A PM2.5 regional severe pollution cause identification system

Abstract

The invention discloses a system for identifying causes of regional heavy pollution of PM2.5, comprising a process module for identifying causes of regional heavy pollution of PM2.5, which first determines whether there is regional pollution, and if there is regional pollution, it determines whether it is regional pollution across districts and counties or regional pollution within districts and counties, and then it is processed, transformed or discharged; a method module for identifying methods of regional pollution across municipalities of PM2.5, which first identifies regional pollution and local pollution, and then determines whether PM2.5 / PM2.5~10 is too high, and finally compares the PM2.5 concentration of each monitoring point; a method module for identifying methods of regional pollution across districts and counties of PM2.5, which determines whether PM2.5 / PM2.5~10 is too high, and then compares the PM2.5 concentration of each monitoring point; a method module for identifying methods of local pollution of PM2.5, which determines whether PM2.5 / PM2.5~10 is too high, and then determines whether PM2.5 and CO are significantly correlated. The present invention accurately identifies the emissions and regional scope of pollution sources that cause heavy pollution weather, scientifically and accurately reduces the spatial scope of emergency control and the number of pollution sources, reduces the social cost of severe air pollution control, and achieves precise pollution control and economic development.

Description

Technical Field

[0001] The present invention relates to the technical field of haze pollution control in areas with severe air pollution, and in particular to a system for identifying causes of regional severe PM2.5 pollution. Background Art

[0002] At present, regional PM2.5 pollution is still one of the main aspects of air pollution problems, and joint prevention and control of regional air pollution remains a key strategic measure for regional PM2.5 pollution control.

[0003] It can be seen that in order to continuously improve the regional PM2.5 pollution situation, it is necessary to further carry out more accurate and refined regional PM2.5 pollution identification. The identification objects need to be further refined to the scale of monitoring points, and the possible causes of pollution source emissions need to be given.

[0004] At present, there are no clearer rules and technical specifications for determining regional severe PM2.5 pollution, making it impossible to achieve regional joint prevention and control of PM2.5.

[0005] To sum up: the main defects are:

[0006] The current city-scale PM2.5 regional severe pollution identification and inspection cannot meet the needs of refined joint prevention and control of air pollution. The identification of PM2.5 regional severe pollution needs to be refined to the monitoring point scale. From the PM2.5 regional severe pollution identification at the monitoring point scale, it is necessary to make clear scientific judgments on whether it is across prefecture-level cities, across counties, or local pollution. The current relatively general city-scale PM2.5 regional severe pollution identification method is obviously not specific enough and cannot provide targeted management measures.

[0007] The PM2.5 regional severe pollution cause identification system proposed in the present invention will be able to overcome the above-mentioned problems and defects, and provide technical methods and support for further promoting the refinement of regional PM2.5 air pollution joint prevention and control. Summary of the invention

[0008] In response to the above technical problems in the related art, the present invention proposes a system for identifying the causes of regional severe PM2.5 pollution, which can overcome the above shortcomings of the prior art methods.

[0009] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows:

[0010] A PM2.5 regional severe pollution cause identification system includes a PM2.5 regional severe pollution cause identification process module, a PM2.5 severe pollution cross-city regional pollution identification method module, a PM2.5 severe pollution cross-district and county regional pollution identification method module, and a PM2.5 severe pollution local pollution identification method module, wherein:

[0011] The PM2.5 regional severe pollution cause identification process module first determines whether there is regional pollution. If there is regional pollution, it is necessary to determine whether it is cross-district or intra-district pollution, whether PM2.5 / PM2.5~10 (α value) is too high, and whether it is primary pollution or secondary pollution, and then it is processed, transformed or discharged.

[0012] The PM2.5 heavy pollution cross-city regional pollution identification method module first identifies regional pollution, including regional pollution and local pollution, then determines whether PM2.5 / PM2.5~10 is too high, and finally compares the PM2.5 concentration of each monitoring point.

[0013] The PM2.5 heavy pollution cross-county regional pollution identification method module first determines whether PM2.5 / PM2.5~10 is too high, and then compares the PM2.5 concentrations of each monitoring point.

