A method, system, device and medium for tracing atmospheric pollutants in an urban near field

Abstract

The application discloses a kind of urban near-field atmospheric pollutant tracing method, system, equipment and medium, belong to near-field atmospheric pollutant tracing technical field.The method includes: based on high-resolution remote sensing and field observation data, construct the three-dimensional parameterized model of urban heterogeneous land surface;Using Reynolds average-based Navier-Stokes equation and turbulence model, obtain the high-resolution meteorological flow field data affected by topography and building;The meteorological flow field data calculated is input Lagrangian diffusion model, simulates the diffusion process of pollution particles in non-uniform wind field;With observation point as particle release location, release particle and track particle in atmospheric flow field in reverse propagation track, simulation obtains the contribution source area range of observation point, quantitatively estimates the contribution of each source point to observation point pollution concentration.The method effectively captures the transmission and diffusion path of pollutants in complex urban environment, provides scientific basis for urban near-field regional pollution tracing.

Description

Technical Field

[0001] This invention relates to the field of near-field air pollutant source tracing technology, and in particular to a method, system, equipment and medium for tracing near-field air pollutants in urban areas. Background Technology

[0002] In recent years, with the rapid pace of industrialization and urbanization, air pollution has become increasingly severe, especially in urban near-field areas. Pollutant emissions exhibit uneven spatial and temporal distribution and localized spikes in concentration, seriously impacting residents' health and the ecological environment. Traditional regional-scale numerical simulation methods for air pollution often employ low spatial resolution, making it difficult to accurately capture the concentration changes and transport and diffusion characteristics of near-field air pollutants caused by local emission sources in complex urban environments. Therefore, there is an urgent need to develop a high-resolution numerical simulation technology capable of tracing near-field pollutant sources at the microscale, in order to provide a scientific basis for atmospheric environmental monitoring, pollution early warning, and precise governance.

[0003] Currently, numerical simulation of air pollution has formed a relatively mature technical system. Research in Europe and North America, for example, has continuously improved numerical models in simulating atmospheric chemical and physical processes and transport and diffusion, developing mesoscale models such as WRF-Chem, GEOS-Chem, and CMAQ, which can effectively reflect the transport, transformation, and deposition processes of air pollutants at regional scales. Meanwhile, the HYSPLIT and FLEXPART models based on the Lagrangian method track the movement of individual pollution particles in the atmosphere using particle tracking algorithms, more intuitively reflecting the migration paths and diffusion characteristics of pollutants, but they rely heavily on high-resolution meteorological data. Therefore, related research combines mesoscale models with Lagrangian algorithms. By calculating accurate meteorological fields through mesoscale models, the Lagrangian models are driven to simulate the migration and diffusion of air pollutants, achieving refined simulation and source tracing of air pollutants.

[0004] While this method can effectively describe large-scale pollutant transport and diffusion processes, it has limitations in handling complex urban blocks, multi-scale turbulence effects, and rapid changes in local pollutant concentrations due to the relatively coarse resolution of meteorological data provided by mesoscale models (typically at the kilometer level). Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of the prior art by providing a method, system, device, and medium for tracing the source of air pollutants in the urban near field, thereby solving the problems of the prior art.

[0006] This invention specifically provides the following technical solution: a method for tracing the source of air pollutants in the near field of an urban area, comprising the following steps:

[0007] Based on high-resolution remote sensing images and UAV data of the urban near-field area, land use type, building form and green cover information of the urban heterogeneous surface are extracted to construct a three-dimensional solid model of the city and a computational fluid dynamics simulation domain.

[0008] Based on the three-dimensional solid model of the city, a Navier-Stokes model based on the Reynolds-averaged method is constructed by using the mass equation, momentum equation and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε. The wind speed and turbulence intensity in the fluid dynamics simulation domain are solved by the Navier-Stokes model in the finite element computing grid to obtain meteorological flow field data with meter-level spatial resolution and hour-level temporal resolution.

[0009] The obtained meteorological flow field data is used as input to the Lagrange random particle diffusion model. The specified observation point is used as the starting position of particle release. The particles are released and the reverse propagation trajectory of the particles in the atmospheric flow field is tracked to simulate the range of the contribution source area of ​​the observation point, which is used as the degree of influence of unit surface emission on unit concentration at the observation point.

[0010] By introducing the actual emission intensity of each area of ​​the Earth's surface, and convolving the degree of influence with the actual emission intensity of each area of ​​the Earth's surface, we can obtain the contribution of the near-field air pollution source areas and each source point to the pollution concentration at the observation point.

