PM2.5 pollution-meteorological comprehensive index construction method

Abstract

The invention discloses a PM2.5 pollution-meteorological comprehensive index construction method, which comprises the following steps: analyzing the correlation between the near-ground PM2.5 concentration of a target research region and a plurality of meteorological elements from a monthly scale, and determining the meteorological elements strongly related to the PM2.5 concentration and strongly related regions of each target research region; averaging meteorological elements strongly related to PM2.5 concentration of each target research area in a strongly related area, and defining an average value or an average value difference as a new meteorological variable for constructing a pollution-meteorological index; and respectively standardizing the time sequence of each new meteorological variable, adding the standardized different time sequences to obtain a comprehensive variable, and finally standardizing the comprehensive variable to obtain a pollution-meteorological index. The method has the advantages that the comprehensive index, namely the PM2.5 pollution-meteorological index, capable of representing the PM2.5 pollution meteorological condition in winter is constructed, and the quality of the meteorological condition related to PM2.5 pollution is known based on the pollution-meteorological index.

Description

Technical Field

[0001] The present invention relates to the fields of atmospheric science and atmospheric environment technology, and in particular to a method for constructing a PM2.5 pollution-meteorological comprehensive index. Background Art

[0002] PM2.5 pollution is a major air pollution problem currently facing Chinese society. In most parts of eastern China, especially in the Beijing-Tianjin-Hebei region and the Yangtze River Delta, PM2.5 pollution incidents frequently occur in winter, seriously threatening human health and traffic safety. In January 2013, the highest hourly PM2.5 concentration in Beijing exceeded 500μg / m 3 . In order to reduce anthropogenic emissions of air pollutants, relevant departments have proposed a series of strict emission control measures. Many recent studies have shown that the concentration of PM2.5 near the ground in eastern China has been on a downward trend since 2013. The PM2.5 data analysis of the Ministry of Ecology and Environment of China found that the concentration of PM2.5 near the ground in China decreased by -3.1±2.2μg / m annually from 2015 to 2019. 3 Many studies have suggested that the decline in near-surface PM2.5 concentrations in eastern China in recent years is mainly due to the reduction in anthropogenic emissions of aerosols and their precursors. The Multiscale Emission Inventory Model of China (MEIC; http: / / www.meicmodel.org) estimates that from 2013 to 2017, national PM2.5 emissions fell by 33%, and PM2.5 precursors SO2, NOx, and NH3 emissions fell by 59%, 21%, and 33%, respectively.

[0003] In addition to changes in anthropogenic emissions, changes in large-scale circulation and local meteorological conditions will also affect the interannual variation and trend of aerosol concentrations. Meteorological conditions at different scales will change the concentration of atmospheric aerosols by changing emissions, affecting the generation, diffusion and deposition of aerosol particles. Many studies have constructed pollution-meteorological indices to characterize the impact of meteorological factors on pollutant concentrations. For example, Zhang et al. (2019) discussed the changes in China's meteorological conditions that affected PM2.5 concentrations from 2013 to 2017 by constructing a comprehensive pollution-meteorological index (PLAM). They believed that compared with 2013, the meteorological conditions in the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta regions of China deteriorated in 2014 and 2015, and improved in 2016 and 2017. Overall, the changes in meteorology and climate from 2013 to 2017 were conducive to the reduction of PM2.5 concentrations. Compared with the winter of 2013, the PM2.5 concentrations in Beijing-Tianjin-Hebei and the Yangtze River Delta decreased by 40.2% and 38.2% in the winter of 2017, respectively, of which the role of changes in meteorological conditions accounted for 49.3% and 80%, respectively. Although many studies have constructed the statistical relationship between pollution and meteorology, no pollution-meteorological index has been constructed separately for each typical region in eastern China (Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Fenwei Plain) in recent years. Summary of the invention

[0004] The purpose of the present invention is to provide a method to solve the above-mentioned problems existing in the prior art.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for constructing a PM2.5 pollution-meteorological comprehensive index comprises the following steps:

[0007] S1. Correlation analysis and determination of strongly correlated meteorological factors:

