Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network
Quantifying methane and nitrous oxide emissions from the UK and Ireland using a national-scale monitoring network
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DOI：
10.5194/acp-15-6393-2015
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发表时间：
2015-01-01
影响因子：
6.3
通讯作者：
O'Doherty, S.
中科院分区：
文献类型：
--
作者：
Ganesan, A. L.;Manning, A. J.;O'Doherty, S.
英国是世界上几个通过立法减少温室气体排放的国家之一。在本研究中，我们展示了2012年8月至2014年8月期间英国和爱尔兰自上而下的甲烷（CH4）和一氧化二氮（N2O）排放量。这些排放量是通过对两国四周四个地点的测量得出的。我们使用分层贝叶斯逆框架来推断通量以及一组描述系统不确定性的协方差参数。我们推断英国的平均总排放量为2.09 (1.65-2.67)Tg / yr(-1) CH4和0.101 (0.068-0.150)Tg / yr(-1) N2O，并发现我们得出的英国估计总体上低于先验排放量，后者主要由人为源组成，自然源的贡献较小。我们使用英国国家大气排放清单（NAEI）的部门分布来确定这些差异是否可归因于特定的源部门。由于农业和废物这两个主要的CH4排放部门在英国的分布不同，我们发现农业CH4排放的清查可能被高估了。我们发现年平均N2O排放量与先前和人为清查结果一致，但我们得出了排放的显著季节性循环。这种季节性可能是由于化肥施用的季节性以及温度和降雨等环境驱动因素的季节性，而这些因素没有反映在年度分辨率清单中。通过层次贝叶斯逆框架，我们量化了不确定性协方差参数，并强调了它们在高分辨率排放估计中的重要性。我们推断CH4和N2O的平均模式误差分别约为20和0.4 ppb，相关时间尺度分别为1.0（0.72-1.43）和2.6（1.9-3.9）天。这些误差是运输模型误差以及由于库存中未解决的排放过程造成的误差的组合。我们在英格兰东部的Tacolneston站发现了最大的CH4误差，这可能是由于垃圾填埋场和北海海上天然气的零星排放造成的。
The UK is one of several countries around the world that has enacted legislation to reduce its greenhouse gas emissions. In this study, we present top-down emissions of methane (CH4) and nitrous oxide (N2O) for the UK and Ireland over the period August 2012 to August 2014. These emissions were inferred using measurements from a network of four sites around the two countries. We used a hierarchical Bayesian inverse framework to infer fluxes as well as a set of covariance parameters that describe uncertainties in the system. We inferred average UK total emissions of 2.09 (1.65-2.67) Tg yr(-1) CH4 and 0.101 (0.068-0.150) Tg yr(-1) N2O and found our derived UK estimates to be generally lower than the a priori emissions, which consisted primarily of anthropogenic sources and with a smaller contribution from natural sources. We used sectoral distributions from the UK National Atmospheric Emissions Inventory (NAEI) to determine whether these discrepancies can be attributed to specific source sectors. Because of the distinct distributions of the two dominant CH4 emissions sectors in the UK, agriculture and waste, we found that the inventory may be overestimated in agricultural CH4 emissions. We found that annual mean N2O emissions were consistent with both the prior and the anthropogenic inventory but we derived a significant seasonal cycle in emissions. This seasonality is likely due to seasonality in fertilizer application and in environmental drivers such as temperature and rainfall, which are not reflected in the annual resolution inventory. Through the hierarchical Bayesian inverse framework, we quantified uncertainty covariance parameters and emphasized their importance for high-resolution emissions estimation. We in-ferred average model errors of approximately 20 and 0.4 ppb and correlation timescales of 1.0 (0.72-1.43) and 2.6 (1.9-3.9) days for CH4 and N2O, respectively. These errors are a combination of transport model errors as well as errors due to unresolved emissions processes in the inventory. We found the largest CH4 errors at the Tacolneston station in eastern England, which may be due to sporadic emissions from landfills and offshore gas in the North Sea.