting model (hereinafter referred to as a region model) simulate sites as the preset region，/>An air quality numerical forecasting model (hereinafter referred to as a block model) with an inner block scale simulates a place as +.>The city was forecasted for 72 hours.
It should be noted that, the invention does not directly forecast the air quality of the forecasting place, but representatively selects the forecasting points around the forecasting place, and characterizes the air quality forecasting result of the forecasting place by the air quality forecasting results of a plurality of forecasting points, so that the data is more direct and the forecasting precision is higher.
Illustratively, weather site selectionEach city represents sites, which are respectively->Station(s)>Station(s)>Station(s)>Station(s)>Station(s)>Station(s)>Station(s)>Station(s)>And (5) a station. Air quality site selection->8 national air quality control sites in city, are respectively +.>、/>、/>、/>、、/>、/>、/>。
S3: the meteorological model and the regional model are set by taking 23.5 DEG N and 113.7 DEG E as central pointsThe resolution of the grid is 3 km, coveringThe grid number is 145 multiplied by 124, the resolution of the sub-grid of the block model is set to be 250m, and the coverage is realizedThe number of sub-grids in the market is 480×480.
S4: the manufactured fine topography data covering the preset area, the fine land utilization data covering the preset area,Updating urban canopy data in static geographic data of a meteorological model, updating manufactured fine land utilization data covering a preset area and 2017 MEIC emission list data covering the preset area in emission list data of the area model, and manufacturing manufactured fine land utilization data covering the preset area>The local city block scale very high resolution manifest data is updated in the manifest data of the block model.
It should be noted that the invention prepares two sets of fine land utilization data covering the preset area with different classification systems, which respectively conform to the land utilization classification systems of the meteorological model and the regional model.
S5: interpolation is carried out on the GFS forecasting product in a meteorological model, a meteorological boundary field is initialized, and simulation is carried outAnd obtaining weather forecast space-time characteristic information with 3 km resolution by using a weather field with 3 km resolution from 18 days of 5 months of 2023 to 20 days of 5 months of 2023.
It should be noted that, the weather forecast space-time characteristic information with 3 km resolution includes a plurality of weather parameters, and the invention selects the following 4 weather elements: and 2m temperature, 2m relative humidity, ground air pressure and 10m wind speed are used for carrying out mode applicability verification on the meteorological model.
It should be noted that, in order to verify the meteorological simulation capability and reliability of the meteorological model, the deviation degree of the simulation value and the observed value is reflected by using the deviation (Bias), the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE); and simultaneously selecting a fitting Index (IOA) to judge the relation between the change trend of the simulation value and the change trend of the observation value. Bias, MAE and RMSE are all dimensional statistics, and the closer they are to 0, the better the simulation effect is; IOAs are dimensionless statistics, and can be generally divided into three levels: i <0.4 is a low fitness, 0.4< |ioa| <0.7 is a significant fit, 0.7< |ioa| <1 is a high fit. The specific calculation formula of the statistics is as follows:
where i=1, 2, … N; n is the sequence length; si and Oi are the respective simulation and observation data;and->Is the average value of simulation and observation data, and the calculation formulas are respectively +.>And->。
It should be noted that the 2m Relative Humidity (RH) is calculated from the mixing ratio (Q2), the ground air pressure (P), and the 2m temperature (T) in the weather mode. The calculation formula is as follows:
wherein e is a natural constant.
It should be noted that, the 10m Wind Speed (WS) is calculated according to the 10m north-south wind speed (U10) and the 10m east-west wind speed (V10) in the meteorological mode. The calculation formula is as follows:
s6: modeling weather forecast space-time characteristic information with 3 km resolution as weather driving field of regional modelAnd (3) obtaining air quality forecast space-time characteristic information with 3 km resolution from 2023, 5, 18 days to 2023, 5, 20 days and 3 km resolution of an air quality field.
It should be noted that, the air quality forecast space-time characteristic information with 3 km resolution includes a plurality of air quality parameters, and the invention selects the following 7 air quality elements: the regional model was validated for mode suitability by 2m carbon monoxide concentration, 2m sulfur dioxide concentration, 2m carbon monoxide concentration, 2m carbon dioxide concentration, 2m ozone concentration, 2m particulate matter concentration (including PM10 and PM 2.5).
S7: air quality at 3 km resolutionAir quality driving field with forecast space-time characteristic information as block model and simulationAnd obtaining 250m resolution air quality forecast space-time characteristic information from 2023, 5, 18 days to 2023, 5, 20 days and 250m resolution air quality field.
It should be noted that, the method for performing downscaling operation on the neighborhood model is an emission redistribution method, which specifically includes the steps of: dividing the forecast grid concentration in the regional model into local emissions and non-local emissions; will be in the block modelAnd (3) redistributing the urban local neighborhood scale emission data to the forecast subgrid, recalculating the forecast subgrid concentration by using a Gaussian diffusion formula, deleting the contribution of the regional model on the forecast subgrid, and reserving the non-local conveying capacity to obtain the forecast subgrid concentration.
It should be noted that, the air quality forecast space-time characteristic information with the resolution of 250 meters includes a plurality of air quality parameters, and the invention selects the following 5 air quality elements: the model suitability of the block model was verified for 2m nitrogen oxide concentration, 2m carbon dioxide concentration, 2m ozone concentration, 2m particulate matter concentration (including PM10 and PM 2.5).
Table 1 is: weather model predictions and observations are made from 18 days 5, 2023, 5, 20 days 20, 72 hours 2023.
Table 2 is: the model predictive value and the observed value result are displayed in the region from 18 days of 5 months of 2023 to 72 hours of 5 months of 2023.
