WO2021208393A1 - Inversion estimation method for air pollutant emission inventory - Google Patents

Inversion estimation method for air pollutant emission inventory Download PDF

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WO2021208393A1
WO2021208393A1 PCT/CN2020/122574 CN2020122574W WO2021208393A1 WO 2021208393 A1 WO2021208393 A1 WO 2021208393A1 CN 2020122574 W CN2020122574 W CN 2020122574W WO 2021208393 A1 WO2021208393 A1 WO 2021208393A1
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emission
inverted
meteorological
concentration
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周颖
张晔华
郎建垒
焦玉方
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北京工业大学
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  • the invention belongs to the technical field of atmospheric environment, and relates to an inversion estimation method of air pollutant emission inventory, in particular to an inversion estimation method of air pollutant emission inventory based on numerical simulation, linear programming and air quality monitoring data.
  • the air pollutant emission inventory is the key basic information for studying the formation mechanism of regional air compound pollution and formulating pollution control plans.
  • the commonly used method for the establishment of traditional emission inventories is the bottom-up method based on statistical yearbook data or field surveys. This method is mainly based on detailed activity level data collection and emission factor selection to achieve pollutant emission estimation; its existence data research work Issues such as large volume and relatively lagging updates.
  • the mass balance method is suitable for pollutants with a short life cycle, such as NOx, but the resulting emission inventory has a low spatial resolution, generally greater than 1°;
  • the Kalman filtering method is based on the assumption of the error probability distribution of the observation data and the pollution source list, considering the model simulation data and the observation data, and recursively fusing the pollution source and the observation data and the covariance of the pollution source. Under the criterion of minimum analysis error, To obtain the optimal solution of the pollution source, it usually requires multiple simulations, which is large and time-consuming;
  • Bayesian methods are mostly based on the Lagrangian particle diffusion model to establish the source-receptor relationship between pollution source emissions and receptor points, while the particle diffusion model only considers physical diffusion and transmission, and does not consider chemical reactions, so it is currently mostly suitable for inactive Pollutants, such as the inversion of halogenated hydrocarbons, have limitations in broadening the scope of application of pollutants.
  • the present invention provides a method for inversion and estimation of air pollutant emission inventory, which can get rid of the limitations of statistical data lag, multiple simulation iterations, etc., and perform a numerical simulation through linear programming. Realize the inversion of the pollutant emission inventory in the study area, simplifying the process of establishing the emission inventory.
  • the invention discloses an inversion estimation method of air pollutant emission inventory, including:
  • Grid division of the study area based on GIS distribute the initial pollution source emission data to the divided network, and obtain a grid emission inventory file suitable for the meteorological-air quality model system;
  • a pollution source emission inventory optimization model is constructed through a linear programming method.
  • said obtaining the initial pollution source emission data of the study area includes:
  • the obtaining the upper and lower emission limits of the area to be inverted includes:
  • preliminary estimates or non-negative constraints can be made based on the empirical estimation results of emissions and the local social and economic conditions to obtain the upper and lower limits of the area to be inverted.
  • the meteorological simulation of the study area based on the meteorological mode with a preset resolution includes:
  • the construction of a pollution source emission inventory optimization model through a linear programming method includes:
  • the pollution source emission inventory optimization model of the corresponding scale (month or day) of the study area is established respectively.
  • the target equation is:
  • CD i TBCD i +ICD i
  • CD i calculated concentration of target area i, ⁇ g/m 3 ;
  • CD 0, i the monitored concentration of target area i, ⁇ g/m 3 ;
  • TBCD i the contribution concentration outside the study area to the target area i, ⁇ g/m 3 ;
  • TBCDL i the lower limit of the contribution concentration outside the study area to the target area i, ⁇ g/m 3 ;
  • TBCDU i the upper limit of the contribution concentration outside the study area to the target area i, ⁇ g/m 3 ;
  • ICD i the contribution concentration of the area to be inverted to the target area i, ⁇ g/m 3 ;
  • ED j the pollutant discharge amount of the area j to be inverted, t;
  • TRD j,i the contribution coefficient of the area j to be inverted to the target area i, ⁇ g/m 3 /(t);
  • EDL j the lower limit of emissions in the region j to be inverted, t;
  • EDU j the upper limit of emissions in the region j to be inverted, t;
  • N the number of regions to be inverted.
  • the invention can realize the rapid establishment and update of the emission inventory of air pollution sources, so that the establishment process of the emission inventory can get rid of the dependence on statistical data with strong lag; for areas that have not yet established a high-resolution emission inventory, the invention can establish an emission inventory in time.
  • a relatively accurate high-resolution emission inventory without the need to carry out large-scale data surveys; at the same time, existing emission inventory results can be verified.
  • the research results can provide scientific and technological support for the research on the formation mechanism of regional air pollution and the formulation of timely and effective air pollution control strategies.
  • Fig. 1 is a flowchart of a method for inversion and estimation of an air pollutant emission inventory disclosed in an embodiment of the present invention
  • Fig. 2 is a schematic diagram of a simulation range of a weather-air quality model disclosed in an embodiment of the present invention
  • Fig. 3 is an inversion result and comparison diagram of pollutant monthly emission disclosed in an embodiment of the present invention; among them, (a) is SO 2 , (b) is NO x ;
  • Fig. 4 is an inversion result of daily emission of pollutants in a representative period disclosed in an embodiment of the present invention; where (a) is SO 2 and (b) is NO x .
  • the present invention provides a method for inversion and estimation of air pollutant emission inventory, including S1 to S7, where the order of S1, S2 to S3, and S4 can be exchanged: specific:
  • the acquired air quality monitoring data are mainly the actual monitored concentrations of pollutants.
  • preliminary estimates or non-negative constraints can be made based on the empirical estimation results of emissions and the local social and economic conditions to obtain the upper and lower limits of the area to be inverted.
  • a linear programming method is used to construct a pollution source emission inventory optimization model; among them, the construction of a pollution source emission inventory optimization model includes:
  • the target equation is the minimum average error between the calculated concentration and the monitored concentration in each target area in the study area:
  • CD i TBCD i +ICD i
  • CD i calculated concentration of target area i, ⁇ g/m 3 ;
  • CD 0, i the monitored concentration of target area i, ⁇ g/m 3 ;
  • TBCD i the contribution concentration outside the study area to the target area i, ⁇ g/m 3 ;
  • TBCDL i the lower limit of the contribution concentration outside the study area to the target area i, ⁇ g/m 3 ;
  • TBCDU i the upper limit of the contribution concentration outside the study area to the target area i, ⁇ g/m 3 ;
  • ICD i the contribution concentration of the area to be inverted to the target area i, ⁇ g/m 3 ;
  • ED j the pollutant discharge amount of the area j to be inverted, t;
  • TRD j,i the contribution coefficient of the area j to be inverted to the target area i, ⁇ g/m 3 /(t);
  • EDL j the lower limit of emissions in the region j to be inverted, t;
  • EDU j the upper limit of emissions in the region j to be inverted, t;
  • N the number of regions to be inverted.
