Scale-dependence of land use effects on water quality of streams in agricultural catchments

Buck, Oliver; Niyogi, Dev K.; Townsend, Colin R.

doi:10.1016/j.envpol.2003.10.018

Cited by 265 publications

(172 citation statements)

References 31 publications

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“…The positive correlation of NO − 3 and agricultural land in the HPS region (located downstream) is consistent with many others studies (Hill 1978;Neill 1989;Johnson and Gage 1997;Tufford et al 1998). There are also weak positive and negative loadings on T, and E. coli temperature is most possibly related to seasonal effects, while E. coli is strongly related to municipal sewage and wastewater treatment plants (Frenzel and Couvillion 2002) are due to agricultural runoff such as livestock waste and fertilizers (Buck et al 2003), industrial effluents, municipal sewage, and existing sewage treatment plants because PO 3− 4 is an important component of detergents (Vega et al 1998). VF6 explains 4.9% of the total variance and has strong positive loading on coliform, while VF7 explained 5.8% of the total variance with strong loading on COD.…”

Section: Figmentioning

confidence: 99%

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

289

196

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This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderatepollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.

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Section: Figmentioning

confidence: 99%

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Juahir

Zain

Yusoff

et al. 2010

Environ Monit Assess

289

196

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“…Buck et al (2004) connected spring floods and Woli et al (2008) floods caused by snow melting with increased nutrient runoff to rivers. In addition, wet seasons (Carroll et al 2013) and high-flow periods (Gonzales-Inca et al 2015) have been associated with increased nutrient inputs to surface waters.…”

Section: Temporal Scalementioning

confidence: 99%

Spatio-temporal aspects of the environmental factors affecting water quality in boreal rivers

Varanka

Hjort

2016

Environ Earth Sci

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River water quality is the outcome of multiple processes and factors, which vary spatially and temporally. In this study, the key spatial and temporal scales and the most important environmental factors explaining river water quality at these scales were analysed by generalized additive models (GAMs). Water quality was studied through total phosphorus (median = 61.9 µg l -1 ) and nitrogen (1388.1 µg l -1 ), pH (6.7) and water colour (143.3 mg Pt l -1 ). Environmental factors covered variables from land use/cover, climate and other landscape characteristics. The spatial scales used were the closest 50 m, 100 m, 200 m, 500 m and 1000 m buffer zones around the river channel, in addition to the entire catchment area. Temporality was studied through the entire year and four periods, which were determined by the natural variation in discharge.In the comparison of spatial scales of environmental factors, the variation in phosphorus, nitrogen and water colour was best explained using environmental data from the broadest scale, the entire catchment. In contrast, the variation in pH was best explained using data from the closest (50 m) buffer zone, the riparian area. In modelling temporal scales, the variation in water quality variables was best explained during discharge maximum periods and when the environmental data covering the entire year was considered. Nutrients were related specifically to agriculture, water colour to lake percentage, and pH to pastures. The results showed the suitability of GAMs in water quality studies.

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“…Human activities such as discharge of industrial and domestic effluents, the use of agricultural chemicals, land use and cover changes are the major factors that influence surface water quality (Peters and Meybeck 2000;Buck et al 2004;Alam et al 2006;Zhang et al 2007;Hussain et al 2008).…”

Section: Anthropogenic Sources Affecting Water Quality In Urban Areasmentioning

confidence: 99%

Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas

Khatri¹,

Tyagi²

2014

Frontiers in Life Science

795

283

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Although water constitutes 71% of the earth's surface, only 0.3% of it is available as fresh water for human use. Moreover, the quality of fresh water in ground and surface systems is of great concern, as potable water needs to have appropriate mineral content. Ground and surface water quality in rural and urban environments is affected by both natural processes and anthropogenic influences. Because of this, water is becoming scarcer as the population increases across the world. Natural processes leading to changes in water quality include weathering of rocks, evapotranspiration, depositions due to wind, leaching from soil, run-off due to hydrological factors, and biological processes in the aquatic environment. These natural processes cause changes in the pH and alkalinity of the water, and also phosphorus loading, increase in fluoride content and high concentrations of sulphates. Anthropogenic factors affecting water quality include impacts due to agriculture, use of fertilizers, manures and pesticides, animal husbandry activities, inefficient irrigation practices, deforestation of woods, aquaculture, pollution due to industrial effluents and domestic sewage, mining, and recreational activities. These anthropogenic influences cause elevated concentrations of heavy metals, mercury, coliforms and nutrient loads. This paper studies the effects of natural processes and human influences in rural and urban aquatic systems. Pollution due to environmental parameters such as heavy metal pollution, heavy metals and bacterial and pathogenic contamination of both urban and rural areas is discussed in detail.

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Scale-dependence of land use effects on water quality of streams in agricultural catchments

Cited by 265 publications

References 31 publications

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

Spatio-temporal aspects of the environmental factors affecting water quality in boreal rivers

Influences of natural and anthropogenic factors on surface and groundwater quality in rural and urban areas

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