CN112102895B - Road sediment polycyclic aromatic hydrocarbon source analysis method for coupling migration conversion process - Google Patents
Road sediment polycyclic aromatic hydrocarbon source analysis method for coupling migration conversion process Download PDFInfo
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Abstract
The invention relates to a road sediment polycyclic aromatic hydrocarbon source analysis method in a coupling migration conversion process, which comprises the following main steps: step 1, collecting a road sediment sample at a set sampling point; step 2, determining the content concentration and the detection limit of various polycyclic aromatic hydrocarbons in a sample by adopting a gas chromatography-mass spectrometer GC/MS (gas chromatography-mass spectrometry) to establish a receptor matrix C and an uncertainty matrix U for describing the content of the sample points and various polycyclic aromatic hydrocarbons; step 3, establishing a source component spectrum library matrix S of the polycyclic aromatic hydrocarbon source; the method can provide technical support for the environmental management department to formulate regional polycyclic aromatic hydrocarbon pollution control countermeasures, so that when the environmental management department faces the road polycyclic aromatic hydrocarbon pollution problem, the pollution source can be rapidly identified through a complete source analysis method, and effective pollution prevention and control can be performed.
Description
Technical Field
The invention relates to the technical field of pollution source analysis, in particular to a road sediment polycyclic aromatic hydrocarbon source analysis method in a coupling migration conversion process.
Background
Along with the development and utilization of natural resources on earth, serious influences are generated on water, soil, atmosphere, environment and the like, and how to prevent and improve the production and living environment of polycyclic aromatic hydrocarbon pollution sources for human survival has been mentioned on an important agenda. At present, two aspects are paid attention to pollution source analysis, one is to judge the source type of main pollutants in an environmental medium, namely pollution source identification (source identification); and secondly, quantitatively calculating the contribution of various pollution sources on the basis of source identification, namely pollution source analysis (source apportionment). Source resolution of environmental pollutants is the basis for pollution control. Pollution source analysis is a method for researching the influence and effect of pollution sources on environmental pollution.
However, there is currently no intensive and targeted study on the resolution of sources of polycyclic aromatic hydrocarbon pollution, a road deposit, coupled with a migratory conversion process. In the existing source analysis, the problem that the collected source component spectrum data is inconsistent with the source component spectrum data obtained through factorization often occurs, and obvious uncertainty is generated in the determination of the source of pollutants. In recent years, research on determination of pollutant sources shows that when polycyclic aromatic hydrocarbon is adsorbed on particles and migrates in the atmosphere, the polycyclic aromatic hydrocarbon is influenced by oxidative free radicals and illumination in the atmosphere, and the photochemical conversion process is converted into other compounds, which is the main factor of attenuation conversion of polycyclic aromatic hydrocarbon in migration. Meanwhile, under the same reaction condition, different polycyclic aromatic hydrocarbons react at different rates, which is one of the reasons why the known source component spectrum data and the calculated source component spectrum data cannot be corresponded. Therefore, the accuracy of the result can be obviously improved by coupling the photochemical pseudo first-order reaction in the migration and transformation process into the source analysis model.
In the prior art, for example, CN110335645a discloses a method for resolving a polycyclic aromatic hydrocarbon pollution source in a water body, which comprises the steps of first extracting the number of main component factors in a polycyclic aromatic hydrocarbon pollution source database in the water body by singular value factorization; then factor decomposition is carried out based on a weighted least square method, and non-negative constraint factor rotation is realized through non-negative constraint least square sum factor rotation, so that a factor load matrix and a factor score matrix with non-negative characteristics are extracted; the method comprises the steps of identifying a polycyclic aromatic hydrocarbon pollution source by using a naive Bayesian method based on factor loading month identification of a pollution source spectrum as an identification problem of a multi-parameter mode; and finally, calculating the factor load pollution source contribution rate by using the identified classification model, and realizing the source analysis of the characteristic pollutants. The pollution source analysis method can realize accurate analysis of the polycyclic aromatic hydrocarbon pollution source data in the water body and improve the analysis rate.