[0011] Preferably, the process of extracting land use type, building morphology, and green space cover information from heterogeneous urban surfaces based on high-resolution remote sensing imagery and UAV data of the urban near-field region, and constructing a three-dimensional urban solid model and computational fluid dynamics simulation domain, specifically involves:

[0012] Remote sensing images are used to extract land cover classification information and elevation models. Data such as building morphology and vegetation parameters on heterogeneous surfaces are obtained by UAV lidar to establish a three-dimensional solid model of the city.

[0013] The extracted land use type, building form, and green space cover parameters are spatially parameterized and fused and registered using a unified coordinate system to establish a computational fluid dynamics simulation domain.

[0014] Preferably, the step of calculating the wind speed and turbulence intensity in the fluid dynamics simulation domain using the Navier-Stokes model in the finite element computational mesh specifically involves:

[0015] In the finite element computational mesh, a drag coefficient and leaf area volume density are added to the momentum equation to construct a source term that characterizes the drag effect of vegetation canopy on wind speed on heterogeneous surfaces. Two source terms describing the turbulent kinetic energy and turbulent dissipation generated by the vegetation canopy are added after the turbulence equation to simulate key meteorological field indicators at the local urban scale. The simulation results include wind speed and turbulence intensity. The wind speed is specifically a three-dimensional wind speed vector field, and the turbulence intensity includes turbulent kinetic energy and dissipation rate.

[0016] Preferably, the step of releasing particles from a designated observation point as the starting position, tracing the reverse propagation trajectory of the particles in the atmospheric flow field, and simulating the contribution source region range of the observation point specifically involves:

[0017] The pollutant observation point is set as the starting point of particle release, and the particles are tracked in the reverse direction of time to simulate the source path of pollutants in the atmosphere.

[0018] In the simulated source tracing path, the Lagrange random particle diffusion model is used to obtain the motion process of particles in a complex non-uniform flow field. The motion state of the particles evolves over time, and the diffusion direction and rate are dynamically updated based on the local wind speed vector, turbulence intensity and turbulence scale within the grid where the particles are located, as the diffusion trajectory.

[0019] When a particle interacts with the ground during its motion, the interaction location is recorded as the potential pollution source contribution location, and the tracking of the particle is terminated; where interaction with the ground is called ground contact.

[0020] The diffusion trajectories and ground contact locations of all backward-released particles are accumulated, and the spatial distribution of all backward-released particles is statistically analyzed to estimate the spatial range and relative contribution of pollutant observation points, which serves as the contribution source area range of the observation points.

[0021] Preferably, the degree of influence is convolved with the actual emission intensity of each area of ​​the land surface to obtain the contribution of the near-field air pollution source areas and each source point to the pollution concentration at the observation point, specifically:

[0022] A grid overlay integral algorithm is used to evaluate the weighted contribution of different source points to the pollution concentration at the observation point, and outputs a spatial distribution map of the dominant pollution source area as the atmospheric pollution source area in the urban near field and the contribution of each source point to the pollution concentration at the observation point.

[0023] This invention provides a near-field air pollutant tracing system for cities, comprising:

[0024] The modeling module, based on high-resolution remote sensing images and UAV data of the urban near-field area, extracts land use information such as building morphology and green space coverage on the urban heterogeneous surface, and constructs a three-dimensional solid model of the city and a computational fluid dynamics simulation domain.

[0025] The simulation module is used to construct a Navier-Stokes model based on the Reynolds-averaged method on the basis of the three-dimensional solid model of the city, through the mass equation, momentum equation and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε. The Navier-Stokes model is used to solve the wind speed and turbulence intensity in the fluid dynamics simulation domain in the finite element computing grid to obtain meteorological flow field data with meter-level spatial resolution and hour-level temporal resolution.

[0026] The tracking module is used to input the obtained meteorological flow field data as the Lagrange random particle diffusion model, with the specified observation point as the starting position of particle release, release particles and track the reverse propagation trajectory of particles in the atmospheric flow field, simulate the contribution source area range of the observation point, and use it as the degree of influence of unit surface emission on unit concentration at the observation point.

[0027] The identification module is used to introduce the actual emission intensity of each area of ​​the land surface, and convolve the influence degree with the actual emission intensity of each area of ​​the land surface to obtain the contribution of the air pollution source areas in the near field of the city and each source point to the pollution concentration at the observation point.

[0028] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described method for tracing the source of air pollutants in the urban near field.

[0029] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method for tracing the source of air pollutants in the urban near field.