[0008] Analyze the correlation between the near-surface PM2.5 concentration and multiple meteorological elements in the target study area on a monthly scale, and determine the meteorological elements and areas with strong correlation with PM2.5 concentration in each target study area;

[0009] S2. Determination of new meteorological variables involved in the construction of the pollution-meteorological index:

[0010] The meteorological elements that are strongly correlated with PM2.5 concentration in each target study area are averaged within the strongly correlated area, and the average value or the difference in the average value is defined as a new meteorological variable for the construction of the pollution-meteorological index;

[0011] S3. Pollution-meteorological index determination:

[0012] The time series of each new meteorological variable is standardized separately, and the standardized time series are added together to obtain a comprehensive variable. Finally, the comprehensive variable is standardized to obtain the pollution-meteorological index.

[0013] Preferably, step S1 specifically includes the following steps:

[0014] S11. Select Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Fenwei Plain as the target research areas;

[0015] S12. Conduct quality control on the hourly observation data of near-surface PM2.5 in winter from 2014 to 2020 of the Ministry of Ecology and Environment of China, and determine the monitoring sites involved in the relevant analysis;

[0016] S13, select meteorological data for relevant analysis from the 6-hour, 1°×1° meteorological reanalysis data of the National Center for Environmental Prediction of the United States;

[0017] S14. Conduct correlation analysis on the hourly PM2.5 observation data of the monitoring stations and the monthly average of meteorological data to determine the positive and negative correlation between the near-ground PM2.5 concentration in the target study area and various meteorological elements.

[0018] Preferably, step S12 specifically includes the following contents:

[0019] S121. For each monitoring station, missing values, duplicate values, zero values, negative values ​​and outliers are eliminated; among them, based on the 3σ rule of normal distribution, data with a distance greater than 3 times the standard deviation from the multi-time average value are considered outliers;

[0020] S122. For the month in which the monthly coverage rate of valid hourly data is not less than 70%, calculate the monthly average value;

[0021] S123. Remove monitoring sites with discontinuous monthly mean values ​​and only retain monitoring sites with continuous monthly mean values.

[0022] Preferably, the meteorological elements involved in the correlation analysis include 2m relative humidity, 2m atmospheric temperature, high-altitude atmospheric temperature, 10m wind speed, boundary layer height, precipitable water, 500hPa potential height, 850hPa potential height, 500hPa zonal wind, 850hPa zonal wind, 500hPa meridional wind, and 850hPa meridional wind.

[0023] Preferably, the meteorological element that is strongly correlated with the PM2.5 concentration is a meteorological element whose correlation with the PM2.5 concentration is greater than a preset positive threshold or less than a preset negative threshold.

[0024] Preferably, the meteorological factors strongly correlated with PM2.5 concentration in each target study area are:

[0025] Beijing-Tianjin-Hebei: 500hpa meridional wind, 850hpa geopotential height east-west difference, local boundary layer height;

[0026] Fenwei Plain: 500hpa geopotential height east-west difference, 850hpa zonal wind, local boundary layer height, 10m wind speed;

[0027] Yangtze River Delta: 500hp zonal wind, 850hpa meridional wind, local and surrounding precipitable rainfall;

[0028] Pearl River Delta: 500hpa meridional wind, 850hpa meridional wind, local and surrounding precipitation.

[0029] Preferably, step S3 specifically comprises:

[0030] S31, using formula 1) to standardize the new meteorological variables used to construct the pollution-meteorological index and sum them to obtain an intermediate variable;

[0031]

[0032] Among them, V i,j is the new meteorological variable; i is a different meteorological variable; j is the target research area; A( ) is the average; S( ) is the standard deviation; X j is an intermediate variable;

[0033] S32, using formula 2) to standardize the intermediate variables to obtain the pollution-meteorological index of the corresponding target study area;

[0034]

[0035] Among them, PMI j is the pollution-meteorological index of the target study area.

