2. MATERIALS AND METHODS
2.1 Study area
The Huashan watershed is located in the lower reaches of the Yangtze
River basin, with a geographical location of
118°08′05″~118°16′52″E and
32°13′14″~32°18′53″N. The watershed is surrounded by
mountains on three sides, which contributes to its high degree of
enclosure. The watershed has an elevation of 28 to 350m and an area of
80.13 km2. The watershed has a warm temperate
semi-humid monsoon climate with an annual average temperature of 15.2℃.
Precipitation is mainly concentrated from May to September, with annual
precipitation of about 1043 mm and annual evaporation of about 922 mm.
The downstream 16km of the watershed is the Chengxi Reservoir, the urban
drinking water source of Chuzhou City, with a storage capacity of 85.3
million m³, providing domestic water for 610,000 residents (Zhang et
al., 2020).
The rivers in the basin are fan-shaped and are fed by the Dongyuan
Tributary, the Zhongyuan Tributary, the Xiyuan Tributary and the
Zhuyuangou Tributary, respectively (Figure 1). The land use types in the
watershed are mainly Orchard, Agriculture and Forest, accounting for
51.25% 24.5% and 17.74% respectively. The predominant soil types in
the watershed are paddy soil and lime soil, with small amounts of
yellow-brown soil and silicate coarse loam. Among them, lime soil and
paddy soil account for 57.69% and 26.92%, respectively (Wang et al.,
2016).
The Huashan watershed is located at
the intersection of the southern end of the North China Land Platform
Block and the northern part of the Yangzi-Qiantang quasi-fold belt. The
regional mountainous and hilly area is characterized by Cambrian and
Ordovician sedimentary limestone, and the parent rocks for soil
formation include weathered products of andesite, coarse sandstone,
conglomerate, limestone, and other rock types. According to the
lithology, groundwater can be classified into two types: carbonate
fracture-karst water and porous water in loose rocks. According to the
burial conditions, the aquifer can be divided into the unconfined and
confined aquifers, and this paper mainly studies unconfined aquifers.
The aquifer in the watershed is mainly composed of Quaternary porous
media, with lithology mainly consisting of silt clay, silty fine sand,
and muddy gravel. The aquifer is relatively thick in the middle, about
10-12 meters, and thinner on both sides, with an average thickness of
7.5 meters (Zhang et al., 2022).
2.2 Sampling and analyses
Sampling was carried out seasonally, from November 2021 to October 2022,
in spring, summer, autumn and winter, respectively, and the spatial
distribution of sampling sites is shown in Figure 1.
When collecting surface water
samples, the sampling point was below 0.3-0.5 m from the water surface
to above 0.3-0.5 m from the water bottom. Before sampling, the
groundwater was pumped for 30 minutes until the conductivity remained
constant. The pH, temperature (T), electrical conductivity (EC),
oxidation-reduction potential (ORP) and dissolved oxygen (DO) were
measured in site using a portable water quality analyzer (HQ30D) that
had been pre-calibrated. The analyzed parameters included anions
(Cl-, SO42-,
NO3-,
NO2-) and cations
(K+,
Na+, Ca2+, Mg2+,
NH4+),
alkalinity, δD-H2O,
δ18O-H2O,
δ15N-NO3-and δ18O-NO3-.
Samples for analyses of
NH4+were filtered by a 0.45 μm syringe-tip filter to avoid
NH4+ volatilization and then were
acidified with reagent quality HCl to a pH of approximately 4.0.
Alkalinity was tested in the field by the Gran titration method. Samples
used to measure other indicators were filtered through 0.45 μm membranes
on site. After filtration, Samples used for testing anion and cation
were collected in 100 ml polyethylene bottles; Samples used to test
δD-H2O, δ18O-H2O,
δ15N-NO3- and
δ18O-NO3- were
collected using 100 ml polyethylene bottles and kept cold on site by ice
bag. All the sampling bottles used above were washed 3 times in advance
with ultrapure water in the laboratory and rinsed 3 times with water
samples to be taken during sampling, no headspace was left in the
sampling bottles during the sampling process and the bottles were
sealed. After sampling, all samples were returned to the laboratory, and
the samples for
δ15N-NO3- and
δ18O-NO3- were
frozen immediately and stored at -20°C. The other samples were stored at
4°C. All indicators were tested within two weeks after sampling.