For another example, patent application number CN2016102205598.7 discloses a polycyclic aromatic hydrocarbon pollution source resolving method, which specifically comprises the steps of: step 1, determining a investigation region of a polycyclic aromatic hydrocarbon pollution source; step 2, carrying out investigation on the polycyclic aromatic hydrocarbon pollution sources in an investigation region, wherein the investigation process comprises the following steps: collecting basic data; (2) field investigation; (3) data processing and analysis; (4) Selecting polycyclic aromatic hydrocarbon pollutants and establishing various monitoring information databases; step 3, analyzing the influence of pollution sources on the environment under different conditions on the basis of the investigation result of the polycyclic aromatic hydrocarbon pollution sources, and judging the main polycyclic aromatic hydrocarbon pollution sources affecting the investigation area; the different cases include: (1) A single pollution source is positioned at an environmental sensitive point, (2) the characteristic identifiers of the polycyclic aromatic hydrocarbon of various emission sources are identified, and a fingerprint spectrum of the polycyclic aromatic hydrocarbon source capable of reflecting the emission characteristics of the pollution source is established; step 5, identifying pollution sources by using a rapid clustering method, and programming by using Matlab software to realize comparison between pollution sources by a computer; the method for identifying the pollution source by applying the rapid clustering method comprises the following steps: firstly, preprocessing and initializing; step two, outputting a training sample pair; step 6, constructing a chemical mass balance source analysis method based on rapid cluster analysis, which comprises the following steps: and (3) carrying out the identification and classification of the pollution sources by using a rapid cluster analysis algorithm and carrying out the pollution source calculation step by using a chemical mass balance method.
For another example, chinese patent application No. 2013107221931.6 discloses a method for determining ecological risk of polycyclic aromatic hydrocarbon in water, which belongs to the field of ecological risk determination. The method comprises the following steps: step 1, screening representative species of the water ecosystem in the area; step 2, obtaining toxicity data of benzo a pyrene; step 3, calculating a benzo a pyrene concentration value HC5 of 95% of species in the protected water ecosystem; step 4, sampling and measuring the type of the polycyclic aromatic hydrocarbon pollutant and the corresponding environmental concentration thereof, and analyzing the concentration distribution characteristics of various polycyclic aromatic hydrocarbons; step 5, calculating an ecological risk quotient Rqi of the specific polycyclic aromatic hydrocarbon pollutant; and step 6, calculating a total ecological risk quotient RQt, and defining specific ecological risks. The method can analyze whether the potential risk caused by the polycyclic aromatic hydrocarbon pollutant is acceptable or not, judge whether the overall level of the ecological risk of the water body is controlled or not, and provide scientific basis for the protection of the water ecological system, the establishment of polycyclic aromatic hydrocarbon pollution control measures and the like.
In summary, in the prior art and the prior patent literature disclosed so far, a method for resolving sources of polycyclic aromatic hydrocarbon pollution considering migration and conversion processes for road sediment is not proposed.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a road sediment polycyclic aromatic hydrocarbon source analysis method in a coupling migration conversion process.
The method for analyzing the road sediment polycyclic aromatic hydrocarbon source in the coupling migration conversion process comprises the following steps:
step 1, collecting road sediment samples at set sampling points;
step 2, determining the content concentration and the detection limit of various polycyclic aromatic hydrocarbons in the sample by adopting a gas chromatography-mass spectrometer GC/MS (gas chromatography-mass spectrometry) to establish a receptor matrix C and an uncertainty matrix U for describing the content of the sample points and various polycyclic aromatic hydrocarbons;
step 3, establishing a source component spectrum library matrix S of the polycyclic aromatic hydrocarbon source;
step 4, inputting the receptor matrix C and the uncertainty matrix U into the EPA-PMF model by using a positive definite matrix factorization method (PMF);
step 5, setting parameters and calculating, and then calculating a matrix F (also called a load matrix) representing the meaning of the source component and a matrix G (also called a factor scoring matrix) representing the source contribution rate from EPA-PMF;
step 6, simulating photochemical pseudo-first-order reactions of different time sequences for the source spectrum S in the source component spectrum library to obtain a source component spectrum reaction matrix S after the simulated migration conversion process on the time sequence t ；
Step 7, converting the simulated migration on the whole time sequence into the whole source component spectrum S t And judging the similarity of the source component types with each row vector in the analyzed load matrix F by using a cosine similarity method, selecting the source component type with the highest similarity to the analyzed source spectrum as the identified source spectrum type, and combining the factor score matrix G to obtain the sources and contribution rates of the polycyclic aromatic hydrocarbon in each road sediment sample in the research area.
