Evaluation of the influence of El Niño–Southern Oscillation on air quality in southern China from long-term historical observations

doi:10.1007/s11783-021-1460-0

Front. Environ. Sci. Eng.

2022, Vol. 16

Issue (2) : 26 https://doi.org/10.1007/s11783-021-1460-0

RESEARCH ARTICLE

Evaluation of the influence of El Niño–Southern Oscillation on air quality in southern China from long-term historical observations

Shansi Wang¹, Siwei Li^1,²(

), Jia Xing³, Jie Yang², Jiaxin Dong¹, Yu Qin⁴, Shovan Kumar Sahu³

¹. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
². State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
³. School of Environment, Tsinghua University, Beijing 100084, China
⁴. Map Institute of Guangdong Province, Guangzhou 510075, China

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Abstract

•Strong ENSO influence on AOD is found in southern China region.

•Low AOD occurs in El Niño but high AOD occurs in La Niña events in southern China.

•Angstrom exponent anomalies reveals the circulation pattern during each ENSO phase.

•ENSO exerts large influence (70.5%) on annual variations of AOD during 2002–2020.

•Change of anthropogenic emissions is the dominant driver for AOD trend (2002–2020).

Previous studies demonstrated that the El Niño–Southern Oscillation (ENSO) could modulate regional climate thus influencing air quality in the low-middle latitude regions like southern China. However, such influence has not been well evaluated at a long-term historical scale. To filling the gap, this study investigated two-decade (2002 to 2020) aerosol concentration and particle size in southern China during the whole dynamic development of ENSO phases. Results suggest strong positive correlations between aerosol optical depth (AOD) and ENSO phases, as low AOD occurred during El Niño while high AOD occurred during La Niña event. Such correlations are mainly attributed to the variation of atmospheric circulation and precipitation during corresponding ENSO phase. Analysis of the angstrom exponent (AE) anomalies further confirmed the circulation pattern, as negative AE anomalies is pronounced in El Niño indicating the enhanced transport of sea salt aerosols from the South China Sea, while the La Niña event exhibits positive AE anomalies which can be attributed to the enhanced import of northern fine anthropogenic aerosols. This study further quantified the AOD variation attributed to changes in ENSO phases and anthropogenic emissions. Results suggest that the long-term AOD variation from 2002 to 2020 in southern China is mostly driven (by 64.2%) by the change of anthropogenic emissions from 2002 to 2020. However, the ENSO presents dominant influence (70.5%) on year-to-year variations of AOD during 2002–2020, implying the importance of ENSO on varying aerosol concentration in a short-term period.

Keywords El Niño–Southern Oscillation Aerosol concentration Aerosol particle size Contribution separation Decadal trend Southern China

Corresponding Author(s): Siwei Li

Issue Date: 17 June 2021

Cite this article:

Shansi Wang,Siwei Li,Jia Xing, et al. Evaluation of the influence of El Niño–Southern Oscillation on air quality in southern China from long-term historical observations[J]. Front. Environ. Sci. Eng., 2022, 16(2): 26.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-021-1460-0
https://academic.hep.com.cn/fese/EN/Y2022/V16/I2/26

Fig.1 The ONI during ENSO development from 2002 to 2020 (unit: °C) (Above 0.5°C is defined as an El Niño event, below −0.5°C is defined as a La Niña event, between −0.5°C and 0.5°C is defined as a neutral event).

Tab.1 ENSO development phase and events division during 2002–2020

Fig.2 Historical trend of anthropogenic emissions of five major pollutants in southern China (units: kiloton).

Fig.3 The time series of peak winter (DJF) anomaly of AOD (black), ONI (yellow) and emission index (blue) (all are processed as standardization data ranged from −1 to 1 interval).

Fig.4 Composite AOD anomalies (color shading) in southern region of China during the development period for the El Niño (a, c, e) and the La Niña event (b, d, f). (a) and (b) for the autumn (SON) developing period, (c) and (d) for the winter (DJF) peak period. (e) and (f) for the following (MAM) decaying period. Dots indicate values significantly above the 95% confidence level. AOD= aerosol optical depth. (the 0 represents the previous year, while the 1 represents the current year).

Fig.5 Same as Fig. 4, except for composite AE anomalies. AE= Angstrom exponent. Dots indicate AE anomalies that exceed the 95% confidence level.

Fig.6 Same as Fig. 4, except for composite spatial patterns of anomalous precipitation (mm/day). Dots indicate precipitation anomalies that exceed the 95% confidence level.

Fig.7 Same as Fig. 4, except for 850-hPa wind anomalies composite. Blue shading color represents wind anomalies in m/s above the 95% confidence level.

Fig.8 (a). The winter (DJF) AOD variation caused by emission and ENSO respectively. Black line is the anomaly value of AOD value (observed value), gray dash line is value equals to zero. Cyan line is the sum of emission and ENSO (fitted value). Green shade shows AOD change caused by emissions and orange shade caused by ENSO events. Red, blue, and gray color bar represents El Niño, La Niña event and Neutral, respectively. (b) The annual AOD variation attributed to emission (green) and ENSO (orange). Y axis is percentage.

1	W Cai, K Li, H Liao, H Wang, L Wu (2017). Weather conditions conducive to Beijing severe haze more frequent under climate change. Nature Climate Change, 7(4): 257–262 https://doi.org/10.1038/nclimate3249
2	W Cai, G Wang, B Dewitte, L Wu, A Santoso, K Takahashi, Y Yang, A Carréric, M J McPhaden (2018). Increased variability of eastern Pacific El Niño under greenhouse warming. Nature, 564(7735): 201–206 https://doi.org/10.1038/s41586-018-0776-9 pmid: 30542166
3	A Capotondi (2015). Extreme La Niña events to increase. Nature Climate Change, 5(2): 100–101 https://doi.org/10.1038/nclimate2509
4	L Chang, J Xu, X Tie, J Wu (2016). Impact of the 2015 El Niño event on winter air quality in China. Scientific Reports, 6(1): 34275 https://doi.org/10.1038/srep34275 pmid: 27671839
5	Z Chen, R Wu, W Chen (2014). Distinguishing interannual variations of the northern and southern modes of the East Asian winter monsoon. Journal of Climate, 27(2): 835–851 https://doi.org/10.1175/JCLI-D-13-00314.1
6	D Ding, J Xing, S Wang, X Chang, J Hao (2019b). Impacts of emissions and meteorological changes on China’s ozone pollution in the warm seasons of 2013 and 2017. Frontiers of Environmental Science & Engineering, 13(5): 76 https://doi.org/10.1007/s11783-019-1160-1
7	D Ding, J Xing, S Wang, K Liu, J Hao (2019a). Estimated contributions of emissions controls, meteorological factors, population growth, and changes in baseline mortality to reductions in ambient PM2.5 and PM2.5-related mortality in China, 2013–2017. Environmental Health Perspectives, 127(6): 67009 https://doi.org/10.1289/EHP4157 pmid: 31232608
8	Y Ding, P Wu, Y Liu, Y Song (2017). Environmental and dynamic conditions for the occurrence of persistent haze events in North China. Engineering, 3(2): 266–271 https://doi.org/10.1016/J.ENG.2017.01.009
9	J Feng, J Li, J Zhu, H Liao (2016). Influences of El Nino Modoki event 1994/1995 on aerosol concentrations over southern China. Journal of Geophysical Research: Atmospheres, 121(4): 1637–1651 https://doi.org/10.1002/2015JD023659
10	J Feng, J Li, J Zhu, H Liao, Y Yang (2017). Simulated contrasting influences of two La Niña Modoki events on aerosol concentrations over eastern China. Journal of Geophysical Research: Atmospheres, 122(5): 2734–2749 https://doi.org/10.1002/2016JD026175
11	H Gao, S Yang (2009). A severe drought event in Northern China in winter 2008–2009 and the possible influences of La Niña and Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 114(D24): D24104 https://doi.org/10.1029/2009JD012430
12	Y Jiang, J Xing, S Wang, X Chang, S Liu, A Shi, S K Sahu (2021). Understand the local and regional contributions on air pollution from the view of human health impacts. Frontiers of Environmental Science & Engineering, 15(5): 11
13	S Kim, S Yoon, J Kim, S Kim (2007). Seasonal and monthly variations of columnar aerosol optical properties over East Asia determined from multi-year MODIS, LIDAR, and AERONET sun/sky radiometer measurements. Atmospheric Environment, 41(8): 1634–1651 https://doi.org/10.1016/j.atmosenv.2006.10.044
14	I Koren, O Altaratz, L A Remer, G Feingold, J V Martins, R H Heiblum (2012). Aerosol-induced intensification of rain from the tropics to the mid-latitudes. Nature Geoscience, 5(2): 118–122 https://doi.org/10.1038/ngeo1364
15	T Lee, M J Mcphaden (2010). Increasing intensity of El Niño in the central-equatorial Pacific. Geophysical Research Letters, 37(14 L14603): n/a https://doi.org/10.1029/2010GL044007
16	T Liu, S Gong, J He, M Yu, Q Wang, H Li, W Liu, J Zhang, L Li, X Wang, S Li, Y Lu, H Du, Y Wang, C Zhou, H Liu, Q Zhao (2017). Attributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China’s Jing-Jin-Ji area. Atmospheric Chemistry and Physics, 17(4): 2971–2980 https://doi.org/10.5194/acp-17-2971-2017
17	Z Liu, D Guan, W Wei, S J Davis, P Ciais, J Bai, S Peng, Q Zhang, K Hubacek, G Marland, R J Andres, D Crawford-Brown, J Lin, H Zhao, C Hong, T A Boden, K Feng, G P Peters, F Xi, J Liu, Y Li, Y Zhao, N Zeng, K He (2015). Reduced carbon emission estimates from fossil fuel combustion and cement production in China. Nature, 524(7565): 335–338 https://doi.org/10.1038/nature14677 pmid: 26289204
18	T Ma, F Duan, K He, Y Qin, D Tong, G Geng, X Liu, H Li, S Yang, S Ye, B Xu, Q Zhang, Y Ma (2019). Air pollution characteristics and their relationship with emissions and meteorology in the Yangtze River Delta region during 2014–2016. Journal of Environmental Sciences, 83: 8–20
19	J Ramon, L Lledo, V Torralba, A Soret, F J Doblas-Reyes (2019). What global reanalysis best represents near-surface winds? Quarterly Journal of the Royal Meteorological Society, 145(724): 3236–3251 https://doi.org/10.1002/qj.3616
20	D Shang, J Peng, S Guo, Z Wu, M Hu. (2021). Secondary aerosol formation in winter haze over the Beijing-Tianjin-Hebei Region, China. Frontiers of Environmental Science & Engineering, 15(2): 13
21	G L Schuster, O Dubovik, B N Holben(2006). Angstrom exponent and bimodal aerosol size distributions. Journal of Geophysical Research-Atmospheres, 111(D07207D7)
22	J Sun, H Li, W Zhang, T Li, W Zhao, Z Zuo, S Guo, D Wu, S Fan (2018). Modulation of the ENSO on winter aerosol pollution in the eastern region of China. Journal of Geophysical Research: Atmospheres, 123(21): 11952–11969 https://doi.org/10.1029/2018JD028534
23	A P K Tai, L J Mickley, D J Jacob (2010). Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change. Atmospheric Environment, 44(32): 3976–3984 https://doi.org/10.1016/j.atmosenv.2010.06.060
24	B Wang, X Luo, J Liu (2020). How robust is the Asian precipitation-ENSO relationship during the industrial warming period (1901–2017)? Journal of Climate, 33(7): 2779–2792 https://doi.org/10.1175/JCLI-D-19-0630.1
25	B Wang, R G Wu, X H Fu (2000). Pacific-East Asian teleconnection: How does ENSO affect East Asian climate? Journal of Climate, 13(9): 1517–1536 https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2
26	X Wang, S Zhong, X Bian, L Yu(2019). Impact of 2015–2016 El Niño and 2017–2018 La Niña on PM2.5 concentrations across China. Atmospheric Environment, 208: 61–73 https://doi.org/10.1016/j.atmosenv.2019.03.035
27	X Wu, Y Xu, R Kumar, M Barth (2019). Separating emission and meteorological drivers of mid-21st-century air quality changes in India based on multiyear global-regional chemistry-climate simulations. Journal of Geophysical Research: Atmospheres, 124(23): 13420–13438 https://doi.org/10.1029/2019JD030988
28	J Xing, X Lu, S Wang, T Wang, D Ding, S Yu, D Shindell, Y Ou, L Morawska, S Li, L Ren, Y Zhang, D Loughlin, H Zheng, B Zhao, S Liu, K R Smith, J Hao (2020a). The quest for improved air quality may push China to continue its CO2 reduction beyond the Paris Commitment. Proceedings of the National Academy of Sciences of the United States of America, 117(47): 29535–29542 https://doi.org/10.1073/pnas.2013297117 pmid: 33168731
29	J Xing, S Zheng, D Ding, J T Kelly, S Wang, S Li, T Qin, M Ma, Z Dong, C Jang, Y Zhu, H Zheng, L Ren, T Y Liu, J Hao (2020b). Deep learning for prediction of the air quality response to emission changes. Environmental Science & Technology, 54(14): 8589–8600 https://doi.org/10.1021/acs.est.0c02923 pmid: 32551547
30	S W Yeh, J S Kug, B Dewitte, M H Kwon, B P Kirtman, F F Jin (2009). El Niño in a changing climate. Nature, 461(7263): 511–514 https://doi.org/10.1038/nature08316 pmid: 19779449
31	Z Yin, H Wang, H Chen (2017). Understanding severe winter haze events in the North China Plain in 2014: Roles of climate anomalies. Atmospheric Chemistry and Physics, 17(3): 1641–1652 https://doi.org/10.5194/acp-17-1641-2017
32	X Yu, Z Wang, H Zhang, J He, Y Li (2020). Contrasting impacts of two types of El Niño events on winter haze days in China’s Jing-Jin-Ji region. Atmospheric Chemistry and Physics, 20(17): 10279–10293 https://doi.org/10.5194/acp-20-10279-2020
33	G Zhang, Y Gao, W Cai, L R Leung, S Wang, B Zhao, M Wang, H Shan, X Yao, H Gao (2019). Seesaw haze pollution in North China modulated by the sub-seasonal variability of atmospheric circulation. Atmospheric Chemistry and Physics, 19(1): 565–576 https://doi.org/10.5194/acp-19-565-2019
34	R H Zhang, Q Li, R N Zhang (2014). Meteorological conditions for the persistent severe fog and haze event over Eastern China in January 2013. Science China. Earth Sciences, 57(1): 26–35 https://doi.org/10.1007/s11430-013-4774-3
35	S Zhao, J Li, C Sun (2016). Decadal variability in the occurrence of wintertime haze in central Eastern China tied to the Pacific Decadal Oscillation. Scientific Reports, 6(1): 27424 https://doi.org/10.1038/srep27424 pmid: 27282140
36	J Zhu, H Liao, J Li (2012). Increases in aerosol concentrations over eastern China due to the decadal-scale weakening of the East Asian summer monsoon. Geophysical Research Letters, 39(9 L09809): n/a https://doi.org/10.1029/2012GL051428
37	Y Zou, Y Wang, Y Zhang, J H Koo (2017). Arctic sea ice, Eurasia snow, and extreme winter haze in China. Science Advances, 3(3): e1602751 https://doi.org/10.1126/sciadv.1602751 pmid: 28345056

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