CN113514378A - PM2.5 regional heavy pollution reason identification system - Google Patents
PM2.5 regional heavy pollution reason identification system Download PDFInfo
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Abstract
The invention discloses a PM2.5 regional heavy pollution reason identification system, which comprises a PM2.5 regional heavy pollution reason identification flow module, a PM2.5 regional heavy pollution reason identification flow module and a PM2.5 pollution detection flow module, wherein the PM2.5 regional heavy pollution reason identification flow module firstly judges whether regional pollution exists, and judges whether regional pollution exists in a cross-county region or a county region, and then the regional pollution exists in the cross-county region or the county region, and is converted or discharged through treatment; the PM2.5 heavy pollution cross-city regional pollution identification method module firstly identifies regional pollution and local pollution, then judges whether PM2.5/PM 2.5-10 is higher or not, and finally compares the PM2.5 concentration of each monitoring point; a PM2.5 heavy pollution cross-district regional pollution identification method module judges whether PM2.5/PM 2.5-10 is higher or not, and then the concentration of PM2.5 at each monitoring point is compared; the PM2.5 heavy pollution local pollution identification method module judges whether PM2.5/PM 2.5-10 is higher or not, and then judges whether PM2.5 and CO are obviously related or not. According to the invention, by accurately identifying the pollution source emission and the area range of the heavy pollution weather, the emergency control space range and the number of pollution sources are scientifically and accurately reduced, the social cost of air heavy pollution treatment is reduced, and accurate pollution control and economic development are realized.
Description
Technical Field
The invention relates to the technical field of haze pollution treatment in severe air pollution areas, in particular to a PM2.5 regional heavy pollution reason identification system.
Background
Air quality, as an environmental public item, is a non-exclusive, non-competitive decision that it is a basic public service that governments must provide. At present, PM2.5 regional pollution is still one of the main aspects of the air pollution problem, and the joint defense and joint control of the regional atmospheric pollution is still the key strategic measure of PM2.5 regional pollution treatment.
In order to promote accurate, scientific, legal and systematic pollution control, cooperatively promote pollution reduction and carbon reduction, continuously improve air … …, deeply develop pollution prevention and control actions, strengthen urban atmospheric quality standard management, promote the cooperative control of fine particulate matters (PM2.5) and ozone (O3), … … continuously improve the air quality of Jingjin Ji and peripheral areas, Fenwei plain and Yanqi area, promote clean heating in northern areas, industrial kiln control and ultra-low emission modification in non-electric industry according to local conditions, accelerate the comprehensive treatment of emission of volatile organic matters, and respectively reduce the total emission of nitrogen oxides and volatile organic matters by more than 10%. In the five years in the future, the air pollution treatment in key areas still is an important footfall for realizing the target requirement of an ecological civilization system and comprehensively promoting the modernized construction of an environmental treatment system and treatment capacity. Aiming at the heavy pollution weather process of Jingjin Ji, peripheral areas and Fenwei plain which appears for many times, related experts read the problems of the cause, the change trend and the like of the heavy pollution, for example, during the occurrence period of various infectious diseases, the social activity is at a lower level, the discharge amount and the discharge intensity of various pollution sources are reduced, the heavy pollution weather still occurs, on one hand, the adverse weather conditions of long-time stillness, strong adverse temperature and high humidity are frequently generated, on the other hand, the uninterruptible production process exists in the resource type industry with high pollution and high energy consumption, and the fixed pollution sources such as centralized heat supply and the like need to be continuously operated. Therefore, in order to continuously improve the regional PM2.5 pollution condition, more accurate and refined regional PM2.5 pollution identification needs to be further carried out, an identification object needs to be further accurate to the scale of a monitoring point, and a possible pollution source emission cause is given.
At present, no more definite PM2.5 regional heavy pollution judgment rule and technical specification are given in the national level, and basically, the rules are defined according to the high-level continuous areas with the urban PM2.5 concentration, and cities with the same high concentration in space are listed as PM2.5 regional heavy pollution areas, such as the kyujin Ji 2+26 city, the fen wei plain 11 city, and the like, but the problems of unclear management target and 'one-bit action' in PM2.5 pollution control exist, and the problem of social cost minimization of the PM2.5 regional joint defense joint control cannot be realized.