  • the present invention provides an inversion estimation method of air pollutant emission inventory based on numerical simulation, linear programming and air quality monitoring data, including:
  • industrial sources can be located to latitude and longitude, and other sources can be refined to the initial emission inventory of districts and counties for grid space allocation.
  • the present invention uses a 3km grid to simulate the source-receptor relationship in various districts and counties of Beijing. Collect the 1° ⁇ 1° resolution meteorological background field data of the National Center for Environmental Prediction (NCEP) during the simulation period and the Beijing area meteorological monitoring data including temperature, pressure, humidity, wind and other meteorological elements, and use the meteorological model WRF to simulate The study area meets the high temporal and spatial resolution meteorological field data required by the air quality model CMAx.
  • NCEP National Center for Environmental Prediction
  • the main parameters of the pollutant source identification technology include the source body (that is, the area to be inverted), the receptor (that is, the target area), and the pollutant identification setting.
  • the details are as follows: In terms of the source body, 17 emission areas are set, namely Dongcheng, Xicheng, Chaoyang, Fengtai, Shijingshan, Haidian, Mentougou, Fangshan, Tongzhou, Shunyi, Changping, Daxing, Huairou, Pinggu, Miyun, Yanqing and other areas outside Beijing; in terms of receptors, select the grid settings of the monitoring stations for each district and county Acceptor; the pollutant is set to SO 2 , NO x , and the simulation range of pollutant source identification is shown in Figure 2.
  • the simulation results are compared with the monitoring data for model verification.
  • Select typical monitoring sites and draw a scatter plot of the daily average monitoring values and daily average simulated values of SO 2 and NO 2.
  • the correlation coefficients between the average daily simulated values of SO 2 and NO 2 and the average daily monitored values are all greater than 0.6, the error does not exceed 43%, and the simulation effect is acceptable.
  • the target equation is the minimum average error between the calculated monthly average concentration of each district and the monitored monthly average concentration:
  • CD i TBCD i +ICD i
  • CD i the calculated monthly average concentration of target district/county i, ⁇ g/m 3 ;
  • CD 0,i Monitoringly average monitoring concentration of target district/county i, ⁇ g/m 3 ;
  • TBCD i the monthly average contribution concentration outside Beijing to the target district/county i, ⁇ g/m 3 ;
  • TBCDL i the lower limit of the monthly average contribution concentration outside Beijing to the target district/county i, ⁇ g/m 3 ;
  • TBCDU i the upper limit of the monthly average contribution concentration outside Beijing to the target district/county i, ⁇ g/m 3 ;
  • ICD i the monthly average contribution concentration of each district/county in Beijing to the target district/county i, ⁇ g/m 3 ;
  • ED j Monthly pollutant emissions of Beijing district and county j, t;
  • TRD j,i the contribution coefficient of the district/county j to be inverted in Beijing to the target district/county i, ⁇ g/m 3 /(t);
  • EDL j the lower limit of emissions from the district j to be inverted, t;
  • EDU j the upper limit of emission of district j to be inverted, t;
  • N the number of districts and counties in Beijing, a total of 16.
  • the objective equation is that the average error between the calculated daily average concentration of each district and the monitored daily average concentration is the smallest:
  • CD i TBCD i +ICD i
  • CD i the calculated daily average concentration of target district/county i, ⁇ g/m 3 ;
  • CD 0, i the monitored daily average concentration of target district and county i, ⁇ g/m 3 ;
  • TBCD i the average daily contribution concentration outside Beijing to the target district/county i, ⁇ g/m 3 ;
  • TBCDL i the lower limit of daily average contribution concentration outside Beijing to the target district/county i, ⁇ g/m 3 ;
  • TBCDU i the upper limit of the daily average contribution concentration outside Beijing to the target district/county i, ⁇ g/m 3 ;
  • ICD i the average daily contribution concentration of each district/county in Beijing to the target district/county i, ⁇ g/m 3 ;
  • ED j the daily pollutant discharge volume of Beijing district and county j, t;
  • TRD j,i the contribution coefficient of the district/county j to be inverted in Beijing to the target district/county i, ⁇ g/m 3 /(t);
  • EDL j the lower limit of emissions from the district j to be inverted, t;
  • EDU j the upper limit of emission of district j to be inverted, t;
  • j the source body district and county (that is, the district and county to be inverted);
  • N the number of districts and counties in Beijing, a total of 16.
  • Figure 3 shows the inversion inventory of SO 2 and NO 2 emissions in the representative months of the four seasons of January, April, July, and October in Beijing, and the comparison results with the inventory established based on detailed survey data using the bottom-up method. It can be seen that the emission inventory obtained through the inversion and estimation of the optimization model is relatively close to the inventory result obtained based on the bottom-up survey, and the emission change trend is consistent in different months.
  • the typical day (7 days each) emission inventory of the typical months in the four seasons of 1, 4, 7, and 10 in each district and county was estimated. Due to the large amount of daily emissions data in various districts and counties, in order to facilitate display, the daily emissions of SO 2 and NO 2 in Beijing are summarized, as shown in Figure 4. It can be found that the emission inventory based on the optimization model can clearly reflect the daily variation of SO 2 and NO 2 emissions in Beijing.

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Abstract

Disclosed is an inversion estimation method for an air pollutant emission inventory, comprising: obtaining initial pollution source emission data of a research region; on the basis of the initial pollution source emission data, obtaining an upper limit and a lower limit of emission of a region to be inverted; performing grid division on the research region on the basis of a GIS, and distributing the initial pollution source emission data to divided grids to obtain a gridded emission inventory file suitable for a meteorological-air quality model system; on the basis of a simulation result of meteorological simulation of the research region and the gridded emission inventory file, establishing a meteorological-air quality model to obtain a source emission-receptor concentration relationship; and constructing a pollution source emission inventory optimization model by means of a linear programming method on the basis of the source emission-receptor concentration relationship, the upper limit and the lower limit of emission of the region to be inverted, and air quality monitoring data. According to the present invention, limitations such as statistical data delay and multiple simulation iterations can be eliminated, one-time numerical simulation is performed, and inversion of the pollutant emission inventory of the research region is realized by means of the linear programming method, thereby simplifying the emission inventory establishment process.

Description

一种大气污染物排放清单的反演估算方法An Inversion Estimation Method of Air Pollutant Emission Inventory 技术领域Technical field
本发明属于大气环境技术领域，涉及一种大气污染物排放清单的反演估算方法，具体涉及一种基于数值模拟、线性规划与空气质量监测数据的大气污染物排放清单的反演估算方法。The invention belongs to the technical field of atmospheric environment, and relates to an inversion estimation method of air pollutant emission inventory, in particular to an inversion estimation method of air pollutant emission inventory based on numerical simulation, linear programming and air quality monitoring data.
