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Distribution Characteristics and Meteorological Prediction Model of Air Negative Oxygen Ions in Fujian
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摘要: 负氧离子是评价空气新鲜和清洁程度的重要指标。利用2018—2021年福建省负氧离子观测站数据分析负氧离子浓度的时空变化特征，并采用多元线性回归方法、多元逻辑回归方法和LightGBM机器学习方法建立负氧离子浓度预测模型。结果表明：福建省负氧离子资源十分丰富，中海拔区（350~550 m）年平均负氧离子浓度最高，低海拔区次之，高海拔区最小。负氧离子浓度日变化特征呈一峰一谷型，04：00—06：00（北京时，下同）达到峰值，12：00—13：00达到谷值；中海拔区负氧离子浓度季节变化较大，季节平均浓度从大到小依次为春季、夏季、冬季、秋季，而高、低海拔区季节变化相对较小。福建省不同海拔地区负氧离子浓度与湿度、降水和能见度均呈显著正相关，负氧离子浓度与气温、风速和气压显著相关，但不同海拔地区的相关性有所不同。机器学习方法对不同海拔地区负氧离子浓度数值的拟合效果比多元线性回归方法有明显提升，对负氧离子浓度等级拟合的准确率比多元逻辑回归方法提高7%~12%，且在绝大部分等级上的准确率均高于多元逻辑回归方法。
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关键词:
- 空气负氧离子;
- 气象因子;
- LightGBM机器学习;
- 时空变化特征;
- 预测模型
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. -
图 3 2019—2021年福州大气颗粒物质量浓度及空气质量指数年变化
Fig. 3 Variation of atmospheric particulate concentration and air quality index in Fuzhou from 2019 to 2021
图 5 中海拔区多元线性回归拟合结果
(黑线为对角线，红线为散点拟合线)
Fig. 5 Multiple linear regression fitting in middle altitude area
(black line is diagonal line, red line is scatter fitting line)
图 6 中海拔区机器学习拟合结果
(黑线为对角线，红线为散点拟合线)
Fig. 6 Machine learning fitting in middle altitude area
(black line is diagonal line, red line is scatter fitting line)
表 1 福建省19个站点年平均NOI浓度空间分布
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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表 2 福建省不同海拔地区NOI浓度与气象因子相关系数
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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表 3 不同海拔地区NOI与气象因子5折交叉验证结果及机器学习模型
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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表 4 不同海拔地区逻辑回归结果准确率统计
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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表 5 不同海拔地区NOI等级与气象因子5折交叉验证结果
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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表 6 不同海拔地区NOI等级机器学习模型的准确率统计
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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