Method for observing NOy concentration pollution zoning by combining earth surface type and foundation remote sensing

Abstract

The invention relates to the technical field of atmospheric pollution zoning recognition, in particular to a method for observing NOy concentration pollution zoning in combination with earth surface types and foundation remote sensing. According to the technical scheme, the method comprises the following steps: carrying out inversion by using a ground-based high-resolution Fourier infrared spectrometer to obtain a concentration time sequence of NO, NO2 and HNO3 in the atmosphere, and calculating the troposphere concentration of the NO, NO2 and HNO3; the troposphere concentrations of NO, NO2 and HNO3 are averagely matched with observation points per hour, and the effective wind direction is determined in an hour vector averaging mode by means of wind direction and wind speed data. According to the method, the improvement of NOy pollution from single-point monitoring to spatial partition identification is realized by fusing foundation remote sensing observation and earth surface type information, and an effective method is provided for accurate traceability and pollution prevention and control.

Description

Technical Field

[0001] This invention relates to the field of atmospheric pollution zoning identification technology, and more particularly to a method that combines land surface type with ground-based remote sensing observation NO y Concentration-based pollution zoning method. Background Technology

[0002] Atmospheric nitrogen oxides (NOx) y Nitric oxide (NO), nitrogen dioxide (NO2), and nitric acid (HNO3) are key reactive nitrogen components in atmospheric chemistry. They primarily originate from emissions from human activities such as transportation and industry, with NO being the most abundant. x NO enters the atmosphere as (NO + NO2), and subsequently undergoes a complex photochemical oxidation process to generate other components. y Not only does it directly harm human health, but as a key precursor in photochemical reactions, NO also dominates the formation rate of near-surface ozone (O3) and the formation process of secondary aerosols, significantly impacting haze and photochemical smog pollution. Therefore, accurately identifying NO... y Understanding the sources of pollution and their chemical transformation processes in the atmosphere is crucial for scientifically formulating pollution prevention and control strategies.

[0003] Currently, regarding NO y The study of pollution characteristics mainly relies on the following technical methods, but all of them have certain limitations:

[0004] First, ground-based remote sensing technology, especially high-resolution Fourier transform infrared spectroscopy, can obtain the column concentrations of key components in the troposphere, such as NO, NO2, and HNO3, over a long period of time and with stable accuracy. These three gases account for a significant portion of tropospheric NO levels. y The relative proportion of pollutants, ranging from 70% to 90% of the total, can effectively reflect the "freshness" and photochemical "aging" of polluted air masses. However, this technology is currently mainly limited to providing time-series concentration changes at a single observation site, lacking effective correlation with spatial information. Although it can obtain high-precision temporal variation data, it cannot directly answer key questions such as where pollutants originate and how the type of underlying surface source region along their transport path affects the composition of pollutants.

[0005] Second, in data analysis, researchers often use NO y The component ratio is used as a diagnostic tool, such as using NO. x / NO y The ratio HNO3 / NO is used to characterize the freshness of a single emission. xRatios are used to characterize the intensity of secondary transformation. However, these ratio indicators only provide an instantaneous "snapshot" of the atmospheric chemical state. In complex real-world environments, especially in cities and surrounding areas with mixed source types, it is difficult to effectively distinguish the specific impacts of different land surface types (such as urban industrial areas, main roads, farmland, and woodlands) on pollution contributions. A single ratio indicator cannot trace pollution characteristics back to specific spatial source areas.

[0006] Third, to trace the sources of pollution, existing studies mostly rely on regional-scale atmospheric trajectory models (such as backward trajectory models) for simulation analysis. However, these models are usually based on meteorological field data and have low spatial resolution, often at the level of several kilometers to tens of kilometers. When facing urban environments with dense buildings and complex local circulation, the accuracy and reliability of the model simulation decrease significantly, making it difficult to accurately capture the fine-scale pollution transport and transformation processes caused by the combined effects of local emission sources and complex underlying surfaces.

