Lequy Emeline, Dubos Nicolas, Witté Isabelle, Pascaud Aude, Sauvage Stéphane, Leblond Sébastien
Natural Heritage Department, National Museum of Natural History, 12 rue Buffon, F-75005, Paris, France.
Centre d'Ecologie et des Sciences de la Conservation (CESCO UMR 7204) & Mécanismes adaptatifs et évolution (MECADEV UMR 7179), Sorbonne Universités, MNHN, CNRS, UPMC, CP51, 55 rue Buffon, 75005 Paris, France.
Environ Pollut. 2017 Jan;220(Pt B):828-836. doi: 10.1016/j.envpol.2016.10.065. Epub 2016 Nov 9.
Air quality biomonitoring has been successfully assessed using mosses for decades in Europe, particularly regarding heavy metals (HM). Assessing robust temporal variations of HM concentrations in mosses requires to better understand to what extent they are affected by the sampling protocol and the moss species. This study used the concentrations of 14 elements measured during four surveys over 15 years in France. Analyses of variance (ANOVA) and a modeling approach were used to decipher temporal variations for each element and adjust them with parameters known to affect concentrations. ANOVA followed by post hoc analyses did not allow to estimate clear trends. A generalized additive mixed modeling approach including the sampling period, the collector and the moss species, plus quadratic effects, was used to analyze temporal variations on repeated sampling sites. This approach highlighted the importance of accounting for non-linear temporal variations in HM, and adjusting for confounding factors such as moss species, species-specific differences between sampling periods, collector and methodological differences in sampling campaigns. For instance, lead concentrations in mosses decreased between 1996 and 2011 following quadratic functions, with faster declines for the most contaminated sites in 1996. On the other hand, other HM showed double trends with U-shaped or hill-shaped curves. The effect of the moss was complex to handle and our results advocate for using one moss species by repeated site to better analyze temporal variations.
在欧洲,几十年来一直成功地利用苔藓进行空气质量生物监测,尤其是针对重金属(HM)。要评估苔藓中重金属浓度的稳健时间变化,需要更好地了解它们在多大程度上受到采样方案和苔藓种类的影响。本研究使用了在法国15年期间进行的四次调查中测得的14种元素的浓度。采用方差分析(ANOVA)和建模方法来解读每种元素的时间变化,并根据已知会影响浓度的参数对其进行调整。方差分析及事后分析无法估计出明确的趋势。采用一种广义相加混合建模方法,该方法包括采样期、采集者和苔藓种类,再加上二次效应,用于分析重复采样地点的时间变化。这种方法突出了考虑重金属非线性时间变化以及调整混杂因素的重要性,这些混杂因素包括苔藓种类、采样期之间的物种特异性差异、采集者以及采样活动中的方法差异。例如,1996年至2011年期间,苔藓中的铅浓度呈二次函数下降,1996年污染最严重的地点下降速度更快。另一方面,其他重金属呈现出U形或山形曲线的双重趋势。苔藓的影响难以处理,我们的结果主张在重复采样地点使用单一苔藓种类,以便更好地分析时间变化。