CN109001386A - A kind of detection method of water body flow connectivity - Google Patents
A kind of detection method of water body flow connectivity Download PDFInfo
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Abstract
The invention discloses a kind of detection methods of water body flow connectivity comprising following steps: S1, takes water sample；S2, macro gene order-checking is carried out to the microorganism in all water samples and carries out sequence assembly, obtain high-quality sequence；S3, high-quality sequence is clustered, forms several taxons, and obtain the taxonomic information of microorganism in each taxon；And S4, clustering carried out to all sampled points according to the taxonomic information and water quality detection result of the microorganism of each sampled point, judge the connectivity of water body runoff.The present invention is additional practical without addition using microorganism as tracer, can gather materials on the spot, environmental protection and economy and nontoxic, has broken the bottleneck of tracer selection；And the detection and analysis of subsequent tracer can be realized using now more mature bioinformatics technique and Clustering Analysis Technology, and it is easy to operate, low in cost, experiment flow can be greatly simplified, while guaranteeing the accuracy of experimental result.
Description
Technical field
The present invention relates to geological exploration technical fields, and in particular to a kind of detection method of water body flow connectivity.
Background technique
The method of the connectivity of traditional Underground water mostly uses the mode of physical and chemical experiment to carry out, i.e., at a certain water source
Point (well or hydrology hole) launches particle or reagent (tracer), if having collected phase in other water source points after a period of time
Same particle or reagent, i.e., it is believed that the two testing site interflow subsurface drainages are interconnected.The experimental result that this method obtains is very
Accurately, thus extremely wide application has been obtained.
Artificial tracer is then broadly divided into physical method connection tracer and chemical method connection tracer.Wherein, physical method connects
Logical tracer mainly passes through asdic method tracer, and the methods of hydrogeological bomb, particle connection tracer are studied.Chemical method connection
Tracer is then mainly tried by using artificial radioisotope, chemical reagent soluble easily in water such as salt ion compound, chemistry
Agent dye function dyestuff such as common pigments and fluorchrome etc., also have sampling directly chemical examination detection reagent concentration ion or
Person's molecular tracer.
However there is also problems, such as particle tracer to be easy to block, be not easy to catch in receiving point for this kind of tracing method
It obtains；Lycopodium powder tracer needs to be separated with heavy-fluid, and long-term just with cadmium heavy-fluid, also easily causes skeleton to deform, joint
It pain and has a dizzy spell；Although artificial radioisotope's precision is high, surrounding water environment can be polluted；And salt ion
The dispensing for closing object not only wastes but also may bring erroneous judgement to result is carried out very much, may be only available for tens of to one two hundred meters
Small range connection test.
Summary of the invention
In view of the foregoing drawbacks, the present invention provides a kind of detection method of water body flow connectivity, using microorganism as
Tracer, it is additional practical without addition, it can gather materials on the spot, environmental protection and economy and nontoxic has broken the bottle of tracer selection
Neck；And the detection and analysis of subsequent tracer can be using now more mature bioinformatics technique and clustering skill
Art is realized, easy to operate, low in cost, can greatly simplify experiment flow, while guaranteeing the accuracy of experimental result.
To achieve the above object, the invention provides the following technical scheme:
Provide a kind of detection method of water body flow connectivity comprising following steps:
S1, several sampled points are determined, and takes corresponding water sample from each sampled point；
S2, macro gene order-checking is carried out to the microorganism in all water samples and carries out sequence assembly, obtain each water
High-quality sequence in quality sample microorganism sequence splicing result；
S3, all high-quality sequences are clustered, form several taxons, and in same taxon microorganism it is excellent
Matter sequence similarity >=97%, and obtain the taxonomic information of microorganism in each taxon；
And S4, according to the taxonomic information and water quality detection result of the microorganism of each sampled point all samplings are clicked through
Row clustering, and judge according to cluster analysis result the connectivity of water body runoff.
Preferably, the step S2 includes:
S21, the macro gene order-checking of MiSeq, the initial data being sequenced are carried out to the microorganism in all water samples
With the preservation of both-end FASTQ format, and the water quality of each sampled point is detected simultaneously, Testing index include: Zn, Cd, Pb,
One or several in SO42-, NO2-, pH；
S22, slip window sampling is used to carry out mass filter to the initial data saved with both-end FASTQ format, and to logical
The sequence for crossing mass filter is spliced, it is desirable that overlap value >=10bp of read1 and read2, and do not allow base mispairing,
Thus the sequence assembly of each water sample microorganism sequencing result is completed；
S23, the microbial gene sequences splicing result for each water sample, according to the base sequence for distinguishing sample
Column index is matched, and is picked out from the microbial gene sequences splicing result of each water sample and the base sequence rope
Draw the ordered sequence of exact matching；
And S24, to the ordered sequence carry out sequence filter, remove chimera sequence, to obtain each water sample
The high-quality sequence of microorganism.