[0007] In summary, existing technologies either lack spatial dimension (e.g., ground-based single-point observations), have insufficient spatial resolution (e.g., trajectory models), or cannot effectively correlate surface information (e.g., ratio indicators), making it difficult to meet the requirements of current refined environmental pollution control for accurate identification and zoning of pollution source areas. Therefore, there is an urgent need to develop a new method that can integrate high-precision ground-based remote sensing observations with high spatial resolution surface type information to achieve accurate identification and zoning of NO. y This application proposes a refined spatial zoning identification method that combines land surface type and ground-based remote sensing observations of NO pollution contribution characteristics. y Concentration-based pollution zoning method. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of existing technologies, such as a lack of spatial dimension (e.g., ground-based single-point observation), insufficient spatial resolution (e.g., trajectory models), or inability to effectively correlate surface information (e.g., ratio indicators), which makes it difficult to meet the requirements of precise identification and zoning of pollution source areas in current refined environmental pollution control. This invention proposes a method that combines surface type with ground-based remote sensing observation NO... y Concentration-based pollution zoning method.

[0009] The technical solution of this invention: a method combining surface type and ground-based remote sensing observation NO y The concentration-based pollution zoning method includes the following steps:

[0010] S1. The concentration time series of NO, NO2 and HNO3 in the atmosphere were obtained by inversion using a ground-based high-resolution Fourier transform infrared spectrometer, and their tropospheric concentrations were calculated.

[0011] S2. Measure the hourly averaged observation points of the tropospheric concentrations of NO, NO2 and HNO3, and determine the effective wind direction using wind direction and speed data in an hourly vector averaging manner.

[0012] S3. Using NO, NO2, and HNO3 as representatives, calculate NO. y Total Amount and Ratio Indicators NO x / NO y and HNO3 / NO x NO x The sum of NO and NO2 is calculated, and the observed samples are classified according to wind direction and sector. The NO values ​​are then statistically analyzed. y Indicator probability distribution;

[0013] S4. Acquire satellite land cover product data and identify the land surface type in the area near the site;

[0014] S5. Divide the land cover data into sectors according to wind direction, calculate the proportion of land surface types in each sector, determine the dominant land surface type, and assign the NO values ​​of the corresponding sectors within the same time period. y The indicators are correlated with the dominant land surface types to identify pollution characteristics dominated by primary emissions or secondary transformation.

[0015] Optionally, in step S1, the maximum spectral resolution of the ground-based high-resolution Fourier transform infrared spectrometer is 0.005 cm⁻¹. -1 The spectral measurement range is 700–4000 cm⁻¹ -1 .

[0016] Optionally, in step S1, when inverting the concentrations of NO, NO2, and HNO3, the prior vertical concentrations calculated using the HITRAN molecular database and the WACCM atmospheric climate model are used, and a nonlinear least squares algorithm is employed for spectral inversion.

[0017] Optionally, in step S1, the band range used for NO inversion is 1900.45-1900.55 cm. -1 The band range used for NO2 inversion is 2914.550–2924.925 cm. -1 The band range used for HNO3 inversion is 868.05–874.00 cm. -1 .

[0018] Optionally, in step S1, when calculating the tropospheric concentration, the vertical concentration from 0 to 12 km is multiplied layer by layer with the dry air column to obtain the tropospheric column concentration.

[0019] Optionally, in step S2, the vector averaging method includes: decomposing the instantaneous wind speed and wind direction into... Components and Calculate the hourly average component, and then obtain the average wind speed and average wind direction.

[0020] Optionally, the Components and The formula for calculating the components is:

[0021] in, Let θ be the instantaneous wind speed and θ be the instantaneous wind direction. The wind direction angle is in radians;

[0022] The hourly average component is calculated as follows:

[0023] in, To ensure an effective number of observations, and These represent the arithmetic mean of all u and v components within one hour, respectively.

[0024] Average wind speed and average wind direction The calculation is as follows:

[0025]

[0026] in, This is a four-quadrant arctangent function, used to calculate the mean wind vector components. Calculate wind direction in radians; mod is the modulo function used to normalize the angle result to the range of 0 to 360 degrees.

[0027] Optionally, in step S3, the wind direction sectors are 12, each sector being 30°.

[0028] Optionally, in step S4, the satellite land cover product data is the LC_Type1 band of MODIS' MCD12Q1 product, and the area near the station is a region with a radius of 5 km centered on the observation station.

[0029] Optionally, in step S5, the criterion for determining the dominant land surface type is: when the proportion of a certain land surface type is greater than 40%, it is defined as the dominant land surface type; otherwise, it is marked as a mixed land type; the NO y Indicators include NO x / NO y HNO3 / NO x and NO y Concentration of NO x / NO y High value and HNO3 / NO x A low value indicates that primary pollutants are the main pollutants emitted, NO x / NO yLow value and HNO3 / NO x A high value indicates that the pollutants are mainly secondary pollutants.