背景技术Background technique
大气污染物排放清单是研究区域大气复合污染形成机制、制定污染控制方案的关键基础信息。传统排放清单建立的常用方法为基于统计年鉴资料或实地调研的自下而上法，此法主要基于详细的活动水平数据收集以及排放因子选取，实现污染物的排放量估算；其存在数据调研工作量大、更新相对滞后等问题。The air pollutant emission inventory is the key basic information for studying the formation mechanism of regional air compound pollution and formulating pollution control plans. The commonly used method for the establishment of traditional emission inventories is the bottom-up method based on statistical yearbook data or field surveys. This method is mainly based on detailed activity level data collection and emission factor selection to achieve pollutant emission estimation; its existence data research work Issues such as large volume and relatively lagging updates.
除自下而上法外，反演方法在污染源清单研究中得到逐步应用。In addition to the bottom-up method, inversion methods have been gradually applied in pollution source inventory research.
在已有的排放反演研究中，质量平衡法适用于生命周期较短的污染物，如NOx，但得到的排放清单空间分辨率较低，一般大于1°；In the existing emission inversion studies, the mass balance method is suitable for pollutants with a short life cycle, such as NOx, but the resulting emission inventory has a low spatial resolution, generally greater than 1°;
卡尔曼滤波法是在假定观测数据和污染源清单的误差概率分布情况下，考虑模式模拟数据和观测数据，逐时递推融合污染源和观测数据以及污染源的协方差，在分析误差最小的准则下，得到污染源的最优解，通常需要进行多次模拟，模拟量大，较为耗时；The Kalman filtering method is based on the assumption of the error probability distribution of the observation data and the pollution source list, considering the model simulation data and the observation data, and recursively fusing the pollution source and the observation data and the covariance of the pollution source. Under the criterion of minimum analysis error, To obtain the optimal solution of the pollution source, it usually requires multiple simulations, which is large and time-consuming;
贝叶斯法大多基于拉格朗日粒子扩散模型建立污染源排放和受体点的源受体关系，而粒子扩散模型仅考虑物理扩散和传输，未考虑化学反应，因此目前多适用于不活泼的污染物，如卤代烃的反演，在拓宽污染物应用范围方面存在局限性。Bayesian methods are mostly based on the Lagrangian particle diffusion model to establish the source-receptor relationship between pollution source emissions and receptor points, while the particle diffusion model only considers physical diffusion and transmission, and does not consider chemical reactions, so it is currently mostly suitable for inactive Pollutants, such as the inversion of halogenated hydrocarbons, have limitations in broadening the scope of application of pollutants.
发明内容Summary of the invention
针对现有技术中存在的上述问题，本发明提供一种大气污染物排放清单的反演估算方法，其可摆脱统计数据滞后性、多次模拟迭代等限制，进行一次数值模拟，通过线性规划方法实现对研究区域污染物排放清单反演，简化了排放清单建立过程。In view of the above-mentioned problems in the prior art, the present invention provides a method for inversion and estimation of air pollutant emission inventory, which can get rid of the limitations of statistical data lag, multiple simulation iterations, etc., and perform a numerical simulation through linear programming. Realize the inversion of the pollutant emission inventory in the study area, simplifying the process of establishing the emission inventory.
本发明公开了一种大气污染物排放清单的反演估算方法，包括：The invention discloses an inversion estimation method of air pollutant emission inventory, including:
获取空气质量监测数据；Obtain air quality monitoring data;
获取研究区域的初始污染源排放数据；Obtain the initial pollution source emission data of the study area;
基于所述初始污染源排放数据，获取待反演区域排放上下限；Based on the initial pollution source emission data, obtain the upper and lower emission limits of the area to be inverted;
基于气象模式对研究区域进行预设分辨率的气象模拟；Perform meteorological simulations with preset resolutions for the study area based on the meteorological model;
基于GIS对所述研究区域进行网格划分，将所述初始污染源排放数据分配到所划分的网络中，得到适用于气象-空气质量模型系统的网格化排放清单文件；Grid division of the study area based on GIS, distribute the initial pollution source emission data to the divided network, and obtain a grid emission inventory file suitable for the meteorological-air quality model system;
基于所述气象模拟的模拟结果和所述网格化排放清单文件，建立气象-空气质量模型，得到满足污染源反演估算时空分辨率要求的源排放-受体浓度关系；Based on the simulation results of the meteorological simulation and the grid emission inventory file, establish a meteorological-air quality model to obtain a source emission-acceptor concentration relationship that meets the requirements of the temporal and spatial resolution of the pollution source inversion estimation;
基于所述源排放-受体浓度关系、待反演区域排放上下限、空气质量监测数据，通过线性规划方法构建污染源排放清单优化模型。Based on the source emission-receptor concentration relationship, the upper and lower emission limits of the area to be inverted, and the air quality monitoring data, a pollution source emission inventory optimization model is constructed through a linear programming method.
作为本发明的进一步改进，所述获取研究区域的初始污染源排放数据，包括：As a further improvement of the present invention, said obtaining the initial pollution source emission data of the study area includes:
基于已有排放清单或经验估计，获取研究区域的的初始污染源排放数据。Based on the existing emission inventory or empirical estimation, obtain the initial pollution source emission data of the study area.
作为本发明的进一步改进，所述获取待反演区域排放上下限，包括：As a further improvement of the present invention, the obtaining the upper and lower emission limits of the area to be inverted includes:
若研究区域已建立过排放清单，则利用不确定性分析方法获取待反演区域排放上下限；If the study area has established an emission inventory, use the uncertainty analysis method to obtain the upper and lower emission limits of the area to be inverted;
若研究区域未建立过排放清单，可根据排放量经验估计结果、结合当地社会经济情况做初步估计或做非负约束，得到待反演区域排放上下限。If the study area has not established an emission inventory, preliminary estimates or non-negative constraints can be made based on the empirical estimation results of emissions and the local social and economic conditions to obtain the upper and lower limits of the area to be inverted.
作为本发明的进一步改进，所述基于气象模式对研究区域进行预设分辨率的气象模拟，包括：As a further improvement of the present invention, the meteorological simulation of the study area based on the meteorological mode with a preset resolution includes:
选取模拟基准年；Select the simulation base year;
收集气象模式所需的地形及土地利用资料；Collect topographic and land use data required for meteorological models;
通过气象模式对研究区域进行模拟；Simulate the study area through meteorological models;
收集所选基准年研究区域内各气象站点各季代表月气象观测数据；Collect the meteorological observation data of each season representative month of each meteorological station in the selected base year study area;
对气象模型模拟结果进行验证。Verify the simulation results of the weather model.
作为本发明的进一步改进，在所述气象-空气质量模型的设置中：As a further improvement of the present invention, in the setting of the weather-air quality model:
以研究区域内的待反演区域作为源体，以监测站点所在的目标区域作为 受体；Use the area to be inverted in the study area as the source and the target area where the monitoring site is located as the receiver;
通过数值模拟研究获取满足污染源反演估算时空分辨率要求的源排放-受体浓度关系。Through numerical simulation research, the source emission-acceptor concentration relationship that satisfies the temporal and spatial resolution requirements of pollution source inversion estimation is obtained.
