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不同时间尺度下武夷山国家公园空气负离子浓度的分布特征。

Different Time Scale Distribution of Negative Air Ions Concentrations in Mount Wuyi National Park.

机构信息

Fujian Meteorological Service Center, Fuzhou 350001, China.

Institute of Meteorological Big Data-Digital Fujian, Minnan Normal University, Zhangzhou 363000, China.

出版信息

Int J Environ Res Public Health. 2021 May 10;18(9):5037. doi: 10.3390/ijerph18095037.

Abstract

The concentration of negative air ions (NAIs) is an important indicator of air quality. Here, we analyzed the distribution patterns of negative air ion (NAI) concentrations at different time scales using statistical methods; then described the contribution of meteorological factors of the different season to the concentration of NAIs using correlation analysis and regression analysis; and finally made the outlook for the trends of NAI concentrations in the prospective using the auto regressive integrated moving average (ARIMA) models. The dataset of NAI concentrations and meteorological factors measured at the fixed stations in the Mountain Wuyi National Park were obtained from the Fujian Provincial Meteorological Bureau. The study showed that NAI concentrations were correlated with relative humidity spanning all seasons. Water was an important factor affecting the distribution of NAI concentrations in different time series. Compared with other ARIMA models, the outlook value of the ARIMA (, ) model was closer to the original data and the errors were smaller. This article provided a unique perspective on the study of the distribution of negative air oxygen ions over time series.

摘要

空气负离子浓度是空气质量的一个重要指标。在这里,我们使用统计方法分析了不同时间尺度下空气负离子(NAI)浓度的分布模式;然后使用相关分析和回归分析描述了不同季节气象因素对 NAI 浓度的贡献;最后使用自回归求和移动平均(ARIMA)模型对未来 NAI 浓度的趋势进行了展望。空气负离子浓度和气象因子数据集是从福建省气象局在武夷山国家公园固定站点测量获得的。研究表明,空气负离子浓度与相对湿度在所有季节都相关。水是影响不同时间序列中空气负离子浓度分布的重要因素。与其他 ARIMA 模型相比,ARIMA(,)模型的展望值更接近原始数据,误差更小。本文为研究时间序列中空气负氧离子的分布提供了一个独特的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/83c5/8126208/f957024a9989/ijerph-18-05037-g001.jpg

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