Further, the sample points set in the step 1 are uniformly distributed by adopting gridding:
step 1.1, setting a sampling point according to the road condition, and collecting a sample of road sediment at the set sampling point;
step 1.2, setting sampling points which are more than the number of the analyzed polycyclic aromatic hydrocarbon types on a road by adopting a mode of uniformly distributing the set sampling points in a gridding way;
step 1.3, cleaning the collected road sediment sample at least three times within the width range of 0.5m from the edge of the motor vehicle lane to the curb by adopting a cleaning brush so as to obtain the maximum amount of cleaning surface sediment comprising particles with various particle diameters;
step 1.4, covering and sealing the collected road sediment sample in a polyethylene plastic bag by using an aluminum foil sheet, then placing the road sediment sample in a sampling box provided with an ice bag, and taking the road sediment sample back to a laboratory in a refrigerating state to be frozen and preserved at the temperature of minus 20 ℃; the mass of each residual sample is more than 300g so as to meet the uniformity of road sediment samples and the use level of experimental detection;
and 1.5, drying the collected road sediment sample, sieving with a 500-mu m-aperture sieve to remove plant residues, sand and stones and impurities, and storing in a refrigerated state in a dark place for later use.
Further, the Polycyclic Aromatic Hydrocarbons (PAHs) in step 2 include: fluorene (Flu), phenanthrene (Phe), anthracene (Ant), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (b) fluoranthene (BbF), benzo (k) fluoranthene (BkF), benzo (a) pyrene (BaP), indene (1, 2, 3-cd) pyrene (IND), benzo (ghi) perylene (BghiP), dibenzo (a, h) anthracene (DbA).
Further, the receptor matrix C in step 1 is shown in the following formula (1):
in formula (1): the horizontal lines represent the concentrations of different polycyclic aromatic hydrocarbons in the same sample, and the vertical lines represent the concentrations of the same polycyclic aromatic hydrocarbons in different samples, for example, m samples are collected in total, n polycyclic aromatic hydrocarbons are analyzed, C ij Namely the concentration of the jth polycyclic aromatic hydrocarbon in the ith sample;
the uncertainty matrix U is shown in the following formula (2):
in formula (2): u (U) ij The uncertainty of the j-th polycyclic aromatic hydrocarbon in the i-th sample is obtained.
Further, element U in uncertainty matrix U described in step 2 ij The generation method is shown in the following formula (3):
in formula (3): u (U) ij MDL, uncertainty of the jth contaminant in the ith sample j For the detection limit of the j-th pollutant, C ij RSD for the concentration of the jth contaminant in the ith sample j Is the relative standard error of the jth contaminant.
Further, the polycyclic aromatic hydrocarbon (polycyclic aromatic hydrocarbon, PAHs) in step 3 is: fluorene (Flu), phenanthrene (Phe), anthracene (Ant), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (b) fluoranthene (BbF), benzo (k) fluoranthene (BkF), benzo (a) pyrene (BaP), indene (1, 2, 3-cd) pyrene (IND), benzo (ghi) perylene (BghiP), dibenzo (a, h) anthracene (DbA).
Further, in the method for establishing the source component spectrum library S of the polycyclic aromatic hydrocarbon source in step 3, the polycyclic aromatic hydrocarbon pollution source sample is collected by itself and detected, so as to obtain the unit content of the polycyclic aromatic hydrocarbon pollutant or the concentration proportion of the pollutant in the potential pollution source, the type of the polycyclic aromatic hydrocarbon is required to be consistent with the type of the polycyclic aromatic hydrocarbon in the receptor matrix, and the source component spectrum library matrix S of the polycyclic aromatic hydrocarbon source is shown in formula (4):
in formula (4): if the concentration of the polycyclic aromatic hydrocarbon of p pollution sources is collected, each sample detects n polycyclic aromatic hydrocarbons of the same kind as the receptor matrix C and the uncertainty matrix U, S kj The concentration of the j-th polycyclic aromatic hydrocarbon in the kth pollution is obtained.
Further, the operation principle of the PMF model in step 4 is that a product of a source component (factor score matrix) G and a load matrix F is generated by a pseudo-random method, such that the product approximates to a receptor matrix C, and then a least squares iterative process is performed, such that a difference matrix E between the product of the factor score matrix G and the load matrix F and the receptor matrix C is as small as possible, and meanwhile, the factor score matrix G and the load matrix F are subjected to non-negative constraint, where a relationship of the receptor matrix C is shown in formula (5):
c=gf ten e.,. (5)
The least square iterative process for making the difference matrix E as small as possible is to establish an objective function Q (E), calculate the minimum value of the objective function Q (E), and when the minimum value is reached after Q (E) is iterated for many times, consider that the factor score matrix G and the load matrix F calculated according to the formula (5) can represent the contribution of each pollution source and the meaning of each pollution source respectively, and establish the objective function Q (E) as shown in the following formula (6):
Min(Q(E))＝Min(∑ i ∑ j (e ij /U ij ) 2 )......(6),
in formula (6): u (U) ij For the element of the ith row and the jth column of the uncertainty matrix U in the formula (2), namely the uncertainty of the ith polycyclic aromatic hydrocarbon in the jth sample, the calculation method is shown in the formula (3), and e ij Is the element of the ith row and jth column of the difference matrix E in equation (5).
