Vol.34, NO.2, 2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Applied Meteorological Science  > 2023  >  34(2): 193-205
Zhang Chungui. Distribution characteristics and meteorological prediction model of air negative oxygen ions in Fujian. J Appl Meteor Sci, 2023, 34(2): 193-205. DOI:  10.11898/1001-7313.20230206.
Citation: Zhang Chungui. Distribution characteristics and meteorological prediction model of air negative oxygen ions in Fujian. J Appl Meteor Sci, 2023, 34(2): 193-205. DOI:  10.11898/1001-7313.20230206.
shu

Distribution Characteristics and Meteorological Prediction Model of Air Negative Oxygen Ions in Fujian

DOI: 10.11898/1001-7313.20230206
  • 1.

    Fujian Institute of Meteorological Science, Fuzhou 350008

  • 2.

    Fujian Key Laboratory of Severe Weather, Fuzhou 350008

  • Received Date: 2022-09-27
  • Rev Recd Date: 2023-01-09
  • Publish Date: 2023-03-31
    Abstract
  • The concentration of negative oxygen ions in air is an important index to evaluate the freshness and cleanliness of air. In recent years, it has become one hot topic concerned by governments and the public. From 2018 to 2021, Fujian has set up a number of observation stations for negative oxygen ions and meteorological factors over the entire province including seashore, mountain, humanities landscape areas, with good representativeness, reliability and continuity. Using the local observations, the spatial and temporal variations of negative oxygen ions concentration in Fujian is analyzed, and the negative oxygen ions concentration and grade prediction models are established based on multiple linear regression method and LightGBM machine learning method. The results show that, negative oxygen ions in Fujian is very rich and is very good for human health. The annual average concentration is between 708-8315 cm-3, which is highest in high altitude, next in low altitude, and the concentration in middle altitude is the smallest. Overall, the annual average concentration of negative oxygen ions of nearly 80% site is beyond the standard of fresh air defined by World Health Organization. The diurnal variation of the concentration of negative oxygen ions show the characteristics of a peak and a trough, with the peak value mainly occurring at 0400-0600 BT and the trough value at 1200-1300 BT. The seasonal variation of negative oxygen ions concentration is more complex. The seasonal variation in the middle altitude area is greater, the seasonal average concentration in descending order is spring, summer, winter and autumn, while the seasonal variation in the high and low altitude area is relatively small. The main meteorological factors affecting the concentration of negative oxygen ions are temperature, humidity, precipitation, wind speed, air pressure and visibility. The concentration of negative oxygen ions is significantly positively correlated with humidity, precipitation and visibility at different altitudes, while the concentration of negative oxygen ions is significantly correlated with air temperature, wind speed and air pressure, but the correlation is different at different altitudes. The comparisons indicate the effects of LightGBM machine learning model are better than those of the traditional multiple linear regression model at different altitudes. The overestimation of negative oxygen ions concentration prediction is significantly improved, and the prediction grade of negative oxygen ions concentration can be improved by up to 12%. The results of logistic regression show that the traditional logistic regression basically has no predictive ability for small samples, while the LightGBM method has good learning ability in the case of small samples or unbalanced samples.
    • FullText(HTML)
    References(46)

  • Fig. 1  Daily variation of NOI concentration in Fujian

    DownLoad: Full-Size Img PowerPoint

    Fig. 2  Ten-day variation of NOI concentration in Fujian

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Variation of atmospheric particulate concentration and air quality index in Fuzhou from 2019 to 2021

    DownLoad: Full-Size Img PowerPoint

    Fig. 4  Seasonal variation of NOI concentration in Fujian

    DownLoad: Full-Size Img PowerPoint

    Fig. 5  Multiple linear regression fitting in middle altitude area

    (black line is diagonal line, red line is scatter fitting line)

    DownLoad: Full-Size Img PowerPoint

    Fig. 6  Machine learning fitting in middle altitude area

    (black line is diagonal line, red line is scatter fitting line)

