Complex terrain pollutant concentration prediction method based on laser radar and numerical simulation

Abstract

The application provides a small-scale complex terrain pollutant concentration field prediction method based on a laser radar and numerical simulation, a coupling module, calculation result of a WRF model is input into a small-scale model CFD to accurately calculate flow field change conditions of a small-scale area, a pretreatment module, pretreatment of conventional monitoring data used in the prediction method is performed to improve calculation accuracy and results, and a correction module, the conventional monitoring data and the CFD model calculation result are combined to reduce calculation errors.

Description

Technical Field

[0001] This invention belongs to the field of pollution forecasting technology, specifically a method for forecasting pollutant concentration fields in small-scale complex terrain based on lidar and numerical simulation. Background Technology

[0002] In recent years, the health hazards posed by atmospheric aerosol particulate matter pollution such as PM2.5 and PM10 have received increasing attention. Current research on pollutants mainly employs two methods: monitoring and numerical simulation. Conventional monitoring methods primarily include two approaches: fixed-point ground-based radar observation and mobile remote sensing technology. Ground-based radar observation involves installing atmospheric particulate matter monitoring lidar or other monitoring equipment in a fixed station, achieving localized detection at a fixed location, thus limiting its application range and scenarios. While mobile remote sensing observation offers greater mobility and flexibility, it cannot achieve simultaneous observation across an entire area. Regarding numerical simulation, due to uncertainties in emission sources, meteorological fields, and the model itself, air quality model forecasts often differ significantly from actual data, especially in small-scale simulations. The main solution currently is data assimilation, coupling the simulation results of air quality numerical models with atmospheric observation data. This effectively improves the initial field of the numerical model and increases forecast accuracy. Assimilation typically uses ground-based particulate matter concentration data or satellite remote sensing results. Summary of the Invention

[0003] To address the aforementioned problems, this invention provides a method for predicting pollutant concentration fields in small-scale complex terrain based on lidar and numerical simulation.

[0004] The technical solution adopted in this invention is as follows: a method for predicting pollutant concentration fields in small-scale complex terrain based on lidar and numerical simulation. This method uses fixed-point lidar data and mobile monitoring vehicle data as data sources, combined with numerical simulation results, to predict the pollutant concentration field within the region. It mainly comprises three parts: a coupling module, a preprocessing module, and a correction module.

[0005] (1) Coupling module: First, the WRF model is used to simulate the local characteristics and seasonal variation characteristics of the region. The influence of large-scale weather patterns on local pollutants is considered. The simulation results are used as the original driving data of the CFD model to simulate small-scale local complex flow field changes, reduce the applicable scale of the simulation, and improve the applicability of the model.

[0006] (2) Preprocessing module: cleans the imported mobile vehicle data (including mobile pollutant data and mobile meteorological data) and lidar data, removes abnormal data, and reassembles the input data.

[0007] (3) Correction module, mainly used to make numerical corrections to CFD simulation results. It mainly consists of two parts: First, using mobile vehicle data to determine pollution sources, and correcting the source strength of pollution sources according to the pollution source type (point source, line source, area source), and correcting the meteorological field according to the mobile vehicle meteorological data; Second, correcting the overall pollutant concentration in the area according to the pollutant data of the fixed-point ground-based lidar.

[0008] The specific steps are as follows: the coupling module inputs the WRF model calculation results downscaled into the small-scale CFD model to accurately calculate the flow field changes in the small-scale region; the preprocessing module preprocesses the conventional monitoring data used in the forecasting method to improve the calculation accuracy and results; and the correction module combines the conventional monitoring data with the CFD model calculation results to reduce calculation errors.

[0009] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0010] This method proposes a small-scale pollutant concentration field prediction method based on lidar and numerical simulation in complex terrain. It uses fixed-point lidar data and mobile vehicle data as data sources, ensuring reliable data quality and high spatiotemporal resolution. It also considers the local characteristics and seasonal variations of the WRF simulation area, the impact of large-scale weather patterns on local pollutants, and the local small-scale complex flow field changes simulated by small-scale CFD, thereby reducing the applicable simulation scale and improving the model's applicability. Through source strength correction and concentration correction, the pollutant concentration is reasonably adjusted, improving the application scenarios of lidar data.

[0011] This method is simple to operate, low in cost, and widely applicable. The coupling method makes it suitable for a wide range of environments, including various complex underlying surface conditions. The simulation results are accurate, and the correction scheme can effectively improve numerical model results and increase forecast accuracy, providing assistance for environmental monitoring and scientific research. This method can be used in scenarios such as environmental surveillance, rapid enforcement, rapid source tracing, air quality assurance, emergency monitoring, and scientific assessment. Attached Figure Description

[0012] Figure 1 This is a flowchart of the present invention;

[0013] Figure 2 This is a schematic diagram of the coupling module driving the CFD mode in this invention using the WRF mode.

[0014] Figure 3 This is a schematic diagram of the parameter selection scheme in the WRF mode of this invention. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0016] Reference Figure 1-3 This paper presents a method for predicting pollutant concentration fields in small-scale complex terrain based on lidar and numerical simulation. It uses fixed-point lidar data and mobile monitoring vehicle data as data sources, combined with numerical simulation results, to predict the pollutant concentration field within the region. The method mainly consists of three parts: a coupling module, a preprocessing module, and a correction module.

[0017] (1) Coupling module: First, the WRF model is used to simulate the local characteristics and seasonal variation characteristics of the region. The influence of large-scale weather patterns on local pollutants is considered. The simulation results are used as the original driving data of the CFD model to simulate small-scale local complex flow field changes, reduce the applicable scale of the simulation, and improve the applicability of the model.

[0018] (2) Preprocessing module: cleans the imported mobile vehicle data (including mobile pollutant data and mobile meteorological data) and lidar data, removes abnormal data, and reassembles the input data.

[0019] (3) Correction module, mainly used to make numerical corrections to CFD simulation results. It mainly consists of two parts: First, using mobile vehicle data to determine pollution sources, and correcting the source strength of pollution sources according to the pollution source type (point source, line source, area source), and correcting the meteorological field according to the mobile vehicle meteorological data; Second, correcting the overall pollutant concentration in the area according to the pollutant data of the fixed-point ground-based lidar.

[0020] The specific steps are as follows: the coupling module inputs the WRF model calculation results downscaled into the small-scale CFD model to accurately calculate the flow field changes in the small-scale region; the preprocessing module preprocesses the conventional monitoring data used in the forecasting method to improve the calculation accuracy and results; and the correction module combines the conventional monitoring data with the CFD model calculation results to reduce calculation errors.

[0021] This invention uses fixed-point lidar data and mobile vehicle data as data sources, ensuring reliable data quality and high spatiotemporal resolution. It also considers the local and seasonal characteristics of the WRF simulation area, the impact of large-scale weather patterns on local pollutants, and the complex local flow field changes in small-scale CFD simulations, thereby reducing the applicable simulation scale and expanding the model's applicability. Through source strength correction and concentration correction, it reasonably adjusts pollutant concentrations and improves the application scenarios of lidar data.

[0022] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting pollutant concentrations in complex terrain based on lidar and numerical simulation, characterized by: Using fixed-point lidar data and mobile monitoring vehicle data as data sources, combined with numerical simulation results, the pollutant concentration field in the region is predicted. It mainly consists of three parts: a coupling module, a preprocessing module, and a correction module. (1) Coupling module: First, the WRF model is used to simulate the local characteristics and seasonal variation characteristics of the region. The influence of large-scale weather patterns on local pollutants is considered. The simulation results are used as the original driving data of the CFD model to simulate small-scale local complex flow field changes, reduce the applicable scale of the simulation, and improve the applicability of the model. (2) Preprocessing module: cleans the imported mobile vehicle data and lidar data, removes abnormal data, and reassembles the input data; wherein, the mobile vehicle data includes mobile pollutant data and mobile meteorological data; (3) Correction module, mainly used to make numerical corrections to CFD simulation results, mainly consists of two parts: First, using mobile vehicle data to determine pollution sources, correcting the source strength of pollution sources according to the pollution source category, and correcting the meteorological field according to the meteorological data of the mobile vehicle; Second, correcting the overall pollutant concentration in the area according to the pollutant data of the fixed-point ground-based lidar; wherein, the pollution source category includes point source, line source and area source. The specific steps are as follows: the coupling module inputs the WRF model calculation results downscaled into the small-scale CFD model to accurately calculate the flow field changes in the small-scale region; the preprocessing module preprocesses the conventional monitoring data used in the forecasting method to improve the calculation accuracy and results; and the correction module combines the conventional monitoring data with the CFD model calculation results to reduce calculation errors.

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