[0014] The PM2.5 heavy pollution local pollution identification method module first determines whether PM2.5 / PM2.5~10 is too high, and then determines whether PM2.5 and CO are significantly correlated.

[0015] Furthermore, identify whether it is regional pollution or local pollution. Regional pollution means that the concentrations at three or more adjacent monitoring points exceed the standard and are similar; local pollution means that the concentrations at one or two adjacent monitoring points exceed the standard.

[0016] Furthermore, a module for identifying the severe PM2.5 pollution across municipalities and counties is provided, which determines whether PM2.5 / PM2.5~10 is too high. If PM2.5 / PM2.5~10 is too high, it is necessary to determine whether the hourly values ​​of SO2 / CO and NO2 / CO decrease during the rising stage of PM2.5 concentration. If the hourly values ​​of O2 / CO and NO2 / CO decrease, it indicates that SO2 and NO2 are converted to form PM2.5. If the hourly values ​​of SO2 / CO and NO2 / CO remain unchanged or increase, it indicates that CPM is converted to form PM2.5. If PM2.5 / PM2.5~10 is not too high, direct emissions lead to high PM2.5 concentration, which is primary pollution. For primary pollution across counties and districts, the pollution source must be a fixed source with large emissions.

[0017] Furthermore, when comparing the PM2.5 concentrations at each monitoring point, since the geographical diffusion conditions in different counties are different, the position of the fixed source can be determined by first comparing the percentile ranking of the pollution day concentration in the annual concentration.

[0018] Furthermore, in the PM2.5 heavy pollution local pollution identification method module, when judging whether PM2.5 / PM2.5~10 is too high, if PM2.5 / PM2.5~10 is too high, it is necessary to judge whether the correlation coefficient between PM2.5 and NO2 is too high, and then judge whether the hourly values ​​of SO2 / CO and NO2 / CO have decreased, and then judge whether the NO2 concentration has increased during the morning and evening peak hours; if PM2.5 / PM2.5~10 is not too high, it means that the concentrations of PM2.5 and PM2.5~10 have both increased, and the high PM2.5 concentration is caused by emissions from fixed sources, which is primary pollution.

[0019] Furthermore, the PM2.5 heavy pollution local pollution identification method module determines whether PM2.5 and CO are significantly correlated. If PM2.5 and CO are not correlated or the correlation coefficient is low, it means that CO has no trend of increasing synchronously with PM2.5 on that day; if PM2.5 and CO are significantly positively correlated, it means that CO and PM2.5 increase synchronously on that day, and the emission source of particulate matter is combustion source.

[0020] The beneficial effects of the present invention are as follows: by accurately identifying the emissions and regional scope of pollution sources that cause heavy pollution weather, the spatial scope of emergency control and the number of pollution sources can be scientifically and accurately reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1 It is a flow chart of the technical method for identifying the cause of PM2.5 pollution (exceeding the standard) of the PM2.5 regional severe pollution cause identification system according to an embodiment of the present invention.

[0023] Figure 2 The process of the PM2.5 pollution (exceeding the standard) cause identification technical method of the PM2.5 regional severe pollution cause identification system according to the embodiment of the present invention is as follows: Figure 2 .

[0024] Figure 3 The process of the PM2.5 pollution (exceeding the standard) cause identification technical method of the PM2.5 regional severe pollution cause identification system according to the embodiment of the present invention is as follows: Figure 3 .

[0025] Figure 4 The process of the PM2.5 pollution (exceeding the standard) cause identification technical method of the PM2.5 regional severe pollution cause identification system according to the embodiment of the present invention is as follows: Figure 4 .

[0026] Figure 5 It is a time series scatter diagram of the 1h value and 24h value of PM2.5 concentration of the system for identifying causes of regional severe PM2.5 pollution according to an embodiment of the present invention.

[0027] Figure 6 It is a statistical schematic diagram of abnormal values ​​of PM2.5 to PM10 concentrations at monitoring points of the PM2.5 regional severe pollution cause identification system according to an embodiment of the present invention.

[0028] Figure 7 It is a statistical diagram of the proportion of secondary pollution days among PM2.5 exceeding standard days according to the PM2.5 regional severe pollution cause identification system described in an embodiment of the present invention.