作为本发明的进一步改进，所述通过线性规划方法构建污染源排放清单优化模型，包括：As a further improvement of the present invention, the construction of a pollution source emission inventory optimization model through a linear programming method includes:
以各目标区域污染物计算浓度与获取的污染物监测浓度的平均误差最小为目标，建立目标方程；Set up a target equation with the goal of minimizing the average error between the calculated concentration of pollutants in each target area and the obtained monitoring concentration of pollutants;
以所述待反演区域排放上下限为限制条件，分别建立研究区域相应尺度(月或日)的污染源排放清单优化模型。Taking the upper and lower emission limits of the area to be inverted as the limiting conditions, the pollution source emission inventory optimization model of the corresponding scale (month or day) of the study area is established respectively.
作为本发明的进一步改进，As a further improvement of the present invention,
所述目标方程为：The target equation is:
Figure PCTCN2020122574-appb-000001
Figure PCTCN2020122574-appb-000001
所述限制条件为：The restrictions are:
1、研究区域外地区对目标区域的浓度贡献：1. Contribution of areas outside the study area to the target area:
TBCDL i≤TBCD i≤TBCDU i TBCDL i ≤TBCD i ≤TBCDU i
2、待反演区域对目标区域的浓度贡献：2. Contribution of the area to be inverted to the concentration of the target area:
Figure PCTCN2020122574-appb-000002
Figure PCTCN2020122574-appb-000002
3、目标区域的计算浓度：3. The calculated concentration of the target area:
CD i＝TBCD i+ICD i CD i =TBCD i +ICD i
4、待反演区域排放量限制：4. Emission limits in the area to be inverted:
EDL i≤ED j≤EDU i EDL i ≤ED j ≤EDU i
其中，in,
ER—研究区域计算浓度的平均误差；ER—The average error of the calculated concentration in the study area;
CD i—目标区域i的计算浓度，μg/m 3； CD i —calculated concentration of target area i, μg/m 3 ;
CD 0，i—目标区域i的监测浓度，μg/m 3； CD 0, i — the monitored concentration of target area i, μg/m 3 ;
TBCD i—研究区域外对目标区域i的贡献浓度，μg/m 3； TBCD i —the contribution concentration outside the study area to the target area i, μg/m 3 ;
TBCDL i—研究区域外对目标区域i的贡献浓度下限，μg/m 3； TBCDL i —the lower limit of the contribution concentration outside the study area to the target area i, μg/m 3 ;
TBCDU i—研究区域外对目标区域i的贡献浓度上限，μg/m 3； TBCDU i —the upper limit of the contribution concentration outside the study area to the target area i, μg/m 3 ;
ICD i—待反演区域对目标区域i的贡献浓度，μg/m 3； ICD i —the contribution concentration of the area to be inverted to the target area i, μg/m 3 ;
ED j—待反演区域j的污染物排放量，t； ED j —the pollutant discharge amount of the area j to be inverted, t;
TRD j,i—待反演区域j对目标区域i的贡献系数，μg/m 3/(t)； TRD j,i —the contribution coefficient of the area j to be inverted to the target area i, μg/m 3 /(t);
EDL j—待反演区域j排放的下限，t； EDL j —the lower limit of emissions in the region j to be inverted, t;
EDU j—待反演区域j排放的上限，t； EDU j —the upper limit of emissions in the region j to be inverted, t;
i—目标区域；i—target area;
j—待反演区域；j—the area to be inverted;
N—待反演区域数量。N—the number of regions to be inverted.
与现有技术相比，本发明的有益效果为：Compared with the prior art, the beneficial effects of the present invention are:
本发明可实现大气污染源排放清单的快速建立与更新，使排放清单的建立过程摆脱对滞后性较强的统计数据依赖；对于尚未建立高分辨率排放清单的地区，通过本发明可及时建立起一套相对准确的高分辨率排放清单，而无需开展大规模数据调查；同时也可对已有的排放清单结果进行校验。研究成果可为区域大气复合污染形成机制研究与及时、有效的大气污染控制策略制定提供科技支撑。The invention can realize the rapid establishment and update of the emission inventory of air pollution sources, so that the establishment process of the emission inventory can get rid of the dependence on statistical data with strong lag; for areas that have not yet established a high-resolution emission inventory, the invention can establish an emission inventory in time. A relatively accurate high-resolution emission inventory without the need to carry out large-scale data surveys; at the same time, existing emission inventory results can be verified. The research results can provide scientific and technological support for the research on the formation mechanism of regional air pollution and the formulation of timely and effective air pollution control strategies.
附图说明Description of the drawings
图1为本发明一种实施例公开的大气污染物排放清单的反演估算方法的流程图；图2为本发明一种实施例公开的气象-空气质量模型的模拟范围示意图；Fig. 1 is a flowchart of a method for inversion and estimation of an air pollutant emission inventory disclosed in an embodiment of the present invention; Fig. 2 is a schematic diagram of a simulation range of a weather-air quality model disclosed in an embodiment of the present invention;
图3为本发明一种实施例公开的污染物月排放反演结果及对比图；其中，(a)为SO 2，(b)为NO x； Fig. 3 is an inversion result and comparison diagram of pollutant monthly emission disclosed in an embodiment of the present invention; among them, (a) is SO 2 , (b) is NO x ;
图4为本发明一种实施例公开的污染物代表时段日排放反演结果；其中，(a)为SO 2，(b)为NO x。 Fig. 4 is an inversion result of daily emission of pollutants in a representative period disclosed in an embodiment of the present invention; where (a) is SO 2 and (b) is NO x .
具体实施方式Detailed ways
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明的一部分实施例，而不是全部的实施例。基 于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本发明保护的范围。In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.
下面结合附图对本发明做进一步的详细描述：The present invention will be further described in detail below in conjunction with the accompanying drawings:
如图1所示，本发明提供一种大气污染物排放清单的反演估算方法，包括S1～S7，其中，S1、S2～S3、S4的顺序可调换：具体的：As shown in Figure 1, the present invention provides a method for inversion and estimation of air pollutant emission inventory, including S1 to S7, where the order of S1, S2 to S3, and S4 can be exchanged: specific:
S1、获取空气质量监测数据，所获取的空气质量监测数据主要为污染物的实际监测浓度。S1. Obtain air quality monitoring data. The acquired air quality monitoring data are mainly the actual monitored concentrations of pollutants.
S2、获取研究区域的初始污染源排放数据；其中，具体获取方法为：S2. Obtain the initial pollution source emission data of the study area; among them, the specific acquisition method is:
基于已有排放清单或经验估计，获取研究区域的初始污染源排放数据。Based on the existing emission inventory or empirical estimation, obtain the initial pollution source emission data of the study area.
S3、基于初始污染源排放数据，获取待反演区域排放上下限；其中，S3. Obtain the upper and lower emission limits of the area to be inverted based on the initial pollution source emission data; among them,
若研究区域已建立过排放清单，则利用不确定性分析方法获取待反演区域排放上下限；If the study area has established an emission inventory, use the uncertainty analysis method to obtain the upper and lower emission limits of the area to be inverted;
若研究区域未建立过排放清单，可根据排放量经验估计结果、结合当地社会经济情况做初步估计或做非负约束，得到待反演区域排放上下限。If the study area has not established an emission inventory, preliminary estimates or non-negative constraints can be made based on the empirical estimation results of emissions and the local social and economic conditions to obtain the upper and lower limits of the area to be inverted.
