Journal
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Volume 18, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/ijerph18095037
Keywords
negative air ions (NAIs); time series; ARIMA model; health
Funding
- Mountain Floods and Geological Disasters Meteorological Protection Project of China Meteorological Administration [FJYS2018-062]
- Fujian Meteorological Service Center Project of 2019 [201903]
- Institute of Meteorological Big Data-Digital Fujian [202010702]
- project of Yongtai County Meteorological Bureau [KH180064A]
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The concentration of negative air ions (NAIs), an important indicator of air quality, was found to be correlated with relative humidity and influenced by water distribution. The ARIMA (0, 1, 1) model showed closer forecast values to the original data with smaller errors.
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 (0, 1, 1) 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.
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