[0030] Compared with the prior art, this application includes at least one of the following beneficial technical effects:

[0031] By combining ground-based remote sensing observations with satellite surface type information, single-point concentration observation data can be transformed into spatially directional pollution zones, clarifying the pollution contribution characteristics in different directions.

[0032] By correlating key chemical ratios with specific land surface types, it is possible to distinguish the pollution contributions of primary emissions and secondary transformations, thereby improving the accuracy of pollution attribution and the ability to analyze mechanisms.

[0033] The method of this invention is based on conventionally available observation data, has a clear process, and is easy to implement in environmental monitoring networks, providing direct technical basis for pollution source identification and control measures formulation.

[0034] This invention improves NOy pollution monitoring from single-point monitoring to spatial zoning identification by integrating ground-based remote sensing observations and land surface type information, providing an effective method for precise source tracing and pollution prevention. Attached Figure Description

[0035] Figure 1 A method combining surface type and ground-based remote sensing observation NO y Flowchart of the concentration-based pollution zoning method;

[0036] Figure 2 This refers to the surface types within a 5 km radius of the observation station in this embodiment;

[0037] Figure 3 NO x / NO y Wind rose chart of indicators;

[0038] Figure 4 HNO3 / NO x Wind rose chart of indicators;

[0039] Figure 5 NO y Wind rose diagram for three indicators. Detailed Implementation

[0040] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0041] Example

[0042] like Figure 1 As shown, this invention proposes a method for zoning NOy concentration pollution by combining land surface type and ground-based remote sensing observation. The steps are described in detail below.

[0043] 1. The ground-based high-resolution Fourier transform infrared spectrometer needs to be installed in an open area, and the maximum spectral resolution of the spectrometer used is 0.005 cm⁻¹. -1 Its spectral measurement range is 700–4000 cm⁻¹. -1 A weather station was set up next to the spectrometer to simultaneously acquire real-time information such as temperature, humidity, air pressure, wind direction, and wind speed.

[0044] This invention uses mid-infrared absorption spectra collected by a spectrometer under clear weather conditions. Then, prior vertical concentrations calculated using the HITRAN molecular database and the WACCM atmospheric climate model, along with a nonlinear least squares algorithm, are used to invert the spectra. The inversion band used in NO is 1900.45–1900.55 cm⁻¹. -1 The inversion band used for NO2 is 2914.550–2924.925 cm⁻¹. -1 The inversion band used for HNO3 is 868.05-874.00 cm⁻¹. -1 The vertical profile concentration of the target gas can be obtained through spectral inversion. Then, the tropospheric column concentration is calculated by multiplying the vertical concentration from 0 to 12 km with the dry air column layer by layer.

[0045] II. Long-term time series data of tropospheric concentrations of NO, NO2, and HNO3 were obtained through inversion. To ensure the accuracy and stability of the inverted data, data with a fitting residual greater than 2% in the spectral inversion were removed. Since spectral acquisition was only conducted under clear weather conditions, and the acquisition times varied slightly, the observation times for NO, NO2, and HNO3 differed slightly. Therefore, the average of the observed tropospheric concentrations of NO, NO2, and HNO3 was taken hourly to ensure the time correspondence of the target gases NO, NO2, and HNO3. For the wind direction and speed data at the time of observation, vector averaging was used to obtain the hourly average. The vector averaging method is as follows: the observed wind speed is decomposed into... Components and The component has two components, while the wind direction is converted into radians.

[0046] , ,

[0047] in Instantaneous wind speed (m / s) Given the instantaneous wind direction (°), calculate the average of all components within the same hour:

[0048]

[0049] in, To ensure an effective number of observations, and These represent the arithmetic mean of all u and v components within one hour, respectively. Finally, the average wind speed for that hour is obtained from the average components. and average wind direction :

[0050]

[0051]

[0052] in, This is a four-quadrant arctangent function, used to calculate the mean wind vector components. Calculate wind direction in radians; mod is the modulo function used to normalize the angle result to the range of 0 to 360 degrees.

