Tian-lun Zhai; Qian-qian Zhang; Long Wang; Hui-wei Wang

doi:10.26599/JGSE.2024.9280022

doi: 10.26599/JGSE.2024.9280022

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出版历程
- 收稿日期: 2023-06-12
- 录用日期: 2024-05-25
- 网络出版日期: 2024-08-10
- 刊出日期: 2024-09-15

Temporal and spatial variations hydrochemical components and driving factors in Baiyangdian Lake in the Northern Plain of China

1.
Hebei and China Geological Survey Key Laboratory of Groundwater Remediation, Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China

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Corresponding author: z_qqian@163.com

摘要

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Abstract: Understanding the temporal and spatial variation of hydrochemical components in large freshwater lakes is crucial for effective management and conversation. In this study, we identify the temporal-spatial characteristics and driving factors of the hydrochemical components in Baiyangdian Lake using geochemical methods (Gibbs diagram, Piper diagram and End-element diagram of ion ratio) and multivariate statistical techniques (Principal component analysis and Correlation analysis). 16 sets of samples were collected from Baiyangdian Lake in May (normal season), July (flood season), and December (dry season) of 2022. Results indicate significant spatial variation in Na⁺, Cl⁻, SO₄²⁻ and NO₃⁻ , suggesting a strong influence of human activities. Cation concentrations exhibit greater seasonal variation in the dry season compared to the flood season, while the concentrations of the four anions show inconsistent seasonal changes due to the combined effects of river water chemical composition and human activities. The hydrochemical type of Baiyangdian Lake is primarily HCO₃·Cl-Na·Ca²⁺, Mg²⁺ and HCO₃⁻ originate mainly from silicate and carbonate rock dissolution, while K⁺, Na⁺ and Cl⁻ originate mainly from sewage and salt dissolution in sediments. SO₄²⁻ may mainly stem from industrial wastewater, while NO₃⁻ primarily originates from animal feces and domestic sewage. Through the use of Principal Component Analysis, it is identified that water-rock interaction (silicate and carbonate rocks dissolution, and dissolution of salt in sediments), carbonate sedimentation, sewage, agricultural fertilizer and manure, and nitrification are the main driving factors of the variation of hydrochemical components of Baiyangdian Lake across three hydrological seasons. These findings suggest the need for effective control of substandard domestic sewage discharge, optimization of agricultural fertilization strategies, and proper management of animal manure to comprehensively improve the water environment in Baiyangdian Lake.
- Hydrochemical variation /
- Sources /
- Human activities /
- Water-rock interaction /
- Multivariate statistical techniques.

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Figure 1. Distribution of sampling sites in Baiyangdian Lake basin

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Figure 2. Piper diagram of Baiyangdian Lake in different seasons (a) Lake water; (b) River water

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Figure 3. Gibbs diagram of Baiyangdian Lake

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Figure 4. Relationship between HCO₃⁻/Na⁺ and Ca²⁺/Na⁺(a), Mg²⁺/Na⁺ and Ca²⁺/Na⁺ (b) in Baiyangdian Lake

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Figure 5. Baiyangdian Lake γ[Ca²⁺+Mg²⁺] and γ[HCO₃-+SO₄²⁻] relationship

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Figure 6. Baiyangdian Lake [SO₄²⁻]/[Ca²⁺] and [NO₃⁻]/[Ca²⁺](a), [Cl⁻] and [NO₃⁻]/[Cl⁻] (b) relationship

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Figure 7. Correlation analysis of Baiyangdian Lake in normal, flood and dry seasons

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1. Basic information of the sampling sites

Sites	Longitude	Latitude	Land use	Potential sources of pollution
L1	115°58′26.96″	38°56′33.15″	Scenic areas, Farmland,	Sewage
L2	115°59′1.98″	38°56′0.07″	Tourist Area	Sewage
L3	116°0′6.68″	38°54′59.17″	Distribution of rural farmland	Agricultural non-point pollution
L4	116°1′29.46″	38°55′6.33″	Near the Observation Deck	—
L5	116°2′11.14″	38°54′0.71″	Village	Sewage
L6	116°3′38.26″	38°543′50.44″	Village	Sewage
L7	116°5′28.23″	38°53′25.56″	Village, near the Farmland	Agricultural non-point pollution and sewage
L8	116°3′12.39″	38°52′46.14″	Village	Sewage
L9	116°1′9.94″	38°51′27.40″	Village	Sewage
L10	116°2′34.59″	38°50′52.66″	Aquaculture intensive area	Feeding fodder and animal excrements
L11	116°2′40.78″	38°49′39.59″	Village, Aquaculture area	Sewage
L12	116°1′33.95″	38°49′46.12″	Village, Large number of aquatic plant distribution	Sewage
L13	115°59′40.70″	38°50′32.51″	Village	Sewage
L14	115°57′21.19″	38°50′59.41″	Village, Farmland	Agricultural non-point pollution and sewage
L15	115°59′6.09″	38°52′26.80″	Village	Sewage
L16	115°59′53.59″	38°53′57.37″	Village	Sewage
R1	115°55′22.3″	38°54′15.75″	Village, Farmland	Undertake domestic sewage and industrial wastewater
R2	115°47′20.12″	38°53′50.23″	Villages along the route, Farmland	Agricultural non-point pollution and sewage
R3	115°46′19.88″	38°54′56.44″	Close to Villages, Farmland, Fishing Sites	Agricultural non-point pollution and sewage
R4	115°52′29.83″	38°47′30.89″	Close to Villages, Farmland	Agricultural non-point pollution and sewage
R5	116°02′1.15″	39°00′44.03″	Village, Farmland	Agricultural non-point pollution and sewage
R6	116°01′5.21″	38°47′20.45″	Farmland along the route, Fewer villages	Agricultural non-point pollution and sewage
R7	115°50′55.11″	38°48′26.68″	Village, Farmland	Agricultural non-point pollution and sewage