    DownLoad: Full-Size Img PowerPoint

    Table  1  Spatial distribution of annual average NOI concentration at 19 stations in Fujian

    序号 站点名称 年平均NOI浓度/cm-3 海拔高度/m 海拔分区
    1 福州气象站 2342 112 低海拔区
    2 平潭流水镇 830 153
    3 厦门鼓浪屿 708 25
    4 莆田湄洲岛 1929 50
    5 诏安九候山 4720 70
    6 诏安江滨公园 2731 18
    7 泉州清源山 3814 447 中海拔区
    8 德化石牛山 2028 510
    9 安溪云中山 2336 356
    10 永定土楼 1567 400
    11 泰宁寨下大峡谷 3797 361
    12 大田气象站 1861 390
    13 武夷山国家公园 8315 377
    14 福州鼓山风景区 722 794 高海拔区
    15 福鼎太姥山 1743 550
    16 屏南白水洋 1242 944
    17 德化县城区 956 629
    18 南靖土楼 2265 730
    19 上杭古田会址 1877 734
    DownLoad: Download CSV

    Table  2  Correlation between NOI concentration and meteorological factors at different altitudes of Fujian

    气象因子 低海拔区 中海拔区 高海拔区
    相关系数 样本量 相关系数 样本量 相关系数 样本量
    气温 0.058** 193960 -0.060** 156201 0.050** 143349
    相对湿度 0.010** 193960 0.125** 156201 0.033** 143349
    降水强度 0.060** 59050 0.087** 81373 0.066** 81702
    风速 -0.059** 103195 0.016** 85841 -0.045** 86777
    气压 -0.082** 57684 -0.008* 90644 0.053** 88809
    小时能见度 -0.011 21683 0.036** 81821 0.052** 84014
    分钟降水量 0.059** 58472 0.076** 81389 0.063** 81702
    分钟能见度 -0.011 21683 0.023** 81849 0.052** 83998
    注：*表示达到0.05显著性水平，**表示达到0.01显著性水平。
    DownLoad: Download CSV

    Table  3  5-fold cross-validation and machine learning model between NOI and meteorological factors at different altitudes

    分区 样本均方根误差E LightGBM机器学习模型
    折数1 折数2 折数3 折数4 折数5
    低海拔区 4552.63 4690.15 4514.25 4270.73 4574.10 气象因子平均得分从高到低排序：X2，X1，X5，X3
    R2=0.165，R=0.407，E=4392
    中海拔区 5754.01 5740.78 5715.85 5660.76 5774.29 气象因子平均得分从高到低排序：X1，X5，X2，X4，X3
    R2=0.207，R=0.455，E=5685
    高海拔区 1468.28 1450.09 1436.00 1402.35 1494.02 气象因子平均得分从高到低排序：X1，X5，X4，X2，X3
    R2=0.193，R=0.439，E=1435
    DownLoad: Download CSV

    Table  4  Accuracy statistics of logistic regression results at different altitudes

    分区 6个等级验证集准确率/% 总体准确率/%
    1 2 3 4 5 6
    低海拔区 38 47 66 0 0 0 63
    中海拔区 49 49 0 0 0 0 49
    高海拔区 71 53 36 0 0 0 50
    DownLoad: Download CSV

    Table  5  5-fold cross validation between NOI grade and meteorological factors at different altitudes

    分区 5折交叉验证结果准确率/% 气象因子平均得分从高到低排序
    折数1 折数2 折数3 折数4 折数5
    低海拔区 69.45 69.35 69.40 68.84 69.73 X5，X1，X2，X3
    中海拔区 59.82 59.54 60.03 60.49 59.32 X5，X1，X2，X4，X3
    高海拔区 56.93 58.24 58.38 57.94 58.81 X1，X5，X2，X4，X3
    DownLoad: Download CSV

    Table  6  Accuracy statistics of NOI grade machine learning at different altitudes

