CN117709600A - Wetland plant optimization method based on quantitative evaluation of new pollutant restoration function - Google Patents
Wetland plant optimization method based on quantitative evaluation of new pollutant restoration function Download PDFInfo
- Publication number
- CN117709600A CN117709600A CN202410019151.5A CN202410019151A CN117709600A CN 117709600 A CN117709600 A CN 117709600A CN 202410019151 A CN202410019151 A CN 202410019151A CN 117709600 A CN117709600 A CN 117709600A
- Authority
- CN
- China
- Prior art keywords
- component
- wetland
- evaluation
- layer
- plants
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000003344 environmental pollutant Substances 0.000 title claims abstract description 132
- 231100000719 pollutant Toxicity 0.000 title claims abstract description 132
- 238000000034 method Methods 0.000 title claims abstract description 71
- 238000005457 optimization Methods 0.000 title claims abstract description 36
- 238000011158 quantitative evaluation Methods 0.000 title claims abstract description 26
- 238000011156 evaluation Methods 0.000 claims abstract description 147
- 230000006870 function Effects 0.000 claims abstract description 107
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 34
- 238000000513 principal component analysis Methods 0.000 claims abstract description 23
- 238000012216 screening Methods 0.000 claims abstract description 21
- 239000000126 substance Substances 0.000 claims description 43
- 239000011159 matrix material Substances 0.000 claims description 37
- 238000004364 calculation method Methods 0.000 claims description 28
- 230000000694 effects Effects 0.000 claims description 27
- 230000008439 repair process Effects 0.000 claims description 27
- 230000006978 adaptation Effects 0.000 claims description 25
- 239000000356 contaminant Substances 0.000 claims description 15
- 230000029553 photosynthesis Effects 0.000 claims description 15
- 238000010672 photosynthesis Methods 0.000 claims description 15
- 230000008635 plant growth Effects 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 13
- 230000008901 benefit Effects 0.000 claims description 13
- 238000005516 engineering process Methods 0.000 claims description 12
- 239000002028 Biomass Substances 0.000 claims description 10
- 244000005700 microbiome Species 0.000 claims description 9
- 230000005068 transpiration Effects 0.000 claims description 9
- 230000005764 inhibitory process Effects 0.000 claims description 8
- 239000002207 metabolite Substances 0.000 claims description 7
- 230000004071 biological effect Effects 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 5
- 229910052760 oxygen Inorganic materials 0.000 claims description 5
- 239000001301 oxygen Substances 0.000 claims description 5
- 238000012502 risk assessment Methods 0.000 claims description 4
- 238000007619 statistical method Methods 0.000 claims description 4
- 238000003306 harvesting Methods 0.000 claims description 3
- KJONHKAYOJNZEC-UHFFFAOYSA-N nitrazepam Chemical compound C12=CC([N+](=O)[O-])=CC=C2NC(=O)CN=C1C1=CC=CC=C1 KJONHKAYOJNZEC-UHFFFAOYSA-N 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000029142 excretion Effects 0.000 claims description 2
- 230000003334 potential effect Effects 0.000 claims description 2
- 230000027756 respiratory electron transport chain Effects 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 abstract description 8
- 238000013441 quality evaluation Methods 0.000 abstract description 2
- 241000196324 Embryophyta Species 0.000 description 252
- 230000009467 reduction Effects 0.000 description 28
- ULGZDMOVFRHVEP-RWJQBGPGSA-N Erythromycin Chemical compound O([C@@H]1[C@@H](C)C(=O)O[C@@H]([C@@]([C@H](O)[C@@H](C)C(=O)[C@H](C)C[C@@](C)(O)[C@H](O[C@H]2[C@@H]([C@H](C[C@@H](C)O2)N(C)C)O)[C@H]1C)(C)O)CC)[C@H]1C[C@@](C)(OC)[C@@H](O)[C@H](C)O1 ULGZDMOVFRHVEP-RWJQBGPGSA-N 0.000 description 20
- 241001494508 Arundo donax Species 0.000 description 14
- 241000234587 Canna Species 0.000 description 11
- 235000005273 Canna coccinea Nutrition 0.000 description 11
- 238000010521 absorption reaction Methods 0.000 description 10
- 230000003044 adaptive effect Effects 0.000 description 10
- 229960003276 erythromycin Drugs 0.000 description 10
- 238000005259 measurement Methods 0.000 description 10
- 241000304459 Tacitus Species 0.000 description 7
- RXZBMPWDPOLZGW-XMRMVWPWSA-N (E)-roxithromycin Chemical compound O([C@@H]1[C@@H](C)C(=O)O[C@@H]([C@@]([C@H](O)[C@@H](C)C(=N/OCOCCOC)/[C@H](C)C[C@@](C)(O)[C@H](O[C@H]2[C@@H]([C@H](C[C@@H](C)O2)N(C)C)O)[C@H]1C)(C)O)CC)[C@H]1C[C@@](C)(OC)[C@@H](O)[C@H](C)O1 RXZBMPWDPOLZGW-XMRMVWPWSA-N 0.000 description 6
- VTNQPKFIQCLBDU-UHFFFAOYSA-N Acetochlor Chemical compound CCOCN(C(=O)CCl)C1=C(C)C=CC=C1CC VTNQPKFIQCLBDU-UHFFFAOYSA-N 0.000 description 6
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 6
- 239000005947 Dimethoate Substances 0.000 description 6
- 238000009825 accumulation Methods 0.000 description 6
- MXWJVTOOROXGIU-UHFFFAOYSA-N atrazine Chemical compound CCNC1=NC(Cl)=NC(NC(C)C)=N1 MXWJVTOOROXGIU-UHFFFAOYSA-N 0.000 description 6
- 238000010219 correlation analysis Methods 0.000 description 6
- MCWXGJITAZMZEV-UHFFFAOYSA-N dimethoate Chemical compound CNC(=O)CSP(=S)(OC)OC MCWXGJITAZMZEV-UHFFFAOYSA-N 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 229960005224 roxithromycin Drugs 0.000 description 6
- 239000010865 sewage Substances 0.000 description 6
- 229960002135 sulfadimidine Drugs 0.000 description 6
- ASWVTGNCAZCNNR-UHFFFAOYSA-N sulfamethazine Chemical compound CC1=CC(C)=NC(NS(=O)(=O)C=2C=CC(N)=CC=2)=N1 ASWVTGNCAZCNNR-UHFFFAOYSA-N 0.000 description 6
- GSDSWSVVBLHKDQ-UHFFFAOYSA-N 9-fluoro-3-methyl-10-(4-methylpiperazin-1-yl)-7-oxo-2,3-dihydro-7H-[1,4]oxazino[2,3,4-ij]quinoline-6-carboxylic acid Chemical compound FC1=CC(C(C(C(O)=O)=C2)=O)=C3N2C(C)COC3=C1N1CCN(C)CC1 GSDSWSVVBLHKDQ-UHFFFAOYSA-N 0.000 description 5
- HEFNNWSXXWATRW-UHFFFAOYSA-N Ibuprofen Chemical compound CC(C)CC1=CC=C(C(C)C(O)=O)C=C1 HEFNNWSXXWATRW-UHFFFAOYSA-N 0.000 description 5
- CMWTZPSULFXXJA-UHFFFAOYSA-N Naproxen Natural products C1=C(C(C)C(O)=O)C=CC2=CC(OC)=CC=C21 CMWTZPSULFXXJA-UHFFFAOYSA-N 0.000 description 5
- 241000519406 Paphiopedilum Species 0.000 description 5
- 239000004098 Tetracycline Substances 0.000 description 5
- 230000009471 action Effects 0.000 description 5
- HKPHPIREJKHECO-UHFFFAOYSA-N butachlor Chemical compound CCCCOCN(C(=O)CCl)C1=C(CC)C=CC=C1CC HKPHPIREJKHECO-UHFFFAOYSA-N 0.000 description 5
- DCOPUUMXTXDBNB-UHFFFAOYSA-N diclofenac Chemical compound OC(=O)CC1=CC=CC=C1NC1=C(Cl)C=CC=C1Cl DCOPUUMXTXDBNB-UHFFFAOYSA-N 0.000 description 5
- 229960001259 diclofenac Drugs 0.000 description 5
- 229960001680 ibuprofen Drugs 0.000 description 5
- ZEKZLJVOYLTDKK-UHFFFAOYSA-N lomefloxacin Chemical compound FC1=C2N(CC)C=C(C(O)=O)C(=O)C2=CC(F)=C1N1CCNC(C)C1 ZEKZLJVOYLTDKK-UHFFFAOYSA-N 0.000 description 5
- 229960002422 lomefloxacin Drugs 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 229960002009 naproxen Drugs 0.000 description 5
- CMWTZPSULFXXJA-VIFPVBQESA-N naproxen Chemical compound C1=C([C@H](C)C(O)=O)C=CC2=CC(OC)=CC=C21 CMWTZPSULFXXJA-VIFPVBQESA-N 0.000 description 5
- 229960001699 ofloxacin Drugs 0.000 description 5
- 238000000746 purification Methods 0.000 description 5
- 238000011002 quantification Methods 0.000 description 5
- 229960002180 tetracycline Drugs 0.000 description 5
- 235000019364 tetracycline Nutrition 0.000 description 5
- 229930101283 tetracycline Natural products 0.000 description 5
- 150000003522 tetracyclines Chemical class 0.000 description 5
- FFSJPOPLSWBGQY-UHFFFAOYSA-N triazol-4-one Chemical compound O=C1C=NN=N1 FFSJPOPLSWBGQY-UHFFFAOYSA-N 0.000 description 5
- HEMJJKBWTPKOJG-UHFFFAOYSA-N Gemfibrozil Chemical compound CC1=CC=C(C)C(OCCCC(C)(C)C(O)=O)=C1 HEMJJKBWTPKOJG-UHFFFAOYSA-N 0.000 description 4
- 230000001186 cumulative effect Effects 0.000 description 4
- 239000003814 drug Substances 0.000 description 4
- 229960003627 gemfibrozil Drugs 0.000 description 4
- DKYWVDODHFEZIM-UHFFFAOYSA-N ketoprofen Chemical compound OC(=O)C(C)C1=CC=CC(C(=O)C=2C=CC=CC=2)=C1 DKYWVDODHFEZIM-UHFFFAOYSA-N 0.000 description 4
- 229960000991 ketoprofen Drugs 0.000 description 4
- 238000005067 remediation Methods 0.000 description 4
- 230000009897 systematic effect Effects 0.000 description 4
- 238000012360 testing method Methods 0.000 description 4
- 244000150187 Cyperus papyrus Species 0.000 description 3
