Ozone pollution gridding tracing method

Abstract

The invention discloses an ozone pollution gridding traceability method. The method comprises the following steps: (1) acquiring a localized basic information data set and carrying out preprocessing; (2) carrying out meteorological field forecasting simulation by utilizing a numerical weather mode WRF and carrying out atmospheric pollutant concentration field forecasting simulation by utilizing an air quality mode CMAQ to obtain a high-resolution atmospheric pollutant spatial and temporal distribution data set; (3) forecasting result evaluation is carried out on a result obtained in the step (2) by comparing hourly ambient air quality monitoring data with meteorological observation network data; (4) constructing a cost model for performing reverse simulation on the adjoint mode to obtain adjoint sensitivity, namely emission source intensity; (5) verifying the adjoint sensitivity and calculating the contribution rate to obtain an emission source causing ozone pollution and a time-space distribution diagram of the contribution rate of the emission source; according to the method, the specific contribution rate of different emission source types of different grids in different time periods to ozone pollution can be obtained in a one-time reverse simulation calculation process.

Description

Technical Field

[0001] The present invention relates to the technical field of ozone pollution prevention and control, and in particular to a grid-based ozone pollution source tracing method. Background Art

[0002] The most effective way to trace the source of ozone pollution is to calculate the sensitivity of ozone precursor emissions and quantify the nonlinear relationship between pollution sources and receptors; the traditional sensitivity analysis technology based on the air quality model (CMAQ) refers to the forward (direct) sensitivity analysis technology, and the specific methods include the strong reduction method (BFM) and the high-order coupled direct method (HDDM). The BFM method based on the CMAQ model is also often called the source switch method, which means that in the model calculation, one of the emission source types is reduced to 0 (i.e. turned off), and then the spatiotemporal distribution changes of atmospheric pollutants are analyzed. In theory, all relevant emission sources need to be turned off one by one to obtain the comprehensive impact of these sources on atmospheric pollution. Therefore, the inevitable disadvantage of this method is that the amount of calculation is very large, and many CMAQ simulation calculations are required, which is difficult to meet the timeliness requirements of the current emergency prevention and control of atmospheric pollution. In addition, since the CMAQ BFM method is an overall linear reduction of the grid area, this method cannot reflect the nonlinear relationship between different sources in the process of atmospheric pollution formation.

[0003] The most widely used forward sensitivity technology at present is the HDDM method based on the CMAQ model. It directly solves the sensitivity equation group of the CMAQ basic model to obtain the slope of the atmospheric pollutant concentration to the change of model parameters. However, the HDDM sensitivity coefficient can only accurately reflect the local sensitivity and because the CMAQ HDDM method is a forward simulation, it also has the defect of large computational complexity. Summary of the invention

[0004] Purpose of the invention: The purpose of the present invention is to provide a grid-based ozone pollution tracing method to solve the problem that existing ozone pollution tracing methods are difficult to efficiently obtain the contribution of all ozone pollution emission sources in a one-time calculation.

[0005] Technical solution: The grid-based ozone pollution tracing method described in the present invention comprises the following steps: (1) Obtain the localization basic information dataset and perform preprocessing; (2) Based on the local basic information dataset and the atmospheric reanalysis data dataset, the numerical weather model WRF is used to simulate the meteorological field forecast and obtain a high-resolution spatiotemporal distribution dataset of meteorological elements; the air quality model CMAQ is used to simulate the atmospheric pollutant concentration field forecast and obtain a high-resolution spatiotemporal distribution dataset of atmospheric pollutants; (3) Evaluate the forecast results by comparing the results obtained in step (2) with the hourly ambient air quality monitoring data and the meteorological observation network data; (4) Based on the high-resolution spatiotemporal distribution data set of atmospheric pollutants, a cost model for inverse simulation of the adjoint model is constructed to obtain the adjoint sensitivity, i.e., the emission source intensity, of different emission source types in different grids at different times; (5) The accompanying sensitivity of different emission source types in different grids at different time periods is verified and the contribution rate is calculated to obtain the spatiotemporal distribution map of the emission sources that cause ozone pollution and their contribution rates.

[0006] Furthermore, in step (1), the localized basic information dataset includes: land use and land cover data, anthropogenic and natural source emission data, historical ground meteorological observation data and ambient air quality monitoring data; the preprocessing is as follows: the localized basic information dataset is gridded based on the numerical model grid configuration, and a standard gridded dataset is generated and stored.

