Meteorological Change and Impacts on Air Pollution: Results From North China

There have been speculations that the severe air pollution experienced in North China was the act of meteorological change in general and a decreasing northerly wind in particular. We conduct a retrospective analysis on 1979–2016 reanalysis data from ERA‐Interim of European Centre for Medium‐Range W...

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Published in:Journal of Geophysical Research: Atmospheres
Main Authors: Xu, Ziping, Chen, Song Xi, Wu, Xiaoqing
Format: Article in Journal/Newspaper
Language:unknown
Published: Springer Science 2020
Subjects:
hypothesis testing
North China
air pollution
meteorological change
Atmospheric and Oceanic Sciences
Science
Arctic
Online Access:https://hdl.handle.net/2027.42/156450
https://doi.org/10.1029/2020JD032423
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/156450
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic hypothesis testing
North China
air pollution
meteorological change
Atmospheric and Oceanic Sciences
Science
spellingShingle hypothesis testing
North China
air pollution
meteorological change
Atmospheric and Oceanic Sciences
Science
Xu, Ziping
Chen, Song Xi
Wu, Xiaoqing
Meteorological Change and Impacts on Air Pollution: Results From North China
topic_facet hypothesis testing
North China
air pollution
meteorological change
Atmospheric and Oceanic Sciences
Science
description There have been speculations that the severe air pollution experienced in North China was the act of meteorological change in general and a decreasing northerly wind in particular. We conduct a retrospective analysis on 1979–2016 reanalysis data from ERA‐Interim of European Centre for Medium‐Range Weather Forecasts over a region in North China to detect meteorological changes over the 38 years. No significant reduction in the northerly wind within the mixing layer is detected. Statistically significant increases are detected in the surface temperature, boundary layer height and dissipation, and significant decreases in relative humidity in the region between the first and second 19‐year periods from 1979 to 2016. We build regression models of PM2.5 on the meteorological variables using data in 2014, 2015, and 2016 to quantify effects of the meteorological changes between the two 19‐year periods on PM2.5 under the emission scenarios of 2014–2016. It is found that despite the warming, dew point temperature had been largely kept under control as the region had gotten dryer. This made the effects of temperature warming largely favorable to PM2.5 reduction as it enhances boundary layer height and dissipation. It is found that the meteorological changes would lead to 1.29% to 2.76% reduction in annual PM2.5 averages with January, March, and December having more than 4% reduction in the 3 years. Thus, the meteorological change in North China had helped alleviate PM2.5 to certain extent and should not be held responsible for the regional air pollution problem.Key PointsMeteorological changes led to 1.9% to 2.7 percent% reduction in annual PM2.5 averages over North China 2014 to 2016 driven by temperature warmingSignificant increases are detected in the surface temperature, boundary layer height, and dissipation and decreases in relative humidityThe meteorological change should not be held responsible for the regional air pollution problem in North China Peer Reviewed ...
format Article in Journal/Newspaper
author Xu, Ziping
Chen, Song Xi
Wu, Xiaoqing
author_facet Xu, Ziping
Chen, Song Xi
Wu, Xiaoqing
author_sort Xu, Ziping
title Meteorological Change and Impacts on Air Pollution: Results From North China
title_short Meteorological Change and Impacts on Air Pollution: Results From North China
title_full Meteorological Change and Impacts on Air Pollution: Results From North China
title_fullStr Meteorological Change and Impacts on Air Pollution: Results From North China
title_full_unstemmed Meteorological Change and Impacts on Air Pollution: Results From North China
title_sort meteorological change and impacts on air pollution: results from north china
publisher Springer Science
publishDate 2020
url https://hdl.handle.net/2027.42/156450
https://doi.org/10.1029/2020JD032423
genre Arctic
genre_facet Arctic
op_relation Xu, Ziping; Chen, Song Xi; Wu, Xiaoqing (2020). "Meteorological Change and Impacts on Air Pollution: Results From North China." Journal of Geophysical Research: Atmospheres 125(16): n/a-n/a.