[0029] Figure 8 It is a schematic diagram of the number of days with high ratios of secondary sources and mobile sources contributing to the PM2.5 regional severe pollution cause identification system according to an embodiment of the present invention.

[0030] Fig. 9 This is a CPM schematic diagram of the PM2.5 secondary pollution days classification from 2019 to 2020 according to the PM2.5 regional heavy pollution cause identification system described in an embodiment of the present invention.

[0031] Fig.10 This is a schematic diagram of dust classification based on the number of PM2.5 single pollution days from 2019 to 2020 according to the PM2.5 regional heavy pollution cause identification system described in an embodiment of the present invention.

[0032] Fig.11 This is a schematic diagram of road dust classification based on the number of PM2.5 single pollution days from 2019 to 2020 according to the PM2.5 regional heavy pollution cause identification system described in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0034] Based on in-depth analysis of air quality data, the main objects of source emission management and technical methods for diagnostic evaluation are proposed, such as the results and technical ideas for the analysis of local sources and external transmission, fixed sources, and surface source contributions that cause PM2.5 severe pollution weather.

[0035] The PM2.5 regional severe pollution cause identification system according to the embodiment of the present invention includes a PM2.5 regional severe pollution cause identification process module, a PM2.5 severe pollution cross-city regional pollution identification method module, a PM2.5 severe pollution cross-district and county regional pollution identification method module and a PM2.5 severe pollution local pollution identification method module.

[0036] The PM2.5 regional severe pollution cause identification process module is as follows: Figure 1-4 As shown, to determine whether there is regional pollution, if there is regional pollution, it is necessary to determine whether it is cross-district or intra-district, determine whether PM2.5 / PM2.5~10 (α value) is high, determine whether it is primary pollution or secondary pollution, and then process it for transformation or discharge.

[0037] The PM2.5 heavy pollution cross-city regional pollution identification method module must first identify regional pollution and local pollution. Regional pollution refers to the concentrations of three or more adjacent monitoring points exceeding the standard and being similar, which is divided into the district and cross-county administrative regions. Local pollution refers to the concentrations of one or two adjacent monitoring points exceeding the standard.

[0038] Then the PM2.5 heavy pollution cross-city / cross-district and county regional pollution identification method modules must determine whether PM2.5 / PM2.5~10 is too high. If PM2.5 / PM2.5~10 is too high, it means that the PM2.5 concentration is high, while the PM2.5~10 concentration has no obvious increase. The sources of only fine particulate matter emissions include secondary pollution (SO2, NO2, VOCS conversion or CPM conversion) and mobile sources. There is no mobile source that can affect multiple districts and counties at the same time, so it is secondary pollution. Then determine whether the hourly values ​​of SO2 / CO and NO2 / CO decrease during the rising stage of PM2.5 concentration (identify the impact of CPM). If the hourly values ​​of SO2 / CO and NO2 / CO decrease, it indicates that SO2 and NO2 are converted to form PM2.5. If the hourly values ​​of SO2 / CO and NO2 / CO remain basically unchanged or increase, it indicates that CPM is converted to form PM2.5. If PM2.5 / PM2.5~10 is not too high, it means that PM2.5 The concentrations of PM2.5 and PM2.5-10 increased, and direct emissions led to high PM2.5 concentrations, which is primary pollution. For primary pollution across districts and counties / within districts and counties, the pollution source must be a fixed source with a large emission volume.

[0039] Finally, compare the PM2.5 concentration at each monitoring point. Due to the different geographical diffusion conditions in different districts and counties, we can first compare the percentile ranking of the pollution day concentration in the annual concentration (or the ratio of the daily average to the annual average) to determine the location of the fixed source. Generally speaking, points with higher concentrations may be closer to the problematic fixed source.

[0040] The PM2.5 heavy pollution local pollution identification method module first determines whether PM2.5 / PM2.5~10 is too high.

[0041] (1) If PM2.5 / PM2.5~10 is too high

[0042] This indicates that the concentration of PM2.5 is high, while the concentration of PM2.5-10 has no significant increase. The sources include secondary pollution (SO2, NO2, VOCS conversion or CPM conversion) and mobile sources.