[0007] Furthermore, step (2) includes the following steps: (21) The mesoscale meteorological forecast model WRF system is used to obtain the spatiotemporal distribution data set of meteorological elements at all grid points every hour. The model grid area configuration adopts three-layer grid nesting, and the corresponding spatial resolutions of the three-layer grid are 27km×27km, 9km×9km, and 3km×3km respectively. The simulation results of the outer grid are used as the initial field and boundary field of the inner grid. The model projection configuration uses the Lambert conformal conic projection, and the meteorological input field data comes from the 1°×1° global reanalysis data provided by the National Center for Environmental Prediction of the United States. (22) The multi-scale air quality model CMAQ system was used to obtain the spatiotemporal distribution data set of atmospheric pollutants at all grid points every hour. The meteorological field driving the model was derived from the spatiotemporal distribution data set of meteorological elements at all grid points every hour. The model grid area configuration adopted a three-layer grid nesting, and the corresponding spatial resolutions of the three layers of grids were 27km×27km, 9km×9km, and 3km×3km, respectively. The simulation results of the outer grid were used as the initial field and boundary field of the inner grid. The model projection configuration used the Lambert conformal conic projection. The initial field and boundary field data sets of CMAQ were both simulation results of the global chemical transport model GEOS-Chem. The CMAQ model running time was set to 1 year. The anthropogenic emission sources used the latest 2017 China MEIC inventory released by Tsinghua University and the biomass combustion source inventory in the global forest fire emission database. The natural emission sources were the simulation results of the MEGAN model.

[0008] Furthermore, in step (22), the geos2cmaq tool is used to match the GEOS-Chem simulation output results with the chemical mechanism of the CMAQ model and convert them into a data format that can be read by CMAQ.

[0009] Furthermore, in step (3), the evaluation includes calculating the mean, standard mean deviation, standard mean error, mean score deviation, and mean score error of the observations and simulations.

[0010] Furthermore, in step (4), first, a cost function is established, and the formula is as follows: ; in, is the cost function, is the ozone pollutant concentration field simulated by the CMAQ model; the ozone pollutant concentration field simulated by the CMAQ model, (i, j) is the grid position, t is the time, and s is the ozone; The adjoint mode formula is as follows: ; in, is the transpose of the Jacobian matrix of the chemical reaction rate, is the accompanying variable, is the mandatory term of the adjoint equation, and the negative sign indicates the reverse simulation; Based on the inverse simulation of the adjoint model, the formula for calculating the adjoint sensitivity of the cost function to different emission source intensities is as follows: ; Where E is the intensity of emission sources.

[0011] Furthermore, in step (5), the accompanying sensitivity result is multiplied by the corresponding emission source intensity through linear approximation to obtain the contribution rate of different emission source types.

[0012] The ozone pollution grid-based source tracing system of the present invention comprises: Preprocessing module: used to obtain localization basic information data set and perform preprocessing; Dataset module: It is used to simulate the meteorological field forecast based on the localized basic information dataset and the atmospheric reanalysis data dataset, and obtain a high-resolution spatiotemporal distribution dataset of meteorological elements using the numerical weather model WRF; it uses the air quality model CMAQ to simulate the atmospheric pollutant concentration field forecast, and obtain a high-resolution spatiotemporal distribution dataset of atmospheric pollutants; Evaluation module: used to evaluate the forecast result by comparing the result obtained in step (2) with the hourly ambient air quality monitoring data and the meteorological observation network data; Emission source intensity module: It is used to construct a cost model for inverse simulation of the adjoint model based on the high-resolution spatiotemporal distribution data set of atmospheric pollutants, and obtain the adjoint sensitivity of different emission source types in different grids at different time periods, i.e., the emission source intensity; Spatiotemporal distribution module: used to verify the accompanying sensitivity of different emission source types in different grids at different time periods and calculate the contribution rate, and obtain the spatiotemporal distribution map of emission sources causing ozone pollution and their contribution rate.

[0013] An electronic device described in the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is loaded into the processor, any one of the ozone pollution grid tracing methods described is implemented.

[0014] A storage medium described in the present invention stores a computer program, characterized in that when the computer program is executed by a processor, it implements any one of the ozone pollution grid tracing methods described.