Further, the method for determining the factor number parameter in step 5 is to input the number of potential factors (the number is determined to be 2-7 in this application, and can be adjusted according to actual needs), and perform one-to-one unitary linear regression on the elements in the corresponding column vectors (i.e. the corresponding values of the same polycyclic aromatic hydrocarbon with different points) in the analog value matrix C' and the acceptor matrix C, as shown in the following formulas (7) - (9):
C′＝GF......(7),
in formula (7): c' is an analog value matrix, and is the product of a factor score matrix G and a load matrix F;
equation (8) is a unitary first-order equation obtained from the model value matrix C' and the receptor matrix C,is of the self-help typeVariable value C' ij Corresponding dependent variable, C' ij Is the element of the ith row and jth column in the analog value matrix C', wherein a j And b j Slope and intercept of a unitary linear function corresponding to the jth contaminant, as determined according to equation (8);
in the formula (9), r 2 j Regression coefficient corresponding to the j-th pollutant, C' ij And C ij The elements of the ith row and jth column of the simulated value matrix C' and the receptor matrix C,and->The mean value of the j-th element, namely the j-th pollutant, in the analog value matrix C' and the receptor matrix C respectively;
by analyzing the slope a of the unitary primary regression equation corresponding to each of the 12 polyaromatic hydrocarbon contaminants j Intercept b j And regression coefficient r 2 j The magnitude of the factor is determined by comparing the ranges of the values when different factor numbers are input, and the reference standard is that the slope a corresponding to most polycyclic aromatic hydrocarbon is that j Are all within the range [0.9,1.1 ]]Within the interval, intercept b j Are all at [ -0.1,0.1]Within the interval, regression coefficient r 2 j Greater than 0.85, the number of factors selected is considered to be within a reasonable range.
Further, the coupling migration conversion process in the step 6 is a photochemical pseudo-first-stage reaction, the reaction raw material is a polycyclic aromatic hydrocarbon source component spectrum at the source starting position, and the reaction formula is the following formula (10):
ln[PAH j /PAH j0 ]＝-k×t......(10)
in formula (10): PAH (PAH) j0 The original concentration of the jth pollutant is S in a source spectrum library matrix S kj ，PAH j For the concentration of the j-th polycyclic aromatic hydrocarbon after the photochemical reaction for t time, the concentrations are combined into a reaction matrix S composed of the simulated reaction source component spectrum t K is a reaction coefficient, and different polycyclic aromatic hydrocarbons are not valuedAnd the k value is 0.0072-0.012.
Further, in step 6, setting a time sequence for the coupled migration conversion process to generate a simulated migration conversion process node, where the time sequence is set for not less than 48 hours, i.e. calculating the formula (10): t=0, t 1 ，t 2 ，……48，t n A series of PAH's such as … … j Values.
Further, the cosine similarity method in step 7 is to judge the similarity method as follows: reaction matrix S on nodes in one-to-one comparison migration conversion process t And selecting a group with highest cosine similarity as a judging basis of source component meanings with the analyzed load matrix F.
Further, the cosine similarity method described in step 7 is performed according to the following formula (11):
in formula (9): A. b are respectively the compared actual measured source component spectral vectors (source component matrix S t In (a) and the resolved source component spectral vector (the row vector in matrix F), n being the vector dimension, i.e. the number of contaminant species, a j And B j The concentration of the j-th pollutant in the two source spectrums is respectively, the larger the cosine similarity is, the more similar the two source spectrums are, the maximum is 1, and the vectors are completely coincident.
The invention has the following advantages:
the road sediment polycyclic aromatic hydrocarbon source analysis method for the coupling migration conversion process has wide practicability by sampling the polycyclic aromatic hydrocarbon pollution source on site and determining the polycyclic aromatic hydrocarbon characteristic pollutant through analysis and calculation.
The road sediment polycyclic aromatic hydrocarbon source analysis method in the coupling migration conversion process can quickly and accurately trace the source of the polycyclic aromatic hydrocarbon pollution source of the urban road, provide technical support for an environmental management department to formulate regional polycyclic aromatic hydrocarbon pollution control countermeasures, and enable the environmental management department to quickly identify the pollution source through a complete source analysis method when facing the road polycyclic aromatic hydrocarbon pollution problem, thereby effectively preventing and controlling pollution.
According to the road sediment polycyclic aromatic hydrocarbon source analysis method in the coupling migration conversion process, a pseudo first-stage photochemical reaction of a time sequence is simulated on a source component spectrum library, so that the unreacted source component spectrum detected at the source and the source component spectrum analyzed at a polluted medium in the migration conversion process are closer to and tend to be consistent in theory, the accuracy of judging the type of a pollution source is improved, and a source analysis result with high reliability is obtained.
Drawings
FIG. 1 is a layout diagram of different sampling points in an urban area in an embodiment of a road sediment polycyclic aromatic hydrocarbon source analysis method in a coupling migration conversion process according to the present invention;
FIG. 2 shows the source and contribution of polycyclic aromatic hydrocarbons at different points in an urban area according to an embodiment of the method for resolving a road sediment polycyclic aromatic hydrocarbon source in a coupled shift conversion process according to the present invention.
Reference numerals: 1-30 sampling points, 31-one loop, 32-two loop and 33-three loop.
Detailed Description
Specific embodiments of the method for resolving a road sediment polycyclic aromatic hydrocarbon source in a coupled mobile conversion process according to the present invention will be described in detail with reference to fig. 1-2 of the present specification.
Step 1, setting sampling points on the basis of geographic position, town type, road area, population, town pattern, pillar industry and surface runoff flushing path information of an object in a research area, and collecting common polycyclic aromatic hydrocarbon pollution sources in the city, including asphalt pavement, concrete and cement pavement abrasion samples; tire, fresh engine oil, waste engine oil traffic source samples; combustion coal dust, jiao Luchen, biomass combustion dust, factory emission dust, and the like.
Road sediment sampling is carried out according to the sample point distribution shown in fig. 1, and 30 road sediment sampling points (BJ 1-BJ 30) are arranged in four loops of urban areas of a certain city in the north part of China, wherein the four road sediment sampling points comprise four residential area sampling points: BJ20, BJ21, BJ24 and BJ 28); commercial area sampling points nine: BJ2, BJ4, BJ6, BJ7, BJ14, BJ15, BJ16, BJ17 and BJ27; main road sampling points are six: BJ1, BJ10, BJ12, BJ18, BJ19 and BJ29; eight road sampling points: BJ3, BJ9, BJ11, BJ13, BJ22, BJ23, BJ25 and BJ 26); park sampling points are three: BJ5, BJ8 and BJ30.
Step 2, measuring content concentrations of fluorene (Flu), phenanthrene (Phe), anthracene (Ant), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (b) fluoranthene (BbF), benzo (k) fluoranthene (BkF), benzo (a) pyrene (BaP), indene (1, 2, 3-cd) pyrene (IND), benzo (ghi) perylene (BghiP) and dibenzo (a, h) anthracene (DbA) polycyclic aromatic hydrocarbon in 30 road sediment samples and 11 polycyclic aromatic hydrocarbon pollution source samples by using a gas chromatography-mass spectrometer (GC/MS), and recording detection limits during measurement; the total concentration of the benzo (b) fluoranthene (BbF) and the benzo (k) fluoranthene (BkF) is recorded as BF because the peak values are not easy to distinguish, so that the total concentration is calculated according to 11 pollutants when 12 polycyclic aromatic hydrocarbons are detected and analyzed.
Step 3, constructing a pollution source component spectrum library matrix S of 11 rows and 11 columns according to the concentration of 11 pollutants in the 11 pollution sources measured in the step 2, and constructing a receptor matrix C of 30 rows and 11 columns according to the concentration of 11 pollutants in 30 samples; according to the relation between the detection limit and the pollutant concentration, an uncertainty matrix U of 30 rows and 11 columns is constructed, and the construction method of each element in the U is as follows:
in the above formula: u (U) ij Namely each element in the uncertainty matrix U, represents the uncertainty of the jth pollutant in the ith sample, MDL j For the detection limit of the j-th pollutant, C ij RSD for the concentration of the jth contaminant in the ith sample j Is the relative standard error of the jth contaminant.
Inputting the receptor matrix C calculated above into EPA-PMF model, setting factor number to 2-7, sequentially performing 6 times of calculation, and comparing each calculationAs a result, it was found that when the number of the set factors is 7, the simulated value and the measured value of the polycyclic aromatic hydrocarbon content of each sample point are subjected to unitary linear relation fitting, and the calculated parameters of the intercept, the slope and the regression coefficient of the correlation can meet the requirements, namely, the slope a corresponding to 11 polycyclic aromatic hydrocarbons is basically ensured j Are all within the range [0.9,1.1 ]]Within the interval, intercept b j Are all at [ -0.1,0.1]Within the interval, regression coefficient r 2 j All greater than 0.85, (Pyr is slightly lower than standard but not so far apart, and is considered substantially satisfactory) the specific results are shown in table 1 below:
TABLE 1 fitting parameters for the PMF model with a factor of 7
From this, 7 sources of a temporary unknown type, temporarily designated PMF1-PMF7, were analyzed, and the contamination contribution rates of these sources to each sample were also determined.
Setting a time sequence for 8-144h of photochemical reaction, setting a simulation sampling point every 8h, and performing pseudo-first-order reaction simulation on a plurality of source component spectrums in a source component spectrum library, wherein the reference k value is shown in the following table 2:
TABLE 2 photochemical reaction coefficients of polycyclic aromatic hydrocarbons
The component changes of the source component spectrums regularly follow a logarithmic curve, so that 11 multiplied by 4=44 source spectrums of representative 8h, 48h, 72h and 144h time nodes are selected to be compared with 7 factors analyzed from a PMF model by a cosine similarity method, and table 3 shows the comparison values of the factors analyzed from the PMF model and a source component spectrum library simulating photochemical reaction, wherein the cosine similarity of industrial, coal-fired and tire is larger than 0.9, and the reliability is higher; except industry and Jiao Luyuan, the cosine similarity decreases along with the reaction time, which indicates that the photochemical reaction time is not long in other source migration, so that the similarity between the original source spectrum and the analysis source spectrum is the highest when judging the meaning, namely the meaning corresponding to the maximum value of the cosine similarity value. Therefore, according to the values calculated by the cosine similarity method in table 3, it is determined that PMF1 represents an industrial source, PMF2 represents Jiao Luyuan, PMF3 represents a waste engine oil source, PMF4 represents a coal-fired source, PMF5 represents a biomass combustion source, PMF6 represents a tire source, and since the cosine similarity of any one of the source spectrum library under all reaction times and PMF7 is not more than 0.6, PMF7 is not considered to belong to any one of the source spectrum library known sources, i.e., the source meaning is unknown.
TABLE 3 comparison of factors resolved by the PMF model with library of source spectra simulating photochemical reactions
FIG. 2 shows the contribution rates of different pollution sources at each point of the final analysis. The abscissa represents the code of different points (as shown in fig. 1), different filling patterns represent different pollution sources, and the contribution rate of different sources at corresponding points can be known by comparing the contribution rate percentage of the ordinate. As can be seen from the figure, the main sources of polycyclic aromatic hydrocarbons in the urban road deposit include industry, fire coal, etc., which contribute to the majority of the sources. However, in different areas, the sources of polycyclic aromatic hydrocarbons are different, and certain unknown sources exist at the same time. Therefore, the result can be used in the treatment process, so that the method has important guiding significance for the treatment of different pollution sources in different areas.
The present invention is not limited to the above-described embodiments, and any modifications, improvements, substitutions, and the like, which can be conceived by those skilled in the art, fall within the scope of the present invention without departing from the spirit of the invention.
Claims (7)
1. A road sediment polycyclic aromatic hydrocarbon source analysis method of a coupling migration conversion process is characterized by comprising the following steps:
step 1, collecting a road sediment sample at a set sampling point;
step 2, determining the content concentration and the detection limit of various polycyclic aromatic hydrocarbons in a sample by adopting a gas chromatography-mass spectrometer GC/MS (gas chromatography-mass spectrometry) to establish a receptor matrix C and an uncertainty matrix U for describing the content of the sample points-various polycyclic aromatic hydrocarbons, wherein the receptor matrix C is shown in the following formula (1):
in formula (1): the horizontal lines represent the concentrations of different polycyclic aromatic hydrocarbons in the same sample, and the vertical lines represent the concentrations of the same polycyclic aromatic hydrocarbons in different samples, for example, m samples are collected in total, n polycyclic aromatic hydrocarbons are analyzed, C ij Namely the concentration of the jth polycyclic aromatic hydrocarbon in the ith sample;
the uncertainty matrix U is shown in the following equation (2):
in formula (2): u (U) ij The uncertainty of the j-th polycyclic aromatic hydrocarbon in the i-th sample is obtained;
element U in uncertainty matrix U ij The generation method is shown in the following formula (3):
in formula (3): u (U) ij MDL, uncertainty of jth polycyclic aromatic hydrocarbon in ith sample j Is the detection limit of the j-th polycyclic aromatic hydrocarbon, C ij For the concentration of the jth polycyclic aromatic hydrocarbon in the ith sample, RSD j Is the relative standard error of the j-th polycyclic aromatic hydrocarbon;
step 3, establishing a source component spectrum library matrix S of the polycyclic aromatic hydrocarbon source;