In summary, the following steps: the main drawbacks mainly present are:
the current urban-scale PM2.5 regional heavy pollution discrimination visit cannot meet the requirement of atmosphere pollution joint defense joint control refinement. PM2.5 regional heavy pollution judgment needs refining to the scale of a monitoring point, and whether the PM2.5 regional heavy pollution of the scale of the monitoring point belongs to a cross-grade city, a cross-county area or a local pollution is judged by PM2.5 regional heavy pollution identification of the scale of the monitoring point, and clear scientific judgment needs to be given. The existing PM2.5 regional heavy pollution discrimination mode with a more general urban scale is obviously not specific enough, and a targeted management measure cannot be provided.
The PM2.5 regional heavy pollution reason identification system provided by the invention can overcome the defects of the problems and provide a technical method and support for further promoting the refinement of regional PM2.5 atmospheric pollution joint defense joint control.
Disclosure of Invention
In view of the above technical problems in the related art, the present invention provides a PM2.5 regional heavy pollution cause identification system, which can overcome the above disadvantages of the prior art methods.
In order to achieve the technical purpose, the technical scheme of the invention is realized as follows:
a PM2.5 regional heavy pollution reason identification system comprises a PM2.5 regional heavy pollution reason identification flow module, a PM2.5 heavy pollution city-crossing regional pollution identification method module, a PM2.5 heavy pollution county-crossing regional pollution identification method module and a PM2.5 heavy pollution local pollution identification method module, wherein,
the PM2.5 regional heavy pollution reason identification flow module firstly judges whether regional pollution exists, whether regional pollution exists is regional in a cross-county or regional in a county, whether the alpha value is high or not, whether the pollution is primary pollution or secondary pollution is judged, and then the pollution is treated, converted or discharged.
The PM2.5 heavy pollution urban regional pollution identification method module is used for firstly identifying regional pollution including regional pollution and local pollution, then judging whether PM2.5/PM 2.5-10 is higher or not, and finally comparing the PM2.5 concentration of each monitoring point.
The PM2.5 heavy pollution cross-district regional pollution identification method module firstly judges whether PM2.5/PM 2.5-10 is higher or not, and then compares the PM2.5 concentration of each monitoring point.
The PM2.5 heavy pollution local pollution identification method module firstly judges whether PM2.5/PM 2.5-10 is higher or not, and then judges whether PM2.5 and CO are obviously related or not.
Further, identifying whether regional pollution or local pollution is detected, wherein the concentration of the regional pollution is beyond the standard and similar for 3 or more adjacent monitoring points; local contamination is that 1 and 2 adjacent monitoring points exceed the standard.
Further, a PM2.5 heavy pollution city-level-crossing/district-county-crossing regional pollution identification method module, wherein whether PM2.5/PM 2.5-10 is higher or not is judged, and if PM2.5 is higher, the concentration increasing stage and SO of PM2.5 need to be judged2/CO、NO2Whether the value decreases in CO hours, if O2/CO、NO2Reduction in the/CO hour value, indicating SO2And NO2Conversion to PM2.5 if SO2/CO、 NO2the/CO hour value does not change or rises, indicating that CPM is converted to form PM 2.5; if PM2.5/PM 2.5-10 is not high, the concentration of PM2.5 is high due to direct emission, the pollution is primary pollution, and for primary pollution across counties/counties, the pollution source is a fixed source with large emission.
Further, comparing the concentration of PM2.5 at each monitoring point, the concentration of pollution on a day can be compared with the concentration of pollution on a year to determine the position of a fixed source due to different geographical diffusion conditions in different counties.
Further, when the PM2.5/PM 2.5-10 is judged to be higher in the PM2.5 heavy pollution local pollution identification method module, if the PM2.5/PM 2.5-10 is higher, the PM2.5 and NO are judged2Whether the correlation coefficient is high or not, and then determining SO2/CO、NO2Whether the hour value of CO is reduced or not and then judging the peak NO in the morning and evening2Whether the concentration is increased; if PM2.5/PM 2.5-10 is not high, it indicates that the concentrations of PM2.5 and PM 2.5-10 are both increased, and the concentration of PM2.5 is high due to the emission of a fixed source, which is primary pollution.
