METHOD FOR ESTIMATING ABUNDANCE AND DISTRIBUTION FEATURES OF ANTIBIOTIC RESISTANCE GENES IN SURFICIAL SEDIMENTS OF LAKE AND RESERVOIR

Abstract

The present application provides a method for estimating abundance and distribution features of antibiotic resistance genes (ARGs) in surfacial sediments of lake and reservoir, including step 1 of obtaining an annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir; step 2 of constructing a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge; and step 3 of calculating annual input total of nitrogen and phosphorus pollutants to be estimated for each geographical location of the lake and reservoir using inverse distance weighting interpolation analysis based on the annual input total of the nitrogen and phosphorus pollutants obtained in step 1, and substituting the calculated annual input total into the linear regression equation to estimate the abundance of each type of ARGs and analyze a distribution feature of the ARGs.

Description

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No. 202211105406.7, filed on Sep. 9, 2022, the entire content of which is hereby incorporated by reference.

TECHNICAL FIELD

The present application relates to the field of ecological environmental evaluation of rivers and lakes, and more particularly, to a method for estimating abundance and distribution features of antibiotic resistance genes in surficial sediments of lake and reservoir.

BACKGROUND

Antibiotics eventually circulate in the water environment through sewage and medical waste, resulting in an increase in the selective pressure of bacteria in the water ecological environment, and inducing the formation of antibiotic resistance bacteria (i.e, ARB) and antibiotic resistance genes (i.e, ARGs) that are transferred to the pathogen of humans and animals, and become a new contaminant of more concern. Because the water flow is relatively slow, the water exchange period is long. The lake and reservoir are often rich in antibiotic pollution and are also rich in ARGs. Therefore, it is urgently necessary to investigate and master a pollution status of ARGs in the lake and reservoir, develop an appropriate ARGs abundance estimation method, and formulate a scientific prevention and control strategy.

The eutrophication of lake and reservoir provides a rich nutritional basis for the growth and reproduction of antibiotic-resistant bacteria and the enrichment of ARGs. The results showed that there was significant correlation between ARGs and nitrogen and phosphorus enrichment in water and sediment in fresh water environment. In terms of the lake and reservoir, most of the nutrients enriched in the water body and sediment are derived from the point source and non-point source pollution discharge at the periphery of the lake and reservoir, while the ARGs in the water body and sediment is partly and directly derived from the basin environment of the lake reservoir, and is transferred to the lake and reservoir along with the point source and non-point source pollution discharge at the periphery. Therefore, the abundance and distribution features of ARGs in the water environment of the lake and reservoir theoretically have a good correlation with the pollution discharge in the basin, and can be predicted by constructing a regression model between the abundance and distribution features and the pollution discharge.

SUMMARY

In view of this, an embodiment of the present application provides a method for estimating abundance and distribution features of antibiotic resistance genes in surficial sediments of lake and reservoir, including:

• • a) obtaining an annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir;
  • b) obtaining abundance of each type of ARGs in the 0˜10 cm sediment of a surface layer of the lake and reservoir in the next year, and analyzing correlation between the abundance of ARGs and the annual input total of the surrounding nitrogen and phosphorus pollutants by using a geographical weighted regression model, so as to construct a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge; and
  • c) calculating an annual input total of nitrogen and phosphorus pollutants to be estimated for each geographical location of the lake and reservoir using Inverse Distance Weighting (IDW) interpolation analysis based on the annual input total of the nitrogen and phosphorus pollutants obtained in step a), and substituting the calculated annual input total of the nitrogen and phosphorus pollutants to be estimated into the linear regression equation constructed in step b) to estimate the abundance of each type of ARGs corresponding to the geographical location of the lake and reservoir and analyze a distribution feature of the ARGs of the lake and reservoir.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating a method for estimating abundance and distribution features of antibiotic resistance genes in surficial sediments of lake and reservoir according to an embodiment of the present application.

EMBODIMENTS OF THE PRESENT DISCLOSURE

In order that an object, a technical solution, and an advantage of the present application may be made clearer, an embodiment of the present application will be further described with reference to the accompanying drawings.

As used herein, the term “coupled” may refer, for example, to “connected.”