2169-897X
2169-8996
https://hdl.handle.net/2027.42/156450
doi:10.1029/2020JD032423
Journal of Geophysical Research: Atmospheres
Tai, A. P., Mickley, L. J., Jacob, D. J., Leibensperger, E., Zhang, L., Fisher, J. A., & Pye, H. ( 2012 ). Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: Implications for PM2.5 sensitivity to climate change. Atmospheric Chemistry and Physics, 12 ( 6 ), 3131 – 3145. https://doi.org/10.5194/acp‐12‐3131‐2012
Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H., & Chen, S. X. ( 2015 ). Assessing Beijing’s PM2.5 pollution: Severity, weather impact, APEC and winter heating. Proceedings of the Royal Society A, 471, 20150257. https://doi.org/10.1098/rspa.2015.0257
Long, X., Bei, N., Wu, J., Li, X., Tian, F., Li, X., Zhao, S., Cao, J., Tie, X., & An, Z. ( 2018 ). Does afforestation deteriorate haze pollution in Beijing–Tianjin–Hebei (BTH), China?. Atmospheric Chemistry and Physics, 18 ( 15 ), 10,869. https://doi.org/10.5194/acp‐18‐10869‐2018
Miao, Y., Hu, X., Liu, S., Qian, T., Xue, M., Zheng, Y., & Wang, S. ( 2015 ). Seasonal variation of local atmospheric circulations and boundary layer structure in the Beijing‐Tianjin‐Hebei region and implications for air quality. Journal of Advances in Modeling Earth Systems, 7, 1602 – 1626. https://doi.org/10.1002/2015MS000522
Pendergrass, D., Shen, L., Jacob, D., & Mickley, L. ( 2019 ). Predicting the impact of climate change on severe wintertime particulate pollution events in Beijing using extreme value theory. Geophysical Research Letters, 46, 1824 – 1830. https://doi.org/10.1029/2018GL080102
Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. ( 2002 ). Lung cancer, cardiopulmonary mortality, and long‐term exposure to fine particulate air pollution. Jama, 287 ( 9 ), 1132 – 1141.
Reinsel, G. C., Miller, A. J., Weatherhead, E. C., Flynn, L. E., Nagatani, R. M., Tiao, G. C., & Wuebbles, D. J. ( 2005 ). Trend analysis of total ozone data for turnaround and dynamical contributions. Journal of Geophysical Research, 110, D16306. https://doi.org/10.1029/2004JD004662
Schwartz, J. ( 2000 ). The distributed lag between air pollution and daily deaths. Epidemiology, 11 ( 3 ), 320 – 326.
Shen, L., Jacob, D. J., Mickley, L. J., Wang, Y., & Zhang, Q. ( 2018 ). Insignificant effect of climate change on winter haze pollution in Beijing. Atmospheric Chemistry and Physics, 18 ( 23 ), 17,489 – 17,496.
Tagaris, E., Manomaiphiboon, K., Liao, K.‐J., Leung, L. R., Woo, J.‐H., He, S., Amar, P., & Russell, A. G. ( 2007 ). Impacts of global climate change and emissions on regional ozone and fine particulate matter concentrations over the United States. Journal of Geophysical Research, 112, D14312. https://doi.org/10.1029/2006JD008262
Tai, A. P., Mickley, L. J., & Jacob, D. J. ( 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
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. ( 2012 ). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93 ( 4 ), 485 – 498. https://doi.org/10.1175/BAMS‐D‐11‐00094.1
Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., & Sulprizio, M. P. ( 2016 ). Why do models overestimate surface ozone in the southeast United States? Atmospheric Chemistry and Physics, 16 ( 21 ), 13,561 – 13,577. https://doi.org/10.5194/acp‐16‐13561‐2016
Wang, H., Chen, H., & Liu, J. ( 2015 ). Arctic sea ice decline intensified haze pollution in eastern China. Atmospheric and Oceanic Science Letters, 8 ( 1 ), 1 – 9.
Wang, X., Wu, J., Chen, M., Xu, X., Wang, Z., Wang, B., Wang, C., Piao, S., Lin, W., & Miao, G. ( 2018 ). Field evidences for the positive effects of aerosols on tree growth. Global Change Biology, 24, 4983 – 4992. https://doi.org/10.1111/gcb.14339
Wuerch, D., Courtois, A., Ewald, C., & Ernct, G. ( 1972 ). A preliminary transport wind and mixing height climatology for St. Louis, Missouri. National Oceanic and Atmospheric Administration Technical Memorandum National Weather Service Central Region, 49, 13.
Xu, P., Chen, Y., & Ye, X. ( 2013 ). Haze, air pollution, and health in China. The Lancet, 382 ( 9910 ), 2067. https://doi.org/10.1016/S0140‐6736(13)62693‐8
Yan, L. ( 2015 ). Influence of wind power development on the heavy haze in Beijing, Tianjin and Hebei (in Chinese). Environmental Protection and Circular Economy, 2015.09, 67 – 71.
Yin, Z., & Wang, H. ( 2016 ). The relationship between the subtropical western pacific SST and haze over north‐central north China plain. International Journal of Climatology, 36 ( 10 ), 3479 – 3491.
Yin, Z., & Wang, H. ( 2017 ). Role of atmospheric circulations in haze pollution in December 2016. Atmospheric Chemistry and Physics, 17 ( 18 ), 11,673.
Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., & Chen, S. X. ( 2017 ). Cautionary tales on air‐quality improvement in Beijing. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473, 20170457. https://doi.org/10.6084/m9.figshare.c.3865483
Zheng, G., Duan, F., Su, H., Ma, Y., Cheng, Y., Zheng, B., Zhang, Q., Huang, T., Kimoto, T., & Chang, D. ( 2015 ). Exploring the severe winter haze in Beijing: The impact of synoptic weather, regional transport and heterogeneous reactions. Atmospheric Chemistry and Physics, 15 ( 6 ), 2969 – 2983. https://doi.org/10.1175/2010JCLI3850.1
Avise, J., Chen, J., Lamb, B., Wiedinmyer, C., Guenther, A., Salathé, E, & Mass, C. ( 2009 ). Attribution of projected changes in summertime us ozone and PM 2.5 concentrations to global changes. Atmospheric Chemistry and Physics, 9 ( 4 ), 1111 – 1124. https://doi.org/10.5194/acp‐9‐1111‐2009
Bühlmann, P. ( 2002 ). Bootstraps for time series. Statistical Science, 17 ( 1 ), 52 – 72.
Benjamini, Y., & Hochberg, Y. ( 1995 ). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, 57 ( 1 ), 289 – 300.
Bojkov, R., Bishop, L., Hill, W., Reinsel, G., & Tiao, G. ( 1990 ). A statistical trend analysis of revised Dobson total ozone data over the Northern Hemisphere. Journal of Geophysical Research, 95 ( D7 ), 9785 – 9807. https://doi.org/10.1029/JD095iD07p09785
Bosq, D. ( 1998 ). Nonparametric statistics for stochastic processes: Estimation and prediction (Vol. 110 ). New York, NY: Springer Science.
Brockwell, P. J., & Davis, R. A. ( 2013 ). Time series: Theory and methods, Springer Series in Statistics. New York, NY: Springer Science & Business Media.
Cai, W., Li, K., Liao, H., Wang, H., & Wu, L. ( 2017 ). Weather conditions conducive to Beijing severe haze more frequent under climate change. Nature Climate Change, 7 ( 4 ), 257.
Chen, L., Guo, B., Huang, J., He, J., Wang, H., Zhang, S., & Chen, S. X. ( 2018 ). Assessing air‐quality in Beijing‐Tianjin‐Hebei region: The method and mixed tales of PM2. 5 and O 3. Atmospheric Environment, 193, 290 – 301.
Chen, S. X., & Tang, C. ( 2005 ). Nonparametric inference of value‐at‐risk for dependent financial returns. Journal of Financial Econometrics, 3 ( 2 ), 227 – 255. https://doi.org/10.1093/jjfinec/nbi012
Chen, H., & Wang, H. ( 2015 ). Haze days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012. Journal of Geophysical Research: Atmospheres, 120, 5895 – 5909. https://doi.org/10.1002/2015JD023225
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Cheng, M. ( 2016 ). Beijing haze is rooted in wind attenuation (in Chinese). China ScienceNet, http://news.sciencenet.cn
Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., & Bauer, P. ( 2011 ). The ERA‐interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137 ( 656 ), 553 – 597. https://doi.org/10.1002/qj.828
Donaldson, K., Li, X., & MacNee, W. ( 1998 ). Ultrafine (nanometre) particle mediated lung injury. Journal of Aerosol Science, 29 ( 5‐6 ), 553 – 560.
Egan, B. A., & Mahoney, J. R. ( 1972 ). Numerical modeling of advection and diffusion of urban area source pollutants. Journal of Applied Meteorology, 11 ( 2 ), 312 – 322. https://doi.org/10.1175/1520‐0450(1972)011h0312:NMOAADi2.0.CO;2
Fan, J., & Yao, Q. ( 2008 ). Nonlinear time series: Nonparametric and parametric methods, Springer Series in Statistics. New York, NY: Springer‐Verlag.
Holzworth, G. C. ( 1972 ). Mixing heights, wind speeds, and potential for urban air pollution throughout the contiguous United States. In United States Environmental Protection Agency publication (Vol. 101, p. 118 ). NC: Research Triangle Park.
Jacob, D. J., & Winner, D. A. ( 2009 ). Effect of climate change on air quality. Atmospheric Environment, 43 ( 1 ), 51 – 63. https://doi.org/10.1016/j.atmosenv.2008.09.051
Küunsch, H. R. ( 1989 ). The jackknife and the bootstrap for general stationary observations. The Annals of Statistics, 17 ( 3 ), 1217 – 1241.
Leung, D. M., Tai, A. P., Mickley, L. J., Moch, J. M., Donkelaar, Aaronvan, Shen, L., & Martin, R. V. ( 2018 ). Synoptic meteorological modes of variability for fine particulate matter (PM 2.5) air quality in major Metropolitan regions of China. Atmospheric Chemistry and Physics, 18 ( 9 ), 6733 – 6748.
Liang, X., Li, S., Zhang, S., Huang, H., & Chen, S. X. ( 2016 ). PM2.5 data reliability, consistency, and air quality assessment in five Chinese cities. Journal of Geophysical Research: Atmospheres, 121, 10,220 – 10,236. https://doi.org/10.1002/2016JD024877
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/156450 2024-09-15T17:52:05+00:00 Meteorological Change and Impacts on Air Pollution: Results From North China Xu, Ziping Chen, Song Xi Wu, Xiaoqing 2020-08-27 application/pdf https://hdl.handle.net/2027.42/156450 https://doi.org/10.1029/2020JD032423 unknown Springer Science Wiley Periodicals, Inc. Xu, Ziping; Chen, Song Xi; Wu, Xiaoqing (2020). "Meteorological Change and Impacts on Air Pollution: Results From North China." Journal of Geophysical Research: Atmospheres 125(16): n/a-n/a. 2169-897X 2169-8996 https://hdl.handle.net/2027.42/156450 doi:10.1029/2020JD032423 Journal of Geophysical Research: Atmospheres Tai, A. P., Mickley, L. J., Jacob, D. J., Leibensperger, E., Zhang, L., Fisher, J. A., & Pye, H. ( 2012 ). Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: Implications for PM2.5 sensitivity to climate change. Atmospheric Chemistry and Physics, 12 ( 6 ), 3131 – 3145. https://doi.org/10.5194/acp‐12‐3131‐2012 Liang, X., Zou, T., Guo, B., Li, S., Zhang, H., Zhang, S., Huang, H., & Chen, S. X. ( 2015 ). Assessing Beijing’s PM2.5 pollution: Severity, weather impact, APEC and winter heating. Proceedings of the Royal Society A, 471, 20150257. https://doi.org/10.1098/rspa.2015.0257 Long, X., Bei, N., Wu, J., Li, X., Tian, F., Li, X., Zhao, S., Cao, J., Tie, X., & An, Z. ( 2018 ). Does afforestation deteriorate haze pollution in Beijing–Tianjin–Hebei (BTH), China?. Atmospheric Chemistry and Physics, 18 ( 15 ), 10,869. https://doi.org/10.5194/acp‐18‐10869‐2018 Miao, Y., Hu, X., Liu, S., Qian, T., Xue, M., Zheng, Y., & Wang, S. ( 2015 ). Seasonal variation of local atmospheric circulations and boundary layer structure in the Beijing‐Tianjin‐Hebei region and implications for air quality. Journal of Advances in Modeling Earth Systems, 7, 1602 – 1626. https://doi.org/10.1002/2015MS000522 Pendergrass, D., Shen, L., Jacob, D., & Mickley, L. ( 2019 ). Predicting the impact of climate change on severe wintertime particulate pollution events in Beijing using extreme value theory. Geophysical Research Letters, 46, 1824 – 1830. https://doi.org/10.1029/2018GL080102 Pope III, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. ( 2002 ). Lung cancer, cardiopulmonary mortality, and long‐term exposure to fine particulate air pollution. Jama, 287 ( 9 ), 1132 – 1141. Reinsel, G. C., Miller, A. J., Weatherhead, E. C., Flynn, L. E., Nagatani, R. M., Tiao, G. C., & Wuebbles, D. J. ( 2005 ). Trend analysis of total ozone data for turnaround and dynamical contributions. Journal of Geophysical Research, 110, D16306. https://doi.org/10.1029/2004JD004662 Schwartz, J. ( 2000 ). The distributed lag between air pollution and daily deaths. Epidemiology, 11 ( 3 ), 320 – 326. Shen, L., Jacob, D. J., Mickley, L. J., Wang, Y., & Zhang, Q. ( 2018 ). Insignificant effect of climate change on winter haze pollution in Beijing. Atmospheric Chemistry and Physics, 18 ( 23 ), 17,489 – 17,496. Tagaris, E., Manomaiphiboon, K., Liao, K.‐J., Leung, L. R., Woo, J.‐H., He, S., Amar, P., & Russell, A. G. ( 2007 ). Impacts of global climate change and emissions on regional ozone and fine particulate matter concentrations over the United States. Journal of Geophysical Research, 112, D14312. https://doi.org/10.1029/2006JD008262 Tai, A. P., Mickley, L. J., & Jacob, D. J. ( 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 Taylor, K. E., Stouffer, R. J., & Meehl, G. A. ( 2012 ). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93 ( 4 ), 485 – 498. https://doi.org/10.1175/BAMS‐D‐11‐00094.1 Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., & Sulprizio, M. P. ( 2016 ). Why do models overestimate surface ozone in the southeast United States? Atmospheric Chemistry and Physics, 16 ( 21 ), 13,561 – 13,577. https://doi.org/10.5194/acp‐16‐13561‐2016 Wang, H., Chen, H., & Liu, J. ( 2015 ). Arctic sea ice decline intensified haze pollution in eastern China. Atmospheric and Oceanic Science Letters, 8 ( 1 ), 1 – 9. Wang, X., Wu, J., Chen, M., Xu, X., Wang, Z., Wang, B., Wang, C., Piao, S., Lin, W., & Miao, G. ( 2018 ). Field evidences for the positive effects of aerosols on tree growth. Global Change Biology, 24, 4983 – 4992. https://doi.org/10.1111/gcb.14339 Wuerch, D., Courtois, A., Ewald, C., & Ernct, G. ( 1972 ). A preliminary transport wind and mixing height climatology for St. Louis, Missouri. National Oceanic and Atmospheric Administration Technical Memorandum National Weather Service Central Region, 49, 13. Xu, P., Chen, Y., & Ye, X. ( 2013 ). Haze, air pollution, and health in China. The Lancet, 382 ( 9910 ), 2067. https://doi.org/10.1016/S0140‐6736(13)62693‐8 Yan, L. ( 2015 ). Influence of wind power development on the heavy haze in Beijing, Tianjin and Hebei (in Chinese). Environmental Protection and Circular Economy, 2015.09, 67 – 71. Yin, Z., & Wang, H. ( 2016 ). The relationship between the subtropical western pacific SST and haze over north‐central north China plain. International Journal of Climatology, 36 ( 10 ), 3479 – 3491. Yin, Z., & Wang, H. ( 2017 ). Role of atmospheric circulations in haze pollution in December 2016. Atmospheric Chemistry and Physics, 17 ( 18 ), 11,673. Zhang, S., Guo, B., Dong, A., He, J., Xu, Z., & Chen, S. X. ( 2017 ). Cautionary tales on air‐quality improvement in Beijing. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 473, 20170457. https://doi.org/10.6084/m9.figshare.c.3865483 Zheng, G., Duan, F., Su, H., Ma, Y., Cheng, Y., Zheng, B., Zhang, Q., Huang, T., Kimoto, T., & Chang, D. ( 2015 ). Exploring the severe winter haze in Beijing: The impact of synoptic weather, regional transport and heterogeneous reactions. Atmospheric Chemistry and Physics, 15 ( 6 ), 2969 – 2983. https://doi.org/10.1175/2010JCLI3850.1 Avise, J., Chen, J., Lamb, B., Wiedinmyer, C., Guenther, A., Salathé, E, & Mass, C. ( 2009 ). Attribution of projected changes in summertime us ozone and PM 2.5 concentrations to global changes. Atmospheric Chemistry and Physics, 9 ( 4 ), 1111 – 1124. https://doi.org/10.5194/acp‐9‐1111‐2009 Bühlmann, P. ( 2002 ). Bootstraps for time series. Statistical Science, 17 ( 1 ), 52 – 72. Benjamini, Y., & Hochberg, Y. ( 1995 ). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B, 57 ( 1 ), 289 – 300. Bojkov, R., Bishop, L., Hill, W., Reinsel, G., & Tiao, G. ( 1990 ). A statistical trend analysis of revised Dobson total ozone data over the Northern Hemisphere. Journal of Geophysical Research, 95 ( D7 ), 9785 – 9807. https://doi.org/10.1029/JD095iD07p09785 Bosq, D. ( 1998 ). Nonparametric statistics for stochastic processes: Estimation and prediction (Vol. 110 ). New York, NY: Springer Science. Brockwell, P. J., & Davis, R. A. ( 2013 ). Time series: Theory and methods, Springer Series in Statistics. New York, NY: Springer Science & Business Media. Cai, W., Li, K., Liao, H., Wang, H., & Wu, L. ( 2017 ). Weather conditions conducive to Beijing severe haze more frequent under climate change. Nature Climate Change, 7 ( 4 ), 257. Chen, L., Guo, B., Huang, J., He, J., Wang, H., Zhang, S., & Chen, S. X. ( 2018 ). Assessing air‐quality in Beijing‐Tianjin‐Hebei region: The method and mixed tales of PM2. 5 and O 3. Atmospheric Environment, 193, 290 – 301. Chen, S. X., & Tang, C. ( 2005 ). Nonparametric inference of value‐at‐risk for dependent financial returns. Journal of Financial Econometrics, 3 ( 2 ), 227 – 255. https://doi.org/10.1093/jjfinec/nbi012 Chen, H., & Wang, H. ( 2015 ). Haze days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012. Journal of Geophysical Research: Atmospheres, 120, 5895 – 5909. https://doi.org/10.1002/2015JD023225 Chen, R., Zhao, Z., & Kan, H. ( 2013 ). Heavy smog and hospital visits in Beijing, China. American Journal of Respiratory and Critical Care Medicine, 188 ( 9 ), 1170 – 1171. Cheng, M. ( 2016 ). Beijing haze is rooted in wind attenuation (in Chinese). China