[0036] The beneficial effects of the present invention are as follows: using atmospheric PM2.5 monitoring data and NECP FNL data, through correlation analysis, a comprehensive index that can characterize the meteorological conditions of PM2.5 pollution in winter is constructed for typical different regions in eastern China (Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Fenwei Plain) - PM2.5 pollution-meteorological index (PMI), and based on the pollution-meteorological index (PMI), the quality of meteorological conditions related to PM2.5 pollution is known. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a principle flow chart of the method in the embodiment of the present invention;

[0038] Figure 2 is the near-surface PM2.5 concentration in Beijing, Tianjin and Hebei, China in the winter of 2014-2020 and a) V500 in the embodiment of the present invention Bd) GD850 B , g) PBLH B ,j)PMI B , the PM2.5 concentration near the surface of the Yangtze River Delta and b) U500 Y , e) V850 Y 、h)PRE Y , k) PMI Y , PM2.5 concentration near the ground in the Pearl River Delta and e)V500 P f) V850 P i) PRE P 1) PMI P Schematic diagram of the time variation of

[0039] Figure 3 is the PM2.5 concentration near the ground in the Fenwei Plain of China in the winter of 2014-2020 and a) GD500 in the embodiment of the present invention F b) U850 F 、c)WS10 F d) PBLH F and e) PMI F Schematic diagram of the time variation of

[0040] Figure 2 and Figure 3 In the figure, the thick black solid line and the numbers on the left represent the monthly changes in PM2.5 concentration; the thin black solid line (dots) and the numbers on the right represent the monthly (annual) changes in meteorological variables or PMI; the black dotted line and the nearby numbers and the numbers on the left represent the interannual change trend of PMI; the numbers in the upper right corner represent the correlation coefficient between PM2.5 concentration and meteorological variables or PMI. DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described here are only used to explain the present invention and are not used to limit the present invention.

[0042] Embodiment 1

[0043] like Figure 1 As shown, in this embodiment, a method for constructing a PM2.5 pollution-meteorological comprehensive index is provided. Using atmospheric PM2.5 monitoring data and NECP FNL data, a comprehensive index that can characterize the PM2.5 pollution meteorological conditions in winter is constructed for typical different regions in eastern China (Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Fenwei Plain) through correlation analysis - PM2.5 pollution-meteorological index (PMI). It specifically includes the following three parts:

[0044] 1. Correlation analysis and determination of strongly correlated meteorological factors

[0045] Analyze the correlation between the near-ground PM2.5 concentration and multiple meteorological elements in the target study area on a monthly scale, and determine the meteorological elements and strongly correlated areas with PM2.5 concentration in each target study area. The specific steps include:

[0046] 1.1. Four regions, namely Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Fenwei Plain, were selected as target research areas.

[0047] 1.2. Conduct quality control on the hourly observation data of near-surface PM2.5 in winter from 2014 to 2020 of the Ministry of Ecology and Environment of China, and determine the monitoring sites involved in relevant analysis.

[0048] 1.3. The meteorological data involved in the relevant analysis were selected from the 6-hourly, 1°×1° meteorological reanalysis data of the National Center for Environmental Prediction of the United States.

[0049] 1.4. Conduct correlation analysis on the hourly PM2.5 observation data of the monitoring stations and the monthly average of meteorological data to determine the positive and negative correlation between the near-ground PM2.5 concentration in the target study area and various meteorological elements.

[0050] In this example, four regions, namely Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Fenwei Plain (FWP), were selected as the main research areas. The hourly observation data of near-surface PM2.5 in winter (December of the current year and January and February of the following year) from the Ministry of Ecology and Environment of China from 2014 to 2020 were used to analyze the interannual variation trend of near-surface PM2.5 concentration in eastern China and the correlation analysis with meteorological conditions. In order to avoid possible data quality problems and ensure the accuracy of trend analysis and correlation analysis, strict quality control was performed on PM2.5 data: for each monitoring station, (1) missing values, duplicate values, zero values, negative values ​​and outliers were eliminated; based on the 3σ rule of normal distribution, data with a distance greater than 3 times the standard deviation from the multi-time average value were considered outliers; (2) for months with a monthly coverage rate of valid hourly data of not less than 70%, the monthly average value was calculated; (3) stations with discontinuous monthly average values ​​were removed, and only stations with continuous monthly average values ​​were retained. After the above quality control, 316 PM2.5 observation stations were retained for analysis. The meteorological data used in this method is the 6-hour, 1°×1° National Center for Environmental Prediction meteorological reanalysis data (NECP FNL). The meteorological variables used for PM2.5 and meteorological correlation analysis mainly include 2m relative humidity, temperature, 10m wind speed, 500hPa and 850hPa meridional wind, zonal wind and geopotential height, etc., as shown in Table 1.