S4、基于气象模式对研究区域进行预设分辨率(高分辨率)的气象模拟；其中，气象模拟的方法为：S4. Perform meteorological simulation with preset resolution (high resolution) on the study area based on the meteorological model; among them, the method of meteorological simulation is:
选取模拟基准年；Select the simulation base year;
收集气象模式所需的地形及土地利用资料；Collect topographic and land use data required for meteorological models;
通过气象模式对研究区域进行模拟；Simulate the study area through meteorological models;
收集所选基准年研究区域内各气象站点各季代表月气象观测数据；Collect the meteorological observation data of each season representative month of each meteorological station in the selected base year study area;
对气象模型模拟结果进行验证。Verify the simulation results of the weather model.
S5、基于GIS对研究区域进行网格划分，将初始污染源排放数据分配到所划分的网络中，得到适用于气象-空气质量模型系统的网格化排放清单文件。S5. Grid the study area based on GIS, distribute the initial pollution source emission data to the divided network, and obtain a grid emission inventory file suitable for the weather-air quality model system.
S6、基于气象模拟的模拟结果和网格化排放清单文件，建立气象-空气质量模型，得到满足污染源反演估算时空分辨率要求的源排放-受体浓度关系；其中，S6. Based on the simulation results of the meteorological simulation and the grid emission inventory file, establish a weather-air quality model to obtain the source emission-receptor concentration relationship that meets the requirements of the temporal and spatial resolution of the pollution source inversion estimation; among them,
收集所选基准年研究区域内各空气质量监测站点各季代表月监测数据，对数值模拟结果进行验证；Collect the monthly monitoring data of each air quality monitoring station in the selected base year study area, and verify the numerical simulation results;
在气象-空气质量模型的设置中：以研究区域内的待反演区域作为源体，以监测站点所在的目标区域作为受体；通过数值模拟研究获取满足污染源反 演估算时空分辨率要求的源排放-受体浓度关系TRD j,i(μg/m 3/(t))(污染物传递系数)。 In the setting of the weather-air quality model: take the area to be inverted in the study area as the source body, and take the target area where the monitoring station is located as the receptor; obtain the source that meets the requirements of the time and space resolution of the pollution source inversion estimation through numerical simulation Emission-acceptor concentration relationship TRD j,i (μg/m 3 /(t)) (pollutant transfer coefficient).
S7、基于源排放-受体浓度关系、待反演区域排放上下限、空气质量监测数据，通过线性规划方法构建污染源排放清单优化模型；其中，构建污染源排放清单优化模型，包括：S7. Based on the source emission-receptor concentration relationship, the upper and lower emission limits of the area to be inverted, and air quality monitoring data, a linear programming method is used to construct a pollution source emission inventory optimization model; among them, the construction of a pollution source emission inventory optimization model includes:
通过数值模拟研究获取满足污染源反演估算时空分辨率要求的源受体关系(污染物传递系数)，以各目标区域污染物计算浓度CD i(μg/m 3)与获取的污染物监测浓度CD 0，i(μg/m 3)的平均误差最小为目标，建立目标方程；以待反演区域排放上下限为限制条件，分别建立研究区域相应尺度(月或日)的污染源排放清单优化模型，实现所需时空分辨率的污染物排放清单反演估算。 Obtain the source-receptor relationship (pollutant transfer coefficient) that meets the requirements of the time-space resolution of pollution source inversion estimation through numerical simulation, and calculate the concentration CD i (μg/m 3 ) of pollutants in each target area and the obtained pollutant monitoring concentration CD The goal is to minimize the average error of 0,i (μg/m 3 ), and establish the target equation; take the upper and lower limits of the emission in the region to be inverted as the limiting conditions, and establish the pollution source emission inventory optimization model of the corresponding scale (month or day) in the study area. To achieve the required spatial and temporal resolution of pollutant emission inventory inversion estimation.
具体的：specific:
目标方程为研究区域内各目标区域计算浓度与监测浓度相比平均误差最小：The target equation is the minimum average error between the calculated concentration and the monitored concentration in each target area in the study area:
Figure PCTCN2020122574-appb-000003
Figure PCTCN2020122574-appb-000003
限制条件为：The restrictions are:
1、研究区域外地区对目标区域的浓度贡献：1. Contribution of areas outside the study area to the target area:
TBCDL i≤TBCD i≤TBCDU i TBCDL i ≤TBCD i ≤TBCDU i
2、待反演区域对目标区域的浓度贡献：2. Contribution of the area to be inverted to the concentration of the target area:
Figure PCTCN2020122574-appb-000004
Figure PCTCN2020122574-appb-000004
3、目标区域的计算浓度：3. The calculated concentration of the target area:
CD i＝TBCD i+ICD i CD i =TBCD i +ICD i
4、待反演区域排放量限制：4. Emission limits in the area to be inverted:
EDL i≤ED j≤EDU i EDL i ≤ED j ≤EDU i
其中，in,
ER—研究区域计算浓度的平均误差；ER—The average error of the calculated concentration in the study area;
CD i—目标区域i的计算浓度，μg/m 3； CD i —calculated concentration of target area i, μg/m 3 ;
CD 0，i—目标区域i的监测浓度，μg/m 3； CD 0, i — the monitored concentration of target area i, μg/m 3 ;
TBCD i—研究区域外对目标区域i的贡献浓度，μg/m 3； TBCD i —the contribution concentration outside the study area to the target area i, μg/m 3 ;
TBCDL i—研究区域外对目标区域i的贡献浓度下限，μg/m 3； TBCDL i —the lower limit of the contribution concentration outside the study area to the target area i, μg/m 3 ;
TBCDU i—研究区域外对目标区域i的贡献浓度上限，μg/m 3； TBCDU i —the upper limit of the contribution concentration outside the study area to the target area i, μg/m 3 ;
ICD i—待反演区域对目标区域i的贡献浓度，μg/m 3； ICD i —the contribution concentration of the area to be inverted to the target area i, μg/m 3 ;
ED j—待反演区域j的污染物排放量，t； ED j —the pollutant discharge amount of the area j to be inverted, t;
TRD j,i—待反演区域j对目标区域i的贡献系数，μg/m 3/(t)； TRD j,i —the contribution coefficient of the area j to be inverted to the target area i, μg/m 3 /(t);
EDL j—待反演区域j排放的下限，t； EDL j —the lower limit of emissions in the region j to be inverted, t;
EDU j—待反演区域j排放的上限，t； EDU j —the upper limit of emissions in the region j to be inverted, t;
i—目标区域；i—target area;
j—待反演区域；j—the area to be inverted;
N—待反演区域数量。N—the number of regions to be inverted.
