A method for pollutant forecasting and tracing based on lagrangian particle transport

Abstract

The present application is a pollution forecasting and tracing method based on Lagrangian particle transport, which is based on the source-receptor relationship (SRR) simulated by the FLEXPART model, combined with the station observation data to correct the emission inventory of pollutants by using the Bayesian optimization method. The real-time prediction of pollutants is carried out by using the corrected emission inventory, which can not only improve the real-time prediction accuracy of pollutants, but also obtain the contribution rate and spatial distribution of each type of pollution source (industry, power, residents and transportation), which can provide corresponding reference information for environmental management, public health protection and national and provincial scale emission reduction decision making.

Description

Technical Field

[0001] This invention relates to the field of pollutant forecasting and source tracing technology, and in particular to a pollutant forecasting and source tracing method based on Lagrange particle transport. Background Technology

[0002] Pollutants have a serious impact on human health. It is extremely important to understand the regional pollution level and formulate reasonable pollution prevention and control measures, but this is inseparable from the site forecasting and source analysis of pollutants.

[0003] Existing technologies that obtain pollutant emission inventories through a "bottom-up" approach suffer from significant uncertainty due to the complexity of emission factors and the time lag in statistical results. Using these inventories to drive atmospheric chemical transport models results in low accuracy for pollutant concentration simulations, making it difficult to meet the pollutant forecasting needs for the next week. Furthermore, commonly used pollution forecasting methods such as WRF-Chemm, CMAQ, and CMAx struggle to simultaneously optimize real-time forecast accuracy at various stations and analyze the sources and contributions of pollutants.

[0004] Therefore, those skilled in the art are dedicated to developing a pollutant forecasting and source tracing method based on Lagrange particle transport to address the shortcomings of the aforementioned existing technologies. Summary of the Invention

[0005] In view of the above-mentioned deficiencies of the prior art, the technical problem to be solved by the present invention is that the future forecast accuracy of pollutants is low and it is difficult to simultaneously analyze the spatial distribution and contribution rate of different pollution sources in the current prior art.

[0006] To achieve the above objectives, this invention provides a pollutant forecasting and source tracing method based on Lagrange particle transport, which includes introducing Bayesian optimization methods into the FLEXPART Lagrange particle transport model to improve the accuracy of site forecasts; and analyzing the contribution of each pollution source (industrial, power, transportation, residential) to the receiver point and the spatial distribution of these pollutant sources by calculating the relationship between receiver points and pollution sources (SRR).

[0007] Furthermore, this invention provides a method for predicting and tracing pollutants based on Lagrange particle transport, comprising the following steps:

[0008] Step 1: Determine the spatial distribution of source-receptor relationships (SRR) using the FLEXPART model to obtain the simulated concentration of pollutants;

[0009] Step 2: Determine whether the pollutant emission inventory has been optimized;

[0010] If optimization is not completed, proceed to step 3-1;

[0011] If optimization has been completed, proceed to step 3-2;

[0012] Step 3-1: Optimize the pollutant emission inventory using a top-down approach; combine receptor point observation data with Bayesian inversion to assimilate the pollutant emission inventory; specifically, optimize the emission flux density by minimizing the mismatch between observed and simulated concentrations to obtain the optimal estimate of pollutant emission flux size x (Mg / grid / month), iterate until all negative emission flux values ​​are greater than 0, and predict the real-time concentration of pollutants based on the posterior optimized flux value to perform real-time pollutant forecasting;

[0013] Step 3-2, Pollutant Source Tracing: Based on the emission proportions of the grid pollutant emission inventory product (MEIC) in industry, power, transportation, and residential sectors, the contribution rate of each pollution source (industry, power, transportation, and residential) to the receiver point and the spatial distribution of these pollutant sources are analyzed by multiplying the pollution source contribution rate of the receiver point by the emission proportion of each grid point.

[0014] Specifically, in step 3-1, the optimal estimated value x is obtained by solving the equation The solution process is shown in Equation 1:

[0015]

[0016] In Equation 1,

[0017] δ o and δ x These represent the standard deviations associated with the observed values ​​and the prior emission inventory, respectively.

[0018]

[0019]

[0020] x、x a and y o Let the pollutant posterior, prior source vector, and observation vector be respectively, and the cost function be described as Equation 2:

[0021]

[0022] In Equation 2,

[0023] M is the SRR source-receptor relationship matrix;

[0024] The real-time concentration of pollutants is predicted based on the optimized flux value from the posterior perspective, as shown in Equation 3.

[0025]

[0026] In Equation 3,

[0027] m is the time series of observations;

[0028] n is the number of emission grids;

[0029] y represents the observed pollutant concentration;

[0030] x represents the magnitude of the pollutant emission flux;

[0031] t represents the time series of the future simulation;

[0032] Specifically, in step 3-2, the calculation formula includes,

[0033] Ft = Ft ind +Ft pow +Ft res +Ft tra (4)

[0034] In Equation 4,

[0035] Ft represents the predicted concentration of pollutants at time t;

[0036] Ft ind This represents the contribution of the industrial source to the predicted concentration at the receptor point at time t;

[0037] Ft pow This represents the contribution of power pollution sources to the predicted concentration at the receptor point at time t;

[0038] Ft res This represents the contribution of resident sources to the predicted concentration at the receptor point at time t;

[0039] Ft tra This represents the contribution of the transport source to the predicted concentration at the receptor point at time t;

[0040]

[0041] In Equation 5,

[0042] Per ind，n This represents the proportion of industrial emissions in the nth emission grid.

