阅读说明：本技术 一种评价生物滤池反冲洗效果的方法 (Method for evaluating backwashing effect of biological filter ) 是由 柏耀辉 林慧 周洁 王东麟 曲久辉 于 2021-05-06 设计创作，主要内容包括：本发明提供一种评价生物滤池反冲洗效果的方法,涉及饮用水处理领域。该方法包括：分别采集生物滤池反冲洗前以及反冲洗后不同恢复时间的滤料样品；提取滤料样品中微生物的DNA并进行基因测序,获得基因测序数据；对基因测序数据进行处理,计算每个滤料样品中微生物的物种丰度,获得物种丰度数据集；以物种丰度数据集作为响应变量,以是否进行反冲洗处理作为解释变量,以反冲洗后的恢复时间作为协变量,建立主响应曲线PRC模型,提取反冲洗后不同恢复时间的典范系数,作为评价生物滤池反冲洗效果的指标。典范系数能对微生物群落结构的恢复程度进行定量评价,用来在微生物层面上表征反冲洗的效果,具有操作方便快捷、准确度高的优点。(The invention provides a method for evaluating the backwashing effect of a biological filter, and relates to the field of drinking water treatment. The method comprises the following steps: respectively collecting filter material samples with different recovery times before and after backwashing of the biological filter; extracting DNA of microorganisms in the filter material sample and performing gene sequencing to obtain gene sequencing data; processing the gene sequencing data, calculating the species abundance of microorganisms in each filter material sample, and obtaining a species abundance data set; and (3) establishing a main response curve PRC model by taking the species abundance data set as a response variable, taking whether backwashing is carried out or not as an explanation variable and taking the recovery time after backwashing as a covariate, and extracting model coefficients of different recovery times after backwashing as indexes for evaluating the backwashing effect of the biological filter. The model coefficient can quantitatively evaluate the recovery degree of the microbial community structure, is used for representing the backwashing effect on the microbial layer surface, and has the advantages of convenience and rapidness in operation and high accuracy.)

1. A method for evaluating the backwashing effect of a biological filter is characterized by comprising the following steps:

(1) respectively collecting filter material samples with different recovery times before and after backwashing of the biological filter;

(2) extracting DNA of microorganisms in the filter material sample collected in the step (1) and performing gene sequencing to obtain gene sequencing data;

(3) processing the gene sequencing data obtained in the step (2), calculating the species abundance of microorganisms in each filter material sample, and obtaining a species abundance data set;

(4) and (4) taking the species abundance data set obtained in the step (3) as a response variable, taking whether backwashing is carried out or not as an explanation variable, taking the recovery time after backwashing as a covariate, establishing a main response curve PRC model, taking the species abundance of microorganisms in the filter material sample before backwashing as a reference value, extracting the canonical coefficients of different recovery times after backwashing, and taking the canonical coefficients as indexes for evaluating the backwashing effect of the biofilter.

2. The method of evaluating the backwashing effect of the biofilter according to claim 1, further comprising: and (3) constructing a loess local weighted regression fitting curve by taking the recovery time after backwashing as an abscissa and the model coefficient as an ordinate, and evaluating the backwashing effect of the biological filter by using the curve.

3. The method for evaluating the backwashing effect of the biofilter according to claim 1 or 2, wherein in the step (3), microorganisms with significant changes of species abundance before and after backwashing and more than 2 times of species abundance changes are screened out by Wilcox non-parametric test, and the species abundance data set is formed by the abundance of the screened microorganisms.

4. The method for evaluating the backwashing effect of the biofilter according to claim 1 or 2, wherein in the step (4), a PRC model of a main response curve is established by using a software package R vegan.

5. The method for evaluating the backwashing effect of the biofilter according to claim 1 or 2, wherein in the step (2), 16S rRNA V3-V4 segment of DNA of the microorganism is subjected to double-ended sequencing.

6. The method for evaluating the backwashing effect of the biofilter according to claim 5, wherein in the step (2), sequencing is performed by using Illumina NovaSeq 6000 and/or Illumina MiSeq high-throughput sequencer, and at least one of PE250, PE150 and PE300 is selected in a sequencing mode.

7. The method for evaluating the backwashing effect of the biofilter according to claim 5, wherein in the step (3), the method for obtaining the abundance set of species comprises:

removing impurities, primers and joints of the obtained raw reads of the original sequencing data, and filtering low-quality reads to obtain high-quality data clean data;

merging double-end data, removing low-quality reads, denoising and removing redundancy to the clean data in sequence to generate amplicon sequence variation ASVs;

species annotation was performed on ASVs and the species abundance of microorganisms in each filter material sample was calculated.