The eutrophication of lake and reservoir provides a rich nutritional basis for the growth and reproduction of antibiotic-resistant bacteria and the enrichment of ARGs. The results showed that there was significant correlation between ARGs and nitrogen and phosphorus enrichment in water and sediment in fresh water environment. In terms of the lake and reservoir, most of the nutrients enriched in the water body and sediment are derived from the point source and non-point source pollution discharge at the periphery of the lake and reservoir, while the ARGs in the water body and sediment is partly and directly derived from the basin environment of the lake reservoir, and is transferred to the lake and reservoir along with the point source and non-point source pollution discharge at the periphery. Therefore, the abundance and distribution features of ARGs in the water environment of the lake and reservoir theoretically have a good correlation with the pollution discharge in the basin, and can be predicted by constructing a regression model between the abundance and distribution features and the pollution discharge.

Referring to FIG. 1, a method for estimating abundance and distribution features of antibiotic resistance genes in surficial sediments of lake and reservoir according to an embodiment of the present application may include steps of S110-S130.

At S110, an annual input total of nitrogen and phosphorus pollutants for each tributary flowing into lake and reservoir is obtained. Th step may be implemented, for example, by a direct measurement method or a model estimation method as described below.

{circle around (1)} Direct Measurement

Water bodies are monthly collected at all tributary inlets around the lake and reservoir to measure conventional water quality parameters while monitoring corresponding flow quantity and flow rate. An annual input total of nitrogen and phosphorus pollutants from each tributary is calculated by a product of the flow quantity and a concentration of the total nitrogen and the total phosphorus in the water quality parameters.

{circle around (2)} Model Estimation

A pollution status of the lake and reservoir basin was analyzed by means of statistical yearbook, actual investigation, and the like, so as to establish a database of planting, rural living, scattered poultry breeding, urban living pollution, and the like. Using an output coefficient method, the annual input total of nitrogen and phosphorus pollutants polluted by such as poultry, a rural and urban life, and aquaculture is estimated by the following equation 1:

$\begin{array}{cc}L={\sum }_{i=1}^n{E}_i\left[A_i\left(I_i\right)\right]+p_1& \left(1\right)\end{array}$ $p_1=\mathrm{cRQ}$

where L is the amount of nutrient; E_i is an output coefficient of the i-th nutrient source; A_i is the area of the i-th class land use type or the number of the i-th class livestock or population; I_i is the nutrient input from the i-th nutrient source, p₁ is the nutrient input from the rainfall, and c is a nutrient concentration (g/m³) of the rainfall itself; R is annual rainfall (m³) in the basin; and Q is a rainfall runoff coefficient.

Further, an annual load total of total nitrogen (TN) and total phosphorus (TP) flowed into the lake and reservoir estimated by the output coefficient method for the poultry, the rural and urban life, and the aquaculture, etc., is coupled to a Soil and Water Assessment Tool (SWAT) hydrological model. Agricultural planting patterns in basin of the lake and reservoir including agricultural management measures such as planting, farming, irrigation, fertilization, etc., are introduced by applying a farmland management component of the SWAT hydrological model. A surface source pollution model of the basin of the lake and reservoir is established by loading the agricultural management measures and a point source pollution. The model is calibrated and validated using the measured water quality data on the basis of the parameter sensitivity analysis. Using the calibrated and validated model, the annual input total of the non-point source pollutant is simulated in a unit of month to obtain the monthly output total of total nitrogen and total phosphorus of each hydrologic research unit (i.e, HRU), and the annual output total of total nitrogen and total phosphorus per unit area of the sub-basin is obtained through watershed summarization and area conversion to calculate and analyze an annual input feature of nitrogen and phosphorus pollutants flowed into the tributary of the lake and reservoir.

At S120, abundance of each type of ARGs in the 0˜10 cm sediment of a surface layer of the lake and reservoir in the next year is obtained, and correlation between the abundance of ARGs and the annual input total of the surrounding nitrogen and phosphorus pollutants is analyzed by using a geographical weighted regression (GWR) model to construct a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge.

