CN111476451A - Water ecological index data processing method - Google Patents

Water ecological index data processing method Download PDF

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CN111476451A
CN111476451A CN202010091488.9A CN202010091488A CN111476451A CN 111476451 A CN111476451 A CN 111476451A CN 202010091488 A CN202010091488 A CN 202010091488A CN 111476451 A CN111476451 A CN 111476451A
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刘桂中
孙长虹
凌文翠
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Beijing Municipal Research Institute of Environmental Protection
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Abstract

The embodiment of the invention provides a water ecological index data processing method, which comprises the following steps: determining the vegetation coverage shoreline proportion of a target water area; determining the river natural shoreline ratio of a target water area; determining the river connectivity of a target water area; determining the guarantee rate of the ecological base flow; a water ecological index dataset is generated. The scheme of the invention provides a water ecological index data processing method, which can improve accurate water ecological index data for water ecological index data processing, so as to more accurately evaluate the bearing capacity of the water environment and prevent the human society from causing irreversible damage to the natural environment.

Description

Water ecological index data processing method
Technical Field
The invention relates to the technical field of data processing, in particular to a water ecological index data processing method.
Background
Due to the high population aggregation, the rapid economic development, the limited resource environment bearing capacity and the special geographical conditions, the severe situations of resource constraint trend and ecosystem degradation are faced. Particularly in the aspect of water environment, although the discharge amount of water pollutants is in a descending trend year by year in recent years, compared with the water environment capacity, the discharge amount of pollutants in partial areas exceeds the maximum allowable discharge amount of the water environment, and as the discharge amount of pollutants far exceeds the environment capacity, the water quality of many rivers exceeds the standard, and the water ecological degradation is serious. In order to enable the social environment to be continuously developed, the water environment needs to be controlled and managed finely, and the current situation of extensive management is broken away; therefore, a technology for accurately analyzing and processing water ecological index data in the bearing capacity of the water environment is urgently needed.
Disclosure of Invention
The invention provides a water ecological index data processing method, which is used for carrying out fine analysis and processing on water ecological data.
In order to solve the technical problems, the invention provides an ecological index data processing method, which solves the problem that the judgment result of the bearing capacity of the water environment is inaccurate due to extensive data processing.
In order to solve the above problems, the present invention provides a method for processing water ecological index data, comprising:
determining the vegetation coverage shoreline proportion of a target water area;
determining the river natural shoreline ratio of a target water area;
determining the river connectivity of a target water area;
determining the guarantee rate of the ecological base flow;
a water ecological index dataset is generated.
In some embodiments, the determining the vegetation coverage shoreline proportion of the target waters comprises: the ratio of the river vegetation covered shoreline to the total shoreline C1;
Figure RE-GDA0002507410100000021
in some embodiments, determining a river natural shoreline ratio for the target water area comprises: determining the river natural shoreline ratio of the target water area by the natural shoreline length and the total river length C2:
Figure RE-GDA0002507410100000022
in some embodiments, determining river connectivity for a target water area comprises: determining the longitudinal connectivity of the river according to the number of hydropower station gates built on the river C3:
Figure RE-GDA0002507410100000023
in some embodiments, determining an eco-based flow assurance rate comprises: the percentage of the actual flow of the reference year and month in the minimum ecological base flow is as follows:
Figure RE-GDA0002507410100000024
in some embodiments, wherein the minimum eco-based flow is calculated by the formula:
WEbmean flow × 10% in nearly 10 years.
The scheme of the invention at least comprises the following beneficial effects:
the scheme of the invention provides a water ecological index data processing method, which can more accurately evaluate the water environment bearing capacity and prevent the human society from causing irreversible damage to the natural environment.
Drawings
FIG. 1 is a schematic flow chart of a water ecological index data processing method in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention provides a method for processing water ecological index data, including:
(1) vegetation coverage shoreline ratio (C1): the river vegetation covers (>3 meters) the proportion of the shoreline to the total shoreline. The influence of the vegetation coverage condition on the bank of the river on the ecology of the river is reflected, the larger the vegetation coverage shoreline proportion is, the better the ecological condition of the river is, and otherwise, the worse the ecological condition of the river is.
Figure RE-GDA0002507410100000031
(2) River natural shoreline ratio (C2): the natural shoreline of the river accounts for the proportion of the total shoreline, and the more the natural shoreline is, the more suitable the growth of the living beings is, and the better the habitat condition of the river is.
Figure RE-GDA0002507410100000032
(3) River connectivity (C3): and the number of gate dams of the hydropower station built in unit length of the river. The fewer gate dams of the hydropower station are reflected, the better the longitudinal connectivity of the river is, the better the space connectivity of nutrient flow and energy flow, the space connectivity of a biological community structure and the space connectivity of information flow are, and the larger the bearing capacity of the water environment is.
Figure RE-GDA0002507410100000033
(4) Ecological-based flow assurance rate (C4): the actual flow of the reference year and month accounts for the percentage of the minimum ecological base flow.
Figure RE-GDA0002507410100000034
Minimum ecological base flux in formula:
WEbthe average flow rate of the product is × 10 percent in nearly 10 years
For those without hydrologic site data, the minimum ecological base flow of the river can be calculated with reference to the relevant parameters of the following table.
TABLE 1 habitat conditions of Water systems at different periods of time
Computing time period division Ecological depth of water (m) Ecological flow velocity (m/s)
Time period T1 (3 months 1-5 months 31 days) 0.6 0.02
Time period T2 (6 months 1-8 months 31 days) 0.8 0.05
Time period T3 (9 month 1-10 month 31) 0.8 0.02
Period T4 (11 month 1 day to next year 2 month 28 days) - -
After the water ecological index data are determined, the water environment bearing capacity data can be analyzed and processed more accurately, and the method comprises the following steps:
index normalization processing: in order to overcome the influence of different dimensions and magnitude of the evaluation indexes on the evaluation result, the evaluation indexes need to be normalized. Processing the forward index by using the formula (1), wherein the larger the value of the forward index is, the larger the bearing capacity of the water environment is; the reverse index is processed by the formula (2), and the larger the value of the reverse index is, the smaller the bearing capacity of the water environment is.
For the forward indicator:
Figure RE-GDA0002507410100000041
for the reverse indicator:
Figure RE-GDA0002507410100000042
in the formula, VjFor normalized index value, Vj≤1；bjThe actual value of the j index; b isjmaxThe upper limit value of the standard value of the interval corresponding to the j-th index actual value; b isjminThe lower limit value of the standard value of the interval corresponding to the j-th index actual value; qjmaxThe upper limit value of the loading degree corresponding to the j index actual value is set; qjminThe lower limit value of the corresponding load degree of the j index actual value is set.
Weight determination: because various influence factors of the bearing capacity of the water environment are mutually connected and restricted, the ambiguity and the uncertainty are very large, and therefore an analytic hierarchy process is required to be selected to determine the weight of each index; the method specifically comprises the following steps:
(1) building a hierarchical model
On the basis of in-depth analysis of actual problems, relevant factors are decomposed into a plurality of layers from top to bottom according to different attributes, and the factors of the same layer depend on or have influence on the factors of the previous layer. The top layer is the target layer, usually only 1 factor, and the second is the index layer.
(2) Construct pairwise comparison and judgment matrix
An important characteristic of the analytic hierarchy process is that the corresponding importance degree grades of two indexes are expressed by the ratio of two importance degrees. The hierarchical structure model determines the membership between the upper and lower elements, and for each element in the same layer, the elements connected with the adjacent upper layer are compared pairwise, and the relative importance or the relative quality degree is determined by using a 1-9 scale scoring method (see table 2). If the matrix B is judged to be (B)ij) n × n, then:
Figure RE-GDA0002507410100000051
TABLE 2 Scale of relative importance and significance
Scale Definition of
1 Of equal importance when compared to two elements
3 Two elements being compared, one being slightly more important than the other
5 Two elements being compared, one being more important than the other
7 One being more important than the other when comparing two elements
9 Two elements being compared, one being absolutely more important than the other
2，4，6，8 Median value of the above two adjacent judgments
1/bi，j The inverse ratio of two elements
(3) Determining relative weight of each element from the decision matrix
For the constructed judgment matrix, the eigenvector corresponding to the maximum eigenvalue can be obtained, and the calculation can be performed by adopting a standard column average method (sum-product method).
The calculation steps are as follows:
computing per-column normalization
Figure RE-GDA0002507410100000052
Averaging canonical columns
Figure RE-GDA0002507410100000061
Vector W ═ W1,w2,…,wn) Where T is the feature vector sought.
Calculating the maximum eigenvalue of the judgment matrix B
Figure RE-GDA0002507410100000062
In the formula (BW)iIs the ith element in the vector BW.
Obtaining the maximum characteristic root lambda of each judgment matrix by using a sum-product methodmaxAnd its corresponding feature vector W. And (4) carrying out consistency check on the judgment matrix according to the consistency ratio CR, and if the judgment matrix has satisfactory consistency, normalizing the eigenvector W to be used as a single-ordering weight vector. Otherwise, the scale of the judgment matrix needs to be properly corrected.
And judging whether the matrix has satisfactory consistency by using a random consistency ratio CR, wherein the matrix is judged to have satisfactory consistency if CR is generally considered to be less than 0.1.
The formula for CR is as follows:
Figure RE-GDA0002507410100000063
Figure RE-GDA0002507410100000064
in the formula, CR is a random consistency ratio; CI is consistencyIndexes; RI is the average random consistency index, which can be found in Table 3; lambda [ alpha ]maxJudging the maximum eigenvalue of the matrix; n is the order of the decision matrix.
TABLE 3 average random consistency index RI standard value
n 1 2 3 4 5 6 7 8 9 10 11 12
RI 0 0 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48
An evaluation model determination step: obtaining a comprehensive index evaluation model SWECC representing the relative size of the water environment bearing capacity of the region by adopting a weighted summation method, namely:
Figure RE-GDA0002507410100000065
in the formula, SWECCThe comprehensive evaluation index of the bearing capacity of the water environment is obtained; siThe fraction value of the ith index in the index layer is obtained; omegaiThe weight of the ith index in the index layer; m is the number of indexes.
Checking the bearing capacity of the water environment according to the standard exceeding index of the concentration of the water pollutants
And calculating the over-standard index of the water pollutant concentration of the region, wherein the over-standard index of the water pollutant concentration is reflected by the comparison value of the annual average concentration monitoring value of the main pollutants and the national current environmental quality standard.
According to the result of the calculation of the standard exceeding index of the concentration of the water pollutants, the bearing capacity S of the water environmentWECCThe necessary checks are performed. When the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state, and the bearing capacity S of the water environmentWECCIn principle, it should be less than 0.5; when the pollutant concentration standard exceeding index is less than-0.2, the water pollutant concentration is in a state of not standard exceeding, and the bearing capacity S of the water environmentWECCIn principle it should be greater than 0.75.
The calculation method of the water pollutant concentration standard exceeding index comprises the following steps:
when i ═ 1, i.e. the water contaminant is DO:
Rwater ijk＝1/(Cijk/Sik)-1
When i is 2, …,6, i.e. the water pollutant is COD respectivelyMn、BOD5、CODCr、NH3N, TP time:
Rwater ijk＝Cijk/Sik-1
Figure RE-GDA0002507410100000071
RWater jk＝max(RWater ijk)，i＝1,2,…,6
Figure RE-GDA0002507410100000072
Wherein R isWater ijkI term water pollutant concentration standard exceeding index R of k section of area jWater ijIs the i-th water pollutant concentration standard exceeding index, R, of the area jWater jkIs the water pollutant concentration standard exceeding index, R, of the kth section of the area jWater jIs the region j water pollutant concentration standard exceeding index, CijkThe annual average concentration monitoring value S of the ith water pollutant of the kth section of the area jikIs the water quality standard limit of the ith water pollutant of the kth section. i is 1,2, …,6, corresponding to DO and COD respectivelyMn、BOD5、CODCr、NH3-N, TP; k is a control section, k is 1,2, …, Nj，NjThe number of control sections in the region j is shown.
When the pollutant concentration standard exceeding index is larger than 0, the water pollutant concentration is in a standard exceeding state; when the pollutant concentration standard exceeding index is between-0.2 and 0, the pollutant concentration is in a state close to standard exceeding; when the pollutant concentration overproof index is less than-0.2, the water pollutant concentration is in an overproof state.
TABLE 4 Standard of the surface Water Environment quality Standard basic project Standard Limit value (Unit: mg/L)
Figure RE-GDA0002507410100000081
Rating scale
Bearing capacity (S) of water environmentWECC) The value range of (1) is between 0 and 1, the magnitude of the value reflects the degree of the bearing capacity of the regional water environment, and the larger the value is, the larger the bearing capacity of the regional water environment is; the smaller value indicates that the water environment in the area has smaller bearing capacity and can not bear larger pressure, and the water environment is very fragile and even at the collapsed edge.
In order to qualitatively evaluate the bearing capacity of the water environment, the value of the bearing capacity of the water environment is divided into different grades, so that the degree of the bearing capacity of the water environment is evaluated. The results of the ranking are shown in table 5.
TABLE 5 comprehensive evaluation of water environment bearing capacity grading
Figure RE-GDA0002507410100000082
Water environment bearing capacity monitoring and early warning
And (4) carrying out early warning grade division on the bearing capacity of the water environment according to the comprehensive evaluation result of the bearing capacity of the water environment, and specifically referring to table 6.
Table 6 early warning grade division of bearing capacity of water environment
Figure RE-GDA0002507410100000083
Proposing a countermeasure proposal
According to the evaluation result of the bearing capacity of the water environment, the main factors influencing the bearing capacity of the water environment in the area are analyzed from the aspects of natural resource conditions, social and economic development, pollutant emission, water environment management and the like by combining the actual condition of the evaluation area.
From two main lines of 'capacity increasing' and 'emission reduction', an ecological water demand guarantee scheme and a water pollutant reduction scheme are provided. Providing a water environment bearing capacity monitoring and early warning mechanism construction scheme, comprising the steps of obtaining and transmitting basic data, constructing a water environment bearing capacity studying and judging system, analyzing and publishing a bearing state, applying a monitoring and early warning result and the like
In the embodiment of the present invention, when performing the water environment bearing capacity data processing, the following parameters may be further referred to: water resource index, water environment index and water ecology index. Specifically, the method comprises the following steps:
water resource index
(1) Water resource exploitation utilization (a 1): the ratio of water usage (industrial, agricultural, domestic, environmental, etc.) to the average total water resource of the drainage basin over the years.
Figure RE-GDA0002507410100000091
(2) Ten thousand GDP water consumption (a 2): the unit GDP (total value of national production) consumes water resource, i.e. the ratio of the total water consumption to the total value of national production (GDP).
Figure RE-GDA0002507410100000092
Figure RE-GDA0002507410100000093
Figure RE-GDA0002507410100000094
(3) Average human water area (a 3): in the district, the area of the water area owned by all people, namely the ratio of the total population to the total area of the water area. The index mainly reflects the natural endowment condition of human owned water resource and also responds to the characteristics of water ecology and water environment.
Figure RE-GDA0002507410100000095
Water environment index
(1) Industrial pollution intensity index (B1): reflecting the pressure of pollutants discharged in the industrial production process of the evaluation area on the ecological environment of the drainage basin.
Figure RE-GDA0002507410100000101
Figure RE-GDA0002507410100000102
Figure RE-GDA0002507410100000103
Figure RE-GDA0002507410100000104
(2) Agricultural pollution intensity index (B2): reflecting the pressure of the pollutants discharged/lost in the agricultural production process of the assessment area on the ecological environment of the drainage basin.
Figure RE-GDA0002507410100000105
Figure RE-GDA0002507410100000106
Figure RE-GDA0002507410100000107
Figure RE-GDA0002507410100000108
(3) Town life pollution intensity index (B3): reflecting the pressure of pollutants discharged from domestic sewage in the urban assessment area on the ecological environment of the drainage basin.
Figure RE-GDA0002507410100000109
Figure RE-GDA00025074101000001010
Figure RE-GDA0002507410100000111
Figure RE-GDA0002507410100000112
(4) Urban non-point source pollution intensity index (B4): reflecting the pressure of pollutants discharged by urban non-point sources in the evaluation area on the ecological environment of the drainage basin.
Figure RE-GDA0002507410100000113
Figure RE-GDA0002507410100000114
Figure RE-GDA0002507410100000115
Figure RE-GDA0002507410100000116
The formula for calculating the discharge amount of urban non-point source pollutants is as follows:
Ggeneral assembly＝∑Gi×10-3
GGeneral assemblyUrban non-point source pollutant emission amount, ton/year; giThe annual pollutant amount of each underlying surface is kilogram per year, and urban underlying surfaces are divided into roofs, traffic pavements, greenbelts and comprehensive lands.
The amount of contaminants on each underlying surface was calculated as follows.
Gi＝0.01αφPSEMC
Wherein α represents a runoff correction factor, phi represents a runoff factor of a drainage area, and P represents an annual precipitation amount and mm represents a standard
Year; s, area of each underlying surface and hectare; EMC-mean concentration of events, mg/l.
The above factors are specifically described as follows:
① runoff correction factor α, generally takes 0.9;
② the runoff coefficient phi of the drainage area is generally adopted as a literature reference value, and the runoff coefficient of different underlying surfaces is different.
TABLE 7 reference table for runoff coefficient values in Beijing City
Figure RE-GDA0002507410100000117
Figure RE-GDA0002507410100000121
The precipitation P in ③ can be obtained from meteorological or hydrological department data.
④ drainage area S, which is the area of different underlays, namely the area of the roof, traffic road, green land and comprehensive land, can be obtained by remote sensing data interpretation.
⑤ event Mean concentration EMC (event Mean concentration), which refers to the flow weighted Mean concentration of pollutants in a single runoff contamination process, i.e. the ratio of total pollutant amount to total runoff amount.
TABLE 8 Event Mean Concentration (EMC) referenceable values in mg/L
Categories COD TN TP Ammonia nitrogen
Roof covering 75.89 8.86 0.084 5.63
Traffic road surface 197.45 7.91 0.28 5.19
Greenbelt 60.71 7 0.3 2.87
Comprehensive land 176.11 5.86 0.12 5.55
The invention provides a method for calculating the watershed water ecological index, which provides more accurate reference data for water environment bearing capacity data processing by establishing a watershed water ecological index data set so as to more accurately evaluate the water environment bearing capacity and prevent the human society from causing irreversible damage to the natural environment.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (6)

1. A method for processing water ecological index data, comprising:
determining the vegetation coverage shoreline proportion of a target water area;
determining the river natural shoreline ratio of a target water area;
determining the river connectivity of a target water area;
determining the guarantee rate of the ecological base flow;
a water ecological index dataset is generated.
2. The method of claim 1, wherein the determining the vegetation coverage shoreline proportion of the target body of water comprises:
the ratio of the river vegetation covered shoreline to the total shoreline C1;
Figure FDA0002383867770000011
3. the water ecological index data processing method of claim 1, wherein determining a river natural shoreline ratio of the target water area comprises:
determining the river natural shoreline ratio of the target water area by the natural shoreline length and the total river length C2:
Figure FDA0002383867770000012
4. the water ecological index data processing method of claim 1, wherein determining river connectivity of a target water area comprises:
determining the longitudinal connectivity of the river according to the number of hydropower station gates built on the river C3:
Figure FDA0002383867770000013
5. the water ecological index data processing method according to claim 1, wherein determining an ecological-based flow assurance rate includes: the percentage of the actual flow of the reference year and month in the minimum ecological base flow is as follows:
Figure FDA0002383867770000014
6. the water ecological index data processing method according to claim 5, wherein the minimum ecological base flow rate is calculated by the following formula:
WEbmean flow × 10% in nearly 10 years.
CN202010091488.9A 2020-02-13 2020-02-13 Water ecological index data processing method Pending CN111476451A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113343413A (en) * 2021-04-22 2021-09-03 中国环境科学研究院 Water environment bearing capacity evaluation method, device, equipment and medium
CN116757897A (en) * 2023-08-21 2023-09-15 中国环境监测总站 Flood season pollution intensity analysis method and system based on data decomposition
CN118296557A (en) * 2024-06-05 2024-07-05 大连中汇达科学仪器有限公司 Water ecology intelligent monitoring method and system based on distributed sensing unit

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CN101918971A (en) * 2007-08-21 2010-12-15 水族指数有限公司 Aqua index
CN108122077A (en) * 2017-12-22 2018-06-05 中国水利水电科学研究院 A kind of water environment safety evaluation method and device
CN110189059A (en) * 2019-06-17 2019-08-30 北京师范大学 A kind of basin water systematic collaboration Bearing Capacity Evaluation index system construction method

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Publication number Priority date Publication date Assignee Title
CN101918971A (en) * 2007-08-21 2010-12-15 水族指数有限公司 Aqua index
CN108122077A (en) * 2017-12-22 2018-06-05 中国水利水电科学研究院 A kind of water environment safety evaluation method and device
CN110189059A (en) * 2019-06-17 2019-08-30 北京师范大学 A kind of basin water systematic collaboration Bearing Capacity Evaluation index system construction method

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113343413A (en) * 2021-04-22 2021-09-03 中国环境科学研究院 Water environment bearing capacity evaluation method, device, equipment and medium
CN116757897A (en) * 2023-08-21 2023-09-15 中国环境监测总站 Flood season pollution intensity analysis method and system based on data decomposition
CN116757897B (en) * 2023-08-21 2023-11-14 中国环境监测总站 Flood season pollution intensity analysis method and system based on data decomposition
CN118296557A (en) * 2024-06-05 2024-07-05 大连中汇达科学仪器有限公司 Water ecology intelligent monitoring method and system based on distributed sensing unit
CN118296557B (en) * 2024-06-05 2024-09-10 大连中汇达科学仪器有限公司 Water ecology intelligent monitoring method and system based on distributed sensing unit

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