[0015] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages: the present invention applies the adjoint operator principle based on the air quality numerical model, constructs a backward adjoint model, and additionally calculates the adjoint sensitivity during the step-by-step calculation of the model. In this way, the specific contribution rate of different emission source types in different grids at different time periods to ozone pollution can be obtained in a reverse simulation calculation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The scatter plot results of the accompanying sensitivity numerical comparison verification of the present invention; Figure 2 is the spatial and temporal distribution result of the contribution rate of ozone concentration of the present invention, Figure 3 It is the contribution percentage value of the ozone concentration in the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0018] like Figure 1 As shown, an embodiment of the present invention provides a grid-based ozone pollution source tracing method, comprising the following steps: (1) Obtain the localized basic information dataset and perform preprocessing; the localized basic information dataset includes: land use and land cover data, anthropogenic and natural source emission data, historical ground meteorological observation data and ambient air quality monitoring data; the preprocessing is as follows: gridding the localized basic information dataset based on the numerical model grid configuration, generating a standard gridded dataset and storing it.

[0019] (2) Based on the localized basic information dataset and the atmospheric reanalysis data dataset, the numerical weather model WRF is used to simulate the meteorological field forecast to obtain a high-resolution meteorological element spatiotemporal distribution dataset; the air quality model CMAQ is used to simulate the atmospheric pollutant concentration field forecast to obtain a high-resolution atmospheric pollutant spatiotemporal distribution dataset; the following steps are included: (21) The mesoscale meteorological forecast model WRF system is used to obtain the spatiotemporal distribution data set of meteorological elements at all grid points every hour. The model grid area configuration adopts three-layer grid nesting, and the corresponding spatial resolutions of the three-layer grid are 27km×27km, 9km×9km, and 3km×3km respectively. The simulation results of the outer grid are used as the initial field and boundary field of the inner grid. The model projection configuration uses the Lambert conformal conic projection, and the meteorological input field data comes from the 1°×1° global reanalysis data provided by the National Center for Environmental Prediction of the United States. (22) The multi-scale air quality model CMAQ system was used to obtain the spatiotemporal distribution data set of atmospheric pollutants at all hourly grid points. The meteorological field driving the model was derived from the spatiotemporal distribution data set of meteorological elements at all hourly grid points. The model grid area configuration adopted a three-layer grid nesting, and the corresponding spatial resolutions of the three layers of grids were 27km×27km, 9km×9km, and 3km×3km, respectively. The simulation results of the outer grid were used as the initial field and boundary field of the inner grid. The model projection configuration used the Lambert conformal conic projection. The initial field and boundary field data sets of CMAQ were both simulation results of the global chemical transport model GEOS-Chem. The CMAQ model running time was set to 1 year. The anthropogenic emission sources used the latest 2017 China MEIC inventory released by Tsinghua University and the biomass combustion source inventory in the global forest fire emission database. The natural emission sources were the simulation results of the MEGAN model. The geos2cmaq tool was used to match the GEOS-Chem simulation output results with the chemical mechanism of the CMAQ model and convert them into a data format readable by CMAQ.

[0020] (3) The results obtained in step (2) are compared with the hourly ambient air quality monitoring data and the meteorological observation network data to evaluate the forecast results; the evaluation includes calculating the mean, standard mean deviation, standard mean error, mean score deviation, and mean score error of the observation and simulation. (4) Based on the high-resolution spatiotemporal distribution data set of atmospheric pollutants, a cost model for inverse simulation of the adjoint model is constructed to obtain the adjoint sensitivity, i.e., the intensity of emission sources, of different types of emission sources in different grids at different times. The details are as follows: First, a cost function is established, and the formula is as follows: ; in, is the cost function, is the ozone pollutant concentration field simulated by the CMAQ model; the ozone pollutant concentration field simulated by the CMAQ model, (i, j) is the grid position, t is the time, and s is the ozone; The adjoint mode formula is as follows: ; in, is the transpose of the Jacobian matrix of the chemical reaction rate, is the accompanying variable, is the mandatory term of the adjoint equation, and the negative sign indicates the reverse simulation; Based on the inverse simulation of the adjoint model, the formula for calculating the adjoint sensitivity of the cost function to different emission source intensities is as follows: ; Where E is the intensity of emission sources.

