AU2020101615A4 - A Method for Source Apportionment of PAHs in Roadway Sediments Coupled with Transport and Transformation Process - Google Patents
A Method for Source Apportionment of PAHs in Roadway Sediments Coupled with Transport and Transformation Process Download PDFInfo
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Abstract
The invention relates to a road sediment polycyclic aromatic hydrocarbon source analysis
method for a coupling migration conversion process, which comprises the following main
steps: Step 1, Collecting road sediment samples at set sampling points; Step 2, Using a gas
chromatography-mass spectrometry GC / MS to determine the content concentration and the
detection limit of multiple polycyclic aromatic hydrocarbon in a sample, so as to establish a
receptor matrix C and an uncertainty matrix U which describe the content of the sampling
point-the multiple polycycle aromatic hydrocarbons; Step 3, Establishing a Source
Composition Library Matrix S for Polycyclic Aromatic Hydrocarbon Sources. The method
described by the invention is to provide the technical support for the environmental
management department to formulate the countermeasure to regional polycyclic aromatic
hydrocarbon pollution, enabling the relevant department to apply complete source analysis
method quickly to identify the pollution source and carry on the effective pollution prevention
and control when such pollution problem occurs.
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Description
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FIG, 1
A Method for Source Apportionment of PAHs in Roadway Sediments Coupled with Transport and Transformation Process
[01] The invention relates to the technical field of pollution source analysis, in particular to a road sediment polycyclic aromatic hydrocarbon source analysis method for a coupling migration conversion process.
[02] With the massive exploitation and utilization of natural resources on the earth, human beings have had a serious impact on water body, soil, atmosphere and environment. How to prevent and improve the production and living environment of polycyclic aromatic hydrocarbons (PAHs), which is resistant to human beings, has been put on the agenda. In that analysis of the pollution source nowadays, attention is paid to two aspect, one is to judge the source type of the main pollutants in the environment medium, which is called source identification; the other is based on the source identification. The quantitative calculation of the contribution of various pollution sources is called source apportionment. Source apportionment of environmental pollutants is the basis of pollution control. Pollution source analysis is a method to study the influence and function of pollution source on environmental pollution.
[03] However, at present, there is a lack of thorough and detailed research on the analysis of PAHs pollution sources in the road sediments with coupled migration and conversion processes. In the existing source analysis, there is often a problem that the collected source component profiles data is not consistent with that obtained by factor decomposition, which will cause significant uncertainty in the determination of pollutant source. In recent years, it has been shown that polycyclic aromatic hydrocarbons (PAHs), when adsorbed on particles and transported in the atmosphere, are affected by oxidation radicals and light in the air which causes photochemical conversion of PAHs into other compounds that is the main factor for PAHs to decline and transform during their migration. At the same time, the reaction rates of different PAHs are different under the same reaction conditions, which is one of the reasons that the above known source component profiles data and the calculated source profile cannot be corresponded. Therefore, coupling pseudo-first-order reactions in photochemistry into the source resolution model will significantly improve the accuracy of the results.
[04] In the prior art, for example, CN110335645A discloses an analytical method for polycyclic aromatic hydrocarbon pollution sources in a water body, which firstly extracts the number of principal component factors in a database of polycyclic aromatic hydrocarbon pollution sources in a water body by using singular value factorization; Then, the weighted least square method is used for factorization and the non-negative constraint factor rotation is realized through the non-negative constraint least square sum factor rotation, and the factor loading matrix and the factor score matrix with non-negative characteristics are extracted; The factor loading monthly identification based on pollution source profile is regarded as a multi-parameter pattern identification problem, and Naive Bayesian method is applied to identify polycyclic aromatic hydrocarbon pollution sources. Finally, the identified classification model is used to calculate the contribution rate of pollution sources under factor loading and realize the source analysis of characteristic pollutants. The pollution source analysis method of the invention can realize accurate analysis of polycyclic aromatic hydrocarbon pollution source data in the water body, and simultaneously improves the analysis rate.
[05] For another example, Patent Application No. CN2016102205598.7 discloses a method for analyzing polycyclic aromatic hydrocarbon (PAH) pollution source. the specific steps are as follows: Step 1, determine the investigation area of the PAH pollution source; Step 2, carry out the investigation of PAHs pollution source within the investigation region; The investigation process includes: (1) Collection of basic data; (2) field investigation; (3) data processing and analysis; (4) selection of polycyclic aromatic hydrocarbon pollutants and establishment of monitoring information database; Step 3: Based on the investigation results of polycyclic aromatic hydrocarbons (PAHs) pollution sources, the influence of pollution sources on the environment is analyzed under different conditions. Identifying the main PAHs pollution sources that affect the investigation area; different circumstances include: (1) single pollution source is located at the environmental sensitive point, (2) identification of characteristic PAHs markers of various emission sources. A fingerprint profile of the polycyclic aromatic hydrocarbon source which can reflect the emission characteristics of the pollution source is established; Step 5, a fast clustering method is applied to identify the pollution sources, and Matlab software is used to program. The method comprises the following steps: A first step of preprocessing and initialization, a second step of outputting a pair of training samples, and a step of identifying the pollution source by using a fast clustering method. A method of chemical mass balance source analysis based on fast cluster analysis is proposed, which includes the following steps: Pollution source identification and classification by using fast clustering analysis algorithm and pollution source calculation by using chemical mass balancing method.