- 239000004100 Oxytetracycline Substances 0.000 description 3
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 3
- 239000003242 anti bacterial agent Substances 0.000 description 3
- 229940088710 antibiotic agent Drugs 0.000 description 3
- 239000002131 composite material Substances 0.000 description 3
- 229940079593 drug Drugs 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 229910052757 nitrogen Inorganic materials 0.000 description 3
- 229960000625 oxytetracycline Drugs 0.000 description 3
- IWVCMVBTMGNXQD-PXOLEDIWSA-N oxytetracycline Chemical compound C1=CC=C2[C@](O)(C)[C@H]3[C@H](O)[C@H]4[C@H](N(C)C)C(O)=C(C(N)=O)C(=O)[C@@]4(O)C(O)=C3C(=O)C2=C1O IWVCMVBTMGNXQD-PXOLEDIWSA-N 0.000 description 3
- 235000019366 oxytetracycline Nutrition 0.000 description 3
- 239000000575 pesticide Substances 0.000 description 3
- 229910052698 phosphorus Inorganic materials 0.000 description 3
- 239000011574 phosphorus Substances 0.000 description 3
- IWVCMVBTMGNXQD-UHFFFAOYSA-N terramycin dehydrate Natural products C1=CC=C2C(O)(C)C3C(O)C4C(N(C)C)C(O)=C(C(N)=O)C(=O)C4(O)C(O)=C3C(=O)C2=C1O IWVCMVBTMGNXQD-UHFFFAOYSA-N 0.000 description 3
- JAYCNKDKIKZTAF-UHFFFAOYSA-N 1-chloro-2-(2-chlorophenyl)benzene Chemical compound ClC1=CC=CC=C1C1=CC=CC=C1Cl JAYCNKDKIKZTAF-UHFFFAOYSA-N 0.000 description 2
- LAXBNTIAOJWAOP-UHFFFAOYSA-N 2-chlorobiphenyl Chemical compound ClC1=CC=CC=C1C1=CC=CC=C1 LAXBNTIAOJWAOP-UHFFFAOYSA-N 0.000 description 2
- NMWSKOLWZZWHPL-UHFFFAOYSA-N 3-chlorobiphenyl Chemical compound ClC1=CC=CC(C=2C=CC=CC=2)=C1 NMWSKOLWZZWHPL-UHFFFAOYSA-N 0.000 description 2
- FPWNLURCHDRMHC-UHFFFAOYSA-N 4-chlorobiphenyl Chemical compound C1=CC(Cl)=CC=C1C1=CC=CC=C1 FPWNLURCHDRMHC-UHFFFAOYSA-N 0.000 description 2
- 239000005411 L01XE02 - Gefitinib Substances 0.000 description 2
- 239000005949 Malathion Substances 0.000 description 2
- 230000002378 acidificating effect Effects 0.000 description 2
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- JXSJBGJIGXNWCI-UHFFFAOYSA-N diethyl 2-[(dimethoxyphosphorothioyl)thio]succinate Chemical compound CCOC(=O)CC(SP(=S)(OC)OC)C(=O)OCC JXSJBGJIGXNWCI-UHFFFAOYSA-N 0.000 description 2
- XGALLCVXEZPNRQ-UHFFFAOYSA-N gefitinib Chemical compound C=12C=C(OCCCN3CCOCC3)C(OC)=CC2=NC=NC=1NC1=CC=C(F)C(Cl)=C1 XGALLCVXEZPNRQ-UHFFFAOYSA-N 0.000 description 2
- 229960002584 gefitinib Drugs 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 229910001385 heavy metal Inorganic materials 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 229960000453 malathion Drugs 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 238000010187 selection method Methods 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 239000002352 surface water Substances 0.000 description 2
- YKAVHPRGGAUFDN-JTQLBUQXSA-N 24464-30-0 Chemical compound O([C@@H]1[C@@H](C)C(=O)O[C@@H]([C@@]2(C)O[C@]3([C@@H]([C@H]2O)C)[C@H](C)C[C@](O3)(C)[C@H](O[C@H]2[C@@H]([C@H](C[C@@H](C)O2)N(C)C)O)[C@H]1C)CC)[C@H]1C[C@@](C)(OC)[C@@H](O)[C@H](C)O1 YKAVHPRGGAUFDN-JTQLBUQXSA-N 0.000 description 1
- 239000002253 acid Substances 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- -1 bichloride Chemical compound 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000007621 cluster analysis Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000005202 decontamination Methods 0.000 description 1
- 230000003588 decontaminative effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000012010 growth Effects 0.000 description 1
- 238000011065 in-situ storage Methods 0.000 description 1
- 239000004615 ingredient Substances 0.000 description 1
- 230000003902 lesion Effects 0.000 description 1
- 239000002075 main ingredient Substances 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 241000894007 species Species 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- SEEPANYCNGTZFQ-UHFFFAOYSA-N sulfadiazine Chemical compound C1=CC(N)=CC=C1S(=O)(=O)NC1=NC=CC=N1 SEEPANYCNGTZFQ-UHFFFAOYSA-N 0.000 description 1
- 229960004306 sulfadiazine Drugs 0.000 description 1
- 229960005404 sulfamethoxazole Drugs 0.000 description 1
- GECHUMIMRBOMGK-UHFFFAOYSA-N sulfapyridine Chemical compound C1=CC(N)=CC=C1S(=O)(=O)NC1=CC=CC=N1 GECHUMIMRBOMGK-UHFFFAOYSA-N 0.000 description 1
- 229960002211 sulfapyridine Drugs 0.000 description 1
- JLKIGFTWXXRPMT-UHFFFAOYSA-N sulphamethoxazole Chemical compound O1C(C)=CC(NS(=O)(=O)C=2C=CC(N)=CC=2)=N1 JLKIGFTWXXRPMT-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Economics (AREA)
- Evolutionary Computation (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Cultivation Of Plants (AREA)
Abstract
The application belongs to the technical field of environmental quality evaluation, and discloses a wetland plant optimization method based on quantitative evaluation of a new pollutant restoration function, which comprises the following steps: determining at least one target criterion layer from an evaluation system of wetland plants about new pollutant restoration functions, acquiring various data information of a plurality of wetland plants to be selected corresponding to each component layer of the target criterion layer, screening key evaluation components from the component layers of the target criterion layer, calculating to obtain comprehensive grading values of the wetland plants to be selected according to the key evaluation components and various data information by combining a membership function value method based on principal component analysis, selecting restoration plants of new pollutants of water from the wetland plants to be selected according to the comprehensive grading values of the wetland plants to be selected, grading and optimizing the plants by combining the membership function value method based on principal component analysis through the plant evaluation system and the key evaluation components, and improving the preferable efficiency of the wetland plants for restoring the new pollutants of the water.
Description
Technical Field
The application relates to the technical field of environmental quality evaluation, in particular to a wetland plant optimization method based on quantitative evaluation of a new pollutant restoration function.
Background
New contaminants are contaminants that have recently been discovered or focused, potentially harmful to human health and the ecological environment, have not been included in regulatory lists or are not sufficiently effective in controlling their risk by existing regulatory measures. Due to the demands of industry and medicine industry, new pollutants burst and grow and frequently appear in various surface water bodies, and the ecological environment and human health risks are high. The sewage treatment plant is a final barrier for blocking pollutants from entering the surface water environment, the treatment efficiency of the traditional pollutants is greatly improved by the existing technology of the sewage treatment plant, the treatment procedure aiming at new pollutants is still lacking, and tail water becomes a main discharge point source for the new pollutants to enter the urban water environment.
The wet land advanced treatment aiming at tail water becomes standard of standard quality improvement, the wet land in-situ repair technology is promoted because of the advantages of green ecology, low cost, sustainable action and the like, the repair effect of polluted water bodies is widely accepted, the plant effect serving as a main body of wet land engineering mainly comprises nutrient storage, filtration, root system oxygen release, microorganism adhesion matrix and landscape effect, the wet land plant selection always takes the principle of local seed adaptation, strong stress resistance, purification capacity, developed root system, vigorous growth and certain economic value as the qualitative principle, the current plant repair effect evaluation is all the specific index effect meeting a certain performance, such as the removal rate in pollution reduction efficiency and biomass in adaptation performance, the evaluation index lacks the representativeness, systemicity and comprehensiveness, and the evaluation method lacks quantification, and the plant material repair screening basis is undefined and non-constitutive. Wetland plants are the main body of application of wetland technology, and a wetland plant optimization method for quantitatively evaluating the repair function of new pollutants is needed.
