Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
Sci Total Environ. 2011 Oct 1;409(21):4572-8. doi: 10.1016/j.scitotenv.2011.07.044. Epub 2011 Aug 26.
Nitrous oxide (N(2)O) is a significant greenhouse gas with a global warming potential that is 300 times than that of carbon dioxide. Soil derived N(2)O emissions usually display a high degree of spatial and temporal variability because of their dependence on soil chemical and physical properties, and climate dependent environmental factors. However, there is little research that incorporates spatial dependence in the estimation of N(2)O emissions allowing for environmental factors in the same model. This study aims to examine the impact of two environmental factors (soil temperature and soil moisture) on N(2)O emissions and explore the spatial structure of N(2)O in the sub-tropical South East Queensland region of Australia. The replicated data on N(2)O emissions and soil properties were collected at a typical sugarcane land site covering 25 uniform grid points across 3600 m(2) between October 2007 and September 2008. A Bayesian conditional autoregressive (CAR) model was used to model spatial dependence. Results showed that soil moisture and soil temperature appeared to have substantially different effects on N(2)O emissions after taking spatial dependence into account in the four seasons. There was a substantial variation in the spatial distribution of N(2)O emission in the different seasons. The high N(2)O emission regions were accompanied by high uncertainty and changed in varying seasons in this study site. Spatial CAR models might be more plausible to elucidate and account for the uncertainty arising from unclear variables and spatial variability in the assessment of N(2)O emissions in soils, and more accurately identify relationships with key environmental factors and help to reduce the uncertainty of the soil parameters.
一氧化二氮（N₂O）是一种重要的温室气体，其全球变暖潜能是二氧化碳的 300 倍。由于土壤化学和物理性质以及气候依赖的环境因素的影响，土壤源 N₂O 排放通常表现出高度的时空变异性。然而，在同一模型中考虑环境因素来估算 N₂O 排放时，很少有研究考虑空间依赖性。本研究旨在检验两个环境因素（土壤温度和土壤湿度）对 N₂O 排放的影响，并探索澳大利亚亚热带东南部地区 N₂O 的空间结构。在 2007 年 10 月至 2008 年 9 月期间，在一个典型的甘蔗土地点上，收集了 25 个均匀网格点上的 N₂O 排放和土壤特性的重复数据，覆盖面积为 3600 平方米。采用贝叶斯条件自回归（CAR）模型来模拟空间依赖性。结果表明，在考虑四个季节的空间依赖性后，土壤湿度和土壤温度对 N₂O 排放的影响似乎有很大的不同。在不同的季节，N₂O 排放的空间分布有很大的变化。在本研究地点，高 N₂O 排放区域伴随着高不确定性，并且在不同季节发生变化。空间 CAR 模型可能更合理地阐明和解释由于不明确变量和土壤中 N₂O 排放的空间变异性而产生的不确定性，并更准确地识别与关键环境因素的关系，有助于降低土壤参数的不确定性。