For example, the total DNA of the sediment microorganisms can be extracted by collecting 0˜10 cm sediment of the surface layer of the lake and reservoir by a random uniform dot distribution method. Each type of ARGs gene-specific primer was used in combination with fluorescent quantitation Polymerase Chain Reaction (PCR) to determine the abundance of each type of ARGs in the surfacial sediment of each sampling point. According to the annual input total of the nitrogen and phosphorus pollutants at the peripheral tributary inlet of the sampling point obtained in step S110, the correlation between the abundance of ARGs and the peripheral nitrogen and phosphorus pollutants is analyzed by using a geographical weighted regression model, so as to constructing a geospatial relationship between the abundance of ARGs and the tributary pollution input. GWR can always perform regression analysis by the following equation 2 starting from the Ordinary Least Square (OLS) regression:

$\begin{array}{cc}{Y}_i={\beta }_0\left(u_i,v_i\right)+{\sum }_{k=1}^{p_2-1}{\beta }_k\left(u_i,v_i\right)X_{\mathrm{ik}}+{\epsilon }_i,i=1,2,\dots ,n& \left(2\right)\end{array}$

where, Y_i s a response variable, (u_i,v_i) represents coordinates of a spatial location i, β₀(u_i,v_i) and β_k(u_i, v_i) represent an intercept and (p₂−1) slope parameters at the location i, respectively, X_ik represents (p₂−1) prediction variables at the location i, p₂ is a total number of parameters to be estimated, and ε_i is an error term at the location i.

A linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge can be constructed according to an analysis result of the geographical weighted regression, as shown in equation 3 below, and the linear regression equation can be inversely verified using field survey data.

$\begin{array}{cc}{y}_{\mathrm{ARGs}}={\mathrm{ax}}_{\mathrm{TN}}-{\mathrm{bx}}_{\mathrm{TP}}+c& \left(3\right)\end{array}$

where, y_ARGs is the abundance of the antibiotic resistance gene ARGs at a point to be measured, X_TN is an annual total nitrogen input pollution load, x_TP is an annual total phosphorus input pollution load, and a, b, and c are intercepts of the linear regression equation.

At S130, an annual input total of nitrogen and phosphorus pollutants to be estimated for each geographical location of the lake and reservoir is calculated by using Inverse Distance Weighting (IDW) interpolation analysis based on the annual input total of the nitrogen and phosphorus pollutants obtained at S110, and the calculated annual input total of the nitrogen and phosphorus pollutants to be estimated is substituted into the linear regression equation constructed at S120 to estimate the abundance of each type of ARGs corresponding to the geographical location of the lake and reservoir, and analyze a distribution feature of the ARGs of the lake and reservoir.

In the process of specifically estimating the abundance and distribution features of the ARGs, the annual input total of the nitrogen and phosphorus pollutants for each tributary of the lake and reservoir in the previous year can be calculated according to the method in S120. On the basis of this, an inverse distance weighted interpolation analysis is performed according to the following equation 4 to calculate the annual input total of nitrogen and phosphorus pollutants virtually flowed into the lake and reservoir for each geographical coordinate point in the whole lake and reservoir water area of the lake and reservoir:

$\begin{array}{cc}\hat{Z}\left(s_0\right)={\sum }_{i=1}^N{\lambda }_{i}Z\left(s_i\right)& \left(4\right)\end{array}$ ${\lambda }_i=\frac{d_{i0}^{-P}}{{\sum }_{i=1}^N{d}_{i0}^{-P}}$ $\sum _{i=1}^N{\lambda }_i=1$

where {circumflex over (Z)}(s₀) represents an interpolation result at s₀, Z(s_i) is an annual pollution load value obtained at s_i, N is the number of tributaries of the lake and reservoir around which the interpolation is performed, λ_i is a weight of each lake and reservoir tributary inlet used in a interpolation calculation process, d_i0 is a distance between the interpolation point and each known lake and reservoir tributary inlet s_i, P is a weighted power index, and the sum of a weight λ_i of each lake and reservoir tributary to an interpolation result is 1.

Further, the abundance of each type of ARGs at each geographical coordinate point of the lake and reservoir is estimated by substituting the annual input total of each geographical coordinate point of the lake and reservoir into the constructed linear regression equation to analyze the distribution feature of ARGs of the lake and reservoir.