In the prior art, chinese patent CN116081820a discloses a method for configuring an economic water purifying plant, comprising: according to river coastal environment, pollutant type and content of polluted water, nutrition characteristics of the water purification plants and action characteristics of the water purification plants on pollutants, the decontamination capability of the water purification plants on total nitrogen, total phosphorus and heavy metals is calculated by using a membership function evaluation method so as to prepare the water purification plants. Chinese patent CN111369176B discloses a python-based water ecological restoration plant community matching method, comprising: and screening out plant varieties capable of recovering the overload multiple of the pollutant to the repair threshold value by using the repair threshold value as a core analysis index based on a matrix analysis method and a recursion method, taking the plant varieties as a water ecological repair plant community matching scheme, and establishing an evaluation index system for the scheme to obtain an optimal matching scheme. The journal paper (evaluation of the sewage purifying capacity of aquatic plants based on fuzzy membership function algorithm) discloses a comprehensive evaluation method for the sewage purifying capacity of different aquatic plants based on fuzzy membership function algorithm, which specifically comprises the following steps: and calculating 4 indexes of the aquatic plants on the removal rate of total nitrogen, total phosphorus, ammonia nitrogen and chemical oxygen demand in the sewage by adopting a fuzzy mathematical membership function so as to determine the purifying capacity of the aquatic plants on the sewage. The evaluation method is mainly aimed at the removal rate of conventional pollutants such as total nitrogen, total phosphorus, ammonia nitrogen, chemical oxygen demand and heavy metals, is only used for analyzing the evaluation result of plant pollution reduction efficiency of conventional pollution restoration of a water body, has the advantages of less conventional pollutant types and quantity, stable property, simple testing method, mature comprehensive restoration technology and low environmental risk, and has complex types, various quantities, changeable properties, complex testing method, high testing cost, high environmental risk and immature restoration technology of new pollutants.
Disclosure of Invention
The method is characterized in that the method is used for grading and optimizing the plants by combining a membership function value method based on principal component analysis through an evaluation system of the wetland plants on the new pollutant restoration function and key evaluation components in the evaluation system, so that the technical problems of undefined and non-systematic screening basis of the plant material in the existing wetland plant optimization method for restoring the new pollutant are solved, quantification, materialization and predictability of the screening method can be realized, the screening thought can be popularized to plant restoration technologies of various environmental media, ecological benefits and economic benefits are considered, and the optimization efficiency of the wetland plant for restoring the new pollutant is improved.
In a first aspect, the present application provides a wetland plant optimization method based on quantitative evaluation of new pollutant remediation function, which is applied to an evaluation system of the wetland plant on the new pollutant remediation function so as to perform optimization on the wetland plant for water new pollutant remediation; the evaluation system takes the comprehensive benefit of the new pollutant phytoremediation technology of the water body as a target layer; the evaluation system comprises a plurality of criterion layers, wherein each criterion layer comprises a plurality of index layers, and each index layer comprises a plurality of component layers; the index layer is an evaluation base corresponding to the criterion layer, and the component layer is an evaluation base corresponding to the index layer;
The wetland plant optimization method based on quantitative evaluation of the new pollutant restoration function comprises the following steps:
determining at least one criterion layer as a target criterion layer from the evaluation system, and acquiring various data information of various component layers of a plurality of wetland plants to be selected, which correspond to the index layers corresponding to the target criterion layer;
screening key evaluation components from the component layers of the target criterion layer;
calculating the comprehensive grading value of each wetland plant to be selected according to the key evaluation component and the data information by a membership function value method based on principal component analysis;
and selecting the repair plants of the new pollutants of the water body from the wetland plants to be selected according to the comprehensive grading values of the wetland plants to be selected.
The wetland plant optimization method based on the quantitative evaluation of the new pollutant restoration function can realize optimization of wetland plants for restoring new pollutants, the wetland plants are scored and optimized by combining a main component analysis-based membership function value method through an evaluation system of the wetland plants on the new pollutant restoration function and key evaluation components in the evaluation system, the technical problem that the restoration plant materials are screened according to an undefined and non-systematic basis in the existing wetland plant optimization method for restoring new pollutants is solved, quantification, materialization and predictability of the screening method can be realized, the screening thought can be popularized to various environmental medium plant restoration technologies, ecological benefits and economic benefits are considered, and the optimization efficiency of the wetland plants for restoring the new pollutants is improved.
Optionally, the criteria layer of the assessment system includes abatement efficacy, adaptive performance, risk assessment, and economic investment.
Optionally, the index layer corresponding to the pollution reduction efficiency comprises a removal proportion, a removal load, an enrichment capacity and an enrichment factor; the index layer corresponding to the adaptation performance comprises plant growth vigor, photosynthesis, transpiration and root system functions; the index layer corresponding to the risk assessment comprises release risk, attenuation potential and biological effect; the index layer corresponding to the economic investment comprises planting types, planting densities and maintenance management.
Optionally, the component layers corresponding to the removal proportion comprise a single substance concentration removal rate, a single substance concentration removal rate and a total substance concentration removal rate; the component layers corresponding to the removal load comprise a single substance removal total amount, a single substance removal total amount and a total substance removal total amount; the component layer corresponding to the enrichment capacity comprises root system pollutant enrichment concentration, stem and leaf pollutant enrichment concentration and whole plant pollutant enrichment concentration; the component layers corresponding to the enrichment factors comprise root system pollutant enrichment coefficients, stem and leaf pollutant enrichment coefficients and whole plant pollutant enrichment coefficients; the component layers corresponding to the transpiration comprise leaf area, plant coverage area, transpiration rate and air hole conductivity; the component layers corresponding to the plant growth vigor comprise biomass, plant height, root length and root number; the component layers corresponding to photosynthesis comprise leaf area, leaf number, photosynthesis rate and electron transfer efficiency; the component layers corresponding to the root system function comprise new root proportion, root system activity, root system porosity and root system oxygen excretion; the component layers corresponding to the release risks comprise an ecological risk level of single substance emission, an ecological risk level of single substance emission and an ecological risk level of total substance emission; the component layers corresponding to the attenuation potential comprise the emission rate of single substance metabolites, the emission rate of single substance metabolites and the emission rate of total substance metabolites; the component layers corresponding to the biological effect comprise an inhibition rate of rhizosphere microorganism biomass, an inhibition rate of rhizosphere microorganism activity and an inhibition rate of rhizosphere microorganism diversity; the component layers corresponding to the planting types comprise purchase prices of single plants and purchase prices of combined plants; the component layers corresponding to the planting density comprise the planting quantity of single plants and the planting quantity of combined plants; the corresponding component layers of maintenance management comprise the frequency of harvesting the plants in year, the frequency of changing seeds in year and the frequency of reseeding the plants in year.
Optionally, determining at least one criterion layer from the evaluation system as a target criterion layer includes:
at least one criterion layer including the pollution reduction effect is extracted from the evaluation system as the target criterion layer.
Optionally, screening key evaluation components from component layers of the target criterion layer includes:
carrying out relevance ranking on each component layer of the target criterion layer by using a Pelson correlation algorithm, a multidimensional difference statistical method and a clustering analysis method to obtain relevance ranking; the relevance ranking is a descending ranking;
and extracting a component layer with a preset percentage before sequencing in the relevance ranking to obtain the key evaluation component.
The wetland plant optimizing method based on the quantitative evaluation of the new pollutant repairing function can realize optimizing wetland plants for repairing new pollutants, performs primary screening on the component layers through the Pelson related algorithm, the multidimensional difference statistical method and the clustering analysis method, screens out key evaluation components with strong correlation in advance, and uses the key evaluation components to represent corresponding index layers, thereby reducing the calculated amount of follow-up grading and improving the optimizing efficiency of the wetland plants for repairing new pollutants.
Optionally, after the key evaluation component is screened from the component layer of the target criterion layer, the method further comprises:
and determining the key evaluation component as a main evaluation component corresponding to the target criterion layer so as to predict the new pollutant restoration function of the water body of the corresponding criterion layer and the criterion layer through the main evaluation component.
The wetland plant optimizing method based on the quantitative evaluation new pollutant restoring function can realize optimizing wetland plants for restoring new pollutants, the key evaluation components are determined to be main evaluation components corresponding to the target criterion layers, when restoring other new pollutants, new pollutant restoring scores of the wetland plants corresponding to the criterion layers can be calculated only by acquiring data of the wetland plants corresponding to the main evaluation components, and when optimizing the wetland plants for restoring other new pollutants, the load is reduced, so that the optimizing efficiency of the wetland plants for restoring the new pollutants is improved.
Optionally, the calculation mode of the comprehensive score value includes a calculation mode of the comprehensive score value of the combined criterion layer; when the calculation mode of the comprehensive grading value of the combined criterion layer is used, calculating the comprehensive grading value of each wetland plant to be selected according to the key evaluation component and the data information by using a membership function value method based on principal component analysis, wherein the calculation method comprises the following steps:
Calculating contribution rates, weight values and performance scores of the wetland plants to be selected on all main components in the target criterion layer according to all data information of the key evaluation components by a main component analysis method;
and calculating the comprehensive grading value of each wetland plant to be selected by adopting a membership function method based on the contribution rate, the weight value and the performance score.
Optionally, the calculation formula of the comprehensive score value specifically includes:
；
；
；
；
wherein C is j A performance score, A, representing the j-th principal component of all target criterion layers of the wetland plants to be selected ji A factor score coefficient, X, for the jth principal component corresponding to the ith component in the component matrix of the principal component ji The expression effect value of the jth main component corresponding to the ith component in the component matrix of the main component is represented by i, i is the ith component, and m is the total quantity of components in the component matrix of the jth main component of the wetland plant to be selected; p (P) j Contribution rate of j-th principal component of all target criterion layers of wetland plants to be selected, P n The sum of the contribution rates of all main components of all target criterion layers of the wetland plants to be selected, W j The method comprises the steps that the weight of the j-th main component of all target criterion layers of the wetland plants to be selected is given, j represents the j-th main component of all target criterion layers, and n is the total number of the main components of all target criterion layers of the wetland plants to be selected; u (U) j Membership function value C of j-th principal component of all target criterion layers of wetland plants to be selected jmin Representing the minimum value in the performance score of the j-th principal component of all target criterion layers of each candidate wetland plant, C jmax The maximum value in the expression score of the j-th principal component representing all target criterion layers of each wetland plant to be selected, D being the wetland plant to be selectedIs a comprehensive evaluation value of the restoration function of the new pollutants in the water body.
