CN111563643A - Water environment index data processing method - Google Patents

Water environment index data processing method Download PDF

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CN111563643A
CN111563643A CN202010091489.3A CN202010091489A CN111563643A CN 111563643 A CN111563643 A CN 111563643A CN 202010091489 A CN202010091489 A CN 202010091489A CN 111563643 A CN111563643 A CN 111563643A
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刘桂中
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 environment index data processing method, which comprises the following steps: determining an industrial pollution intensity index, an agricultural pollution intensity index, a town living pollution intensity index and a city non-point source pollution intensity index of a target water area; and generating a water environment index data set. The scheme of the invention provides a water environment index data processing method, which can provide an accurate data processing method for the water environment index 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 environment index data processing method
Technical Field
The invention relates to the technical field of data processing, in particular to a water environment index data processing method.
Background
Due to the high population gathering, the rapid economic development, the limited resource and 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, water resources are deficient, although the discharge amount of water pollutants is in a descending trend year by year in recent years, compared with the capacity of the water environment, the discharge amount of pollutants in partial areas exceeds the maximum allowable discharge amount of the water environment, and the water quality of many rivers exceeds the standard because the discharge amount of the pollutants far exceeds the environment capacity. 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 precisely analyzing and processing various data of the water environment is urgently needed.
Disclosure of Invention
The invention provides a water environment index data processing method, which is used for carrying out fine analysis and processing on water environment data.
In order to solve the technical problems, the invention provides a water environment index data processing method, which solves the problem of inaccurate water environment bearing capacity judgment result caused by extensive data processing.
Aiming at the problems, the technical problem to be solved by the invention is to provide a water environment index data processing method, which comprises the following steps:
determining an industrial pollution intensity index, an agricultural pollution intensity index, a town living pollution intensity index and a city non-point source pollution intensity index of a target water area;
and generating a water environment index data set.
In some embodiments, the determining the industrial pollution intensity index comprises: determining an industrial pollution intensity index B1 through industrial discharged pollutants:
Figure BDA0002383867630000021
Figure BDA0002383867630000022
Figure BDA0002383867630000023
Figure BDA0002383867630000024
in some embodiments, the determining the agricultural pollution intensity index comprises: determining an agricultural pollution intensity index B2 through agricultural discharged pollutants:
Figure BDA0002383867630000025
Figure BDA0002383867630000026
Figure BDA0002383867630000027
Figure BDA0002383867630000028
in some embodiments, the determining the urban area source pollution intensity index comprises: determining the urban life pollution intensity index B3 through pollutants discharged by urban life:
Figure BDA0002383867630000029
Figure BDA00023838676300000210
Figure BDA00023838676300000211
Figure BDA00023838676300000212
in some embodiments, the determining the town life pollution intensity index includes: determining the urban living pollution intensity index B4 through pollutants discharged from urban non-point sources:
Figure BDA0002383867630000031
Figure BDA0002383867630000032
Figure BDA0002383867630000033
Figure BDA0002383867630000034
in some embodiments, the determining the town life pollution intensity index includes: 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.
In some embodiments, the amount of contaminants on each underlying surface is calculated by the following equation:
Gi＝0.01αφPSEMC
in the formula: alpha-runoff correction factor; phi represents the runoff coefficient of the drainage area; p-annual precipitation in mm
Year; s, area of each underlying surface and hectare; EMC-mean concentration of events, mg/l.
The scheme of the invention at least comprises the following beneficial effects:
according to the scheme, the water environment index data processing method is provided, and the water environment index data set is established, so that an accurate data processing method can be provided for the water environment index, the bearing capacity of the water environment can be evaluated more accurately, and the irreversible damage of the human society to the natural environment is prevented.
Drawings
Fig. 1 is a schematic flow chart of a water environment 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 environment index data, including:
(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 watershed.
Figure BDA0002383867630000041
Figure BDA0002383867630000042
Figure BDA0002383867630000043
Figure BDA0002383867630000044
(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 BDA0002383867630000045
Figure BDA0002383867630000046
Figure BDA0002383867630000047
Figure BDA0002383867630000048
(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 BDA0002383867630000049
Figure BDA0002383867630000051
Figure BDA0002383867630000052
Figure BDA0002383867630000053
(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 watershed.
Figure BDA0002383867630000054
Figure BDA0002383867630000055
Figure BDA0002383867630000056
Figure BDA0002383867630000057
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
In the formula: alpha-runoff correction factor; phi represents the runoff coefficient of the drainage area; p-annual precipitation in mm
Year; s, area of each underlying surface and hectare; EMC-mean concentration of events, mg/l.
The above factors are specifically described as follows:
firstly, a runoff correction coefficient alpha is generally 0.9;
secondly, the runoff coefficient phi of the drainage area is generally adopted as a reference value of a document, and the runoff coefficients of different underlying surfaces are different.
TABLE 1 reference table for runoff coefficient values
Figure BDA0002383867630000058
Figure BDA0002383867630000061
And the annual precipitation P can be obtained through the data of the meteorological department or the hydrological department.
And fourthly, the area S of the drainage area refers to the areas of different underlying surfaces, namely the areas of the roof, the traffic road surface, the green land and the comprehensive land, and can be obtained through remote sensing data interpretation.
Event Mean concentration emc (event Mean concentration), which means the flow weighted Mean concentration of the pollutants in the primary runoff pollution process, i.e. the ratio of the total pollutant amount to the total runoff amount. Without actual monitoring data, the Event Mean Concentration (EMC) in the table below can be used.
Table 2 Event Mean Concentration (EMC) referenceable value table units: 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
After the watershed water environment index data are determined, the water environment bearing capacity data can be more accurately analyzed and processed, and therefore risk assessment is achieved. The method specifically comprises the following steps:
index normalization processing: in order to overcome the influence of different dimensions and magnitude levels 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 BDA0002383867630000062
for the reverse indicator:
Figure BDA0002383867630000071
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 j-th item indicates the lower limit value of the corresponding load degree of the actual value.
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 feature 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 upper and lower layer elementsThe membership relationship between elements is that for each element in the same layer, the elements connected with the adjacent upper layer are compared pairwise respectively, and the relative importance or the relative quality degree is determined by using a 1-9 scale scoring method (see table 3). If the matrix B is judged to be (B)ij) n × n, then:
Figure RE-GDA0002507405960000072
TABLE 3 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-GDA0002507405960000081
Averaging canonical columns
Figure RE-GDA0002507405960000082
Vector W ═ W1,w2,…,wn) Where T is the feature vector sought.
Calculating the maximum eigenvalue of the judgment matrix B
Figure RE-GDA0002507405960000083
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-GDA0002507405960000084
Figure RE-GDA0002507405960000085
in the formula, CR is a random consistency ratio; CI is a consistency index; RI is the average random consistency index, which can be found in Table 4; lambda [ alpha ]maxJudging the maximum eigenvalue of the matrix; n is the order of the decision matrix.
TABLE 4 average random consistency index RI standard value
Figure BDA0002383867630000086
Figure BDA0002383867630000091
An evaluation model determination step: obtaining a comprehensive index evaluation model S representing the relative magnitude of the water environment bearing capacity of the region by adopting a weighted summation methodWECCNamely:
Figure BDA0002383867630000092
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 water pollutant concentration standard exceeding index, the method is used for calculating the concentration standard exceeding index of the water pollutantBearing capacity S of 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 water environment bearing capacity SWECCIn 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 BDA0002383867630000093
RWater jk＝max(RWater ijk)，i＝1,2,…,6
Figure BDA0002383867630000094
Wherein R isWater ijkI term water pollutant concentration standard exceeding index R of k section of area jWater ijIs the ith water pollutant concentration standard exceeding index, R, of the region jWater jkIs the water pollutant concentration superscript index R of the kth section of the region 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 controlled cross-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 superstandard index is less than-0.2, the water pollutant concentration is in a state of not exceeding the standard.
TABLE 5 Standard basic project Standard Limit value (unit: mg/L) of surface water environment quality standard
Figure BDA0002383867630000101
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 water environment in the region, and the larger the value is, the larger the bearing capacity of the water environment in the region 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 values of the bearing capacity of the water environment are 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 6 comprehensive evaluation grade division of bearing capacity of water environment
Figure BDA0002383867630000102
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 7.
Table 7 early warning grade division of bearing capacity of water environment
Figure BDA0002383867630000111
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 BDA0002383867630000112
(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 BDA0002383867630000113
Figure BDA0002383867630000114
Figure BDA0002383867630000121
(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 innate endowment condition of human beings owned by water resources and also responds to the characteristics of water ecology and water environment.
Figure BDA0002383867630000122
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 watershed.
Figure BDA0002383867630000123
Figure BDA0002383867630000124
Figure BDA0002383867630000125
Figure BDA0002383867630000126
(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 BDA0002383867630000127
Figure BDA0002383867630000128
Figure BDA0002383867630000129
Figure BDA0002383867630000131
(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 BDA0002383867630000132
Figure BDA0002383867630000133
Figure BDA0002383867630000134
Figure BDA0002383867630000135
(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 watershed.
Figure BDA0002383867630000136
Figure BDA0002383867630000137
Figure BDA0002383867630000138
Figure BDA0002383867630000139
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
In the formula: alpha-runoff correction factor; phi represents the runoff coefficient of the drainage area; p-annual precipitation in mm
Year; s, area of each underlying surface and hectare; EMC-mean concentration of events, mg/l.
The above factors are specifically described as follows:
firstly, a runoff correction coefficient alpha is generally 0.9;
secondly, the runoff coefficient phi of the drainage area is generally adopted as a reference value of a document, and the runoff coefficients of different underlying surfaces are different.
Runoff coefficient value reference table
Categories Reference value
Roof covering 0.90
Traffic road surface 0.80
Greenbelt 0.20
Comprehensive land 0.60
And the annual precipitation P can be obtained through the data of the meteorological department or the hydrological department.
And fourthly, the area S of the drainage area refers to the areas of different underlying surfaces, namely the areas of the roof, the traffic road surface, the green land and the comprehensive land, and can be obtained through remote sensing data interpretation.
Event Mean concentration emc (event Mean concentration), which means the flow weighted Mean concentration of the pollutants in the primary runoff pollution process, i.e. the ratio of the total pollutant amount to the total runoff amount. Without actual monitoring data, the Event Mean Concentration (EMC) in the table below can be used.
Event Mean Concentration (EMC) referable value table unit: 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
Synthesis ofLand for use 176.11 5.86 0.12 5.55
Water ecology data:
(1) vegetation coverage shoreline ratio (C1): river vegetation covers (>3 meters) the proportion of the shoreline in 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 BDA0002383867630000151
(2) River natural shoreline ratio (C2): the natural bank line of the river accounts for the total bank line, and the more the natural bank line is, the more suitable the biological growth is, and the better the habitat condition of the river is.
Figure BDA0002383867630000152
(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 colony structure and the space connectivity of information flow are, and the larger the bearing capacity of the water environment is.
Figure BDA0002383867630000153
(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 BDA0002383867630000154
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-based flux of the river can be calculated with reference to the relevant parameters in the table below.
Habitat conditions of water systems at different time intervals
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) - -
According to the scheme, the water environment index data processing method is provided, and the water environment index data set is established, so that an accurate data processing method can be provided for the water environment index, the bearing capacity of the water environment can be evaluated more accurately, and the irreversible damage of the human society to the natural environment is prevented.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it is obvious to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements should be construed as the protection scope of the present invention.

Claims (7)

1. A water environment index data processing method is characterized by comprising the following steps:
determining an industrial pollution intensity index, an agricultural pollution intensity index, a town living pollution intensity index and a city non-point source pollution intensity index of a target water area;
and generating a water environment index data set.
2. The method for processing the water environment index data according to claim 1, wherein the determining the industrial pollution intensity index comprises:
determining an industrial pollution intensity index B1 through industrial emission pollutants:
Figure FDA0002383867620000011
Figure FDA0002383867620000012
Figure FDA0002383867620000013
Figure FDA0002383867620000014
3. the water environment index data processing method according to claim 1, wherein the determining the agricultural pollution intensity index comprises:
determining an agricultural pollution intensity index B2 through pollutants emitted by agriculture:
Figure FDA0002383867620000015
Figure FDA0002383867620000016
Figure FDA0002383867620000017
Figure FDA0002383867620000018
4. the method for processing the water environment index data according to claim 1, wherein the determining the urban non-point source pollution intensity index comprises:
determining the urban life pollution intensity index B3 through pollutants discharged by urban life:
Figure FDA0002383867620000021
Figure FDA0002383867620000022
Figure FDA0002383867620000023
Figure FDA0002383867620000024
5. the water environment index data processing method according to claim 1, wherein the determining the town pollution intensity index comprises:
determining the urban living pollution intensity index B4 through pollutants discharged from urban non-point sources:
Figure FDA0002383867620000025
Figure FDA0002383867620000026
Figure FDA0002383867620000027
Figure FDA0002383867620000028
6. the water environment index data processing method according to claim 4, wherein the determining the town pollution intensity index comprises:
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.
7. The method for processing the water environment index data according to claim 5, wherein the pollutant amount of each underlying surface is calculated by the following formula:
Gi＝0.01αφPSEMC
in the formula: alpha-runoff correction factor; phi represents the runoff coefficient of the drainage area; p-annual precipitation, mm/year; s, area of each underlying surface and hectare; EMC-mean concentration of events, mg/l.
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Application publication date: 20200821