实施例：Examples:
本发明提供一种基于数值模拟、线性规划与空气质量监测数据的大气污染物排放清单的反演估算方法，包括：The present invention provides an inversion estimation method of air pollutant emission inventory based on numerical simulation, linear programming and air quality monitoring data, including:
S1、选取基准年为2013年，选取1、4、7、10四个月作为四季的代表月，作为模拟时段。充分收集北京地区污染物排放信息后，通过进一步更新和完善得到反演所需的初始排放信息，北京之外地区排放信息从MEIC清单(Multi-resolution Emission Inventory for China)中获得。S1. Select the base year as 2013, and select 1, 4, 7, and 10 as the representative months of the four seasons as the simulation period. After fully collecting pollutant emission information in Beijing, the initial emission information required for inversion is obtained through further update and improvement. Emission information outside Beijing is obtained from the MEIC (Multi-resolution Emission Inventory for China).
S2、利用空间地理信息处理技术(Geographical Information System)将工业源可定位至经纬度、其他源可细化至区县的初始排放清单进行网格空间分配。S2. Using spatial geographic information processing technology (Geographical Information System), industrial sources can be located to latitude and longitude, and other sources can be refined to the initial emission inventory of districts and counties for grid space allocation.
S3、基于污染物来源识别技术建立北京地区源排放-受体浓度关系：本发明采用3km网格对北京各个区县源受体关系进行模拟。收集该模拟时段内的美国环境预报中心(NCEP)1°×1°分辨率气象背景场数据及包括温、压、湿、风等各气象要素的北京地区气象监测资料，利用气象模型WRF模拟得到研究区域符合空气质量模型CMAx要求的高时空分辨率气象场数据。S3. Establishing the source emission-receptor concentration relationship in Beijing based on the pollutant source identification technology: The present invention uses a 3km grid to simulate the source-receptor relationship in various districts and counties of Beijing. Collect the 1°×1° resolution meteorological background field data of the National Center for Environmental Prediction (NCEP) during the simulation period and the Beijing area meteorological monitoring data including temperature, pressure, humidity, wind and other meteorological elements, and use the meteorological model WRF to simulate The study area meets the high temporal and spatial resolution meteorological field data required by the air quality model CMAx.
污染物来源识别技术主要参数包括源体(即待反演区域)设置、受体(即 目标区域)设置、识别污染物设置，具体如下：源体方面，设置17个排放区域，分别为东城、西城、朝阳、丰台、石景山、海淀、门头沟、房山、通州、顺义、昌平、大兴、怀柔、平谷、密云、延庆以及京外其他区域；受体方面，针对各区县选择监测站点所在的网格设置受体；污染物设置为SO 2、NO x，污染物来源识别模拟范围如图2所示。根据收集到的环境质量浓度监测数据，将模拟结果与监测数据作对比进行模型验证。选取典型监测站点，将SO 2、NO 2日均监测值与日均模拟值绘制散点图。SO 2、NO 2日均模拟值与日均监测值相关系数均大于0.6，误差不超过43％，模拟效果可接受。 The main parameters of the pollutant source identification technology include the source body (that is, the area to be inverted), the receptor (that is, the target area), and the pollutant identification setting. The details are as follows: In terms of the source body, 17 emission areas are set, namely Dongcheng, Xicheng, Chaoyang, Fengtai, Shijingshan, Haidian, Mentougou, Fangshan, Tongzhou, Shunyi, Changping, Daxing, Huairou, Pinggu, Miyun, Yanqing and other areas outside Beijing; in terms of receptors, select the grid settings of the monitoring stations for each district and county Acceptor; the pollutant is set to SO 2 , NO x , and the simulation range of pollutant source identification is shown in Figure 2. According to the collected environmental quality concentration monitoring data, the simulation results are compared with the monitoring data for model verification. Select typical monitoring sites and draw a scatter plot of the daily average monitoring values and daily average simulated values of SO 2 and NO 2. The correlation coefficients between the average daily simulated values of SO 2 and NO 2 and the average daily monitored values are all greater than 0.6, the error does not exceed 43%, and the simulation effect is acceptable.
4)基于线性规划方法，以北京各区县污染物计算浓度与监测浓度(μg/m 3)平均误差最小为目标，建立目标方程；以各区县(待反演区域)排放上下限作为限制条件，建立北京区县级污染源月排放和日排放优化估算模型。 4) Based on the linear programming method, with the goal of minimizing the average error between the calculated concentration of pollutants and the monitored concentration (μg/m 3 ) in Beijing districts and counties, the goal equation is established; the upper and lower limits of emissions in each district and county (area to be inverted) are used as limiting conditions. Establish an optimized estimation model of monthly and daily emissions from pollution sources at the county level in Beijing.
一、北京市各区县月排放优化估算模型1. Optimized estimation model of monthly emissions in all districts and counties of Beijing
目标方程为各区县计算月均浓度与监测月均浓度相比平均误差最小：The target equation is the minimum average error between the calculated monthly average concentration of each district and the monitored monthly average concentration:
Figure PCTCN2020122574-appb-000005
Figure PCTCN2020122574-appb-000005
限制条件为：The restrictions are:
1、北京外地区对目标区县的月均浓度贡献：1. The monthly average concentration contribution of areas outside Beijing to the target districts and counties:
TBCDL i≤TBCD i≤TBCDU i TBCDL i ≤TBCD i ≤TBCDU i
2、北京各区县对目标区县的月均浓度贡献：2. Contributions of Beijing's districts and counties to the target districts and counties' monthly average concentration:
Figure PCTCN2020122574-appb-000006
Figure PCTCN2020122574-appb-000006
3、北京目标区县的计算月均浓度：3. The calculated monthly average concentration of the target districts and counties in Beijing:
CD i＝TBCD i+ICD i CD i =TBCD i +ICD i
4、北京各区县排放量限制：4. Emission limits in various districts and counties of Beijing:
EDL i≤ED j≤EDU i EDL i ≤ED j ≤EDU i
其中，in,
ER—北京各区县计算月均浓度的平均误差；ER—The average error in the calculation of monthly average concentration in all districts and counties of Beijing;
CD i—目标区县i的计算月均浓度，μg/m 3； CD i —the calculated monthly average concentration of target district/county i, μg/m 3 ;
CD 0，i—目标区县i的的监测月均浓度，μg/m 3； CD 0,i —Monthly average monitoring concentration of target district/county i, μg/m 3 ;
TBCD i—北京外对目标区县i的月均贡献浓度，μg/m 3； TBCD i —the monthly average contribution concentration outside Beijing to the target district/county i, μg/m 3 ;
TBCDL i—北京外对目标区县i的月均贡献浓度下限，μg/m 3； TBCDL i —the lower limit of the monthly average contribution concentration outside Beijing to the target district/county i, μg/m 3 ;
TBCDU i—北京外对目标区县i的月均贡献浓度上限，μg/m 3； TBCDU i —the upper limit of the monthly average contribution concentration outside Beijing to the target district/county i, μg/m 3 ;
ICD i—北京各区县对目标区县i的月均贡献浓度，μg/m 3； ICD i —the monthly average contribution concentration of each district/county in Beijing to the target district/county i, μg/m 3 ;
ED j—北京区县j的污染物月排放量，t； ED j —Monthly pollutant emissions of Beijing district and county j, t;
TRD j,i—北京待反演区县j对目标区县i的贡献系数，μg/m 3/(t)； TRD j,i —the contribution coefficient of the district/county j to be inverted in Beijing to the target district/county i, μg/m 3 /(t);
EDL j—待反演区县j排放的下限，t； EDL j —the lower limit of emissions from the district j to be inverted, t;
EDU j—待反演区县j排放的上限，t； EDU j —the upper limit of emission of district j to be inverted, t;
i—受体区县(即目标区县)；i—Recipient districts and counties (ie target districts and counties);
j—源体区县(即待反演区县)；j-source body districts and counties (that is, districts and counties to be inverted);
N—北京区县数量，共16个。N—the number of districts and counties in Beijing, a total of 16.