Optionally, the calculation mode of the comprehensive score value includes a calculation mode of a criterion score value of a comprehensive single criterion layer; when the calculation mode of the criterion scoring value of the comprehensive single criterion layer is used, calculating the comprehensive scoring value of each wetland plant to be selected according to the key evaluation component and the data information by a membership function value method based on principal component analysis, and further comprising:
calculating the contribution rate, the weight value and the performance score of each main component in each target criterion layer of the wetland plant to be selected according to the key evaluation components and the data information belonging to the same criterion layer by using the main component analysis method;
and calculating the criterion scoring value of each wetland plant to be selected on the basis of the contribution rate, the weight value and the performance score by adopting the membership function method, wherein the criterion scoring value of each target criterion layer is used for calculating the comprehensive scoring value of each wetland plant to be selected.
The beneficial effects are that: according to the wetland plant optimization method based on quantitative evaluation of the new pollutant restoration function, through an evaluation system of the wetland plant on the new pollutant restoration function and key evaluation components in the evaluation system, the plants are scored and optimized by combining a membership function value method based on principal component analysis, the technical problems that the basis of the selection of the restored plant materials is undefined and is not systematic in the existing wetland plant optimization method based on the new pollutant restoration are solved, the quantification, the materialization and the predictability of the selection method can be realized, the selection thinking can be popularized to various environmental medium plant restoration technologies, ecological benefits and economic benefits are considered, and the optimization efficiency of the wetland plant for restoring the new pollutant is improved.
Drawings
Fig. 1 is a flowchart of a preferred method for quantitatively evaluating a new contaminant repair function based on wetland plants provided in an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
Example 1
Referring to fig. 1, fig. 1 is a flowchart of a wetland plant optimization method based on quantitative evaluation of new pollutant restoration function according to a first embodiment of the present application, which is applied to an evaluation system of the new pollutant restoration function of a wetland plant to optimize the wetland plant for restoration of new pollutant in a water body; the evaluation system comprises a plurality of criterion layers, wherein each criterion layer comprises a plurality of index layers, and each index layer comprises a plurality of component layers; the index layer is an evaluation base corresponding to the criterion layer, and the component layer is an evaluation base corresponding to the index layer; the wetland plant optimization method based on quantitative evaluation of the new pollutant restoration function comprises the following steps:
step S101, determining at least one criterion layer as a target criterion layer from an evaluation system of wetland plants about new pollutant restoration function, and acquiring various data information of each component layer of a plurality of to-be-selected wetland plants corresponding to the index layer corresponding to the target criterion layer;
step S102, screening out key evaluation components from component layers of a target criterion layer;
step S103, calculating and obtaining comprehensive grading values of the wetland plants to be selected according to the key evaluation components and various data information by a membership function value method based on principal component analysis;
And step S104, selecting the repair plants of the new pollutants of the water body from the wetland plants to be selected according to the comprehensive grading values of the wetland plants to be selected.
According to the wetland plant optimization method based on the quantitative evaluation of the new pollutant restoration function, through an evaluation system of the wetland plant on the new pollutant restoration function and key evaluation components in the evaluation system, the plants are scored and optimized by combining a membership function value method based on principal component analysis, the technical problems that the basis of the selection of the restoration plant materials is undefined and not systematic in the existing wetland plant optimization method based on the new pollutant restoration are solved, the quantification, the materialization and the predictability of the selection method can be realized, the selection thought can be popularized to various environmental medium plant restoration technologies, ecological benefits and economic benefits are considered, and the optimization efficiency of the wetland plant for the restoration of the new pollutant in the water body is improved.
Example two
The method for selecting the repairing plants by adopting the wetland plant optimization method based on quantitative evaluation of the new pollutant repairing function in the wetland park in the area B of A city comprises the following specific steps:
step one, before executing step S101 of the first embodiment, an evaluation system of the wetland plant about a new pollutant repairing function is pre-established, where the evaluation system uses a comprehensive pollutant repairing effect as a target layer, the target layer includes a plurality of criterion layers, each criterion layer includes a plurality of indicator layers, each indicator layer includes a plurality of component layers, where the indicator layers are evaluation bases of the corresponding criterion layers, and the component layers are evaluation bases of the corresponding indicator layers.
The evaluation system of the wetland plants on the repair function of the new pollutants is specifically shown in the following table 1:
TABLE 1 evaluation System of wetland plants for New pollutant remediation function
；
The rightmost element in the table is a judgment standard and is expressed as the repair effect of the corresponding component layer. "lower and better" means that the new contaminant restoration effect of the wetland plant is better as the performance data of the corresponding component layer is lower; "higher and better" means that the new contaminant restoration effect of the wetland plant is better as the performance data of the corresponding component layers is higher.
Step S101 is specifically executed based on step S101 in the first embodiment, where a target criterion layer is determined according to actual needs, for example, the target criterion layer determined in the present embodiment is a pollution reduction performance and an adaptation performance, and according to a nearby principle, a wetland plant to be selected is selected near a wetland park in a city B area (the selected wetland plant includes, but is not limited to, a wetland plant such as canna, arundo donax, papyrus and pinaster, and the present embodiment uses canna, arundo donax, papyrus and pinaster as an example), and various data information (including the plant characteristic data such as root number, root length, etc. of the wetland plant to be selected) about the pollution reduction performance criterion layer and the component layer of the adaptation performance of the wetland plant to be selected is obtained by means of literature review, field monitoring, field sampling, laboratory instrument analysis, etc.
After determining the target criterion layer, each item of data information of the component layer of the target criterion layer can be acquired according to the following proposal:
1) Acquiring various data information of the corresponding component layers according to the target index layer;
when the target criterion layer comprises a pollution reduction efficiency criterion layer, determining at least two target criterion layers of the wetland plants to be selected, including the removal proportion and the removal load, from the index layers corresponding to the pollution reduction efficiency criterion layer, thereby acquiring various data information of the component layers corresponding to the target criterion layers;
when the target criterion layer comprises adaptation performance, determining at least two target index layers of the wetland plants to be selected, including plant growth vigor and photosynthesis, from index layers corresponding to the adaptation performance, so as to acquire various data information of component layers corresponding to the target index layers;
when the target criterion layer comprises a risk evaluation criterion layer, determining at least two target criterion layers of the wetland plants to be selected, including release risk and attenuation potential, from the index layers corresponding to the risk evaluation criterion layer, so as to acquire various data information of the component layers corresponding to the target criterion layers;
when the target criterion layer comprises an economic input criterion layer, determining at least two target criterion layers of the wetland plants to be selected, including the planting type and the planting density, from the index layers corresponding to the economic input criterion layer, thereby acquiring various data information of the component layers corresponding to the target criterion layers.
2) Extracting various data information of a target component layer;
when the target index layer comprises a removal proportion, acquiring at least one target component layer of the wetland plant to be selected, which comprises a single substance removal rate, from the component layers corresponding to the removal proportion, so as to acquire various data information of the target component layer;
when the target index layer comprises a load removal index layer, acquiring at least one target component layer of the wetland plant to be selected, including the total removal amount of single substances, from the component layers corresponding to the load removal index layer;
when the target index layer comprises enrichment capacity, acquiring at least one target component layer of the wetland plant to be selected, including the concentration of root system pollutant enrichment, from the component layer corresponding to the enrichment capacity, so as to acquire various data information of the target component layer;
when the target index layer comprises an enrichment factor index layer, acquiring at least one target component layer of the wetland plant to be selected, including the enrichment coefficient of root system pollutants, from the component layers corresponding to the enrichment factor index layer, thereby acquiring various data information of the target component layer;
when the target index layer comprises a plant growth index layer, acquiring at least one target component layer of the wetland plant to be selected, including biomass, from the component layers corresponding to the plant growth index layer, thereby acquiring various data information of the target component layer;
When the target index layer comprises photosynthesis, acquiring at least one target component layer of the wetland plant to be selected, including the leaf area, from the component layers corresponding to the photosynthesis, so as to acquire various data information of the target component layer;
when the target index layer comprises a transpiration index layer, acquiring at least one target component layer of the wetland plant to be selected, including the leaf area, from the component layers corresponding to the transpiration index layer, thereby acquiring various data information of the target component layer;
when the target index layer comprises a root system removing functional index layer, acquiring at least one target component layer of the wetland plant to be selected, including the new root proportion, from the component layers corresponding to the root system functional index layer, thereby acquiring various data information of the target component layer;
when the target index layer comprises a release risk index layer, acquiring at least one target component layer of the wetland plant to be selected, including the ecological risk level of single substance emission, from the component layers corresponding to the release risk index layer, thereby acquiring various data information of the target component layer;
when the target index layer comprises an attenuation potential index layer, acquiring at least one target component layer of the wetland plant to be selected, including the emission rate of single substance metabolites, from the component layers corresponding to the attenuation potential index layer, thereby acquiring various data information of the target component layer;
When the target index layer comprises a biological effect index layer, acquiring at least one target component layer of the wetland plant to be selected, including the inhibition rate of rhizosphere microorganism biomass, from the component layers corresponding to the biological effect index layer, so as to acquire various data information of the target component layer;
when the target index layer comprises a planting type index layer, acquiring at least one target component layer of the wetland plant to be selected, including the purchase price of a single plant, from the component layers corresponding to the planting type index layer, so as to acquire various data information of the target component layer;
when the target index layer comprises a planting density index layer, acquiring at least one target component layer of the wetland plants to be selected, including the planting quantity of single plants, from the component layers corresponding to the planting density index layer, so as to acquire various data information of the target component layer;
when the target index layer comprises a maintenance management index layer, at least one target component layer of the wetland plant to be selected, including the annual harvesting frequency of the plant, is acquired from the component layers corresponding to the maintenance management index layer, so that various data information of the target component layer is acquired.
Wherein, some component layers have different expression effects aiming at different new pollutant components, each expression effect is one component of the component layer, for example, a single substance removal rate, a root system pollutant concentration, a stem and leaf pollutant concentration, a whole plant pollutant concentration, a root system pollutant enrichment factor, a stem and leaf pollutant enrichment factor, a whole plant pollutant enrichment factor, a single substance emission concentration elevation rate, a single substance concentration and ecological risk reduction rate, a rhizosphere microorganism biomass and a diversity inhibition rate all belong to the component layers; the other component layers have only one effect and thus only one component.