[0030] Compared with the prior art, the present invention has the following significant advantages:

[0031] This invention constructs a 3D urban solid model and a fluid dynamics simulation domain based on remote sensing imagery and UAV data. It employs the Navier-Stokes equations based on the Reynolds-averaged method to solve for wind speed and turbulence intensity within a finite element computational grid, obtaining meteorological flow field data with meter-level spatial resolution and hour-level temporal resolution. Through high-precision simulation of complex flow fields, building influences, and local turbulence effects, it effectively improves subsequent particle tracking accuracy. Furthermore, using the obtained meteorological flow field data as input, it simulates the contribution source region range of observation points by backward particle release based on a Lagrange random particle diffusion model. By convolving with a high-resolution pollutant emission inventory, we can obtain the atmospheric pollution source regions in the urban near-field and the contribution of each source to the pollution concentration at the observation point. This allows for the inverse solution of the pollutant diffusion process using particle tracking, tracing the pollution sources and their contribution to the concentration distribution in a specific area. Through the coupling of these two methods, we can not only obtain the fine transport characteristics of pollutants in the urban near-field region, breaking through the limitations of traditional numerical models in simulating fine-scale pollutant transport and providing a more refined and accurate scientific basis for urban environmental governance, but also help to construct a dynamic correlation model between pollution sources and pollution fields, thereby achieving accurate location and quantitative assessment of pollution sources. Attached Figure Description

[0032] Figure 1 The simulation analysis map provided by this invention; wherein, Figure 1 (a) is the Yangtze River Delta Ecological Green Integrated Development Demonstration Zone. Figure 1 (b) represents the land use type of the simulated area;

[0033] Figure 2 The CFD flow field simulation diagram provided by the present invention; wherein, Figure 2 (a), (b), (c), (d), (e), (f), (g), and (h) are CFD flow field simulation diagrams at 11, 14, 17, 20, 23, 02, 05, and 08 points, respectively.

[0034] Figure 3 A comparison chart of the simulation results of the observation point contribution area and the FFP model provided by this invention; wherein, Figure 3 (a) is a simulation diagram of the contribution area of ​​the observation point. Figure 3 (b) is the result of the FFP model;

[0035] Figure 4 Comparison of crosswind contribution functions in the observation point contribution area provided by this invention;

[0036] Figure 5 The contribution map of the observation point contribution area and the pollutant source area provided by this invention; wherein, Figure 5 (a) represents the area contributed by the observation point. Figure 5 (b) contributes to the pollutant source area;

[0037] Figure 6 The flowchart illustrates a method for tracing the source of air pollutants in the urban near field, as provided by this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] Computational fluid dynamics (CFD) methods, with their advantages of high resolution and flexible mesh generation, can perform detailed simulations of complex terrain, building cluster effects, and local meteorological conditions. Currently, numerical simulation methods based on the combination of CFD and Lagrange models are gradually becoming a hot research direction in high-resolution near-field atmospheric pollution source tracing. The core of this approach lies in its ability to take into account both large-scale meteorological driving effects and finely characterize the transport and diffusion processes of pollutants at local scales.

[0040] This invention leverages the high-resolution flow field description provided by CFD methods and the precise simulation of pollutant trajectories using Lagrange models. By inputting observation point data and a background emission inventory, it reconstructs the diffusion and transport processes of key pollution sources in the atmosphere, identifies the main influencing factors of the diffusion process, and analyzes the spatial distribution and variation patterns of major contributing sources. This provides a scientific basis for environmental governance and supports precise pollution control decisions. It can improve the spatiotemporal resolution of air pollution source tracing, providing more accurate and real-time scientific support for urban atmospheric environmental governance, pollution early warning, and emergency response, thereby playing a crucial role in improving residents' living environment, reducing health risks, and promoting green and low-carbon development.

[0041] like Figure 6 As shown, this invention provides a method for tracing the source of air pollutants in the near field of an urban area, specifically including the following steps:

[0042] Step S1: Based on high-resolution remote sensing images and UAV data of the urban near-field area, extract land use information such as building morphology and green space coverage on the urban heterogeneous surface, construct a three-dimensional urban entity model, and establish a simulation domain for flow field simulation.

[0043] High-resolution remote sensing imagery was used to classify land use in the study area, identifying surface types such as impervious surfaces, green spaces, and water bodies. Combining imagery and point cloud data acquired through UAV oblique photography, three-dimensional geometric features of urban buildings, including height, outline, and morphology, were extracted to construct a refined three-dimensional urban solid model. Simultaneously, vegetation vertical structure parameters were obtained using UAV lidar or image inversion methods, and leaf area density (LAD) was calculated. The vegetated area was modeled as a three-dimensional porous medium; the LAD parameter characterizes the density of the vegetation area and has a significant impact on airflow resistance and turbulent diffusion.

[0044] In terms of simulation parameter settings, the surface roughness length z0m was allocated according to different land use types, with z0m set to 0.004m for impermeable surfaces and building surfaces, and 0.0002m for water bodies. All parameters were uniformly converted to the simulation coordinate system and mapped to the terrain boundary to ensure physical consistency in subsequent simulations.

