Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality
Multivariate receptor modeling with widely dispersed Lichens as bioindicators of air quality
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DOI：
10.1002/env.2785
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发表时间：
2022-12-25
期刊：
影响因子：
1.7
通讯作者：
St Clair，Larry L.
中科院分区：
文献类型：
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作者：
Heiner，Matthew;Grimm，Taylor;St Clair，Larry L.
生物监测研究通过地衣中空气中元素的积累模式来评估空气质量，通常通过关注狭窄的地理区域和短时间窗口来控制变异性。利用美国山间地区广泛存在的岩石状地衣样本，我们调查了一般污染源的积累模式是否在广泛的地理和时间尺度上可检测到。我们开发了一种新的贝叶斯多变量受体建模(BMRM)方法，该方法通过(I)对每个样本的源贡献进行正则化和(Ii)将估计的地衣次生化学作为一个因素来提高对候选污染源的检测和区分。通过模拟研究，我们展示了当贡献真正稀疏时缩小贡献的明显优势，就像来自分散的采集点的异质样本所期望的那样。我们对比了使用标准和稀疏BMRM以及正矩阵分解(PMF)的分析。稀疏模型更好地维护了源身份，通过在基本配置文件上提供信息的先前分布指定了源身份。我们提倡定量剖面匹配，这表明PMF主要捕捉地衣次生化学基线剖面的变化。PMF和BMRM的结果都表明，最明显的特征与风尘沉积有关，而空间格局暗示了零星的人为影响。
Biomonitoring studies evaluating air quality via airborne element accumulation patterns in lichens typically control variability by focusing on narrow geographic regions and short time windows. Using samples of the widespread “rock‐posy” lichen sampled across the Intermountain Region of the United States, we investigate whether accumulation patterns of generic pollution sources are detectable on broad geographic and temporal scales. We develop a novel Bayesian multivariate receptor modeling (BMRM) approach that sharpens detection and discrimination of candidate pollution sources through (i) regularization of source contributions to each sample and (ii) incorporating estimated lichen secondary chemistry as a factor. Through a simulation study, we demonstrate a distinct advantage in shrinking contributions when they are truly sparse, as would be expected with heterogeneous samples from dispersed collection sites. We contrast analyses employing both standard and sparse BMRMs, and positive matrix factorization (PMF). The sparse model better maintains source identity, as specified though informative prior distributions on elemental profiles. We advocate quantitative profile matching, which reveals that PMF primarily captures variations of the baseline profile for lichen secondary chemistry. Both PMF and BMRM results suggest that the most detectable signatures relate to aeolian dust deposition, while spatial patterns hint at sporadic anthropogenic influence.