Step S102 is specifically executed based on the step S102 of the first embodiment, and is specifically executed by performing Person correlation analysis on each component layer of each target criterion layer by using function drawing software (such as origin 2022), performing multidimensional difference statistical analysis and cluster analysis on each component layer of each target criterion layer by using statistical analysis software (such as SPSS 26.0), performing calculation by using a correlation decision coefficient greater than 0.60, a difference significance level greater than 0.05 and a cluster similarity greater than 60% as calculation criteria based on a correlation decision coefficient, a difference significance level and a cluster similarity, performing automatic calculation by using an algorithm carried by the software, obtaining a correlation ranking of each component layer of each target criterion layer by calculating, extracting a component layer of a preset percentage (such as the first 60% but not limited thereto) before ordering in the correlation ranking, and obtaining a key evaluation component of the target criterion layer, wherein the key evaluation component can basically reflect the repairing function of the corresponding target criterion layer. The key evaluation components of the embodiment are obtained through analysis of function drawing software and statistical analysis software and comprise single-class substance concentration removal rate and plant growth vigor component layers. The calculation criteria of the correlation determination coefficient, the difference significance level and the clustering similarity can be adjusted according to actual needs.
The correlation determination coefficient and the difference significance level are in a negative correlation relationship, namely the higher the correlation determination coefficient is, the lower the difference significance level is, and the lower the correlation determination coefficient is, the higher the difference significance level is; the correlation decision coefficient and the cluster similarity are positive correlation, the cluster similarity represents the tightness degree among the principal components, and the higher the cluster similarity is, the higher the ranking of the correlation rank is.
Specifically, the key evaluation components are determined to be the main evaluation components corresponding to the target criterion layer, so that when the new pollutant repairing plants are optimized later, the new pollutant repairing functions of the corresponding target layer and the criterion layer of the wetland plants can be predicted through the main evaluation components only by acquiring data of the wetland plants corresponding to the main evaluation components, and the load is reduced when the wetland plants for water new pollutant repairing are optimized later, thereby being beneficial to improving the optimizing efficiency of the wetland plants for water new pollutant repairing.
Step four is performed based on step S103 of the first embodiment, specifically, the step four is performed by using statistical analysis software (such as software of SPSS 26.0, etc.), and a principal component analysis method is applied to calculate and obtain, for each principal component in the target criterion layer, a contribution rate, a component matrix and a factor score coefficient of each principal component (the principal component may be formed by all target criterion layers (or the same target criterion layer, when the calculation mode of the comprehensive score value of the combined criterion layer is used, all target criterion layers, and the same target criterion layer when the calculation mode of the comprehensive score value of the combined criterion layer is used) of the wetland plant to be selected, and the specific component of the principal component may be determined by using a component matrix, where the specific determination process is the prior art, and is not described in detail herein).
The contribution rates are ordered according to the order from high to low, the contribution rates are sequentially added in order until the sum of the contribution rates exceeds a preset extraction percentage (i.e., the cumulative contribution rate exceeds the preset extraction percentage, such as 80%, but not limited thereto), the principal component corresponding to the accumulated contribution rate is extracted, and the specific components of the extracted principal component are determined in combination with the component matrix (the component matrix is the degree of influence of any component layer component with respect to all component layer components of the target criterion layer (or the same target criterion layer). In this way, the total number of main components with the cumulative contribution rate greater than 80% in the same index layer in the single-class substance concentration removal rate in this embodiment is determined to be 4, which specifically includes: a first main component reflecting the absorption amounts of triazolone, bichloride, ibuprofen, gemfibrozil, erythromycin dehydrate, roxithromycin and sulfadimidine, a second main component reflecting the absorption amounts of dimethoate, butachlor, lomefloxacin, ofloxacin, tetracycline and erythromycin, a third main component reflecting the absorption amounts of malathion, naproxen, ketoprofen, oxytetracycline and tetracycline, and a fourth main component reflecting the absorption amounts of atrazine, acetochlor and ofloxacin; determining two main components with the accumulated contribution rate of each component layer of plant growth vigor being more than 90% in the same index layer, specifically comprising: a fifth main component reflecting root length, number of leaves, plant height, stem weight, leaf weight, total weight of the subsurface and total weight of the ground, and a sixth main component reflecting root number, root weight and total weight of the subsurface.
The weight calculation formula is specifically as follows:
；
wherein P is j Contribution rate of jth principal component of all target criterion layers (or the same target criterion layer) of the wetland plants to be selected, P n The sum of the contribution rates of all main components of all target criterion layers of the wetland plants to be selected, W j The j-th principal component of all target criterion layers of the wetland plants to be selected is weighted, j represents the j-th principal component of all target criterion layers, and n is the total number of the principal components of all target criterion layers of the wetland plants to be selected.
The calculation formula of the performance score is specifically as follows:
；
wherein C is j A performance score, A, representing the j-th principal component of all target criterion layers of the wetland plants to be selected ji Factor scoring system for the j-th principal component corresponding to the i-th component in the component matrix of the principal componentNumber, X ji The j-th main component corresponds to the i-th component in the component matrix of the main component, i is the i-th component, and m is the total number of components in the component matrix of the j-th main component of the wetland plant to be selected (the sum of the numbers of all components affecting the main component in all target criterion layers is m).
In sum, the contribution rate, the ingredient matrix and the factor score coefficient of each main ingredient in the wetland plants to be selected (namely canna, arundo donax, paphiopedilum and Graptopetalum) are calculated.
When the sum of the contribution rates of the plurality of principal components exceeds the preset extraction percentage, it is described that the plurality of principal components can basically reflect the repair function (repair evaluation) of the corresponding target criterion layer.
The index score of the index layer corresponding to each main component in canna, arundo donax, paphiopedilum and Graptopetalum is calculated according to the contribution rate, the component matrix and the factor score coefficient of the main component of the target criterion layer corresponding to each key evaluation component in canna, arundo donax, paphiopedilum and Graptopetalum, and the calculation formula of the index score is specifically as follows:
；
；
wherein U is j Membership function value C of the j-th principal component of all target criterion layers (or the same target criterion layer) of the wetland plants to be selected j A performance score, C, representing the j-th principal component of all target criterion layers of the selected wetland plants jmin Representing the minimum value in the performance score of the j-th principal component of all target criterion layers of each candidate wetland plant, C jmax And D represents the maximum value in the expression scores of the j-th main component of all target criterion layers of each wetland plant to be selected, and D is the comprehensive evaluation value of the water body new pollutant restoration function of the wetland plant to be selected.
In other embodiments, when the method of calculating the criterion score value of the integrated single criterion layer is used, the "same target criterion layer" may be used instead of the "all target criterion layers" mentioned in the fourth step, and the criterion score value D of each target criterion layer is finally calculated. Calculating a weighted sum of criterion scoring values of each target criterion layer of each wetland plant to be selected (wherein the weighted sum of the weights can be set according to actual needs, for example, the weights are set according to the proportion of the number of key evaluation components in each target criterion layer to the sum of the numbers of key evaluation components in all target criterion layers), and taking the weighted sum as the comprehensive scoring value of the wetland plant to be selected; or, calculating the sum of the criterion scoring values of the target criterion layers of each wetland plant to be selected as the comprehensive scoring value of the wetland plant to be selected.
To sum up, the combined score values of canna, arundo donax, paphiopedilum and Graptopetalum with respect to all target criterion layers are calculated.
Step five is executed based on step S104 of the first embodiment, specifically, the comprehensive score values of the wetland plants to be selected and the corresponding wetland plants to be selected are ranked according to the order from high to low, a comprehensive score value ranking is obtained, and the repair plants of the pollutants to be repaired are selected according to the comprehensive score values or the comprehensive score value ranking, for example, the comprehensive score value ranking is that the arundo donax, canna, papyrus and pinaster indicate that the new pollutants of the arundo donax have the strongest repair function, so that the arundo donax is selected as the repair plant.
Example III
The wetland plant optimization method based on quantitative evaluation of the new pollutant restoration function in the third embodiment of the present application is applied to the evaluation system of the wetland plant related to the new pollutant restoration function in the first embodiment so as to optimize the wetland plant for restoring the new pollutant in the water body, and the specific process includes:
step S301, obtaining a plant species of a wetland to be selected, including: then the wetland plants such as flowers, paphiopedilum, rhizoma acori graminei, arundo donax, canna, graptopetalum and the like;
Obtaining new contaminant species, including: 20 kinds of substances such as pesticides, acidic drugs and antibiotics.
Step S302, based on literature report and measured data, acquiring data corresponding to a pollution reduction efficacy criterion layer and adaptive performance: acquiring data corresponding to the removal proportion and enrichment capacity of the wetland plants to be selected in the pollution reduction efficiency criterion layer on new pollutants, namely acquiring a plurality of component data corresponding to the removal proportion, including the removal rate of single-class substance concentration, and a plurality of component data corresponding to the enrichment capacity, including the enrichment concentration of single-class substances in the stems and leaves; and acquiring a plurality of component data of plant growth vigor and photosynthesis corresponding component layers of the wetland plants to be selected in the adaptability criterion layer, wherein the component data comprise biomass, root weight, plant height, root length, root number, leaf number and the like.
Step S303, inputting component data (biomass, root weight, plant height, root length, root number and leaf number) of two index layers of plant growth vigor and photosynthesis in the six kinds of wetland plants to be selected into SPSS22 statistical software, and carrying out standardized processing on all the data.
And step S304, judging and screening key evaluation components by utilizing correlation analysis of SPSS22 statistical software, and then carrying out comprehensive evaluation value calculation by utilizing a principal component analysis dimension reduction technology and a membership function value method.