[0043] First, determine whether PM2.5 and NO2 are significantly correlated, and whether the correlation coefficient is high (to distinguish the impact of mobile sources)

[0044] 1) PM2.5 / NO2 correlation coefficient is high

[0045] This indicates that the concentrations of PM2.5 and NO2 are increasing simultaneously, and emissions from surrounding mobile sources need to be controlled.

[0046] PM2.5 / NO2 correlation coefficient is not high

[0047] 2) It shows that NO2 has no synchronous increasing trend with PM2.5, and is not pollution from mobile sources, but secondary pollution. There may be CPM sources and sources emitting water vapor near the monitoring point.

[0048] Secondly, determine whether the hourly values ​​of SO2 / CO and NO2 / CO decrease during the rising stage of PM2.5 concentration (identify the impact of CPM)

[0049] ①SO2 / CO, NO2 / CO hourly values ​​decreased

[0050] SO2 and NO2 are transformed into PM2.5, and it is necessary to control the emission of SO2 and NO2 in the area. Then determine whether the NO2 concentration increases significantly during the morning and evening peaks:

[0051] The NO2 concentration has increased significantly, indicating the need to control emissions from surrounding mobile sources;

[0052] The NO2 concentration has not increased significantly, indicating that emissions from surrounding surface sources need to be controlled.

[0053] ②The hourly values ​​of SO2 / CO and NO2 / CO remain basically unchanged or increase

[0054] It shows that CPM is converted into PM2.5

[0055] (2) If PM2.5 / PM2.5~10 is not too high, it means that the concentrations of PM2.5 and PM2.5~10 are both elevated, and the high PM2.5 concentration is caused by emissions from fixed sources, which is primary pollution.

[0056] Then determine whether PM2.5 and CO are significantly correlated (distinguishing the impact of dust)

[0057] 1) PM2.5 and CO are not correlated or the correlation coefficient is low

[0058] This indicates that CO did not increase synchronously with PM2.5 on that day, and emission sources that produce particulate matter from combustion basically emit CO, so it is mainly affected by sources that emit fine particulate matter.

[0059] Then determine whether PM2.5 and NO2 are significantly correlated, and the correlation coefficient is high (distinguishing the impact of muck trucks)

[0060] ①The PM2.5 / NO2 correlation coefficient is high, indicating that the concentrations of PM2.5 and NO2 increase simultaneously, which is caused by road dust (dump trucks).

[0061] ②The PM2.5 / NO2 correlation coefficient is not high, indicating that NO2 has no synchronous increasing trend with PM2.5, which may be caused by emissions from fixed sources.

[0062] 2) PM2.5 and CO are significantly positively correlated

[0063] This indicates that CO and PM2.5 increased simultaneously on that day, and the source of particulate matter emission was combustion. For local pollution, it may be caused by surface sources, especially emissions from residents' stoves, and it may also be related to mobile sources.

[0064] Then determine the correlation coefficient between PM2.5, SO2 and NO2 (by percentile ranking)

[0065] ③The correlation coefficient between PM2.5 and SO2 is larger, indicating that fixed and surface source emissions from coal burning have a greater impact on PM2.5.

[0066] ④The correlation coefficient between PM2.5 and NO2 is larger, indicating that mobile source emissions have a greater impact on PM2.5.

[0067] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are described in detail below through specific usage methods.

[0068] like Figure 1 As shown, according to the PM2.5 regional severe pollution cause identification system described in an embodiment of the present invention, the 24h and 1h pollutant concentration data of 15 national, provincial and municipal monitoring points in the urban area from 2019.1.1 to 2020.12.31SY are taken as an example. Figure 4As shown, the time series scatter plot of the 1-hour and 24-hour values ​​of PM2.5 concentrations shows that there are 3 1-hour outliers and 2 24-hour outliers at the Xilan Street monitoring point in Qindu District, and they were eliminated.

[0069] First, PM2.5~PM10 are analyzed, and the case of PM2.5>PM10 is determined as an abnormal value, accounting for 4.18% and 2.07% of the 1h value and 24h value sample sets respectively. The abnormal data of the monitoring point are as follows Figure 5 shown.