二、北京市各区县日排放优化估算模型2. Optimized estimation model of daily emissions in all districts and counties of Beijing
目标方程为各区县计算日均浓度与监测日均浓度相比平均误差最小：The objective equation is that the average error between the calculated daily average concentration of each district and the monitored daily average concentration is the smallest:
Figure PCTCN2020122574-appb-000007
Figure PCTCN2020122574-appb-000007
限制条件为：The restrictions are:
1、北京外地区对目标区县的日均浓度贡献：1. Contributions of areas outside Beijing to the target districts and counties:
TBCDL i≤TBCD i≤TBCDU i TBCDL i ≤TBCD i ≤TBCDU i
2、北京各区县对目标区县的日均浓度贡献：2. Contributions of Beijing's districts and counties to the target districts and counties:
Figure PCTCN2020122574-appb-000008
Figure PCTCN2020122574-appb-000008
3、北京目标区县的计算日均浓度：3. The calculated daily average concentration of the target districts and counties in Beijing:
CD i＝TBCD i+ICD i CD i =TBCD i +ICD i
4、北京各区县排放量限制：4. Emission limits in various districts and counties of Beijing:
EDL i≤ED j≤EDU i EDL i ≤ED j ≤EDU i
其中，in,
ER—北京各区县计算日均浓度的平均误差；ER—The average error in the calculation of daily average concentration in all districts and counties of Beijing;
CD i—目标区县i的计算日均浓度，μg/m 3； CD i —the calculated daily average concentration of target district/county i, μg/m 3 ;
CD 0，i—目标区县i的的监测日均浓度，μg/m 3； CD 0, i — the monitored daily average concentration of target district and county i, μg/m 3 ;
TBCD i—北京外对目标区县i的日均贡献浓度，μg/m 3； TBCD i —the average daily contribution concentration outside Beijing to the target district/county i, μg/m 3 ;
TBCDL i—北京外对目标区县i的日均贡献浓度下限，μg/m 3； TBCDL i —the lower limit of daily average contribution concentration outside Beijing to the target district/county i, μg/m 3 ;
TBCDU i—北京外对目标区县i的日均贡献浓度上限，μg/m 3； TBCDU i —the upper limit of the daily average contribution concentration outside Beijing to the target district/county i, μg/m 3 ;
ICD i—北京各区县对目标区县i的日均贡献浓度，μg/m 3； ICD i —the average daily contribution concentration of each district/county in Beijing to the target district/county i, μg/m 3 ;
ED j—北京区县j的污染物日排放量，t； ED j —the daily pollutant discharge volume of Beijing district and county j, t;
TRD j,i—北京待反演区县j对目标区县i的贡献系数，μg/m 3/(t)； TRD j,i —the contribution coefficient of the district/county j to be inverted in Beijing to the target district/county i, μg/m 3 /(t);
EDL j—待反演区县j排放的下限，t； EDL j —the lower limit of emissions from the district j to be inverted, t;
EDU j—待反演区县j排放的上限，t； EDU j —the upper limit of emission of district j to be inverted, t;
i—受体区县(即目标区县)；i—Recipient districts and counties (ie target districts and counties);
j—源体区县(即待反演区县)；j—the source body district and county (that is, the district and county to be inverted);
N—北京区县数量，共16个。N—the number of districts and counties in Beijing, a total of 16.
模型优化结果：Model optimization results:
基于月排放优化模型，估算了北京市SO 2、NO 2的排放。图3给出了北京1月、4月、7月、10月四个季节代表月SO 2、NO 2的排放反演清单以及与基于详细调查数据利用自下而上法建立清单的对比结果。可以看出，通过优化模型反演估算得到的排放清单与基于自下而上调查得到的清单结果较为接近，不同月份排放变化趋势一致。 Based on the monthly emission optimization model, the emissions of SO 2 and NO 2 in Beijing are estimated. Figure 3 shows the inversion inventory of SO 2 and NO 2 emissions in the representative months of the four seasons of January, April, July, and October in Beijing, and the comparison results with the inventory established based on detailed survey data using the bottom-up method. It can be seen that the emission inventory obtained through the inversion and estimation of the optimization model is relatively close to the inventory result obtained based on the bottom-up survey, and the emission change trend is consistent in different months.
基于日排放优化模型，估算了各区县1、4、7、10四个季节典型月份的典型日(各7天)排放清单。由于各区县日排放数据量较大，为便于展示，将其汇总得到北京市SO 2、NO 2日排放，如图4所示。可以发现基于优化模型得到的排放清单可明显反映出北京SO 2、NO 2排放日变化差异。 Based on the daily emission optimization model, the typical day (7 days each) emission inventory of the typical months in the four seasons of 1, 4, 7, and 10 in each district and county was estimated. Due to the large amount of daily emissions data in various districts and counties, in order to facilitate display, the daily emissions of SO 2 and NO 2 in Beijing are summarized, as shown in Figure 4. It can be found that the emission inventory based on the optimization model can clearly reflect the daily variation of SO 2 and NO 2 emissions in Beijing.
以上仅为本发明的优选实施例而已，并不用于限制本发明，对于本领域的技术人员来说，本发明可以有各种更改和变化。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。The above are only preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

  1. 一种大气污染物排放清单的反演估算方法，其特征在于，包括：A method for inversion and estimation of air pollutant emission inventory, which is characterized in that it includes:
    获取空气质量监测数据；Obtain air quality monitoring data;
    获取研究区域的初始污染源排放数据；Obtain the initial pollution source emission data of the study area;
    基于所述初始污染源排放数据，获取待反演区域排放上下限；Based on the initial pollution source emission data, obtain the upper and lower emission limits of the area to be inverted;
    基于气象模式对研究区域进行预设分辨率的气象模拟；Perform meteorological simulations with preset resolutions for the study area based on the meteorological model;
    基于GIS对所述研究区域进行网格划分，将所述初始污染源排放数据分配到所划分的网络中，得到适用于气象-空气质量模型系统的网格化排放清单文件；Grid division of the study area based on GIS, distribute the initial pollution source emission data to the divided network, and obtain a grid emission inventory file suitable for the meteorological-air quality model system;
    基于所述气象模拟的模拟结果和所述网格化排放清单文件，建立气象-空气质量模型，得到满足污染源反演估算时空分辨率要求的源排放-受体浓度关系；Based on the simulation results of the meteorological simulation and the grid emission inventory file, establish a meteorological-air quality model to obtain a source emission-acceptor concentration relationship that meets the requirements of the temporal and spatial resolution of the pollution source inversion estimation;
    基于所述源排放-受体浓度关系、待反演区域排放上下限、空气质量监测数据，通过线性规划方法构建污染源排放清单优化模型。Based on the source emission-receptor concentration relationship, the upper and lower emission limits of the area to be inverted, and the air quality monitoring data, a pollution source emission inventory optimization model is constructed through a linear programming method.
  2. 如权利要求1所述的反演估算方法，其特征在于，所述获取研究区域的初始污染源排放数据，包括：The inversion estimation method according to claim 1, wherein said obtaining the initial pollution source emission data of the study area comprises:
    基于已有排放清单或经验估计，获取研究区域的初始污染源排放数据。Based on the existing emission inventory or empirical estimation, obtain the initial pollution source emission data of the study area.
  3. 如权利要求1所述的反演估算方法，其特征在于，所述获取待反演区域排放上下限，包括：The inversion estimation method according to claim 1, wherein said obtaining the upper and lower emission limits of the area to be inverted comprises:
    若研究区域已建立过排放清单，则利用不确定性分析方法获取待反演区域排放上下限；If the study area has established an emission inventory, use the uncertainty analysis method to obtain the upper and lower emission limits of the area to be inverted;
    若研究区域未建立过排放清单，可根据排放量经验估计结果、结合当地社会经济情况做初步估计或做非负约束，得到待反演区域排放上下限。If the study area has not established an emission inventory, preliminary estimates or non-negative constraints can be made based on the empirical estimation results of emissions and the local social and economic conditions to obtain the upper and lower limits of the area to be inverted.
  4. 如权利要求1所述的反演估算方法，其特征在于，所述基于气象模式对研究区域进行预设分辨率的气象模拟，包括：The inversion estimation method according to claim 1, wherein said performing a meteorological simulation with a preset resolution on the research area based on a meteorological model comprises:
    选取模拟基准年；Select the simulation base year;
    收集气象模式所需的地形及土地利用资料；Collect topographic and land use data required for meteorological models;
    通过气象模式对研究区域进行模拟；Simulate the study area through meteorological models;
    收集所选基准年研究区域内各气象站点各季代表月气象观测数据；Collect the meteorological observation data of each season representative month of each meteorological station in the selected base year study area;
    对气象模型模拟结果进行验证。Verify the simulation results of the weather model.
  5. 如权利要求1所述的反演估算方法，其特征在于，在所述气象-空气质 量模型的设置中：The inversion estimation method according to claim 1, wherein in the setting of the weather-air quality model:
    以研究区域内的待反演区域作为源体，以监测站点所在的目标区域作为受体；Use the area to be inverted in the study area as the source and the target area where the monitoring site is located as the receptor;
    通过数值模拟研究获取满足污染源反演估算时空分辨率要求的源排放-受体浓度关系。Through numerical simulation research, the source emission-acceptor concentration relationship that satisfies the temporal and spatial resolution requirements of pollution source inversion estimation is obtained.
  6. 如权利要求1所述的反演估算方法，其特征在于，所述通过线性规划方法构建污染源排放清单优化模型，包括：The inversion estimation method according to claim 1, wherein said constructing an optimization model of a pollution source emission inventory through a linear programming method comprises:
    以各目标区域污染物计算浓度与获取的污染物监测浓度的平均误差最小为目标，建立目标方程；Set up a target equation with the goal of minimizing the average error between the calculated concentration of pollutants in each target area and the obtained monitoring concentration of pollutants;
    以所述待反演区域排放上下限为限制条件，分别建立研究区域相应尺度的污染源排放清单优化模型。Taking the upper and lower emission limits of the area to be inverted as the limiting conditions, the pollution source emission inventory optimization model of the corresponding scale of the study area is established respectively.
  7. 如权利要求6所述的反演估算方法，其特征在于，The inversion estimation method according to claim 6, characterized in that:
    所述目标方程为：The target equation is:
    Figure PCTCN2020122574-appb-100001
    Figure PCTCN2020122574-appb-100001
    所述限制条件为：The restrictions are:
    1、研究区域外地区对目标区域的浓度贡献：1. Contribution of areas outside the study area to the target area:
    TBCDL i≤TBCD i≤TBCDU i TBCDL i ≤TBCD i ≤TBCDU i
    2、待反演区域对目标区域的浓度贡献：
    Figure PCTCN2020122574-appb-100002
    2. Contribution of the area to be inverted to the concentration of the target area:
    Figure PCTCN2020122574-appb-100002
    3、目标区域的计算浓度：3. The calculated concentration of the target area:
    CD i＝TBCD i+ICD i CD i =TBCD i +ICD i
    4、待反演区域排放量限制：4. Emission limits in the area to be inverted:
    EDL i≤ED j≤EDU i EDL i ≤ED j ≤EDU i
    其中，in,
    ER—研究区域计算浓度的平均误差；ER—The average error of the calculated concentration in the study area;
    CD i—目标区域i的计算浓度，μg/m 3； CD i —calculated concentration of target area i, μg/m 3 ;
    CD 0，i—目标区域i的监测浓度，μg/m 3； CD 0, i — the monitored concentration of target area i, μg/m 3 ;
    TBCD i—研究区域外对目标区域i的贡献浓度，μg/m 3； TBCD i —the contribution concentration outside the study area to the target area i, μg/m 3 ;
    TBCDL i—研究区域外对目标区域i的贡献浓度下限，μg/m 3； TBCDL i —the lower limit of the contribution concentration outside the study area to the target area i, μg/m 3 ;
    TBCDU i—研究区域外对目标区域i的贡献浓度上限，μg/m 3； TBCDU i —the upper limit of the contribution concentration outside the study area to the target area i, μg/m 3 ;
    ICD i—待反演区域对目标区域i的贡献浓度，μg/m 3； ICD i —the contribution concentration of the area to be inverted to the target area i, μg/m 3 ;
    ED j—待反演区域j的污染物排放量，t； ED j —the pollutant discharge amount of the area j to be inverted, t;
    TRD j,i—待反演区域j对目标区域i的贡献系数，μg/m 3/(t)； TRD j,i —the contribution coefficient of the area j to be inverted to the target area i, μg/m 3 /(t);
    EDL j—待反演区域j排放的下限，t； EDL j —the lower limit of emissions in the region j to be inverted, t;
    EDU j—待反演区域j排放的上限，t； EDU j —the upper limit of emissions in the region j to be inverted, t;
    i—目标区域；i—target area;
    j—待反演区域；j—the area to be inverted;
    N—待反演区域数量。N—the number of regions to be inverted.
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