Forescasting chemical recovery from acidification

酸沉降被认为是一种主要的地区性污染物，对整个北美东部敏感的陆地和水生生态系统产生了负面影响。强酸的沉积加速了土壤中盐基阳离子的淋失，人们越来越担心土壤盐基饱和度的下降可能导致森林健康和生产力的下降。碱性贫瘠土壤的酸性沉积也导致地表水pH值下降和无机单体铝浓度增加。已开发了许多面向过程的动态模型来研究地表水对酸沉积的响应。动态酸化模型可以很容易地用于提供对未来土壤或地表水化学的估计。因此，动态模型正越来越多地在区域基础上应用，以评估减少酸沉积的益处并预测恢复的时间。拟议的研究计划将有助于在拟议的未来减排下更好地预测土壤和水的未来化学状态。这项研究具有创新性和新颖性，因为将开发预测建模工具来确定人为影响带来的环境风险。这项研究将主要集中在酸沉降上，尽管将考虑土地利用变化和气候的影响。该方法将侧重于连接或嵌套现有的生物地球化学和水文模型。将利用新的统计和建模技术来量化模型预测中的不确定性。该研究计划将主要集中在已建立的长期监测点。这些地点的现有信息将辅之以对其他状态变量的测量，目的是为计算机模型的应用发展一个综合监测网络。这种类型的网络方法对于综合各个站点的结果和向决策者提供一致的信息是必不可少的。预计研究成果将为未来减排政策的制定和实施提供科学依据。

Acid deposition is recognised as a major regional pollutant that has had negative impacts on sensitive terrestrial and aquatic ecosystems across eastern North America. Deposition of strong acids has accelerated base cation leaching from soils, and there is increasing concern that declining soil base saturation could result in a decline in forest health and productivity. Acidic deposition to base poor soils has resulted also in decreases in surface water pH and increases in the concentration of inorganic monomeric aluminium. A number of dynamic process-oriented models have been developed to investigate the response of surface waters to acid deposition. Dynamic acidification models can be readily used to provide an estimate of the future soil or surface water chemistry. As such, dynamic models are being increasingly applied on a regional basis to assess the benefits of reduced acid deposition and predict times to recovery. The proposed research program will contribute to the better prediction of the future chemical status of soils and waters under proposed future emission reductions. The research is innovative and novel in that predictive modelling tools will be developed to determine environmental risk from anthropogenic impacts. The research will principally focus on acid deposition, although the influence of land-use change and climate will be considered. The methodology will focus on linking or nesting existing biogeochemical and hydrological models. Novel statistical and modelling techniques will be utilised to quantify uncertainty in model predictions. The research program will focus principally on established long-term monitoring sites. Existing information for these sites will be supplemented with measurements of additional state variables with the objective to develop an integrated monitoring network for the application of computer models. This type of network approach is essential for synthesising the results of individual sites and for providing a coherent message to policy makers. It is anticipated that the results of the research program will provide the scientific underpinning for the development and implementation of future emission reduction polices.