[0043] Per pow，n This represents the proportion of electricity emitted by the nth emission grid.

[0044] Per res，n This represents the proportion of residential emissions in the nth emission grid.

[0045] Per tra，n This represents the proportion of traffic emissions from the nth emission grid.

[0046] St n This represents the contribution of the nth emission grid to the receptor point concentration at time t;

[0047] By adopting the above scheme, the pollutant forecasting and source tracing method based on Lagrange particle transport disclosed in this invention has the following advantages:

[0048] (1) The present invention provides a pollutant forecasting and source tracing method based on Lagrange particle transport, which applies the Bayesian optimization method to the FLEXPART Lagrange particle transport model, and further improves the site forecasting accuracy of pollutants by assimilating the emission inventory of pollutants; improves the accuracy of heavy pollution weather forecasting, and provides assistance to decision-making departments to carry out control in advance and prevent and control air pollution.

[0049] (2) The present invention provides a pollutant forecasting and source tracing method based on Lagrange particle transport. By analyzing in real time the sources and spatial distribution of pollution that cause the concentration of pollution at the observation station, the distribution of pollution sources in a pollution process can be clearly understood, and targeted solutions can be proposed to protect human life and property safety.

[0050] In summary, this invention discloses a pollutant forecasting and source tracing method based on Lagrange particle transport. It applies Bayesian optimization to the FLEXPART Lagrange particle transport model, further improving the accuracy of pollutant site forecasts by assimilating the pollutant emission inventory. This enhances the accuracy of heavy pollution weather prediction, providing assistance to decision-making departments in proactive control and prevention of air pollution. Real-time analysis of the pollution sources and spatial distribution causing pollution concentrations at observation stations allows for a clearer understanding of the pollution source distribution during a pollution event, leading to more targeted solutions.

[0051] The following will further explain the concept, specific technical solution and technical effects of the present invention in conjunction with specific embodiments, so as to fully understand the purpose, features and effects of the present invention. Attached Figure Description

[0052] Figure 1 This is a flowchart of a pollutant forecasting and source tracing method based on Lagrange particle transport according to the present invention;

[0053] Figure 2 This is a schematic diagram illustrating the contribution rates of different pollutant emission sources in an embodiment of the present invention;

[0054] Figure 3 This is a schematic diagram showing the spatial distribution of the contribution rates of various pollution sources to the pollution concentration at the Beijing monitoring station, according to an embodiment of the present invention.

[0055] Figure 2 Among them, (a) industrial emission sources; (b) electricity emission sources; (c) residential emission sources; and (d) transportation emission sources;

[0056] Figure 3 In the middle, (a) industry; (b) electricity; (c) housing; (d) transportation. Detailed Implementation

[0057] The following describes several preferred embodiments of the present invention to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, which are described exemplarily, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0058] Example: A pollutant forecasting and source tracing method based on Lagrange particle transport, according to the present invention, is used for pollutant forecasting and source tracing in the Beijing-Tianjin-Hebei region.

[0059] Step 1: Determine the spatial distribution of source-receptor relationships (SRR) using the FLEXPART model to obtain the simulated concentration of pollutants; the 30-day SRR is determined by releasing 10,000 particles from potential sources per hour.

[0060] Step 2: Determine whether the pollutant emission inventory has been optimized;

[0061] If optimization is not completed, proceed to step 3-1;

[0062] If optimization has been completed, proceed to step 3-2;

[0063] Step 3-1: Optimize the pollutant emission inventory using a top-down approach; combine receptor point observation data with Bayesian inversion to assimilate the pollutant emission inventory; specifically, optimize the emission flux density by minimizing the mismatch between observed and simulated concentrations to obtain the optimal estimate of pollutant emission flux size x (Mg / grid / month), iterate until all negative emission flux values ​​are greater than 0, and predict the real-time concentration of pollutants based on the posterior optimized flux value to perform real-time pollutant forecasting;

[0064] The optimal estimate x is obtained by solving the equation The solution process is shown in Equation 1:

[0065]

[0066] In Equation 1,

[0067] δ o and δ x These represent the standard deviations associated with the observed values ​​and the prior emission inventory, respectively.

[0068]

[0069]

[0070] x、x aand y o Let the pollutant posterior, prior source vector, and observation vector be respectively, and the cost function be described as Equation 2:

[0071]

[0072] In Equation 2,

[0073] M is the SRR source-receptor relationship matrix;

[0074] The real-time concentration of pollutants is predicted based on the optimized flux value from the posterior perspective, as shown in Equation 3.

