Modelling uncertainty for decision making on ammonia mitigation with trees in the landscape (MUDMAT).

利用景观中的树木对氨氮减排决策的不确定性进行建模 (MUDMAT)。

• 批准号: NE/T004185/1
• 负责人: David Cameron
• 金额: $ 7.93万
• 依托单位: NERC CEH (Up to 30.11.2019)
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FT004185%2F1
• 关键词: Modelling; uncertainty; decision; making; ammonia

In the agricultural landscape there are competing needs of making the best economical use of the land for food production and the use of land to mitigate against Nitrogen pollution from agriculture. Agricultural practises accounts for over 80% of ammonia emissions within the UK with releases from livestock housing and manure management through storage and spreading. Deposition of nitrogen in the form of ammonia can cause eutrophication and acidification effects on semi-natural ecosystems, leading to species composition changes and reduced biodiversity. The Clean Air Strategy 2019 has given significant focus to the impact of ammonia emissions and the subsequent atmospheric nitrogen load on ecosystems together with the particulate form of ammonia affecting human health outcomes. Over 60% of the UK's semi-natural habitats exceed their environmental limit for nitrogen deposition. Mitigation measures have been proposed by Government to support farmers in providing reductions in ammonia emissions. One effective abatement strategy is to plant tree shelter-belts downwind of livestock housing and slurry stores to 'scavenge' ammonia. (10-25% Bealey et al. 2014). Trees are particularly effective scavengers of air pollutants due to their effect on turbulence. Because of their rougher surface and high surface area trees are particularly effective scavengers of air pollutants. Recently a decision support tool has been created to help land managers quickly predict the potential of tree planting to mitigate in ammonia pollution based on simple user input choices. This web-based decision tool can help resolve the competing interests of using agricultural land for food production and pollution mitigation, by allowing the land manager to assess tree planting strategies that maximise ammonia abatement for the minimum use of land. Therefore, the web tool has proved to be very popular with key food producers (e.g. egg industry), and agricultural decision makers notably pollution regulators, conservation bodies and also planners.However there is no quantification of the reliability of the predictions made from the web tool. This is a key omission and hampers its use in decision making. Since models are never perfect representations of the landscape, model predictions are only as good as the quantification of how certain those predictions are. Compounding this, in the web tool, the computationally expensive coupled turbulence-deposition model MODDAAS-THETIS is replaced with a very simple empirical model and there is at present no way of quantifying the reduced accuracy of predictions associated with this simplification.Here we propose to use statistical methods to create a faster but quantifiably traceable emulator of MODDAAS-THETIS which will replace the empirical model in the web tool and will also allow uncertainty of the predictions from MODDAAS-THETIS to be quantified through the emulator.Our key objectives are to:1) Provide a decision tool that will help land managers to make the most economically efficient use of the agricultural land whilst minimising ammonia pollution. 2) Make a step-change improvement in decision making concerning the mitigation of ammonia pollution in the landscape by bringing state-of-the-art statistical methods to a web-based decision analysis system by quantifying the uncertainty of the predictions made. 3) Significantly improve the model behind the web-based tool making it traceable statistically to the underlying process-based model (MODDAAS-THETIS) through emulation and increasing the tree belt planting options available to the decision makers for predicting the mitigation of ammonia in the tool.4) Establish a methodology for emulating the multivariate spatial and temporal output from pollution transport models in the landscape making it possible to quantify uncertainty in the predictions from these computationally-expensive models.

在农业方面，既要最经济地利用土地生产粮食，又要利用土地减轻农业氮素污染，这两方面的需求相互竞争。农业实践占英国氨排放的80%以上，其中牲畜舍和粪便管理通过储存和传播产生的氨排放。以氨形式沉积的氮会对半自然生态系统造成富营养化和酸化效应，导致物种组成变化和生物多样性减少。2019年清洁空气战略非常重视氨排放和随后的大气氮负荷对生态系统的影响，以及氨的颗粒形式对人类健康结果的影响。英国超过60%的半自然栖息地超过了环境中氮沉积的极限。政府提出了缓解措施，以支持农民减少氨排放。一种有效的减排策略是在牲畜舍和泥浆仓库的下风向种植树木防护带，以“清除”氨。(10-25%Bealey等人)2014年)。由于树木对湍流的影响，它们是空气污染物的特别有效的清道夫。由于树木的表面更粗糙，表面积更大，因此对空气污染物的清除特别有效。最近，开发了一个决策支持工具，帮助土地管理人员根据简单的用户输入选择，快速预测植树以缓解氨污染的潜力。这一基于网络的决策工具可以帮助解决将农业用地用于粮食生产和减轻污染的利益冲突，使土地管理者能够评估植树策略，以最大限度地减少土地使用中的氨排放。因此，事实证明，网络工具在主要食品生产商(如蛋业)和农业决策者中非常受欢迎，特别是污染监管机构、保护机构和规划者。然而，网络工具做出的预测的可靠性没有量化。这是一个关键的遗漏，阻碍了它在决策中的使用。由于模型从来都不是景观的完美代表，所以模型预测只有量化这些预测的确定性才是最好的。更复杂的是，在网络工具中，计算代价高昂的湍流-沉积耦合模型MODDAAS-THEIS被一个非常简单的经验模型取代，目前还没有办法量化与这种简化相关的预测的降低精度。在这里，我们建议使用统计方法来创建一个更快但可定量跟踪的MODDAAS-THEIS仿真器，它将取代网络工具中的经验模型，并允许通过仿真器量化MODDAAS-THEIS预测的不确定性。我们的主要目标是：1)提供一个决策工具，帮助土地管理者最经济地有效地使用农业土地，同时将氨污染降至最低。2)通过量化预测的不确定性，将最先进的统计方法引入基于网络的决策分析系统，从而在景观中缓解氨污染的决策方面做出阶段性的改进。3)显著改进基于网络的工具背后的模型，使其可通过仿真从统计上追溯到基于过程的基础模型(MODDAAS-THEIS)，并增加决策者可用于预测工具中氨的缓解的林带种植选择。4)建立一种方法来模拟景观中污染传输模型的多变量空间和时间输出，从而能够量化这些计算昂贵的模型预测的不确定性。