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A Hierarchical Model for Estimating Long-Term Trend of Atrazine Concentration in the Surface Water of the Contiguous US

机译:估算美国连续地表水中阿特拉津浓度长期趋势的层次模型

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Atrazine is a herbicide frequently detected in both surface and groundwater in the United States (U.S.), but its spatiotemporal distribution and concentration trends have only been analyzed recently at regional or local scales. We employed a Bayesian hierarchical modeling approach to assess spatial and seasonal variation in atrazine concentration trends between 1990 and 2010 for the contiguous U.S. A Markov chain Monte Carlo simulation algorithm was used to address the problem of left-censored data (i.e., atrazine concentration values below method reporting levels). We observed opposing temporal trends in the northern (flat or decreasing) and southern (increasing) regions of the U.S. This spatial variation in temporal trends can be partially explained by the relative amount of cropland in the region. Flat or decreasing trends in the north are more likely in regions with high cropland coverage while positive trends in the south are more likely in regions with low cropland coverage.
机译:在美国(美国),.去津是一种经常在地表水和地下水中发现的除草剂,但其时空分布和浓度趋势最近才在区域或地方范围内得到分析。我们采用贝叶斯分层建模方法评估了美国在1990年至2010年之间阿特拉津浓度趋势的空间和季节变化。使用了马尔可夫链蒙特卡罗模拟算法来解决左删失数据的问题(即阿特拉津浓度值低于方法报告级别)。我们在美国北部(平坦或下降)和南部(增长)区域观察到相反的时间趋势。时间趋势的这种空间变化可以部分通过该地区耕地的相对数量来解释。在耕地覆盖率高的地区,北部的趋势趋于平坦或下降,而在耕地覆盖率低的地区,南部的趋势更趋于积极。

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