Assessment of temporal and spatial variations in water quality using multivariate statistical methods: a case study of the Xin'anjiang River, China

doi:10.1007/s11783-014-0736-z

Front. Environ. Sci. Eng.

2014, Vol. 8

Issue (6) : 895-904 https://doi.org/10.1007/s11783-014-0736-z

RESEARCH ARTICLE

Assessment of temporal and spatial variations in water quality using multivariate statistical methods: a case study of the Xin'anjiang River, China

Xue LI¹,Pengjing LI¹,Dong WANG²,Yuqiu WANG^1,^*(

)

1. College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
2. Chinese Academy for Environmental Planning, Water Environment Institute, Beijing 100012, China

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Abstract

This study evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River through the use of multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), correlation analysis, and principal component analysis (PCA). The water samples, measured by ten parameters, were collected every month for three years (2008–2010) from eight sampling stations located along the river. The hierarchical CA classified the 12 months into three periods (First, Second and Third Period) and the eight sampling sites into three groups (Groups 1, 2 and 3) based on seasonal differences and various pollution levels caused by physicochemical properties and anthropogenic activities. DA identified three significant parameters (temperature, pH and E.coli) to distinguish temporal groups with close to 76% correct assignment. The DA also discovered five parameters (temperature, electricity conductivity, total nitrogen, chemical oxygen demand and total phosphorus) for spatial variation analysis, with 80.56% correct assignment. The non–parametric correlation coefficient (Spearman R) explained the relationship between the water quality parameters and the basin characteristics, and the GIS made the results visual and direct. The PCA identified four PCs for Groups 1 and 2, and three PCs for Group 3. These PCs captured 68.94%, 67.48% and 70.35% of the total variance of Groups 1, 2 and 3, respectively. Although natural pollution affects the Xin'anjiang River, the main sources of pollution included agricultural activities, industrial waste, and domestic wastewater.

Keywords Xin'anjiang River multivariable statistical analysis temporal variation spatial variation water quality

Corresponding Author(s): Yuqiu WANG

Issue Date: 17 November 2014

Cite this article:

Xue LI,Pengjing LI,Dong WANG, et al. Assessment of temporal and spatial variations in water quality using multivariate statistical methods: a case study of the Xin'anjiang River, China[J]. Front. Environ. Sci. Eng., 2014, 8(6): 895-904.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-014-0736-z
https://academic.hep.com.cn/fese/EN/Y2014/V8/I6/895

Fig.1 Study area and eight monitoring sites

parameters		HSLX	HJDQ	HKD	HDD	YL	PK	NYK	JK
Temp	range	5.4–31.5	5.6–32.5	5.5–34	5.6–34.2	4.4–29.9	5.3–30.1	3.5–31	4.2–32
	mean	17.67	17.92	18.33	18.59	18.39	18.53	18.64	19.74
	SE	1.31	1.33	1.36	1.34	1.30	1.27	1.27	1.30
	SD	7.83	7.96	8.15	8.01	7.82	7.61	7.62	7.79
pH	range	7.13–8.51	7.1–8.88	6.86–8.92	7.08–8.7	6.88–8.47	6.89–8.51	6.82–8.78	7.05–8.88
	mean	7.74	7.72	7.69	7.74	7.70	7.73	7.67	7.68
	SE	0.06	0.07	0.08	0.07	0.06	0.06	0.06	0.07
	SD	0.37	0.41	0.48	0.39	0.35	0.34	0.38	0.39
EC	range	3.1–18.6	4.2–37.7	4.2–22	4.52–22.5	15.1–69.5	13–64.2	8.6–31.8	7.8–24.9
	mean	7.02	22.78	12.13	12.81	32.42	29.20	17.18	13.76
	SE	0.49	1.39	0.81	0.71	2.40	2.18	0.95	0.60
	SD	2.93	8.37	4.85	4.24	14.40	13.08	5.71	3.59
DO	range	7.0–14.0	6.1–14.6	7.2–14.4	7.3–16.3	5.5–15.2	6.1–15.5	5.7–13.7	6.1–80.2
	mean	9.35	9.51	9.38	10.02	9.06	9.69	9.24	11.10
	SE	0.28	0.33	0.34	0.36	0.32	0.37	0.31	2.00
	SD	1.68	1.99	2.02	2.17	1.94	2.23	1.83	11.97
COD_Mn	range	0.6–3.8	1–5.8	1.3–3.8	1.0–3.0	1.7–5.7	1.5–4.2	1–3.8	1–3.2
	mean	1.59	2.34	2.16	2.06	3.31	2.87	2.33	1.88
	SE	0.10	0.15	0.10	0.09	0.17	0.11	0.11	0.09
	SD	0.59	0.90	0.57	0.53	1.00	0.64	0.67	0.52
N H 4 + - N	range	0.066–0.659	0.077–0.863	0.049–0.832	0.071–0.927	0.148–0.992	0.142–0.902	0.048–0.806	0.064–0.704
	mean	0.24	0.30	0.31	0.36	0.53	0.43	0.30	0.18
	SE	0.03	0.03	0.04	0.04	0.05	0.04	0.04	0.02
	SD	0.15	0.18	0.22	0.24	0.28	0.26	0.21	0.12
COD_Cr	range	0–9	0–17	2.2–12	0–18	5.8–19	5.0–16.0	0–16	0–10
	mean	4.31	7.92	7.38	7.38	11.16	9.88	7.93	6.06
	SE	0.40	0.48	0.34	0.57	0.56	0.40	0.50	0.41
	SD	2.39	2.86	2.04	3.42	3.38	2.39	3.00	2.49
TN	range	0.35–1.9	0.48–2.52	0.56–1.86	0.9–2.12	0.94–4.58	0.98–4.34	0.95–2.44	0.082–1.82
	mean	0.96	1.21	1.17	1.44	2.50	2.26	1.61	1.28
	SE	0.06	0.07	0.05	0.06	0.14	0.13	0.07	0.05
	SD	0.34	0.40	0.32	0.36	0.85	0.79	0.42	0.31
TP	range	0.02–0.15	0.03–0.18	0–0.14	0.03–0.14	0.04–0.2	0.04–0.18	0.02–0.16	0.008–0.09
	mean	0.06	0.07	0.06	0.08	0.11	0.08	0.07	0.04
	SE	0.00	0.01	0.00	0.00	0.01	0.01	0.00	0.00
	SD	0.03	0.03	0.03	0.03	0.04	0.04	0.03	0.02
E.coli	range	170–2400	220–3500	430–5400	430–3500	330–5400	490–5400	230–5400	220–3500
	mean	599.44	835.56	1174.08	1025.42	1523.06	1523.33	1121.11	832.50
	SE	93.36	139.51	164.46	115.06	191.65	197.10	207.12	148.25
	SD	560.17	837.03	986.75	690.38	1149.93	1182.61	1242.74	889.51

Tab.1 Water quality parameters with range, mean value, standard error of mean and standard deviation of Xin'anjiang River system

Fig.2 (a) landscape characteristic gradients, (b) trends of temperature, precipitation, slope, population density and flow

Fig.3 Dendrogram showing temporal similarities of monitoring periods

Tab.2 Classification functions for discriminant analysis of temporal variation

Fig.4 Dendrogram showing sampling site clusters

Tab.3 Classification functions for discriminant analysis of spatial variation

Tab.4 Loadings of 10 experimental variables on factor analysis parameters for three spatial clusters

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