[0051] Table 1 Meteorological variables involved in correlation analysis

[0052] Meteorological variables Variable Description RH2 2m relative humidity T2 2m atmospheric temperature T Upper air temperature WS10 10m wind speed PBL Boundary layer height PRE Precipitable Water GH500 500hpa geopotential height GH850 850hpa geopotential height U500 500hpa zonal wind U850 850hpa zonal wind V500 500hpa Meridional Wind V850 850hpa Meridional Wind

[0053] Since the impact of meteorological factors on PM2.5 pollution varies greatly with seasons and regions, winter PM2.5-meteorological indices were constructed for the four target regions (Beijing-Tianjin-Hebei, Fenwei Plain, Yangtze River Delta, and Pearl River Delta).

[0054] The correlation between regional near-surface PM2.5 concentration and multiple meteorological elements was analyzed on a monthly scale, and the meteorological elements and strongly correlated areas with PM2.5 concentration were determined. Specifically, the monthly average concentration of near-surface PM2.5 in Beijing-Tianjin-Hebei, Fenwei Plain, Yangtze River Delta and Pearl River Delta in winter from 2014 to 2020 and the monthly average values ​​of multiple meteorological elements (Table 1) were correlated. The following results were obtained: The near-surface PM2.5 concentration in Beijing-Tianjin-Hebei in winter has a significant positive correlation with the 500hpa meridional wind and the east-west difference of 850hpa potential height in high latitudes, and has a significant negative correlation with the local boundary layer height. Combined with the movement of the East Asian Trough and the Siberian High, when the 500hpa north wind speed is small and the 850hpa potential height difference between the east and the west decreases, it means that the 500hpa East Asian Trough is shallow, the Siberian High is weak, or there is abnormal high pressure in the east. At this time, the dry and cold air from the north is not easy to enter Beijing-Tianjin-Hebei, and the boundary layer is low. It is not conducive to the diffusion and transmission of pollutants in both horizontal and vertical directions, and it is easy to form PM2.5 pollution; the near-surface PM2.5 concentration in the Yangtze River Delta in winter is significantly negatively correlated with the 500hpa zonal wind in high latitudes, the 850hpa meridional wind in mid-latitudes, and the local and surrounding precipitable water. The increase in the 500hpa zonal westerly wind at 40°N-50°N and the weakening of the 850hpa north wind in mid-latitudes mean that the East Asian Trough is shallow. At this time, there is less cold air activity in the north, which is not conducive to the transmission of pollutants from Beijing-Tianjin-Hebei to the Yangtze River Delta, but is conducive to the transportation of clean and humid air from the sea from south to north, which will reduce the PM2.5 concentration in the Yangtze River Delta. The input of humid air from the south is conducive to the formation of precipitation. The enhancement of PM2.5 wet deposition will further reduce the PM2.5 concentration in the Yangtze River Delta; the near-ground PM2.5 concentration in the Pearl River Delta in winter is significantly negatively correlated with the 500hpa meridional wind, 850hpa meridional wind and local and surrounding precipitable rainfall in the mid-latitudes; the near-ground PM2.5 concentration in the Fenwei Plain in winter is significantly positively correlated with the east-west difference of 500hpa potential height in the high latitudes, and significantly negatively correlated with the 850hpa zonal westerly wind, 10m wind speed and local boundary layer height.

[0055] 2. Determination of new meteorological variables involved in the construction of the pollution-meteorological index

[0056] The meteorological elements that are strongly correlated with PM2.5 concentration in each target study area are averaged within the strongly correlated area, and the average value or the difference in the average values ​​is defined as a new meteorological variable for the construction of the pollution-meteorological index.

