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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (3) : 163-173     DOI: 10.6046/zrzyyg.2024074
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Air freshness monitoring technology based on meteorology and remote sensing
ZHANG Chungui1,2(), PENG Jida1,2
1. Fujian Institute of Meteorological Science, Fuzhou 350008, China
2. Fujian Key Laboratory of Severe Weather, Fuzhou 350008, China
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Abstract  

The concentrations of negative oxygen ions and particulate matter 2.5 (PM2.5) serve as important indicators in the assessment of the degrees of air freshness and cleanliness. Based on 2018-2022 data from 50 negative oxygen ion observation stations affiliated with the Fujian meteorological departments, along with the ecological parameters such as aerosol, vegetation index, and surface brightness temperature obtained by satellite-based remote sensing inversion, this study built estimation models for the concentrations of negative oxygen ions and PM2.5 using the Cubist machine learning method. Accordingly, it developed an air freshness index (AFI), and the fine-scale mesh-based monitoring of regional air freshness was achieved. The results show that the estimation model for the negative oxygen ion concentration yielded goodness of fit of 0.838 and 0.526 for the training and test sets, respectively. In comparison, the estimation model for the PM2.5 concentration exhibited goodness of fit of 0.968 and 0.867 for the training and test sets, respectively. Then, this study developed the AFI by comprehensively considering negative oxygen ions and PM2.5. Then, this study graded the AFI using the frequency quartiles of the statistical data series combined with the spatiotemporal changes in negative oxygen ions. The results indicate that the AFI monitoring results based on meteorology, remote sensing, and machine learning algorithms are consistent with the actual conditions.
Keywords negative oxygen ion      PM2.5      air freshness index      satellite remote sensing      machine learning     
ZTFLH:  TP751.1  
Issue Date: 03 September 2024
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Chungui ZHANG
Jida PENG
Cite this article:   
Chungui ZHANG,Jida PENG. Air freshness monitoring technology based on meteorology and remote sensing[J]. Remote Sensing for Natural Resources, 2024, 36(3): 163-173.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024074     OR     https://www.gtzyyg.com/EN/Y2024/V36/I3/163
Fig.1  Map of main NOI stations in Fujian
Fig.2  Main technical flow chart of air freshness monitoring based on meteorological and remote sensing
类型 影响因子 相关系数 样本数/个
气象因子 温度 -0.060**① 156 201
湿度 0.125** 156 201
风速 0.016** 85 841
气压 -0.008* 90 644
能见度 0.036** 81 821
生态环境因子 NDVI 0.115** 5 867
NDMI -0.112** 5 867
NDSI -0.105** 5 867
VSWI 0.117** 5 867
LST -0.021* 5 867
AOD -0.062** 5 867
Tab.1  Linear correlation coefficients between NOI concentration and meteorological factors and ecological environment factors
类型 影响因子 相关系数 样本数/个
气象因子 温度 -0.065**① 117 798
湿度 0.013** 117 798
风速 -0.055** 117 798
气压 0.055* 117 798
能见度 -0.137** 117 798
生态环境因子 NDVI -0.233** 117 798
AOD 0.110** 117 798
Tab.2  Linear phase relationship between PM2.5 concentration and meteorological factors and ecological environmental factors
Fig.3  Optimization results of Cubist algorithm committees and instances
Fig.4  Training and testing results of Cubist machine learning model for NOI concentration prediction
Fig.5  Training and testing results of Cubist machine learning model for PM2.5 concentration prediction
Fig.6  Frequency distribution of AFI and its logarithm
空气清新等级 景观类型 低海拔地区 中海拔地区 高海拔地区 平均值
1 国家公园 — 7 637 — 4 001
生态保护区 2 336 5 410 2 054
森林公园 4 720 2 224~3 626 —
2 山区乡镇 4 470 2 617 2 231~3 701 2 464
郊野公园 2 731 2 048 —
名胜景区 — 1 566 712~2 097
3 县气象站 1 930 1 586 — 1 610
县城郊区 1 861 — 956
海滨景区 1 725 — —
海滨乡镇 772~883 — —
Tab.3  
百分位数
(部分)		 ln(AFI) AFI NOI浓度/
(个·cm-3)		 PM2.5质量浓
度/(μg·m-3)
15% 3.6 37 993 26.8
20% 3.8 45 1 198 26.6
25% 4.0 54 1 347 24.9
30% 4.2 65 1 422 21.9
35% 4.3 77 1 491 19.4
40% 4.5 93 1 595 17.2
45% 4.7 111 1 628 14.7
50% 4.9 131 1 656 12.6
55% 5.0 156 1 741 11.2
60% 5.2 187 1 780 9.5
65% 5.4 226 1 925 8.5
70% 5.6 275 2 171 7.9
75% 5.8 339 2 221 6.3
80% 6.1 426 2 511 5.9
85% 6.3 561 2 802 5.0
90% 6.8 791 3 644 4.6
91% 6.8 856 4 040 4.5
95% 7.2 1343 4 935 3.7
Tab.4  Normal distribution percentiles of AFI and its logarithm
等级 等级说明 NOI浓度界限值/
(个·cm-3)		 AFI界限值/
(μg·m-3)
1 非常清新 ≥2 800 ≥561
2 很清新 [2 000,2 800) [226,561)
3 清新 [1 600,2 000) [93,226)
4 较不清新 [1 200,1 600) [45,93)
5 不清新 <1200 <45
Tab.5  AFI based on meteorological and remote sensing factors
Fig.7  Fine grid monitoring map of air fresh in Fujian
气候康养福地 空气清新度监测模型 气候康养福地 空气清新度监测模型
AFI等级 NOI浓度估算值/
(个·cm-3)		 AFI等级 NOI浓度估算值/
(个·cm-3)
福州市闽清县七叠温泉 1级 3 165 龙岩市新罗区培斜村 1级 3 007
福州市福清市大山村 2级 2 298 三明市漳平市永福镇 2级 2 115
厦门市同安区顶村村 1级 3 525 三明市大田县屏山乡 2级 2 064
厦门市海沧区森林公园 2级 2 284 三明市明溪县夏阳乡 1级 2 937
莆田市仙游县西苑乡 1级 3 092 三明市尤溪县度假区 2级 2 525
泉州市德化县国宝乡 1级 3 410 南平市政和县杨源乡 3级 1 867
泉州市永春县牛姆林区 1级 3 134 南平市建阳区黄坑镇 2级 2 530
泉州市永春县苏坑镇 1级 3 021 南平市延平区上洋村 2级 2 088
泉州市南安市向阳乡 1级 3 159 宁德市福安市白云山 1级 2 834
漳州市南靖县土楼景区 1级 2 904 宁德市屏南县寿山乡 2级 2 567
漳州市龙海区鹭凯庄园 2级 2 163 宁德市屏南县龙潭村 1级 2 812
龙岩市武平县城厢镇 2级 2 567 宁德市古田县泮洋乡 1级 3 167
Tab.6  Estimated values of air fresh index and NOI concentration in 24 “climate healthy good places” in Fujian
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