Table 3 is: street model predictions and observations from 18 days 5 in 2023 to 72 hours 5 in 2023.
TABLE 1
TABLE 2
TABLE 3 Table 3
While the preferred embodiments of the present invention have been described in detail, the various features of the disclosed embodiments of the present invention may be combined with each other in any manner, and the combinations are not described in the present specification in an exhaustive manner for the sake of brevity and resource saving. The present invention is not limited to the above-described embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present invention, and these equivalent modifications and substitutions are intended to be included in the scope of the present invention as defined in the appended claims.
Claims (10)
1. The block scale air quality forecasting method is characterized by comprising the following steps of:
s1: preparing an initial basic data set, wherein the initial basic data set comprises initial weather forecast driving data, fine topography data, fine land utilization data, urban canopy data, mesoscale high-resolution emission list data and neighborhood scale extremely high-resolution emission list data;
s2: determining initial forecast information, wherein the initial forecast information comprises a forecast place and a forecast duration;
s3: determining a forecasting range according to the forecasting place; determining the size of a forecast grid and the size of a forecast sub-grid according to the forecast places and the forecast range;
s4: updating the initial basic data set in a block-scale air quality forecasting model, wherein the block-scale air quality forecasting model comprises a mesoscale weather numerical forecasting model, a mesoscale air quality numerical forecasting model and a block-scale air quality numerical forecasting model; the resolution of the mesoscale weather numerical forecasting model and the mesoscale air quality numerical forecasting model is the forecasting grid size; the resolution of the neighborhood scale air quality numerical forecasting model is the forecasting sub-grid size;
s5: obtaining mesoscale weather forecast space-time characteristic information in the forecast duration and the forecast range through the mesoscale weather numerical forecast model;
s6: obtaining mesoscale air quality forecasting space-time characteristic information in the forecasting duration and the forecasting range through the mesoscale air quality numerical forecasting model;
s7: and inputting the mesoscale air quality forecasting space-time characteristic information and the ultra-high resolution emission list data of the forecasting range in the neighborhood scale air quality numerical forecasting model to perform downscaling operation, so as to obtain the forecasting time length and the forecasting space-time characteristic information of the forecasting range in the neighborhood scale air quality forecasting.
2. The block-scale air quality prediction method according to claim 1, wherein in S1, the initial weather prediction driving data uses GFS prediction products; the fine topography data uses HWSD soil data sets and SRTM topography data; the medium-scale high-resolution emission inventory data uses a MEIC inventory; the neighborhood scale extremely high resolution emissions inventory data uses neighborhood scale emissions data within a forecast range, including neighborhood scale road movement source data, neighborhood scale population emissions data, neighborhood scale shipping emissions data, and neighborhood scale industrial source data.
3. The block scale air quality forecasting method according to claim 2, wherein the fine land utilization data are made into two sets of fine land utilization data with different classification systems according to classification setting of a mesoscale weather numerical forecasting model and a mesoscale air quality numerical forecasting model.
4. The block-scale air quality prediction method according to claim 1, wherein in S3, the prediction range is centered at the prediction site, a preset east-west direction is long, and a preset north-south direction is wide.
5. The neighborhood-scale air quality prediction method according to claim 1, wherein in said S3, said prediction grid size is set based on said prediction horizon, and said prediction subgrid size is set based on a prediction place.
6. The block scale air quality prediction method according to claim 1, wherein in S4, the mesoscale weather numerical prediction model is a weather model, the mesoscale air quality numerical prediction model is a regional model, and the block scale air quality numerical prediction model is a block model.
7. The block scale air quality prediction method according to claim 1, wherein in S5, initial weather prediction driving data, fine topography data, and city canopy data are input into the mesoscale weather numerical prediction model.
8. The block scale air quality prediction method according to claim 1, wherein in S6, mesoscale weather prediction spatiotemporal characteristic information, fine land utilization data, mesoscale high resolution emission inventory data are input into the mesoscale air quality numerical prediction model.
9. The method for forecasting the air quality of the block scale according to claim 1, wherein the method for performing the downscaling operation on the block scale air quality numerical forecasting model in S7 is an emission redistribution method, and specifically comprises the following steps:
dividing the forecast grid concentration in the mesoscale air quality numerical forecast model into local emission and non-local emission;
and redistributing the neighborhood scale emission data of the prediction range to the prediction subgrid in the neighborhood scale air quality numerical prediction model, recalculating the concentration of the prediction subgrid by using a Gaussian diffusion formula, deleting the contribution of the middle scale air quality numerical prediction model on the prediction subgrid, and reserving non-local conveying capacity to obtain the concentration of the prediction subgrid.
10. The neighborhood-scale air quality prediction method of claim 1, wherein the neighborhood-scale air quality prediction spatiotemporal characteristic information comprises in-air、/>、/>、/>、/>The concentration of one or more of the above.
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| US20190139163A1 (en) * | 2014-10-31 | 2019-05-09 | Research Foundation Of The City University Of New York | Method for forecasting energy demands that incorporates urban heat island |
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| CN110941790A (en) * | 2019-09-27 | 2020-03-31 | 成都信息工程大学 | High-resolution numerical value-based low-altitude flight meteorological information processing method for unmanned aerial vehicle |
| WO2021208393A1 (en) * | 2020-04-15 | 2021-10-21 | 北京工业大学 | Inversion estimation method for air pollutant emission inventory |
| CN116467920A (en) * | 2022-12-30 | 2023-07-21 | 北京市科学技术研究院城市安全与环境科学研究所 | Method for simulating, tracing and optimizing list of atmospheric pollutants on block scale |
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