1) The plant optimization evaluation method taking plant adaptability as a criterion layer (taking plant growth vigor and photosynthesis as key evaluation indexes) comprises the following calculation process:
firstly, carrying out correlation analysis on the related indexes of the adaptability of the wetland plants to be selected, and primarily judging the components with the most influence (the difference significance level is less than 0.05), such as overground total weight, leaf number, leaf weight, root length, plant height, stem weight, underground total weight, root weight and root number, wherein the specific correlations among the components of the corresponding component layers of the adaptability of the wetland plants to be selected are shown in table 2:
TABLE 2 results of the adaptation properties of the wetland plants to be selected on the correlation between the components of the component layers
；
It should be noted that ". Times" is on the order of 0.05, indicating that these two components are significantly related; the "×" is on the scale of 0.01, indicating that the two components are very significantly related.
Principal component analysis is performed on components with significant influence correlation of the primary screening, principal component analysis results of adaptation performance of the wetland plants to be selected in table 3 are available, feature values of principal components 1, 2 and 3 are larger than 0.7, variance accumulation contribution rate is larger than 94%, the 3 principal components are extracted, a principal component matrix (table 4) is constructed, a matrix equation is established, and the three principal components are listed according to the component matrix, and the expression score matrix equation:
PCA 1 =0.150*X 3 +0.160*X 4 +0.160*X 8 +0.159*X 9 ；
PCA 2 =0.345*X 1 +0.214*X 2 +0.615*X 5 ；
PCA 3 =0.578*X 6 +0.259*X 7 ；
Wherein PCA 1 、PCA 2 、PCA 3 The performance scores, X, for three principal components with variance cumulative contribution greater than 94% 1 、X 2 、X 3 、X 4 、X 5 、X 6 、X 7 、X 8 、X 9 The number, length, number of leaves, plant height, root weight, stem weight, leaf weight, total weight under the ground and total weight above the ground are normalized.
TABLE 3 principal component analysis results of adaptation properties of wetland plants to be selected
；
TABLE 4 principal component matrix of adaptation properties of the wetland plants to be selected
；
The numerical values of the performance effects of the standardized treatment of the plant growth vigor and photosynthesis component data are shown in table 5:
TABLE 5 expression efficacy values of data of plant growth vigor and photosynthesis components for adaptive performance
；
Substituting the scores of the main components of the six kinds of wetland plants to be selected into a membership function formula to obtain the weight value and membership function value of each main component, and finally calculating the evaluation score value of the adaptation performance criterion layer of the wetland plants to be selected, wherein the evaluation results (membership function value and comprehensive score value) of the adaptation performance criterion layer of the wetland plants to be selected are shown in table 6, and U is as follows A1 、U A2 、U A3 The result shows that the comprehensive adaptability of the grassleaf sweelflag rhizome, the Graptopetalum album and the arundo donax is optimal, and the grassleaf sweelflag rhizome can be used as the preferable plant for evaluating the adaptability criterion.
TABLE 6 membership function value and comprehensive score value of adaptive Performance criterion layer of wetland plants to be selected
；
2) The calculation process of the plant optimization method taking the pollution reduction efficiency of plants as a target criterion layer is as follows:
taking a component layer corresponding to the enrichment capacity and the removal proportion of the wetland plants to be selected as a key evaluation component:
a) First, a correlation analysis is performed between 20 new pollutant absorption amounts (the new pollutant absorption amount can represent a new pollutant repairing function of enrichment capability of the wetland plant to be selected), and the preliminary screening significantly influences related components (the difference significant level is < 0.05), wherein the new pollutants comprise 20 new pollutants such as diclofenac, dimethoate, erythromycin, sulfadimidine, roxithromycin, erythromycin dehydratum, ibuprofen, tetracycline, gemfibrozil, oxytetracycline, lomefloxacin, butachlor, triazolone, naproxen, ofloxacin, ketoprofen, acetochlor and atrazine.
Principal component analysis is performed on components with significant influence correlation of the primary screening, principal component analysis results of enrichment capacity of the wetland plants to be selected in table 7 are obtained, characteristic values of principal components 1, 2, 3 and 4 are larger than 1, variance accumulation contribution rate is larger than 95%, 4 principal components are extracted, a principal component matrix (table 8) is constructed, a matrix equation is established, and the three principal components are listed according to the component matrix, wherein the expression score matrix equation is:
PCB 1 =0.137*Y 6 +0.137*Y 7 +0.124*Y 9 +0.117*Y 11 +0.133*Y 16 +0.138*
Y 17 +0.138*Y 19 ；
PCB 2 =0.125*Y 1 +0.213*Y 5 +0.213*Y 12 +0.12*Y 13 +0.042*Y 15 +0.184*
Y 18 ；
PCB 3 =0.319*Y 2 -0.284*Y 8 -0.241*Y 10 +0.266*Y 14 +0.22*Y 15 ；
PCB 4 =0.397*Y 3 +0.385*Y 4 -0.302*Y 15 ；
Wherein the PCB 1 、PCB 2 、PCB 3 、PCB 4 A performance score, X, of 4 principal components with a variance cumulative contribution greater than 95% 1 ~X 19 The values of the performance effects of the wetland plants to be selected after standardized treatment with respect to the absorption amounts of dimethoate, malathion, atrazine, acetochlor, butachlor, triazolone, diclofenac, naproxen, ibuprofen, ketoprofen, gemfibrozil, lomefloxacin, ofloxacin, oxytetracycline, tetracycline, anhydroerythromycin, roxithromycin, erythromycin and sulfadimidine are respectively shown.
TABLE 7 results of principal component analysis of enrichment Capacity of wetland plants to be selected
；
TABLE 8 principal component matrix of enrichment Capacity of wetland plants to be selected
；
The numerical values of the performance effects after the standardized treatment of the data of each component of the adaptive performance are specifically shown in table 9:
TABLE 9 Performance Effect values for the data of the adaptive Performance Components
；
Substituting the scores of the main components of the six wetland plants to be selected into a membership function formula to obtain the weight value and membership function value of each main component, and finally calculating the comprehensive evaluation value of the pollution reduction efficiency index (enrichment capacity) of the wetland plants to be selected, wherein the evaluation results (membership function value and comprehensive evaluation value) of the wetland plants to be selected when the enrichment capacity index is used as the key evaluation index of the pollution reduction efficiency are shown in table 10, wherein U is as follows B1 、U B2 、U B3 The results of the membership function values of three main components with the variance accumulation contribution rate of more than 94% of the wetland plants to be selected show that the comprehensive evaluation values of the pollution reduction efficiency of canna, arundo donax and rhizoma acori graminei are highest, and the method can be used as the preferable plants for evaluating the pollution reduction efficiency criterion layer.
TABLE 10 membership function value and comprehensive score value of enrichment ability of wetland plants to be selected
；
B) Taking the removal proportion of the wetland plants to be selected as a key evaluation index of a pollution reduction efficacy criterion layer, performing correlation analysis, sequencing and principal component analysis aiming at components with obvious influence correlation of the primary screening, obtaining principal component analysis results of the removal proportion of the wetland plants to be selected in the table 11, wherein the principal component 1, 2 and 3 eigenvalues are more than 0.5, the variance accumulation contribution rate is more than 95%, extracting 3 principal components, constructing a principal component matrix, and listing the expression score matrix equation of the three principal components according to the component matrix:
PCC 1 =0.101*Z 4 +0.104*Z 5 +0.102*Z 9 ；
PCC 2 =0.350*Z 6 +0.329*Z 7 +0.015*Z 10 +0.244*Z 11 ；
PCC 3 =0.286*Z 1 +0.107*Z 2 +0.217*Z 3 +0.668*Z 8 +0.357*Z 12 +0.697*Z 13 ；
wherein PCC 1 、PCC 2 、PCC 3 The performance scores of 3 principal components with variance accumulation contribution rate greater than 95%, Z 1 ~Z 13 The values of the performance effects of the wetland plants to be selected with respect to the removal ratio of each new pollutant are respectively given.
TABLE 11 principal component analysis results of removal ratio of wetland plants to be selected
；
Substituting the scores of all main components of six wetland plants to be selected into a membership function formula to obtain the weight value and membership function value of each main component, finally calculating the comprehensive evaluation value of the pollution reduction efficiency index (removal proportion) of the wetland plants to be selected, wherein the evaluation results (membership function value and comprehensive evaluation value) of the wetland plants to be selected when the removal proportion is used as the key evaluation index of the pollution reduction efficiency are shown in table 12, and the result shows that the comprehensive evaluation value of the pollution reduction efficiency of Graptopetalum album, lesion flowers and canna/arundo donax is the highest, and can be used as the preferred plant for evaluating the pollution reduction efficiency criterion layer.
TABLE 12 membership function value and comprehensive score value of removal ratio of wetland plants to be selected
；
In step S305, the plant preference evaluation is performed on a single criterion layer, and the following comprehensive evaluation is developed by combining the soil reduction efficiency and the adaptive performance composite criterion layer of the wetland plant to be selected, and the calculation process of the plant preference evaluation method using the plant soil reduction efficiency+adaptive performance composite criterion layer is as follows:
the components corresponding to the adaptation performance of the wetland plants to be selected, including plant growth vigor, photosynthesis, transpiration and indexes corresponding to the pollution reduction efficiency, including removal proportion and enrichment capacity, are utilized to carry out correlation analysis as above, main component analysis is carried out aiming at the components (correlation significance level < 0.05) with significance influence correlation of the primary screening, the main components are obtained from a table 13, the characteristic values of main components 1, 2 and 3 are greater than 4.5, the variance accumulation contribution rate is greater than 83%, 3 main components are extracted, and a main component matrix is constructed.
TABLE 13 principal component analysis results of adaptation property, removal ratio and enrichment ability of wetland plants to be selected
；
A1-A41 are standardized data of plant adaptability, enrichment capacity and removal proportion of the wetland to be selected.