[0045] After completing the 3D modeling, a computational fluid dynamics simulation domain was established based on the constructed 3D solid model of the city. The simulation domain was divided using an unstructured mesh, with a denser mesh placed near the ground, buildings, and vegetation areas to improve local analytical accuracy. Surface parameters, building boundaries, and LAD data were all mapped to corresponding mesh cells and imported into the flow field simulation system as boundary inputs, providing high-precision terrain input conditions for subsequent wind field and pollutant transport simulations.

[0046] Step S2: Based on the three-dimensional solid model of the city, a Navier-Stokes model based on the Reynolds-averaged method is constructed by using the mass equation, momentum equation and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε. The wind speed and turbulence intensity of the fluid dynamics simulation domain are solved in the finite element computing grid using the Navier-Stokes model to obtain meteorological flow field data with meter-level spatial resolution and hour-level temporal resolution.

[0047] CFD simulation describes the dynamic behavior of flow fields by solving the Navier-Stokes equations, especially physical processes such as wind speed and turbulence under heterogeneous surfaces. The CFD part provides an essential flow field background for the LS model. Especially in complex terrain, the CFD model can capture the changes in local wind speed and turbulence caused by factors such as terrain undulation and vegetation, which can effectively improve the particle tracking accuracy of the subsequent LS model. The method adopts the Navier-Stokes model based on the Reynolds average method (RANS) to simulate the high-resolution flow field under the influence of heterogeneous terrain surfaces. The RANS model mainly consists of the mass equation, the momentum equation, and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε. The mass and momentum equations are shown in equations (1) and (2):

[0048] ;

[0049] ;

[0050] in, x i ( i=1,2,3 )express x, y, z Average components in three directions; x j express Pulsating components in three directions; u i ( i=1,2,3 () represents the wind speed components in three directions; t For time; ρ air density; μ It is the molar viscosity coefficient; μ t The turbulent viscosity coefficient; S u Vegetation canopy source terms added to this study.

[0051] The turbulence equations are shown in equations (3), (4) and (5):

[0052] ;

[0053] ;

[0054] ;

[0055] in, G k It is the turbulent kinetic energy generated by the average velocity gradient. σ k and σ ε yes k and ε The turbulent Prandtl number. The constants involved in the equation are: (C m ,k, , , , )=(0.09,0.4,1.0,1.3,1.44,1.92), C m for The empirical constant.

[0056] In the finite element computational mesh, this invention uses Raupach et al. to add a drag coefficient to the momentum equation. C d Leaf area and volume density LAD (m 2 / m 3 Construct source terms to characterize the wind speed drag effect of vegetation canopy on heterogeneous surfaces. This part ensures that the CFD method quantifies the hindering effect of surface vegetation on turbulence:

[0057] ;

[0058] in, The wind speed is the average wind speed (m / s). u i for ( i=u, v, w The wind speed component (m / s) in the direction of ); The two source terms added to the turbulence equation and To describe the turbulent kinetic energy and turbulent dissipation generated by the vegetation canopy (Equations 7 and 8):

[0059] ;

[0060] ;

[0061] in, β p This represents the fraction of average kinetic energy converted into turbulence. β d This represents the dimensionless coefficient after turbulent dissipation. c ε4 ,and c ε5 Let be a constant, and let its values ​​be ( ). β p , β d , c ε4 , c ε5 =(1,4,1.5,1.5). Turbulent kinetic energy and turbulent dissipation rate are key parameters in CFD flow field simulation, affecting the stability of the flow field, wind speed distribution, and particle trajectory. In this invention, the turbulence equation provides necessary turbulence data for CFD simulation (Equations 1-8), directly affecting the particle diffusion process in the LS model, and simulating key meteorological field indicators at the local urban scale; the simulation results include wind speed and turbulence intensity, where wind speed is specifically a three-dimensional wind speed vector field, and turbulence intensity includes turbulent kinetic energy and dissipation rate.

[0062] Step S3: Input the obtained meteorological flow field data into the Lagrange random particle diffusion model, take the specified observation point as the starting position of particle release, release the particles and track the reverse propagation trajectory of the particles in the atmospheric flow field, simulate the contribution source area range of the observation point, and use it as the degree of influence of unit surface emission on unit concentration at the observation point.

[0063] A Lagrange random particle model is used to simulate the backward propagation of observation points. By releasing a large number of particles and tracking their reverse propagation trajectories in the atmospheric flow field, the location of the source region influencing the observation points and its spatial distribution probability are identified. The model uses a set of random particles to represent scalar transport; the particles are transported by the airflow in the downwind direction and diffused by three-dimensional turbulence. Besides particle transport in the turbulent flow field, the evolution of particle velocity and position follows a stochastic Markov process, and their trajectories can be described by the generalized Langevin equation. The flow field data provided by CFD serves as the basic input for particle trajectory evolution.

