Spatio-temporal variations of water quality in Yuqiao Reservoir Basin, North China

Yi‐chong, Xu; Ruqin, Xie; Wang, Yuqiu; Sha, Jingeng

doi:10.1007/s11783-014-0702-9

Cited by 18 publications

(14 citation statements)

References 35 publications

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“…Kumarasamy et al (2014) revealed that Ca, Mg, Na, K, HCO 3 , Cl, H 4 SiO 4 , SO 4 , NO 2 , and PO 4 affect the seasonal and spatial variations and that water quality was in the first place determined more by natural weathering processes than by anthropogenic. Xu et al (2014) identified T, pH, SS, THard, DO, NO 3 -N, and TP as significant variables affecting spatial variations, and T, pH, and NO 2 -N were identified as significant variables affecting temporal variation. They identified four latent pollution sources for water quality variations: nutrient pollution, organic pollution, inorganic pollution, and natural pollution.…”

Section: Source Pollution Identificationmentioning

confidence: 93%

“…The results showed that there were two different water qualities in the catchment area. It implied that for rapid assessment of water quality, only one station in each cluster may serve as good in spatial assessment of the water quality as the whole network (Li et al 2014;Xu et al 2014;Dobsa et al 2014). The CA technique reduced the need for numerous sampling stations.…”

Section: Cluster Analysismentioning

confidence: 99%

“…Li et al (2014) evaluated the temporal and spatial variations of water quality data sets for the Xin'anjiang River with multivariate statistical techniques and used hierarchical CA to classify the 12 months into three periods and the eight sampling sites into three groups based on seasonal differences and various pollution levels. Xu et al (2014) employed multivariate statistical techniques to assess the spatial-temporal variations and identify potential pollution sources of Yugiao Reservoir Basin, North China, and used hierarchical CA to classify seven monitoring sites into two groups and months into three periods (low-flow, normal-flow, and high-flow periods). Bu et al (2014) examined spatial-temporal variations of water quality in the Taizi River by multivariate statistical analysis of data from the 67 sampling sites in the mainstream and major tributaries of the river during dry and rainy seasons and used hierarchical CA to classify the mainstream and tributaries of the river into three clusters…”

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confidence: 99%

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Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria

Ogwueleka

2015

Environ Monit Assess

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Multivariate statistical techniques, such as cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA), were used to investigate the temporal and spatial variations and to interpret large and complex water quality data sets collected from the Kaduna River. Kaduna River is the main tributary of Niger River in Nigeria and represents the common situation of most natural rivers including spatial patterns of pollutants. The water samples were collected monthly for 5 years (2008-2012) from eight sampling stations located along the river. In all samples, 17 parameters of water quality were determined: total dissolved solids (TDS), pH, Thard, dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), NH4-N, Cl, SO4, Ca, Mg, total coliform (TColi), turbidity, electrical conductivity (EC), HCO3 (-), NO3 (-), and temperature (T). Hierarchical CA grouped 12 months into two seasons (dry and wet seasons) and classified eight sampling stations into two groups (low- and high-pollution regions) based on seasonal differences and different levels of pollution, respectively. PCA/FA for each group formed by CA helped to identify spatiotemporal dynamics of water quality in Kaduna River. CA illustrated that water quality progressively deteriorated from headwater to downstream areas. The results of PCA/FA determined that 78.7 % of the total variance in low pollution region was explained by five factor, that is, natural and organic, mineral, microbial, organic, and nutrient, and 87.6 % of total variance in high pollution region was explained by six factors, that is, microbial, organic, mineral, natural, nutrient, and organic. Varifactors obtained from FA indicated that the parameters responsible for water quality variations are resulted from agricultural runoff, natural pollution, domestic, municipal, and industrial wastewater. Mann-Whitney U test results revealed that TDS, pH, DO, T, EC, TColi, turbidity, total hardness (THard), Mg, Ca, NO3 (-), COD, and BOD were identified as significant variables affecting temporal variation in river water, and TDS, EC, and TColi were identified as significant variables affecting spatial variation. In addition, box-whisker plots facilitated and supported multivariate analysis results. This study illustrates the usefulness of multivariate statistical techniques for classification and processing of large and complex data sets of water quality parameters, identification of latent pollution factors/sources and their spatial-temporal variations, and determination of the corresponding significant parameters in river water quality.

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Section: Source Pollution Identificationmentioning

confidence: 93%

Section: Cluster Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria

Ogwueleka

2015

Environ Monit Assess

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“…[17]. ANOVA discloses significant seasonal variance in temperature, pH, conductivity and BIC compared to significant site contrasts for Cl, Mg, Na, K, NO 3 − N and P [59] and [60]. The BIC, Cl, Ca, Mg, Na, K, nitrate N and total P display virtually a matching situation of withdrawal throughout highest growth periods of macrophytes and once more pickup in post senescence period [2] and [61].…”

Section: Resultsmentioning

confidence: 99%

A Retrospective Environmental Assessment of the Natural Heritage of Dal Lake for Prospective Implication Scenarios

Bhat¹,

Khan²

2019

ESD

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Monitoring at 30 sampling sites of Dal Lake is carried out to assess its spatial-temporal heterogeneity under human pressures. P surpass critical eutrophic index ( 0.05 mgL ≤ -1 ) while nitrate-N persist beneath it ( 0.5 mgL ≤ -1 ). Semi-drainage hydrology recuperates basin volume. Autotrophic assimilation and biocalcification episodes drop conductivity. Anionic prevalence of HCO3and Clexist along Ca > Mg > Na > K cationic progression. Autochthonous sediment OM at typical < 10 C/N is the conventional nutrient source. Higher temperature and lower N:P ratio during summer develop P internal loading process. Cr, Ni and Zn exceed the sediment quality guidelines. OM enriched sediments and calcite co-precipitation curtails PTE mobility. Biomass parameters establish similar variations except species turn-over. OM immobilizes nutrients and OC provisions denitrification. Dal Lake exposed to anthro-urban intensification depends on comprehensive management interventions like National Plan for Conservation of Aquatic Ecosystems vital for insitu leniency control and lake-front improvement.

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“…River water pollutants usually come from highly seasonal-dependent transport pathways, such as ditches and creeks discharges, groundwater seepage, storm water runoff and atmospheric deposition 2 . Previous studies found that water quality at Yuqiao Reservoir Basin in China was clustered temporally by hydrological conditions (i.e., river water flow), but spatially by water quality parameters (i.e., water temperature, pH, suspended solids, total hardness, dissolved oxygen, nitrate and total phosphorus) 13 . Driving effects of water quality and hydrological factors varied significantly across spatial distributions, due to differences in geographical environments, water salinity and water pollution 4 .…”

Section: Introductionmentioning

confidence: 99%

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

Yang

Liang

Qin³

et al. 2021

Sci Rep

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It’s vital to explore critical indicators when identifying potential pollution sources of urban rivers. However, the variations of urban river water qualities following temporal and spatial disturbances were highly local-dependent, further complicating the understanding of pollution emission laws. In order to understand the successional trajectory of water qualities of urban rivers and the underlying mechanisms controlling these dynamics at local scale, we collected daily monitoring data for 17 physical and chemical parameters from seven on-line monitoring stations in Nanfeihe River, Anhui, China, during the year 2018. The water quality at tributaries were similar, while that at main river was much different. A seasonal ‘’turning-back” pattern was observed in the water quality, which changed significantly from spring to summer but finally changed back in winter. This result was possibly regulated by seasonally-changed dissolved oxygen and water temperature. Linear mixed models showed that the site 2, with the highest loads of pollution, contributed the highest (β = 0.316, P < 0.001) to the main river City Water Quality Index (CWQI) index, but site 5, the geographically nearest site to main river monitoring station, did not show significant effect. In contrast, site 5 but not site 2 contributed the highest (β = 0.379, P < 0.001) to the main river water quality. Therefore, CWQI index was a better index than water quality to identify potential pollution sources with heavy loads of pollutants, despite temporal and spatial disturbances at local scales. These results highlight the role of aeration in water quality controlling of urban rivers, and emphasized the necessity to select proper index to accurately trace the latent pollution sources.

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Spatio-temporal variations of water quality in Yuqiao Reservoir Basin, North China

Cited by 18 publications

References 35 publications

Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria

Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria

A Retrospective Environmental Assessment of the Natural Heritage of Dal Lake for Prospective Implication Scenarios

A novel assessment considering spatial and temporal variations of water quality to identify pollution sources in urban rivers

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