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2. Hydrochemical parameters, analytical method, equipment and detection limits.

Parameters	Analytical method	Analytical equipment	Detection limit
pH EC(μS/cm) DO(mg/L)	Electrode method	HQ40D, HACH, United States	0.01 0.01 0.05
Nitrate [NO₃⁻](mg/L)	Spectrophotometry	Perkin-Elmer Lambda 35, United States	0.664
Chloride [Cl⁻](mg/L)			1.0
Sulfate [SO₄²⁻](mg/L)			0.75
Potassium [K⁺](mg/L)	Inductively coupled plasma-mass spectrometry	Agilent 7500ce ICP-MS, Tokyo, Japan	0.05
Sodium [Na⁺](mg/L)			0.01
Calcium [Ca²⁺](mg/L)			4.0
Magnesium [Mg²⁺](mg/L)			3.0
Bicarbonate [HCO₃⁻](mg/L)	Acid–base titration	—	5.0

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Table 1. Statistical table of chemical components of Baiyangdian Lake

Parameter	Mean value			Range			Variable coefficient(%)			National standard
Parameter	NS	FS	DS	NS	FS	DS	NS	FS	DS	National standard
pH	8.67	8.41	7.41	8.17–10.2	7.73–8.87	7.31–7.54	6.51	4.58	0.856	6.0–9.0
EC	945	719	848	704–1199	540–984	706–1089	15.2	21.3	12.9	–
DO	8.03	8.41	7.72	4.99–11.0	4.59–11.7	4.25–9.27	25.6	29.8	26.1	5.0
K⁺	6.30	6.39	8.33	5.45–8.62	4.12–8.95	5.15–10.1	12.7	26.9	16.8	–
Na⁺	103	72.7	75.5	59.4–175	32.6–141	34.9–113	31.2	55.4	31.5	–
Ca²⁺	50.8	41.1	56.4	22.3–69.5	32.9–49.9	47.5–63.3	25.9	12.4	8.39	–
Mg²⁺	26.2	23.1	24.8	19.8–31.4	19.9–29.2	20.0–29.2	14.6	13.6	9.36	–
HCO₃^–	197	206	243	134–285	178–227	216–266	21.0	7.10	5.51	–
Cl^–	116	72.8	86.0	82.8–153	40.3–124	28.8–144	16.1	40.9	42.3	250
SO₄^2–	73.3	93.0	92.5	33.8–137	39.9–149	24.4–202	34.4	34.3	50.3	250
NO₃^–	2.36	1.68	2.34	0.050–5.98	0.574–4.73	0.448–8.91	58.0	67.4	98.9	44.3
Note: pH is dimensionless. EC unit is μs/cm. The unit of other indicators is mg/L. NS: Normal season; FS: Flood season; DS: Dry season; National standard: Class III standard of the national surface water quality standard, Chinese (GB 3838—2002).

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Table 2. Statistical table of chemical components of the river flows into Baiyangdian Lake

Parameter	Mean value			Range			Variable coefficient（%）			National standard
Parameter	NS	FS	DS	NS	FS	DS	NS	FS	DS	National standard
pH	8.66	8.38	8.59	8.42–8.76	7.65–8.46	7.51–9.39	1.66	4.48	8.71	6.0–9.0
EC	721	661	1188	357–1324	331–1379	370–2270	47.2	64.7	52.5	–
DO	7.11	7.99	7.33	5.22–8.81	5.55–8.77	5.11–8.49	17.4	18.0	15.8	5.0
K⁺	6.64	5.86	9.97	2.18–14.0	2.80–13.0	2.80–25.4	82.4	52.7	77.2	–
Na⁺	73.1	68.8	151	8.28–233	13.0–212	13.8–413	136	104	91.9	–
Ca²⁺	45.4	41.3	52.9	44.6–45.9	32.1–52.7	35.2–80.3	1.39	19.2	29.3	–
Mg²⁺	20.4	16.8	21.8	11.5–26.9	13.5–24.9	11.5–36.9	39.4	22.7	41.4	–
HCO₃^–	192	191	272	149–303	137–259	161–471	33.1	22.2	44.7	–
Cl^–	76.4	61.1	120	16.3–173	7.17–162	13.9–234	77.0	104	67.6	250
SO₄^2–	55.4	91.1	96.8	30.8–98.5	34.1–181	21.0–212	52.0	65.6	82.7	250
NO₃^–	7.13	6.80	12.7	3.20–10.6	1.20–11.8	8.02–20.5	34.9	65.0	37.9	44.3