[0064] ;

[0065] ;

[0066] in, i and j In three-dimensional space, they are 1, 2, and 3 respectively. For position vectors, x , y , z These refer to the flow direction, lateral direction, and vertical position, respectively. It is a velocity vector. t For time, and For drift coefficient and random acceleration coefficient, The Wiener process is a Gaussian-distributed process.

[0067] Particle drift coefficient It is an important parameter describing the average motion trend in turbulence, and is controlled by the flow field wind speed distribution and turbulence characteristics. Its value is usually determined by the velocity distribution of the flow field and the turbulence intensity.

[0068] ;

[0069] in, For turbulent kinetic energy, For turbulent dissipation rate, This represents the velocity gradient.

[0070] To quantify the statistical regularity of particle landing locations, their ground contribution probability density function can be calculated, expressed as: ;

[0071] in Represents surface unit The particle impact probability density, in m -2 ; N This represents the total number of particles; The area of ​​the ground grid; For indicator functions, when the first i The value is 1 if a particle falls into the cell, otherwise it is 0.

[0072] Based on equation (12), a contribution source region function (Footprint Function) is further defined, which is the influence of unit source region emissions on the concentration at the observation point, with units of s·m -2 The formula is:

[0073] ;

[0074] in, To contribute source region functions, M Let be the total particle emission, and be the weight or relative contribution of each particle. If all particles have the same weight, it simplifies to equation (12) multiplied by a constant coefficient. This function characterizes the unit emission at a point in the source region in response to the instantaneous concentration at the observation point, and is a key function for atmospheric pollutant inversion and assessment.

[0075] By integrating high-resolution CFD flow field simulation results as the driving wind field, the CFD flow field simulation results are as follows: Figure 2 As shown, and by updating particle transport parameters in real time under irregular terrain and heterogeneous surface conditions, this invention can effectively capture the influence characteristics of local wind fields on the distribution of contributing source areas, significantly improve the spatial resolution and physical reliability of pollution source estimation, and is especially suitable for source area identification and source tracing analysis tasks in complex urban environments.

[0076] Specifically, using a designated observation point as the starting position for particle release, the particles are released and their reverse propagation trajectory in the atmospheric flow field is tracked to simulate the contribution source region range of the observation point.

[0077] The pollutant observation point is set as the starting point of particle release, and the particles are tracked in the reverse direction of time to simulate the source path of pollutants in the atmosphere.

[0078] In the simulated source tracing path, the Lagrange random particle diffusion model is used to obtain the motion process of particles in a complex non-uniform flow field. The motion state of the particles evolves over time, and the diffusion direction and rate are dynamically updated based on the local wind speed vector, turbulence intensity and turbulence scale within the grid where the particles are located, as the diffusion trajectory.

[0079] The interaction location of a particle with the ground during its motion is recorded as a potential pollution source contribution location, and the tracking of the particle is terminated; the interaction with the ground is called ground contact.

[0080] The diffusion trajectories and ground contact locations of all backward-released particles are accumulated, and the spatial distribution of all backward-released particles is statistically analyzed to estimate the spatial range and relative contribution of pollutant observation points, which serves as the contribution source area range of the observation points.

[0081] Step S4: Introduce the actual emission intensity of each area of ​​the land surface, and convolve the influence degree with the actual emission intensity of each area of ​​the land surface to obtain the contribution of the air pollution source areas and each source point in the near field of the city to the pollution concentration at the observation point.

[0082] After calculating the particle back-emission and contribution source region function, high-resolution pollutant emission inventory data is further introduced to perform quantitative calculations for pollutant source tracing. Contribution source region function A high-resolution pollutant emission inventory characterizes the extent to which unit surface emissions affect unit concentrations at the observation point. What is provided is the actual emission intensity of various areas of the Earth's surface (such as CO2, NO2, etc. per unit time). x PM 2.5 Equivalent emissions, in μg∙m -2 ∙s -1 Multiplying the two values ​​yields the actual contribution of this region to the pollutant concentration at the observation point:

[0083] ;

[0084] In the formula, C is the predicted concentration at the observation point caused by emissions from the source region, and A is the simulation domain range.

[0085] By combining CFD and LS models, this source tracing method offers more accurate and efficient calculations when dealing with complex urban flow fields. CFD provides detailed flow field and turbulence statistics, while the LS model estimates the contribution of the source region by tracing particles in reverse. The combination of the two can more accurately reflect particle diffusion processes under complex terrain and meteorological conditions, thereby improving the spatial accuracy and reliability of atmospheric pollutant source tracing.