Further, the PM2.5 heavy pollution local pollution identification method module judges whether the PM2.5 is significantly related to the CO, and if the PM2.5 is not related to the CO or the correlation coefficient is low, it indicates that the CO does not have a synchronous rising trend with the PM2.5 on the same day; if PM2.5 and CO are obviously and positively correlated, the CO and PM2.5 are synchronously increased on the same day, and the emission source of the particulate matters is a combustion source.
The invention has the beneficial effects that: by accurately identifying the pollution source emission and the area range of the heavily polluted weather, the emergency control space range and the number of the pollution sources can be scientifically and accurately reduced, the 'one-time' negative influence on execution of the heavily polluted emergency control measures is reduced, the social cost of air heavy pollution control is reduced, accurate pollution control and ordered economic development are realized, and the government invalid policy intervention caused by the supply of environmental public goods is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a flowchart of a PM2.5 pollution (out-of-standard) cause identification technology method of a PM2.5 regional heavy pollution cause identification system according to an embodiment of the present invention.
Fig. 2 is a flowchart ii of a PM2.5 pollution (out-of-standard) cause identification technique method of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the present invention.
Fig. 3 is a flow chart of a PM2.5 pollution (out-of-standard) cause identification technical method of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the invention.
Fig. 4 is a fourth flowchart of a PM2.5 pollution (out-of-compliance) cause identification technique method of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the present invention.
Fig. 5 is a schematic time-series scatter plot diagram of the PM2.5 concentration 1h value and the 24h value of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the present invention.
Fig. 6 is a statistical schematic diagram of abnormal values of the concentrations of PM2.5 to PM10 at monitoring points of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the invention.
Fig. 7 is a statistical diagram of secondary pollution daily ratios in PM2.5 standard exceeding days of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the present invention.
Fig. 8 is a schematic diagram of the number of days of contribution of the secondary source to the moving source on the higher ratio day of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the present invention.
Fig. 9 is a PM2.5 secondary pollution day number classification CPM schematic diagram in 2019-2020 of the PM2.5 regional heavy pollution cause identification system according to the embodiment of the present invention.
Fig. 10 is a schematic diagram of classified raise dust of PM2.5 pollution days in 2019-2020 in a PM2.5 regional heavy pollution cause identification system according to an embodiment of the present invention.
Fig. 11 is a schematic diagram of classified road raise dust of PM2.5 pollution days per day in 2019-2020 according to the PM2.5 regional heavy pollution cause identification system in the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments that can be derived by one of ordinary skill in the art from the embodiments given herein are intended to be within the scope of the present invention.
Based on the deep analysis of air quality data, main objects of source emission management and diagnostic evaluation technical methods are provided, such as research and judgment results and technical ideas of local source and external transmission, fixed source and non-point source contribution and the like of the weather causing PM2.5 heavy pollution.
The PM2.5 regional heavy pollution reason identification system comprises a PM2.5 regional heavy pollution reason identification flow module, a PM2.5 heavy pollution city-crossing regional pollution identification method module, a PM2.5 heavy pollution county-crossing regional pollution identification method module and a PM2.5 heavy pollution local pollution identification method module.
The PM2.5 regional heavy pollution cause identification process module, as shown in fig. 1 to 4, determines whether there is regional pollution, and determines whether there is regional pollution, whether it is regional across or regional within a county, whether the α value is high, whether it is primary pollution or secondary pollution, and then converts or discharges the pollution through treatment.
The PM2.5 heavy pollution urban-level regional pollution identification method module is used for identifying regional pollution and local pollution, wherein the regional pollution means that the concentrations of 3 or more adjacent monitoring points exceed standards and are similar, the regional pollution is divided into a local district and a cross-county-level administrative district, and the local pollution means that 1 monitoring point and 2 adjacent monitoring points exceed standards.
