Vol.34, NO.2, 2023
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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.
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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.
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  • Fig. 1  Daily variation of NOI concentration in Fujian

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

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

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    Fig. 4  Seasonal variation of NOI concentration in Fujian

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    Fig. 5  Multiple linear regression fitting in middle altitude area

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

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    Fig. 6  Machine learning fitting in middle altitude area

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

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    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
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    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。
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    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
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    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
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    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
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    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
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    • Received : 2022-09-27
    • Accepted : 2023-01-09
    • Published : 2023-03-31
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