A method for accurately identifying a monitoring section of a river water body COD affected by natural factors

Abstract

The application provides a precise identification method for monitoring sections of COD of river water bodies affected by natural factors, and relates to the technical field of water environment monitoring and evaluation. The method comprises classifying monitoring sections and determining corresponding catchment areas, screening candidate background affected sections according to the correlation between land use structure and upstream COD concentration change, excluding sections affected by point sources, surface sources or historical human disturbance, and then dividing background sections with greater and smaller background influences according to the annual average of COD. The method can reduce the confusion between natural background dominant sections and human pollution affected sections.

Description

Technical Field

[0001] This invention relates to the field of water environment monitoring and evaluation technology, and in particular to a method for accurately identifying monitoring sections of river water COD affected by natural factors. Background Technology

[0002] Chemical oxygen demand (COD) is a commonly used comprehensive indicator of organic pollution in surface water environmental quality assessment. River COD is influenced not only by anthropogenic activities such as industrial wastewater, urban sewage, and agricultural non-point source pollution, but also by the natural background conditions of the watershed. In watersheds with a high proportion of forests, grasslands, wetlands, and water bodies, soil humus leaching, litter decomposition, natural runoff transport, organic matter input from riverbanks, and upstream water inflow can all contribute to maintaining relatively high levels of COD in river water. Such elevated COD levels have natural background attributes, and it cannot be concluded that a monitoring section is affected by anthropogenic pollution simply because the monitored concentration exceeds the surface water environmental quality standard limit.

[0003] Existing water environment baseline assessment work has already considered the impact of natural geographical conditions, land cover type, hydrological processes, and aquatic ecological status on water quality indicators, and has included water bodies or monitoring sections affected by natural background values ​​within the scope of water environment assessment and management. However, in the actual determination of river monitoring sections, existing methods mostly rely on survey conclusions or experience-based judgments, lacking continuous discrimination rules for monitoring sections. Especially when multiple monitoring sections are set up upstream and downstream of the same river, the contributions of upstream water, local runoff input, and the impact of anthropogenic pollution are easily intertwined. It is difficult to accurately identify monitoring sections whose chemical oxygen demand is mainly affected by natural factors based solely on section water quality concentration or single land use information.

[0004] Therefore, the identification of monitoring sections affecting the background of chemical oxygen demand (COD) in river waters still faces problems such as unclear boundaries, difficulty in separating the sources of influence, and insufficient consistency of results. If monitoring sections dominated by natural background are identified as sections affected by anthropogenic pollution, it can easily lead to water environment assessment results deviating from the actual causes; conversely, if monitoring sections affected by point source pollution, non-point source pollution, or historical anthropogenic interference are identified as background sections, the targeted nature of pollution control and water environment management will be weakened. Based on these problems, it is necessary to establish a precise identification method for monitoring sections affecting COD in river waters under the influence of natural factors, focusing on monitoring section classification, catchment area determination, background influence screening, and exclusion of anthropogenic influences. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a precise identification method for monitoring sections of river water COD affected by natural factors. By classifying river monitoring sections, determining the corresponding catchment area, conducting background influence screening, and excluding human influence, the identification process of monitoring sections of COD affected by natural factors has a continuous basis for judgment, reducing the confusion between sections dominated by natural background and sections affected by human pollution.

[0006] To achieve the above objectives, the present invention provides the following solution:

[0007] A method for accurately identifying monitoring sections of river water COD affected by natural factors, comprising:

[0008] The monitoring sections of the target river are classified, with the monitoring section located at the uppermost part of the river being identified as the upstream monitoring section, and the monitoring section located in the middle or lower reaches of the river and with other monitoring sections upstream being identified as the transit monitoring section.

[0009] The watershed is divided into small watersheds based on the monitoring section as the outlet, and the catchment area of ​​the monitoring section is determined. The catchment area of ​​the upstream monitoring section is the complete catchment area above the upstream monitoring section, and the catchment area of ​​the transit monitoring section is the catchment area with the transit monitoring section as the outlet minus the catchment area of ​​the upstream adjacent monitoring section.

[0010] Based on the land use structure within the catchment area, the background impact of upstream monitoring sections and transit monitoring sections is initially screened to obtain the first candidate background impact section set;

[0011] For transit monitoring sections that were not included in the first candidate background impact section set, supplementary screening was conducted based on the proportion of the catchment area of ​​the monitoring point to the total catchment area and the correlation of COD concentration changes between the adjacent upstream monitoring section and the transit monitoring section, to obtain the second candidate background impact section set.

[0012] Human influence is eliminated from the monitoring sections in the first and second candidate background influence section sets. Monitoring sections affected by point sources, area sources, or historical human interference are removed to obtain the background influence section set to be classified.

[0013] Based on the annual average COD value of each monitoring section in the set of background impact sections to be classified, the monitoring sections with an annual average COD value greater than the Class III threshold are identified as background sections with greater background impact, and the monitoring sections with an annual average COD value not greater than the Class III threshold are identified as background sections with less background impact.

[0014] Preferably, the monitoring sections of the target river are classified, including:

[0015] Obtain the longitudinal position of each monitoring section in the target river and the upstream and downstream relationships between each monitoring section;

[0016] The monitoring section that has no upstream monitoring section and directly receives the water from the source is designated as the upstream monitoring section;

[0017] Monitoring sections that have other monitoring sections upstream and are affected by both upstream water inflow and local runoff are designated as transit monitoring sections.

[0018] Preferably, the monitoring section is used as the outlet to divide the watershed into smaller watersheds, and the catchment area of ​​the monitoring section is determined, including:

[0019] Using the upstream monitoring section as the outlet, the complete catchment area between the source and the upstream monitoring section is determined, and the complete catchment area is taken as the catchment range of the upstream monitoring section;

[0020] Using the transit monitoring section as the outlet, the catchment area flowing into the transit monitoring section is determined, and the catchment area of ​​the upstream adjacent monitoring section is subtracted from the catchment area flowing into the transit monitoring section to obtain the catchment range of the transit monitoring section.

[0021] Preferably, based on the land use structure within the catchment area, a preliminary background impact screening is performed on upstream monitoring sections and transit monitoring sections to obtain a first candidate set of background impact sections, including:

[0022] Interpret the land cover within the catchment area to obtain the land use structure;

[0023] Identify forest land, grassland, wetland, cultivated land, water area, construction land, and bare land based on land use structure;

[0024] Forest land, grassland, wetland and water area are designated as ecological land;

[0025] Calculate the proportion of ecological land area to the total catchment area, and include upstream monitoring sections and transit monitoring sections where the proportion of ecological land area to the total catchment area is not less than 90% into the first candidate background impact section set.

[0026] Preferably, for transit monitoring sections not included in the first candidate background impact section set, supplementary screening is conducted based on the proportion of the catchment area of ​​the monitoring point to the total catchment area and the correlation of COD concentration changes between adjacent upstream monitoring sections and transit monitoring sections, resulting in a second candidate background impact section set, including:

[0027] Among the transit monitoring sections that have never been included in the first candidate background impact section set, those with ecological land area accounting for less than 90% of the total catchment area are identified as target transit monitoring sections.

[0028] Calculate the proportion of the catchment area of ​​the monitoring point at the target transit monitoring section to the total catchment area;

[0029] A correlation test was conducted on the COD concentration changes between the nearby upstream monitoring section and the target transit monitoring section;

[0030] When the catchment area of ​​the monitoring point accounts for less than 1% of the total catchment area, and the COD concentration change trend of the adjacent upstream monitoring section is the same and significantly correlated with that of the target transit monitoring section, the target transit monitoring section will be included in the second set of candidate background influence sections.

[0031] Preferably, the monitoring sections in the first and second candidate background influence section sets are subject to human influence exclusion, including:

[0032] From the first set of background impact sections and the second set of background impact sections, identify the monitoring sections within the catchment area where there are fixed sewage outlets;

[0033] Obtain the discharge water quality of fixed sewage outlets and the proportion of protein-like substances in dissolved organic matter at the corresponding monitoring sections;

[0034] When the water quality at the discharge outlet is worse than Class III, or when the proportion of protein-like substances in dissolved organic matter is greater than 20%, the corresponding monitoring section will be designated as a monitoring section affected by point sources.

[0035] Preferably, the monitoring sections in the first and second candidate background influence section sets are subject to human influence exclusion, including:

[0036] Calculate the contribution rate of non-point source pollution to Class III water quality at each monitoring section in the first and second candidate background influence section sets.

[0037] The contribution rate of non-point source pollution to Class III water quality is calculated using the following formula:

[0038] ;

[0039] in, The contribution rate of non-point source pollution to Class III water quality. As for the pollution load of rural life, Pollution load from livestock and poultry farming Annual runoff at the cross-section;

[0040] When the contribution rate of non-point source pollution to Class III water is greater than 1%, the corresponding monitoring section will be identified as a monitoring section affected by non-point source pollution.

[0041] Preferably, the monitoring sections in the first and second candidate background influence section sets are subject to human influence exclusion, including:

[0042] Obtain the attached algal community data of each monitoring section in the first candidate background influence section set and the second candidate background influence section set;

[0043] The Shannon-Wiener diversity index of terrestrial algae was calculated based on terrestrial algal community data.

[0044] When the Shannon-Wiener diversity index of attached algae is less than 2, the corresponding monitoring section is identified as a monitoring section affected by historical human interference.

[0045] Preferably, monitoring sections affected by point sources, area sources, or historical human interference are removed to obtain a set of background impact sections to be classified, including:

[0046] By merging the monitoring sections affected by point sources, the monitoring sections affected by area sources, and the monitoring sections affected by historical human interference, a set of sections with human influence excluded is obtained.

[0047] After merging the first candidate background impact section set and the second candidate background impact section set, the monitoring sections in the human impact exclusion section set are removed to obtain the background impact section set to be classified.

[0048] Preferably, the classification is based on the annual average COD value of each monitoring section in the set of background impact sections to be classified, including:

[0049] Obtain COD monitoring data for each monitoring section in the set of background impact sections to be classified;

[0050] The annual average COD value of each monitoring section was calculated based on COD monitoring data;

[0051] The annual average COD value of each monitoring section was compared with the Class III threshold, which is the Class III limit value for COD in the "Surface Water Environmental Quality Standard".

[0052] When the annual average COD value is greater than the Class III threshold, the corresponding monitoring section will be identified as a background section that is significantly affected by the background.

[0053] When the annual average COD value is not greater than the Class III threshold, the corresponding monitoring section will be identified as a background section with less background influence.

[0054] The present invention discloses the following beneficial effects:

[0055] This invention first divides the monitoring sections of the target river into upstream monitoring sections and transit monitoring sections, and then determines the corresponding catchment area based on the monitoring section as the outlet. This allows monitoring sections at different hydrological locations to be identified according to their corresponding inflow relationships and catchment impact ranges. On this basis, monitoring sections that may be affected by natural background are first screened based on land use structure. Then, for transit monitoring sections not included in the initial screening results, supplementary screening is conducted by combining the catchment area ratio of the monitoring point and the correlation between upstream and downstream COD concentration changes. This can reduce misjudgments caused by relying solely on single water quality concentrations or single land use information. Furthermore, monitoring sections affected by point sources, non-point sources, or historical human interference are removed from the candidate sections. Based on the annual average COD, the remaining monitoring sections are divided into background sections with greater background influence and background sections with less background influence. This makes the identification process of monitoring sections affected by natural factors in river water COD a continuous judgment chain from section classification, catchment area limitation, background influence screening to human influence exclusion, reducing confusion between sections dominated by natural background and sections affected by human pollution. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of a method provided in an embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] The purpose of this invention is to provide a method for accurately identifying monitoring sections of river water COD affected by natural factors. The method takes the monitoring sections of the target river as the identification object and determines the monitoring sections affected by natural factors based on the section's hydrological location, catchment area, land use structure, upstream water inflow correlation, and the exclusion of human influences.

[0060] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0061] Figure 1 The method flowchart provided in the embodiments of the present invention is as follows: Figure 1 As shown, this invention provides a method for accurately identifying monitoring sections of river water COD affected by natural factors, including:

[0062] Step 100: Classify the monitoring sections of the target river, identify the monitoring section located at the uppermost part of the river as the upstream monitoring section, and identify the monitoring section located in the middle or lower reaches of the river and with other monitoring sections upstream as the transit monitoring section.

[0063] Step 200: Divide the watershed into smaller watersheds based on the monitoring section as the outlet and determine the catchment area of ​​the monitoring section; the catchment area of ​​the upstream monitoring section is the complete catchment area above the upstream monitoring section, and the catchment area of ​​the transit monitoring section is the catchment area with the transit monitoring section as the outlet minus the catchment area of ​​the upstream adjacent monitoring section.

[0064] Step 300: Based on the land use structure within the catchment area, conduct preliminary screening of background impact on upstream monitoring sections and transit monitoring sections to obtain the first candidate background impact section set;

[0065] Step 400: For the transit monitoring sections not included in the first candidate background impact section set, supplementary screening is carried out based on the proportion of the catchment area of ​​the monitoring point to the total catchment area and the correlation of COD concentration changes between the adjacent upstream monitoring section and the transit monitoring section to obtain the second candidate background impact section set.

[0066] Step 500: Perform human influence exclusion on the monitoring sections in the first candidate background influence section set and the second candidate background influence section set, and remove the monitoring sections affected by point source influence, area source influence or historical human interference to obtain the background influence section set to be classified.

[0067] Step 600: Classify the monitoring sections in the set of background impact sections according to their annual average COD value. The monitoring sections with an annual average COD value greater than the Class III threshold are identified as background sections with greater background impact, and the monitoring sections with an annual average COD value not greater than the Class III threshold are identified as background sections with less background impact.

[0068] This embodiment uses monitoring sections of the target river as the identification object. Based on the section's hydrological location, catchment area, land use structure, upstream water inflow correlation, and the results of excluding anthropogenic influences, it determines the monitoring sections whose COD is affected by natural factors. Background-affected section database A1 is the first candidate set of background-affected sections, background-affected section database A2 is the second candidate set of background-affected sections, exclusion section database B1 is the set of sections excluded for point source influences, exclusion section database B2 is the set of sections excluded for non-point source influences, and exclusion section database C is the set of sections excluded for historical anthropogenic interference. This implementation method, based on the judgment chain of monitoring section classification, catchment area determination, background influence screening, and anthropogenic influence exclusion, identifies high-COD sections dominated by natural factors from multiple monitoring sections of the target river.

[0069] Specifically, this embodiment first classifies the monitoring sections of the target river. Monitoring sections located at the very upstream of the river, without any upstream tributaries flowing into them and directly receiving water from the source, are designated as upstream monitoring sections. Monitoring sections located in the middle or lower reaches of the river, where other monitoring sections already exist upstream, are designated as transit monitoring sections. Transit monitoring sections receive water from upstream and may be affected by the cumulative impact of human activities within their local catchment area; therefore, their catchment areas need to be determined separately from those of the upstream monitoring sections, and they should be included in the subsequent assessment separately.

[0070] After classifying the monitoring sections, each monitoring section is used as an outlet to divide the watershed into smaller watersheds, and the catchment area of ​​each monitoring section is determined. The catchment area refers to the source area of ​​the water that can flow into the corresponding monitoring section. For upstream monitoring sections, the complete catchment area upstream of the upstream monitoring section is determined as the catchment area of ​​the upstream monitoring section. This complete catchment area covers the entire watershed from the source to the upstream monitoring section. For transit monitoring sections, the catchment area with the transit monitoring section as the outlet is first determined, and then the catchment area of ​​the upstream adjacent monitoring section is subtracted from this catchment area. The area after subtraction is determined as the catchment area of ​​the transit monitoring section. Through the above processing, the area already covered by the upstream adjacent monitoring section can be avoided by double-counting, and the independent catchment influence range of the transit monitoring section can be determined.

[0071] Subsequently, background impact screening was conducted on upstream and transit monitoring sections based on land use structure. Specifically, land cover within the catchment area was interpreted using remote sensing imagery combined with GIS spatial analysis. The remote sensing imagery could be Landsat or Sentinel series imagery. Land use types within the catchment area were identified based on the interpretation results. These land use types included forest land, grassland, wetland, cultivated land, water area, construction land, and bare land. Forest land, grassland, wetland, and water area were designated as ecological land, representing land types dominated by natural background and with minimal human disturbance. The proportion of ecological land area to the total catchment area was calculated. When the proportion of ecological land area to the total catchment area of ​​the upstream or transit monitoring section was not less than 90%, the corresponding monitoring section was included in the background impact section database A1. This screening was used to identify monitoring sections with lower human activity intensity and where COD was more likely to be affected by natural factors based on land use structure.

[0072] For transit monitoring sections not included in the background-affected section database A1, further supplementary screening based on upstream inflow impact was conducted. Specifically, among the transit monitoring sections not included in the background-affected section database A1, those where the proportion of ecological land area to the total catchment area was less than 90% were selected. The proportion of the catchment area of ​​the monitoring point to the total catchment area of ​​the transit monitoring section was calculated. When the proportion of the catchment area of ​​the monitoring point to the total catchment area was less than 1%, it indicated that the local catchment impact range of the transit monitoring section was small, and the impact of local human activities on COD concentration changes was relatively limited. Further correlation tests were conducted between the COD concentration changes of adjacent upstream monitoring sections and the transit monitoring section. If the COD concentration change trends of adjacent upstream monitoring sections and the transit monitoring section were the same, and the significance level was [not specified], then [further correlation analysis was conducted]. If the value is less than 0.05, the COD concentration changes of the two are considered to be significantly correlated, indicating that the high COD value of this transit monitoring section is mainly affected by upstream natural water flow rather than local human activities. Transit monitoring sections that meet the above conditions are included in the background-affected section database A2.

[0073] After the background-affected section databases A1 and A2 are formed, human influence is eliminated from the monitoring sections in the background-affected section databases A1 and A2.

[0074] First, monitoring sections affected by existing point sources are excluded. Specifically, it is necessary to identify whether there are fixed sewage outlets within the catchment area of ​​each monitoring section. Fixed sewage outlets include factory sewage outlets, direct discharge outlets from livestock farms, and other point sources that can directly input pollution. For monitoring sections with fixed sewage outlets within the catchment area, the water quality at the outlets is obtained, and the proportion of protein-like substances in dissolved organic matter at the corresponding monitoring section is also obtained. When the water quality at the outlet is worse than Class III, or the proportion of protein-like substances in dissolved organic matter is greater than 20%, the corresponding monitoring section is determined to be significantly affected by point sources, and the corresponding monitoring section is included in the exclusion section database B1. Protein-like substances are used to reflect the characteristics of point source pollution such as domestic sewage and industrial wastewater.

[0075] Secondly, monitoring sections affected by existing non-point source pollution were excluded. Non-point source pollution includes the impact of dispersed anthropogenic activities such as the discharge of rural domestic sewage and the runoff of livestock and poultry manure. For each monitoring section in the background-affected section pools A1 and A2, the contribution rate of non-point source pollution to Class III water quality was calculated. The contribution rate of non-point source pollution to Class III water quality Determined according to the following formula:

[0076]

[0077] In the formula, The contribution rate of non-point source pollution to Class III water quality; The pollution load from rural domestic activities is expressed in kg / a. Calculated using population size and per capita COD production; Pollution load from livestock and poultry farming, expressed in kg / a. The value of Q is calculated based on the number of livestock and poultry farms and the COD production per unit of livestock and poultry; Q represents the annual runoff of the cross-section, in ten thousand m³. 3 Q is obtained through hydrological station data. If the contribution rate of non-point source pollution to Class III water quality... If the contribution of non-point source pollution to Class III water exceeds 1%, the corresponding monitoring section is considered to be significantly affected by non-point source pollution, and the corresponding monitoring section will be included in the exclusion section database B2.

[0078] Secondly, monitoring sections significantly affected by historical human disturbances were excluded. Historical human disturbances include activities that could alter the aquatic ecosystem's state in the long term, such as deforestation and land reclamation from lakes. For each monitoring section in the background-affected section databases A1 and A2, algal community data for the corresponding monitoring sections were obtained, and the Shannon-Wiener diversity index H of the algae was calculated based on this data. The Shannon-Wiener diversity index H of the algae was determined according to the following formula:

[0079]

[0080] In the formula, The Shannon-Wiener diversity index represents the number of epiphytic algae; S represents the number of epiphytic algae species. Let be the relative abundance of the i-th species, and let the relative abundance be the abundance of the th species. The proportion of individuals of a particular species to the total number of individuals. The Shannon-Wiener diversity index (H) of attached algae is used to characterize the community diversity of aquatic ecosystems; the lower the index, the stronger the degree of historical disturbance to the ecosystem. When the Shannon-Wiener diversity index of attached algae... When the value is less than 2, the corresponding monitoring section is determined to be subject to significant historical human interference, and the corresponding monitoring section is included in the exclusion section database C.

[0081] Finally, the monitoring sections after human influence exclusion were classified according to their degree of background influence. Specifically, the monitoring sections in exclusion section databases B1, B2, and C were removed from background-affected section databases A1 and A2, resulting in the remaining monitoring sections, which constitute the set of background-affected sections to be classified. COD monitoring data for each of the remaining monitoring sections were obtained, and the annual average COD value for each section was calculated based on the COD monitoring data. The annual average COD value of each monitoring section was compared with the Class III threshold of the "Surface Water Environmental Quality Standard". When the annual average COD value was not greater than the Class III threshold (i.e., not greater than 20 mg / L), the corresponding monitoring section was identified as a background section with relatively low background influence; when the annual average COD value was greater than the Class III threshold (i.e., greater than 20 mg / L), the corresponding monitoring section was identified as a background section with relatively high background influence. The background sections that are significantly affected by the background are those monitoring sections where high COD values ​​are mainly dominated by natural factors after eliminating point source influence, non-point source influence, and historical human interference. Natural factors include natural degradation of soil organic matter, humus migration, decomposition of litter, release of organic matter from rock weathering, and upstream natural water input.

[0082] Specifically, taking a mountain river as an example, the river is 50 km long and has five monitoring sections along its course, designated S1, S2, S3, S4, and S5. S1 is located at the very upstream of the river and serves as the upstream monitoring section; S2, S3, S4, and S5 are located in the middle or lower reaches of the river, and each section has an adjacent upstream monitoring section, serving as the transit monitoring sections.

[0083] First, the five monitoring sections were classified, with S1 designated as the upstream monitoring section and S2, S3, S4, and S5 as transit monitoring sections. Then, each monitoring section was used as an outlet to divide the watershed into smaller basins, and the catchment area of ​​each section was determined. The catchment area of ​​S1 is the entire watershed upstream of S1, with an area of ​​10 km². The catchment area of ​​S2, with itself as its outlet, is 20 km². Subtracting the 10 km² catchment area of ​​S1, the catchment area of ​​S2 is 10 km². S3, S4, and S5 were determined using the same method as S2, with their respective monitoring sections as outlets, and the catchment areas of adjacent upstream monitoring sections were subtracted to obtain the catchment areas of the corresponding monitoring sections.

[0084] Land cover within the catchment areas corresponding to S1 to S5 was interpreted, and the proportion of ecological land area to the total catchment area of ​​each monitoring section was calculated based on the interpretation results. S1 has an ecological land area ratio of 95%, of which forest land accounts for 80% and water area accounts for 15%, therefore S1 is included in the background-affected section database A1. S2 has an ecological land area ratio of 85%, which is less than 90%, therefore it is not included in the background-affected section database A1. S3 has an ecological land area ratio of 92%, therefore S3 is included in the background-affected section database A1. S4 has an ecological land area ratio of 88%, which is less than 90%, therefore it is not included in the background-affected section database A1. S5 has an ecological land area ratio of 89%, which is less than 90%, therefore it is not included in the background-affected section database A1. Therefore, the background-affected section database A1 includes S1 and S3.

[0085] For sections S2, S4, and S5, which are not included in the background-affected section database A1 and whose ecological land area accounts for less than 90%, further supplementary screening was conducted based on the impact of upstream water inflow. The catchment area of ​​monitoring point S2 accounts for 0.5% of the total catchment area, less than 1%, and S2 is significantly correlated with the COD concentration change of the adjacent upstream monitoring section S1 (p=0.03, less than 0.05). Therefore, S2 is included in the background-affected section database A2. The catchment area of ​​monitoring point S4 accounts for 2% of the total catchment area, greater than 1%, and is not included in the background-affected section database A2. The catchment area of ​​monitoring point S5 accounts for 0.8% of the total catchment area, less than 1%, but the correlation between S5 and the COD concentration change of the adjacent upstream monitoring section S4 is not significant (p=0.12). Therefore, it is not included in the background-affected section database A2. Thus, the background-affected section database A2 includes S2.

[0086] After obtaining the background-affected section databases A1 and A2, human-induced impacts were eliminated for each monitoring section. An investigation revealed that there was a livestock farm's sewage outlet within the catchment area of ​​S4, and the water quality at the outlet was worse than Class III. Therefore, S4 was determined to be significantly affected by existing point sources, and S4 was included in the elimination section database B1.

[0087] When assessing the impact of non-point source pollution on S3, the rural domestic pollution load R1 is taken as 500 kg / a, the livestock and poultry breeding pollution load R2 as 300 kg / a, and the annual runoff Q of the cross-section as 1 million m³. R1 is determined based on the rural population and per capita COD production within the S3 catchment area, R2 is determined based on the number of livestock and poultry farms and COD production per unit of livestock and poultry within the S3 catchment area, and Q is determined based on the annual runoff data of the corresponding hydrological station. The contribution rate of non-point source pollution to Class III water quality is calculated using the formula:

[0088]

[0089] Since the contribution rate p of non-point source pollution to Class III water in S3 is 4%, which is greater than 1%, it is determined that S3 is significantly affected by the existing non-point source pollution, and S3 is included in the exclusion section database B2.

[0090] Further investigation was conducted to determine the historical human disturbance impact on section S5. Algal community data for section S5 were obtained, and the Shannon-Wiener diversity index was calculated based on this data. The Shannon-Wiener diversity index for section S5 was 1.5, which is less than 2, indicating a high degree of historical human disturbance to the aquatic ecosystem of the river segment where section S5 is located. Therefore, section S5 was included in the exclusion section database C.

[0091] After eliminating human-induced influences, monitoring sections in the elimination section databases B1, B2, and C are removed from the background-affected section databases A1 and A2. Since S3 has already been included in the elimination section database B2, S4 in B1, and S5 in C, the remaining sections are S1 and S2. S1 originates from the background-affected section database A1, and S2 originates from the background-affected section database A2.

[0092] The remaining cross-sections S1 and S2 were classified according to their degree of background influence. COD monitoring data for S1 and S2 were obtained, and the annual average COD value was calculated. The annual average COD value of S1 was 18 mg / L, which is not greater than the Class III threshold of 20 mg / L in the "Surface Water Environmental Quality Standard". Therefore, S1 was identified as a background cross-section with relatively low background influence. The annual average COD value of S2 was 25 mg / L, which is greater than the Class III threshold of 20 mg / L in the "Surface Water Environmental Quality Standard". Therefore, S2 was identified as a background cross-section with relatively high background influence.

[0093] Further investigation was conducted into the natural environment and human activities in the S2 watershed. The S2 watershed is a primary forest area, and no industrial or agricultural pollution input was found. The high COD value of S2 was mainly caused by the natural degradation of soil organic matter, leaching of humus, and transport by natural upstream water. These results demonstrate that the method described in this embodiment can identify background sections from multiple monitoring sections where COD is primarily influenced by natural factors, after excluding point source pollution, non-point source pollution, and historical human interference.

[0094] The beneficial effects of this invention are as follows:

[0095] This invention first classifies the monitoring sections of the target river and determines the catchment area according to upstream monitoring sections and transit monitoring sections. Then, it screens monitoring sections that may be affected by the natural background based on land use structure and the correlation with upstream water inflow, which can avoid the bias caused by judging based on a single water quality concentration or a single land use information. At the same time, this invention further excludes monitoring sections affected by point source pollution, non-point source pollution, and historical human interference, so that sections with significant human impact are not included in the final background section classification results. On this basis, the remaining monitoring sections are divided into background sections with significant background impact and background sections with less significant background impact according to the annual average COD value. Thus, the identification of monitoring sections affected by natural factors of COD has a clear section range, catchment area, and basis for excluding human impact, reducing the confusion between sections dominated by natural background and sections affected by human pollution.

[0096] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0097] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for accurately identifying monitoring sections of river water COD affected by natural factors, characterized in that, include: The monitoring sections of the target river are classified, with the monitoring section located at the uppermost part of the river being identified as the upstream monitoring section, and the monitoring section located in the middle or lower reaches of the river and with other monitoring sections upstream being identified as the transit monitoring section. The monitoring section is used as the outlet to divide the watershed into small watersheds and determine the catchment area of ​​the monitoring section; the catchment area of ​​the upstream monitoring section is the complete catchment area above the upstream monitoring section, and the catchment area of ​​the transit monitoring section is the catchment area with the transit monitoring section as the outlet minus the catchment area of ​​the upstream adjacent monitoring section. Based on the land use structure within the catchment area, the upstream monitoring section and the transit monitoring section are initially screened for background impact to obtain a first set of candidate background impact sections. For the transit monitoring sections that were not included in the first candidate background impact section set, a second candidate background impact section set was obtained by supplementing the screening based on the proportion of the catchment area of ​​the monitoring point to the total catchment area and the correlation of COD concentration changes between the adjacent upstream monitoring sections and the transit monitoring sections. Human influence is eliminated from the monitoring sections in the first candidate background influence section set and the second candidate background influence section set. Monitoring sections affected by point source influence, area source influence or historical human interference are removed to obtain the background influence section set to be classified. Based on the annual average COD value of each monitoring section in the set of background impact sections to be classified, the monitoring sections with an annual average COD value greater than the Class III threshold are identified as background sections with greater background impact, and the monitoring sections with an annual average COD value not greater than the Class III threshold are identified as background sections with less background impact.

2. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, The monitoring sections of the target river are classified, including: Obtain the longitudinal position of each monitoring section in the target river and the upstream and downstream relationships between each monitoring section; The upstream monitoring section is defined as the monitoring section that has no upstream monitoring section and directly receives the water from the source. The monitoring section that has other monitoring sections upstream and is affected by both upstream water and local runoff is identified as the transit monitoring section.

3. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, The monitoring section is used as the outlet to divide the watershed into smaller watersheds, and the catchment area of ​​the monitoring section is determined, including: Using the upstream monitoring section as the outlet, the complete catchment area from the source to the upstream monitoring section is determined, and the complete catchment area is taken as the catchment range of the upstream monitoring section; Using the transit monitoring section as the outlet, the catchment area flowing into the transit monitoring section is determined, and the catchment area of ​​the upstream adjacent monitoring section is subtracted from the catchment area flowing into the transit monitoring section to obtain the catchment range of the transit monitoring section.

4. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, Based on the land use structure within the catchment area, a preliminary background impact screening is conducted on the upstream monitoring section and the transit monitoring section to obtain a first candidate set of background impact sections, including: The land cover within the catchment area is interpreted to obtain the land use structure; Based on the land use structure, forest land, grassland, wetland, cultivated land, water area, construction land and bare land are identified; Forest land, grassland, wetland and water area are designated as ecological land; Calculate the proportion of ecological land area to the total area of ​​the catchment area, and include the upstream monitoring section and the transit monitoring section where the proportion of ecological land area to the total area of ​​the catchment area is not less than 90% into the first candidate background impact section set.

5. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, For the transit monitoring sections not included in the first candidate background impact section set, a second candidate background impact section set is obtained by supplementing the screening based on the proportion of the catchment area of ​​the monitoring point to the total catchment area and the correlation of COD concentration changes between the adjacent upstream monitoring sections and the transit monitoring sections, including: Among the transit monitoring sections that have never been included in the first candidate background impact section set, the transit monitoring sections in which the proportion of ecological land area to the total area of ​​the catchment range is less than 90% are identified as target transit monitoring sections. Calculate the proportion of the catchment area of ​​the monitoring point of the target transit monitoring section to the total catchment area; A correlation test was conducted on the COD concentration changes between the adjacent upstream monitoring section and the target transit monitoring section; When the catchment area of ​​the monitoring point accounts for less than 1% of the total catchment area, and the COD concentration change trend of the adjacent upstream monitoring section is the same as and significantly correlated with that of the target transit monitoring section, the target transit monitoring section is included in the second set of candidate background influence sections.

6. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, Human-induced impact exclusion is performed on the monitoring sections in the first and second candidate background impact section sets, including: From the first set of candidate background impact sections and the second set of candidate background impact sections, determine the monitoring sections within the catchment area where there are fixed sewage outlets; Obtain the discharge water quality of the fixed sewage outlet and the proportion of protein-like substances in the dissolved organic matter at the corresponding monitoring section; When the water quality at the discharge outlet is worse than Class III, or when the proportion of protein-like substances in the dissolved organic matter is greater than 20%, the corresponding monitoring section will be identified as a monitoring section affected by point sources.

7. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, Human-induced impact exclusion is performed on the monitoring sections in the first and second candidate background impact section sets, including: Calculate the contribution rate of non-point source pollution to Class III water quality for each monitoring section in the first candidate background impact section set and the second candidate background impact section set; The contribution rate of non-point source pollution to Class III water quality is calculated using the following formula: ； in, The contribution rate of non-point source pollution to Class III water quality. As for the pollution load of rural life, Pollution load from livestock and poultry farming The annual runoff of the cross section; When the contribution rate of non-point source pollution to Class III water is greater than 1%, the corresponding monitoring section will be identified as a monitoring section affected by non-point source pollution.

8. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, The step of eliminating human influence on the monitoring sections in the first set of candidate background influence sections and the second set of candidate background influence sections includes: Obtain the attached algal community data of each monitoring section in the first candidate background influence section set and the second candidate background influence section set; The Shannon-Wiener diversity index of attached algae was calculated based on the attached algal community data. When the Shannon-Wiener diversity index of the attached algae is less than 2, the corresponding monitoring section is identified as a monitoring section affected by historical human interference.

9. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, After removing monitoring sections affected by point sources, area sources, or historical human interference, a set of background impact sections to be classified is obtained, including: By merging the monitoring sections affected by point sources, the monitoring sections affected by area sources, and the monitoring sections affected by historical human interference, a set of sections with human influence excluded is obtained. After merging the first set of candidate background impact sections and the second set of candidate background impact sections, the monitoring sections in the set of human impact exclusion sections are removed to obtain the set of background impact sections to be classified.

10. The method for accurately identifying monitoring sections of river water COD affected by natural factors according to claim 1, characterized in that, The classification based on the annual average COD value of each monitoring section in the set of background impact sections to be classified includes: Obtain COD monitoring data for each monitoring section in the set of background impact sections to be classified; The annual average COD value of each monitoring section is calculated based on the COD monitoring data. The annual average COD value of each monitoring section is compared with the Class III threshold, which is the Class III limit value for COD in the "Surface Water Environmental Quality Standard". When the annual average COD value is greater than the Class III threshold, the corresponding monitoring section will be identified as the background section that is more affected by the background. When the annual average COD value is not greater than the Class III threshold, the corresponding monitoring section will be determined as the background section with less background influence.

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