Then, the PM2.5 heavy pollution city-level-crossing/district-county-crossing regional pollution identification method module judges whether PM2.5/PM 2.5-10 is higher or not, if PM2.5/PM 2.5-10 is higher, the concentration of PM 2.5-10 is not obviously increased, and only the source of the emission of fine particulate matters comprises secondary pollution (SO) (SO 2.5-10 concentration is not obviously increased)2、NO2VOCS conversion or CPM conversion) and mobile sources, there is no mobile source that can affect multiple counties simultaneously, SO it is secondary pollution, and then the stage of PM2.5 concentration increase, SO, is judged2/CO、NO2Whether the/CO hour value has dropped (recognition of CPM effects), if SO2/CO、NO2Reduction in the/CO hour value, indicating SO2And NO2Conversion to PM2.5 if SO2/CO、NO2the/CO hour value is essentially unchanged or increased, indicating that CPM is converted to form PM 2.5; if PM2.5/PM 2.5-10 is not high, the concentration of PM2.5 and PM 2.5-10 is increased, and the concentration of PM2.5 is high due to direct emission, which is primary pollution. For primary pollution across/within a county, the pollution source must be a fixed source with a large emission.
And finally, comparing the PM2.5 concentration of each monitoring point, wherein due to different geographical diffusion conditions of different counties, the position of the fixed source can be judged by comparing the ranking percentile height (or the ratio of the current day average value to the current year average value) of the pollution day concentration in the annual concentration, and under the general condition, the point with higher concentration is possibly close to the problem fixed source.
The PM2.5 heavy pollution local pollution identification method module firstly judges whether PM2.5/PM 2.5-10 is higher or not.
(1) If PM2.5/PM 2.5-10 is higher
The result shows that the concentration of PM2.5 is higher, but the concentration of PM 2.5-10 is not obviously increased. Sources include secondary pollution (SO)2、NO2VOCS conversion or CPM conversion) and mobile sources.
First, PM2.5 and NO were judged2Whether significant correlation exists or not and the correlation coefficient is higher (distinguishing the influence of moving sources)
1)PM2.5/NO2The correlation coefficient is higher
Description of PM2.5 and NO2The concentration increases synchronously, and the discharge of peripheral mobile sources needs to be controlled.
PM2.5/NO2The correlation coefficient is not high
2) Description of NO2There is no tendency to rise in synchrony with PM2.5, not mobile source pollution, there may be a CPM source near the monitoring point, and a source of exhaust water vapor, for secondary pollution.
Next, the increase stage of PM2.5 concentration, SO2/CO、NO2Whether the CO hour value decreases (recognition of CPM impact)
①SO2/CO、NO2Reduction in the hourly CO value
SO2And NO2Conversion to PM2.5, SO in the region to be controlled2And NO2And (5) discharging. Then determine the morning and evening peak NO2Whether the concentration is obviously increased:
a significant increase in NO2 concentration indicates a need to control emissions from moving sources around;
NO2 concentration did not rise significantly indicating the need to control ambient source emissions.
②SO2/CO、NO2The value of/CO hour is basically unchanged or increased
Indicating CPM conversion to PM2.5
(2) If PM2.5/PM 2.5-10 is not high, it indicates that the concentrations of PM2.5 and PM 2.5-10 are both increased, and the concentration of PM2.5 is high due to the emission of a fixed source, which is primary pollution.
Then judging whether PM2.5 and CO are obviously related (distinguishing the dust effect)
1) PM2.5 and CO are irrelevant or have low correlation coefficient
Indicating that CO does not have a synchronous rising trend with PM2.5 on the day, and the emission source of the particulate matters generated by combustion basically emits CO, so the emission source is mainly influenced by the source of the fine particulate matters.
Then, PM2.5 and NO were judged2Whether the correlation is significant or not and the correlation coefficient is higher (distinguishing the influence of the slag car)
①PM2.5/NO2The correlation coefficient is higher, which shows that PM2.5 and NO2The concentration is synchronously increased, and dust is raised for the road (slag car).
②PM2.5/NO2The correlation coefficient is not high, which indicates that NO2There is no synchronous rising trend with PM2.5, probably due to fixed source emissions.
2) PM2.5 and CO are significantly positively correlated
The CO and PM2.5 rise synchronously on the same day, the emission source of particulate matters is a combustion source, and the emission source of the particulate matters can be a non-point source for local pollution, particularly the emission of a resident stove and the like, and can also be related to a mobile source.
