کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6316009 | 1619157 | 2016 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A field-based method to derive macroinvertebrate benchmark for specific conductivity adapted for small data sets and demonstrated in the Hun-Tai River Basin, Northeast China
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم محیط زیست
شیمی زیست محیطی
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چکیده انگلیسی
Ionic mixtures, measured as specific conductivity, have been increasingly concerned because of their toxicities to aquatic organisms. However, identifying protective values of specific conductivity for aquatic organisms is challenging given that laboratory test systems cannot examine more salt-intolerant species nor effects occurring in streams. Large data sets used for deriving field-based benchmarks are rarely available. In this study, a field-based method for small data sets was used to derive specific conductivity benchmark, which is expected to prevent the extirpation of 95% of local taxa from circum-neutral to alkaline waters dominated by a mixture of SO42â and HCO3â anions and other dissolved ions. To compensate for the smaller sample size, species level analyses were combined with genus level analyses. The benchmark is based on extirpation concentration (XC95) values of specific conductivity for 60 macroinvertebrate genera estimated from 296 sampling sites in the Hun-Tai River Basin. We derived the specific conductivity benchmark by using a 2-point interpolation method, which yielded the benchmark of 249 μS/cm. Our study tailored the method that was developed by USEPA to derive aquatic life benchmark for specific conductivity for basin scale application, and may provide useful information for water pollution control and management.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Environmental Pollution - Volume 216, September 2016, Pages 902-910
Journal: Environmental Pollution - Volume 216, September 2016, Pages 902-910
نویسندگان
Qian Zhao, Xiaobo Jia, Rui Xia, Jianing Lin, Yuan Zhang,