[0057] Meteorological elements that are strongly correlated with PM2.5 concentration are meteorological elements whose correlation with PM2.5 concentration is greater than a preset positive threshold (such as 0.5) or less than a preset negative threshold (such as -0.5). For variables with consistent physical meanings, for example, the magnitude of the 500hpa meridional wind and the zonal wind can both characterize the degree of development of the East Asian trough to a certain extent, and the one with a higher correlation coefficient is used for the construction of PMI. For example, the intensity of the 500hpa meridional wind V500B, the east-west difference of the 850hpa geopotential height GD850B, and the boundary layer height PBLHB that affect the near-surface PM2.5 concentration in Beijing-Tianjin-Hebei; multiple meteorological variables are also defined for the Yangtze River Delta, the Pearl River Delta, and the Fenwei Plain. The names of each meteorological variable and the regional range involved in the average calculation are shown in Table 2. The correlation coefficients of these meteorological variables with the regional near-surface PM2.5 concentration are all greater than 0.5 or less than -0.5.

[0058] Table 2 Meteorological variables involved in constructing PMI

[0059]

[0060] 3. Pollution-Weather Index Determination:

[0061] The time series of each new meteorological variable is standardized separately, and the standardized time series are added together to obtain a comprehensive variable. Finally, the comprehensive variable is standardized to obtain the pollution-meteorological index.

[0062] Since these new variables are not independent of each other and have different units, the time series of each variable are standardized separately, and then the different time series are added to obtain the comprehensive variable. Finally, the comprehensive variable is standardized to obtain PMI. The calculation formula is:

[0063]

[0064] The new meteorological variables used to construct PM I are standardized and summed to obtain the intermediate variable X by formula 1) j . V i,j is the new meteorological variable, i represents different meteorological variables, j represents different regions, A() represents the average, and S() represents the standard deviation. j Standardize to get the PMI of a certain area j .

[0065] Finally, the winter PM2.5 pollution-meteorological comprehensive index (PMI) of Beijing-Tianjin-Hebei, Fenwei Plain, Yangtze River Delta and Pearl River Delta is obtained:

[0066]

[0067]

[0068] PMI in the formula B 、PMI F 、PMI Y and PMI P Represents the PM2.5 pollution-meteorological comprehensive index in Beijing-Tianjin-Hebei, Fenwei Plain, Yangtze River Delta and Pearl River Delta respectively. B , X F , X Y and X P As an intermediate variable, it is the result of standardization of different meteorological variables. The PMI index finally calculated is positively correlated with the regional PM2.5 concentration. The size of the PMI index can represent the quality of meteorological conditions related to PM2.5 pollution. The smaller the PMI index, the better the meteorological conditions, which is less conducive to the formation of PM2.5 pollution.

[0069] Embodiment 2

[0070] The pollution meteorological index established in Example 1 is used to analyze the influencing factors of PM2.5 changes in different regions of my country in recent years. Figure 2 The temporal changes of PM2.5 concentration, PMI and their meteorological components in the Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Pearl River Delta in winter from 2014 to 2020 are given. B 、850hpa geopotential height east-west difference GD850 B , boundary layer height PBLH B and PMI B ( Figure 2 The correlation coefficients of a, d, g and j in the figure are 0.68, 0.66, -0.65 and 0.71 respectively. The near-surface PM2.5 concentration in the Yangtze River Delta region in winter and the 500hpa zonal wind U500 Y 、850hpa meridian wind V850 Y , Precipitable Water Y and PMI Y ( Figure 2 The correlation coefficients of b, e, h and k in the figure are -0.55, -0.57, -0.51 and 0.71 respectively. The near-surface PM2.5 concentration in the Pearl River Delta in winter and the 500hpa meridional wind V500 P 、850hpa meridian wind V850 P , Precipitable Water P and PMI P ( Figure 2 The correlation coefficients of c, f, i and l) in the figure are -0.58, -0.65, -0.53 and 0.65 respectively. Figure 3The temporal changes of PM2.5 concentration, PMI and its meteorological component variables in the Fenwei Plain in winter from 2014 to 2020 are given. F 、850hpa zonal wind U850 F 、10m wind speed WS10 F , boundary layer height PBLH F and PMI F The correlation coefficients are 0.6, -0.57, -0.6, -0.7 and 0.71 respectively. Compared with a single meteorological variable, PMI has a stronger correlation with the near-ground PM2.5 concentration in each region.

