Urban fine-scale day-by-day atmospheric pollutant concentration estimation method

Abstract

The invention relates to an urban fine-scale day-by-day atmospheric pollutant concentration estimation method, which comprises the following steps: acquiring urban region multi-source data, and performing multi-scale space-time matching processing; constructing a global estimation model of the atmospheric pollutants and training the global estimation model; obtaining the annual global atmospheric pollutant concentration under the fine scale of the urban area, and converting the annual global atmospheric pollutant concentration into a global atmospheric pollutant concentration weight; calculating a day-by-day local atmospheric pollutant concentration estimation residual error; and estimating to obtain the urban fine-scale day-by-day atmospheric pollutant concentration. The method has the beneficial effects that global features and local changes are comprehensively considered, the spatial resolution of an existing data product in a city is effectively improved, a high-precision hectometer-level day-by-day atmospheric pollutant data product is obtained, a polluted area can be positioned more accurately, and the method has a wide application prospect. Therefore, formulation and implementation of accurate prevention and control measures of urban atmospheric pollution are more scientifically guided, and the method has important application value.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and more specifically, to a method for estimating the daily concentration of atmospheric pollutants in a fine-scale city. Background Art

[0002] Intensive human activities in cities lead to the emission of a large amount of pollutants, resulting in a significant decline in the quality of the atmospheric environment. A large number of studies have shown that atmospheric pollutants, especially fine particulate matter PM2.5, can have various adverse effects on human health. In order to achieve precise prevention and control of atmospheric pollutants in urban areas, timely and accurate grasp of the spatial and temporal distribution of atmospheric pollutants within the city is the key to solving the problem.

[0003] At present, my country has built a relatively complete ground air quality monitoring network, which can monitor the air quality of key urban areas in real time. However, the spatial distribution of limited ground monitoring stations is still relatively sparse compared to the entire urban area, making it difficult to reflect the distribution of atmospheric pollutants in the entire region. With the development of artificial intelligence and satellite remote sensing technology, a large number of studies have combined ground station monitoring with satellite remote sensing technology to estimate the distribution of atmospheric pollutants in a large range, making up for the shortcomings of ground station monitoring. However, due to the limitations of satellite hardware, the current spatial resolution of satellite observations is low, only reaching the kilometer scale, and it is difficult to reflect the refined distribution of atmospheric pollutants within the city. Therefore, there is an urgent need for an estimation method for the concentration distribution of atmospheric pollutants at a fine urban scale to meet the actual needs of urban air pollution control. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies of the prior art and to propose a method for estimating the daily concentration of atmospheric pollutants in a city at a fine scale.

[0005] First, a method for estimating the daily concentration of atmospheric pollutants in a city at a fine scale is provided, including:

[0006] Step 1: Obtain multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data;

[0007] Step 2: Perform multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution spatiotemporal dataset and a fine-scale spatiotemporal dataset;

[0008] Step 3: Based on the low-resolution spatiotemporal dataset, a global estimation model of atmospheric pollutants is constructed and trained, and model parameters are iteratively optimized;

[0009] Step 4: Use the trained global estimation model and the fine-scale spatiotemporal data set to obtain the annual global atmospheric pollutant concentrations at the fine scale of the urban area and convert them into global atmospheric pollutant concentration weights;

[0010] Step 5: Calculate the daily local atmospheric pollutant concentration estimation residual based on the low-resolution daily satellite remote sensing inversion data;

[0011] Step 6: Combine the fine-scale spatiotemporal dataset, the global air pollutant concentration weights and the daily local air pollutant concentration estimation residuals to estimate the fine-scale daily air pollutant concentrations in the city.