    分区 6个等级验证集准确率/% 总体准确率/%
    1 2 3 4 5 6
    低海拔区 55 57 75 0 28 0 70
    中海拔区 56 67 67 38 28 0 61
    高海拔区 61 60 56 28 19 0 59
    DownLoad: Download CSV
  • [1]
    Zhang J C. The form of air negation ion and its law of density decline. Journal of Textile Basic Sciences, 1994, 7(4): 306-309. https://www.cnki.com.cn/Article/CJFDTOTAL-FGJK404.003.htm
    [2]
    Xia L B. The negative ions beneficial to human health. Popular Medicine, 1981(7): 36-37.
    [3]
    Liu G T. The effects of Beidaihe convalescent environment on health. Chinese Journal of Convalescent Medicine, 2004, 13(1): 7-10. doi:  10.3969/j.issn.1005-619X.2004.01.006
    [4]
    Wang Z G. The brief analysis of promoting effect of forest health on human health. Xiandai Horticulture, 2020(1): 106-108. doi:  10.3969/j.issn.1006-4958.2020.01.050
    [5]
    Hyun J, Lee S G, Hwang J. Application of corona discharge-generated air ions for filtration of aerosolized virus and inactivation of filtered virus. Journal of Aerosol Science, 2017, 107: 31-40. doi:  10.1016/j.jaerosci.2017.02.004
    [6]
    Zhang C Y, Wu Z N, Li Z H, et al. Inhibition effect of negative air ions on adsorption between volatile organic compounds and environmental particulate matter. Langmuir, 2020, 36(18): 5078-5083. doi:  10.1021/acs.langmuir.0c00109
    [7]
    Jiang S, Ma A, Ramachandran S. Negative air ions and their effects on human health and air quality improvement. Int J Mol Sci, 2018, 19: 2966. doi:  10.3390/ijms19102966
    [8]
    Chu C H, Chen S R, Wu C H, et al. The effects of negative air ions on cognitive function: An event-related potential(ERP) study. Int J Biometeorol, 2019, 63(10): 1309-1317. doi:  10.1007/s00484-019-01745-7
    [9]
    Zeng S C, Su Z Y, Chen B G. The review on forest negative air ions in China. Journal of Nanjing Forestry University(Nat Sci Ed), 2006, 30(5): 107-111. doi:  10.3969/j.issn.1000-2006.2006.05.026
    [10]
    Li L, Du Q, Liu T N, et al. The research progress of air negative ions. Modernizing Agriculture, 2017(12): 30-31. doi:  10.3969/j.issn.1001-0254.2017.12.016
    [11]
    Peng W, Li M W, Wang H, et al. A review of the research progress of negative air ion at home and abroad and its positive role in forest health. Journal of Temperate Forestry Research, 2020, 3(3): 11-14. doi:  10.3969/j.issn.2096-4900.2020.03.003
    [12]
    Shao H R, He Q T, Yan H P, et al. The spatio-temporal changes of negative air ion concentrations in Beijing. Journal of Beijing Forestry University, 2005, 27(3): 35-39. https://www.cnki.com.cn/Article/CJFDTOTAL-BJLY200503008.htm
    [13]
    Liu H J, Xia S G, Ding Z F, et al. The analysis and evaluation on aero-anion concentration in Jiuhua Mountain. Journal of Chinese Urban Forestry, 2012, 10(5): 14-17. doi:  10.3969/j.issn.1672-4925.2012.05.006
    [14]
    Mao C Z, Yu N L, Du J L, et al. The characteristic comparison of negative oxygen ion between typical urban and forest areas. Meteor Sci Technol, 2014, 42(6): 1083-1088. doi:  10.3969/j.issn.1671-6345.2014.06.023
    [15]
    Liao R J, Yan X J, Jiang B, et al. The variation characteristics of negative air ions concentrations and influencing factors in Lingjiushan Mountain National Forest Health Base. Zhejiang For Sci Technol, 2021, 41(5): 36-41. doi:  10.3969/j.issn.1001-3776.2021.05.006
    [16]
    Chen B H, Ying J H, Jin Q F, et al. The distribution characteristics of air negative oxygen ions in Baiyunshan National Forest Park. Journal of Zhejiang Agricultural Sciences, 2019, 60(2): 337-339. doi:  10.16178/j.issn.0528-9017.20190249
    [17]