[0071] from Figure 2 In the j, Figure 2 From the l in the figure, we can see that there was no significant trend change in the PMI of Beijing-Tianjin-Hebei and the Pearl River Delta in the winter of 2014-2020. The average PMI of Beijing-Tianjin-Hebei in winter each year was 0.2, 0.1, 1.0, -1.4, -0.1, 0.8 and -0.6, respectively, and that of the Pearl River Delta was 1.0, -0.4, 0.3, 0.75, -1.1, -0.7 and 0.3, respectively. In these seven years, the weather conditions in Beijing-Tianjin-Hebei region were the worst in winter 2016 (PMI B =1.0), which is most conducive to the formation of PM2.5 pollution. The meteorological conditions for PM2.5 pollution were the best in winter 2017 (PMI B =-1.4), which is the least favorable for the formation of PM2.5 pollution. The weather conditions for PM2.5 pollution in the Pearl River Delta region were the worst in winter 2014 (PMI P =1.0), the PM2.5 pollution meteorological conditions were the best in winter 2018 (PMI P =-1.1). Unlike the Beijing-Tianjin-Hebei region and the Pearl River Delta, the PMI in the Yangtze River Delta showed a significant downward trend from 2014 to the winter of 2020, with an average annual decline of 0.33 ( Figure 2 k), which shows that the meteorological conditions affecting PM2.5 pollution in the Yangtze River Delta region in winter from 2014 to 2020 have generally improved, becoming less and less conducive to the formation of PM2.5 pollution. Compared with other years, the meteorological conditions for PM2.5 pollution in the Yangtze River Delta region in winter 2014 were the worst, with an average winter PMI of 1.3. From the perspective of the PMI component variables in the Yangtze River Delta, the U500 in winter from 2014 to 2020 Y 、V850 Y and PRE Y The increase is the main reason for the improvement of winter meteorological conditions in the Yangtze River Delta ( Figure 2 b, e, and h in Figure 2). The increase in these three variables may be related to the weakening of the East Asian winter monsoon. The PMI in the Fenwei Plain did not show a significant downward trend during the winter of 2014-2020 ( Figure 3 e). Similar to the Beijing-Tianjin-Hebei region, the Fenwei Plain had the worst PM2.5 pollution meteorological conditions in winter 2016, with an average winter PMI of 0.9.

[0072] Therefore, through the constructed pollution-meteorological index, combined with the trend change of near-surface PM2.5 concentration in winter from 2014 to 2020, since there is no obvious trend change in the meteorological conditions of PM2.5 pollution itself, the downward trend of near-surface PM2.5 concentration in Beijing-Tianjin-Hebei and the Pearl River Delta in winter is mainly caused by changes in anthropogenic source emissions, while for the Yangtze River Delta region, the improvement of PM2.5 pollution meteorological conditions is also an important reason for the decline in near-surface PM2.5 concentration in the Yangtze River Delta in winter from 2014 to 2020. The increase in near-surface PM2.5 concentration in the Fenwei Plain in winter from 2014 to 2016 was mainly the result of the deterioration of pollution meteorological conditions.

[0073] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0074] The present invention provides a method for constructing a PM2.5 pollution-meteorological comprehensive index. By using atmospheric PM2.5 monitoring data and NECP FNL data, a comprehensive index, PM2.5 pollution-meteorological index (PMI), which can characterize the meteorological conditions of PM2.5 pollution in winter is constructed for typical different regions in eastern China (Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Fenwei Plain) through correlation analysis. The quality of meteorological conditions related to PM2.5 pollution is obtained based on the pollution-meteorological index (PMI).