[0086] To verify the applicability and feasibility of the proposed high-resolution near-field atmospheric pollutant source tracing method based on computational fluid dynamics and the Lagrange model in complex urban environments, such as... Figure 1 As shown, the Dalianhu flux monitoring station located in Qingpu District, Shanghai, was selected as the research object for numerical simulation and source analysis. The underlying surface types in this area are diverse, including buildings, forests, water bodies, green spaces, and impervious surfaces, providing typical heterogeneous surface conditions, which are suitable for evaluating the performance of the proposed method in source region identification, characterization of contributing source region structure, and quantification of pollution source contributions.

[0087] This case study uses flux observation data from July 18, 2023, as a basis. It employs surface meteorological information acquired by unmanned aerial vehicles (UAVs) to accurately reproduce the wind field structure within the simulated area, and then uses this information to drive a CFD model for turbulence statistics calculations. The simulation area is centered on the observation tower, with a range of 4km × 4km, and the CFD output resolution is 20m, fully covering the possible source region. Surface parameters such as canopy height (LAD) are set according to different land use types to ensure that the heterogeneity of the underlying surface is reasonably represented in the CFD simulation.

[0088] Table 1 Data Source

[0089]

[0090] CFD simulations were conducted using Fluent software on the Beijing Parallel Cloud Supercomputing Platform. The resulting wind field distribution revealed local disturbances caused by differences in underlying surface type and topographic relief. The simulation results showed that at a height of 2m, there were significant differences in wind speed over woodlands and water bodies. Due to the high roughness of the woodlands, the wind speed was significantly reduced, while the wind speed was higher and the streamlines were smoother in the water bodies and farmland areas.

[0091] The footprint estimation employs the STILT model framework, using CFD-simulated wind speed and turbulence statistics as input fields, and performs offline inverse Lagrange trajectory tracking calculations. The model utilizes a well-mixed drift term construction scheme, with a time step set to 5% of tL, and a partial reflection boundary condition of β=0.5 is applied when particles contact the ground surface to account for energy dissipation and viscous effects. Particles are instantaneously released at a height of 30m, with a quantity of 1000. Their inverse trajectories in the wind field are tracked, and the final landing distribution is statistically analyzed to estimate the probability density function of the ground source contribution.

[0092] To analyze the performance of source region contribution functions and atmospheric pollutant source point contribution estimations under complex urban environments, CFD simulation results from 20:00 on July 18, 2023, were selected for calculation. During this period, wind speeds were stable and turbulence intensity was low, meeting atmospheric stability conditions and providing a favorable background for clearly identifying the main control range of the contributing source region. The model used CFD-driven wind field and turbulence parameters for particle backtracking. The simulation results of the contributing source region were compared with the calculation results of the classic FFP model, as shown below. Figure 3 As shown, the differences in performance of the evaluation methods on the spatial structure of the contributing source region are illustrated.

[0093] The results show that the main axis of the source region in both models is consistent, exhibiting a downwind elliptical expansion, indicating that the overall shape of the source region is dominated by wind direction under the influence of the mean wind field. However, significant differences exist in the detailed distribution. The CFD-LS model estimates a larger spatial range of the source region, with significant flux contributions still present between approximately 150–300 m from the observation point, while the source region of the FFP model is essentially attenuated within this range. Correspondingly, the CFD-LS model has a lower peak probability density in the source region and a smoother distribution, reflecting that this method more fully considers the local turbulent disturbances and trajectory dispersion caused by the underlying surface.

[0094] In particular, in the comparison of crosswind source region functions, the crosswind source region of the FFP model exhibits a typical Gaussian-type rapid decay, while the CFD-LS model shows a wider crosswind expansion and a more significant tail drag phenomenon in certain non-uniform underlying surface areas (such as the boundary between woodland and water bodies). Specifically, for example... Figure 4 As shown. This difference illustrates that the CFD-LS model can more realistically reflect the lateral diffusion characteristics of particle trajectories in a three-dimensional turbulent field in environments significantly affected by variations in terrain and surface roughness.

[0095] Based on the estimation of the source region function, the calculated source region probability density function is further fused with the high-resolution emission inventory of the region to estimate the contribution of different surface sources to the observed pollutant concentrations. This inventory data covers NO from typical urban underlying surface types such as buildings, roads, industry, and green spaces. X The CO2 emission information, with a resolution of 100m and a time step of 1 hour, adequately meets the registration requirements of this study in terms of spatial and temporal resolution. When performing convolution operations with the source region probability density function, a grid superposition integral algorithm is used to evaluate the weighted contribution of different source points to the pollution concentration at the observation point, and outputs a spatial distribution map of the dominant pollution source region, serving as the near-field atmospheric pollution source region of the city and the contribution of each source point to the pollution concentration at the observation point. The pollutant emission inventory possesses high spatial resolution and hourly temporal resolution, including emission information from stationary pollution sources, mobile pollution sources, and area sources. The input emission inventory data represents the emission intensity of atmospheric pollutants within the gridded area at different times.

[0096] The results of the source region map and pollutant contribution map at the observation points are as follows: Figure 5As shown, the relative contribution intensity and spatial distribution characteristics of pollutant concentrations at the observation points from different source points are revealed. The results indicate that the main pollution contribution at the observation points originates from major traffic arteries and building clusters within the upwind 200–400 m region. These areas have high emission intensity in the emission inventory and are also high-weighted in the footprint function, resulting in a significantly high product value in the contribution map. In contrast, green spaces and woodlands located downwind or with higher roughness, even with some emission intensity, contribute less to the observation points due to their lower particle trajectory contact probability.

[0097] In summary, the CFD-LS model, by incorporating high-resolution flow fields and turbulence statistics, significantly enhances its response to underlying surface heterogeneity, especially under stable atmospheric conditions, enabling more accurate identification of distant contribution areas and lateral disturbance characteristics. This provides more refined data support for tracing the sources and controlling air pollutants, and offers more accurate and real-time scientific methods for urban air pollution control, pollution early warning, and emergency response.

[0098] Based on the above methods and statements, the present invention provides a method for tracing the source of air pollutants in the urban near field, including: a modeling module, a simulation module, a tracking module and an identification module.

[0099] The modeling module, based on high-resolution remote sensing imagery and UAV data of the urban near-field area, extracts land use information such as building morphology and green space cover from heterogeneous urban surfaces to construct a 3D urban solid model and a computational fluid dynamics simulation domain. The simulation module, based on the 3D urban solid model, constructs a Navier-Stokes model based on the Reynolds-averaged method using the mass equation, momentum equation, and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε. The Navier-Stokes model is then used to solve for wind speed and turbulence intensity in the fluid dynamics simulation domain within a finite element computational mesh, achieving meter-level spatial resolution. The system uses hourly-level temporal resolution meteorological flow field data as input to a Lagrange random particle diffusion model. It releases particles from a specified observation point as the starting position and tracks their reverse propagation trajectory in the atmospheric flow field, simulating the contribution source area range of the observation point. This range serves as the degree of influence of unit surface emissions on the unit concentration at the observation point. The identification module incorporates the actual emission intensity of each surface region and convolves this influence with the actual emission intensity of each surface region to obtain the contribution of the near-field atmospheric pollution source regions and each source point to the pollution concentration at the observation point.

[0100] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a method for tracing the source of air pollutants in the urban near field.

[0101] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0102] The present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of a method for tracing the source of air pollutants in the urban near field.

[0103] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0104] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for tracing the source of air pollutants in the near field of an urban area, characterized in that, include: Based on high-resolution remote sensing images and UAV data of the urban near-field area, land use type, building form and green cover information of the urban heterogeneous surface are extracted to construct a three-dimensional solid model of the city and a computational fluid dynamics simulation domain. Based on the aforementioned three-dimensional solid model of the city, a Navier-Stokes model based on the Reynolds-averaged method is constructed using the mass equation, momentum equation, and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε. The Navier-Stokes model is then used to solve for wind speed and turbulence intensity in the fluid dynamics simulation domain within a finite element computational grid, obtaining meteorological flow field data with meter-level spatial resolution and hour-level temporal resolution. Specifically, in the finite element computational grid, a drag coefficient and leaf area volume density are added to the momentum equation to construct a source term characterizing the drag effect of vegetation canopy on wind speed in heterogeneous surfaces. Two source terms describing the turbulent kinetic energy and turbulent dissipation generated by the vegetation canopy are added after the turbulence equation to simulate key meteorological field indicators at the local urban scale. The simulation results include wind speed and turbulence intensity; wind speed is specifically a three-dimensional wind speed vector field, and turbulence intensity includes turbulent kinetic energy and dissipation rate. The obtained meteorological flow field data is used as input to the Lagrange stochastic particle diffusion model. The specified observation point is taken as the starting position for particle release. Particles are released and their reverse propagation trajectory in the atmospheric flow field is tracked to simulate the contribution source region range at the observation point, representing the degree of influence of unit surface emissions on unit concentration at the observation point. Specifically, this includes: The pollutant observation point is set as the starting point of particle release, and the particles are tracked in the reverse direction of time to simulate the source path of pollutants in the atmosphere. In the simulated source tracing path, the Lagrange random particle diffusion model is used to obtain the motion process of particles in a complex non-uniform flow field. The motion state of the particles evolves over time, and the diffusion direction and rate are dynamically updated based on the local wind speed vector, turbulence intensity and turbulence scale within the grid where the particles are located, as the diffusion trajectory. When a particle interacts with the ground during its motion, the interaction location is recorded as the potential pollution source contribution location, and the tracking of the particle is terminated; where interaction with the ground is called ground contact. The diffusion trajectories and ground contact locations of all backward-released particles are accumulated, and the spatial distribution of all backward-released particles is statistically analyzed to estimate the spatial range and relative contribution of pollutant observation points, which serves as the contribution source area range of the observation points. By incorporating the actual emission intensity of each area of ​​the Earth's surface, the degree of influence is convolved with the actual emission intensity of each area of ​​the Earth's surface to obtain the contribution of the near-field air pollution source regions and each source point to the pollution concentration at the observation point. Specifically, a grid superposition integral algorithm is used to evaluate the weighted contribution of different source points to the pollution concentration at the observation point, and a spatial distribution map of the dominant pollution source regions is output as the contribution of the near-field air pollution source regions and each source point to the pollution concentration at the observation point.