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Table 3. Driving factors of chemical components in Baiyangdian Lake water

Parameter	Normal season					Flood season		Dry season
Parameter	PC1	PC2	PC3	PC4	PC5	PC1	PC2	PC1	PC2	PC3	PC4
pH	0.798	−0.179	−0.306	−0.107	−0.327	0.945	0.284	0.026	0.163	−0.200	0.846
DO	0.817	0.242	−0.374	−0.135	−0.119	0.927	0.219	−0.229	−0.132	0.335	0.767
K⁺	0.879	−0.241	0.341	0.102	0.120	0.670	0.733	0.907	0.184	0.113	−0.011
Na⁺	0.933	−0.029	0.040	0.012	0.330	0.629	0.765	0.857	−0.113	0.395	−0.010
Ca²⁺	0.329	0.689	−0.404	0.397	0.175	−0.296	−0.906	−0.050	0.957	0.108	0.109
Mg²⁺	−0.203	0.895	−0.148	−0.262	0.150	−0.797	−0.087	−0.148	0.889	0.270	−0.048
HCO₃⁻	−0.043	0.021	0.994	0.011	0.003	0.578	0.675	0.881	−0.430	−0.004	−0.028
Cl⁻	0.060	−0.009	−0.002	0.078	0.985	0.454	0.881	0.274	0.183	0.792	−0.004
NO₃⁻	−0.121	−0.081	0.018	0.964	0.090	0.434	−0.783	−0.887	0.118	0.187	0.318
SO₄²⁻	0.266	0.816	0.052	0.439	−0.106	0.654	0.566	−0.075	0.199	0.927	0.037
Eigenvalue	3.39	2.09	1.58	1.33	1.10	6.97	1.76	3.56	2.57	1.38	1.14
Variance contribution rate (%)	33.9	20.9	15.8	13.3	11.0	69.7	17.6	35.6	25.7	13.8	11.4
Driving factor	Dissolution of salt in sediments	Dissol-ution of silicate and carbonate	Carbonate sedime-ntation	Fertilizer Manu-re	Sewage	Lixivi-ation	Sewage, manure, fertilizers, and carbonate sedimentation	Dissolution of salt, fertili-zers, and manu-re	Silicate and carbonate dissolution and carbon-ate sedime-ntation	Sewage	Nitrifi-cation

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[13]	ZHU Wei, TANG Wen, LIU Qiang, ZHANG Mei-gui2017: Analysis on variation characteristics of geothermal response in Liaoning Province, Journal of Groundwater Science and Engineering, 5, 336-342.
[14]	SONG Chao, HAN Gui-lin, WANG Pan, SHI Ying-chun, HE Ze2017: Hydrochemical and isotope characteristics of spring water discharging from Qiushe Loess Section in Lingtai, northwestern China and their implication to groundwater recharge, Journal of Groundwater Science and Engineering, 5, 364-373.
[15]	GUO Li-jun, YAN Ya-ya, GUO Li-na, MA Jin-long, LV Ming-yu2016: GIS-based spatial and temporal changes of land occupation caused by mining activities-a study in eastern part of Hubei Province, Journal of Groundwater Science and Engineering, 4, 60-68.
[16]	GONG Xiao-ping, JIANG Guang-hui, CHEN Chang-jie, GUO Xiao-jiao, ZHANG Hua-sheng2015: Specific yield of phreatic variation zone in karst aquifer with the method of water level analysis, Journal of Groundwater Science and Engineering, 3, 192-201.
[17]	ZHOU Yang-xiao, Parvez Sarwer Hossain, Nico van der Moot2015: Analysis of travel time, sources of water and well protection zones with groundwater models, Journal of Groundwater Science and Engineering, 3, 363-374.
[18]	Chang-li LIU, Chao SONG, Hong-bing HOU, Xiu-yan WANG, Yun ZHANG, Jun-kun WANG, Jian-mei JIANG, Li-xin PEI, Bo SONG2014: The Impact of Human Activities on CO2 Intake by Carbonate Weathering: A Case Study of Conglin Karst Ridge-trough at Fuling Town, Chongqing, China, Journal of Groundwater Science and Engineering, 2, 29-38.
[19]	2013: The Study of Statistical Damage Constitutive Models of Rock and Its Parameters Based on Lade-Duncan Criterion, Journal of Groundwater Science and Engineering, 1, 74-79.
[20]	2013: ESR Signal Intensity and Crystallinity of Quartz from Three Major Asian Dust Sources: Implication for Tracing the Provenances of Eolian Dust, Journal of Groundwater Science and Engineering, 1, 53-67.