Department of Chemistry, Skidmore College, 815 North Broadway, Saratoga Springs, NY, USA,
Environ Monit Assess. 2014 Jun;186(6):3391-414. doi: 10.1007/s10661-014-3625-9. Epub 2014 Feb 20.
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.
对纽约州萨拉托加县凯亚德罗萨溪流域 8 条支流的土地利用模式、地质、土壤和主要溶质浓度之间的关系进行了研究,使用 Pearson 相关系数和多元回归分析。为每个采样点划定了次流域,并使用 GIS 确定了这 8 个次流域的土地利用模式。四个土地利用类别(城市发展、农业、森林和湿地)构成了次流域土地的 99%以上。11 个水质参数之间高度正相关,且与城市发展相关。多元回归模型表明,城市发展是 11 个相同参数(电导率、TN、TP、NO[Formula: see text]、Cl(-)、HCO(-)3、SO9(2-)4、Na(+)、K(+)、Ca(2+)和 Mg(2+))的最有力预测因子。调整后的 R(2)值在 19%至 91%之间,表明这些模型平均解释了样本中这 11 个参数 64%的方差,当 Mg(2+)被排除时为 70%。更为常见的 R(2)值在 29%至 92%之间,这些 11 个参数的平均值为 68%,当 Mg(2+)被排除时为 72%。流域中森林覆盖率越高,水质改善越大。水质变量与城市发展之间的强烈关联表明,在所有 8 个采样点,这些 11 个水质参数都可能来自城市,这表明即使在开发程度较低的较小区域,也可能检测到城市溪流综合征。还暗示了 Ca(2+)和 HCO(-)3可能的城市来源。