U.S. Geological Survey, 425 Jordan Road, Troy, New York 12180, United States.
Environ Sci Technol. 2013 Jun 4;47(11):5904-12. doi: 10.1021/es303758e. Epub 2013 May 13.
Mercury (Hg) bioaccumulation factors (BAFs) for game fishes are widely employed for monitoring, assessment, and regulatory purposes. Mercury BAFs are calculated as the fish Hg concentration (Hg(fish)) divided by the water Hg concentration (Hg(water)) and, consequently, are sensitive to sampling and analysis artifacts for fish and water. We evaluated the influence of water sample timing, filtration, and mercury species on the modeled relation between game fish and water mercury concentrations across 11 streams and rivers in five states in order to identify optimum Hg(water) sampling approaches. Each model included fish trophic position, to account for a wide range of species collected among sites, and flow-weighted Hg(water) estimates. Models were evaluated for parsimony, using Akaike's Information Criterion. Better models included filtered water methylmercury (FMeHg) or unfiltered water methylmercury (UMeHg), whereas filtered total mercury did not meet parsimony requirements. Models including mean annual FMeHg were superior to those with mean FMeHg calculated over shorter time periods throughout the year. FMeHg models including metrics of high concentrations (80th percentile and above) observed during the year performed better, in general. These higher concentrations occurred most often during the growing season at all sites. Streamflow was significantly related to the probability of achieving higher concentrations during the growing season at six sites, but the direction of influence varied among sites. These findings indicate that streamwater Hg collection can be optimized by evaluating site-specific FMeHg-UMeHg relations, intra-annual temporal variation in their concentrations, and streamflow-Hg dynamics.
汞(Hg)生物积累因子(BAF)被广泛应用于监测、评估和监管目的。Hg BAF 的计算方法是将鱼类 Hg 浓度(Hg(fish))除以水 Hg 浓度(Hg(water)),因此,Hg(fish)和 Hg(water)的采样和分析方法会影响 BAF 值。为了确定最优的 Hg(water)采样方法,我们评估了水样采集时间、过滤和汞形态对 11 条河流和溪流中鱼类和水中汞浓度之间模型关系的影响,这些河流和溪流分布在五个州。每个模型都包含鱼类的营养级,以解释在各采样点采集的广泛物种范围,并考虑了流量加权的 Hg(water)估算值。通过使用赤池信息量准则(Akaike's Information Criterion)评估模型的简约性。较好的模型包括过滤的甲基汞(FMeHg)或未过滤的甲基汞(UMeHg),而过滤的总汞不符合简约性要求。包括年均 FMeHg 的模型优于那些在全年较短时间内计算平均 FMeHg 的模型。包括年内观察到的高浓度(第 80 百分位数及以上)的 FMeHg 模型通常表现更好。这些更高的浓度通常在所有采样点的生长季节最常出现。在六个采样点,径流量与生长季节高浓度(第 80 百分位数及以上)的出现概率之间存在显著关系,但影响方向因采样点而异。这些发现表明,可以通过评估特定地点的 FMeHg-UMeHg 关系、其浓度的年内时间变化以及径流量-汞动态来优化溪流 Hg 采集。