[0021] (5) Verify the accompanying sensitivity of different emission source types in different grids at different times and calculate the contribution rate to obtain the spatiotemporal distribution map of the emission sources causing ozone pollution and their contribution rates. Among them, the contribution rate of different emission source types is obtained by multiplying the accompanying sensitivity results with the corresponding emission source intensity through linear approximation. Verify the accompanying sensitivity of different emission source types in different grids at different times. Figure 1 As shown, Figure 1 The scatter plot results for the comparison and verification of the adjoint sensitivity values ​​for all grid points in the grid within the 3-hour simulation period with the numerical mode of every 10-minute step, the obtained accuracy R2 is 0.99, which verifies the high accuracy of CMAQ adjoint simulation; Figure 2 For southern and northern China, the spatiotemporal distribution results of the total contribution rate of different emission source types to ozone concentration at different grid points during the total study period were obtained based on the CMAQ adjoint model simulation. The grid point value reflects the contribution rate to the concentration of ozone pollutants. Figure 3 As shown, typical urban cluster areas in China, including the Pearl River Delta, Yangtze River Delta, and Beijing-Tianjin-Hebei regions, were selected as emission source areas, and the total contribution percentage of their ozone precursor emissions to the ozone concentration in southern China and northern China in the study area was quantified. The total contribution percentage (%) of different grid points for different emission source types (NOx and VOCs) to the ozone concentration level during the total study period was achieved to quantify the results of grid-based tracing of ozone pollution.

[0022] This embodiment also provides an ozone pollution grid tracing system, including: Preprocessing module: used to obtain localization basic information data set and perform preprocessing; Dataset module: It is used to simulate the meteorological field forecast based on the localized basic information dataset and the atmospheric reanalysis data dataset, and obtain a high-resolution spatiotemporal distribution dataset of meteorological elements using the numerical weather model WRF; it uses the air quality model CMAQ to simulate the atmospheric pollutant concentration field forecast, and obtain a high-resolution spatiotemporal distribution dataset of atmospheric pollutants; Evaluation module: used to evaluate the forecast result by comparing the result obtained in step (2) with the hourly ambient air quality monitoring data and the meteorological observation network data; Emission source intensity module: It is used to construct a cost model for inverse simulation of the adjoint model based on the high-resolution spatiotemporal distribution data set of atmospheric pollutants, and obtain the adjoint sensitivity of different emission source types in different grids at different time periods, i.e., the emission source intensity; Spatiotemporal distribution module: used to verify the accompanying sensitivity of different emission source types in different grids at different time periods and calculate the contribution rate, and obtain the spatiotemporal distribution map of emission sources causing ozone pollution and their contribution rate.

[0023] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when loaded into the processor, implements any one of the ozone pollution grid-based source tracing methods described above.

[0024] This embodiment also provides a storage medium, which stores a computer program, characterized in that when the computer program is executed by a processor, it implements any one of the ozone pollution grid tracing methods described.

Claims

1. A grid-based ozone pollution source tracing method, characterized in that: The following steps are involved: (1) Obtain the localization basic information dataset and perform preprocessing; (2) Based on the local basic information dataset and the atmospheric reanalysis data dataset, the numerical weather model WRF is used to simulate the meteorological field forecast and obtain a high-resolution spatiotemporal distribution dataset of meteorological elements; the air quality model CMAQ is used to simulate the atmospheric pollutant concentration field forecast and obtain a high-resolution spatiotemporal distribution dataset of atmospheric pollutants; (3) Evaluate the forecast results by comparing the results obtained in step (2) with the hourly ambient air quality monitoring data and the meteorological observation network data; (4) Based on the high-resolution spatiotemporal distribution data set of atmospheric pollutants, a cost model for inverse simulation of the adjoint model is constructed to obtain the adjoint sensitivity, i.e., the emission source intensity, of different emission source types in different grids at different times; (5) The accompanying sensitivity of different emission source types in different grids at different time periods is verified and the contribution rate is calculated to obtain the spatiotemporal distribution map of the emission sources that cause ozone pollution and their contribution rates.

2. A grid-based ozone pollution source tracing method according to claim 1, characterized in that: In step (1), the localized basic information dataset includes: land use and land cover data, anthropogenic and natural source emission data, historical ground meteorological observation data and ambient air quality monitoring data; the preprocessing is as follows: the localized basic information dataset is gridded based on the numerical model grid configuration, and a standard gridded dataset is generated and stored.