Writing a principal component equation according to the component matrix result:
writing a scoring matrix equation of the principal component according to the component matrix result:
1) The score matrix equation for adaptation performance:
PC 1 =0.04*A 1 +0.041*A 2 +0.048*A 3 +0.049*A 4 +0.037*A 6 +0.051*A 7 +0.048*A 8 +0.053*A 9 ；
2) Removing the proportional score matrix equation:
PC 2 =0.031*A 10 +0.031*A 11 +0.03*A 12 +0.044*A 13 +0.035*A 14 +0.028*A 18 ；
3) Enrichment ability score matrix equation:
PC 3 =0.081*A 23 +0.083*A 28 +0.006*A 29 +0.084*A 30 +0.008*A 31 +0.081*A 32 ；
wherein, PC 1 、PC 2 、PC 3 Performance scores for fitness, removal rate, and enrichment capacity, respectively, A 1 、A 2 、A 3 、A 4 、A 6 、A 7 、A 8 、A 9 The numerical value of the performance effect of the components corresponding to the plant adaptation performance of the wetland to be selected after standardized treatment is A 10 、A 11 、A 12 、A 13 、A 14 、A 18 The numerical value of the performance effect of the components corresponding to the enrichment capacity of the wetland plants to be selected after standardized treatment is A 23 、A 28 、A 29 、A 30 、A 31 、A 32 And (5) the numerical value of the performance effect of the components corresponding to the removal proportion of the wetland plants to be selected after the standardized treatment.
The plant adaptability, enrichment capacity and removal proportion components of the wetland to be selected with obvious correlation are included into a membership function, and the comprehensive benefit of the wetland to be selected for restoring new pollutants in the water body is inspected. The membership function value and the comprehensive evaluation value of the plant composite index of the wetland to be selected are shown in Table 14, wherein U is 1 、U 2 、U 3 The values of the adaptive performance, the removal ratio and the enrichment ability were the highest, and the values of the comprehensive evaluation of the flowers, arundo donax and canna were 0.554, 0.530 and 0.517, respectively, as can be seen from the evaluation results (membership function value and comprehensive evaluation value) of the comprehensive pollution reduction performance and the adaptive performance of table 14.
For plants repaired by new pollutants in the water body, the evaluation score of the pollution reduction efficiency criterion layer can be referred, or other criterion layers including the pollution reduction efficiency can be selected according to the actual requirements and conditions to obtain the comprehensive evaluation value.
TABLE 14 membership function value and comprehensive evaluation value of adaptability, removal proportion and enrichment ability of wetland plants to be selected
；
Step S306, the new pollutant repairing function of the wetland plant can be predicted by one-measurement multi-evaluation of each criterion layer, each index layer and each component layer of the evaluation system of the plant on the new pollutant repairing function of the wetland plant:
1) Through one-measurement multi-evaluation of the adaptation performance, the new pollutant restoration function of the wetland plant corresponding to the adaptation performance can be predicted through the corresponding key evaluation component of the adaptation performance. Based on the relevance among the physiological index values in the process of phytoremediation of the new pollutants, the physiological index values are ordered in descending order according to a significant relevance relation (the significant relevance relation is less than 0.05), and one measurement index is selected as the total weight of the ground, the leaf number and the length of She Chonggen according to the result, and the adaptability of the wetland plants to the restoration of the new pollutants can be predicted through the key evaluation components.
2) Through one-measurement multi-evaluation of the enrichment capacity, the new pollutant restoration function of the wetland plant corresponding to the enrichment capacity can be predicted through the corresponding key evaluation component of the enrichment capacity. According to the sorting significant correlation (the significant correlation is < 0.05) and the correlation determination coefficient (the correlation determination coefficient is > 0.60), the new pollutant absorption amount of the plants is sorted in descending order, a test substance is selected as a result, the first-level multi-evaluation substance is naproxen, ibuprofen, gefitinib, ofloxacin, erythromycin dehydrate, roxithromycin, erythromycin, sulfadimidine, acetochlor and dimethoate, the second-level multi-evaluation substance is butachlor, lomefloxacin, tetracycline, atrazine and triazolone, and the enrichment capacity of the wetland plants on the new pollutants can be predicted through the enrichment types and enrichment concentration values of the plants on the new pollutants.
3) Through one-measurement multi-evaluation of the removal proportion, the new pollutant restoration function of the wetland plants corresponding to the removal proportion can be predicted through the corresponding key evaluation components of the removal proportion. Based on the correlation between the removal proportion of the wetland plants corresponding to the new pollutants, the removal proportion is sorted in descending order according to the sorting significant correlation (the significant correlation is < 0.05) and the correlation determination coefficient (the correlation determination coefficient is > 0.60), and the result is that one measured substance is atrazine, the first-level multi-evaluation substance is diclofenac, erythromycin dehydrate, roxithromycin, sulfamethazine, sulfadiazine and sulfapyridine, the second-level multi-evaluation substance is naproxen, ibuprofen, gefitinib, ketoprofen and sulfamethoxazole, and the removal proportion of the wetland plants to the new pollutants can be predicted through the removal types and the removal proportion values of the plants to the new pollutants.
4) Through one-measurement multi-evaluation of the physiological indexes of the enrichment capacity, the enrichment capacity of the wetland plants to the new pollutants such as pesticide (acetochlor, dimethoate), antibiotics (erythromycin, lomefloxacin), acid drugs (gemfibrozil, diclofenac) and the like can be predicted through the key evaluation components corresponding to the enrichment capacity and the adaptation performance, the correlation between the absorption capacity of the new pollutants of the wetland plants and the physiological index values is predicted, the prediction range can be expanded through the combination of clustering similarity, and the comprehensive functions of the enrichment capacity of the wetland plants to the new pollutants and the adaptation performance can be predicted through the physiological index values and the absorption capacity of the new pollutants.
5) Through one-measurement multi-evaluation of the physiological indexes of the removal proportion, the new pollutant restoration function of the wetland plants corresponding to the removal proportion and the physiological indexes can be predicted through the corresponding key evaluation components of the removal proportion and the adaptation performance, the relevance of the removal proportion and the physiological indexes is ordered in descending order according to the ordered obvious relevance relation (the obvious relevance relation < 0.05) and the relevance determination coefficient (the relevance determination coefficient > 0.60), the one-measurement indexes are selected as root numbers, leaf weights and total weight on the ground, the enrichment capacity of the plants on pesticides (atrazine, dimethoate, acetochlor, butachlor, triazolone), antibiotics (erythromycin dehydrat, roxithromycin, sulfamethazine, sulfadimethyrimidine), acidic drugs (diclofenac) and the like can be predicted through direct measurement of the one-measurement indexes, and the comprehensive function of the removal proportion of the new pollutants corresponding to the wetland plants can be predicted through the removal rate of the physiological indexes and the new pollutants by combining the clustering similarity.
In summary, the representative components and the representative indexes which can be obtained in a simple manner are selected, so that other component information of the same index of the wetland plant or other index information of the same standard layer can be predicted, and the new pollutant repairing function of the index layer and the standard layer corresponding to the wetland plant can be predicted.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (10)
1. A wetland plant optimization method based on quantitative evaluation of new pollutant restoration function, which is characterized by being applied to an evaluation system of the new pollutant restoration function of wetland plants so as to optimize the wetland plants for water new pollutant restoration; the evaluation system takes the comprehensive benefit of the new pollutant phytoremediation technology of the water body as a target layer; the evaluation system comprises a plurality of criterion layers, wherein each criterion layer comprises a plurality of index layers, and each index layer comprises a plurality of component layers; the index layer is an evaluation base corresponding to the criterion layer, and the component layer is an evaluation base corresponding to the index layer;
the wetland plant optimization method based on quantitative evaluation of the new pollutant restoration function comprises the following steps:
determining at least one criterion layer as a target criterion layer from the evaluation system, and acquiring various data information of various component layers of a plurality of wetland plants to be selected, which correspond to the index layers corresponding to the target criterion layer;
screening key evaluation components from the component layers of the target criterion layer;
calculating the comprehensive grading value of each wetland plant to be selected according to the key evaluation component and the data information by a membership function value method based on principal component analysis;
And selecting the repair plants of the new pollutants of the water body from the wetland plants to be selected according to the comprehensive grading values of the wetland plants to be selected.
2. The wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 1, wherein the guideline layer of the evaluation system comprises pollution abatement efficacy, adaptation performance, risk assessment, and economic investment.
3. The wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 2, wherein the index layer corresponding to the abatement efficacy comprises removal ratio, removal load, enrichment capacity, and enrichment factor; the index layer corresponding to the adaptation performance comprises transpiration, plant growth vigor, photosynthesis and root system functions; the index layer corresponding to the risk assessment comprises release risk, attenuation potential and biological effect; the index layer corresponding to the economic investment comprises planting types, planting densities and maintenance management.
4. The wetland plant optimization method based on quantitative evaluation of new contaminant restoration function according to claim 3, wherein the component layers corresponding to the removal ratio include a single substance concentration removal rate, and a total substance concentration removal rate; the component layers corresponding to the removal load comprise a single substance removal total amount, a single substance removal total amount and a total substance removal total amount; the component layer corresponding to the enrichment capacity comprises root system pollutant enrichment concentration, stem and leaf pollutant enrichment concentration and whole plant pollutant enrichment concentration; the component layers corresponding to the enrichment factors comprise root system pollutant enrichment coefficients, stem and leaf pollutant enrichment coefficients and whole plant pollutant enrichment coefficients; the component layers corresponding to the transpiration comprise leaf area, plant coverage area, transpiration rate and air hole conductivity; the component layers corresponding to the plant growth vigor comprise biomass, plant height, root length and root number; the component layers corresponding to photosynthesis comprise leaf area, leaf number, photosynthesis rate and electron transfer efficiency; the component layers corresponding to the root system function comprise new root proportion, root system activity, root system porosity and root system oxygen excretion; the component layers corresponding to the release risks comprise an ecological risk level of single substance emission, an ecological risk level of single substance emission and an ecological risk level of total substance emission; the component layers corresponding to the attenuation potential comprise the emission rate of single substance metabolites, the emission rate of single substance metabolites and the emission rate of total substance metabolites; the component layers corresponding to the biological effect comprise an inhibition rate of rhizosphere microorganism biomass, an inhibition rate of rhizosphere microorganism activity and an inhibition rate of rhizosphere microorganism diversity; the component layers corresponding to the planting types comprise purchase prices of single plants and purchase prices of combined plants; the component layers corresponding to the planting density comprise the planting quantity of single plants and the planting quantity of combined plants; the corresponding component layers of maintenance management comprise the frequency of harvesting the plants in year, the frequency of changing seeds in year and the frequency of reseeding the plants in year.
