Mapping and modeling airborne urban phenanthrene distribution using vegetation biomonitoring.
Mapping and modeling airborne urban phenanthrene distribution using vegetation biomonitoring.
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DOI：
10.1016/j.atmosenv.2013.05.056
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发表时间：
2013-10
影响因子：
5
通讯作者：
E. Noth;S. Hammond;G. Biging;I. Tager
中科院分区：
文献类型：
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作者：
E. Noth;S. Hammond;G. Biging;I. Tager
为了捕捉城市环境中菲的空间分布，我们利用杰弗里松树（Pinus jeffreyi）进行植被生物监测。表征大都市地区多环芳烃（PAH）浓度空间变化的主要挑战是以足够精细的分辨率采样以观察潜在的空间格局。然而，野外和室内研究表明，多环芳烃进入植物的主要途径是通过空气进入植物叶片，这使得植被生物监测成为检测这些化合物空间分布的可行方法。先前的研究表明，菲对健康有不利影响，它是城市空气中含量最多的多环芳烃之一。在冬季的一个早晨，我们在弗雷斯诺市的91个地点采集了99个松针样本，并对其内针中的多环芳烃进行了分析。所有99个松针样品均检测到菲，平均浓度为41.0 ng g - 1，中位数为36.9 ng g - 1，标准偏差为28.5 ng g - 1鲜重。第90百分位浓度与第10百分位浓度之比为3.3。菲分布的Moran′i值为0.035，具有显著的统计学意义，空间聚类程度较高。我们实施了土地利用回归来拟合我们的数据模型。我们的模型能够解释数据中适度的变化（R2= 0.56），这可能反映了弗雷斯诺菲的主要来源。模拟空气中菲的空间分布受公路、铁路和工商圈的影响。
To capture the spatial distribution of phenanthrene in an urban setting we used vegetation biomonitoring with Jeffrey pine trees (Pinus jeffreyi). The major challenge in characterizing spatial variation in polycyclic aromatic hydrocarbon (PAH) concentrations within a metropolitan area has been sampling at a fine enough resolution to observe the underlying spatial pattern. However, field and chamber studies show that the primary pathway through which PAHs enter plants is from air into leaves, making vegetation biomonitoring a feasible way to examine the spatial distribution of these compounds. Previous research has shown that phenanthrene has adverse health effects and that it is one of the most abundant PAHs in urban air. We collected 99 pine needle samples from 91 locations in Fresno in the morning on a winter day, and analyzed them for PAHs in the inner needle. All 99 pine needle samples had detectable levels of phenanthrene, with mean concentration of 41.0 ng g−1, median 36.9 ng g−1, and standard deviation of 28.5 ng g−1fresh weight. The ratio of the 90th:10th percentile concentrations by location was 3.3. The phenanthrene distribution had a statistically significant Moran'sIof 0.035, indicating a high degree of spatial clustering. We implemented land use regression to fit a model to our data. Our model was able to explain a moderate amount of the variability in the data (R2= 0.56), likely reflecting the major sources of phenanthrene in Fresno. The spatial distribution of modeled airborne phenanthrene shows the influences of highways, railroads, and industrial and commercial zones.