[0053] 3. The tropospheric concentrations of NO, NO2, and HNO3 calculated in step 3 are further added together to calculate the NO concentration. y Tropospheric concentration, calculated as NO y =NO + NO2 + HNO3. Then, calculate NO. y The ratio index: Ratio NO x / NOy This can be used to characterize the compositional proportion of newly generated nitrogen oxide primary pollutants, including NO. x =NO + NO2; Ratio HNO3 / NO x It can characterize the intensity of photochemical reactions and the proportion of secondary pollution formation. The main source of HNO3 in the troposphere is the reaction between OH radicals and NO2. This process is a typical secondary pollution and generally occurs in atmospheric conditions where photochemical reactions are more active.

[0054] The calculated values ​​were matched with wind direction and speed data, a wind rose diagram was created, and the average values ​​of 12 wind direction sectors were compared across different directions, based on a 30° division. y Differences in indicators.

[0055] 4. Retrieve the land cover product MCD12Q1 from the Moderate Resolution Imaging Spectroradiometer (MODIS) in NASA Earthdata Search, download the HDF file for the LC_Type1 band, and load the dataset into the Geographic Information System (GIS) software QGIS.

[0056] Using QGIS's buffer mask clipping tool, a 5 km radius buffer zone centered on the observation station was created, and then clipped using a mask to obtain the land cover area within a 5 km radius of the observation point. The band information within the clipped result was rendered and classified to obtain typical land surface type zones of different colors, such as grassland, cultivated land, urban built-up land, forest, and water bodies. Figure 2 As shown, the area surrounding the observation station is a transitional zone between urban and rural areas, with water bodies dominating the south, farmland dominating the north, and mixed construction areas and grassland wetlands in the east and west directions.

[0057] 5. Using QGIS, land cover data is divided into 12 wind direction sectors with a 30° buffer zone. For each sector, QGIS's intersection tool is used to overlay the wind direction sector layer with the land cover data, outputting land cover information for each wind direction sector. Then, QGIS's field calculator is used to count the number of pixels for each category, and the proportion of different land cover categories in each sector is calculated based on the total number of pixels in each sector. When the proportion of a certain land cover type is greater than 40%, that category is designated as the "dominant land cover type"; otherwise, it is marked as a "mixed land cover type." The land cover types and NO... y Connect the indicators and plot NO. x / NO y HNO3 / NO x with NO y Wind rose diagrams for three indicators, such as Figure 3 , Figure 4 , Figure 5 shown. NO yThe highest column concentrations were observed in the east-northeast direction, an area primarily characterized by urban and construction land, indicating the most significant pollutant emissions and accumulation in this direction, making it a major pollution source area. The southward direction was dominated by water bodies and mixed surface types, with the highest HNO3 / NOx ratio, suggesting that the air mass underwent strong photochemical transformation downwind. x Large amounts are oxidized to HNO3; the eastern and northeastern regions are mainly farmland and cities, where NO... x / NO y The ratio is relatively high, indicating that the photochemical degree of the air mass in this direction is low, representing relatively fresh emissions. Overall, nitrogen oxides near the site area tend to accumulate more in eastern urban areas, undergo stronger photochemical transformation in the south, and have fresh air mass inputs in the northeast.

[0058] This invention creatively integrates high-precision ground-based remote sensing observations with satellite surface classification information to achieve NO... y This invention represents a groundbreaking breakthrough in pollution characterization, moving from single-point temporal data to spatial zoning, successfully addressing the core challenge of spatial pollution attribution using traditional observation techniques. It not only utilizes wind sector analysis and key chemical ratios to qualitatively identify the dominant pollution types of primary emissions and secondary transformations in different locations, but also incorporates high-resolution satellite land cover data to precisely anchor pollution characteristics to the actual surface types at a fine scale around the observation points. This significantly improves the spatial resolution and accuracy of pollution attribution, avoiding the uncertainties of traditional trajectory models. This technical approach transcends simple concentration monitoring, revealing pollution formation mechanisms from the perspective of atmospheric chemical process diagnosis. Furthermore, the invention relies on universally applicable data sources and a clear technical route, facilitating operational deployment and long-term application within existing monitoring networks. It provides environmental management departments with an intuitive and scientific decision-making tool, enabling them to accurately trace sources, assess chemical transformation trends, and quantify emission reduction effects, ultimately providing crucial technical support for the refined prevention and control of regional pollution.