8. The method for evaluating the backwashing effect of the biofilter according to claim 7, wherein clear data is processed using QIIME2 to generate ASVs, wherein double ended data is merged using QIIME vseearch join-pairs; removing low quality reads according to a quality score phred >4 using q2-quality-filter q-score-join; the tessellation is identified and filtered for denoising using qiime deblur dense-16S.

9. The method of evaluating the effect of a biofilter backwash according to claim 7, wherein species annotation is performed using a previously trained Naive Bayes classifier using q2-feature-classifier of QIIME 2.

10. A method of assessing the effectiveness of biofilter backwash as claimed in claim 9, wherein said classifier is trained using Greengenes 13_8 operating the V4 segment in the sequence of taxon OTUs in 99% clusters.

Technical Field

The invention relates to the technical field of drinking water treatment, in particular to a method for evaluating the backwashing effect of a biological filter.

Background

The drinking water biofilter is widely used for removing various organic matters, inorganic matters and particles, but biomass and non-biological particle amount in a filter layer are continuously accumulated along with the prolonging of the operation time of the biofilter, so that the head loss of the biofilter is obviously increased, thereby causing the blockage of a filter bed and influencing the filtering performance of the biofilter. In addition, the stable and active biofilm thin layer planted in the filter layer is the key for maintaining good filtering performance, and when the biofilm is too thick, the diffusion of nutrients and oxygen to the biofilm is limited. Therefore, the biofilter of the drinking water treatment plant needs to be periodically back-flushed. On one hand, the expansion volume of the filter layer can be increased by the agitation action of air through backwashing water and air scouring, so that pollutants and other non-biological particles trapped on the surface of the filter material are stripped, and on the other hand, the biomass of the biological membrane attached to the filter layer is properly controlled and maintained through backwashing, so that the adsorption or biotransformation capacity of the indigenous microbial community on the pollutants is ensured.

The research shows that the biological membrane in the biological filter can exert the biodegradation function to the maximum extent and keep the high-efficiency filtering performance when not interfered by any interference, so that the community structure of the biological membrane microorganisms of the filtering layer after backwashing is changed, and the effluent quality and the water yield of the biological filter are seriously influenced. However, along with the extension of the retention time, the community structure of the indigenous microorganisms in the filter layer shows a recovery trend, and when the community structure of the biofilm of the filter layer is basically recovered to the level before back flushing, the next filtration period can be started, so that the biological filter tank still has good filtration performance. The backwashing is important for maintaining the functions of the biological filter, and the backwashing strength and frequency of the backwashing are more direct interference to indigenous microbial communities, so the evaluation of the backwashing effect of the biological filter and the evaluation of the recovery time of a filter layer biofilm are particularly important.

Drink at presentThe performance evaluation of the biological filter is mostly based on the biomass, the biological activity, the organic matter removal rate and the like in the biological filter. For example, Chinese non-patent document "study on influence of temperature and backwashing on biological filter for drinking water" uses SOUR method to characterize activity of biofilm, uses lipophos method to measure biomass, evaluates performance of filter after backwashing based on total biomass loss before and after backwashing and total biological activity, and also studies on organic matter, ammonia nitrogen and NO in backwashing₂ ^--effect of N removal rate. For another example, in the non-patent document "the influence of backwashing on the operation efficiency and the microbial community structure of the biological aerated filter" in China, the backwashing effect is evaluated by monitoring the removal of main pollutants in the filter at each stage of the backwashing period and analyzing the influence of backwashing on the microbial community structure and the microbial community density. However, the change of microbial community structure cannot be characterized by quantitative indexes, the backwashing effect cannot be reflected intuitively, and indexes such as biomass, biological activity, organic matter removal rate and the like can be quantified, but the problems of complex detection process, low precision and large error are generally caused. Therefore, the research of a method which can quantify the backwashing effect of the biological filter, is convenient and quick to operate and has high accuracy is an urgent technical problem to be solved in the field.

Disclosure of Invention

Therefore, the technical problem to be solved by the invention is to overcome the defects of complex detection process, low precision and large error of the method for evaluating the backwashing effect of the biological filter tank in the prior art, so that the method for evaluating the backwashing effect of the biological filter tank based on the PRC model of the main response curve is provided.

The invention provides a method for evaluating the backwashing effect of a biological filter, which comprises the following steps:

(1) respectively collecting filter material samples with different recovery times before and after backwashing of the biological filter;

(2) extracting DNA of microorganisms in the filter material sample collected in the step (1) and performing gene sequencing to obtain gene sequencing data;

(3) processing the gene sequencing data obtained in the step (2), calculating the species abundance of microorganisms in each filter material sample, and obtaining a species abundance data set;

(4) and (4) taking the species abundance data set obtained in the step (3) as a response variable, taking whether backwashing is carried out or not as an explanation variable, taking the recovery time after backwashing as a covariate, establishing a main response curve PRC model, taking the species abundance of microorganisms in the filter material sample before backwashing as a reference value, extracting the canonical coefficients of different recovery times after backwashing, and taking the canonical coefficients as indexes for evaluating the backwashing effect of the biofilter.

Further, the method further comprises: and (3) constructing a loess local weighted regression fitting curve by taking the recovery time after backwashing as an abscissa and the model coefficient as an ordinate, and evaluating the backwashing effect of the biological filter by using the curve.

Further, in the step (3), the Wilcox nonparametric test is used for screening out microorganisms with the species abundance changing significantly and more than 2 times before and after backwashing, and the abundance of the screened microorganisms is used for forming the species abundance data set.

Further, in step (4), the primary response curve PRC is modeled using the R software package vegan.

Further, in step (2), 16S rRNA V3-V4 segment of the microbial DNA was double-ended sequenced.

Further, in step (2), sequencing is performed using Illumina NovaSeq 6000 and/or Illumina MiSeq high-throughput sequencer, and the sequencing mode selects at least one of PE250, PE150, and PE 300.

Further, in step (3), the method for obtaining the abundance set of species comprises:

removing impurities, primers and joints of the obtained raw reads of the original sequencing data, and filtering low-quality reads to obtain high-quality data clean data;

merging double-end data, removing low-quality reads, denoising and removing redundancy to the clean data in sequence to generate amplicon sequence variation ASVs;

species annotation was performed on ASVs and the species abundance of microorganisms in each filter material sample was calculated.

Further, clean data was processed using QIIME2 to generate ASVs.

Further, merging the double-ended data by using qiime vsearch join-calls; removing low quality reads according to a quality score phred >4 using q2-quality-filter q-score-join; the tessellation is identified and filtered for denoising using qiime deblur dense-16S.

Further, species annotation was performed using a pre-trained Naive Bayes classifier using q2-feature-classifier from QIIME 2.

Further, the classifier was trained using Greengenes 13_8 to operate on the V4 segments in the sequence of taxon OTUs in 99% clusters.

The technical scheme of the invention has the following advantages:

1. according to the method for evaluating the backwashing effect of the biofilter, provided by the invention, the species abundance of microorganisms is obtained by extracting, sequencing and analyzing DNA of the microorganisms, a Primary Response Curve (PRC) model is established, and then a model coefficient is extracted to quantitatively evaluate the recovery degree of a biofilm microbial community structure in a filter layer, so that the backwashing effect can be represented on a microbial layer. The invention also researches the structural difference of the microbial community before and after the back washing through the principal co-ordinate analysis (PCoA), and compares the results with the results obtained by the evaluation method provided by the invention, thus proving the reliability of the evaluation method.

2. According to the method for evaluating the backwashing effect of the biological filter, provided by the invention, the recovery time after backwashing is taken as an abscissa and the canonical coefficient is taken as an ordinate, a loess local weighted regression fitting curve is constructed, the backwashing effect of the biological filter is evaluated by the curve, the recovery degree of a microbial community can be intuitively reflected by the curve, and the period of the optimal backwashing effect can be determined more favorably.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

FIG. 1 is a schematic view showing the structures of a quartz sand filter simulation reactor (a) and a manganese sand filter simulation reactor (b) in example 1 of the present invention;

FIG. 2 is a PRC main response curve before and after backwashing of the quartz sand filter simulation reactor in example 1 of the present invention;

FIG. 3 is a PRC main response curve before and after backwashing of the quartz sand filter simulation reactor in example 1 of the present invention;

FIG. 4 is a PCoA diagram of the community structure change of 0h before and after the back flushing of the quartz sand filter simulation reactor in example 2 of the invention;

FIG. 5 is a PCoA diagram of the community structure change of a manganese sand filter simulation reactor for 0h before and after back flushing in example 2 of the invention;

FIG. 6 is a PCoA diagram showing the structural change of a community 48 hours before and after back flushing of a quartz sand filter simulation reactor in example 2 of the present invention;

FIG. 7 is a PCoA diagram showing the structural change of the community 48 hours before and after the back flushing of the manganese sand filter simulation reactor in example 2 of the invention.

Description of the drawings:

1-a filter column; 2-water inlet pipe; 3-water outlet pipe; 4-an overflow pipe; 5-a peristaltic pump; 6, an air pump; 7-quartz sand filter material; 8-manganese sand filter material.

Detailed Description

The following examples are provided to further understand the present invention, not to limit the scope of the present invention, but to provide the best mode, not to limit the content and the protection scope of the present invention, and any product similar or similar to the present invention, which is obtained by combining the present invention with other prior art features, falls within the protection scope of the present invention.

The examples do not show the specific experimental steps or conditions, and can be performed according to the conventional experimental steps described in the literature in the field. The raw materials or equipment used are all conventional products which can be obtained commercially, including but not limited to the raw materials or equipment used in the examples of the present application.

Example 1

The embodiment provides a method for evaluating the backwashing effect of a biological filter, which comprises the following steps:

(1) construction of sand filter simulation reactor

As shown in fig. 1, in order to simulate the backwashing treatment of the sand filter, a quartz sand filter simulation reactor (a) and a manganese sand filter simulation reactor (b) are constructed, and each reactor consists of a filter column 1, a water inlet pipe 2, a water outlet pipe 3, an overflow pipe 4, a peristaltic pump 5 and an air pump 6, wherein the water inlet pipe 2 is arranged at the top of the filter column 1, the water outlet pipe 3 is arranged at the bottom of the filter column 1, the overflow pipe 4 is arranged on the side wall close to the top of the filter column 1, the peristaltic pump 5 and the air pump 6 are respectively connected with the water inlet pipe 2, a quartz sand filter material 7 is filled in the filter column 1 of the quartz sand filter simulation reactor, and a manganese sand filter material 8 is filled in the filter column 1 of the manganese sand filter simulation reactor. Water and gas are pumped into the filter column 1 by adjusting the peristaltic pump 5 and the air pump 6, the water flows out from the water outlet pipe 3 at the bottom of the filter column 1 after passing through the filter material filled in the filter column 1, and the water flows out from the overflow pipe 4 when the water level in the filter column 1 is too high.

(2) Filter material sample collection and backwash treatment

The collected filter material samples are respectively from a quartz sand filter tank of a water purification plant of Liuwan of Jiangsu and a manganese sand filter tank of a new water purification plant.

And respectively filling the collected quartz sand filter material samples and manganese sand filter material samples into filter columns of a quartz sand filter simulation reactor and a manganese sand filter simulation reactor, wherein each filter material sample is provided with 3 parallel experiments, and the total number of the filter columns is 6. The experiment was carried out in three stages:

in the first stage, backwashing is started, the operation is carried out for 15 days according to the empty bed contact time of 4 hours after the backwashing is finished, and the collection time of the sample to be detected is set to be 0 hour, 6 hours, 12 hours, 24 hours, 48 hours, 72 hours, 7d and 15d before the backwashing and after the backwashing, and the total sampling time is 9;

in the second stage, backwashing is carried out after the first stage is finished, the operation is carried out for 15 days according to the empty bed contact time of 2h after the backwashing is finished, and the collection time of the sample to be detected is set to be 0h, 6h, 12h, 24h, 48h, 72h, 7d and 15d before the backwashing and after the backwashing, and 9 sampling times are provided;

and in the third stage, performing backwashing after the second stage is finished, operating for 15 days according to the empty bed contact time of 0.5h after the backwashing is finished, and setting the collection time of the sample to be detected as 0h, 6h, 12h, 24h and 48h before backwashing and after backwashing, wherein the total time is 6 sampling times.

The collected sample to be tested is a surface filter material (thickness about 1cm) containing a biological membrane.

The backwashing conditions are as follows: the air-water mixed back flushing is adopted, the expansion rate is about 33.3 percent, the expansion volume is about 5cm, and the air-blast strength is 100mL/(min m)²) The water impact strength is 90L/(h.m)²) And backwashing for 5 min. The parameters are controlled by controlling the peristaltic pump and the air pump.

The empty bed contact time is controlled by adjusting the flow rate.

(3) DNA extraction and 16S rRNA sequencing

0.5g of DNA of the microbial community in the sample to be tested was extracted using a DNeasy Power Soil Kit (DNeasy Power Soil Kit, QIAGEN, Germany).

The extracted V3-V4 segment of the microbial DNA (515F/806R primer pair, front primer sequence 515F: GTGCCAGCMGCCGCGGTAA, rear primer sequence 806R: GGACTACHVGGGTWTCTAAT) was double-ended sequenced using an Illumina NovaSeq 6000 high throughput sequencer, PE250 was selected for sequencing mode, yielding 34,319,502 raw reads as a total.

(4)16S rRNA sequencing data processing

Carrying out impurity removal, primer removal, joint removal and low-quality reads filtration on raw reads in sequence to obtain high-quality data clear data;

clean data obtained was processed using pipeline from QIIME 2: merging paired end data by using qime v search join-pairs, removing low-quality reads according to a quality score phred >4 by using q2-quality-filter q-score-join, identifying and filtering a chimera by using qime de blu dense-16S for de-noising, directly removing redundancy of the de-noised sequence, and generating Amplicon Sequence Variation (ASVs);

species annotation was performed using a q2-feature-classifier from QIIME2 using a previously trained Naive Bayes classifier trained using Greengenes 13_8 on V4 segments of 99% clustering operations taxon OTUs sequences that have been trimmed to contain only 250 bases from the 16S region that has been sequenced (V4 segment, 515F/806R primer pair); the annotated ASV relative abundance table was exported into R (v3.4.3) and a stacked histogram was generated using the ggplot2 package to visualize the relative abundance of microorganisms in different taxa in each sample.

(5) Establishing a main response curve model

And screening microbial genera of which the relative abundance changes remarkably (p is less than 0.05) and is more than 2 times before and after backwashing by Wilcox nonparametric inspection, and acquiring the species abundances of the microbial genera to form a species abundance data set.

And (3) carrying out significance test on the species abundance data set of the quartz sand filter material obtained under the three empty bed contact times by using ANOISM test, and finding that no significant difference exists between groups (p is greater than 0.05). Similarly, the species abundance data sets of the manganese sand filter material obtained under the three empty bed contact times are not obviously different through inspection. Thus, three sets of data were used together for modeling to determine that empty bed contact time did not have a significant effect on microbial community changes.

A Primary Response Curve (PRC) model is built by using an R software package vegan, and a function for executing PRC is PRC (), and a specific running code "mod < -PRC (response ═ gene, flow ═ back moving, time ═ time)". Wherein, the response variable is a species abundance data set (response), the interpretation variable is whether to perform backwashing treatment (treatment), and the recovery time after backwashing is used as a covariate (time).

After modeling is finished, taking the species abundance of microorganisms in the filter material sample before backwashing as a reference value, extracting model coefficients (canonical coefficient) of different recovery times after backwashing, and executing the following commands: "coeff < -prc _ summary $ coefficients". The recovery time is used as an abscissa and the canonical coefficient is used as an ordinate to construct a loess (local weighted regression) fitting curve, i.e., a PRC curve, as shown in FIGS. 2 to 3. The canonical factor can quantify the degree of difference between the species abundance response of the treatment group and the reference group, wherein the relative abundance of each microorganism before backwashing is used as the reference group, the relative abundance of each microorganism after backwashing is used as the treatment group, and a larger canonical factor indicates a larger difference between the groups (a value of 0 indicates no difference), and an increase (or decrease) in the value reflects an increase (or decrease) in the relative abundance level of the total genus.

As shown in FIGS. 2 to 3, in both the quartz sand filter and the manganese sand filter, the biofilm microbial community structure shows a similar response mode, after back washing, the model coefficient is decreased at 0h, the relative abundance of the corresponding microbial community is reduced, and the relative abundance of the microbial community is gradually increased along with the prolonging of the recovery time, so that the community structure is recovered, and the community structure is basically recovered to the community structure mode before back washing at 48h to 72h (the model coefficient is close to 0, which indicates no difference between groups).

Example 2

This example utilizes a principal co-ordinates analysis (PCoA) based on the distance of the day-cuts to study differences in microbial community structure before and after backwash. Using ANOSIM statistical test, calculating the difference degree of species composition between 0h before and 48h after back washing to form a PCoA graph (shown in figures 4 and 5), and calculating the difference degree of species composition between 48h before and 48h after back washing to form the PCoA graph (shown in figures 6 and 7).

As shown in the figures 4-7, the microbial community structure before backwashing and 0h after backwashing has obvious difference, but the microbial community structure gradually recovers to before backwashing after 48h (the point at 48h in the PCoA diagram is closer to the point before backwashing), and no obvious difference exists, which indicates that the community structure is basically recovered after 48h, and the backwashing effect reaches the best.

Therefore, in example 2, the conclusion obtained by analyzing the structural change of the microbial community is basically the same as that in example 1, and it is proved that the evaluation method provided by the invention is reliable, the backwashing effect can be accurately evaluated on the microbial layer, and the quantitative index of the biological community recovery degree after the sand filter is backflushed is given, so that the period of the optimal backwashing effect can be determined. Therefore, the method provided by the invention can be used for evaluating the backwashing effect of the biological filter.

It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.