[06] As another example, Chinese Patent Application No. 2013107221931.6 discloses an ecological risk determination method for polycyclic aromatic hydrocarbons in water bodies, belonging to the field of ecological risk determination. The invention comprises the following steps: Step 1, screening representative species of the aquatic ecosystem in the region; Step 2, Obtaining toxicity data of benzo pyrene; Step 3, calculating that benzo pyrene concentration value HC5 of 95% of the species in the protect aquatic ecosystem; Step 4, sampling and measuring that type of polycyclic aromatic hydrocarbon pollutants and their corresponding environmental concentrations, and analyzing the concentration distribution characteristics of various polycyclic aromatic hydrocarbons; Step 5, calculating that ecological risk quotient RQi of the specific polycyclic aromatic hydrocarbon pollutant; Step 6: Calculating the total ecological risk quotient RQt to define the specific ecological risk. The invention can analyze whether the potential risks caused by polycyclic aromatic hydrocarbon pollutants are acceptable, and judge whether the overall level of water ecological risks should be controlled, thus providing scientific basis for the protection of water ecological systems, the formulation 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 analyzing the source of polycyclic aromatic hydrocarbon contamination in consideration of a migration conversion process for road sediments has not been proposed.
[07] In order to overcome the defects in the prior art, the invention provides a method for analyzing the source of polycyclic aromatic hydrocarbons in the road sediment in the coupled migration conversion process.
[08] The method for analyzing the source of PAHs in the road sediments in the coupled migration and conversion process of the present invention comprises the following steps:
Step 1: Collecting the road sediment samples at the set sample points;
Step 2: Determining The content concentration and the detection limit of a plurality of polycyclic aromatic hydrocarbon in a sample by using a gas chromatography-mass spectrometry (GC / MS), in order to establish a receptor matrix C and an uncertainty matrix U which describe that content of sample-multiple polycyclic aromatic hydrocarbons;
Step 3, Establishing a source component library matrix S of the polycyclic aromatic hydrocarbon source;
Step 4. The receiver matrix C and the uncertainty matrix U are input into the model EPA-PMF using a positive definite matrix factorization (PMF) method;
Step 5: After the parameters are set and the calculation is performed, a matrix F (also called loading matrix) representing the meaning of the source components and a matrix G (also known as factor score matrix) expressing the source contribution rate are calculated from the EPA-PMF.
Step 6: Simulating actinic pseudo-first-order reactions of different time series for the source profiles S in the source data library, and obtain a new source profile matrix with reaction, the matrix St, after a simulated migration and transformation process on the time series;
Step 7. Comparing each row vector in St and matrix F, which represent the all possible transformation of the original sources with different reaction times and the simulated source profiles from loading matrix, respectively, judging their similarity by using cosine similarity method, and selecting the source component class with the highest degree of similarity for the source profiles as the identified source type, combining the factor score matrix G. The source and contribution rate of PAHs in various road sediment samples in the study area were obtained.
[09] Further, the set sample points in step 1 are uniformly distributed by means of gridding:
Step 1.1: Setting a sample point according to the road condition, and collecting a sample of the road sediment at the set sample point;
Step 1.2: For the set sample points, set more sample points than the number of types of polycyclic aromatic hydrocarbons to be analyzed on the road by uniformly arranging the sample points in a grid;
Step 1.3, A cleaning brush is use to clean that collected road sediment sample at least three time within the width of the edge of the motor vehicle lane from the kerb stone by a width of 0.5m, so as to obtain the maximum amount of cleaning surface sediment including particle of different sizes;
Step 1.4: The collected road deposit samples are covered with aluminium foil, sealed in a polyethylene plastic bag, and then put into a sampling box with an ice bag. In that refrigerate state, the sample is taken back to the laboratory for freezing preservation at -20°C, and the mas of each reserved sample needs to be more than 300g, so as to meet the uniformity of the road sediment sample and the dosage of the experimental detection;
Step 1.5: After the collected road sediment samples are dried, pass through a sieve with a pore diameter of 500m to remove plant residues, sand, sand and impurities, and store them for standby under the condition of cold storage without light.
[010] Further, In step 2, that polycyclic aromatic hydrocarbon (PAHs) include Fluorene (Flu), Phenanthrene (Phe), Anthracene (Ant), Pyrene (Pyr), Benzo(a)Anthracene (BaA), Beno(b)Fluoranthene (BbF), Benzo(k)Fluoranthene (BkF), Benzo(a)Pyrene (BaP), Indenes(1,2,3-cd)Pyrene (IND), Beno(ghi)Perylene (Bghip), Dibenzo(a, h)Anthracene (DbA).
[011] Further, the acceptor matrix C in step 1 is shown in the following formula (1):
_C 1 1 C1 2 C1n C2 1 C= CY.
.C.1 --- Cm
[012] In that formula (1), the row is the concentration of different polycyclic aromatic hydrocarbon in the same sample, and the column is the concentrations of the same type of PAH in different sample; if m samples are collected, analyze n types of PAHs, Cij is the concentration ofjth polycyclic aromatic hydrocarbon in the ith sample;
[013] The uncertainty matrix U is shown in the following formula (2):
U 11 U1 2 "' U1 U2 1
_U_1 ... Um . (2),
[014] In formula (2): Uji is, the uncertainty of the existence of jth polycyclic aromatic hydrocarbon in the ith sample.
[015] Further, the method for generating the element Ujj in the uncertainty matrix U in step 2 is as shown in the following formula (3):
" MDLj (if C1 j!5MDLr)
LU = J(RSD; x C1 ) 2 + MDI ( if C, > MDL()
[016] In the formula (3), Ujj is the uncertainty of the jth pollutant in the ith sample, MDLjis the detection limit of the jth pollutant, Cij is the concentration of the ith sample, and RSDj is the relative standard error of the jth pollutant.
[017] Further, the polycyclic aromatic hydrocarbon of Step 3, PAHs are: Fluorene (Flu), Phenanthrene (Phe), Anthracene (Ant), Pyrene (Pyr), Benzo(a)Anthracene (BaA), Beno(b)Fluoranthene (BbF), Benzo(k)Fluoranthene (BkF), Benzo(a)Pyrene (BaP), Indenes(1,2,3-cd)Pyrene (IND), Beno(ghi)Perylene (Bghip), Dibenzo(a, h)Anthracene (DbA).
[018] Further, the method for establishing the source component database S of the polycyclic aromatic hydrocarbon source in step 3 is as follows: The sample of polycyclic aromatics source is collected by itself for detection, In order to obtain that unit content or the concentration ratio of the polycyclic aromatic hydrocarbon pollutants in the potential pollution source, the type of the obtain polycycle aromatic hydrocarbon needs to be consistent with that of the receptor matrix, The source component library matrix S of the polycyclic aromatic hydrocarbon source is shown in formula (4):
-S1 1 S12 ~ S1n S2 1 S= Skj
S,1 ... S- (4)
[019] In that formula (4), if the concentration of the polycyclic aromatic hydrocarbon of p pollution source are collected, n type of the same type of polycyclic aromatics as the receptor matrix C and the uncertainty matrix U are detected for each sample, Skj is the concentration of the jth polycyclic aromatic hydrocarbon in the kth pollution.
[020] Further, the operation principle of the PMF model described in Step 4 is to generate the product of the source component (factor score matrix) G and the loading matrix F by a pseudo-random method so that the product approximates the receptor matrix C. In that iteration process of the least square method, the difference matrix E between the product of the factor score matrix G and the loading matrix F and the receptor matrix C is as small as possible, Meanwhile, a non-negative constraint is applied to the factor score matrix G and the loading matrix F, wherein the relationship of the receptor matrix C is shown in Formula (5):
C = GF + E.(5),
[021] In that iteration process of the least square method, which makes the difference matrix E as small as possible, an objective function Q (E) is establish, a minimum value of the objective function Q (E) is obtained, That is, it is considered that the factor score matrix G and the loading matrix F obtained according to the formula (5) can respectively represent the contribution of each pollution source and the meaning of each pollutant source, The objective function Q (E) is established as shown in the following formula (6):
Min(Q(E))= Min(Z (ej/Uj)) .(6),
[022] In the formula (6), Uij is the element in the ith row and the jth column of the uncertainty matrix U in formula (2), that is, the uncertainty of the ith polycyclic aromatic hydrocarbon in the jth sample. Is the element in row i and column j of the difference matrix E in equation (5).
[023] Further, the method for determining the factor quantity parameter in step 5 is to input the number of potential factors (determined as 2-7 in this application, which can be adjusted according to actual needs). A one-to-one linear regression is performed on the elements in the corresponding column vectors in the simulated value matrix and the receptor matrix C (i.e., the corresponding values of the same polycyclic aromatic hydrocarbons of different samples), as shown in the following formulas (7) - (9):
Cij = a C'j; + by .(8).
r2 Ei(C'1 _G(EI E (C',j - - )2E1 ) (C(C - C)) 2 2 1 1 - F) . (9),
[024] In the formula (7), C' is an analog value matrix, which is the product of the factor score matrix G and the loading matrix F;
[025] Formula (8) C' is a one-dimensional primary equation obtained from the analog value matrix and the receptor matrix C, and C, is a dependent variable corresponding to the value of the independent variable. C' 1 is the element in the ith row and jth column in the matrixC' of analog values, where aj and bj is the slope and intercept of the one-dimensional linear function corresponding to the jth pollutant obtained according to the formula (8);
[026] In the formula (9), r21 is the regression coefficient corresponding to the jth pollutant, and C'jj and Cij is the element of row i and column j in the simulated value matrix C'andthereceptormatrixC,C'and C, respectively; is values of thejth column elements in the simulation value matrix C' and the receptor matrix C , which is namely the average value of the jth pollutant;
[027] By analyzing the slope a, intercept bj and regression coefficient r21 of the one-dimensional regression equation corresponding to each of the 12 polycyclic aromatic hydrocarbon pollutants, and comparing the ranges of these values when different factor quantities are input to determine the appropriate factor quantities, The reference standard is that when the slope aj of most PAHs is within the range [0.9, 1.1] and [-0.1, 0.1], respectively. The regression coefficient r 2j is larger than 0.85, the number of factors selected is considered to be within the reasonable range.