In the case where the annual input total of pollutant for the entire lake and reservoir calculated in S110 is dispersed to a lake region, a particular point in the lake region has the total of pollutant received for one year at the point. The sum of the total of pollutant inputted at all points in the lake region is the total in S110. The abundance of the resistance gene at a particular point can be estimated based on a linear regression equation according to an annual input total of the pollutant at that point. In the case where the abundance of the resistance genes of all points in the lake region is known, the distribution features of the resistance genes in the lake region can be obtained.

The pollution concentration and distribution features of the ARGs in the surface sediments of the lake and reservoir can be easily, accurately and quickly estimated by constructing the geographical weighted regression model between the annual discharge total of nitrogen and phosphorus pollutants in each tributary of the basin and the measured abundance of the ARGs based on better correlation between the abundance and distribution features of the ARGs in the water environment of the lake and reservoir and the pollution discharge in the basin, thereby providing a scientific guidance for the pollution control and abatement of the ARGs in the lake and reservoir.

EXAMPLE

Taking Erhai Lake as an example, the abundance and distribution features of ARGs in the surfacial sediments of Erhai Lake are estimated below.

The geographical and ecological environment of Erhai Lake is summarized as follows.

Erhai Lake (where north latitude is 25° 36′−25° 58′ and east longitude is 100° 06′-100° 18′) is the second largest plateau freshwater lake in Yunnan Province, located in a north-south intermountain basin, with an elevation of 1974 meters. The area of the water region is 252 square kilometers and an average water depth is 10.8 meters. The north is Eryuan basin and Dengchuan basin, and main rivers flowing into the lake include Miju River, Luoshi River and Yongan River. The west is the Tibetan-Yunnan fold system, the Diancang Mountain screen is located on the west side of the Erhai Lake, and main rivers flowing into the lake are Cangshan Shibaxi revers. The southeast rivers flowing into the lake are such as Balo River, Yulong River, Baita River, Fengtaiqing and so on. It is not only the main source of drinking water in Dali City, but also an important source of water for domestic use and industrial and agricultural production. It is an important force for regulating the climate of Dali City and promoting the agricultural development of the whole basin and even the sustainable development of the whole economy and society, and is called the “mother lake” of the Dali people. In recent years, the problem of water environment in Erhai Lake has become more and more serious. With the rapid development of agricultural industry in the basin, especially the rapid growth of economic crop planting area and poultry breeding scale, the amount of chemical fertilizer is increased, the structure of fertilization is unreasonable, etc., and poultry feces and urine have not been effectively utilized and treated, which threatens the water quality of Erhai Lake. Among them, nitrogen and phosphorus are the primary pollutants in Erhai Lake, and non-point source pollution in rural regions and farmland is an important cause of eutrophication in Erhai Lake. Hundreds of thousands of pigs, cattle, and sheep and millions of poultry are produced every year in the basin.

1) Determination of Pollution Load in Erhai Lake Basin in 2018

A pollution status of the Erhai lake basin was analyzed by means of statistical yearbook, actual investigation, and the like, so as to establish a database of planting, rural living, scattered poultry breeding, urban living pollution and the like. The survey scope covered 17 towns in Dali City and Eryuan County. The data came from Dali City Statistical Yearbook, Eryuan County Statistical Yearbook, Environmental Protection Statistical Yearbook, China Natural Resources Database and investigation data of various towns. Survey data is started in 2014 as the base year. The output coefficient method was used to respectively estimate the load amount of pollutants such as total nitrogen (TN) and total phosphorus (TP) in the poultry, the rural and urban life, and the aquaculture, etc.