[0070] After removing the outlier samples, the frequency distribution of each monitoring point was statistically analyzed. The statistical distribution of PM2.5 data in the heating period and the non-heating period was observed separately. It was found that the kurtosis of the ratio of the heating period and the non-heating period was significantly reduced, the data range was larger, and there were more high ratio data, that is, there was a significant difference in the ratio distribution between the heating period and the non-heating period, and standards can be formulated separately. The 75th and 90th percentiles of the days when PM2.5 did not exceed the standard at each monitoring point (2019-2020) were statistically analyzed, and the results are shown in Table 1.

[0071]

[0072] Table 1--PM2.5 / PM2.5~10 quantile statistics for days when PM2.5 does not exceed the standard

[0073] According to the above standards, the proportion of PM2.5 exceeding the standard on each monitoring point that may be caused by secondary pollution is calculated, such as Figure 6 As shown in the data, the proportion of secondary PM2.5 pollution in the urban area is relatively lower than that in Qindu District. According to the 90th percentile of the days without exceeding the standard, the proportion of secondary pollution days among the days with exceeding the standard is 21.1-54.4%, with a standard deviation of 9.8%; according to the 75th percentile of the days without exceeding the standard, the proportion of secondary pollution days among the days with exceeding the standard is 56.5-80.8%, with a standard deviation of 6.7%.

[0074] Based on experience, Xianyang Meteorological Station is a control point with fewer emission sources around it, so PM2.5 is more likely to come from secondary pollution. The monitoring point ranks highest in the statistics of the proportion of secondary pollution days according to the 75th percentile value, so the 75th percentile value is selected as the standard for judging that the ratio is too high. It is believed that the days when the ratio exceeds this range are when the PM2.5 increase rate is significantly higher than that of coarse particulate matter, and there is a high probability of PM2.5 secondary pollution.

[0075] The analysis of the high PM2.5 / PM2.5~10 ratio, first, identified the impact of mobile sources, and calculated the correlation coefficient between the daily PM2.5 concentration and the hourly value of NO2 concentration at each monitoring point from 2019 to 2020. The results of variance analysis showed that: ① The correlation between NO2 and PM2.5 concentrations was significantly different between the heating period and the non-heating period, and the correlation in the heating period was greater than that in the non-heating period; ② During the heating period, the correlation between NO2 and PM2.5 concentrations was significantly different between PM2.5 exceeding days and PM2.5 not exceeding days, and the correlation weakened on PM2.5 exceeding days; during the non-heating period, the correlation between NO2 and PM2.5 concentrations was not significantly different between PM2.5 exceeding days and PM2.5 not exceeding days.

[0076] Therefore, standards are set for the correlation between NO2 and PM2.5 during the heating period and the non-heating period. The standards are set during the heating period based on the statistical distribution of days without exceeding the standard, and the standards are set uniformly during the non-heating period without distinguishing whether PM2.5 exceeds the standard. As shown in Table 2, among the days with PM2.5 exceeding the standard with high PM2.5 / PM2.5~10, the number of days with PM2.5 exceeding the standard due to mobile sources and secondary sources is distinguished by whether the correlation between NO2 and PM2.5 concentration is high, such as Figure 7 As shown, the impact of secondary sources accounts for 63% to 92%.

[0077]

[0078]

[0079] Table 2 - High standard of correlation coefficient between NO2 and PM2.5

[0080] Second, identify the impact of CPM, calculate the number of hours of daily PM2.5 concentration increase (Ct-Ct-1≥0) and the number of hours of SO2 / CO and NO2 / CO increase at each monitoring point between 2019 and 2020, and count the proportion of hours of SO2 / CO and NO2 / CO increase during the daily PM2.5 concentration increase period, which are hereinafter recorded as βSO2 and βNO2. (For example: Assume that there are 24 1h values ​​in a day, of which there are 12 hours of PM2.5 increase, and during this period, there are 8h SO2 / CO increase, then the ratio βSO2 is 8 / 12=0.75). The results of variance analysis show that β is significantly different between the heating period and the non-heating period, and between the PM2.5 exceeding days and the PM2.5 non-exceeding days. During the heating period and on the PM2.5 exceeding days, the synchronous increase rate of SO2 and NO2 and PM2.5 is reduced. Therefore, standards are set for the indicator β in the heating period and the non-heating period respectively, and the standards are set based on the statistical distribution of the non-exceeding days.