3. The grid-based ozone pollution tracing method according to claim 1 is characterized in that: Step (2) includes the following steps: (21) The mesoscale meteorological forecast model WRF system is used to obtain the spatiotemporal distribution data set of meteorological elements at all grid points every hour. The model grid area configuration adopts three-layer grid nesting, and the corresponding spatial resolutions of the three-layer grid are 27km×27km, 9km×9km, and 3km×3km respectively. The simulation results of the outer grid are used as the initial field and boundary field of the inner grid. The model projection configuration uses the Lambert conformal conic projection, and the meteorological input field data comes from the 1°×1° global reanalysis data provided by the National Center for Environmental Prediction of the United States. (22) The multi-scale air quality model CMAQ system was used to obtain the spatiotemporal distribution data set of atmospheric pollutants at all grid points every hour. The meteorological field driving the model was derived from the spatiotemporal distribution data set of meteorological elements at all grid points every hour. The model grid area configuration adopted a three-layer grid nesting, and the corresponding spatial resolutions of the three layers of grids were 27km×27km, 9km×9km, and 3km×3km, respectively. The simulation results of the outer grid were used as the initial field and boundary field of the inner grid. The model projection configuration used the Lambert conformal conic projection. The initial field and boundary field data sets of CMAQ were both simulation results of the global chemical transport model GEOS-Chem. The CMAQ model running time was set to 1 year. The anthropogenic emission sources used the latest 2017 China MEIC inventory released by Tsinghua University and the biomass combustion source inventory in the global forest fire emission database. The natural emission sources were the simulation results of the MEGAN model.

4. A grid-based ozone pollution source tracing method according to claim 3, characterized in that: In step (22), the geos2cmaq tool is used to match the GEOS-Chem simulation output results with the chemical mechanism of the CMAQ model and convert them into a data format that can be read by CMAQ.

5. The grid-based ozone pollution tracing method according to claim 1 is characterized in that: In step (3), the evaluation includes calculating the mean, standard mean deviation, standard mean error, mean score deviation, and mean score error of the observations and simulations.

6. The grid-based ozone pollution tracing method according to claim 1 is characterized in that: In step (4), first, establish the cost function, the formula is as follows: ； in, is the cost function, is the ozone pollutant concentration field simulated by the CMAQ model; the ozone pollutant concentration field simulated by the CMAQ model, (i, j) is the grid position, t is the time, and s is the ozone; The adjoint mode formula is as follows: ； in, is the transpose of the Jacobian matrix of the chemical reaction rate, is the accompanying variable, is the mandatory term of the adjoint equation, and the negative sign indicates the reverse simulation; Based on the inverse simulation of the adjoint model, the formula for calculating the adjoint sensitivity of the cost function to different emission source intensities is as follows: ； Where, is the intensity of emission sources.

7. The grid-based ozone pollution source tracing method according to claim 1 is characterized in that: In step (5), the concomitant sensitivity result is multiplied by the corresponding emission source intensity through linear approximation to obtain the contribution rate of different emission source types.

8. A grid-based ozone pollution tracing system, characterized in that: include: Preprocessing module: used to obtain localization basic information data set and perform preprocessing; Dataset module: It is used to simulate the meteorological field forecast based on the localized basic information dataset and the atmospheric reanalysis data dataset, and obtain a high-resolution spatiotemporal distribution dataset of meteorological elements using the numerical weather model WRF; it uses the air quality model CMAQ to simulate the atmospheric pollutant concentration field forecast, and obtain a high-resolution spatiotemporal distribution dataset of atmospheric pollutants; Evaluation module: used to evaluate the forecast result by comparing the result obtained in step (2) with the hourly ambient air quality monitoring data and the meteorological observation network data; Emission source intensity module: It is used to construct a cost model for inverse simulation of the adjoint model based on the high-resolution spatiotemporal distribution data set of atmospheric pollutants, and obtain the adjoint sensitivity of different emission source types in different grids at different time periods, i.e., the emission source intensity; Spatiotemporal distribution module: used to verify the accompanying sensitivity of different emission source types in different grids at different time periods and calculate the contribution rate, and obtain the spatiotemporal distribution map of emission sources causing ozone pollution and their contribution rate.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is loaded into a processor, it implements a gridded ozone pollution source tracing method according to any one of claims 1 to 7.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, an ozone pollution gridding source tracing method according to any one of claims 1-7 is implemented.

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