[028] Further, in step 6, the coupled migration conversion process is a pseudo first-order photochemical reaction, the reaction raw material is a polycyclic aromatic hydrocarbon source component profile at the source start, and the reaction formula is the following formula (10):
In [PAHj/PAHjO] = -k x t.....(10),
[029] In the formula (10), PAHjO is the original concentration of the jth pollutant,
namely is Skj of Source library matrix S, PAH is after t-time photochemical reaction,
the concentration of the jth polycyclic aromatic hydrocarbon is combined to form a reaction matrix St composed of the component profile of the simulated reaction source, and k is the reaction coefficient, and the k values of different polycyclic aromatic hydrocarbons are different, with k values ranging from 0.0072 to 0.012.
[030] Further, in step 6, a certain time sequence is set for the coupled migration and transformation process to generate a node for simulating the migration and translation process, and the time series is set to be not less than 48 hours. That is, in
formula (10): T = 0, tl, t2,. 48, tn,...a series of the value of PAH.
[031] In step 7, the cosine similarity method is used to judge the similarity between the reaction matrix St which considered the migration and transformation process and the analyzed loading matrix F. The group with the highest cosine similarity is selected as the basis for judging the meanings of the source components.
[032] Further, in step 7, the cosine similarity method is used to judge the similarity according to the following formula (11):
Cosinesimilarity AA[\ .||B|||B +B _ AB; IJAIIII L 2 2(11)
[033] In that formula (9), A and B are respectively the compare actual measured source component profile vector (each row vector in the source component matrix St) and the decomposited source component profile vector (row vectors in the matrix F), and n is the vector dimension, Aj and Bj respectively is concentrations of the jth pollutant in the two source profiles, the greater the cosine similarity, the more similar the two source profiles are, and the maximum is 1, which means the vectors are completely coincident.
[034] The present invention has the following advantageous effects:
[035] In that method for analyze the polycyclic aromatic hydrocarbon source of the road sediments in the couple migration and conversion process, the characteristic pollutants of the polycycle aromatic hydrocarbon are determined through analysis and calculation by on-site sample of a polycycle aromatics pollution source, It has a wide range of practicality.
[036] In that method for resol the polycyclic aromatic hydrocarbon source of the road sediments in the couple migration and conversion process, the source of polycyclic aromatics source of urban roads can be quickly and accurately trace, To provide technical support for the environmental management department to formulate regional PAHs pollution control countermeasures, so that when environmental management departments face the PAHs road pollution problems, they can quickly identify the pollution sources through a complete source analysis method, In order to carry out effective pollution prevention and control.
[037] In that road sediment polycyclic aromatic hydrocarbon source analysis method of the couple migration conversion process, a pseudo first-order photochemical reaction of a time series is simulated on a source data library, The non-reacted source component profiles detected at the source is theoretically more close to and more consistent with that precipitated at the contaminated medium through the migration and conversion process. In order to improve that accuracy of judging the type of the pollution source and obtain the source analysis result with high reliability.
[038] FIG. 1 is a layout diagram of different sampling points in a certain urban area according to an embodiment of the method for analyzing polycyclic aromatic hydrocarbons in road sediments in the coupled migration and conversion process of the present invention;
[039] FIG. 2 shows the source and contribution rate of polycyclic aromatic hydrocarbons at different sampling points in a certain urban area according to an embodiment of the method for source analysis of road sediments in the coupled migration and conversion process of the present invention.
[040] Reference: 1-30 Sampling points, 31-2nd ring road, 32-3rd ring road, 33 4th ring road.
[041] The following is a specific embodiment of the method for analyzing the source of PAHs in the road sediment of the couple migration and conversion process of the present invention will be clearly and completely described with reference to FIGS. 1-2 of this specification.
[042] Step 1: Sample points are set based on the geographic location, town type, road area, population, town pattern, pillar industry and surface runoff scouring path information of the research area. In that collection city common polycyclic aromatic hydrocarbon pollution source, Including asphalt pavement, concrete and cement pavement wear samples; tires, fresh engine oil, waste engine oil traffic source samples; combustion and industrial samples such as coal dust, coke oven dust, biomass combustion dust, factory emission dust, etc.