[0059] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A method combining land surface type and ground-based remote sensing observation NO y The method for zoning pollution by concentration is characterized by, Includes the following steps: S1. The concentration time series of NO, NO2 and HNO3 in the atmosphere were obtained by inversion using a ground-based high-resolution Fourier transform infrared spectrometer, and their tropospheric concentrations were calculated. S2. Measure the hourly averaged observation points of the tropospheric concentrations of NO, NO2 and HNO3, and determine the effective wind direction using wind direction and speed data in an hourly vector averaging manner. S3. Using NO, NO2, and HNO3 as representatives, calculate NO. y Total Amount and Ratio Indicators NO x / NO y and HNO3 / NO x NO x The sum of NO and NO2 is calculated, and the observed samples are classified according to wind direction and sector. The NO values ​​are then statistically analyzed. y Indicator probability distribution; S4. Acquire satellite land cover product data and identify the land surface type in the area near the site; S5. Divide the land cover data into sectors according to wind direction, calculate the proportion of land surface types in each sector, determine the dominant land surface type, and assign the NO values ​​of the corresponding sectors within the same time period. y The indicators are correlated with the dominant land surface types to identify pollution characteristics dominated by primary emissions or secondary transformation.

2. The method according to claim 1 that combines surface type with ground-based remote sensing observation NO y The method for zoning pollution by concentration is characterized by, In step S1, the maximum spectral resolution of the ground-based high-resolution Fourier transform infrared spectrometer is 0.005 cm⁻¹. -1 The spectral measurement range is 700–4000 cm⁻¹ -1 .

3. The method according to claim 1 that combines surface type with ground-based remote sensing observation NO y The method for zoning pollution by concentration is characterized by, In step S1, when retrieving the concentrations of NO, NO2, and HNO3, the prior vertical concentrations calculated using the HITRAN molecular database and the WACCM atmospheric climate model are used, and a nonlinear least squares algorithm is employed for spectral inversion.

4. The method according to claim 1 that combines surface type with ground-based remote sensing observation NO y The method for zoning pollution by concentration is characterized by, In step S1, the band range used for NO inversion is 1900.45–1900.55 cm. -1 The band range used for NO2 inversion is 2914.550–2924.925 cm. -1 The band range used for HNO3 inversion is 868.05–874.00 cm. -1 .

5. A method for combining surface type and ground-based remote sensing observation NO as described in claim 1 y The method for zoning pollution by concentration is characterized by, In step S1, when calculating the tropospheric concentration, the vertical concentration from 0 to 12 km is multiplied layer by layer with the dry air column to obtain the tropospheric column concentration.

6. The method according to claim 1 that combines surface type with ground-based remote sensing observation NO y The method for zoning pollution by concentration is characterized by, In step S2, the vector averaging method includes: decomposing instantaneous wind speed and wind direction into... Components and Calculate the hourly average component, and then obtain the average wind speed and average wind direction.

7. A method for combining surface type and ground-based remote sensing observation NO as described in claim 6 y The method for zoning pollution by concentration is characterized by, The Components and The formula for calculating the components is: , in, Let θ be the instantaneous wind speed and θ be the instantaneous wind direction. The wind direction angle is in radians; The hourly average component is calculated as follows: , in, To ensure an effective number of observations, and These represent the arithmetic mean of all u and v components within one hour, respectively. Average wind speed and average wind direction The calculation is as follows: , ， in, This is a four-quadrant arctangent function, used to calculate the mean wind vector components. Calculate wind direction in radians; mod is the modulo function used to normalize the angle result to the range of 0 to 360 degrees.

8. The method for zoning NOy concentration pollution by combining surface type and ground-based remote sensing observation as described in claim 1, characterized in that, In step S3, there are 12 wind direction sectors, each sector is 30°.

9. The method for zoning NOy concentration pollution by combining surface type and ground-based remote sensing observation as described in claim 1, characterized in that, In step S4, the satellite land cover product data is the LC_Type1 band of MODIS' MCD12Q1 product, and the area near the station is a region with a radius of 5 km centered on the observation station.

10. The method for zoning NOy concentration pollution by combining surface type and ground-based remote sensing observation as described in claim 1, characterized in that, In step S5, the criterion for determining the dominant land surface type is: when the proportion of a certain land surface type is greater than 40%, it is defined as the dominant land surface type; otherwise, it is marked as a mixed land type. y Indicators include NO x / NO y HNO3 / NO x and NO y Concentration, of which NO x / NO y High value and HNO3 / NO x A low value indicates that primary pollutants are the main pollutants emitted, NO x / NO y Low value and HNO3 / NO x A high value indicates that the pollutants are mainly secondary pollutants.

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