5. The wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 2, wherein determining at least one of the criterion layers from the evaluation system as a target criterion layer comprises:
at least one of the criterion layers including abatement efficacy is extracted from the evaluation system as the target criterion layer.
6. A wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 3, wherein screening key evaluation components from component layers of the target criterion layer comprises:
carrying out relevance ranking on each component layer of the target criterion layer by using a Pelson correlation algorithm, a multidimensional difference statistical method and a clustering analysis method to obtain relevance ranking; the relevance ranking is a descending ranking;
and extracting a component layer with a preset percentage before sequencing in the relevance ranking to obtain the key evaluation component.
7. The wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 1, further comprising, after screening key evaluation components from the component layers of the target criterion layer:
And determining the key evaluation component as a main evaluation component corresponding to the target criterion layer so as to predict the new pollutant restoration function of the water body of the corresponding criterion layer and the criterion layer through the main evaluation component.
8. The wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 1, wherein the calculation means of the comprehensive score value comprises calculation means of a comprehensive score value of a combination criterion layer; when the calculation mode of the comprehensive grading value of the combined criterion layer is used, calculating the comprehensive grading value of each wetland plant to be selected according to the key evaluation component and the data information by using a membership function value method based on principal component analysis, wherein the calculation method comprises the following steps:
calculating contribution rates, weight values and performance scores of the wetland plants to be selected on all main components in the target criterion layer according to all data information of the key evaluation components by a main component analysis method;
and calculating the comprehensive grading value of each wetland plant to be selected by adopting a membership function method based on the contribution rate, the weight value and the performance score.
9. The wetland plant optimization method based on quantitative evaluation of new contaminant repair function according to claim 8, wherein the calculation formula of the comprehensive score value is specifically:
；
；
；
；
wherein C is j A performance score, A, representing the j-th principal component of all target criterion layers of the wetland plants to be selected ji A factor score coefficient, X, for the jth principal component corresponding to the ith component in the component matrix of the principal component ji The expression effect value of the jth main component corresponding to the ith component in the component matrix of the main component is represented by i, i is the ith component, and m is the total quantity of components in the component matrix of the jth main component of the wetland plant to be selected; p (P) j Contribution rate of j-th principal component of all target criterion layers of wetland plants to be selected, P n The sum of the contribution rates of all main components of all target criterion layers of the wetland plants to be selected, W j The method comprises the steps that the weight of the j-th main component of all target criterion layers of the wetland plants to be selected is given, j represents the j-th main component of all target criterion layers, and n is the total number of the main components of all target criterion layers of the wetland plants to be selected; u (U) j Membership function value C of j-th principal component of all target criterion layers of wetland plants to be selected jmin Representing the minimum value in the performance score of the j-th principal component of all target criterion layers of each candidate wetland plant, C jmax Representative of each wetland plant to be selectedThe maximum value in the expression score of the jth main component of the target criterion layer is D, and the D is the comprehensive evaluation value of the water new pollutant restoration function of the wetland plant to be selected.
10. The method for optimizing wetland plants based on quantitative evaluation of new contaminant repair function according to claim 8, wherein the means for calculating the integrated score value comprises means for calculating a criterion score value of a single criterion layer; when the calculation mode of the criterion scoring value of the comprehensive single criterion layer is used, calculating the comprehensive scoring value of each wetland plant to be selected according to the key evaluation component and the data information by a membership function value method based on principal component analysis, wherein the calculation mode comprises the following steps:
calculating the contribution rate, the weight value and the performance score of each main component in each target criterion layer of the wetland plant to be selected according to the key evaluation components and the data information belonging to the same criterion layer by using the main component analysis method;
and calculating the criterion scoring value of each wetland plant to be selected on the basis of the contribution rate, the weight value and the performance score by adopting the membership function method, wherein the criterion scoring value of each target criterion layer is used for calculating the comprehensive scoring value of each wetland plant to be selected.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410019151.5A CN117709600A (en) | 2024-01-05 | 2024-01-05 | Wetland plant optimization method based on quantitative evaluation of new pollutant restoration function |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410019151.5A CN117709600A (en) | 2024-01-05 | 2024-01-05 | Wetland plant optimization method based on quantitative evaluation of new pollutant restoration function |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117709600A true CN117709600A (en) | 2024-03-15 |
Family
ID=90144470
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410019151.5A Pending CN117709600A (en) | 2024-01-05 | 2024-01-05 | Wetland plant optimization method based on quantitative evaluation of new pollutant restoration function |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117709600A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109377041A (en) * | 2018-10-16 | 2019-02-22 | 河海大学 | A kind of two-phase evaluation method about shipping business |
| CN110689244A (en) * | 2019-09-17 | 2020-01-14 | 长沙凯天工研院环保服务有限公司 | Screening method for ecological restoration plants of abandoned mine |
| CN115907518A (en) * | 2022-11-03 | 2023-04-04 | 桂林理工大学 | Method for selecting land plants for ecological restoration of mines |
| CN116183843A (en) * | 2022-12-29 | 2023-05-30 | 河北农业大学 | Tomato fruit taste evaluation method |
| CN116821628A (en) * | 2023-07-19 | 2023-09-29 | 长江水利委员会长江科学院 | Multi-factor coupled fishway effect indirect evaluation and problem diagnosis method |
| CN116910438A (en) * | 2023-05-11 | 2023-10-20 | 重庆市农业科学院 | Method for evaluating cadmium resistance of sweet sorghum and method for comprehensively evaluating cadmium pollution restoration capability of sweet sorghum |
-
2024
- 2024-01-05 CN CN202410019151.5A patent/CN117709600A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109377041A (en) * | 2018-10-16 | 2019-02-22 | 河海大学 | A kind of two-phase evaluation method about shipping business |
| CN110689244A (en) * | 2019-09-17 | 2020-01-14 | 长沙凯天工研院环保服务有限公司 | Screening method for ecological restoration plants of abandoned mine |
| CN115907518A (en) * | 2022-11-03 | 2023-04-04 | 桂林理工大学 | Method for selecting land plants for ecological restoration of mines |
| CN116183843A (en) * | 2022-12-29 | 2023-05-30 | 河北农业大学 | Tomato fruit taste evaluation method |
| CN116910438A (en) * | 2023-05-11 | 2023-10-20 | 重庆市农业科学院 | Method for evaluating cadmium resistance of sweet sorghum and method for comprehensively evaluating cadmium pollution restoration capability of sweet sorghum |
| CN116821628A (en) * | 2023-07-19 | 2023-09-29 | 长江水利委员会长江科学院 | Multi-factor coupled fishway effect indirect evaluation and problem diagnosis method |
Non-Patent Citations (1)
| Title |
|---|
| 袁学良 等: "《煤炭行业 循环经济发展理论与应用》", 31 December 2010, 济南：山东大学出版社, pages: 137 - 139 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107561242B (en) | Evaluation method for quality of soil of compound contaminated farmland | |
| Roy et al. | Stream macroinvertebrate response to catchment urbanisation (Georgia, USA) | |
| Taft et al. | Floristic quality assessment for vegetation in Illinois, a method for assessing vegetation integrity | |
| Mack | Developing a wetland IBI with statewide application after multiple testing iterations | |
| Zahedifar | Assessing alteration of soil quality, degradation, and resistance indices under different land uses through network and factor analysis | |
| Wu et al. | Applying a multivariate statistical analysis model to evaluate the water quality of a watershed | |
| Arscott et al. | Role of rarity and taxonomic resolution in a regional and spatial analysis of stream macroinvertebrates | |
| Gani et al. | Assessing the impact of land use and land cover on river water quality using water quality index and remote sensing techniques | |
| Yagow et al. | Use of biological indicators in TMDL assessment and implementation | |
| CN116384748A (en) | Industrial enterprise environmental health risk classification method based on environmental monitoring | |
| Rosenberg et al. | Establishing reference conditions for benthic invertebrate monitoring in the Fraser River catchment, British Columbia, Canada | |
| CN107273669B (en) | The monitoring method of ecological dredging engineering water Effect of Ecological Restoration | |
| Giulivo et al. | Assessing the effects of hydrological and chemical stressors on macroinvertebrate community in an Alpine river: The Adige River as a case study | |
| CN115439022A (en) | Risk assessment method and system for environmental pollution after manure returning to field in farm | |
| CN115271500A (en) | Comprehensive assessment method for remediation effect of heavy metal contaminated farmland soil | |
| Lazaridou-Dimitriadou et al. | Contribution to the ecological quality of Aliakmon river (Macedonia, Greece): a multivariate approach | |
| CN117709600A (en) | Wetland plant optimization method based on quantitative evaluation of new pollutant restoration function | |
| Keleher et al. | Bioassessment of artesian springs in the Bonneville Basin, Utah, USA | |
| Kennedy et al. | Predicting interactions between wetland vegetation and the soil-water and surface-water environment using diversity, abundance and attribute values | |
| Paller et al. | A multimetric index using macroinvertebrate data collected with artificial substrates | |
| Vermonden et al. | The influence of environmental factors and dredging on chironomid larval diversity in urban drainage systems in polders strongly influenced by seepage from large rivers | |
| Davis et al. | Wetland bioassessment manual (macroinvertebrates) | |
| Detenbeck et al. | Comparability of a regional and state survey: effects on fish IBI assessment for West Virginia, USA | |
| CN118243888B (en) | Comprehensive water quality evaluation method and system for coupling biological and non-biological elements | |
| Wassie | Comparative study of macro invertebrates and diatoms as bioindicators of river water quality in Addis Ababa |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |