CN113030303A - Prediction method for high-risk area of disinfection by-products of long-distance water supply pipe network - Google Patents
Prediction method for high-risk area of disinfection by-products of long-distance water supply pipe network Download PDFInfo
- Publication number
- CN113030303A CN113030303A CN202110215856.0A CN202110215856A CN113030303A CN 113030303 A CN113030303 A CN 113030303A CN 202110215856 A CN202110215856 A CN 202110215856A CN 113030303 A CN113030303 A CN 113030303A
- Authority
- CN
- China
- Prior art keywords
- dbps
- water supply
- temperature
- risk
- area
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N2030/042—Standards
- G01N2030/045—Standards internal
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analyzing Non-Biological Materials By The Use Of Chemical Means (AREA)
Abstract
The invention provides a high-risk area prediction method of disinfection by-products (DBPs), which comprises the following steps: uniformly distributed sampling points are arranged in a pipe network coverage area, controlled DBPs (direct-base plasma) detection is carried out at a certain frequency, and DBPs content data of the sampling points are obtained; carrying out numerical simulation on monitoring point data of the DBPs by means of ArcGIS software by adopting an inverse distance weight method to obtain a DBPs distribution simulation diagram covering the whole water supply area in different sampling periods; taking the average value of multiple monitoring data as a standard, and defining areas with DBPs higher than the average value in different sampling periods; and (4) performing image superposition by adopting Photoshop software, comparing areas with sampling periods higher than the average value, and drawing up a high-risk prediction area of the DBPs by taking the overlapping rate not less than 50% as a reference. The method can realize the identification of the high-risk area of the controlled DBPs of the large-scale water supply network through numerical simulation and overlay analysis, has the advantages of high efficiency, repeatability, accuracy, reliability and the like, can simplify the conventional DBPs monitoring of the long-distance water supply network, and provides technical reference for the operation and maintenance of water service groups.
Description
Technical Field
The application relates to the technical field of drinking water safety guarantee, in particular to a method for predicting high-risk areas of water supply pipe network disinfection by-products (DBPs).
Technical Field
The development of society and economy, resident's high quality life are ensured, and city drinking water safety becomes one of the key links. Along with the enlargement of urban scale and the promotion of urban and rural integrated process in China, the coverage area of a water supply network is larger and larger, and a long-distance multipoint chlorine-adding type water supply network for multi-water-source combined water supply is a main form of a pipe network in most large and medium-sized cities and urban and rural combined water supply systems.
Chlorine disinfection is an effective means for disinfecting water outlet of a water plant and inhibiting microorganism breeding of a water supply pipe network, plays a great role in preventing the spread of waterborne diseases, but chlorine is easy to react with organic matters in water to generate chlorinated organic matters, generally called DBPs. Over the past four decades, over seven hundred DBPs have been identified, including trihalomethanes, haloacetic acids, haloacetonitrile, haloketones, nitrosamines, and other novel DBPs. Trichloromethane, dibromomonochloromethane, monobromomethane and tribromomethane are trihalomethanes which have different carcinogenic levels respectively, and DBPs generally have a 'three-cause' effect, so countries and organizations such as world health organization, America, European Union and China generally carry out limit regulation on two major types of DBPs (dichloromethanes and haloacetic acids) which are generally detected in high content in drinking water. In order to ensure the water quality safety in the pipe network, chlorine with higher concentration is added before water is discharged from a water plant or chlorine is added in the middle of water supply to ensure that residual chlorine with certain concentration still exists in a peripheral pipe network. The precursor of DBPs and free chlorine continuously react in the process of transporting water in a pipe network, so that DBPs are increased, and compared with the effluent of a water plant, the content of DBPs in the pipe network is increased. Bacterial metabolites in the biological membrane in the pipe network, organic matters adsorbed by pipe wall corrosion substances, corroded pipe walls and the like can promote DBPs to be generated, and the risk that the DBPs in the pipe network exceed the standard is further increased. In conclusion, the chlorine input and the contact time are taken as main factors, a plurality of factors influence the level of DBPs (direct Water pipe networks), large-scale long-distance water supply pipe networks DBPs have relatively stable high-risk water supply areas, the risk areas are identified by a certain technical means, and the technical support can be provided for the target supervision of water quality of water supply, and the chlorination strategy adjustment and the regular monitoring of the long-distance water supply pipe networks by water service groups.
Disclosure of Invention
The invention aims to provide a method for establishing a DBPs high-risk area prediction model, so as to realize rapid DBPs detection and high-risk early warning by identifying the DBPs risk area of a water supply network and provide a basic theory for seeking a solution for subsequent DBPs exceeding standards and sudden pollution accidents.
In order to realize the technical effects, the invention adopts the following technical scheme: a prediction method for a high-risk area of disinfection byproducts of a long-distance water supply pipe network comprises the following steps:
s1, uniformly distributed sampling points are arranged in a pipe network coverage area, water samples of the sampling points are collected at a certain time frequency, monitoring point water samples of each sampling point in different sampling periods are obtained, and the monitoring point water samples are stored in a refrigerator at 4 ℃;
s2, analyzing the DBPs content in the water samples of each monitoring point through a GC-ECD to obtain DBPs monitoring data of the water samples of each monitoring point, wherein the average value of the DBPs monitoring data of the water samples of the monitoring points in different sampling periods of each sampling point is used as a DBPs standard value;
s3, performing numerical simulation on the DBPs monitoring data of each sampling point by ArcGIS software by adopting an inverse distance weight method to obtain a DBPs distribution simulation diagram covering the whole water supply area in different sampling periods;
s4, adopting Photoshop software to demarcate an area where DBPs monitoring data of each sampling point are higher than standard values of corresponding DBPs in DBPs distribution simulation graphs of different sampling periods;
s5, overlapping images by adopting Photoshop software, comparing areas higher than the standard value of the DBPs in the DBPs distribution simulated images of different sampling periods, and drawing a high-risk prediction area of the DBPs by taking the overlapping rate not less than 50% as a reference.
In the invention, the certain time frequency refers to a water sample which takes months as interval points and samples a sampling point once every month.
Preferably, in S4, the method for defining the area where the DBPs monitoring data of each sampling point is higher than the DBPs standard value in the DBPs distribution simulation diagram of each different sampling period includes the following steps:
step 1, marking a distribution area in which DBPs monitoring data exceed DBPs standard values in a DBPs distribution simulation diagram by using a dotted line in Photoshop software;
and 2, correspondingly repeating the step 1 on DBPs monitoring data in different sampling periods, and respectively identifying regions with various DBPs higher than the average value.
Preferably, the specific operation of drawing up the high-risk prediction region of DBPs in S5 includes the following steps:
step 3, importing the area maps with the single DBPs detected in different sampling periods higher than the standard value into Photoshop software;
step 4, comparing areas with different sampling periods higher than a standard value in Photoshop software by adopting a graph stacking method, and drawing up a high-risk prediction area of DBPs (direct-base-specific primers) by taking the overlapping rate not less than 50% as a reference;
and 5, repeating the step 3 and the step 4 for each content distribution simulation graph obtained in different sampling periods to obtain a prediction graph of the high-risk area of each DBPs.
Preferably, the DBPs comprise trihalomethanes, haloacetic acids or haloacetonitrile.
Preferably, the analysis of the content of the trihalomethane or the haloacetonitrile in the S2 comprises the following steps:
step 6: firstly, taking 25mL of water sample, and adding the water sample into a 40mL reagent bottle with a screw cap and a polytetrafluoroethylene bottle cap;
and 7: adding 4g of anhydrous sodium sulfate dried at 120 ℃ for 2h into a water sample in the reagent bottle, adding 2mL of an extracting agent, shaking for 5min, and standing for layering for 5 min;
and 8: and (4) taking 1mL of the upper organic phase, filling the upper organic phase into a gas phase vial, and finally performing detection analysis on the GC-ECD.
Preferably, the GC-ECD detection and analysis method is to use an internal standard method for detection, a liquid-liquid extraction method is adopted for pretreatment, and methyl tert-butyl ether containing 150 mug/L of internal standard substance 1, 2 dibromopropane is used as an extracting agent.
Preferably, the detection conditions of the GC-ECD are as follows:
the chromatographic conditions are that the column type is an Rtx-5ms capillary column, the temperature of a sample inlet is 200 ℃, the carrier gas is high-purity nitrogen, the total flow is 50mL/min, the purging flow is 3.6mL/min, and the sample is injected without shunting; the temperature raising program is that the initial temperature is 35 ℃, the temperature is kept for 1min, the temperature is raised to 70 ℃ at 10 ℃/min, the temperature is raised to 120 ℃ at 20 ℃/min, the temperature is raised to 200 ℃ at 10 ℃/min, the temperature is raised to 240 ℃ at 20 ℃/min, the temperature is kept for 5min, and the ECD temperature is 250 ℃.
In the present invention, the Rtx-5ms capillary column is 30 m.times.0.25 mm.times.0.25 μm, SHIMADZU.
Preferably, the analysis of the content of the halogenated acetic acid in the S2 includes the following steps:
step 9, taking 30mL of water sample to be detected in a 40mL reagent bottle with a screw opening and a polytetrafluoroethylene bottle cap;
step 10, adding 2mL of sulfuric acid into the water to be detected in the reagent bottle, then adding 4g of anhydrous sodium sulfate, shaking up to completely dissolve the sodium sulfate, and obtaining a water sample;
step 11, adding 3mL of methyl tert-butyl ether containing 300 mug/L of internal standard substance after a water sample is cooled, oscillating for 5min, and then standing for 5min to enable the mixture to be layered to obtain a sample;
step 12, taking 2.5mL of organic phase at the upper part of the sample, adding the organic phase into a 20mL derivatization reagent bottle, and immediately adding 2mL acidified methanol into the extract;
step 13, placing the derivatization reagent bottle into a water bath kettle at 50 ℃ for derivatization, taking out after 2 hours, and standing and cooling for 10min to obtain a derivatization reagent;
step 14, adding 7mL of sodium sulfate solution with the concentration of 150g/L into the derivatization reagent, oscillating and standing, and then completely taking out the lower-layer liquid;
and step 15, adding 1.0mL of saturated sodium bicarbonate solution into the residual solution, oscillating, deflating and standing for 3min, and taking an upper organic phase into a 2.0mL gas-phase bottle, wherein the GC-ECD can be used for analysis and detection.
In the present invention, the acidified methanol volume ratio is: sulfuric acid: methanol =1: 10.
Preferably, in the analysis of the content of the halogenated acetic acid in S2, the method for measuring the halogenated acetic acid is a combination of liquid-liquid microextraction and acidic methanol esterification.
In the present invention, the method for measuring halogenated acetic acids is based on the U.S. USEPA552.3 method.
Preferably, the GC-ECD detection conditions of the halogenated acetic acid are as follows:
in the GC chromatographic analysis method, the column type is an Rtx-5ms capillary column, the temperature of a sample inlet is 210 ℃, split-flow sample injection is not carried out, carrier gas is high-purity nitrogen, the total flow is 50mL/min, and the purging flow is 3.6 mL/min; the temperature raising program is that the initial temperature is kept at 35 ℃ for 8min, the temperature is raised to 200 ℃ at 8 ℃/min, the temperature is kept for 15min, and the ECD temperature is 280 ℃.
In the present invention, the Rtx-5ms capillary column is 30 m.times.0.25 mm.times.0.25 μm, SHIMADZU.
According to the technical scheme, the invention has the following advantages:
(1) the achievement of the invention can provide a reliable method for identifying the DBPs risk area of the large and medium-sized urban multi-water-source water supply network.
(2) Further, monitoring key points are determined, the water quality monitoring process of the pipe network is simplified from aspects of monitoring point selection and the like, corresponding technical services are provided for water affair monitoring departments, and theoretical bases are provided for operation and basic data acquisition of the pipe network.
(3) The achievement of the invention can provide a quick, simple and convenient technical means for quick identification of water quality risks and provide basic theoretical support for risk management of large and medium-sized pipe networks.
Drawings
FIG. 1: a diagram of a DBPs distribution simulation of trihalomethanes in example 1 of the present invention;
FIG. 2: the areas with higher trihalomethane content than the average value in example 1 of the present invention;
FIG. 3: a high risk zone prediction plot for trihalomethane in example 1 of the invention;
FIG. 4: is a prediction chart of the halogenated acetonitrile high-risk zone in the embodiment 1 of the invention;
FIG. 5: is a prediction chart of the high-risk area of the haloacetic acid in the embodiment 2 of the invention.
Detailed Description
The technical solution of the present invention is further specifically described below by way of specific examples in conjunction with the accompanying drawings.
Embodiments of the present invention will be described in detail below with reference to examples. The following examples are merely illustrative of the present invention and should not be construed as limiting the scope of the invention. The reagents or instruments are not indicated by manufacturers, and are all conventional products which can be purchased through normal channels.
The following are examples: and predicting DBPs high-risk areas in a typical urban and rural homogeneous water supply network of the long triangle.
The method specifically comprises the following steps:
and S1, collecting a water sample. Samples were taken from selected points in drinking water treatment and water supply piping systems in Huzhou, Zhejiang province. The sampling time is 5 months in 2018, 6 months in 2019 to 1 month in 2020. The city west waterworks in Huzhou city take the Dongtao xi and the tiger pool reservoirs as water sources, and the Taihu lake waterworks take water from the Taihu lake.
The water sample is taken and immediately taken back to the laboratory, filtered by a 0.45 mu m filter membrane and stored in a brown bottle and stored in a shading environment at 4 ℃.
S2, example 1: trihalomethane and haloacetonitrile were detected by internal standard method, pretreated by liquid-liquid extraction method, and methyl tert-butyl ether (MTBE) containing 150. mu.g/L internal standard 1, 2 dibromopropane was used as extractant. Firstly, taking 25mL of water sample, adding the water sample into a 40mL reagent bottle with a screw-top polytetrafluoroethylene bottle cap, adding 4g of anhydrous sodium sulfate dried at 120 ℃ for 2h into the water sample, then adding 2mL of an extracting agent, shaking for 5min, standing for layering for 5min, taking 1mL of an upper organic phase, filling the upper organic phase into a gas phase bottle, and then carrying out detection analysis on the gas phase bottle on GC-ECD. The GC chromatographic analysis method of trihalomethane and halogenated acetonitrile comprises the following steps:
the column type is an Rtx-5ms capillary column (30 m multiplied by 0.25mm multiplied by 0.25 mu m, SHIMADZU), the temperature of a sample inlet is 200 ℃, carrier gas is high-purity nitrogen, the total flow is 50mL/min, the purging flow is 3.6mL/min, and split-flow sample introduction is not carried out; the temperature raising program starts at 35 deg.C, keeps for 1min, raises the temperature to 70 deg.C at 10 deg.C/min, raises the temperature to 120 deg.C at 20 deg.C/min, raises the temperature to 200 deg.C at 10 deg.C/min, raises the temperature to 240 deg.C at 20 deg.C/min, and keeps for 5 min. The ECD temperature was 250 ℃.
Example 2: the method for measuring the halogenated acetic acid is based on the United states USEPA552.3 method, namely a liquid-liquid microextraction combined acidic methanol esterification method is adopted. The method comprises the following steps: taking 30mL of a water sample to be detected in a 40mL reagent bottle with a screw-top polytetrafluoroethylene bottle cap, adding 2mL of sulfuric acid into the bottle, then adding 4g of anhydrous sodium sulfate, shaking uniformly to completely dissolve the sodium sulfate, adding 3mL of methyl tert-butyl ether containing 300 mug/L of an internal standard substance (1, 2-dibromopropane) after the water sample is cooled, shaking for 5min, standing for 5min to separate the mixture, adding 2.5mL of an organic phase at the upper part of a sample into a 20mL derivatization reagent bottle, and immediately adding 2mL of acidified methanol (1: 10) into an extract liquor. And (3) placing the reagent bottle into a water bath kettle at 50 ℃ for derivatization, taking out after 2h, and standing and cooling for 10 min. And then adding 7mL of sodium sulfate solution with the concentration of 150g/L into the derivatization reagent, oscillating and standing, then completely taking out the lower layer liquid, adding 1.0mL of saturated sodium bicarbonate solution into the residual solution, oscillating, deflating and standing for 3min, then taking the upper organic phase into a 2.0mL gas phase bottle, and carrying out analysis and detection by using GC-ECD.
The GC chromatographic analysis method of the halogenated acetic acid comprises the following steps: the column type is Rtx-5ms capillary column (30 m × 0.25mm × 0.25 μm, SHIMADZU), the injection inlet temperature is 210 ℃, the split-flow injection is not carried out, the carrier gas is high-purity nitrogen, the total flow is 50mL/min, and the purging flow is 3.6 mL/min; temperature raising program the initial temperature is kept at 35 ℃ for 8min, raised to 200 ℃ at 8 ℃/min and kept for 15 min. The ECD temperature was 280 ℃.
S3, carrying out numerical simulation on the monitoring point data of the DBPs by means of ArcGIS software by adopting an inverse distance weight method to obtain a DBPs distribution simulation diagram covering the whole water supply area in different sampling periods, wherein the diagram is as shown in the figure 1:
s4, importing the content distribution simulation diagram into Photoshop software, and demarcating areas with DBPs content higher than the average value in different sampling periods by using the average value of multiple monitoring data as a standard and using dotted lines; repeating the steps to divide the areas with the content of all sampling periods higher than the average value as shown in figure 2.
S5, in Photoshop software, overlapping the areas with DBPs content higher than the average value in different sampling periods by adopting a graph overlapping method, and drawing a high risk prediction area of DBPs (taking trihalomethane as an example, as shown in FIG. 3) by taking the overlapping rate not less than 50% as a reference. And repeating the steps to obtain a prediction map of the high-risk area of each DBPs.
According to the invention, DBPs in a typical urban and rural homogeneous water supply network of a long triangle are investigated, and the results show that trihalomethane, haloacetic acid and haloacetonitrile are detected, wherein the detection rate is 100%.
In summary, the invention aims to realize rapid DBPs detection and high-risk early warning by establishing a DBPs high-risk area prediction method and identifying the DBPs high-risk area of a water supply network, and provides a basic theory for seeking a solution for subsequent DBPs exceeding standards and sudden pollution accidents.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (10)
1. A prediction method for a high-risk area of disinfection byproducts of a long-distance water supply pipe network is characterized by comprising the following steps of: the method comprises the following steps:
s1, uniformly distributed sampling points are arranged in a pipe network coverage area, water samples of the sampling points are collected at a certain time frequency, monitoring point water samples of each sampling point in different sampling periods are obtained, and the monitoring point water samples are stored in a refrigerator at 4 ℃;
s2, analyzing the DBPs content in the water samples of each monitoring point through a GC-ECD to obtain DBPs monitoring data of the water samples of each monitoring point, wherein the average value of the DBPs monitoring data of the water samples of the monitoring points in different sampling periods of each sampling point is used as a DBPs standard value;
s3, performing numerical simulation on the DBPs monitoring data of each sampling point by ArcGIS software by adopting an inverse distance weight method to obtain a DBPs distribution simulation diagram covering the whole water supply area in different sampling periods;
s4, adopting Photoshop software to demarcate an area where DBPs monitoring data of each sampling point are higher than standard values of corresponding DBPs in DBPs distribution simulation graphs of different sampling periods;
s5, overlapping images by adopting Photoshop software, comparing areas higher than the standard value of the DBPs in the DBPs distribution simulated images of different sampling periods, and drawing a high-risk prediction area of the DBPs by taking the overlapping rate not less than 50% as a reference.
