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HJ 838-2017: Technical guideline for deriving nutrient criteria for lakes
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Technical guideline for deriving nutrient criteria for lakes
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HJ 838-2017
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Standard similar to HJ 838-2017 HJ 511 HJ 945.3 HJ 943 Basic data | Standard ID | HJ 838-2017 (HJ838-2017) | | Description (Translated English) | Technical guideline for deriving nutrient criteria for lakes | | Sector / Industry | Environmental Protection Industry Standard | | Word Count Estimation | 22,242 | | Date of Issue | 2017-06-09 | | Date of Implementation | 2017-09-01 | | Issuing agency(ies) | Ministry of Ecology and Environment | HJ 838-2017: Technical guideline for deriving nutrient criteria for lakes---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
Technical guideline for deriving nutrient criteria for lakes
National Environmental Protection Standard of the People 's Republic of China
Lake nutrient benchmarking
Technical guide
2017-06-09 released
2017-09-01 implementation
Ministry of Environmental Protection released
I directory
Preface .ii
1 Scope of application
2 normative reference documents
3 Terms and definitions 1
4 Nutrition Benchmarking Technical Process 1
5 Data collection and requirements
6 Candidate indicators and screening
7 benchmark value derivation 5
8 Benchmark Validation and Auditing 9
9 Nutrition Benchmark Application ..10
Appendix A (normative) Assessment of land ecosystem health status
Appendix B (normative appendix) Refer to the lakes screening technique
Appendix C (normative appendices) inflection point analysis
Appendix D (normative appendix) ancient lakes
Foreword
In order to implement the Environmental Protection Law of the People's Republic of China, the Law of the People's Republic of China on the Prevention and Control of Water Pollution and the Action Plan for Water Pollution Control,
Scientific and standardized development of lake nutrient benchmarks, the development of this standard.
This standard specifies the procedures, methods and technical requirements for lake nutrient benchmarking.
Appendix A to Appendix D of this standard are normative appendices.
This standard is the first release.
This standard is a guiding standard.
This standard is organized by the Ministry of Environmental Protection Science and Technology Standards Division.
The main drafting unit of this standard. China Environmental Science Research Institute (State Key Laboratory of Environmental Benchmarking and Risk Assessment).
This standard is approved by the Ministry of Environmental Protection on June 9,.2017.
This standard has been implemented since September 1,.2017.
This standard is explained by the Ministry of Environmental Protection.
Guidance on the development of lake nutrient benchmarks
1 Scope of application
This standard specifies the technical methods for the development of lake nutrient benchmarks, including nutrient benchmarking techniques, data collection and requirements,
Candidate indicators and screening, benchmarking, benchmark validation and review, and nutrient benchmarking.
This standard applies to guide the development of regional lake nutrient benchmarks in China, and the reference to the development of reservoirs and individual lake nutrient reference can be implemented.
2 normative reference documents
This standard refers to the terms of the following documents. For undated references, the valid version applies to this standard.
Water quality - Determination of total phosphorus - Ammonium molybdate spectrophotometric method
HJ 636 Water quality - Determination of total nitrogen - Alkaline persulfate digestion - Ultraviolet spectrophotometric method
Determination of chlorophyll content - Spectrophotometric method
3 terms and definitions
The following terms and definitions apply to this standard.
Nutrient
Refers to the measurement, evaluation or prediction of water status or eutrophication status indicators.
3.2 nutrient criteria
Refers to the nutrient concentration or level of the nutrient produced by the ecological effect of the lake that does not endanger its water function or use.
3.3 reference state condition
Refers to the state of the least affected or considered to be the best possible condition.
Refer to the lake reference lake
Refers to lakes that are not artificially affected or are artificially affected and are of minimal use.
4 Nutrition benchmarks to develop technical processes
The lake nutrient benchmarking process is shown in Figure 1.
Figure 1 Nutrient benchmarking Technical flow chart
5 Data collection and requirements
5.1 Data sources
3 data sources mainly for environmental monitoring agencies, research institutes and other institutions to collect data in a standard way. For other sources of data (public
Development documents), should support the inspection documents to ensure that sampling, measurement and analysis methods are consistent.
5.2 Data Filtering Principles
Nutrition benchmarks need to be based on a large amount of data, the required data should be consistent with the following principles.
(1) Data integrity principles. For areas where monitoring data is complete, such as the need to meet the classification of lakes and the development of benchmarks,
Its work is mainly for the collection of existing data, analysis and screening; for the lack of monitoring data or insufficient areas, should be carried out in a timely manner
And monitor the work to meet the data requirements.