    Li Q Y, Li G F, Liao J Y, et al. The study on the characteristics of negative air ion concentrations and its influential factors in Hunan Forest Botanical Garden. Hunan Forestry Science & Technology, 2019, 46(1): 18-23. https://www.cnki.com.cn/Article/CJFDTOTAL-HLKJ201901004.htm
    [18]
    Wang Y L. The study on daily change rule of negative oxygen ion concentration in Taikuanhe Nature Reserve. Shanxi Forestry Science and Technology, 2017, 46(4): 11-14. doi:  10.3969/j.issn.1007-726X.2017.04.004
    [19]
    Shao H R, He Q T. The forest and air anion. World Forestry Research, 2000, 13(5): 19-23. doi:  10.3969/j.issn.1001-4241.2000.05.004
    [20]
    Meng J J, Zhang Y. The distribution of air anion concentration above ground at some scenic sites in Guangxi. Research of Environmental Sciences, 2004, 17(3): 25-27. https://www.cnki.com.cn/Article/CJFDTOTAL-HJKX200403008.htm
    [21]
    Ji Y K, Zhou Y B, Mi S H, et al. The study on the concentration of negative ions in air of Qipanshan Scenic Area. Journal of Liaoning Forestry Science & Technology, 2007(3): 16-21. https://www.cnki.com.cn/Article/CJFDTOTAL-LNLK200703005.htm
    [22]
    Ma J H, Dong Z Z, Yang Y Y, et al. The variation features of negative ion concentration and its correlation with meteorological factors in Changsha. Journal of Anhui Agri Sci, 2014, 42(28): 9872-9874. doi:  10.3969/j.issn.0517-6611.2014.28.084
    [23]
    He N, Lin M L, Zhao H Y, et al. The fluctuation of negative oxygen ion concentration and its influence of meteorological elements in Xiangtan. Journal of Guizhou Meteorology, 2016, 40(2): 65-69. https://www.cnki.com.cn/Article/CJFDTOTAL-GZQX201602012.htm
    [24]
    Tan J, Chen Z H, Luo X R, et al. The distribution characteristics of air negative oxygen ion concentration and the influence of meteorological conditions in tourist attractions in Hubei Province. Resources and Environment in the Yangtze Basin, 2017, 26(2): 314-322. https://www.cnki.com.cn/Article/CJFDTOTAL-CJLY201702018.htm
    [25]
    Chen B H, Ying J H, Jin Q F. The distribution characteristics and influencing factors of air negative oxygen ions in Lishui City. Journal of Zhejiang Agricultural Sciences, 2018, 59(8): 1444-1448. https://www.cnki.com.cn/Article/CJFDTOTAL-ZJNX201808036.htm
    [26]
    Jin Q, Yan J, Yang Z B, et al. The spatial-temporal characteristics of spring air negative oxygen ions and its relationship with environmental factors in Hubei. Meteor Sci Technol, 2015, 43(4): 728-733. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201504029.htm
    [27]
    He Z Q, Liu Q C, Lin L C, et al. The research on the aero anion concentration's diurnal variation and its correlation with environmental factors and meteorological factors of urban areas. Journal of Anhui Agri Sci, 2015, 43(28): 260-262. https://www.cnki.com.cn/Article/CJFDTOTAL-AHNY201528095.htm
    [28]
    Huang S C, Xu C Y, Zhou J L, et al. The path analysis on negative air ion concentration and the meteorological environment in urban and forest zone. Meteor Mon, 2012, 38(11): 1417-1422. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201211013.htm
    [29]
    Cong J, Sun L J. The distribution of negative oxygen ion concentration and the establishment of prediction model in Dalian City. Journal of Meteorology and Environment, 2010, 26(4): 44-47. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201004009.htm
    [30]
    Gu X L, Qian Y Z, Bao Y J, et al. The distribution of oxygen anion concentration and forecasting model in Ningbo and its application in tourism meteorology. Journal of Meteorology and Environment, 2013, 29(6): 128-133. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201306020.htm
