基于机器学习方法的气溶胶对西藏高原地区雨季降水的影响

doi:10.3969/j.issn.1673-503X.2024.03.017

Abstract

Abstract:

Using the precipitation data from 2001 to 2021 and the data of atmospheric pollutants such as PM_2.5, sulfate and nitrate, we chose Lazi as the study area and use the machine learning (ML) method to decipher the complex relationship between precipitation and its influencing factors. After that, the contribution of each input variable to the precipitation in was is quantified by this ML method. The results indicate that the dew point temperature is the most crucial elements affecting the precipitation in Lazi, contributing 74% to precipitation in rainy season and 66% to precipitation in non-rainy season. Among aerosol components, nitrate shows the greatest influence on precipitation, accounting for 61% and 71% to the precipitation in rainy and non-rainy seasons, respectively. This result means that nitrate aerosols play an important role in precipitation.

Key words: High altitude region, Precipitation mechanism, Machine learning algorithm

CLC Number:

Ciren DAWA,Zhui LUO,Yihang HONG,Zhuoga CIREN. Impact of aerosol on rainy season precipitation in Xizang Plateau based on machine learning method[J]. Journal of Meteorology and Environment, 2024, 40(3): 138-144.

Figures/Tables 4

Fig.1

Table 1

Fig.2

Fig.3

References 24

1	常姝婷. 全球变暖背景下青藏高原夏季大气水汽特征及对区域气候的影响[D]. 兰州: 兰州大学, 2018.
2	路红亚, 杜军, 袁雷, 等. 1971—2012年珠穆朗玛峰地区极端降水事件变化研究[J]. 冰川冻土, 2014, 36 (3): 563- 572.
3	刘蕾, 陈茂钦, 蓝柳茹, 等. 桂北山区两次突发性大暴雨触发及维持机制分析[J]. 气象与环境学报, 2022, 38 (5): 15- 24.
4	刘达之, 姚聃, 梁旭东. 基于四维变分同化的"6·23"阜宁龙卷大涡模拟研究[J]. 气象与环境学报, 2022, 38 (6): 1- 9.
5	邢莉, 傅宗玫. 有机气溶胶对中国境内云凝结核数量的贡献研究[J]. 北京大学学报: 自然科学版, 2015, 51 (1): 13- 23.
6	张瑶, 吴昊, 张东梅, 等. 超大城市新粒子生成事件对云凝结核贡献分析[J]. 环境科学学报, 2022, 42 (9): 372- 383.
7	Huang R J , Zhang Y L , Bozzetti C , et al. High secondary aerosol contribution to particulate pollution during haze events in China[J]. Nature, 2014, 514 (7521): 218- 222. doi: 10.1038/nature13774
8	Dao X , Lin Y C , Cao F , et al. Introduction to the national aerosol chemical composition monitoring network of China: objectives, current status, and outlook[J]. Bulletin of the American Meteorological Society, 2019, 100 (12): ES337- ES351. doi: 10.1175/BAMS-D-18-0325.1
9	Geng G N , Zhang Q , Tong D , et al. Chemical composition of ambient PM_2.5 over China and relationship to precursor emissions during 2005-2012[J]. Atmospheric Chemistry and Physics, 2017, 17 (14): 9187- 9203. doi: 10.5194/acp-17-9187-2017
10	Liu S G , Geng G N , Xiao Q Y , et al. Tracking daily concentrations of PM_2.5 chemical composition in China since 2000[J]. Environmental Science & Technology, 2022, 56 (22): 16517- 16527.
11	Bao M Y , Zhang Y L , Cao F , et al. Highly time-resolved characterization of carbonaceous aerosols using a two-wavelength Sunset thermal-optical carbon analyzer[J]. Atmospheric Measurement Techniques, 2021, 14 (6): 4053- 4068. doi: 10.5194/amt-14-4053-2021
12	Yu M Y , Zhang Y L , Xie T , et al. Quantification of fossil and non-fossil sources to the reduction of carbonaceous aerosols in the Yangtze River Delta, China: insights from radiocarbon analysis during 2014-2019[J]. Atmospheric Environment, 2023, 292, 119421. doi: 10.1016/j.atmosenv.2022.119421
13	Miyakawa T , Kanaya Y , Komazaki Y , et al. Intercomparison between a single particle soot photometer and evolved gas analysis in an industrial area in Japan: implications for the consistency of soot aerosol mass concentration measurements[J]. Atmospheric Environment, 2016, 127, 14- 21. doi: 10.1016/j.atmosenv.2015.12.018
14	Hong Y H , Cao F , Fan M Y , et al. Impacts of chemical degradation of levoglucosan on quantifying biomass burning contribution to carbonaceous aerosols: a case study in Northeast China[J]. Science of the Total Environment, 2022, 819, 152007. doi: 10.1016/j.scitotenv.2021.152007
15	姜建芳, 侯丽丽, 齐梦溪, 等. 天津市采暖季PM_2.5中碳组分污染特征及来源分析[J]. 生态环境学报, 2020, 29 (6): 1181- 1188.
16	黄炯丽, 陈志明, 莫招育, 等. 广西玉林市大气PM₁₀和PM_2.5中有机碳和元素碳污染特征分析[J]. 环境科学, 2018, 39 (1): 27- 37.
17	Shin J Y , Ro Y , Cha J W , et al. Assessing the applicability of random forest, stochastic gradient boosted model, and extreme learning machine methods to the quantitative precipitation estimation of the radar data: a case study to gwangdeoksan radar, South Korea, in 2018[J]. Advances in Meteorology, 2019, 2019, 6542410.
18	Li Q L , Zhu Q Y , Xu M W , et al. Estimating the impact of COVID-19 on the PM_2.5 levels in China with a satellite-driven machine learning model[J]. Remote Sensing, 2021, 13 (7): 1351. doi: 10.3390/rs13071351
19	Grange S K , Carslaw D C . Using meteorological normalisation to detect interventions in air quality time series[J]. Science of the Total Environment, 2019, 653, 578- 588. doi: 10.1016/j.scitotenv.2018.10.344
20	Grange S K , Carslaw D C , Lewis A C , et al. Random forest meteorological normalisation models for Swiss PM₁₀ trend analysis[J]. Atmospheric Chemistry and Physics, 2018, 18 (9): 6223- 6239. doi: 10.5194/acp-18-6223-2018
21	Grange S K , Uzu G , Weber S , et al. Linking Switzerland's PM₁₀ and PM_2.5 oxidative potential (OP) with emission sources[J]. Atmospheric Chemistry and Physics, 2022, 22 (10): 7029- 7050. doi: 10.5194/acp-22-7029-2022
22	Lovric M , Pavlovic K , Vukovic M , et al. Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning[J]. Environmental Pollution, 2021, 274, 115900. doi: 10.1016/j.envpol.2020.115900
23	Wright M N , Ziegler A . Ranger: a fast implementation of random forests for high dimensional data in C++ and R[J]. Journal of Statistical Software, 2017, 77 (1): 1- 17.
24	Liu F T, Ting K M, Zhou Z H. Isolation forest[C]//Proceedings of 2008 Eighth IEEE International Conference on Data Mining. Pisa: IEEE, 2008: 413-422.

变量	全年	雨季	非雨季	显著性分析
降水量/mm	37.3±64.9	98.0±82.9	8.6±20.0	P < 0.01
U10/(m·s^-1)	0.8±0.8	0.1±0.5	1.2±0.7	P < 0.01
V10/(m·s^-1)	0.9±0.7	0.7±0.6	1.0±0.7	P>0.05
D2M/℃	-6.8±11.1	5.6±2.6	-12.7±8.4	P < 0.01
T2M/℃	4.7±5.3	10.7±1.3	1.9±4.0	P < 0.01
LAI_HV/(m²·m^-2)	0.0±0.0	0.0±0.0	0.0±0.0	P>0.05
LAI_LV/(m²·m^-2)	0.7±0.1	0.8±0.0	0.7±0.0	P>0.05
SP/(hPa)	590.0±2.9	591.8±1.5	589.2±3.0	P < 0.01
PM_2.5/(μg·m^-3)	11.7±4.5	9.1±1.4	13.0±4.9	P < 0.01
SO₄^2-/(μg·m^-3)	2.1±0.7	1.8±0.5	2.3±0.7	P < 0.01
NO₃^-/(μg·m^-3)	2.4±1.1	1.4±0.3	2.8±1.0	P < 0.01
NH₄⁺/(μg·m^-3)	1.9±0.7	1.3±0.3	2.2±0.7	P < 0.01
OM/(μg·m^-3)	3.8±1.3	3.1±0.7	4.2±1.4	P < 0.01
OC/(μg·m^-3)	2.4±0.8	1.9±0.4	2.6±0.9	P < 0.01
BC/(μg·m^-3)	0.7±0.3	0.6±0.1	0.8±0.3	P < 0.01
SOC/(μg·m^-3)	1.0±0.4	0.7±0.2	1.1±0.4	P < 0.01
Others/(μg·m^-3)	0.8±2.7	0.8±1.2	0.8±3.1	P < 0.01
SOC/OC	0.4±0.1	0.4±0.1	0.4±0.1	P < 0.01

[1]	Jinmeng CUI,Chunhua CONG,Yi ZHENG,Jinmin CHEN,Ming YANG,Jikang WANG,Hongli LIU,Xuguang DONG. Analysis of air pollution characteristics and meteorological conditions in Shandong province during 2013-2021 [J]. Journal of Meteorology and Environment, 2024, 40(3): 46-54.
[2]	Zhongjie LI,Chang'an YAN,Yali LI,Dawei ZHANG,Jianwu SHI. Study of PM_2.5 mass concentration evolution and potential source diffusion simulation in Kunming [J]. Journal of Meteorology and Environment, 2024, 40(3): 26-36.
[3]	Jinglan LUO,Chao HU,Dongmin TANG,Xueqin PENG,Yike GAO. Comparative analysis of ozone pollution cases in Meishan city based on unmanned aerial vehicle vertical observation [J]. Journal of Meteorology and Environment, 2024, 40(3): 17-25.
[4]	Lei YAO,Jiaren YAN,Ruixiang LIU. A polluted weather analysis in Lianyungang area based on unmanned aerial vehicle vertical observation [J]. Journal of Meteorology and Environment, 2024, 40(3): 9-16.
[5]	Xueyan MA,Meiling SUN,Ling GUO,Xiaojia WANG. Spatio-temporal distribution of negative oxygen ion concentration in different functional areas of Tianjin city in 2022 and their relationships with meteorological conditions [J]. Journal of Meteorology and Environment, 2024, 40(2): 59-68.
[6]	Wei WU,Tie-liang SHAN. Transport and case study of heavy air pollution in Luohe during autumn and winter using backward trajectory analysis [J]. Journal of Meteorology and Environment, 2022, 38(3): 65-74.
[7]	Xiao-yu YAN, Yuan-yuan YANG, Xiao-hui GOU, Jian-jun LIU, Zhan-sheng SU, Bao-guo WU, Xiao-li GONG. Analysis of actinic flux and its influence factors in Yinchuan based on TUV model [J]. Journal of Meteorology and Environment, 2022, 38(3): 127-136.
[8]	Xiao-xiao LI, Hu-jia ZHAO, Yan-jun MA, Xiao-chu LIU, Si-xu LI, Xiao-lan LI, Yang-feng WANG, Ye HONG. Aerosol vertical variations and the origin analysis on a pollution event in Shenyang on March 2021 [J]. Journal of Meteorology and Environment, 2022, 38(2): 46-54.
[9]	Hui ZHANG,Chu WU,Jing-wei ZHANG,Xiao-ping LIN,Jia-quan LIANG,Su LIU,Yan-fang GUO,Cheng-liu LI. Characteristics of primary pollutants of air quality and their relationships with meteorological conditions in Heyuan [J]. Journal of Meteorology and Environment, 2022, 38(1): 40-47.
[10]	An-ke GUO,Xiao-ge YIN,Zhi-min WANG,Zhi-qiang LI,Zhe LI,Ji ZHANG,Lu CHEN,Hao-yun HUANG. A study on the response relationship between air pollution process and meteorological wind field in an industrial park [J]. Journal of Meteorology and Environment, 2022, 38(1): 23-32.
[11]	Wei-jie LI,Hu-jia ZHAO,Quan-liang CHEN,Lin-chang AN,Jie ZHANG,Xu-xin WANG. Variation characteristics of air pollutants in Sichuan region during January-March of 2020 [J]. Journal of Meteorology and Environment, 2022, 38(1): 15-22.
[12]	Wei GUO, Ling-yun ZHU, Wen-ya WANG, Xing-ai GAO, Peng-wei CHENG, Yue-jun ZHANG. Evaluation of air quality improvement in Taiyuan during Second National Youth Games [J]. Journal of Meteorology and Environment, 2021, 37(6): 36-43.
[13]	Tulinisha,Da-wei AN,Chao ZHANG,Bi-xin YU,Chun-yan CHEN. Characteristics of dust weather in southern Xinjiang based on hourly data [J]. Journal of Meteorology and Environment, 2021, 37(5): 34-40.
[14]	Yan-jun MA, Hu-jia ZHAO, Yu-fei LIU, Xiao-lan LI, Yang-feng WANG, Yun-hai ZHANG, Ye HONG. Analysis of aerosol concentration variation and weather characteristics of heavy pollution events in northeast China [J]. Journal of Meteorology and Environment, 2021, 37(5): 13-19.
[15]	Ning-wei LIU, Wei-jun QUAN, Wan-hui REN, Xiao-lan LI, Li-guang LI, Di WANG. Ozone pollution and the related effects of meteorological factors in Liaoning province, China [J]. Journal of Meteorology and Environment, 2021, 37(5): 27-33.

Impact of aerosol on rainy season precipitation in Xizang Plateau based on machine learning method

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 24

Related Articles 15

Metrics

Comments

Recommended 10