(2) the least data. the monitoring data should include at least total phosphorus, total nitrogen, chlorophyll a and transparency. Other data include judgment
Basic data for the degree of nutrient input (information on pollutant emission data, land use, etc.).
5.3 Data quality evaluation
Trusted data refers to the use of standard methods to collect data, should be from the following aspects of the data quality evaluation.
(1) monitoring site. with a clear site information, including latitude and longitude and other geographical information related to the reference.
(2) monitoring indicators and analysis methods. the same monitoring indicators should be a unified standard analysis method. If a standard method is used
Obtain too little monitoring data, you can use other standard methods to get the data.
(3) laboratory quality control. meet the laboratory quality control requirements of the monitoring data can be used.
(4) Data duration. Monitoring data for at least 3 consecutive years in the past 10 years, if not required Supplementary monitoring.
(5) monitoring frequency. under normal circumstances, need to be monitored within a natural year by month; or at least in a natural year in the spring,
Summer, autumn monitoring once.
(6) representative lake data. should be randomly selected representative of the lake monitoring data. Representative lakes require an area greater than 10 km2,
The number reached more than 80% of the total number of lakes in the area. If the above requirements are not met, additional monitoring is required.
6 Candidate indicators and screening
Nutritional benchmark candidates include nutrient indicators, biological indicators and auxiliary indicators.
6.1 Nutritional indicators
6.1.1 Phosphorus
The content of total phosphorus (TP) in water samples was analyzed by GB 11893, including all organic and inorganic, dissolved and particulate phosphorus,
For μg/L or mg/L. TP is a mandatory indicator of the nutrient base. Phosphates can be used as an alternative to nutrient benchmarks.
6.1.2 Nitrogen
The contents of total nitrogen (TN) in water samples were analyzed by HJ 636, including all nitrate nitrogen, nitrite nitrogen, ammonia nitrogen and total organic
Nitrogen in μg/L or mg/L. TN is a mandatory indicator of the nutrient base. Ammonia nitrogen, nitrate nitrogen, nitrite nitrogen can be used as a nutrient
Optional indicators for benchmarks.
Biological Indicators
6.2.1 chlorophyll a
The content of chlorophyll a (Chl a) was analyzed by SL 88, which directly reflected the biomass of algae in μg/L or mg/m3. Chl a is a battalion
A mandatory indicator for the basis of the baseline.
6.2.2 Transparency
Transparency (SD) can be used as a predictor of the outbreak of lacustrine green algae blooms in cm. SD is not suitable for water
Large nuts with higher chroma (≥30 mg Pt/L) or higher concentrations of inorganic suspended solids.
6.2.3 Dissolved Oxygen
Dissolved oxygen (DO) can be used as a potential early warning indicator of nutritional status changes in mg/L.
6.2.4 Total Organic Carbon
Organic carbon can be used to determine the weight of live substances and is the basis for the classification and definition of nutritional status in mg/L. Total organic carbon package
Including particulate organic carbon and dissolved organic carbon.
6.2.5 Large aquatic plants
Large aquatic plants are potential users of imported plant nutrients, whose community composition or abundance is directly related to nutrient concentration,
A key indicator of the ecological status of the park. Large-scale aquatic plants may be considered as nutrient reference candidates for more comprehensive monitoring of lakes
index. The total biomass of large aquatic plants in lakes is calculated using the formula (1)
BCSATSMB (1)
Where. TSMB - total biomass of large aquatic plants, mg/L;
SA - lake surface area, Km2;
C - large aquatic plant coverage,%;
B - the average biomass of the sample taken, mg/L.
6.2.6 Biomass structure
The changes of community structure of diatoms, blue-green algae, zooplankton, fish and benthic macrofauna in lakes were measured by Shannon-Wiener
Diversity index (Shannon Wiener's diversity index) or biological integrity index (index of biological integrity, IBI),
Quantitative analysis of biome structure.
6.3 Auxiliary indicators
6.3.1 Temperature
The temperature probe is directly inserted into the sampling point measurement, and the influence of temperature on nutrient-algal growth response is considered in the classification of the lake.
6.3.2 pH value
Using the measurement accuracy of 0.1 pH meter determination, in the classification of lakes need to consider the lake water pH value.
6.3.3 Conductivity
The electrical conductivity of lake water was measured by conductivity meter in μS/cm. Conductivity is very sensitive to changes in salinity and can be used to indicate
Nutrient enrichment, but not applicable to areas containing higher concentrations of calcium carbonate or dissolved salts.