2. The method for tracing the source of air pollutants in the urban near field as described in claim 1, characterized in that, The high-resolution remote sensing imagery and UAV data of the urban near-field area are used to extract land use type, building morphology, and green space cover information from heterogeneous urban surfaces, and to construct a three-dimensional urban solid model and a computational fluid dynamics simulation domain, specifically: Remote sensing images are used to extract land cover classification information and elevation models. UAV lidar is used to obtain data on building morphology and vegetation parameters on heterogeneous surfaces to establish a three-dimensional solid model of the city. The extracted land use type, building form, and green space cover parameters are spatially parameterized and fused and registered using a unified coordinate system to establish a computational fluid dynamics simulation domain.

3. A near-field air pollutant source tracing system for cities, characterized in that, include: The modeling module, based on high-resolution remote sensing images and UAV data of the urban near-field area, extracts land use type, building form and green space cover information on the urban heterogeneous surface, and constructs a three-dimensional solid model of the city and a computational fluid dynamics simulation domain. The simulation module is used to construct a Navier-Stokes model based on the Reynolds-averaged method, using the mass equation, momentum equation, and two turbulence equations based on turbulent viscosity k and turbulent dissipation rate ε, based on the three-dimensional solid model of the city. The Navier-Stokes model is then used to solve for wind speed and turbulence intensity in the fluid dynamics simulation domain within a finite element computational grid, obtaining meteorological flow field data with meter-level spatial resolution and hour-level temporal resolution. Specifically, in the finite element computational grid, a drag coefficient and leaf area volume density are added to the momentum equation to construct a source term characterizing the drag effect of vegetation canopy on wind speed in heterogeneous surfaces. Two source terms describing the turbulent kinetic energy and turbulent dissipation generated by the vegetation canopy are added after the turbulence equation to simulate key meteorological field indicators at the local urban scale. The simulation results include wind speed and turbulence intensity; wind speed is specifically a three-dimensional wind speed vector field, and turbulence intensity includes turbulent kinetic energy and dissipation rate. The tracking module is used to input the obtained meteorological flow field data into the Lagrange random particle diffusion model. It uses a specified observation point as the starting position for particle release, releases particles, and tracks their reverse propagation trajectory in the atmospheric flow field. This simulates the contribution source region range at the observation point, representing the impact of unit surface emissions on unit concentration at that observation point. Specifically, it includes: The pollutant observation point is set as the starting point of particle release, and the particles are tracked in the reverse direction of time to simulate the source path of pollutants in the atmosphere. In the simulated source tracing path, the Lagrange random particle diffusion model is used to obtain the motion process of particles in a complex non-uniform flow field. The motion state of the particles evolves over time, and the diffusion direction and rate are dynamically updated based on the local wind speed vector, turbulence intensity and turbulence scale within the grid where the particles are located, as the diffusion trajectory. When a particle interacts with the ground during its motion, the interaction location is recorded as the potential pollution source contribution location, and the tracking of the particle is terminated; where interaction with the ground is called ground contact. The diffusion trajectories and ground contact locations of all backward-released particles are accumulated, and the spatial distribution of all backward-released particles is statistically analyzed to estimate the spatial range and relative contribution of pollutant observation points, which serves as the contribution source area range of the observation points. The identification module is used to introduce the actual emission intensity of each area of ​​the land surface, and convolve the influence degree with the actual emission intensity of each area of ​​the land surface to obtain the contribution of the air pollution source areas and each source point in the urban near field to the pollution concentration at the observation point. Specifically, a grid superposition integral algorithm is used to evaluate the weighted contribution of different source points to the pollution concentration at the observation point, and outputs the spatial distribution map of the dominant pollution source areas as the contribution of the air pollution source areas and each source point in the urban near field to the pollution concentration at the observation point.

4. A computer device, characterized in that, The device includes a memory and a processor, wherein the memory stores a program that, when executed by the processor, causes the processor to perform the steps of a method for tracing the source of air pollutants in the urban near field as described in any one of claims 1 to 2.

5. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for tracing the source of air pollutants in the urban near field according to any one of claims 1 to 2.

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