• 关键词：酸雨  遗传算法  BP网络  降水化学数据

• 基金项目：国家自然科学基金(49875026)资助

• 唐桂刚
• 中国科学院生态环境研究中心, 北京 100085

• 白乃彬
• 中国科学院生态环境研究中心, 北京 100085

• 摘要：收集了111组包含Ca²⁺、Mg²⁺等8种离子浓度和pH值的我国降水化学数据, 并用遗传神经网络(Genetic Neural Networks, GNN)方法建立了这些离子浓度和pH值关系的BP网络模式.运用这些模式计算了各离子浓度对pH值的贡献, 结果表明, 我国各地降水pH值与各地区酸性离子与碱性离子相互作用相关, 其中碱性离子的中和作用起着主导作用.

• Abstract：data of precipitation chemistry in China which contained the concentrations of eight ions such as Ca²⁺, Mg²⁺, K⁺, Na⁺, NH₄⁺, SO₄^2-, NO₃^-Cl^-and the pH values were collected.Based on these data, some BP neural network models that correlated the concentrations of these ions and pH values were built by genetic neural networks (GNN).By these models the contribution to the pH of each ion concentration was given out results indicated that the difference on ability for the soil particle to neutralize acidity of the precipitation is a very important reason of acid rain distribution in China.

• Key words：acid rain  genetic algorithm  BP neural networks  precipitation chemistry data

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