[043] Road sediment sampling is carried out according to the distribution of sample points shown in Figure 1. thirty road sediment sample points (BJ-BJ30) are set in the fourth ring road of a city in northern China which includes four sampling points in residential areas: BJ20, BJ21, BJ24 and BJ28; Nine sampling points in the commercial district: BJ2, BJ4, BJ6, BJ7, BJ14, BJ15, BJ16, BJ17 and BJ27 ; Six
sampling points for trunk roads: BJ1, BJ1O, BJ12, BJ18, BJ19 and BJ29; Eight road sampling points: BJ3, BJ9, BJ11, BJ13, BJ22, BJ23, BJ25 and BJ26; Three sampling points in the park: BJ5, BJ8 and BJ30.
[044] Step 2: A gas chromatography-mass spectrometer (GC / MS) was used for the collected road sediment samples and the source samples. Fluorene (Flu), Phenanthrene (Phe), Anthracene (Ant), Pyrene (Pyr), Benzo(a)Anthracene (BaA), Beno(b)Fluoranthene (BbF), Benzo(k)Fluoranthene (BkF), Benzo(a)Pyrene (BaP), Indenes(1,2,3-cd)Pyrene (IND), Beno(ghi)Perylene (Bghip), Dibenzo(a, h)Anthracene (DbA) in 30 samples of road sediments and 11 samples of PAHs pollution sources are tested. Also, the detection limits are tested; The total concentration of Benzo(b)Fluoranthene (BbF) and Benzo(k) Fluoranthracen (BkF), which are difficult to distinguish due to their peak values, is recorded as BF. Therefore, although 12 kinds of polycyclic aromatic hydrocarbons were detected, 11 kinds of pollutants were calculated for analysis.
[045] Step 3, According to the concentrations of 11 pollutants in the 11 pollution sources measured in step 2, a pollution source data library matrix S of 11 rows and 11 columns is constructed, and according to 11 pollutant concentrations in the 30 samples measured, A 30-row and 11-column receptor matrix C is constructed, and according to the relationship between the detection limit and the pollutant concentration, each element in the uncertainty matrix U is constructed by the following formula:
U = MDL; (when Cy <; MDL;)
Ui = 2 (RSD x Cij) + MDLj2 (when Cij > MDL;)
[046] In the above equation, Ujj is each element in the uncertainty matrix U represents the uncertainty of the jth pollutant in the ith sample,MDLjis the detection limit of the jth pollutant, and Cii is the concentration of the j. RSDj is pollutants in the ith sample. Is the relative standard error of the jth pollutant.
[047] The above-calculated receptor matrix C is input to the EPA-PMF model, the number of factors is set to 2-7, the operations are performed six times in turn, and the results of each operation are compared with each other. The simulated value and the measured value of the polycyclic aromatic hydrocarbon content of each sample point are subjected to one-dimensional linear relationship fitting, and the calculated relevant intercept a , slope bj and regression coefficient parameters r 2 j can meet the requirements, That is, it is basically ensured that the slopes and intercept of 11 polycyclic aromatic hydrocarbons are within the range of [0.9, 1.1] and [-0.1, 0.1] respectively. The regression coefficient is larger than 0.85, (Pyr is slightly lower than the standard, but the difference is not big, so it is considered that it basically meets the requirements) The specific results are shown in Table 1 below:
[048] Table 1 Fitting Parameters when PMF Model Setting Factor is 7
Polycyclic Regression aromatic intercept Slope coefficients hydrocarbons
Flu 0.001 0.982 0.999
Phe -0.004 1.007 0.943
Ant 0.003 0.942 0.992
Pyr 0.015 0.897 0.959
BaA 0.003 0.985 0.998
Chr 0.005 0.958 0.929
BF 0.003 0.976 0.961
BaP 0.000 0.993 0.994
IND 0.010 0.965 0.989
BghiP -0.010 1.042 0.977
DbA 0.002 0.981 0.996
[049] Thus, seven unknown types of sources, tentatively named as PMF1-PMF7, were analyzed, and the different contribution rates of these sources to each sample were also calculated.
[050] Set the time series as 8-144h for photochemical reaction, set a simulated sampling point every 8h, and simulate pseudo-first-order reaction for many source component profiles in the source component data library, and the value of reference k is shown in Table 2 below:
[051] Table 2 Photochemical Reaction Coefficients of Polycyclic Aromatic Hydrocarbons.
PAHs k(h-1) PAHs k(h-1)
Flu 0.0067 BF(BbF) 0.0072
Phe 0.0083 BF(BkF) 0.0114
Ant 0.0132 BaP 0.0135
Pyr 0.0047 Ind 0.0065
BaA 0.0125 BghiP 0.012
Chr 0.0051 DbA 0.007
[052] The variation of the components from the source component profiles follows the logarithmic curve regularly, and therefore, the 11 x 4 = 44 source profiles of the representative time nodes of 8h, 48h, 72h and 144h were compared with the 7 factors analyzed from the PMF model by the cosine similarity method. Table 3 shows the comparison value between the factors analyzed by the PMF model and the source component data library of the simulated photochemical reaction, in which the cosine similarity of industrial, coal-fired and tire is greater than 0.9, and the reliability is high. The cosine similarity decreases with the reaction time, indicating that the photochemical reaction time occurring in the migration of other sources is not long, so the similarity between the original source profile and the resolved source profile is the highest when the meaning is judged. That is, the corresponding meaning when the cosine similarity value takes the maximum value. Therefore, according to the values calculated by the cosine similarity method in Table 3, it is determined that PMF 1 represents an industrial source, PMF 2 represents a coke oven source, PMF 3 represents a used machine oil source, PMF 4 represents a coal combustion source, Since the cosine similarity between PMF7 and any source profile library at all reaction times is no more than 0.6, it is considered that PMF 7 does not belong to any known source, i.e. the source meaning is unknown.
[053] Table 3 Comparison of PMF model-resolved factors with source component libraries simulating photochemical reactions.
duration PMF1 PMF2 PMF3 PMF4 PMF5 PMF6 PMF7
Cosine maximu 0.9062 0.7614 0.7472 0.9182 0.7945 0.9359 0.5933 origin m al Source industry Fresh Used oil Burning Biomass Tires Coke oven meaning motor oil coal burning Cosine maximu 0.908 0.764 0.7447 0.9181 0.7918 0.9354 0.5884 8h m Source industry Fresh Used oil Burning Biomass Tires Coke oven meaning motor oil coal burning Cosine maximu 0.9148 0.7714 0.7249 0.9165 0.7763 0.9262 0.5657 48h m Source industry Used oil Used oil Burning Biomass Tires Coke oven meaning coal burning Cosine maximu 0.9175 0.7787 0.708 0.9144 0.7661 0.9164 0.5524 72h m Source industry Coke oven Used oil Burning Biomass Tires Coke oven meaning coal burning Cosine maximu 0.9192 0.7964 0.6641 0.9023 0.7333 0.8787 0.5183 144h m Source industry Coke oven industry Burning Biomass Tires Coke oven meaning coal burning Final judgt Seanin industry Coke oven Used oil Burning buri Tires unknown mentmeng
[054] Figure 2 is the contribution rate of different pollution sources at each point finally analyzed. The abscissa indicates the codes of different samples (as shown in FIG. 1), and different filling patterns represent different pollution sources. the contribution rate of different sources at the corresponding samples can be found by comparing the percentage contribution rate on the ordinate. It can be seen from the figure that the main sources of PAHs in the urban road sediments include industry and coal combustion, which account for most of the sources. However, the sources of PAHs are different in different areas, and there are some unknown sources. Therefore, it is of great guiding significance to make use of this result in the treatment of different pollution sources in different areas.
[055] The present invention is not limited to the above-described embodiments, and any modification, improvement, or substitution that may occur to those skilled in the art without departing from the essential contents of the invention falls within the scope of protection of this invention.
[056] Although the invention has been described with reference to specific examples, it will be appreciated by those skilled in the art that the invention may be embodied in many other forms, in keeping with the broad principles and the spirit of the invention described herein.
[057] The present invention and the described embodiments specifically include the best method known to the applicant of performing the invention. The present invention and the described preferred embodiments specifically include at least one feature that is industrially applicable
Claims (12)
1. A method for source resolution of PAHs in road sediments with coupled migration and transformation process is characterized by comprising the following steps:
Step 1; Collect a road sediment sample at the set sample point.
Step 2; Using a gas chromatography-mass spectrometry GC / MS to determine
the content concentration and the detection limit of multiple polycyclic aromatic
hydrocarbon in a sample, so as to establish a receptor matrix C and an uncertainty
matrix U which describe the content of the sampling point-the multiple polycycle
aromatic hydrocarbons;
Step 3; Establishing a Source Composition Library Matrix S for Polycyclic
Aromatic Hydrocarbon Sources.
Step 4; The receiver matrix C and the uncertainty matrix U are input into the
model EPA-PMF by using a positive definite matrix factorization (PMF) method;
Step 5; After the parameters are set and the calculation is performed, a matrix
F (also called loading matrix) representing the meaning of the source components and
a matrix G (also known as factor score matrix) expressing the source contribution rate
are calculated from the EPA-PMF;
Step 6; Simulate actinic pseudo-first-order reactions of different time series
for the source profile S in the source component data library, and obtain a source
component profile reaction matrix St after a simulated migration and transformation
process on the time series;
In step 7; The simulated migration on the full time series is transformed into
all source component profiles St and each row vector in the analyzed loading matrix F, and the similarity thereof is judged by using the cosine similarity method, Selecting the source component class with the highest degree of similarity for the parsed source profile as the identified source type, combining the factor score matrix G, The source and contribution rate of PAHs in various road sediment samples in the study area were obtained.
2. According to Claim 1, analyzing the polycyclic aromatic hydrocarbon source of the road sediment in the coupled migration transformation process, the arranged sample points in the step 1 adopt grid uniform distribution points;
Step 1.1; Setting a sample point according to the road condition, and collecting
a sample of the road sediment at the set sample point;
Step 1.2; For the set sample points, set more sample points than the number of
types of polycyclic aromatic hydrocarbons to be analyzed on the road by uniformly
arranging the sample points in a grid;
In step 1.3; A cleaning brush is use to clean that collected road sediment
sample at least three time within the width of the edge of the motor vehicle lane from
the kerb stone by a width of 0.5m, so as to obtain the maximum amount of cleaning
surface sediment including particle of different sizes;
Step 1.4; The collected road sediment samples are covered with aluminum foil,
sealed in a polyethylene plastic bag, then put into a sampling box with an ice bag, and
brought back to the laboratory for cryopreservation at -20 °C under the refrigeration
state. The mass of each sample should be more than 300g, so as to meet the uniformity
of road sediment samples and the dosage of experimental detection;
Step 1.5; After the collected road sediment samples are dried, pass through a
sieve with a pore diameter of 500m to remove plant residues, sand, sand and
impurities, and store them for standby under the condition of cold storage without light.
3. As described in Claim 1, analyzing the source of the road sediment polycyclic aromatic hydrocarbon according to the couple migration and conversion process, In step 2, that polycyclic aromatic hydrocarbon (PAHs) include Fluorene (Flu), Phenanthrene (Phe), Anthracene (Ant), Pyrene (Pyr), Benzo(a)Anthracene (BaA), Beno(b)Fluoranthene (BbF), Benzo(k)Fluoranthene (BkF), Benzo(a)Pyrene (BaP), Indenes(1,2,3-cd)Pyrene (IND), Beno(ghi)Perylene (Bghip), Dibenzo(a, h)Anthracene (DbA).
4. 4. In that method for analyze the source of the road sediment polycyclic aromatic hydrocarbon according to the couple migration transformation process as claimed in claim 1, the acceptor matrix C in step 1 is as shown in the following formula (1):
_C 1 1 C1 2 Cin C2 1 C= CY.
_Cm1 ... Cmn (1) In that formula (1), the row is the concentration of different polycyclic aromatic hydrocarbon in the same sample, and the column is the concentrations of the same type of PAH in different sample; if m samples are collected, analyze n types of PAHs, That is, the concentration of jth polycyclic aromatic hydrocarbon in the ith sample; The uncertainty matrix U is shown in the following formula (2):
U 11 U1 2 ' Uin U2 1
_Um1 ... Umn. - ( 2 )
In formula (2): Ujj is the uncertainty of the existence of jth polycyclic aromatic
hydrocarbon in the ith sample.
5. In that method for analyze the source of the road sediment polycyclic aromatic hydrocarbon according to the couple migration and conversion process as claimed in claim 1, The method for generating elements in the uncertainty matrix U described in step 2 is shown in the following formula (3):Ui
U =-MDLj ( if Cj < MDI-)
UY = (RSD x C1 )z+ MDL ( if C 1> MDL)
In the formula (3), Uj is the uncertainty of the jth pollutant in the ith sample,
MDLi is the detection limit of the jth pollutant, Cij is the concentration of the ith sample,
and RSDj is the relative standard error of the jth pollutant.
6. The method for source resolution of road sediments polycyclic aromatic hydrocarbons according to the coupled migration and conversion process as claimed in claim 1, characterized in that, in step 3, the polycylic aromatic hydrocarbons, PAHs include: Fluorene (Flu), Phenanthrene (Phe), Anthracene (Ant), Pyrene (Pyr), Benzo(a)Anthracene (BaA), Beno(b)Fluoranthene (BbF), Benzo(k)Fluoranthene (BkF), Benzo(a)Pyrene (BaP), Indenes(1,2,3-cd)Pyrene (IND), Beno(ghi)Perylene (Bghip), Dibenzo(a, h)Anthracene (DbA).
7. The method for source resolution of road sediment polycyclic aromatic hydrocarbons according to the coupled migration and conversion process of claim 1, characterized in that. In step 3, that method for establish the source component profile database S of the polycyclic aromatic hydrocarbon source is as follows: The concentration ratio of polycyclic aromatics pollutant in the potential pollution sources is obtained by collect the samples of the pollution sources by itself for detection, The type of the obtained polycyclic aromatic hydrocarbon shall be consistent with that of the receptor matrix. the source component library matrix S of the polycyclic aromatics source is shown in formula (4):
-S11 S12 "' S1n' S21 S= : Ski
-Sp1 ... S (4)
In that formula (4), if the concentration of the polycyclic aromatic hydrocarbon of p pollution source are collected, n type of the same type of polycyclic aromatics as the receptor matrix C and the uncertainty matrix U are detected for each sample,Skj is the
concentration of the jth polycyclic aromatic hydrocarbon in the kth pollution.