2. The method for predicting the high-risk areas of the DBPs in the long-distance water supply network according to claim 1, wherein the step of defining the areas with DBPs monitoring data of each sampling point higher than the standard value of the DBPs in the DBPs distribution simulation diagram of each different sampling period in S4 comprises the following steps:
step 1, marking a distribution area in which DBPs monitoring data exceed DBPs standard values in a DBPs distribution simulation diagram by using a dotted line in Photoshop software;
and 2, correspondingly repeating the step 1 on DBPs monitoring data in different sampling periods, and respectively identifying regions with various DBPs higher than the average value.
3. The method for predicting the high-risk areas of the DBPs in the long-distance water supply network according to claim 1, wherein the specific operation of drawing up the high-risk prediction areas of the DBPs in S5 comprises the following steps:
step 3, importing the area maps with the single DBPs detected in different sampling periods higher than the standard value into Photoshop software;
step 4, comparing areas with different sampling periods higher than a standard value in Photoshop software by adopting a graph stacking method, and drawing up a high-risk prediction area of DBPs (direct-base-specific primers) by taking the overlapping rate not less than 50% as a reference;
and 5, repeating the step 3 and the step 4 for each content distribution simulation graph obtained in different sampling periods to obtain a prediction graph of the high-risk area of each DBPs.
4. The method of claim 1, wherein the DBPs comprise trihalomethanes, haloacetic acids, or haloacetonitrile.
5. The method for predicting the high-risk areas of the DBPs in the long-distance water supply pipe network according to claim 4, wherein the analysis of the content of the trihalomethane or the haloacetonitrile in the S2 comprises the following steps:
step 6: firstly, taking 25mL of water sample, and adding the water sample into a 40mL reagent bottle with a screw cap and a polytetrafluoroethylene bottle cap;
and 7: adding 4g of anhydrous sodium sulfate dried at 120 ℃ for 2h into a water sample in the reagent bottle, adding 2mL of an extracting agent, shaking for 5min, and standing for layering for 5 min;
and 8: and (4) taking 1mL of the upper organic phase, filling the upper organic phase into a gas phase vial, and finally performing detection analysis on the GC-ECD.
6. The method for predicting the DBPs high-risk area of the long-distance water supply pipe network according to claim 5, wherein the detection and analysis method of the GC-ECD is detection by using an internal standard method, pretreatment is performed by using a liquid-liquid extraction method, and methyl tert-butyl ether containing 150 mug/L of an internal standard substance 1, 2 dibromopropane is used as an extracting agent.
7. The method for predicting the high-risk areas of the DBPs of the long-distance water supply pipe network according to claim 5, wherein the detection conditions of the GC-ECD are as follows:
the chromatographic conditions are that the column type is an Rtx-5ms capillary column, the temperature of a sample inlet is 200 ℃, the carrier gas is high-purity nitrogen, the total flow is 50mL/min, the purging flow is 3.6mL/min, and the sample is injected without shunting; the temperature raising program is that the initial temperature is 35 ℃, the temperature is kept for 1min, the temperature is raised to 70 ℃ at 10 ℃/min, the temperature is raised to 120 ℃ at 20 ℃/min, the temperature is raised to 200 ℃ at 10 ℃/min, the temperature is raised to 240 ℃ at 20 ℃/min, the temperature is kept for 5min, and the ECD temperature is 250 ℃.