    [31]
    Wang B, Xie F Y, Zhang Z X, et al. The forecast model establishment of air negative oxygenion in Yuxi. Plateau Meteor, 2015, 34(1): 251-257. https://www.cnki.com.cn/Article/CJFDTOTAL-GYQX201501026.htm
    [32]
    Zhang Y, Chen L Y, Liu T, et al. The change characteristics and prediction model about concentration of anions in the Emei Mountain scenic spot. Journal of Meteorology and Environment, 2018, 34(2): 61-68. https://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201802008.htm
    [33]
    Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi:  10.11898/1001-7313.20210101
    [34]
    Liu N, Xiong A Y, Zhang Q, et al. Development of basic dataset of severe convective weather for artificial intelligence training. J Appl Meteor Sci, 2021, 32(5): 530-541. doi:  10.11898/1001-7313.20210502
    [35]
    Mi Q C, Gao X N, Li Y, et al. Application of deep learning method to drought prediction. J Appl Meteor Sci, 2022, 33(1): 104-114. doi:  10.11898/1001-7313.20220109
    [36]
    Yin X Y, Hu Z Q, Zheng J F, et al. Filling in the dual polarization radar echo occlusion based on deep learning. J Appl Meteor Sci, 2022, 33(5): 581-593. doi:  10.11898/1001-7313.20220506
    [37]
    Han N F, Yang L, Chen M X, et al. Machine learning correction of wind, temperature and humidity elements in Beijing-Tianjin-Hebei Region. J Appl Meteor Sci, 2022, 33(4): 489-500. doi:  10.11898/1001-7313.20220409
    [38]
    Meng L N, Sun Y X, Li K, et al. The measurement of vertical variation of air negative oxygen ions in Beijing Xiangshan. Urban Environment & Urban Ecology, 2014, 27(1): 12-15.
    [39]
    Xie Z X. Influence of Regional Transport on Ambient Air Quality in Key Cities of Fujian Province. Fuzhou: Fujian Provincial Department of Science and Technology, 2019.
    [40]
    Lin X B, Liu A M, Lin Y, et al. Weather Forecast Technical Manual of Fujian Province. Beijing: China Meteorological Press, 2013.
    [41]
    Lu S J, Wang Y. Climate of Fujian. Beijing: China Meteorological Press, 2012.
    [42]
    Xu J, Ding G A, Yan P, et al. Componential characteristics and sources identification of PM2.5 in Beijing. J Appl Meteor Sci, 2007, 18(5): 645-654. http://qikan.camscma.cn/article/id/20070599
    [43]
    Pu W W, Zhao X J, Zhang X L. Effect of meteorological factors on PM2.5 in late summer and early autumn of Beijing. J Appl Meteor Sci, 2011, 22(6): 716-723. http://qikan.camscma.cn/article/id/20110609
    [44]
    Luan T, Guo X L, Zhang T H, et al. The scavenging process and physical removing mechanism of pollutant aerosols by different precipitation intensities. J Appl Meteor Sci, 2019, 30(3): 279-291. doi:  10.11898/1001-7313.20190303
    [45]
    National Forestry Administration. Technical Specification for Observation of Air Negative Oxygen on Concentration. Beijing: Standards Press of China, 2016.
    [46]
    Liu H Z, Xu H, Bao H J, et al. Application of machine learning classification algorithm to precipitation-induced landslides forecasting. J Appl Meteor Sci, 2022, 33(3): 282-292. doi:  10.11898/1001-7313.20220303
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中
  • -->

Catalog

    Figures(6)  / Tables(6)

    Get Citation
    PDF
    XML
    Article views (889) PDF downloads(95) Cited by()
    • Received : 2022-09-27
    • Accepted : 2023-01-09
    • Published : 2023-03-31
    Journal Online
    Contact

    © 2020 Journal of Applied Meteorological Science   京ICP备15006967号-1

    Supported by Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return