A critical catchment area threshold is set to 5 km² based on the SWAT hydrological model in the Erhai Lake Basin to generate 545 sub-basins. The land use, soil type area threshold is set to 10% and the slope is 20%, to generate 1977 hydrological response units. The model of surface source pollution in Erhai Lake basin is established by loading agricultural management measures and point source pollution (poultry breeding and living pollution). The agricultural production mode and fertilization mode of 500 households in Erhai Lake basin are statistically recorded. According to the statistical result, a representative mainstream agricultural production mode is selected in each town of Erhai Lake basin for scenario simulation from fourrotation modes including broad bean-rice, garlic-rice, garlic-corn and rape-rice. 2014 will be used as a model warm-up period and 2015-2016 will used as calibration and validation periods. A lake entrance of Miju River is selected as a water quality verification station, and the water quantity, total nitrogen and total phosphorus data of the lake entrance of Miju River are used in a model calibration phase from January to December 2015, and the corresponding data of the lake entrance of Miju River are used in a model verification phase from January to December 2016. On the basis of the verification of hydrological parameter calibration, the concentrations of total nitrogen and total phosphorus in the river are calibrated.

According to the characteristics of the sewage interception project around the lake, the pollution of rural life and poultry breeding is subtracted from the surface source pollution model of the Erhai Lake basin, and six sewage disposal plants (point source pollution) are added. the calibrated and verified model is used to simulate surface source pollution load in 2018 in a unit of month to obtain a month output amount of the total nitrogen and total phosphorus of each of the hydrological response unit, and the annual average output amount of total nitrogen and total phosphorus per unit area of the sub-basin is obtained by basin summarization and area conversion. In 2018, the rainfall in Erhai Lake basin was 1000.7 mm, and the precipitation was concentrated from July to October. Because the catchment area of the rivers flowing into the lake is relatively smaller, the fluctuation law of runoff and rainfall is almost the same. The simulation results of TN and TP of each river flowing into the Erhai Lake in 2018 are shown in Table 1.

TABLE 1
Annual input amount of TN and TP of each
river flowing into ErHai Lake in 2018
			Coordinates of entrance
	TN	TP	flowing into the Lake
Tributary name	(tom )	(ton )	East Longitude	North latitude
Yangnang River	2.136	0.233	100.2246881	25.62233509
Tingming River	0.336	0.011	100.2170062	25.63848925
Mocan River	31.597	0.529	100.2107084	25.67140022
Qinghi River	10.699	0.093	100.2106118	25.67525842
Heilong River	20.818	0.253	100.2079082	25.68320165
Bahe River	19.717	1.117	100.2053118	25.69629743
Zhonghe River	9.298	0.933	100.191493	25.71725539
Tao River	3.548	0.077	100.1800346	25.73094181
Mei River	0.599	0.023	100.1763278	25.7336818
Yinxian River	1.049	0.061	100.172857	25.73452746
Shuangyuan	0.813	0.058	100.1637697	25.73909878
River
Baishi River	0.776	0.137	100.1534271	25.74988369
Lingquan River	1.463	0.058	100.1491141	25.7602811
Jin River	4.464	0.153	100.1495218	25.76729595
Mangyong River	6.907	0.270	100.1466894	25.79598872
Yang River	2.489	0.201	100.1490927	25.81569288
Wanhua River	8.020	0.424	100.1356387	25.86078758
Xiayi River	0.518	0.036	100.1178074	25.89787402
Zongshu River	0.566	0.037	100.1156616	25.90864446
Yulong River	9.654	0.786	100.2583122	25.70712488
Fengtaoqing	24.401	0.428	100.2195168	25.82858573
River
Boluo River	190.109	25.824	100.2718735	25.61676284
Banta River	12.710	1.351	100.2808106	25.63827887
Yongan River	11.826	0.809	100.1578903	25.95248818
Luoshi River	92.086	6.011	100.1008987	25.94230061
Miju River	591.624	36.335	100.1342011	25.9287158

2) Geographical Weighted Regression Relationship Analysis Between Nitrogen and Phosphorus Pollution Load Flowing into Erhai Lake and Abundance of Antibiotic Resistance Gene (ARGs) in Surfacial Sediments of Erhai Lake{circle around (1)} survey of ARGs distribution features of surfacial sediments In Erhai Lake

Ten sampling points were randomly and evenly disposed in Erhai, and the coordinates of each of the sampling points are shown in Table 2. In March 2019, 0˜10 cm sediment was collected from a surface layer of each point to extract the total DNA of the sediment microorganisms. Each type of ARGs gene-specific primer is used in combination with fluorescence quantitative PCR (qPCR) to determine the abundance of each type of ARGs in the surfacial sediment of each sampling point. The results are shown in Table 2 below.

TABLE 2
Actual investigation results of abundance of the ARGs (copies/16S
rRNA copy) in surfacial sediments in Erhai Lake in 2019
	Total	ARGs gene class
Number	Longitude	Latitude	ARGs	Amin	β-Lactam	FCA	MGE	MLSB	Sulf	Tet
E1	100.1142883	25.93087712	0.03701	0.00054	0.00022	0.01735	0.00467	0.00059	0.01355	0.00009
E2	100.147934	25.94384423	0.03650	0.00102	0.00030	0.01315	0.00306	0.00028	0.01859	0.00010
E3	100.1434708	25.91790858	0.03609	0.00270	0.00098	0.00796	0.00595	0.00093	0.01707	0.00050
E4	100.1527405	25.87127181	0.01759	0.00032	0.00039	0.01009	0.00309	/	0.00346	0.00024
E5	100.2059555	25.87559652	0.01957	0.00040	0.00027	0.01113	0.00171	/	0.00554	0.00053
E6	100.1881027	25.78814479	0.01454	0.00021	0.00002	0.00944	0.00344	0.00002	0.00119	0.00022
E7	100.1881027	25.7341892	0.02171	0.00033	0.00027	0.01449	0.00196	/	0.00445	0.00021
E8	100.223465	25.69970044	0.01666	0.00014	0.00008	0.01157	0.00116	/	0.00364	0.00006
E9	100.2674103	25.6693792	0.01695	0.00034	0.00007	0.01058	0.00277	/	0.00310	0.00008
E10	100.2399445	25.60809439	0.01846	0.00103	0.00034	0.00682	0.00198	/	0.00801	0.00028

Where Amin refers to an aminoglycoside; B-Lactam refers to B-lactam; MGE refers to a moveable gene element; Sulf refers to sulfonamides; and Tet refers to tetracycline.

{circle around (2)} Geographical weighted regression analysis

According to the annual load amount of nitrogen and phosphorus pollution load of 26 tributaries in the periphery of 10 sampling points in Erhai Lake, correlation between the abundance of ARGs in Table 2 and the nitrogen and phosphorus pollution load of 26 tributaries flowing into Erhai Lake in Table 1 is analyzed by using the geographical weighted regression model, so as to construct a geospatial relationship between the abundance of each type of ARGs in the lake and reservoir and pollution input from the tributaries. A linear regression equation between the abundance of each type of ARGs and nitrogen and phosphorus discharge is constructed according to the results of the geographical weighted regression analysis. The results are shown in Table 3.

TABLE 3
Linear regression analysis between abundance of ARGs in surfacial sediments of
Erhai Lake and nitrogen and phosphorus discharge from tributaries of Erhai Lake
		calibration
ARGs type	Linear regression equation	R2	P
Total ARGs	yARGs = 0.000214xTN − 0.001275xTP + 0.013171	0.954	0.000191
FCA	yFCA = 0.000083xTN − 0.000684xTP + 0.009631	0.929	0.000585
Sulf	ySul = 0.000091xTN − 0.000381xTP + 0.001408	0.968	0.000078
Amin	YAmin = 0.000006xTN − 0.000007xTP + 0.000117	0.961	0.000131
β-Lactam	yβ-Lactam = 0.000002xTN − 0.000008xTP + 0.000112	0.850	0.003761
MGEs	yMGEs = 0.000027xTN − 0.000166xTP + 0.001776	0.856	0.003370
MLSB	yMLSB = 0.000004xTN − 0.000026xTP − 0.000089	0.963	0.000115
Tet	yTet = 0.000001xTN − 0.000003xTP − 0.000205	0.332	0.157260

Where, a unit of x is ton and a unit of y is copies/16S rRNA copy.

{circle around (3)} Abundance and Distribution of ARGs in Surface Sediments of Erhai Lake

In the process of specifically estimating the abundance and distribution features of the ARGs of Erhai Lake, the load total of the nitrogen and phosphorus pollutants of each tributary flowing into Erhai Lake in the previous year is calculated according to the method in step 1. On the basis of this, an inverse distance weighted interpolation analysis is used to calculate load amount of nitrogen and phosphorus pollutants virtually flowed into the lake and reservoir for each geographical coordinate point in the Erhai Lake, and the calculated TN and TP are substituted into the linear regression equation of the corresponding gene in Table 3, so as to calculate the abundance of ARGs at various locations in the Erhai Lake, thereby judging the distribution features of ARGs.

The examples of the present application have been described in detail above, but the content is only a preferred embodiment of the present application and should not be considered as limiting the scope of implementation of the present application. Any modifications or substitutions can be readily conceivable by those skilled in the art within the technical scope of the disclosure of this application, without departing from the spirit and scope of the technical solution of this application, which are intended to be encompassed within the scope of the claims of this application. Accordingly, the scope of protection of the present application is defined by the appended claims.

Claims

1. A method for estimating abundance and distribution features of antibiotic resistance genes (ARGs) in surfacial sediments of lake and reservoir, comprising: a) obtaining an annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir;b) obtaining abundance of each type of ARGs in the 0˜10 cm sediment of a surface layer of the lake and reservoir in the next year, and analyzing correlation between the abundance of ARGs and the annual input total of the surrounding nitrogen and phosphorus pollutants by using a geographical weighted regression model, so as to construct a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge; andc) calculating annual input total of nitrogen and phosphorus pollutants to be estimated for each geographical location of the lake and reservoir using inverse distance weighting interpolation analysis based on the annual input total of the nitrogen and phosphorus pollutants obtained in step a, and substituting the calculated annual input total of the nitrogen and phosphorus pollutants to be estimated into the linear regression equation constructed in step b to estimate the abundance of each type of ARGs corresponding to the geographical location of the lake and reservoir and analyze a distribution feature of the ARGs of the lake and reservoir.

2. The method of claim 1, wherein the step a includes: estimating the annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir by using a pollutant annual input total estimation model based on a status of a social and economic production activity in a studied basin of the lake and reservoir; orcalculating the annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir by using water quality and hydrological monitoring data of the tributary flowing into the lake and reservoir.

3. The method of claim 2, further comprising: estimating, by the pollutant annual input total estimation model applying an output coefficient method, a total nitrogen (TN) and total phosphorus (TP) pollutant index load amount of poultry, rural and urban life, and aquaculture pollution, respectively, from a pollutant generation stage, a pollutant loss stage, and a pollutant inflow stage, and couple the estimated pollutant index load amount to a SWAT hydrological model to simulate the annual input total of the nitrogen and phosphorus pollutant of the tributary flowing into the lake and reservoir.

4. The method of claim 2, wherein when the pollutant annual input total estimation model coupled to a SWAT hydrological model applies a farmland management component of the SWAT hydrological model, the method further comprises: determining, by the farmland management component, farm production time, fertilization time, and fertilization amount to introduce agricultural planting patterns in the basin of the lake and reservoir including agricultural management measures and estimate farmland soil pollutant amount flowing into the lake and reservoir in combination with rainfall time and rainfall amount of the basin, wherein the agricultural management measures include planting, farming, irrigation, fertilization.

5. The method of claim 2, wherein the pollutant annual input total estimation model uses an output coefficient method to estimate the annual input total of nitrogen and phosphorus pollutants of each tributary flowing into the lake and reservoir by a following equation:

6. The method of claim 1, wherein the correlation between the abundance of ARGs and the annual input total of the nitrogen and phosphorus pollutants in the peripheral tributaries is analyzed in the step b using the geographical weighted regression model to construct a geospatial relationship between the distribution feature of each type of ARGs in the lake and reservoir and the pollution input, i.e., a linear regression equation between the abundance of each type of ARGs and the nitrogen and phosphorus discharge, wherein the geographical weighted regression model always performs regression analysis by a following equation starting from the Ordinary Least Square regression:

7. The method of claim 1, wherein, in the step b, the linear regression equation represented by a following equation is constructed using the abundance of each type of ARGs in the surfacial sediments of field-investigated lake and reservoir, and the constructed linear regression equation is inversely validated using the abundance of each type of ARGs in the surfacial sediments of field-investigated lake and reservoir:

8. The method of claim 1, wherein the inverse distance weighting interpolation analysis is performed according to a following equation: