A Quantitative Prediction Method of Pollution Degree of Offshore Water Based on Pollution Indicator Bacteria

A technology for water pollution and prediction methods, which is applied in general water supply conservation, testing water, measuring devices, etc., and can solve the problems of inaccurate data, inaccurate reflection of microbial composition information, and prediction of water pollution in sea areas.

Active Publication Date: 2021-11-02
NINGBO UNIV
View PDF8 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] (2) In the prior art, the degree of sea water pollution is not predicted by measuring the microbial community, resulting in inaccurate data for predicting the degree of sea water pollution in the prior art;
[0009] (3) In the existing technology, high-throughput sequencing is not used to accurately reflect the microbial composition information, resulting in inaccurate detection of dominant species, rare species and some unknown species in the sea area, that is, inaccurate prediction of pollution indicator bacteria

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A Quantitative Prediction Method of Pollution Degree of Offshore Water Based on Pollution Indicator Bacteria
  • A Quantitative Prediction Method of Pollution Degree of Offshore Water Based on Pollution Indicator Bacteria
  • A Quantitative Prediction Method of Pollution Degree of Offshore Water Based on Pollution Indicator Bacteria

Examples

Experimental program
Comparison scheme
Effect test

Embodiment example

[0086] From August 15th to 28th, 2013, a total of 82 sampling points in 8 areas were selected from the routine monitoring project of the Marine Environmental Monitoring Center, and surface water samples at a depth of 0.5m were collected; The measured values ​​of pollution at sampling points (Table 1); the other part was subjected to high-throughput sequencing after pretreatment such as filtration, DNA extraction, and PCR amplification, to obtain the number of reads of microbial OTUs in each sample, and convert them into relative abundances. A total of 82 samples from different sea areas were used as samples for screening pollution indicator bacteria and establishing a prediction model. The relative abundance of indicator bacteria screened by random forest was used as an independent variable, and a method for quantitatively evaluating the pollution status of sea areas was established. Implementation process reference figure 1 .

[0087] Table 1 The physical and chemical proper...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention belongs to the technical field of quantitatively predicting the degree of pollution of water bodies, and discloses a method for quantitatively predicting the degree of pollution of offshore water bodies based on pollution indicator bacteria groups, which obtains the physical and chemical index data of the water body in the research area to calculate the comprehensive water quality pollution index OPI, and obtains different The microbial community composition information in the sea water samples, using the random forest algorithm to screen out the pollution indicator bacteria that can indicate the pollution status, and then use the relative abundance and weight of each microorganism in the pollution indicator bacteria combination as the independent variable for quantitative prediction Degree of water pollution. The present invention establishes a quantitative determination model of water quality pollution based on random forest algorithm, directly and quickly identifies the severity of water quality deterioration through fewer pollution indicator bacteria, is applicable to water quality evaluation of water bodies in different environments, and provides reliable water quality monitoring and treatment. basis.

Description

technical field [0001] The invention belongs to the technical field of predicting the degree of pollution of water bodies, and in particular relates to a method for quantitatively predicting the degree of pollution of offshore water bodies based on pollution indicator bacteria groups. Background technique [0002] Currently, the closest prior art: [0003] As a collection area, the nearshore sea area receives a large amount of terrestrial sources. The impact of human activities on the nearshore ecological environment is increasing. With the increasing discharge of coastal cities from industry, agriculture and aquaculture, it is finally collected into the ocean through the river system. This has resulted in a natural pollution gradient from the near shore to the far sea, and the coastal environment is threatened by increasingly serious pollution and nutrient salts, which has brought serious harm to the ecological environment. Frequent outbreaks of toxic red tides in recent y...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Patents(China)
IPC IPC(8): G01N33/18
CPCG01N33/1866Y02A20/20
Inventor 熊金波宣丽霞裘琼芬
Owner NINGBO UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products