[0012] Preferably, the satellite remote sensing inversion data is low-resolution daily atmospheric pollutant fine particulate matter PM 2.5 Remote sensing inversion data; land use data is fine-scale annual classification data, covering water bodies, trees, flooded vegetation, crops, built-up areas, bare land and pasture types; ground digital elevation model data is fine-scale raster data; road network data is annual line vector data, and the road network includes expressways, trunk roads, first-class highways, second-class highways and third-class highways; ground station monitoring data is hourly real-time monitoring data of the national ground air quality monitoring network

[0013] Preferably, step 2 comprises:

[0014] Step 2.1: Based on the satellite remote sensing inversion data, a low-resolution scale standard grid is constructed. In a unified spatial coordinate system, the proportion of the area occupied by each land use type in the low-resolution scale standard grid, the mean of the ground digital elevation model data, and the total length of each road are counted, and the atmospheric fine particulate matter PM is calculated at the same time. 2.5 The annual mean of remote sensing inversion data is used to obtain a low-resolution spatiotemporal dataset;

[0015] Step 2.2: Construct a fine-scale standard grid in a unified spatial range and coordinate system, and calculate the proportion of each land use type in the fine-scale standard grid, the mean of the ground digital elevation model data, and the total length of each road. 2.5 The remote sensing inversion data was obtained by using the nearest neighbor resampling method to obtain the daily PM 2.5 A fine-scale dataset.

[0016] Preferably, in step 3, the global estimation model of air pollutants is a supervised machine learning model, and the supervised machine learning model is any one of a random forest regression model, a Cubist regression model and an XGBoost regression model; the formula of the supervised machine learning model is expressed as:

[0017] PM2.5 global= F(DEM, Road, LU water , L.U. veg , L.U. wet , L.U. agri , L.U. build , L.U. bare , L.U. farm )

[0018] In the above formula, PM2.5 global Indicates PM 2.5 where F represents any supervised machine learning model, DEM represents digital surface elevation, Road represents the total length of various roads, LU represents the proportion of each land use type, and the subscripts water, veg, wet, agri, build, bare and farm represent water, trees, flooded vegetation, crops, built-up areas, bare land and pasture, respectively.

[0019] Preferably, in step 4, the spatiotemporal dataset of elevation, road length, and proportion of each land use type at a fine scale constructed in step 2.2 is used as the input of the global estimation model to obtain the annual global atmospheric pollutant concentration at a fine scale in the urban area; and the global PM is obtained by standardized calculation within the range of the low-resolution scale standard grid. 2.5 Concentration weight, its formula is expressed as:

[0020]

[0021] In the above formula, Represents the PM calculated after normalization of the i-th fine-scale grid within a low-resolution scale standard grid. 2.5 Concentration weight, For the corresponding PM 2.5 The global concentration is calculated by normalization, where the average value of all weights within each low-resolution scale standard grid is 1.

[0022] Preferably, step 5 comprises:

[0023] Step 5.1: Based on the low-resolution daily satellite remote sensing inversion data, the inverse distance weighted method is used to obtain the fine-scale daily urban area PM 2.5 Spatial distribution;

[0024] Step 5.2: Convert the fine-scale daily urban area PM 2.5 Daily PM obtained by nearest neighbor resampling method during spatial distribution and spatiotemporal matching 2.5 Subtract the data;

[0025] Step 5.3: Within each low-resolution scale standard grid, the residuals are standardized to obtain the daily local atmospheric pollutant concentration estimation residuals, which are expressed as follows:

[0026]

[0027] In the above formula, Represents the PM calculated after normalization of the i-th fine-scale grid within a low-resolution scale standard grid. 2.5 The local residual of the concentration, and Represent the fine-scale PM obtained based on the inverse distance weighting method and the nearest neighbor resampling method respectively. 2.5 Data, mean and σ represent all fine-scale PM within the low-resolution standard grid. 2.5 The mean and standard deviation of the local residuals for concentration.

[0028] Preferably, the calculation formula of step 6 is:

[0029]

[0030] In the above formula, represents the PM of the kth fine-scale grid in the urban area 2.5 Estimate the concentration.