Preferably, sequence filter is carried out using Qiime, chimera sequence is removed using the uchime method in mothur software
Column.
Preferably, the step S3 includes:
S31, it calls the method for uclust to cluster all high-quality sequences in Qiime, forms several taxons,
And in same taxon high-quality sequence sequence similarity >=97%；
S32, the maximum length sequence chosen in each taxon are to represent sequence, and the BLAST method in Qiime is called to carry out
Sequence alignment obtains the taxonomic information of microorganism in each taxon, forms the microorganism of each sampled point water sample
Type-quantitative attribute information.
Preferably, the step S3 further includes step S33, removes Abundances are less than total sequence item number 0.001%
Taxon.
Preferably, in the step S4, to the sampled point carry out clustering method include: hierarchical clustering algorithm,
One or more of Ward minimum variance clustering algorithm, partition clustering algorithm and sort algorithm.
Preferably, in the step S4, it is superimposed Ward minimum variance cluster result respectively using PCA and NMDS algorithm and carries out
After dimensionality reduction projection, the cluster result between sampled point is obtained according to the difference between the sample prescription that projection result is explained and is confirmed, and
The connectivity of groundwater flow is determined according to the result.
Preferably, in the step S4, before the connectivity for judging water body runoff, using PCA algorithm to the micro- life of sampled point
Object information and environmental factor carry out comprehensive analysis, to obtain influence of the environmental factor for sampled point water quality, further according to environment because
The connectivity of influence auxiliary judgment water body runoff of the element for sampled point water quality.
Compared with prior art, the beneficial effects of the present invention are: the proposition of the invention is with microorganism in underground water
As the technical solution of natural food essence agent, do not need using additional tracer, not will increase economic cost will not cause
Environmental pollution, and the detection and analysis of subsequent tracer can be using now more mature bioinformatics technique and cluster
Analytical technology is realized, easy to operate, low in cost, can greatly simplify experiment flow, while guaranteeing the accuracy of experimental result.
Detailed description of the invention
Fig. 1 is the detection method flow chart of water body flow connectivity in embodiment one；
Fig. 2 is sampling point distributions figure and reference numeral figure in embodiment one；
Fig. 3 is both-end FASTQ format instance graph in embodiment one；
Fig. 4 a is the clustering tree that single connection aggregating algorithm obtains in embodiment one；
Fig. 4 b is to be fully connected the clustering tree that aggregating algorithm obtains in embodiment one；
Fig. 4 c is the clustering tree that UPGMA aggregating algorithm obtains in embodiment one；
Fig. 4 d is the clustering tree that Ward aggregating algorithm obtains in embodiment one；
Fig. 5 a is the fusion level value figure of single connection aggregating algorithm in embodiment one；
Fig. 5 b is the fusion level value figure that aggregating algorithm is fully connected in embodiment one；
Fig. 5 c is the fusion level value figure of UPGMA algorithm in embodiment one；
Fig. 5 d is the fusion level value figure of Ward aggregating algorithm in embodiment one；
Fig. 6 a is the single connection aggregating algorithm clustering tree after cutting in embodiment one；
Fig. 6 b is to be fully connected aggregating algorithm clustering tree after cutting in embodiment one；
Fig. 6 c is the UPGMA algorithm clustering tree after cutting in embodiment one；
Fig. 6 d is the Ward aggregating algorithm clustering tree after cutting in embodiment one；
Fig. 7 is the PCA algorithm ordering chart that Ward aggregating algorithm result is superimposed in embodiment one；
Fig. 8 is the NMDS algorithm ordering chart that Ward aggregating algorithm result is superimposed in embodiment one；
Fig. 9 is the groundwater flow connected graph that NMDS algorithm and the acquisition of Ward aggregating algorithm result are superimposed in embodiment one；
Figure 10 is the groundwater flow connected graph obtained in embodiment one by traditional physical and chemical experiment；
Figure 11 is the double sequence figures of distance that PCA algorithm is formed in embodiment one.