Then, PM2.5 and SO were judged2With NO2Magnitude of correlation coefficient (in percentile rank)
③ PM2.5 and SO2The correlation coefficient is larger, which shows that the PM2.5 is influenced more by the emission of the coal-fired fixed source and the non-point source.
PM2.5 and NO2Correlation coefficient is moreLarge, indicates that the moving source emissions have a greater impact on PM 2.5.
In order to facilitate understanding of the above-described technical aspects of the present invention, the above-described technical aspects of the present invention will be described in detail below in terms of specific usage.
As shown in fig. 1, according to the PM2.5 regional heavy pollution cause identification system in the embodiment of the present invention, by taking 24h and 1h pollutant concentration data of 15 national city control monitoring points in a data range of 2019.1.1 to 2020.12.31SY as an example, as shown in fig. 4, time series scatter diagrams of PM2.5 concentration 1h values and 24h values show that there are 31 h value abnormal values and 2 24h value abnormal values respectively at west street monitoring points in the qin city area, and a rejection process is adopted.
First, analyzing PM2.5 to PM10, and determining that PM2.5> PM10 is abnormal, wherein the abnormal data of the monitoring points account for 4.18% and 2.07% in the sample sets of 1h value and 24h value, respectively, as shown in fig. 5.
After the abnormal value samples are removed, the frequency distribution of each monitoring point is counted, the statistical distribution is observed for the data of PM2.5 in the heating period and the non-heating period respectively, the peak degree of the off-air ratio is found to be obviously reduced, the data range is larger, the high ratio data are more, namely the ratio distribution of the heating period and the non-heating period is obviously different, and the standards can be set respectively. 75 quantiles and 90 quantiles of the non-standard days of the PM2.5 of each monitoring point are counted (2019-2020), and the results are shown in table 1.
TABLE 1 statistic table for PM2.5 not exceeding standard day PM2.5/PM 2.5-10 quantiles
And counting the proportion possibly caused by secondary pollution in the standard exceeding days of PM2.5 of each monitoring point according to the standard, wherein the proportion of the secondary pollution of PM2.5 in urban areas is relatively lower than that in the metropolitan areas as shown in FIG. 6. Making a standard according to a 90-degree grading value of a non-overproof day, wherein the secondary pollution day in the overproof day accounts for 21.1-54.4%, and the standard deviation is 9.8%; and (4) making a standard according to a 75-degree grading value of the standard-exceeding day, wherein the secondary pollution day accounts for 56.5-80.8% in the standard-exceeding day, and the standard deviation is 6.7%.
According to the judgment of the experience, the weather station in the Yanyang city serves as a control point, the peripheral emission sources are less, and the PM2.5 is more likely to be caused by secondary pollution. And (4) the monitoring points are ranked highest in the secondary pollution daily ratio statistics according to the standard established by the 75 quantile values, so that the 75 quantile values are selected as the standard for judging the higher ratio. The date when the ratio exceeds this range is considered to be the case where the PM2.5 increase rate is significantly higher than that of the coarse particulate matter, and there is a greater probability that the PM2.5 secondary pollution exists.
Analyzing the high ratio of PM2.5/PM 2.5-10, firstly, identifying the influence of a moving source, calculating the PM2.5 concentration and NO at each monitoring point every day in 2019-20202Correlation coefficient of the value of the concentration hour. The results of the anova showed: (ii) NO2The correlation with PM2.5 concentration is obviously different between the heating period and the non-heating period, and the correlation of the heating period is greater than that of the non-heating period; heating period NO2The correlation with the PM2.5 concentration is obviously different between the PM2.5 standard exceeding days and the PM2.5 standard not exceeding days, and the correlation of the PM2.5 standard exceeding days is weakened; non-heating period, NO2The correlation with the PM2.5 concentration was not significantly different between the PM2.5 out-of-standard days and the PM2.5 out-of-standard days.
Thus, for NO2The PM2.5 correlation is respectively made into standards in the heating period and the non-heating period, the standard is made in the heating period based on statistical distribution of days which do not exceed standards, and the standard is made in the non-heating period without distinguishing whether the PM2.5 exceeds the standards. As shown in Table 2, in the days when PM2.5/PM 2.5-10 is higher than standard, NO is added2And (3) counting the number of PM2.5 exceeding days for distinguishing the influence of the moving source and the secondary source according to whether the correlation with the PM2.5 concentration is high, wherein the proportion of the secondary source influence is 63-92% as shown in FIG. 7.