[0081] By extracting the days when PM2.5 did not exceed the standard, the statistical distribution of β was observed and it was found that β was approximately normally distributed, with the mean value of each monitoring point being around 0.5, that is, during the period when PM2.5 concentration increased, SO2 and NO2 increased synchronously with PM2.5 in about 1 / 2 of the period. The specific calculation standards of β at each monitoring point during the heating period and the non-heating period are shown in Table 3.

[0082]

[0083]

[0084] According to the standard statistics in Table 3, the number of days with secondary PM2.5 pollution from 2019 to 2020 is classified as follows: Figure 8 shown.

[0085] The ratio is not too high, indicating that the concentrations of PM2.5 and PM2.5-10 have increased, which is a primary pollution. To identify the impact of dust sources, the correlation coefficient between the daily PM2.5 concentration and the hourly value of CO concentration at each monitoring point between 2019 and 2020 was first calculated. The results of variance analysis showed that the correlation between CO and PM2.5 concentrations was significantly different between the heating period and the non-heating period. During the heating period, the correlation between CO and PM2.5 concentrations was significantly different between PM2.5 exceeding days and PM2.5 not exceeding days, and the correlation on PM2.5 exceeding days was enhanced. During the non-heating period, the correlation between CO and PM2.5 concentrations was not significantly different between PM2.5 exceeding days and PM2.5 not exceeding days. Therefore, standards for the correlation between CO and PM2.5 are formulated separately during the heating period and the non-heating period. The standards are formulated based on the statistical distribution of non-exceeding days during the heating period, and the standards are formulated uniformly during the non-heating period without distinguishing whether PM2.5 exceeds the standard.

[0086] The correlation coefficient between CO and PM2.5 is used to screen the days when the ratio is not too high, that is, the source of PM2.5 on a pollution day. When the correlation coefficient between CO and PM2.5 is low or uncorrelated, PM2.5 is more likely to come from particulate matter emitted from non-combustion sources, such as dust sources. For the standard with a low coefficient, it is determined according to the 25% percentile value of the statistics, and the standard with a negative correlation coefficient is adjusted to 0. The results are shown in Table 4.

[0087]

[0088]

[0089] Table 4 - CO and PM 2.5 The correlation coefficient is low

[0090] According to the statistics in Table 4, the number of PM2.5 pollution days from 2019 to 2020 is classified as follows: Fig. 9Further develop the correlation standard between NO2 and PM2.5 concentrations, and subdivide dust sources into road dust or other dust.

[0091] Identify the impact of fixed sources / mobile sources. For the samples of days exceeding the standard in 4.1 that exclude the impact of dust sources, determine the correlation coefficients among PM2.5, SO2 and NO2 (ranked by percentile). If the correlation coefficient between PM2.5 and SO2 is larger, then the impact of fixed coal-fired sources and surface sources on PM2.5 is greater; if the correlation coefficient between PM2.5 and NO2 is larger, then mobile source emissions have a greater impact on PM2.5.

