School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.
Bowburn Consultancy, 11 Monteigne Drive, Bowburn, Durham DH6 5QB, UK.
Sci Total Environ. 2016 Oct 15;568:671-678. doi: 10.1016/j.scitotenv.2016.02.163. Epub 2016 Mar 9.
Freshwater acidification continues to be a major problem affecting large areas of Europe, and while there is evidence for chemical recovery, similar evidence for biological recovery of freshwaters is sparse. The need for a methodology to identify waterbodies impacted acidification and to assess the extent of biological recovery is relevant to the EU Water Framework Directive, which requires methods to quantify differences in biology between impacted and unimpacted or reference sites. This study presents a new WFD-compliant metric based on diatoms (Diatom Acidification Metric: DAM) for assessing the acidification status of rivers. A database of 558 benthic diatom samples and associated water chemistry data was assembled. Diatom taxa were assigned to one of 5 indicator classes on the basis of their pH optimum, assessed using Gaussian logistic regression, and these indicator values used to calculate a DAM score for each site using weighted averaging. Reference sites were selected on the basis of their acid neutralising capacity (ANC) and calcium concentration, and a regression model developed to predict expected DAM for each site using pH and total organic carbon (TOC) concentration. Site-specific DAM scores were used to calculate ecological quality ratios ranging from ≥1, where the diatom assemblage showed no impact, to (theoretically) 0, when the diatom assemblage was indicative of major anthropogenic activities. The boundary between 'high' and 'good' status was defined as the 25th percentile of Ecological Quality Ratios (EQRs) of all reference sites. The boundary between 'good' and 'moderate' status was set at the point at which nutrient-sensitive and nutrient-tolerant taxa were present in equal relative abundance. The methodology was evaluated using long-term data from 11 sites from the UK Uplands Waters Monitoring Network and is shown to perform well in discriminating naturally acid from acidified sites.
淡水酸化仍是影响欧洲大片地区的主要问题，尽管有化学恢复的证据，但类似的淡水生物恢复证据却很少。需要有一种方法来识别受酸化影响的水体，并评估生物恢复的程度，这与欧盟水框架指令有关，该指令要求采用方法来量化受影响和未受影响或参照点之间的生物学差异。本研究提出了一种基于硅藻的新的符合 WFD 要求的指标（硅藻酸化指标：DAM），用于评估河流的酸化状况。组装了一个包含 558 个底栖硅藻样本和相关水质数据的数据库。根据 pH 最优值，使用高斯逻辑回归将硅藻分类群分配到 5 个指示类之一，并用这些指示值使用加权平均法为每个地点计算 DAM 得分。参考地点是根据其酸中和能力 (ANC) 和钙浓度选择的，并开发了一个回归模型，使用 pH 和总有机碳 (TOC) 浓度预测每个地点的预期 DAM。使用特定于地点的 DAM 得分来计算生态质量比，范围从≥1（表明硅藻组合没有受到影响）到（理论上）0（表明硅藻组合指示主要人为活动）。“高”和“好”状态之间的边界定义为所有参考地点生态质量比 (EQR) 的第 25 百分位数。“好”和“中等”状态之间的边界设定为在营养敏感和营养耐受分类群具有相等相对丰度的点。该方法使用来自英国高地水域监测网络的 11 个站点的长期数据进行了评估，结果表明该方法在区分自然酸化和酸化站点方面表现良好。