TABLE 2 NO2Higher standard of correlation coefficient with PM2.5
Second, knowThe CPM influence is distinguished, the hours of the PM2.5 concentration rise (Ct-Ct-1 is more than or equal to 0) of each monitoring point per day in 2019-2020 years and SO are calculated2/CO、NO2Hours of CO rise, and counting SO in the rising time period of PM2.5 concentration every day2/CO、NO2Ratio of/CO in the rise hours, hereinafter referred to as β SO2And beta NO2. (example: assume 24 1h values in a day, where there are 12 hours of PM2.5 rise, during which there is 8hSO2CO is increased, the ratio is beta SO 28/12 ═ 0.75). The result of the variance analysis shows that the beta is obviously different between the heating period and the non-heating period, between the PM2.5 standard exceeding days and the PM2.5 standard not exceeding days, and the SO is added at the PM2.5 standard exceeding days in the heating period2And NO2The rate of rise decreases in synchronism with PM 2.5. Therefore, standards are respectively established for the index beta in the heating period and the non-heating period, and the standards are established based on statistical distribution of days which do not exceed standards.
Extracting PM2.5 on days without exceeding standard, observing the statistical distribution of beta to find that the beta is approximately normal distribution, wherein the average value of each monitoring point is about 0.5, namely during the rising period of PM2.5 concentration, about 1/2 time periods of SO2And NO2Rising synchronously with PM 2.5. The specific criteria for calculating beta for each monitoring point during the heating and non-heating periods are shown in table 3.
The classification of the number of days of secondary pollution days in year 2019-2020 PM2.5 according to the standard statistics of Table 3 is shown in FIG. 8.
The ratio is not high, which indicates that the concentrations of PM2.5 and PM 2.5-10 are increased, and the pollution is primary pollution. The influence of a dust source is identified, and the correlation coefficient of the PM2.5 concentration and the CO concentration small value of each monitoring point every day is calculated between 2019 and 2020. The anova results showed that the correlation of CO to PM2.5 concentration was significantly different between the heating and non-heating periods. In the heating period, the correlation between the concentrations of CO and PM2.5 is obviously different between the days when PM2.5 exceeds the standard and the days when PM2.5 does not exceed the standard, the correlation between the concentrations of CO and PM2.5 is enhanced, and in the non-heating period, the correlation between the concentrations of CO and PM2.5 does not have obvious difference between the days when PM2.5 exceeds the standard and the days when PM2.5 does not exceed the standard. Therefore, the standards are respectively established for the correlation between CO and PM2.5 in the heating period and the non-heating period, the standard is established in the heating period based on the statistical distribution of the non-exceeding days, and the non-heating period does not distinguish whether PM2.5 exceeds the standard or not and establish the standard uniformly.
The correlation coefficient of CO and PM2.5 is used for screening the source of PM2.5 on days when the ratio is not too high, i.e. a pollution day, and when the correlation coefficient of CO and PM2.5 is too low or not correlated, PM2.5 has a greater probability of coming from particulate matters emitted by non-combustion sources, such as a dust source. For the criterion with a lower coefficient, it was determined according to the statistical 25% percentile, and for the criterion with a negative correlation coefficient calculated, it was adjusted to 0, with the results shown in table 4.
TABLE 4 CO and PM2.5Standard of low correlation coefficient
The classification of the number of days per day of PM2.5 contamination in 2019-2020 according to the standard statistics in Table 4 is shown in FIG. 9. Further elaboration of NO2The dust source is subdivided into road dust or other dust, according to the correlation standard with the PM2.5 concentration.
Identifying fixed source/mobile source influence, and judging PM2.5 and SO for the standard-exceeding day sample with 4.1 dust source influence eliminated2With NO2Size of correlation coefficient (in percentile ranking), PM2.5 and SO2The correlation coefficient is larger, the influence of the emission of the coal-fired fixed source and the non-point source on PM2.5 is larger; PM2.5 and NO2The larger the correlation coefficient, the greater the influence of the mobile source emissions on PM 2.5.