Specific embodiment
For a clearer understanding of the technical characteristics, objects and effects of the present invention, now control attached drawing is described in detail
A specific embodiment of the invention.
Embodiment one:
Fig. 1 shows a kind of detection method of water body (such as underground water) flow connectivity of the invention, which is characterized in that packet
Include following steps:
S1, several sampled points are determined, and takes corresponding water sample from each sampled point；Specifically, this reality
It applies in example, determines 12 sampled points altogether within the scope of the experimental study of the Yunnan mining area Zhe Hai, point three periods carry out underground water and take
Sample is influenced with eliminating time factor to result bring, and reduces the randomness and uncertainty in experiment by repeated sample,
12 the specific of sampled point are distributed and number as shown in Figure 2, wherein two observation points are located at earth's surface (e, f), remaining is the hydrology
Hole (a-d) and civilian well (g-l)；
S2, macro gene order-checking is carried out to the microorganism in all water samples and carries out sequence assembly, obtain each water
High-quality sequence in quality sample microorganism sequence splicing result；Specifically, the step S2 includes:
S21, the macro genome survey of MiSeq is carried out to the microorganism in all water samples in Illumina MiSeq platform
Sequence, the initial data being sequenced are saved with both-end FASTQ format as shown in Figure 3, wherein every four row is sequenced as one
Reads, wherein the first behavior sequence names (with beginning), the second behavior sequence, third behavior "+" number, fourth line are the second row
Base quality information corresponding to sequence；And the water quality of each sampled point is detected simultaneously, Testing index include: Zn, Cd,
Pb、SO4 2-、NO2-, one or several in pH, the results are shown in Table 1 for the water quality detection of each sampled point；
1 sampled point water quality detection of table
Wherein, the step of carrying out MiSeq macro gene order-checking include:
(1) DNA is extracted: conventional method extracts the microbial DNA in each water sample；
(1) area 16s rDNA V expands: carrying out PCR trial test for the specific area V of sample DNA and carries out high-volume PCR expansion
Increase；16S rDNA gene is the gene for encoding prokaryotes small subunit ribosome, and molecular size is moderate, and mutation rate is small, is thin
The most frequently used and the most useful mark in fungus strain system means of taxonomic research, 16S rDNA gene order include 9 variable regions and 10 guarantors
Defending zone, conserved region sequence reflects the affiliation between species, and variable region sequences can then embody the difference between species；
(2) glue recovery purifying: being tapped and recovered for target stripe, the sample purified；
(3) each sample is quantitative: using BioTek microplate reader to each sample amounts；
(4) DNA double end is repaired: by the collective effect of 3 ' -5 ' exonucleases and polymerase, being repaired with prominent end
The DNA fragmentation at end；
(5) 3 ' ends introduce " A " base: introducing single base " A " at the end of DNA fragmentation 3 ' for repairing smooth, 3 ' ends of connector
Containing single base " T ", to guarantee that DNA fragmentation can be connected with connector by " A " " T " complementary pairing, and prevent connector from connecting
DNA insertion is connected with each other during DNA fragmentation；
(6) jointing: under the action of ligase, it is incubated for connector and DNA fragmentation containing label, it is made to be connected；
(7) DNA fragmentation that both ends are connected with connector, while DNA amplification text selectively enriched DNA fragments: are enriched with using PCR
Library.PCR should use less recurring number as far as possible, and PCR amplification Chinese library is avoided mistake occur；
(8) it verifies library: quantifying library using Pico green and sepectrophotofluorometer method, use Agilent
2100 pairs of PCR rich segments carry out quality control, the clip size and distribution in validating DNA library；
(9) uniform and mix library: Multi-example DNA library (multiplexed DNA libraries) uniforms extremely
It is mixed in equal volume after 10nM.It if not Multi-example DNA, then only needs after every sample is diluted to 10nM, without further mixing；
(10) machine is sequenced on: the library mixed (10nM) is gradually carried out upper machine sequencing after dilution is quantified to 4~5pM；
S22, slip window sampling is used to carry out mass filter to the initial data saved with both-end FASTQ format, window is big
Small is 5bp, and step-length 1bp is moved since first base positions, it is desirable that base average quality >=Q20 (i.e. base in window
Accuracy rate >=99%), from first lower than sequence is truncated from Q20, sequence length >=150bp is finally required, and be impermissible for mould