[0092] To sum up, with the help of the above-mentioned technical scheme of the present invention, by accurately identifying the emissions and regional scope of pollution sources that cause heavy pollution weather, the emergency control space scope and the number of pollution sources can be scientifically and accurately reduced.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for identifying causes of regional severe PM2.5 pollution, characterized in that: It includes the PM2.5 regional severe pollution cause identification process module, PM2.5 severe pollution cross-city regional pollution identification method module, PM2.5 severe pollution cross-district regional pollution identification method module and PM2.5 severe pollution local pollution identification method module, among which, The PM2.5 regional heavy pollution cause identification process module first determines whether there is regional pollution. If there is regional pollution, it is necessary to determine whether it is cross-county regional or regional within the county, determine whether PM2.5 / PM2.5~10 is high, and determine whether it is primary pollution or secondary pollution, and then process, transform or discharge; the judgment method is: if it is cross-county regional, then continue to determine whether PM2.5 / PM2.5~10 is high, if so, it is determined to be secondary pollution, if not, it is determined to be primary pollution; if it is regional within the county, then continue to determine whether PM2.5 / PM2.5~10 is high, if so, it is determined to be secondary pollution, if not, it is determined to be primary pollution; The PM2.5 heavy pollution cross-city regional pollution identification method module first identifies regional pollution, including regional pollution and local pollution, then determines whether PM2.5 / PM2.5~10 is high, and finally compares the PM2.5 concentration of each monitoring point; The PM2.5 heavy pollution cross-county regional pollution identification method module first determines whether PM2.5 / PM2.5~10 is too high, and then compares the PM2.5 concentrations of each monitoring point; The PM2.5 heavy pollution local pollution identification method module first determines whether PM2.5 / PM2.5~10 is high, and then determines whether PM2.5 and CO are significantly correlated; when determining whether PM2.5 / PM2.5~10 is high in the PM2.5 heavy pollution local pollution identification method module, if PM2.5 / PM2.5~10 is high, it is necessary to determine whether the correlation coefficient between PM2.5 and NO2 is high. If the PM2.5 / NO2 correlation coefficient is high, it means that PM2 .5 and NO2 concentrations increase simultaneously, and it is necessary to control emissions from surrounding mobile sources; secondly, determine whether the hourly values ​​of SO2 / CO and NO2 / CO have decreased. If so, SO2 and NO2 are converted to form PM2.5, and it is necessary to control SO2 and NO2 emissions in the area; then determine whether NO2 concentrations increase during peak hours in the morning and evening. If NO2 concentrations increase significantly, it means that it is necessary to control emissions from surrounding mobile sources; if PM2.5 / PM2.5~10 is not too high, it means that both PM2.5 and PM2.5~10 concentrations have increased, and fixed source emissions have caused high PM2.5 concentrations, which is primary pollution; the PM2.5 heavy pollution is local pollution In the identification method module, determine whether PM2.5 and CO are significantly correlated. If PM2.5 and CO are not correlated or the correlation coefficient is low, it means that CO has no synchronous increase trend with PM2.5 on that day; if PM2.5 and CO are significantly positively correlated, it means that CO and PM2.5 increase synchronously on that day, and the emission source of particulate matter is the combustion source. Then determine the correlation coefficient between PM2.5, SO2 and NO2; if the correlation coefficient between PM2.5 and SO2 is larger, it means that coal-fired fixed sources and surface sources have a greater impact on PM2.5; if the correlation coefficient between PM2.5 and NO2 is larger, it means that mobile source emissions have a greater impact on PM2.5; PM2.5 heavy pollution cross-city / cross-district / county regional pollution identification method module, which determines whether PM2.5 / PM2.5~10 is too high. If PM2.5 / PM2.5~10 is too high, it is necessary to determine whether the hourly values ​​of SO2 / CO and NO2 / CO decrease during the rising stage of PM2.5 concentration. If the hourly values ​​of SO2 / CO and NO2 / CO decrease, it indicates that SO2 and NO2 are converted to form PM2.

5. If the hourly values ​​of SO2 / CO and NO2 / CO remain unchanged or increase, it indicates that CPM is converted to form PM2.

5. If PM2.5 / PM2.5~10 is not too high, direct emissions lead to high PM2.5 concentration, which is primary pollution. For primary pollution across districts and counties / districts and counties, the pollution source must be a fixed source with large emissions; Finally, the PM2.5 concentration at each monitoring point was compared. Due to the different geographical diffusion conditions in different districts and counties, the location of the fixed source was determined by first comparing the percentile ranking of the pollution day concentration in the annual concentration or the ratio of the daily average to the annual average.

2. The system for identifying causes of regional severe PM2.5 pollution according to claim 1, characterized in that: Identify whether it is regional pollution or local pollution. Regional pollution means that the concentrations at three or more adjacent monitoring points exceed the standard and are similar; local pollution means that the concentrations at one or two adjacent monitoring points exceed the standard.

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