8. The method for predicting the high-risk areas of the DBPs in the long-distance water supply pipe network according to claim 4, wherein the halogenated acetic acid content analysis in S2 comprises the following steps:
step 9, taking 30mL of water sample to be detected in a 40mL reagent bottle with a screw opening and a polytetrafluoroethylene bottle cap;
step 10, adding 2mL of sulfuric acid into the water to be detected in the reagent bottle, then adding 4g of anhydrous sodium sulfate, shaking up to completely dissolve the sodium sulfate, and obtaining a water sample;
step 11, adding 3mL of methyl tert-butyl ether containing 300 mug/L of internal standard substance after a water sample is cooled, oscillating for 5min, and then standing for 5min to enable the mixture to be layered to obtain a sample;
step 12, taking 2.5mL of organic phase at the upper part of the sample, adding the organic phase into a 20mL derivatization reagent bottle, and immediately adding 2mL acidified methanol into the extract;
step 13, placing the derivatization reagent bottle into a water bath kettle at 50 ℃ for derivatization, taking out after 2 hours, and standing and cooling for 10min to obtain a derivatization reagent;
step 14, adding 7mL of sodium sulfate solution with the concentration of 150g/L into the derivatization reagent, oscillating and standing, and then completely taking out the lower-layer liquid;
and step 15, adding 1.0mL of saturated sodium bicarbonate solution into the residual solution, oscillating, deflating and standing for 3min, and taking an upper organic phase into a 2.0mL gas-phase bottle, wherein the GC-ECD can be used for analysis and detection.
9. The method for predicting the DBPs high risk area of the long-distance water supply pipe network according to claim 8, wherein in the analysis of the content of the halogenated acetic acid in S2, the method for measuring the halogenated acetic acid is a combination of liquid-liquid microextraction and acidic methanol esterification.
10. The method for predicting the high-risk areas of the DBPs in the long-distance water supply pipe network according to claim 8, wherein the detection conditions of the GC-ECD of the halogenated acetic acid are as follows:
in the GC chromatographic analysis method, the column type is an Rtx-5ms capillary column, the temperature of a sample inlet is 210 ℃, split-flow sample injection is not carried out, carrier gas is high-purity nitrogen, the total flow is 50mL/min, and the purging flow is 3.6 mL/min; the temperature raising program is that the initial temperature is kept at 35 ℃ for 8min, the temperature is raised to 200 ℃ at 8 ℃/min, the temperature is kept for 15min, and the ECD temperature is 280 ℃.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110215856.0A CN113030303B (en) | 2021-02-26 | 2021-02-26 | Prediction method for high-risk area of disinfection byproducts of long-distance water supply pipe network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110215856.0A CN113030303B (en) | 2021-02-26 | 2021-02-26 | Prediction method for high-risk area of disinfection byproducts of long-distance water supply pipe network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113030303A true CN113030303A (en) | 2021-06-25 |
| CN113030303B CN113030303B (en) | 2023-03-14 |
Family
ID=76461620
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110215856.0A Active CN113030303B (en) | 2021-02-26 | 2021-02-26 | Prediction method for high-risk area of disinfection byproducts of long-distance water supply pipe network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113030303B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306021A (en) * | 2011-08-16 | 2012-01-04 | 上海交通大学 | Joint debugging and joint control method for guaranteeing biosafety of pipe network water quality based on real-time ArcGis |
| CN104834821A (en) * | 2015-05-13 | 2015-08-12 | 中国环境科学研究院 | Method for determining population exposure area according to environmental health risk assessment on river pollution specific pollutants and application of method |
| CN105654236A (en) * | 2015-12-29 | 2016-06-08 | 北京师范大学 | Pollution risk evaluation method for underground water type drinking water source region |
| CN112147244A (en) * | 2020-09-07 | 2020-12-29 | 同济大学 | Method for identifying high-risk disinfection byproducts in water, device and application thereof |
-
2021