Anions (Cl-, SO42-,
NO3-) and cations
(K+, Na+, Ca2+,
Mg2+) were analyzed using ion chromatography
(ICS-900), and the test results were evaluated using the charge balance
error, yielding errors within ±5%.
NH4+ and
NO2- were analyzed using a
spectrophotometer (Shimazu UV-2600) with a test accuracy of 0.01 mg/ L;
hydrogen and oxygen isotopes were analyzed using a stable isotope mass
spectrometer (Isoprime 100) with a testing accuracy of ±0.03‰ and ±0.02‰
for δD-H2O and
δ18O-H2O, respectively. The nitrogen
and oxygen isotopes of nitrate were analyzed using denitrifying bacteria
method, with the bacterial strain being Pseudomonas aureofaciens (ATCC
13985). The test instrument was TraceGas coupled with continuous flow
isotope ratio mass spectrometer (IRMS), with USGS32, USGS34, and USGS35
used as standards. The data was calibrated using the two-point
correction method.
Figure1 Location of the Huashan watershed and sampling
sites
2.3 Source apportionments
calculation
The Bayesian isotope mixture model, SIAR, was used to calculate the
contribution of different potential pollution sources. Based on the R
platform, the SIMMR package was used for analysis and calculation, and
the model expression is as follows (Parnell et al., 2010):
(1)
where Xij represents the value of isotope j of mixture i
(where i=1, 2, 3, …, N, j=1, 2, 3, …, J), Pk is the
estimated proportion of source k, Sjk is the value of
isotope j of source k, with mean μjk and standard
deviation ωjk, Cjk is the fractionation
factor of isotope j of source k (following a normal distribution with
mean λjk and variance τjk), and
εjk is the residual error of isotope j of mixture k
(with mean 0 and standard deviation σj). The parameters
of the SIAR model are mainly the number of iterations, the sampling
interval and the number of decays, which are set to 500,000, 15 and
5000, respectively.
In this study, the end-member values of
δ15N and
δ18O from different sources were selected through
self-testing in the study area and referencing to nearby regions (Table
1). The end-member values of δ15N and
δ18O in atmospheric deposit were obtained through
self-testing in the study area, while the δ15N values
in soil organic nitrogen, manure and sewage, and nitrogen fertilizer
were referenced from another study conducted in a similar geographical
and climatic basin (Zhang et al., 2018). The end-member values of
δ18O in soil organic nitrogen, manure and sewage, and
nitrogen fertilizer were designated as the same values, which had
undergone microbial processes and were different from atmospheric
deposition sources (Yi et al., 2017). Based on the microbial
nitrification model theory (Kendall, 1998), we used the collected
samples and the δ18O of oxygen to predict the
δ18O-nitrate of nitrification products, as shown in
the following formula:
(2)
The δ18O value of atmospheric oxygen is 23.5%.
(Aravena and Mayer, 2010)
Table 1 End-members ofδ15N and δ18O inputs for
the Bayesian isotope mixing model.
2.4 Uncertainty analysis and Sensitivity
analysis
Uncertainty analysis uses probabilistic statistics to evaluate the
results of the SIAR model. UI90 is defined as the
cumulative frequency distribution of 0.95 minus the cumulative frequency
distribution of 0.05 and then divided by 0.9 (Ji et al. 2017).
UI90 can represent the intensity of uncertainty under
high probability conditions (90%), thus eliminating the effect of small
probability (10%) extremes. Uncertainty analysis can quantify the
uncertainty of model output, and help to better identify the main
sources of pollution.
Sensitivity analysis was performed by adjusting the mean values of
δ15N and δ18O for different
potential nitrate sources separately. The sensitivity of the nitrate
source assignment in the study area to the mean values of
δ15N and δ18O from different sources
was analyzed by varying the input values. Sensitivity analysis can help
optimize the design of sampling schemes and improve the accuracy of
model allocation results.