In conclusion, by means of the technical scheme, the pollution source emission and the area range of the heavily polluted weather are accurately identified, the emergency control space range and the number of the pollution sources can be scientifically and accurately reduced, the 'one-time' negative influence of the execution of the heavily polluted emergency control measures is reduced, the social cost of air heavily polluted control is reduced, the accurate pollution control and the ordered economic development are realized, and the invalid policy intervention of government failure caused by the supply of environmental public goods by the government is reduced.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A PM2.5 regional heavy pollution reason identification system is characterized by comprising a PM2.5 regional heavy pollution reason identification flow module, a PM2.5 heavy pollution city-crossing regional pollution identification method module, a PM2.5 heavy pollution county-crossing regional pollution identification method module and a PM2.5 heavy pollution local pollution identification method module,
the PM2.5 regional heavy pollution reason identification flow module firstly judges whether regional pollution exists, whether regional pollution exists is regional in a cross-county or regional in a county, whether the alpha value is high or not, whether the pollution is primary pollution or secondary pollution is judged, and then the pollution is treated, converted or discharged;
the PM2.5 heavy pollution cross-market regional pollution identification method module is used for identifying regional pollution including regional pollution and local pollution, judging whether PM2.5/PM 2.5-10 is higher or not, and finally comparing the PM2.5 concentration of each monitoring point;
the PM2.5 heavy pollution cross-district regional pollution identification method module firstly judges whether PM2.5/PM 2.5-10 is higher or not, and then compares the PM2.5 concentration of each monitoring point;
the PM2.5 heavy pollution local pollution identification method module firstly judges whether PM2.5/PM 2.5-10 is higher or not, and then judges whether PM2.5 and CO are obviously related or not.
2. The PM2.5 regional heavy pollution cause identification system according to claim 1, wherein the regional pollution is local pollution, and the regional pollution is that the concentration of 3 or more adjacent monitoring points exceeds the standard and is similar; local contamination is that 1 and 2 adjacent monitoring points exceed the standard.
3. The PM2.5 regional heavy pollution cause identification system according to claim 1, wherein the PM2.5 heavy pollution cross-city/cross-district regional pollution identification method module judges whether PM2.5/PM 2.5-10 is higher, if PM2.5 is higher, it is necessary to judge the PM2.5 concentration increasing stage and SO2/CO、NO2Whether the value decreases in CO hours, if O2/CO、NO2Reduction in the/CO hour value, indicating SO2And NO2Conversion to PM2.5 if SO2/CO、NO2the/CO hour value does not change or rises, indicating that CPM is converted to form PM 2.5; if PM2.5/PM 2.5-10 is not high, the concentration of PM2.5 is high due to direct emission, the pollution is primary pollution, and for primary pollution across counties/counties, the pollution source is a fixed source with large emission.
4. The PM2.5 regional heavy pollution cause identification system according to claim 1, wherein the PM2.5 concentrations of monitoring points are compared, and due to different geographical diffusion conditions of different counties, the concentration of pollution day in the annual concentration can be compared first to judge the position of a fixed source.
5. The PM2.5 regional heavy pollution cause identification system according to claim 1, wherein when the PM2.5/PM 2.5-10 is judged to be higher in the PM2.5 heavy pollution local pollution identification method module, if the PM2.5/PM 2.5-10 is higher, the PM2.5 and NO are judged to be higher2Whether the correlation coefficient is high or not, and then determining SO2/CO、NO2Whether the hour value of CO is reduced or not and then judging the peak NO in the morning and evening2Whether the concentration is increased; if PM2.5/PM 2.5-10 is not high, it indicates that the concentrations of PM2.5 and PM 2.5-10 are both increased, and the concentration of PM2.5 is high due to the emission of a fixed source, which is primary pollution.
6. The system for identifying the PM2.5 regional heavy pollution reason according to claim 1, wherein the PM2.5 regional heavy pollution local pollution identification method module judges whether the PM2.5 and the CO are significantly related, and if the PM2.5 and the CO are not related or the correlation coefficient is low, the CO does not have a synchronous rising trend with the PM2.5 on the same day; if PM2.5 and CO are obviously and positively correlated, the CO and PM2.5 are synchronously increased on the same day, and the emission source of the particulate matters is a combustion source.
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