[0075]

[0076] In Equation 3,

[0077] m is the time series of observations;

[0078] n is the number of emission grids;

[0079] y represents the observed pollutant concentration;

[0080] x represents the magnitude of the pollutant emission flux;

[0081] t represents the time series of the future simulation;

[0082] Step 3-2, Pollutant Source Tracing; Based on the grid-based pollutant emission inventory product (MEIC), the proportion of emissions from industry, power, transportation, and residential sectors (e.g., Figure 2 As shown in the figure, the time resolution is monthly, which means that it is assumed that the emission ratio of each pollution source does not change within a month; by calculating the pollution emission source contribution rate of the receiver point multiplied by the emission ratio of each grid point, the contribution of each pollution source (industry, power, transportation, and residential) to the receiver point and the spatial distribution of these pollutant sources are analyzed.

[0083] The calculation formula includes,

[0084] Ft = Ft ind +Ft pow +Ft res +Ft tra (4)

[0085] In Equation 4,

[0086] Ft represents the predicted concentration of pollutants at time t;

[0087] Ft ind This represents the contribution of the industrial source to the predicted concentration at the receptor point at time t;

[0088] Ft pow This represents the contribution of power pollution sources to the predicted concentration at the receptor point at time t;

[0089] Ft res This represents the contribution of resident sources to the predicted concentration at the receptor point at time t;

[0090] Ft tra This represents the contribution of the transport source to the predicted concentration at the receptor point at time t;

[0091]

[0092] In Equation 5,

[0093] Per ind，n This represents the proportion of industrial emissions in the nth emission grid.

[0094] Per pow，n This represents the proportion of electricity emitted by the nth emission grid.

[0095] Per res，n This represents the proportion of residential emissions in the nth emission grid.

[0096] Per tra，n This represents the proportion of traffic emissions from the nth emission grid.

[0097] St n This represents the contribution of the nth emission grid to the receptor point concentration at time t;

[0098] like Figure 3 As shown;

[0099] In summary, this patented technical solution applies the Bayesian optimization method to the FLEXPART Lagrange particle transport model, further improving the accuracy of pollutant site forecasts by assimilating the pollutant emission inventory; it also improves the accuracy of heavy pollution weather forecasts, providing assistance to decision-making departments in early control and prevention of air pollution. By analyzing the pollution sources and spatial distribution that cause the pollution concentration at the observation station in real time, it is possible to gain a clearer understanding of the distribution of pollution sources during a pollution process and propose more targeted solutions.

[0100] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A Lagrangian particle transport based method for pollutant prediction and source apportionment, characterized in that, The method comprises the following steps: Step 1, determining the spatial distribution of source-receptor relationship (SRR) by FLEXPART model to obtain the simulated concentration of pollutants; Step 2, determining whether the pollutant emission inventory has been optimized; If not, proceed to step 3-1; If yes, proceed to step 3-2; Step 3-1, optimizing the pollutant emission inventory by a "top-down" method; assimilating the pollutant emission inventory by using the Bayesian inversion method combined with the observation data of the receptor point; specifically, optimizing the emission flux density by minimizing the mismatch between the observation and the simulated concentration to obtain the best estimated value x of the pollutant emission flux, iteratively calculating until all the negative emission flux values are greater than 0, and then predicting the real-time concentration of the pollutant according to the posterior optimized flux value to perform real-time prediction of the pollutant; Step 3-2, tracing the source of the pollutant; based on the grid pollutant emission inventory product (MEIC) in the industrial, power, transportation and residential emission proportion, the contribution rate of the pollutant emission source of the receptor point is multiplied by the emission proportion of each grid point to analyze the contribution of each pollution source to the receptor point and the spatial distribution of these sources of pollution; In the step 3-1, the best estimate value is obtained by solving the equation as shown in Equation 1: （1） In formula 1, and and represent the standard deviation associated with the observations and the prior emission inventory, respectively; ； ； and are the posterior and prior source vectors of pollutants, respectively, and the observation vector, and the cost function is described as in Equation 2: （2） In formula 2, M is the SRR source-receptor relationship matrix; The prediction of the real-time concentration of the pollutant according to the posterior optimized flux value is as formula 3; （3） In formula 3, m is the time series of observation; n is the number of emission grids; is the pollutant concentration observation; is the pollutant emission flux magnitude; t is the time series of future simulation; In the step 3-2, The calculation formula comprises, （4） In formula 4, represents the predicted concentration of the pollutant at time t; This represents the contribution of the industrial source to the predicted concentration at the receptor point at time t; This represents the contribution of power pollution sources to the predicted concentration at the receptor point at time t; This represents the contribution of resident sources to the predicted concentration at the receptor point at time t; This represents the contribution of the transport source to the predicted concentration at the receptor point at time t; （5） In formula 5, This represents the proportion of industrial emissions in the nth emission grid. This represents the proportion of electricity emitted by the nth emission grid. This represents the proportion of residential emissions in the nth emission grid. represents the proportion of traffic emissions from the nth emission grid; represents the contribution of the nth emission grid to the receptor point concentration at time t.

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