- 2021-02-26 CN CN202110215856.0A patent/CN113030303B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102306021A (en) * | 2011-08-16 | 2012-01-04 | 上海交通大学 | Joint debugging and joint control method for guaranteeing biosafety of pipe network water quality based on real-time ArcGis |
| CN104834821A (en) * | 2015-05-13 | 2015-08-12 | 中国环境科学研究院 | Method for determining population exposure area according to environmental health risk assessment on river pollution specific pollutants and application of method |
| CN105654236A (en) * | 2015-12-29 | 2016-06-08 | 北京师范大学 | Pollution risk evaluation method for underground water type drinking water source region |
| CN112147244A (en) * | 2020-09-07 | 2020-12-29 | 同济大学 | Method for identifying high-risk disinfection byproducts in water, device and application thereof |
Non-Patent Citations (6)
| Title |
|---|
| 刘俊萍等: "供水管网终端消毒副产物分布特征及预测模型", 《环境科学》 * |
| 刘瑞凯等: "GIS技术在长输油气管道风险分析与决策中的应用", 《当代化工》 * |
| 张谱等: "基于GIS实现供水管网管理的可视化", 《中国给水排水》 * |
| 朱慧敏等: "基于GIS栅格数据的城市河网水质综合评价模型构建", 《环境与健康杂志》 * |
| 梁德全等: "基于Photoshop的教育专题地图的制作设计与分析――以重庆市高等教育发展研究为例", 《安徽农业科学》 * |
| 王玲玲等: "基于GIS的城市供水管网爆管预测预警信息系统", 《中国给水排水》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113030303B (en) | 2023-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gao et al. | Systematic evaluation of biomarker stability in pilot scale sewer pipes | |
| Zhang et al. | Antibiotics in the offshore waters of the Bohai Sea and the Yellow Sea in China: occurrence, distribution and ecological risks | |
| Hong et al. | Trophic state, natural organic matter content, and disinfection by-product formation potential of six drinking water reservoirs in the Pearl River Delta, China | |
| Gao et al. | Occurrence and distribution of antibiotics and antibiotic resistance genes in water of Liaohe River Basin, China | |
| Zhang et al. | Effects of ecological protection and restoration on phytoplankton diversity in impounded lakes along the eastern route of China's South-to-North Water Diversion Project | |
| Comer-Warner et al. | Seasonal variability of sediment controls of nitrogen cycling in an agricultural stream | |
| Yokouchi et al. | The seasonal variation of selected natural and anthropogenic halocarbons in the Arctic troposphere | |
| Audet et al. | Greenhouse gas emissions from urban ponds in Denmark | |
| Peng et al. | Pharmaceutically active compounds (PhACs) in surface sediments of the Jiaozhou Bay, north China | |
| CN113030303B (en) | Prediction method for high-risk area of disinfection byproducts of long-distance water supply pipe network | |
| Ahmed et al. | In-sewer stability of selected analgesics and their metabolites | |
| Zhou et al. | Space-for-time substitution leads to carbon emission overestimation in eutrophic lakes | |
| Hu et al. | Modeling riverine N2O sources, fates, and emission factors in a typical river network of eastern China | |
| CN102519929A (en) | Method for predicting formation potential of nitrogenous disinfection by-product dichloro acetamide in water | |
| Zhang et al. | Identification of nitrogen sources and cycling along freshwater river to estuarine water continuum using multiple stable isotopes | |
| Chen et al. | Occurrence of pharmaceuticals in Calgary's wastewater and related surface water | |
| Lin et al. | Effect of dissolved organic matter on pre-equilibrium passive sampling: A predictive QSAR modeling study | |
| Li et al. | Seasonal variation of sea surface pH and its controls in the Jiaozhou Bay, China | |
| Ye et al. | Using machine learning methods to predict VOC emissions in chemical production with hourly process parameters | |
| Zhang et al. | Antibiotics in a seasonal ice-sealed reservoir: Occurrence, temporal variation, prioritization, and source apportionment | |
| Badaró et al. | Multivariate analysis of the distribution and formation of trihalomethanes in treated water for human consumption | |
| CN112147244A (en) | Method for identifying high-risk disinfection byproducts in water, device and application thereof | |
| Yi et al. | Occurrence and distribution of PPCPs in water from two largest urban lakes of China: First perspective from DGT in-situ measurement | |
| Zhang et al. | Direct measurements of dissolved N2 and N2O highlight the strong nitrogen (N) removal potential of riverine wetlands in a headwater stream | |
| Liu et al. | Oceanic emissions of methyl halides and effect of nutrients concentration on their production: A case of the western Pacific Ocean (2° N to 24° N) |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |