天津市负氧离子浓度变化特征分析及预测模型筛选

Characteristics of Negative Oxygen Ion Concentration and Its Prediction Model in Tianjin

利用天津市梁庄子站(森林区)、市区站(居民区)2019年6月至2022年12月的负氧离子浓度数据、气象、环境要素数据,分析逐日负氧离子浓度不同时间尺度上的变化特征及其与不同要素之间的关系,并构建最优预测模型。结果表明:①森林区和居民区负氧离子浓度均呈“一峰一谷”型日变化特征,森林区存在“两峰一谷”型月变化特征,而居民区不明显;②森林区负氧离子浓度为(春季,冬季)>秋季>夏季,居民区为夏季>春季>秋季>冬季;③居民区负氧离子浓度明显低于森林区,不同区域存在不同的影响要素;④构建森林区逐日负氧离子浓度预测模型更适合使用随机森林法,构建居民区逐日负氧离子浓度预测模型更适合使用随机梯度下降法。

Using observation data of the negative oxygen ion(NOI)concentration,meteorological and environmental elements from June 2019 to December 2022 in Liangzhuangzi station(forest zone)and urban station(residential zone),the change characteristics of daily NOI concentration in different time scales and its relationship with different factors in Tianjin are analyzed.The best prediction model of NOI concentration based on machine learning is built.The results show that:(1)The NOI concentration in forest and urban zones shows“single peak-single valley”pattern in daily scale,and in forest zone,there is“double peak-single valley”pattern in monthly scale,but in urban zone,there is no significant pattern.(2)The NOI concentration in forest zone is ranked as spring and winter>autumn>summer,and that in urban zone is ranked as summer>spring>autumn>winter.(3)The NOI concentration in urban zone is lower than that in forest zone,and there are different influencing factors in different zones.(4)The random forest method is more suitable for building the prediction model of NOI concentration in forest zone,and the stochastic gradient descent method is more suitable for urban zone.