Land use
Land use type is an important indicator of lake selection and land ecosystem health assessment, and is also an early warning of lake eutrophication
index. Should map the land use type map, indicating the percentage of land use type, focusing on changes in forest land into agricultural or urban land changes
Situation, taking into account the natural waterfront ratio and lake shore buffer width and other habitat.
6.4 Index screening
Should be used for correlation analysis, principal component analysis, reduced dimension correspondence analysis, typical correspondence analysis and other methods, screening and algae growth has a clear phase
The relationship between the response indicators.
(1) total phosphorus, total nitrogen (reason indicator) and chlorophyll a, transparency (response index) for the lake nutrient reference to develop mandatory
Standard
(2) the impact of natural and human factors such as geography, climate and history, and the key indicators affecting the nutritional status of lakes in different regions
A certain difference, according to local conditions, appropriate to increase the characteristics of indicators.
(3) For drinking water sources and other important water environment functional areas, need to select the early warning indicators such as land use.
(4) the indicators used should be standard monitoring and analysis methods, easy to national promotion.
7 reference value derivation
The lakes are divided into smaller lakes by man-made activities and disturbed by artificial activities in accordance with the health status of terrestrial ecosystems (Appendix A)
Park. The lake where the health of the land ecosystem is good and above is a lake with less disturbance by human activities. Other lakes are
For activities to disturb larger lakes.
The area is subject to human activity disturbances. Smaller lake nutrient benchmarks are developed using statistical analysis, which is disturbed by human activities. Larger lake nutrients
Benchmarking uses a pressure-response model approach.
7.1 Statistical analysis
The statistical analysis method includes reference to the lake method, the lake population distribution method and the third method. According to the data available in the area lakes, choose one
Species or several methods to determine the nutrient baseline.
7.1.1 Refer to the Lake Act
When the number of reference lakes in the area exceeds 10% of the total amount of lakes, priority may be given to the use of reference lake method to determine the nutrient baseline.
Specific derivation of the technical process (Figure 2) is as follows.
(1) to determine the area within the reference lake. Refer to Appendix B for reference to lake screening techniques.
(2) data screening. select the area reference to the lake all the original data.
(3) data distribution test. the reference to the lake all the data for normal distribution test (such as t test, F test), in line with the normal
The cloth can be used to derive the reference value. If it does not conform to the normal distribution, it is necessary to identify the outliers and extreme values and transform it by means of logarithmic conversion
(Based on 10), re-check until the normal distribution.
(4) nutrient reference value derivation. consistent with the normal distribution test data for frequency distribution analysis (according to the order of water quality from high to low
6 do not arrange), select the 25% point (the transparency of the frequency distribution using the opposite end) as a nutrient reference value (see Figure 3).
Figure 2 refers to the lakes method to derive the nutrient baseline technical process
Figure 3 (a) refer to the lakes method and (b) the lakes population distribution method
7.1.2 Lakes population distribution method
When the number of reference lakes can not reach 10% of the total number of lakes, the lake population distribution method can be used.
Row reference lake screening. The specific method is as follows.
(1) data screening. select the area of the lake all the original data.
(2) data distribution test. with the reference to the lake law.
(3) nutrient reference value derivation. consistent with the normal distribution test data for frequency distribution analysis (according to the order of water quality from high to low
Do not arrange), select the next 25% point (the transparency of the frequency distribution using the opposite end) as a nutrient reference value (see Figure 3).
7.1.3 Trichotomy
When the reference number of lakes in the area can not reach 10% of the total number of lakes, it is also possible to use a three-point method. The method does not need to be referenced
Lake screening. The specific method is as follows.
(1) data screening. select the area of all the data in the lake water quality of the best 1/3 data.
(2) data distribution test. with the reference to the lake law.
7 (3) Nutrient reference value derivation. The median of the 1/3 data obtained (50% of the frequency distribution) is taken as the nutrient reference value.
7.2 Pressure - response model method
Pressure-response model includes linear regression model, classification regression tree model, Bayesian inflection point analysis and nonparametric inflection point analysis
Method, the need to use four models to determine the nutrient reference value.
In one of the following two cases, the classification regression tree model method, Bayesian inflection point analysis and nonparametric inflection point analysis
Nutrient reference value. (1) the relationship between the response index and the nutrient concentration can not be expressed in a linear relationship, showing a non-linear, non-normal and different
Qualitative; (2) lake water quality indicators can not meet the linear regression in the set conditions.
7.2.1 Linear regression model method
Linear regression model includes simple linear regression model and multiple linear regression model, simple linear regression model of the specific derivation of the technical flow
As follows (Figure 4).
Figure 4 Linear regression model to derive the nutrient benchmarking process
(1) Data screening. Select the average of the data of all the lakes from April to September for linear regression analysis; for model fitting
The number of independent samples is not less than 20.
(2) Data test. whether the test data meet the following conditions. 1) whether the linear regression equation reflects the nutrient concentration and response to the indicators
Relationship; 2) whether the nutrient concentration sampling satisfies the normal distribution; 3) whether the size of the nutrient concentration sampling variance is within the forecast interval; 4)
Whether the data samples used are independent of each other. If you do not meet the above assumptions, you need to identify outliers and extreme values, and logarithmic conversions
10 for the base).
(3) linear regression model established. the test data into the linear regression equation (2), using the least squares method for the model
Fitting to get a and b.
ya bx (2)
Where. y - Chl a, SD estimate, μg/L, cm;
8x - nitrogen and phosphorus concentration monitoring, mg/L;
A - intercept, dimensionless;
B - linear regression slope, dimensionless.
(2) Model evaluation. The correlation coefficient (R2), root mean square (RMSE), the relationship between residual and fitting value, residual and cumulative
Rate percentage relationship and other parameters, evaluation model fit degree.
(5) reference value derivation. taking into account the international and China's lake nutrition status and functional requirements, Chl a range of 2 ~ 5 μg/L *, to
90% confidence interval, the use of equation (2) derived nitrogen and phosphorus reference value.
7.2.2 Classification regression tree model method
The classification regression tree model method can quantitatively reflect the impact of different predictive indicators (such as nutrients, etc.) on the response index (Chl a)
Indicator change threshold. The use of a classification regression tree model to determine the nutrient baseline does not require a reference value for the response indicator. The specific method is as follows.
(1) Data screening. Select the average of the data of all the lakes from April to September in the region to classify the regression tree model. According to forecast
The number of indicators, determine the amount of data required for the model fit, the ratio of the number of independent samples to the number of predicted indicators should be greater than or equal to 10.
(2) Classification regression tree model establishment. including tree construction, stop, pruning and optimal tree selection four steps.
(3) Important predictive indicators. Based on the selection of potential predictive indicators, according to the classification regression tree model to determine the impact of response indicators
An important predictor of volatility.
(4) the reference value derived. the optimal tree node corresponding to the nutrient concentration and Chl a mean value is the reference value.
7.2.3 Bayesian inflection point analysis method
The use of inflection point analysis of nutrient concentration transition inflection point is the nutrient reference value. Bayesian inflection point analysis gives the inflection point
Probably the probability distribution of the position occurs and the transition point with the greatest probability is taken as the nutrient reference value. The specific method is as follows.
(1) data screening. select the area of all the lakes from April to September the average of the data for inflection point analysis. Bayesian inflection points are required
To analyze whether the response indicator conforms to a normal distribution, logarithmic conversions (10 basis numbers) are required for response indicators that do not conform to the normal distribution.
(2) model construction. meet the requirements of the data in accordance with the concentration gradient from low to high, in the pressure indicators and response indicators built between
In the response relation, the probability point is the transition point. The principles of the Bayesian inflection point analysis are given in Appendix C.
(3) nutrient reference value derivation. 90% confidence interval, using bootstrap (bootstrap) simulation to determine the reference value.
7.2.4 Nonparametric inflection point analysis
Using the nonparametric inflection point analysis method to find the relationship between the pressure index and the response index of the transition inflection point, that is, the nutrient reference value. Specific derivation
Methods as below.
(1) data screening. select the area of all the lakes from April to September the average of the data for inflection point analysis. This method does not need to be positive
State distribution test.
(2) model construction. meet the requirements of the data in accordance with the concentration gradient from low to high, in the pressure indicators and response indicators built between
* Note. Chl a is based on the fact that there is a significant positive correlation between Chl a and algal toxins. Chl a can be used to evaluate the intracellular alginate content. In order to protect
The World Health Organization (WHO) recommends that the reference value for algal toxins in drinking water be set at 1 μg/L. Domestic and foreign studies have shown that algal toxins and Chl
The ratio of a is about 0.2 to 0.5. At the same time, the empirical value of Chl a is 2 ~ 5μg/L, so Chl a is in the range of 2 ~ 5 μg/L,
To ensure the realization of drinking water function, and help to prevent the lake "under-protection" and "over-protection" phenomenon.
In the response relation, the maximum deviation corresponds to the transition point. The principle of the nonparametric inflection point analysis is given in Appendix C.
(3) nutrient reference value derivation. 90% confidence interval, using bootstrap (bootstrap) simulation to determine the reference value.
7.3 Comprehensive evaluation of the reference value
The preliminary evaluation of the baseline valu...
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