step 4, inputting the receptor matrix C and the uncertainty matrix U into the EPA-PMF model by using a positive definite matrix factorization method PMF;
step 5, setting parameters and calculating, and then calculating a matrix F representing the meaning of the source component and a matrix G representing the source contribution rate from EPA-PMF;
step 6, simulating photochemical pseudo-first-order reactions of different time sequences for the source spectrum S in the source component spectrum library to obtain a source component spectrum reaction matrix S after the simulated migration conversion process on the time sequence t The reaction formula is the following formula (10):
ln[PAH j /PAH j0 ]＝-k×t......(10)，
in formula (10): PAH (PAH) j0 The original concentration of the jth pollutant is S in a source spectrum library matrix S kj ，PAH j For the concentration of the j-th polycyclic aromatic hydrocarbon after the photochemical reaction for t time, the concentrations are combined into a reaction matrix S composed of the simulated reaction source component spectrum t K is a reaction coefficient, the k values of different polycyclic aromatic hydrocarbons are different, and the k value is 0.0072-0.012;
setting a certain time sequence of the coupled migration conversion process to generate a simulated migration conversion process node, and setting the time sequence to be not less than 48 hours, namely calculating the formula (10): t=0, t 1 ，t 2 ，……48，t n … … series of PAHs j A value;
step 7, converting the simulated migration over the whole time sequence into a whole source component spectrum S t The similarity of the source component types with each row vector in the analyzed load matrix F is judged by a cosine similarity method, the source component type with the highest similarity to the analyzed source spectrum is selected as the identified source spectrum type, the factor score matrix G is combined, the sources and the contribution rates of the polycyclic aromatic hydrocarbon in each road sediment sample in the research area are obtained, and the similarity is judged by the cosine similarity method which comprises the following steps: reaction matrix S on nodes in one-to-one comparison migration conversion process t Selecting a group with highest cosine similarity as a judging basis of source component meanings with the analyzed load matrix F; the cosine similarity method is judged according to the following formula (11):
in the formula (11): A. b are the compared actual measured source component spectral vectors, i.e. source component matrix S t The source component spectrum vector which is analyzed in the matrix F is the row vector in the matrix F, n is the vector dimension and represents the type number of pollutants A j And B j The concentration of the j-th pollutant in the two source spectrums is respectively, the larger the cosine similarity is, the more similar the two source spectrums are, the maximum is 1, and the vectors are completely coincident.
2. The method for analyzing the road sediment polycyclic aromatic hydrocarbon source in the coupling migration conversion process according to claim 1, wherein the set sample points in the step 1 are uniformly distributed by adopting gridding:
step 1.1, setting a sampling point according to the road condition, and collecting a sample of road sediment at the set sampling point;
step 1.2, setting sampling points which are more than the number of the analyzed polycyclic aromatic hydrocarbon types on a road by adopting a mode of uniformly distributing the set sampling points in a gridding way;
step 1.3, cleaning the collected road sediment sample at least three times within the width range of 0.5m from the edge of the motor vehicle lane to the curb by adopting a cleaning brush so as to obtain the maximum amount of cleaning surface sediment comprising particles with various particle diameters;
step 1.4, covering and sealing the collected road sediment samples in a polyethylene plastic bag by using an aluminum foil sheet, then placing the road sediment samples in a sampling box provided with an ice bag, carrying the road sediment samples back to a laboratory in a refrigerating state, and freezing and preserving the road sediment samples at the temperature of minus 20 ℃, wherein the mass of each residual sample is more than 300g so as to meet the uniformity of the road sediment samples and the use level of experimental detection;
and 1.5, drying the collected road sediment sample, sieving with a 500-mu m-aperture sieve to remove plant residues, sand and stones and impurities, and storing in a refrigerated state in a dark place for later use.
3. The method for resolving a road sediment polycyclic aromatic hydrocarbon source coupled to a mobile conversion process according to claim 1, wherein step 2 the Polycyclic Aromatic Hydrocarbon (PAHs) comprises: fluorene (Flu), phenanthrene (Phe), anthracene (Ant), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (b) fluoranthene (BbF), benzo (k) fluoranthene (BkF), benzo (a) pyrene (BaP), indene (1, 2, 3-cd) pyrene (IND), benzo (ghi) perylene (BghiP), dibenzo (a, h) anthracene (DbA).