[0075] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for constructing a PM2.5 pollution-meteorological comprehensive index, characterized by: The following steps are included: S1. Correlation analysis and determination of strongly correlated meteorological factors: Analyze the correlation between the near-surface PM2.5 concentration and multiple meteorological elements in the target study area on a monthly scale, and determine the meteorological elements and areas with strong correlation with PM2.5 concentration in each target study area; S2. Determination of new meteorological variables involved in the construction of the pollution-meteorological index: The meteorological elements that are strongly correlated with PM2.5 concentration in each target study area are averaged within the strongly correlated area, and the average value or the difference in the average value is defined as a new meteorological variable for the construction of the pollution-meteorological index; S3. Pollution-meteorological index determination: The time series of each new meteorological variable is standardized separately, and the standardized time series are added together to obtain a comprehensive variable. Finally, the comprehensive variable is standardized to obtain the pollution-meteorological index.

2. The method for constructing a PM2.5 pollution-meteorological comprehensive index according to claim 1, characterized in that: Step S1 specifically includes the following steps: S11. Select Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Fenwei Plain as the target research areas; S12. Conduct quality control on the hourly observation data of near-surface PM2.5 in winter from 2014 to 2020 of the Ministry of Ecology and Environment of China, and determine the monitoring sites involved in the relevant analysis; S13, select meteorological data for relevant analysis from the 6-hour, 1°×1° meteorological reanalysis data of the National Center for Environmental Prediction of the United States; S14. Conduct correlation analysis on the hourly PM2.5 observation data of the monitoring stations and the monthly average of meteorological data to determine the positive and negative correlation between the near-ground PM2.5 concentration in the target study area and various meteorological elements.

3. The method for constructing a PM2.5 pollution-meteorological comprehensive index according to claim 2, characterized in that: Step S12 specifically includes the following contents: S121. For each monitoring station, missing values, duplicate values, zero values, negative values ​​and outliers are eliminated; among them, based on the 3σ rule of normal distribution, data with a distance greater than 3 times the standard deviation from the multi-time average value are considered outliers; S122. For the month in which the monthly coverage rate of valid hourly data is not less than 70%, calculate the monthly average value; S123. Remove monitoring sites with discontinuous monthly mean values ​​and only retain monitoring sites with continuous monthly mean values.

4. The method for constructing a PM2.5 pollution-meteorological comprehensive index according to claim 2, characterized in that: The meteorological elements involved in the relevant analysis include 2m relative humidity, 2m atmospheric temperature, high-altitude atmospheric temperature, 10m wind speed, boundary layer height, precipitable water, 500hPa geopotential height, 850hPa geopotential height, 500hPa zonal wind, 850hPa zonal wind, 500hPa meridional wind, and 850hPa meridional wind.

5. The method for constructing a PM2.5 pollution-meteorological comprehensive index according to claim 2, characterized in that: Meteorological elements that are strongly correlated with PM2.5 concentration are meteorological elements whose correlation with PM2.5 concentration is greater than a preset positive threshold or less than a preset negative threshold.

6. The method for constructing a PM2.5 pollution-meteorological comprehensive index according to claim 5, characterized in that: The meteorological factors that are strongly correlated with PM2.5 concentration in each target study area are: Beijing-Tianjin-Hebei: 500hpa meridional wind, 850hpa geopotential height east-west difference, local boundary layer height; Fenwei Plain: 500hpa geopotential height east-west difference, 850hpa zonal wind, local boundary layer height, 10m wind speed; Yangtze River Delta: 500hp zonal wind, 850hpa meridional wind, local and surrounding precipitable rainfall; Pearl River Delta: 500hpa meridional wind, 850hpa meridional wind, local and surrounding precipitation.

7. The method for constructing a PM2.5 pollution-meteorological comprehensive index according to claim 1, characterized in that: Step S3 specifically comprises: S31, using formula 1) to standardize the new meteorological variables used to construct the pollution-meteorological index and sum them to obtain an intermediate variable; Among them, V i,j is the new meteorological variable; i is a different meteorological variable; j is the target research area; A( ) is the average; S( ) is the standard deviation; X j is an intermediate variable; S32, using formula 2) to standardize the intermediate variables to obtain the pollution-meteorological index of the corresponding target study area; Among them, PMI j is the pollution-meteorological index of the target study area.

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