Base is pasted, soft FLASH (version1.2.7) software is further utilized, is attached to by the sequence of mass filter, it is desirable that
Overlap value >=10bp of read1 and read2, and do not allow base mispairing, thus complete each water sample microorganism sequencing
As a result sequence assembly；
S23, the microbial gene sequences splicing result for each water sample, according to the base sequence for distinguishing sample
Column index is matched, and is picked out from the microbial gene sequences splicing result of each water sample and the base sequence rope
Draw the ordered sequence of exact matching；That is, using scheduled index (Index) information as screening criteria, accordingly from each water quality sample
Corresponding sequence is found in the microbial gene sequences splicing result of product, when certain section of sequence and scheduled index (Index) information
When exact matching, that is, think that this section of sequence is ordered sequence；
And S24, the ordered sequence progress sequence filter to each water sample, chimera sequence is removed, it is every to obtain
The high-quality sequence of one water sample microorganism；Specifically, the PCR amplification during building library due to high-flux sequence can generate it is chimeric
Body sequence (chimera sequence) can generate the sequencing mistake such as point mutation in sequencing procedure, in order to guarantee to analyze result
Accuracy needs that ordered sequence is further filtered and removed chimera processing；Qiime is used in the present invention as a result,
(version 1.7.0) carries out sequence filter, is removed using the uchime method in mothur software (version 1.31.2)
Chimera sequence；
S3, all high-quality sequences are clustered, form several taxons, and in same taxon microorganism it is excellent
Matter sequence similarity >=97%, and the taxonomic information of microorganism in each taxon is obtained, the taxonomic information includes
Type and quantity information；Specifically, the step S3 includes:
S31, it calls the method for uclust to cluster all high-quality sequences in Qiime, forms several taxons,
And in same taxon high-quality sequence sequence similarity >=97%；Specifically, OTU (Operational Taxonomic
Units, activity classification unit) it is, for the ease of being analyzed, artificially to give certain in phylogenetics or population genetic study
The same mark of one taxon (such as: strain belongs to, kind, grouping etc.) setting；In DNA sequence analysis, by sequence according to
Certain similitude point is classified as many taxons, and wherein each taxon is exactly an OTU, and the present embodiment is chosen
Qiime software, and the method for calling wherein uclust is clustered all high-quality sequences by sequence similarity 97%, if being formed
Dry taxon；
S32, the maximum length sequence chosen in each taxon are to represent sequence, and the BLAST method in Qiime is called to carry out
Sequence alignment obtains the taxonomic information of microorganism in each taxon, and as unit of sampled point, counts shape by arranging
At microbe species-quantitative attribute information of each sampled point water sample；
And S33, remove Abundances be less than total sequence item number 0.001% taxon；Specifically, step S31
In may contain extremely low-abundance OTU in the original OTU list that is formed of several taxons, these OTU may be in sample standard
Introduced during standby and sequencing etc., cannot true reflected sample microbiologic population's composition；Therefore, in order to guarantee analyze result
Accuracy, need to carry out OTU to simplify processing, the standard of simplifying is 0.001% for removing Abundances and being less than total sequence item number
OTU, it is subsequent analysis using the OTU list after as shown in Table 2 simplify；
Each sampled point microorganism sequencing result data of table 2
It should be noted that being made of for sequencing data of the invention the two-dimensional table of 36*8528 in table 2, wherein indulging
Three periods of coordinate representation corresponding 12 sampled points (from a1 to l3), the sample prescription number as in this experiment, totally 36；It is horizontal
The OTU coding of microorganism is corresponded in each sampled point of coordinate representation, each coding (example denovo27548) represents a kind of micro- life
Object；Data indicate quantity of the corresponding microorganism in each corresponding sample prescription in table；
And S4, according to the taxonomic information and water quality detection result of the microorganism of each sampled point all samplings are clicked through
Row clustering, and judge according to cluster analysis result the connectivity of water body runoff；Specifically, being clustered to all sampled points
The method of analysis include: hierarchical clustering algorithm (including single connection Molecule cluster, be fully connected Molecule cluster, average polymerization cluster
(i.e. following UPGMA)), Ward minimum variance clustering algorithm, partition clustering algorithm (including K-means, PAM) and sequence calculate
One or more of method；