4. The method for resolving a road sediment polycyclic aromatic hydrocarbon source coupled to a mobile conversion process according to claim 1, wherein the polycyclic aromatic hydrocarbon (polycyclic aromatic hydrocarbon, PAHs) in step 3 comprises: fluorene (Flu), phenanthrene (Phe), anthracene (Ant), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (b) fluoranthene (BbF), benzo (k) fluoranthene (BkF), benzo (a) pyrene (BaP), indene (1, 2, 3-cd) pyrene (IND), benzo (ghi) perylene (BghiP), dibenzo (a, h) anthracene (DbA).
5. The method for analyzing a road sediment polycyclic aromatic hydrocarbon source according to the coupled migration conversion process of claim 1, wherein the method for establishing the source component spectrum library S of the polycyclic aromatic hydrocarbon source in step 3 is as follows: the method comprises the steps of automatically collecting a polycyclic aromatic hydrocarbon pollution source sample for detection, and obtaining the concentration proportion of polycyclic aromatic hydrocarbon pollutants in a potential pollution source, wherein the type of the obtained polycyclic aromatic hydrocarbon is required to be consistent with the type of polycyclic aromatic hydrocarbon of a receptor matrix, and a source component spectrum library matrix S of the polycyclic aromatic hydrocarbon source is shown as a formula (4):
in formula (4): if the concentration of the polycyclic aromatic hydrocarbon of p pollution sources is collected, each sample detects n polycyclic aromatic hydrocarbons of the same kind as the receptor matrix C and the uncertainty matrix U, S kj Namely the concentration of the jth polycyclic aromatic hydrocarbon in the kth pollution source.
6. The method for analyzing a road sediment polycyclic aromatic hydrocarbon source coupled to a mobile conversion process according to claim 1, wherein the operation of the EPA-PMF model in step 4 includes: generating the product of the factor score matrix G and the load matrix F by using a pseudo-random method, enabling the product to be similar to the receptor matrix C, and then enabling a difference matrix E between the product of the factor score matrix G and the load matrix F and the receptor matrix C to be as small as possible through a least square iterative process, and simultaneously carrying out non-negative constraint on the factor score matrix G and the load matrix F, wherein the relation of the receptor matrix C is shown as a formula (5):
C＝GF+E......(5)，
in the above equation (5), the iterative process of the least squares method for making the difference matrix E as small as possible is: establishing an objective function Q (E), solving the minimum value of the objective function Q (E), and when the Q (E) converges to the minimum value after a plurality of iterations, namely considering that the factor score matrix G and the load matrix F obtained according to the formula (5) can respectively represent the contribution of each pollution source and the meaning of each pollution source, wherein the establishment mode of the objective function Q (E) is shown as the formula (6):
in the formula (6), U ij For the element of the ith row and the jth column of the uncertainty matrix U in the formula (2), namely the uncertainty of the ith polycyclic aromatic hydrocarbon in the jth sample, the calculation method is shown in the formula (3), and e ij Is the element of the ith row and jth column of the difference matrix E in equation (5).
7. The method for analyzing the road sediment polycyclic aromatic hydrocarbon source in the coupled migration conversion process according to claim 1, wherein the parameter determining method in the step 5 is to input the number of potential factors, which is determined as 2 to 7 in the present application, and perform one-to-one unitary linear regression on the elements in the corresponding column vectors in the analog value matrix C' and the acceptor matrix C, as shown in the following formulas (7) - (9):
C′＝GF......(7),
in formula (7): c' is an analog value matrix, and is the product of a factor score matrix G and a load matrix F;
equation (8) is a unitary first-order equation obtained from the model value matrix C' and the receptor matrix C,take the value C 'for the independent variable' ij Corresponding dependent variable, C' ij Is the element of the ith row and jth column in the analog value matrix C', wherein a j And b j Slope and intercept of a unitary linear function corresponding to the jth contaminant, as determined according to equation (8);
in the formula (9), r 2 j Regression coefficient corresponding to the j-th pollutant, C' ij And C ij The elements of the ith row and jth column of the simulated value matrix C' and the receptor matrix C,and->The mean value of the j-th element, namely the j-th pollutant, in the analog value matrix C' and the receptor matrix C respectively;
analyzing the slope a of a unitary primary regression equation corresponding to each of 12 polycyclic aromatic hydrocarbon pollutants j Intercept b j And regression coefficient r 2 j The magnitude of the factor is determined by comparing the ranges of the values when different factor numbers are input, and the reference standard is that when the slope a corresponding to the polycyclic aromatic hydrocarbon is j Are all within the range [0.9,1.1 ]]Within the interval, intercept b j Are all at [ -0.1,0.1]Within the interval, regression coefficient r 2 j Greater than 0.85, the number of factors selected is considered to be within a reasonable range.
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