Literature DB >> 24554019

Urban stream syndrome in a small, lightly developed watershed: a statistical analysis of water chemistry parameters, land use patterns, and natural sources.

Judith A Halstead1, Sabrina Kliman, Catherine White Berheide, Alexander Chaucer, Alicea Cock-Esteb.   

Abstract

The relationships among land use patterns, geology, soil, and major solute concentrations in stream water for eight tributaries of the Kayaderosseras Creek watershed in Saratoga County, NY, were investigated using Pearson correlation coefficients and multivariate regression analysis. Sub-watersheds corresponding to each sampling site were delineated, and land use patterns were determined for each of the eight sub-watersheds using GIS. Four land use categories (urban development, agriculture, forests, and wetlands) constituted more than 99 % of the land in the sub-watersheds. Eleven water chemistry parameters were highly and positively correlated with each other and urban development. Multivariate regression models indicated urban development was the most powerful predictor for the same eleven parameters (conductivity, TN, TP, NO[Formula: see text], Cl(-), HCO(-)3, SO9(2-)4, Na(+), K(+), Ca(2+), and Mg(2+)). Adjusted R(2) values, ranging from 19 to 91 %, indicated that these models explained an average of 64 % of the variance in these 11 parameters across the samples and 70 % when Mg(2+) was omitted. The more common R (2), ranging from 29 to 92 %, averaged 68 % for these 11 parameters and 72 % when Mg(2+) was omitted. Water quality improved most with forest coverage in stream watersheds. The strong associations between water quality variables and urban development indicated an urban source for these 11 water quality parameters at all eight sampling sites was likely, suggesting that urban stream syndrome can be detected even on a relatively small scale in a lightly developed area. Possible urban sources of Ca(2+) and HCO(-)3 are suggested.

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Year:  2014        PMID: 24554019     DOI: 10.1007/s10661-014-3625-9

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


  27 in total

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  9 in total

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4.  Spatial and temporal analysis of land cover changes and water quality in the Lake Issaqueena watershed, South Carolina.

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8.  Spatial Regression and Prediction of Water Quality in a Watershed with Complex Pollution Sources.

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9.  Ecological River Health Assessment Using Multi-Metric Models in an Asian Temperate Region with Land Use/Land Cover as the Primary Factor Regulating Nutrients, Organic Matter, and Fish Composition.

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  9 in total

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