Small-scale high-resolution atmospheric pollution source tracking identification method and system

Abstract

The invention discloses a small-scale high-resolution atmospheric pollution source tracking identification method and system, and the method comprises the steps: transmitting the obtained environment data of a to-be-detected position to an improved random reverse time Lagrange transport model, and carrying out the processing of the data, so as to obtain a small-scale high-resolution atmospheric pollution source result of the to-be-detected position, and carrying out the tracking identification of the small-scale high-resolution atmospheric pollution source. According to the improved random inverse-time Lagrange transport model, a calculation formula of height variation is obtained by using a new vertical interpolation calculation method on the basis of a Lagrange atmospheric pollution diffusion model, and a relationship between the random inverse-time Lagrange transport model and WRF is established; small-scale high-resolution atmosphere pollution source tracking identification is realized through a diffusion method of a random reverse-time Lagrange transport model, and the model is a backward time simulation model based on the random-time reverse-time Lagrange transport model, so that the efficiency is improved compared with forward time simulation.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring technology, and in particular to a small-scale and high-resolution atmospheric pollution source tracking and identification method and system. Background Art

[0002] After different types of pollutants emitted by various industries and various types of air pollution sources enter the atmosphere, they go through transmission, physical and chemical processes, and affect the concentrations of different types of pollutants in different regions and locations at different times. The impact mechanism is both physical and chemical, and the impact mechanism and degree of impact are different at different times, under different meteorological conditions, and at different locations. The concentration of air pollutants at the same location may be affected by tens of millions of pollution sources. In order to improve air quality and reduce the concentration of pollutants in the air, it is necessary to track and identify air pollution sources and accurately evaluate the degree of impact of each pollution source, so as to scientifically and specifically control the emissions of air pollution sources, efficiently reduce the degree of air pollution, and save governance costs.

[0003] Atmospheric pollution sources include small-scale and high-resolution atmospheric pollution sources, which mainly include industrial enterprises, domestic stoves and heating boilers, transportation, etc. These small-scale and high-resolution atmospheric pollution sources have a significant impact on the quality of the atmospheric environment due to their universality and persistence. Therefore, effective measures need to be taken to control and manage them in order to reduce harm to human comfort, health and welfare as well as the environment. However, in the prior art, there are no methods and systems for tracking and identifying small-scale and high-resolution atmospheric pollution sources. Therefore, the problem of tracking and identifying small-scale and high-resolution atmospheric pollution sources has become an issue that urgently needs to be solved in the production process of enterprises. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a small-scale and high-resolution atmospheric pollution source tracking and identification method and system to solve the problem that small-scale and high-resolution atmospheric pollution sources cannot be tracked and identified in the prior art.

[0005] To achieve the above objectives, the present invention provides a small-scale and high-resolution atmospheric pollution source tracking and identification method. Specifically, the method comprises the following steps:

[0006] 1) Obtaining environmental data at the location to be measured; the environmental data includes air quality monitoring data and meteorological data;

[0007] 2) Transmit the environmental data to the improved stochastic reverse-time Lagrangian transport model to obtain the small-scale and high-resolution atmospheric pollution source results at the location to be measured;

[0008] The improved random reverse Lagrangian transport model is based on the Lagrangian atmospheric pollution diffusion model, and the original calculation formula for calculating the height change using the equal height equation is converted into a calculation formula for obtaining the height change using a new vertical interpolation calculation method, so as to establish the relationship between the random reverse Lagrangian transport model and WRF, and replace the original standard HYSPLIT diffusion algorithm with the diffusion method of the random reverse Lagrangian transport model;

[0009] The new vertical interpolation method uses the same equations in the WRF model to calculate layer thickness.

[0010] The method of the present invention has the following advantages: the method of the present invention obtains environmental data, and processes the environmental data using an improved random reverse Lagrangian transport model based on the Lagrangian atmospheric pollution diffusion model, and the model establishes a connection between the random reverse Lagrangian transport model and WRF by using a new interpolation method to achieve a tight coupling between STILT (random reverse Lagrangian transport model) and WRF, and then the atmospheric pollution source result can be obtained by using the diffusion method of the random reverse Lagrangian transport model, that is, the method of the present invention can achieve the tracking and identification of small-scale high-resolution atmospheric pollution sources. And the time used for the forward time simulation is a hundred times the time used for the backward time simulation, and the model used in the present invention is a random reverse Lagrangian transport model, which is based on the random time inverse Lagrangian transport model and is a backward time simulation model, so the method of the present invention is more efficient in terms of efficiency.

[0011] Furthermore, the equation of the new vertical interpolation calculation method is: Among them, Δz is the height change, μ d is the WRF dry mass, σ top and σ bot are the top and bottom sigma levels, μ d =p dhs -p dht , Where σ is the sigma level, p dh 、p dhs and p dht are the dry hydrostatic pressures at the target location, the surface, and the top of the model, respectively.

[0012] Furthermore, the diffusion method of the random reverse-time Lagrangian transport model is as follows: when particles are transported to the model, the original time step stops and a new time step starts, so as to adopt a fractional time step.

[0013] Furthermore, the small-scale and high-resolution atmospheric pollution source results are particle position information text files.

[0014] Furthermore, the particle position information text file is displayed in one or more forms of a migration diagram, a pie chart, and a bar chart.

[0015] To achieve the above-mentioned purpose, the present invention also provides a small-scale high-resolution atmospheric pollution source tracking and identification system. Specifically, the system includes a data acquisition module, a data processing module and a display module. The data acquisition module is used to transmit the acquired environmental data at the position to be measured to the data processing module, and the environmental data includes air quality monitoring data and meteorological data. The data processing module is used to transmit the small-scale high-resolution atmospheric pollution source results at the position to be measured obtained after data processing to the display module for display. The data processing module includes an improved stochastic reverse-time Lagrangian transport model;

[0016] The improved random reverse Lagrangian transport model is based on the Lagrangian atmospheric pollution diffusion model, and the original calculation formula for calculating the height change using the equal height equation is converted into a calculation formula for obtaining the height change using a new vertical interpolation calculation method, so as to establish the relationship between the random reverse Lagrangian transport model and WRF, and replace the original standard HYSPLIT diffusion algorithm with the diffusion method of the random reverse Lagrangian transport model;

[0017] The new vertical interpolation method uses the same equations in the WRF model to calculate layer thickness.

[0018] Furthermore, the equation of the new vertical interpolation calculation method is: Among them, Δz is the height change, μ d is the WRF dry mass, σ top and σ bot are the top and bottom sigma levels, μ d =p dhs -p dht , Where σ is the sigma level, p dh 、p dhs and p dht are the dry hydrostatic pressures at the target location, the surface, and the top of the model, respectively.

[0019] Furthermore, the diffusion method of the random reverse-time Lagrangian transport model is as follows: when particles are transported to the model, the original time step stops and a new time step starts, so as to adopt a fractional time step.

[0020] Furthermore, the small-scale high-resolution atmospheric pollution source results are text files of particle position information.

[0021] Furthermore, the display module is used to display the particle position information text file in one or more forms of a migration diagram, a pie chart, and a bar chart.

[0022] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the following specifically cites a preferred embodiment and describes it in detail with the accompanying drawings as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a schematic diagram of the framework of the improved stochastic reverse-time Lagrangian transport model of the present invention.

[0024] Figure 2 This is a pollution source analysis diagram obtained by using the method of the present invention.

[0025] Figure 3 This is a time series diagram of regional source analysis obtained by using the method of the present invention.

[0026] Figure 4 This is a time series diagram of pollutant industry source analysis obtained by using the method of the present invention. DETAILED DESCRIPTION

[0027] The technical scheme of the present invention will be clearly and completely described below in conjunction with specific embodiments, but those skilled in the art should understand that the embodiments described below are only used to illustrate the present invention and should not be regarded as limiting the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Small-scale and high-resolution atmospheric pollution source tracking and identification system embodiment

[0029] The system of this embodiment includes a data acquisition module, a data processing module and a display module. The data acquisition module is used to transmit the environmental data acquired at the test location to the data processing module, and the environmental data includes air quality monitoring data and meteorological data. The data processing module is used to transmit the small-scale and high-resolution atmospheric pollution source results at the test location obtained after data processing to the display module for display.

[0030] The data processing module of this embodiment processes the corresponding data using an improved random reverse-time Lagrangian transport model to obtain small-scale and high-resolution atmospheric pollution source results, thereby realizing the tracking and identification of small-scale and high-resolution atmospheric pollution sources.

[0031] like Figure 1The above is a schematic diagram of the framework of the improved stochastic reverse-time Lagrangian transport model in this embodiment, which specifically adopts STILT diffusion, WRF vertical interpolation, Hanna turbulence and Lagrangian time scale, no convection, boundary layer stability estimated by heat and momentum fluxes, and a new STILT format mixing layer depth calculated using the improved Richardson number.