Specifically, hierarchical clustering and Ward minimum variance clustering algorithm are directed to, using OTU table described in table 2 as journey
The input of sequence simultaneously carries out data normalization, then is utilized respectively different similarity distance calculating methods and is calculated, such as Fig. 4 a-4d
Shown, final cluster result is visualized in a manner of dendrogram；Further, for four kinds in Fig. 4 a-4d
Cluster result (namely merges water as shown in Fig. 5 a-5d by calculating the diversity numerical value at the fusion of clustering tree Zhong Liangge branch
Level values) determine height that clustering tree is cut out, it is after cutting cut out thus to obtain the clustering tree after cutting out as shown in figures 6 a-6d
Trimming X every cluster subtree below is a grouping, and shows hierarchical clustering and Ward as shown in table 3 in table form
Minimum variance cluster result；As can be seen from Table 3, when using hierarchical clustering algorithm and Ward minimum variance clustering algorithm: k,
L, d, j are one group, and the group result that g, h, i are one group, f is one group is relatively uniform, and grouping is also more obvious；
For partition clustering algorithm, it is still OTU table described in input table 2 first；Next step, when using K-
When means algorithm, the packet count selected is 4 groups and 6 groups；When using PAM algorithm, packet count is selected as 4 groups, and passes through table
3 show the cluster result of K-means algorithm Yu PAM algorithm；
3 K-means algorithm of table and PAM algorithm cluster result
As shown in Table 3, in the result that partition clustering obtains be grouped k, l, j, d and grouping g, h, i it is relatively uniform, the result with
The classification results that hierarchical clustering and Ward minimum variance clustering algorithm obtain are consistent.
Summarize hierarchical clustering, Ward minimum variance clustering algorithm and partition clustering as a result, obtaining 4 data of table, therefrom
As can be seen that the result of Clustering Model average polymerization cluster (UPGMA) best in hierarchical clustering and Ward minimum variance cluster
It is completely the same.
4 cluster result of table summarizes
Further, on the basis of the above, since the attribute of each sample prescription is excessive, the step of the present embodiment
It is superimposed Ward minimum variance cluster result respectively using PCA and NMDS algorithm in S4 and carries out dimensionality reduction projection, and according to projection result
It explains that the difference between the sample prescription that confirms obtains the cluster result between sampled point, and groundwater flow is determined according to the result
Connectivity.
Specifically, Fig. 7 show superposition Ward minimum variance clustering algorithm in PCA algorithm as a result, there it can be seen that
Since the underground water of connection has mobility, so microorganism adapts to groundwater environment and the grouping that is formed to groundwater flow
Connectivity has indicative significance, while also providing new reference frame for sampled point division；
Further, the present embodiment is superimposed Ward minimum variance clustering algorithm (such as Fig. 8 institute also in NMDS algorithm ordering chart
Show), by calculating the Ward clustering of Bray-Curtis matrix, then extract four groups, and to belonging to difference in NMDS figure
Group sample prescription be marked, finally obtained cluster result as shown in figure 9, four group cluster results be respectively { a, b, c, d, e },
{ f }, { h, i, g }, { k, l, j } is almost the same with the result (such as Figure 10) that is obtained by traditional physical and chemical experiment.
Preferably, in step S4, before the connectivity for judging water body runoff, also using PCA algorithm to the micro- life of sampled point
Object information and environmental factor carry out comprehensive analysis, to obtain influence of the environmental factor for sampled point water quality, further according to environment because
The connectivity of influence auxiliary judgment water body runoff of the element for sampled point water quality.As shown in figure 11, wherein EC is conductivity, and Eh is
Oxidation-reduction potential, DO are dissolved oxygen, which explains the relationship between environmental factor and sampled point water quality, and the length of arrow is got over
It is long, it indicates to influence bigger；Two arrow angles are smaller, show that correlation is bigger, meanwhile, it can also intuitively find out a, b, c sampled point
Mainly influenced by pH value；E is mainly influenced by sulfate ion and f is mainly influenced by heavy metal ion, this with two
Sample is contaminated even more serious match in earth's surface and close to slag muck；Remaining sampled point is then mainly by nitrate
It influences, shows that main well is not affected by the pollution of heavy metal ion in slag muck, there is no safety is hidden for the water in Drinking Water for Residents well
Suffer from.
In conclusion the proposition of the invention using in underground water microorganism as natural food essence agent technical solution,
It is not needed using additional tracer, and not will increase economic cost will not cause environmental pollution, and the inspection of subsequent tracer
Survey and analysis can be using now more mature bioinformatics technique and Clustering Analysis Technology realization, easy to operate, costs
It is cheap, experiment flow can be greatly simplified, while guaranteeing the accuracy of experimental result.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of detection method of water body flow connectivity, which comprises the steps of:
S1, several sampled points are determined, and takes corresponding water sample from each sampled point；
S2, macro gene order-checking is carried out to the microorganism in all water samples and carries out sequence assembly, obtain each water quality sample
High-quality sequence in product microorganism sequence splicing result；
S3, all high-quality sequences are clustered, form several taxons, and in same taxon microorganism high-quality sequence
Column similarity >=97%, and obtain the taxonomic information of microorganism in each taxon；
And S4, according to the taxonomic information and water quality detection result of the microorganism of each sampled point all sampled points are gathered
Alanysis, and judge according to cluster analysis result the connectivity of water body runoff.
2. detection method as described in claim 1, which is characterized in that the step S2 includes:
S21, the macro gene order-checking of MiSeq is carried out to the microorganism in all water samples, the initial data being sequenced is with double
It holds FASTQ format to save, and the water quality of each sampled point is detected simultaneously, Testing index includes: Zn, Cd, Pb, SO4 2-、
NO2-, one or several in pH；
S22, slip window sampling is used to carry out mass filter to the initial data that saves with both-end FASTQ format, and to passing through matter
The sequence of amount filtering is spliced, it is desirable that overlap value >=10bp of read1 and read2, and do not allow base mispairing, thus
Complete the sequence assembly of each water sample microorganism sequencing result；
S23, the microbial gene sequences splicing result for each water sample, according to the base sequence rope for distinguishing sample
Row matching is introduced, picks out from the microbial gene sequences splicing result of each water sample and has been indexed with the base sequence
Complete matched ordered sequence；
And S24, to the ordered sequence carry out sequence filter, remove chimera sequence, to obtain the micro- life of each water sample
The high-quality sequence of object.
3. detection method as claimed in claim 2, which is characterized in that sequence filter is carried out using Qiime, it is soft using mothur
Uchime method in part removes chimera sequence.
4. detection method as described in claim 1, which is characterized in that the step S3 includes:
S31, it calls the method for uclust to cluster all high-quality sequences in Qiime, forms several taxons, and same
Sequence similarity >=97% of high-quality sequence in one taxon；
S32, the maximum length sequence chosen in each taxon are to represent sequence, and the BLAST method in Qiime is called to carry out sequence
It compares, obtains the taxonomic information of microorganism in each taxon, form the microbe species-of each sampled point water sample
Quantitative attribute information.
5. detection method as claimed in claim 5, which is characterized in that the step S3 further includes step S33, removes Abundances
Less than 0.001% taxon of total sequence item number.
6. detection method as described in claim 1, which is characterized in that in the step S4, clustered to the sampled point
The method of analysis includes: in hierarchical clustering algorithm, Ward minimum variance clustering algorithm, partition clustering algorithm and sort algorithm
It is one or more of.
7. detection method as claimed in claim 6, which is characterized in that in the step S4, distinguished using PCA and NMDS algorithm
After being superimposed the progress dimensionality reduction projection of Ward minimum variance cluster result, according to the difference between the sample prescription that projection result is explained and is confirmed
Cluster result between different acquisition sampled point, and determine according to the result connectivity of groundwater flow.
8. detection method as claimed in claim 7, which is characterized in that in the step S4, in the connection for judging water body runoff
Property before, comprehensive analysis is carried out to sampled point microbial information and environmental factor using PCA algorithm, to obtain environmental factor for adopting
The influence of sampling point water quality, the connectivity of the influence auxiliary judgment water body runoff further according to environmental factor for sampled point water quality.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109859799A (en) * | 2019-01-29 | 2019-06-07 | 安图实验仪器(郑州)有限公司 | Based on the mass spectrometric weighting microorganism clustering methodology of microorganism |
| CN113554066A (en) * | 2021-06-30 | 2021-10-26 | 山东大学 | Coastal zone underground brine storage interlayer hydraulic connection identification method and system |
| CN115841847A (en) * | 2022-12-12 | 2023-03-24 | 哈尔滨因极科技有限公司 | Microorganism information measuring and extracting system and method |
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| CN113554066A (en) * | 2021-06-30 | 2021-10-26 | 山东大学 | Coastal zone underground brine storage interlayer hydraulic connection identification method and system |
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| CN115841847A (en) * | 2022-12-12 | 2023-03-24 | 哈尔滨因极科技有限公司 | Microorganism information measuring and extracting system and method |
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