[0031] In a second aspect, a system for estimating daily atmospheric pollutant concentrations at a fine scale in a city is provided, which is used to execute any of the methods described in the first aspect, including:

[0032] The first acquisition module is used to acquire multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data;

[0033] A matching module, used for performing multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution spatiotemporal dataset and a fine-scale spatiotemporal dataset;

[0034] A construction module is used to construct and train a global estimation model of atmospheric pollutants based on the low-resolution spatiotemporal dataset, and iteratively optimize model parameters;

[0035] The second acquisition module is used to use the trained global estimation model and the fine-scale spatiotemporal data set to obtain the annual global atmospheric pollutant concentration at the fine scale of the urban area and convert it into the global atmospheric pollutant concentration weight;

[0036] A calculation module is used to calculate the residuals of the daily local atmospheric pollutant concentration estimation based on the low-resolution daily satellite remote sensing inversion data;

[0037] The estimation module is used to combine the fine-scale spatiotemporal data set, the global atmospheric pollutant concentration weights and the daily local atmospheric pollutant concentration estimation residuals to estimate the fine-scale daily atmospheric pollutant concentrations in the city.

[0038] According to a third aspect, a computer storage medium is provided, wherein a computer program is stored in the computer storage medium; when the computer program is executed on a computer, the computer executes any method described in the first aspect.

[0039] In a fourth aspect, an electronic device is provided, including:

[0040] Memory, used to store computer programs;

[0041] A processor is used to execute the computer program to implement any method as described in the first aspect.

[0042] The beneficial effects of the present invention are as follows: the present invention first constructs a global estimation model of atmospheric pollutants based on a low-resolution spatiotemporal data set, and based on the scale-invariant hypothesis, obtains the global atmospheric pollutant concentration trend distribution weights at a fine scale in the urban area, further considers the daily local changes of atmospheric pollutants, calculates the daily atmospheric pollutant estimation residuals, and uses the residuals to correct the daily atmospheric pollutant concentrations calculated by the global trend weights, and finally obtains the daily atmospheric pollutant concentrations at a fine scale in the city. Compared with the existing kilometer-level atmospheric pollutant products, the present invention comprehensively considers the global characteristics and local changes, effectively improves the spatial resolution of the existing data products within the city, and obtains high-precision daily atmospheric pollutant data products at the hundred-meter level, which can more accurately locate the polluted area, thereby more scientifically guiding the formulation and implementation of precise prevention and control measures for urban atmospheric pollution, and has important application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of a method for estimating daily atmospheric pollutant concentrations at a fine scale in a city according to an embodiment of the present invention;

[0044] Figure 2 A scatter plot for verifying the method for estimating daily atmospheric pollutant concentrations at a fine scale in a city according to an embodiment of the present invention;

[0045] Figure 3 This is a spatial distribution diagram of the method for estimating daily atmospheric pollutant concentrations at a fine scale in a city according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further described below in conjunction with embodiments. The description of the following embodiments is only used to help understand the present invention. It should be noted that for ordinary persons in the art, without departing from the principle of the present invention, the present invention can also be modified in some ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

[0047] Embodiment 1:

[0048] To solve the problems of the prior art, Example 1 of the present application provides a method for estimating the daily concentration of atmospheric pollutants in a city at a fine scale, such as Figure 1 As shown, including:

[0049] Step 1: Obtain multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data.

[0050] Specifically, the satellite remote sensing inversion data is a low-resolution (1 km) daily atmospheric pollutant fine particulate matter PM 2.5 Remote sensing inversion data; land use data is annual classification data at a fine scale (10 meters), covering water bodies, trees, flooded vegetation, crops, built-up areas, bare land and pasture types; ground digital elevation model data is raster data at a fine scale (30 meters); road network data is annual line vector data, and the road network includes expressways, trunk lines, first-class highways, second-class highways and third-class highways; ground station monitoring data is hourly real-time monitoring data of the national ground air quality monitoring network.

[0051] Step 2: Perform multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution scale spatiotemporal dataset and a fine-scale spatiotemporal dataset.