[0032] The system of this embodiment applies the stochastic inverse time Lagrangian transport (STILT) model, which is a tool for determining surface fluxes from atmospheric concentration data between distributed sources or sinks on land. STILT simulates the impact of upstream on the observation location (receptor) by tracking the evolution of the particle system backward in time. The analyzed wind field is interpolated to the position of each particle, and based on the observed meteorological parameters (local meteorological conditions such as temperature, humidity, wind speed, wind direction, sunshine, etc. are called meteorological parameters) statistics, the Markov chain process is used to simulate turbulent motion.

[0033] 1) Theory of the tracking and identification method used in this embodiment - Lagrangian particle diffusion model:

[0034] The stochastic reverse-time Lagrangian transport model is based on the assumption that turbulent diffusion can be modeled using a Markov chain: the particle velocity vector u can be decomposed into the average components and turbulent component u′, the turbulent component is as follows:

[0035] u′(t+Δ)=R(Δt)u′(t)+u"(t), (1)

[0036] Where u" is a random vector, R is the autocorrelation coefficient, and Δt is the time step. The autocorrelation coefficient R in formula (1) is in exponential form R(Δt) = exp(-Δt / T L i), where T L i is the Lagrangian time scale in the i direction (i = u, horizontal; i = w, vertical). L i is a decorrelation time scale, which determines the randomness of the particle motion (T Li = 0) or downwind advection (T Li The random velocity u" in formula (1) is defined by the relationship:

[0037] u″=λ[1-R 2 (Δt)] 1 / 2 , (2)

[0038] where λ is usually taken from a Gaussian distribution with mean 0 and standard deviation σi, representing the distribution of random velocities. Turbulence is characterized by the properties of the set of particle trajectories obtained by integrating equation (1).

[0039] The total amount of tracer at the source is divided equally into the number of particles, and the particle density at the receptor is the tracer concentration. If time is reversible, a reverse time run starting at the receptor will produce the same number of particles at the source. Therefore, emissions with more particles upstream will affect the receptor concentration, and the particle density obtained from the reverse time simulation provides the "influence density": in the reverse time model, the position and time of the particles, the tracer concentration at the receptor varies with the flux, and a receptor-oriented framework can be implemented that defines the upstream influence on the receptor tracer observation. The backward time particle position maps the influence function I(x r ,t r |x,t), we can quantitatively relate the source S(x,t) to the position x r and time t r The tracer concentration C(x r ,t r ) to contact:

[0040]

[0041] Where I(x r ,t r |x,t) is in 1 volume -1 ,C(x r ,t r ) is the mixing ratio unit, for example, ppm, and S(x,t) is the mixing ratio unit per unit time (ppm*h -1 ). Assume that the fluid element is at x and time t, I(x r ,t r |x,t) represents the position x r and time t r The first term on the RHS of equation (3) represents the initialization time t0 and t r The second term represents the contribution of tracer advection from the initial tracer field C(x,t0) to the receptor concentration.

[0042] Influence function I(x r ,t r |x,t) and the particle position are formally established as follows:

[0043] I(x r ,t r |x,t) is represented by (x,t) from (x r ,t r ) The density of the particles transferred in the opposite direction r(x r ,t r |x,t), using the total number of particles N tot Normalization:

[0044]

[0045] The function in equation (4) simply indicates whether particle p exists at position x; I(x r ,t r The domains of S(x,t) and S(x,t) are continuous in space and time, and can only be replaced by discrete volumes (Δx, Δy, Δz) and finite time intervals (τ) at finite resolution. Integrating over finite volume and time units, applying equation (4), the influence function is:

[0046]

[0047] Therefore, the time- and volume-integrated influence functions can be simply calculated by Δt p,m,i,j,k to quantify the total time each particle p spends in volume cell i, j, k at time step m. Equation (5) provides the source-receptor matrix elements, which directly relate the sources with finite spatiotemporal resolution to the receptor concentrations via equation (3).

[0048] "Surface" flux F(x,y,t) (in moles time -1 ) and S(x,t)(unit is mixing ratiotime -1 ) can be established by diluting the surface tracer flux into an atmospheric column of height h where the turbulent mixing intensity is sufficient to thoroughly mix with the surface signal in one time step, as follows:

[0049]

[0050] In the formula is the average density below h, m air is the molar mass of air. Given the discrete surface flux F(x i ,y j ,t m ), connecting the surface flux to the receptor concentration ΔC m,i,j (x r ,t r ) changes in the "footprint" element f(x r ,t r |x i ,y j ,t m ) can be derived as follows. Replace equation (6) with the first term S(x, t) on the RHS of equation (3) and integrate the discrete volume and time elements to obtain:

[0051]

[0052] Surface flux F(xi ,y j ,t m The unit of ) is (μmol·m -2 ·s -1 ), ΔC is in ppm, f(x r ,t r |x i ,y j ,t m ) is in ppm (μmol -1 ·m 2 ·s), f(x r ,t r |x i ,y j ,t m ) can be derived from the particle position by combining the expressions in brackets in equations (5) and (7):

[0053]

[0054] The forward time simulation takes a hundred times longer than the backward time simulation, and the demonstration of time reversibility in the model shows that a single backward time simulation generates I(x r ,t r |x,t) and I(x) generated by multiple forward time runs r ,t r |x,t) are the same, which significantly saves the computational workload.

[0055] 2) Stochastic Inverse Lagrangian Transport (STILT) model calculation method:

[0056] The STILT model is built on the source code of the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) system and uses the mean advection scheme of HYSPLIT, but with a different turbulence module. The turbulence parameterization is described as follows:

[0057] Assume the horizontal Lagrangian time scale T Lu is the 3-hour speed. The parameter T that controls the vertical turbulence Lw and σ w It is expressed as a function of the boundary layer height, roughness length, Monin-Obukhov length, convective velocity scale, and friction velocity. Due to the general unavailability of atmospheric boundary layer height (PBL) in meteorology, the parameters of the atmospheric boundary layer height have been modified to specify the atmospheric boundary layer height as the height where the potential temperature is 2°K greater than the ground temperature. The new parameterization is a modified Richardson number method generalized to unstable, neutral, and stable conditions. The new parameterization significantly reduces the bias of the predicted PBL (boundary layer height).

[0058] STILT uses a unique way to transfer particles between vertical levels to meet the well-mixed criterion. It is worth noting that in inhomogeneous turbulent environments, most numerical treatments violate the well-mixed criterion by allowing initially well-mixed particles to accumulate in low-turbulence regions. We adopt a reflection / transmission scheme for Gaussian turbulence to meet the well-mixed criterion in the strongly inhomogeneous environment of PBL. In STILT, σ w and T Lw The vertical variation of w is approximated by discontinuous layers of constant value. A typical configuration of STILT has 17 layers below 10 km altitude, with denser coverage near the ground (5 layers below 1 km). i , from the lower layer (z i- ) and upper layer (z i+ ) below the interface between the particles, with a vertical velocity of w i The particles from below (z i- ) and above (z i+ ) approaches below the interface between the layers with probability A and speed w t Random transmission, or w i reflection:

[0059]

[0060] The same applies to particles moving downward and approaching the interface from above by reversing the up and down variables. The air density is represented by r. Equation (9) ensures that the initially well-mixed particles remain well-mixed in a Gaussian turbulent environment.

[0061] 3) Fusion of the stochastic inverse-time Lagrangian transport (STILT) model and the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model:

[0062] The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model has multiple user options to estimate the stability of the atmosphere, calculate velocity variance through turbulence parameterization, determine the depth of the mixing layer, and simulate turbulent particle motions. The model can simulate particle emission, transport, diffusion, deposition, and chemical reactions in a Lagrangian framework by directly calculating turbulent particle motions or by calculating statistical distributions.

[0063] The Stochastic Inverse Time Lagrangian Transport (STILT) model is a Lagrangian particle diffusion system used to connect receptors to upwind sources. STILT has been widely used to interpret greenhouse gas observations and estimate their emissions. STILT consists of two parts: 1) an atmospheric diffusion module written in Fortran code that simulates particle transport; and 2) a high-level module written in R that manages batch simulations, provides single-node and multi-node parallelization methods, and processes the output of the Fortran executable to determine source-receptor relationships and estimate emissions.

[0064] 3.1) WRF vertical interpolation scheme.

[0065] HYSPLIT uses a new vertical interpolation scheme specifically for meteorological input files derived from Weather Research and Forecasting (WRF) model output files. This scheme simulates the vertical coordinate transformation in the WRF model as part of the tight coupling achieved between STILT and WRF by using the same equations in the WRF model to calculate layer thicknesses. The vertical coordinate system in WRF is a topographically variable dry hydrostatic pressure coordinate system defined as:

[0066]

[0067] Where σ is the sigma level, p dh 、p dhs and p dht are the dry hydrostatic pressures at the target location, the surface, and the top of the model, respectively.

[0068] In the original HYSPLIT solution, the height change Δz is calculated using the constant height equation:

[0069]

[0070] Where P bot and P top are the pressure at the bottom and top of the layer respectively; R dry is the dry gas constant (287 J K -1 kg -1 );T vtop and T vbot are the virtual temperatures at the top and bottom of the layer, respectively; g is gravity. The Δz in the new STILT scheme is estimated as:

[0071]

[0072] Where μ d is the dry mass of WRF (dry hydrostatic surface pressure minus WRF top pressure); σ top and σ bot are the top and bottom sigma levels respectively; α d is the WRF dry inverse density.

[0073] 3.2) Calculation of mixing layer depth.

[0074] The new method for estimating boundary layer depth in HYSPLIT uses a modified Richardson number method that includes the excess temperature in the convective case as a function of the virtual potential temperature, friction velocity, and convective vertical velocity. In this method, the mixing height is given by the modified volumetric Richardson number R i estimate:

[0075]

[0076] θ v and θ vsc are the virtual temperature at height z and the superheat temperature caused by surface convection; under stable conditions, θ vsc =θ s (surface potential temperature), under unstable conditions, θ vsc Including package overtemperature:

[0077]

[0078] ρ sl and ρ z are the densities at the top of the surface and at height z, respectively; u * is the friction speed; T * is the friction temperature; w * is the convection velocity scale. T * and w * Defined as:

[0079] T * =-H(ρC p u * ) -1

[0080] w * =|gu * T * z i T -1 | 1 / 3 ,

[0081] Where H is the sensible heat flux, C p is the thermal constant of dry air, T is the temperature. The mixing height is set to height z, at which R i Equal to the critical Richardson number R icr =0.25.

[0082] The existing options for the mixed layer depth used in HYSPLIT include: (1) using the mixed layer depth directly from the meteorological input file, (2) estimating the mixed layer depth using temperature profiles, and (3) estimating the mixed layer depth using TKE profiles.

[0083] 3.3) Hanna turbulence parameterization.

[0084] The Hanna turbulence parameterization has been incorporated into HYSPLIT. The vertical velocity variance calculated using the Hanna format is derived from the friction velocity, the convective velocity scale, and the mixing layer height. When the Obukhov stability length scale L is greater than or less than zero, the vertical velocity variance w is defined ′2 for:

[0085]

[0086] When the Obukhov length scale is less than zero, the vertical velocity variance is defined as follows: If z / z i <0.3,

[0087]

[0088] If 0.3 ≤ z / z i <0.4:

[0089]

[0090] If 0.4 ≤ z / z i <0.96:

[0091]

[0092] If z / z i ≥0.96:

[0093]

[0094] Where L is the Obukhov length. HYSPLIT does not distinguish between the velocity variances of the tailwind and the crosswind, assuming that the u and v velocity variances are the same. When the Hanna format is used to calculate the vertical velocity variance, the horizontal velocity variance is defined as:

[0095] u′ 2 =υ′ 2 =0.5w′ 2 .

[0096] HYSPLIT assumes that the u and v velocity variances are the same for all existing turbulence parameterizations within the model.

[0097] Existing turbulence parameterizations available in HYSPLIT include: (1) Beljaars-Holtslag scheme, (2) Kantha-Clayson scheme and (3) TKE scheme.

[0098] 3.4) Hanna vertical Lagrangian time scale.

[0099] The Hanna vertical Lagrangian timescale has been incorporated into HYSPLIT. The Hanna vertical Lagrangian timescale varies in space and time. The Hanna vertical Lagrangian timescale can be used with all turbulence parameterizations in HYSPLIT. Under steady-state conditions, the Hanna vertical Lagrangian timescale T is defined. Lw for:

[0100]

[0101] For unstable conditions, if And z-z0>-L, T Lw for:

[0102]

[0103] if And z-z0<-L, T Lw for:

[0104]

[0105] In other cases:

[0106]

[0107] where z0 is the aerodynamic roughness length.

[0108] 3.5) STILT diffusion scheme.

[0109] The STILT diffusion scheme differs from the standard HYSPLIT diffusion algorithm in that it uses a fractional time step. The transmission / reflection scheme of Thomson et al. includes a very thin model layer above the mixing layer of the diffusion scheme in order to maintain a well-mixed distribution of particles and prevent particles from being trapped above the mixing layer. This is summarized below.

[0110] Each time a particle is transported up or down to a model level interface, the time step is stopped and a new time step is started, using a fractional time step. When particles reach interfaces between vertical levels of the model, the transmission / reflection scheme of Thomson et al. is used to maintain a well-mixed distribution of particles across the interface. For the vertical turbulent velocity w′ i When the upward moving particles reach the model interface, the ratio α up When it rises to a random number greater than 0 to 1, the particle moves at a speed of w' tup propagates, otherwise at a vertical speed of -w′ i Reflection from the interface:

[0111]

[0112] Where ρ is the density, zi+ and z i- Represent the interface above and below the current grid cell respectively. For particles moving downward to the model interface, if the ratio α dp is greater than a random number between 0 and 1, the particle moves at a speed of w′ tdn transmission, otherwise at speed -w′ i reflection:

[0113]

[0114] 3.6) Convection

[0115] Two new convection schemes have been adapted from STILT and incorporated into HYSPLIT. The second convection scheme incorporated into HYSPLIT is the Grell convection scheme. The existing convection options in HYSPLIT are extreme convection schemes based on a minimum CAPE threshold. When the CAPE exceeds a user-defined minimum threshold, rapid convective mixing is simulated. The extreme convection method vertically mixes particles in all grid cells with a positive CAPE throughout the unstable layer defined by the convection limit. This scheme randomly assigns vertical positions below the Z LOC The particles are at the surface and the convective height limit Z LOC The random redistribution is weighted by density. This scheme assumes that the updrafts and downdrafts in the convection are strong enough to leave a perfectly mixed column after each convective event. The scheme aims to provide an upper bound on the effect of subgrid-scale vertical redistribution caused by convective cloud transport.

[0116] 4) Conduct application evaluation on the model of this embodiment.

[0117] HYSPLIT model simulations were run with new options for the STILT model and evaluated with tracer releases observed in the Trans-Appalachian Tracer Experiment (CAPTEX) and the Trans-North American Experiment (ANATEX).

[0118] HYSPLIT simulations are evaluated using statistical ranks. The ranks include (1) the linear correlation coefficient R2; (2) the fractional bias (FB), which defines a standardized measure of bias; (3) the spatial figure of merit (FMS), which defines the percentage of overlap between the measured and predicted areas; and (4) the Kolmogorov-Smirnov parameter (KS), which defines the maximum interpolation between two cumulative distributions. These four statistical parameters are normalized to between 0 and 1 and then summed:

[0119]

[0120] Ranks range from 0 (worst) to 4 (exactly consistent with observations), and two simulations are statistically significantly different if the ranks differ by about 0.1.

[0121] The results show that the new diffusion scheme of STILT is more suitable than the simulation of the HYSPLIT diffusion module, but requires more computing resources. In the vertical direction, the STILT diffusion is smaller than the HYSPLIT diffusion due to the inclusion of the Thomson reflection / transmission scheme, which reduces the outgassing of the boundary layer. The HYSPLIT simulation results of the tracer released in the boundary layer show that the STILT scheme simulates higher surface concentrations than the HYSPLIT diffusion scheme.

[0122] The STILT model is primarily used to track air parcels back in time, identifying upstream source areas that influence tracer concentrations. The updated model reproduces the capability of forward-time simulations initialized at CAPTEX tracer release locations using backward-time simulations initialized at CAPTEX measurement locations.

[0123] When running HYSPLIT in STILT mode, the masses of the particles are summed and divided by the density to output a mixing ratio, the minimum concentration and layer are optionally varied with the mixed layer depth, and a text file of particle position information is output. HYSPLIT is run with STILT diffusion, WRF vertical interpolation, Hanna turbulence and Lagrangian time scales, no convection, boundary layer stability estimated from heat and momentum fluxes, and a new STILT format mixed layer depth calculated using an improved Richardson number.

[0124] The reversibility test shows that the reverse diffusion simulation has similar performance characteristics to the forward diffusion simulation, that is, the accuracy of the system of this embodiment can be guaranteed.

[0125] The system of this embodiment is established based on the STILT model of the Lagrangian atmospheric pollution diffusion model. The uniqueness of the STILT model is that, as a backward trajectory model, it can reversely simulate the movement trajectory of air mass particles at any time and place. STILT estimates the source of particles under specific weather indicators based on the weather model, and wraps the particles as the model time goes backwards. Similar to the forward trajectory, there is an additional random component on the air mass, which means that the path of each air mass is different. However, this random component is quite small, and the movement of the air mass will maintain the same direction and speed. In the atmospheric pollution source tracking and identification system, the anti-phase trajectory path set of thousands of groups is mapped as the source area.

[0126] The system of this embodiment has the following functions: 1. Integration of multiple data sources: The atmospheric pollution source tracking and identification system uses publicly available online maps as the basis for interactive visualization, integrating multiple air quality monitoring and meteorological data sets, including: air quality monitoring data at the district, county, town and street scale, high-resolution rapidly updated assimilated meteorological models, etc. 2. Real-time pollution source tracing: The backward trajectory of polluted air masses at any point at any time can be tracked and identified, and the monitoring site data and pollution source information can be superimposed to quickly identify the type of pollution, lock the target, and implement targeted governance. 3. The air quality data, meteorological data and potential source areas in the upwind direction of any time period in the past can be analyzed to gain a deeper understanding of the pollution patterns in high-value areas.

[0127] The atmospheric pollution source tracking and identification system can track primary pollutants, such as PM2.5, NO2, VOCs, etc. It can also track secondary pollutants, such as O3.

[0128] Based on the above analysis, the system of this embodiment is established based on the STILT model of the Lagrangian atmospheric pollution diffusion model. The uniqueness of the STILT model is that, as a backward trajectory model, it can reversely simulate the movement trajectory of air mass particles at any time and place. STILT estimates the source of particles under specific weather indicators based on the weather model, and wraps the particles as the model time goes backwards. Similar to the forward trajectory, there is an additional random component on the air mass, which means that the path of each air mass is different. However, this random component is quite small, and the movement of the air mass will maintain the same direction and speed. In the atmospheric pollution source tracking and identification system, the anti-phase trajectory path set of thousands of groups is mapped to the source area.

[0129] The display module in the system of this embodiment can display the pollutant source analysis, regional source analysis and industry source analysis, and can display the results in the form of migration diagrams, pie charts or bar charts. The specific display content is as follows: Figure 2 , Figure 3 as well as Figure 4 shown.

[0130] like Figure 2The figure shows a schematic diagram showing the pollution source analysis. Pollution source identification and tracking displays pollution source analysis in the form of migration diagrams and pie charts. The pollutants, resolution, time and receptors in the display interface can be modified to obtain pollution source analysis schematics for multiple pollutants under different circumstances, realizing custom pollutants, time resolution, time and receptors. The migration diagram intuitively shows the path of particle transmission. The pie chart displays content including regional source analysis and industry source analysis. Specifically, the regional source pie chart shows multiple areas of pollutant sources, and uses the area size to intuitively show the main areas of pollutant sources. The industry source pie chart shows multiple industry sources in a specific area, and also intuitively shows the main industries of pollution sources in the area, so that the pollution source can be clearly determined based on the displayed content.

[0131] like Figure 3 The figure shows a schematic diagram showing regional source analysis. The regional identification of pollution source identification tracking displays a time series diagram of regional source analysis in the form of a bar chart. The pollutant type, time resolution, and start and end time in the display interface can be modified to obtain regional source analysis of different pollutants in different time periods. The bar chart of regional source analysis analyzes the pollution sources from different cities and the pollution sources from different regions in the same city. In the bar chart of pollution sources from different cities, the pollution sources of different cities in different time periods are displayed. Different colors are used in the columns in each specific time period to represent the pollution sources of different cities. The larger the area of ​​the same column color in the same time period, the more urban pollution sources are represented by the column color. In the bar chart of pollution sources from different regions of the same city, the pollution sources of different regions in the same city in different time periods are displayed. Different colors are used in the columns in each specific time period to represent the pollution sources of different regions in the same city. The larger the area of ​​the same column color in the same time period, the more regional pollution sources are represented by the column color.

[0132] like Figure 4The following is a schematic diagram showing the industry source analysis. The industry identification of pollution source identification tracking displays the time series diagram of pollution source analysis in the form of a bar chart. You can customize pollutants, time resolution, and start and end time to obtain industry source analysis of different pollutants in different time periods. The bar chart of industry source analysis analyzes the pollution industry sources from a city and the pollution industry sources from a certain area of ​​the city. The bar chart of pollution industry sources from a city shows the pollution sources of different industries in the city at different time periods. Different colors are used in the bar chart to represent the pollution sources of different industries. The larger the area of ​​the same column color in the same time period, the more pollution sources of the industry in this city represented by the column color. The bar chart of pollution industry sources from a certain area of ​​the city shows the pollution sources of different industries in a certain area of ​​the city at different time periods. Different colors are used in the bar chart to represent the pollution sources of different industries. The larger the area of ​​the same column color in the same time period, the more pollution sources of the industry in this city represented by the column color.