8. In that method for analyze the source of the road sediment polycyclic aromatic hydrocarbon according to the couple migration and conversion process as described in claim 1, The operation of the PMF model of step 5 comprises generating a product of the factor score matrix g and the loading matrix f by a pseudo-random method such that the product approximates the receptor matrix C, and then performing an iterative process by a least square method, Such that the difference matrix E between the product of the factor score matrix G and the loading matrix F and the receptor matrix C is as small as possible, while a non-negative constraint is applied to the factor scoring matrix G, The relationship of the receptor matrix C is shown in equation (5):
C = GF + E. (5), In the above formula (5), the least square iteration process for making the difference matrix E as small as possible is to establish the objective function Q (E), find the minimum value of the objective functions Q (E), When Q (E) converges to the minimum value after multiple iterations, it is considered that the factor score matrix G and the loading matrix F obtained according to the formula (5) can respectively represent the contribution of each pollution source and the meaning of each pollutant source, The objective function Q (E) is established as shown in formula (6):
Min(Q (E)) = Min(J, (ey /Ui ) )
(6)
In the formula (6), Uij is the element in the ith row and the jth column of the uncertainty
matrix U in formula (2), that is, the uncertainty of the ith polycyclic aromatic hydrocarbon in the jth sample. eij is the element in row i and column j of the difference
matrix E in equation (5).
9. In that method for analyze the source of the road sediment polycyclic aromatic
hydrocarbon according to the couple migration transformation process in claim 1, the
method for determining the factor quantity parameter in step 5 is that the potential factor
quantity is input, In this application, 2-7 are determined, and one-to-one linear
regression is performed on the elements in the corresponding column vectors in the
analog value matrix and the receptor matrix C, as shown in the following formulas (7)
-(9):
C =GF .(7)
C,- = a C',; + b .(8)
r r2 - (ET 1 (C',j - EM = (C'ij - )2 E (C -)2 )(Cj . (9),
In the formula (7), C' is an analog value matrix, which is the product of the factor score matrix G and the loading matrix F;
Formula (8) C' is a one-dimensional primary equation obtained from the analog value matrix and the receptor matrix C, and C is a dependent variable
corresponding to the value of the independent variable. C' is the element in the ith row
and jth column in the matrixC' of analog values, whereas and bj is the slope and
intercept of the one-dimensional linear function corresponding to the jth pollutant
obtained according to the formula (8);
In the formula (9), r 2 is the regression coefficient corresponding to the jth
pollutant, andC'gj is the element of row i and column j in the simulated value matrixC'
and the receptor matrix C, respectively; C'andC, respectively, is the jth element in
the simulated value matrixC' and the receptor matrix C, and is the mean value of the j th pollutant;
Analyze the slopea, intercept band regression coefficient r2j of the one
dimensional regression equation corresponding to each of the 12 polycyclic aromatic hydrocarbon pollutants, comparing the ranges of these values when different factors are input to determine the appropriate factor number. The reference standard is that when the slope aj of most PAHs is within the range [0.9, 1.1], the intercept bj is [-0.1, 0.1],
the regression coefficient r 2 is larger than 0.85, the number of factors selected is
considered to be within the reasonable range.
10. In that method for analyze the source of the road sediment polycyclic aromatic hydrocarbon according to the couple migration transformation process as claimed in claim 1, the coupling migration transformation process as described in Step 6 is a photochemical pseudo first-order reaction. The reaction raw material is the source component profile of polycyclic aromatic hydrocarbons at the origin, and the reaction formula is the following formula (10):
In [PAHj/PAHjo] = -k x t.(10),
In the formula (10), PAHjO is the original concentration of the jth pollutant,
which is Skj of the source profile library matrix S, PAHjis the concentration of this jth
polycyclic aromatic hydrocarbon after the photochemical reaction for t time in the
source library matrix S, In that reaction matrix St compose of the simulated reaction
source component profile, k is the reaction coefficient, and the value of k is 0.0072
0.012 for different polycyclic aromatic hydrocarbon.
11. In that method for analyze the source of the road sediment polycyclic aromatic
hydrocarbon according to the couple migration and conversion process as claimed in
claim 1, in Step 6, a certain time sequence is set for the coupled migration and
transformation process to generate a node for simulating the migration and translation
process, and the time series is set to be no less than 48 hours, that is, t = 0, tl, t2 in
formula (10) is calculated. .48, tn, .etc.
12. In that method for analyzing the source of the road sediment polycyclic aromatic
hydrocarbon according to the couple migration and conversion process as claimed in
claim 1, in Step 7, that cosine similarity method is use to judge the similarity by
comparing the reaction matrix St which considered the migration and transformation
process with the analyzed loading matrix F one by one, The group with the highest
cosine similarity is selected as the judging basis of the meaning of the source
component, and the cosine similarity judging method is based on the following formula
(11):
Cosine imilarity = A-AB ||AJ - ||B||_ 1 A
Y- 14 I(11I),
In that formula (11), A and B are respectively the compare actual measured
source component profile vector, that is, each row vector in the source component
matrix St and the decomposited source components profile vector are the row vectors
in the matrix F, and n is the vector dimension, Indicates the number of types of
pollutants, Ajand Bj is the concentration of the jth pollutant in the two source profiles,
the larger the cosine similarity is, the more similar the two sources profiles are, and the
maximum is 1, i.e. the vectors completely overlap.
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