[0052] Step 2 includes:

[0053] Step 2.1: Based on the satellite remote sensing inversion data, a low-resolution scale (1 km) standard grid is constructed. In a unified spatial coordinate system, the proportion of the area occupied by each land use type in the low-resolution scale standard grid, the mean of the ground digital elevation model data, and the total length of each road are counted, and the atmospheric fine particulate matter PM is calculated at the same time. 2.5 The annual mean of remote sensing inversion data is used to obtain a low-resolution spatiotemporal dataset;

[0054] Step 2.2: Based on the constructed low-resolution scale standard grid, a fine-scale (100-meter) standard grid is further constructed in its unified spatial range and coordinate system. The proportion of the area occupied by each land use type in the 100-meter standard grid (the total number of land use grids in each 10-meter grid in the 100-meter standard grid / 100), the mean of the ground digital elevation model data (the mean of all 30-meter digital elevation model grids covered in the 100-meter standard grid) and the total length of each road (the sum of the lengths of all roads covered in the 100-meter standard grid) are counted to calculate the low-resolution daily PM 2.5 The remote sensing inversion data was obtained by using the nearest neighbor resampling method to obtain the daily PM 2.5 100m resolution dataset.

[0055] Step 3: Based on the low-resolution spatiotemporal dataset, a global estimation model for atmospheric pollutants is constructed and trained, and model parameters are iteratively optimized.

[0056] In step 3, the global estimation model of air pollutants is a supervised machine learning model, and the supervised machine learning model is any one of a random forest regression model, a Cubist regression model, and an XGBoost regression model; the formula of the supervised machine learning model is expressed as follows:

[0057] PM2.5 global = F(DEM, Road, LU water , L.U. veg , L.U. wet , L.U. agri , L.U. build , L.U. bare , L.U. farm )

[0058] In the above formula, PM2.5 global Indicates PM 2.5 where F represents any supervised machine learning model, DEM represents digital surface elevation, Road represents the total length of various roads, LU represents the proportion of each land use type, and the subscripts water, veg, wet, agri, build, bare and farm represent water, trees, flooded vegetation, crops, built-up areas, bare land and pasture, respectively.

[0059] Step 4: Use the trained global estimation model in combination with the fine-scale spatiotemporal dataset to obtain the annual global atmospheric pollutant concentrations at the fine scale of the urban area and convert them into global atmospheric pollutant concentration weights.

[0060] Step 5: Calculate the daily local atmospheric pollutant concentration estimation residuals based on the low-resolution daily satellite remote sensing inversion data.

[0061] Step 6: Combine the fine-scale spatiotemporal dataset, the global air pollutant concentration weights and the daily local air pollutant concentration estimation residuals to estimate the fine-scale daily air pollutant concentrations in the city.

[0062] Embodiment 2:

[0063] Based on Example 1, Example 2 of the present application provides a more specific method for estimating the daily concentration of atmospheric pollutants in a fine-scale city, including:

[0064] Step 1: Obtain multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data.

[0065] Step 2: Perform multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution scale spatiotemporal dataset and a fine-scale spatiotemporal dataset.

[0066] Step 3: Based on the low-resolution spatiotemporal dataset, a global estimation model for atmospheric pollutants is constructed and trained, and model parameters are iteratively optimized.

[0067] Step 4: Use the trained global estimation model in combination with the fine-scale spatiotemporal dataset to obtain the annual global atmospheric pollutant concentrations at the fine scale of the urban area and convert them into global atmospheric pollutant concentration weights.

[0068] In step 4, based on the scale-invariant hypothesis, PM 2.5 The relationship between concentration and dependent variables (elevation, road length, and proportion of each land use type) remains unchanged and applicable at a fine scale. Therefore, the global estimation model trained at a low-resolution scale can be used to take the above-constructed spatiotemporal dataset of elevation, road length, and proportion of each land use type at a fine scale as the model input to obtain the annual global concentration of atmospheric pollutants at a fine scale in the urban area. Furthermore, considering that the global concentration is an annual trend result, it reflects the PM concentration of each 100-meter grid in the urban area. 2.5 The distribution trend of concentration needs to be converted into global PM in subsequent calculations 2.5 Concentration weights, so in order to ensure the reliability of the data, the global PM 2.5 Concentration weight, its formula is expressed as:

[0069]

[0070] In the above formula, Represents the PM calculated after normalization of the i-th fine-scale grid within a low-resolution scale standard grid. 2.5 Concentration weight, For the corresponding PM 2.5 The global concentration is calculated by normalization, where the average value of all weights within each low-resolution scale standard grid is 1.

[0071] Step 5: Calculate the daily local atmospheric pollutant concentration estimation residuals based on the low-resolution daily satellite remote sensing inversion data.

[0072] In step 5, the purpose of the daily local atmospheric pollutant concentration estimation residual is to correct the uncertainty based on the global trend estimation and obtain a more reliable daily atmospheric pollutant concentration estimation result.

[0073] Specifically, step 5 includes:

[0074] Step 5.1: Consider the PM inversion of daily satellite remote sensing at low resolution 2.5 Based on the first law of geography, i.e., geographical objects or attributes are interrelated in spatial distribution, and the correlation between similar objects is greater, the inverse distance weighting method is used to obtain the fine-scale daily urban area PM 2.5 Spatial distribution;

[0075] Step 5.2: Convert the fine-scale daily urban area PM 2.5 Daily PM obtained by nearest neighbor resampling method during spatial distribution and spatiotemporal matching 2.5 Subtract the data;

[0076] Step 5.3: Within each low-resolution scale standard grid, the residuals are standardized to obtain the daily local atmospheric pollutant concentration estimation residuals, which are expressed as follows:

[0077]

[0078] In the above formula, Represents the PM normalized by the i-th 100-meter fine-scale grid within a 1-kilometer standard grid. 2.5 The local residual of the concentration, and Represent the fine-scale PM obtained based on the inverse distance weighting method and the nearest neighbor resampling method respectively. 2.5 Data, mean and σ represent all fine-scale PM within the 1 km scale standard grid 2.5 The mean and standard deviation of the local residuals for concentration.

[0079] Step 6: Combine the fine-scale spatiotemporal dataset, the global air pollutant concentration weights and the daily local air pollutant concentration estimation residuals to estimate the fine-scale daily air pollutant concentrations in the city.

[0080] The calculation formula for step 6 is:

[0081]

[0082] In the above formula, represents the PM of the kth fine-scale grid in the urban area 2.5 The estimated concentration is further verified with ground station monitoring results to ensure the accuracy of the estimated results.

[0083] The effect of the present invention is further analyzed by specific experimental results below:

[0084] First, we collected data from the central urban area of ​​Ningbo from January 1 to December 31, 2019, including the daily 1km spatial resolution PM2.5 data from the China High-resolution and High-quality Near-Surface Air Pollutants (CHAP) dataset. 2.5 ESRI's 10m spatial resolution land use data, Open Street Map's road network vector data, USGS's 30m spatial resolution DEM data, and the National Environmental Monitoring Center's ground air quality monitoring station's PM 2.5 The concentration data is subjected to multi-scale spatiotemporal matching according to the aforementioned method of the present invention to obtain a processed multi-scale spatiotemporal data set.

[0085] Then, according to the above method of the present invention, a global estimation model of atmospheric pollutants based on random forest regression was constructed based on a low-resolution spatiotemporal dataset for training, and then combined with a high-resolution spatiotemporal dataset, it was applied to a fine scale of 100 m to calculate the standardized global PM 2.5 The daily global trend results are estimated after concentration weighting, and the daily local residuals are calculated to correct the results. Figure 2 The results show that the proposed model estimates the PM at fine scale. 2.5 R 2 The RMSE was 0.98 and 2.46 μg / m 3 , the slope of the fitting line is 0.98, and the estimation accuracy is high. Figure 3 As shown in the figure, compared with the original atmospheric pollutant data with a spatial resolution of 1 km, the model of the present invention can achieve PM with a higher spatial resolution. 2.5 The estimation can more accurately depict the spatial distribution details of atmospheric pollutants within the city, and can better promote the precise and in-depth control of urban air pollution.

[0086] It should be noted that the parts in this embodiment that are the same or similar to those in Embodiment 1 can be referenced to each other and will not be described in detail in this application.

[0087] Embodiment 3:

[0088] Based on Examples 1 and 2, Example 3 of the present application provides a system for estimating daily atmospheric pollutant concentrations at a fine scale in a city, including:

[0089] The first acquisition module is used to acquire multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data;

[0090] A matching module, used for performing multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution spatiotemporal dataset and a fine-scale spatiotemporal dataset;

[0091] A construction module is used to construct and train a global estimation model of atmospheric pollutants based on the low-resolution spatiotemporal dataset, and iteratively optimize model parameters;

[0092] The second acquisition module is used to use the trained global estimation model and the fine-scale spatiotemporal data set to obtain the annual global atmospheric pollutant concentration at the fine scale of the urban area and convert it into the global atmospheric pollutant concentration weight;

[0093] A calculation module is used to calculate the residuals of the daily local atmospheric pollutant concentration estimation based on the low-resolution daily satellite remote sensing inversion data;

[0094] The estimation module is used to combine the fine-scale spatiotemporal data set, the global atmospheric pollutant concentration weights and the daily local atmospheric pollutant concentration estimation residuals to estimate the fine-scale daily atmospheric pollutant concentrations in the city.

[0095] Specifically, the system provided in this embodiment is a system corresponding to the method provided in Embodiments 1 and 2. Therefore, the parts in this embodiment that are the same or similar to Embodiments 1 and 2 can be referenced to each other and will not be repeated in this application.

[0096] In summary, the present invention provides a method for estimating the concentration of atmospheric pollutants on a daily basis at a fine scale in a city, which can achieve high spatial resolution of PM 2.5 Accurate estimation has important application value.

Claims

1. A method for estimating daily urban air pollutant concentrations at a fine scale, characterized in that: include: Step 1: Obtain multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data; Step 2: Perform multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution spatiotemporal dataset and a fine-scale spatiotemporal dataset; Step 3: Based on the low-resolution spatiotemporal dataset, a global estimation model of atmospheric pollutants is constructed and trained, and model parameters are iteratively optimized; Step 4: Use the trained global estimation model and the fine-scale spatiotemporal data set to obtain the annual global atmospheric pollutant concentrations at the fine scale of the urban area and convert them into global atmospheric pollutant concentration weights; Step 5: Calculate the daily local atmospheric pollutant concentration estimation residual based on the low-resolution daily satellite remote sensing inversion data; Step 6: Combine the fine-scale spatiotemporal dataset, the global air pollutant concentration weights and the daily local air pollutant concentration estimation residuals to estimate the fine-scale daily air pollutant concentrations in the city.

2. The method for estimating daily urban fine-scale atmospheric pollutant concentrations according to claim 1 is characterized in that: The satellite remote sensing inversion data is a low-resolution daily atmospheric pollutant fine particulate matter PM 2.5 Remote sensing inversion data; land use data is annual classification data with fine scale, covering water bodies, trees, flooded vegetation, crops, built-up areas, bare land and pasture types; ground digital elevation model data is raster data with fine scale; road network data is annual line vector data, and the road network includes expressways, trunk roads, first-class highways, second-class highways and third-class highways; The ground station monitoring data is the real-time hourly monitoring data of the national ground air quality monitoring network.

3. The method for estimating daily urban fine-scale atmospheric pollutant concentrations according to claim 2 is characterized in that: Step 2 includes: Step 2.1: Based on the satellite remote sensing inversion data, a low-resolution scale standard grid is constructed. In a unified spatial coordinate system, the proportion of the area occupied by each land use type in the low-resolution scale standard grid, the mean of the ground digital elevation model data, and the total length of each road are counted, and the atmospheric fine particulate matter PM is calculated at the same time. 2.5 The annual mean of remote sensing inversion data is used to obtain a low-resolution spatiotemporal dataset; Step 2.2: Construct a fine-scale standard grid in a unified spatial range and coordinate system, and calculate the proportion of each land use type in the fine-scale standard grid, the mean of the ground digital elevation model data, and the total length of each road. 2.5 The remote sensing inversion data was obtained by using the nearest neighbor resampling method to obtain the daily PM 2.5 A fine-scale dataset.

4. The method for estimating daily urban air pollutant concentrations at a fine scale according to claim 3 is characterized in that: In step 3, the global estimation model of air pollutants is a supervised machine learning model, and the supervised machine learning model is any one of a random forest regression model, a Cubist regression model, and an XGBoost regression model; the formula of the supervised machine learning model is expressed as: PM2.5 global =F(DEM,Road,LU water ,THE veg ,THE wet ,THE agri ,THE build ,THE bare ,THE farm ) In the above formula, PM2.5 global Indicates PM 2.5 where F represents any supervised machine learning model, DEM represents digital surface elevation, Road represents the total length of various roads, LU represents the proportion of each land use type, and the subscripts water, veg, wet, agri, build, bare and farm represent water, trees, flooded vegetation, crops, built-up areas, bare land and pasture, respectively.

5. The method for estimating daily urban fine-scale atmospheric pollutant concentrations according to claim 4 is characterized in that: In step 4, the spatiotemporal dataset of elevation, road length, and proportion of each land use type at a fine scale constructed in step 2.2 is used as the input of the global estimation model to obtain the annual global atmospheric pollutant concentration at a fine scale in the urban area; The global PM is calculated by standardization within the low-resolution standard grid. 2.5 Concentration weight, its formula is expressed as: In the above formula, Represents the PM calculated after normalization of the i-th fine-scale grid within a low-resolution standard grid. 2.5 Concentration weight, For the corresponding PM 2.5 The global concentration is calculated by normalization, where the average value of all weights within each low-resolution scale standard grid is 1.

6. The method for estimating daily urban air pollutant concentrations at a fine scale according to claim 5 is characterized in that: Step 5 includes: Step 5.1: Based on the low-resolution daily satellite remote sensing inversion data, the inverse distance weighted method is used to obtain the fine-scale daily urban area PM 2.5 Spatial distribution; Step 5.2: Convert the fine-scale daily urban area PM 2.5 Daily PM obtained by nearest neighbor resampling method in the process of spatial distribution and spatiotemporal matching 2.5 Subtract the data; Step 5.3: Within each low-resolution scale standard grid, the residuals are standardized to obtain the daily local atmospheric pollutant concentration estimation residuals, which are expressed as follows: In the above formula, Represents the PM calculated after normalization of the i-th fine-scale grid within a low-resolution standard grid. 2.5 The local residual of the concentration, and Represent the fine-scale PM obtained based on the inverse distance weighting method and the nearest neighbor resampling method respectively. 2.5 Data, mean and σ represent all fine-scale PM within the low-resolution standard grid. 2.5 The mean and standard deviation of the local residuals for concentration.

7. The method for estimating daily urban air pollutant concentrations at a fine scale according to claim 6, characterized in that: The calculation formula for step 6 is: In the above formula, represents the PM of the kth fine-scale grid in the urban area 2.5 Estimate the concentration.

8. A system for estimating the concentration of daily atmospheric pollutants at a fine scale in a city, characterized in that: Used to perform the method according to any one of claims 1 to 7, comprising: The first acquisition module is used to acquire multi-source data of urban areas, including satellite remote sensing inversion data, land use data, ground digital elevation model data, road network data and ground site monitoring data; A matching module, used for performing multi-scale spatiotemporal matching processing on the multi-source data of the urban area to obtain a processed multi-scale spatiotemporal dataset; the multi-scale spatiotemporal dataset includes a low-resolution spatiotemporal dataset and a fine-scale spatiotemporal dataset; A construction module is used to construct and train a global estimation model of atmospheric pollutants based on the low-resolution spatiotemporal dataset, and iteratively optimize model parameters; The second acquisition module is used to use the trained global estimation model and the fine-scale spatiotemporal data set to obtain the annual global atmospheric pollutant concentration at the fine scale of the urban area and convert it into the global atmospheric pollutant concentration weight; A calculation module is used to calculate the residuals of the daily local atmospheric pollutant concentration estimation based on the low-resolution daily satellite remote sensing inversion data; The estimation module is used to combine the fine-scale spatiotemporal data set, the global atmospheric pollutant concentration weights and the daily local atmospheric pollutant concentration estimation residuals to estimate the fine-scale daily atmospheric pollutant concentrations in the city.

9. A computer storage medium, characterized in that The computer storage medium stores a computer program; when the computer program is executed on a computer, the computer executes any one of the methods described in claims 1 to 7.

10. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the method according to any one of claims 1 to 7.

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