[0133] Small-scale and high-resolution atmospheric pollution source tracking and identification method embodiment

[0134] The method of the present invention includes that the specific method flow and the improved stochastic reverse-time Lagrangian transport model have been introduced in detail in the embodiment of the small-scale high-resolution atmospheric pollution source tracking and identification system, and will not be repeated here.

[0135] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A small-scale and high-resolution atmospheric pollution source tracking and identification method, characterized in that: The steps include: 1) Obtaining environmental data at the location to be measured, wherein the environmental data includes air quality monitoring data and meteorological data; 2) Transmit the environmental data to the improved stochastic reverse-time Lagrangian transport model to obtain the small-scale and high-resolution atmospheric pollution source results at the location to be measured; The improved random reverse Lagrangian transport model is based on the Lagrangian atmospheric pollution diffusion model, and the original calculation formula for calculating the height change using the equal height equation is converted into a calculation formula for obtaining the height change using a new vertical interpolation calculation method, so as to establish the relationship between the random reverse Lagrangian transport model and WRF, and replace the original standard HYSPLIT diffusion algorithm with the diffusion method of the random reverse Lagrangian transport model; The new vertical interpolation method uses the same equations in the WRF model to calculate layer thickness.

2. The small-scale and high-resolution atmospheric pollution source tracking and identification method according to claim 1 is characterized in that: The equation of the new vertical interpolation calculation method is: Among them, Δz is the height change, μ d is the WRF dry mass, σ top and σ bot are the top and bottom sigma levels, μ d =p dhs -p dht , Where σ is the sigma level, p dh 、p dhs and p dht are the dry hydrostatic pressures at the target location, the surface, and the top of the model, respectively.

3. The small-scale and high-resolution atmospheric pollution source tracking and identification method according to claim 1 is characterized in that: The diffusion method of the stochastic reverse-time Lagrangian transport model is as follows: when a particle is transported to the model, the original time step is stopped and a new time step is started, so as to adopt a fractional time step.

4. The small-scale and high-resolution atmospheric pollution source tracking and identification method according to claim 1 is characterized in that: The small-scale high-resolution atmospheric pollution source result is a particle position information text file.

5. The small-scale and high-resolution atmospheric pollution source tracking and identification method according to claim 4 is characterized in that: The particle position information text file is displayed in one or more forms of a migration graph, a pie chart, and a bar chart.

6. A small-scale high-resolution atmospheric pollution source tracking and identification system, comprising a data acquisition module, a data processing module and a display module, wherein the data acquisition module is used to transmit the acquired environmental data at the position to be measured to the data processing module, the environmental data including air quality monitoring data and meteorological data, and the data processing module is used to transmit the small-scale high-resolution atmospheric pollution source results at the position to be measured obtained after data processing to the display module for display, characterized in that: The data processing module includes an improved stochastic reverse-time Lagrangian transport model; The improved random reverse Lagrangian transport model is based on the Lagrangian atmospheric pollution diffusion model, and the original calculation formula for calculating the height change using the equal height equation is converted into a calculation formula for obtaining the height change using a new vertical interpolation calculation method, so as to establish the relationship between the random reverse Lagrangian transport model and WRF, and replace the original standard HYSPLIT diffusion algorithm with the diffusion method of the random reverse Lagrangian transport model; The new vertical interpolation method uses the same equations in the WRF model to calculate layer thickness.

7. The small-scale and high-resolution atmospheric pollution source tracking and identification system according to claim 6 is characterized in that: The equation of the new vertical interpolation calculation method is: Among them, Δz is the height change, μ d is the WRF dry mass, σ top and σ bot are the top and bottom sigma levels, μ d =p dhs -p dht , Where σ is the sigma level, p dh 、p dhs and p dht are the dry hydrostatic pressures at the target location, the surface, and the top of the model, respectively.

8. The small-scale and high-resolution atmospheric pollution source tracking and identification system according to claim 6 is characterized in that: The diffusion method of the stochastic reverse-time Lagrangian transport model is as follows: when a particle is transported to the model, the original time step is stopped and a new time step is started, so as to adopt a fractional time step.

9. The small-scale and high-resolution atmospheric pollution source tracking and identification system according to claim 6 is characterized in that: The results of small-scale and high-resolution atmospheric pollution sources are text files with particle position information.

10. The small-scale and high-resolution atmospheric pollution source tracking and identification system according to claim 9 is characterized in that: The display module is used to display the particle position information text file in one or more forms of a migration diagram, a pie chart, and a bar chart.

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