ScienceNet, http://news.sciencenet.cn Dee, D. P., Uppala, S., Simmons, A., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M., Balsamo, G., & Bauer, P. ( 2011 ). The ERA‐interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137 ( 656 ), 553 – 597. https://doi.org/10.1002/qj.828 Donaldson, K., Li, X., & MacNee, W. ( 1998 ). Ultrafine (nanometre) particle mediated lung injury. Journal of Aerosol Science, 29 ( 5‐6 ), 553 – 560. Egan, B. A., & Mahoney, J. R. ( 1972 ). Numerical modeling of advection and diffusion of urban area source pollutants. Journal of Applied Meteorology, 11 ( 2 ), 312 – 322. https://doi.org/10.1175/1520‐0450(1972)011h0312:NMOAADi2.0.CO;2 Fan, J., & Yao, Q. ( 2008 ). Nonlinear time series: Nonparametric and parametric methods, Springer Series in Statistics. New York, NY: Springer‐Verlag. Holzworth, G. C. ( 1972 ). Mixing heights, wind speeds, and potential for urban air pollution throughout the contiguous United States. In United States Environmental Protection Agency publication (Vol. 101, p. 118 ). NC: Research Triangle Park. Jacob, D. J., & Winner, D. A. ( 2009 ). Effect of climate change on air quality. Atmospheric Environment, 43 ( 1 ), 51 – 63. https://doi.org/10.1016/j.atmosenv.2008.09.051 Küunsch, H. R. ( 1989 ). The jackknife and the bootstrap for general stationary observations. The Annals of Statistics, 17 ( 3 ), 1217 – 1241. Leung, D. M., Tai, A. P., Mickley, L. J., Moch, J. M., Donkelaar, Aaronvan, Shen, L., & Martin, R. V. ( 2018 ). Synoptic meteorological modes of variability for fine particulate matter (PM 2.5) air quality in major Metropolitan regions of China. Atmospheric Chemistry and Physics, 18 ( 9 ), 6733 – 6748. Liang, X., Li, S., Zhang, S., Huang, H., & Chen, S. X. ( 2016 ). PM2.5 data reliability, consistency, and air quality assessment in five Chinese cities. Journal of Geophysical Research: Atmospheres, 121, 10,220 – 10,236. https://doi.org/10.1002/2016JD024877 IndexNoFollow hypothesis testing North China air pollution meteorological change Atmospheric and Oceanic Sciences Science Article 2020 ftumdeepblue https://doi.org/10.1029/2020JD032423 2024-07-30T04:06:07Z There have been speculations that the severe air pollution experienced in North China was the act of meteorological change in general and a decreasing northerly wind in particular. We conduct a retrospective analysis on 1979–2016 reanalysis data from ERA‐Interim of European Centre for Medium‐Range Weather Forecasts over a region in North China to detect meteorological changes over the 38 years. No significant reduction in the northerly wind within the mixing layer is detected. Statistically significant increases are detected in the surface temperature, boundary layer height and dissipation, and significant decreases in relative humidity in the region between the first and second 19‐year periods from 1979 to 2016. We build regression models of PM2.5 on the meteorological variables using data in 2014, 2015, and 2016 to quantify effects of the meteorological changes between the two 19‐year periods on PM2.5 under the emission scenarios of 2014–2016. It is found that despite the warming, dew point temperature had been largely kept under control as the region had gotten dryer. This made the effects of temperature warming largely favorable to PM2.5 reduction as it enhances boundary layer height and dissipation. It is found that the meteorological changes would lead to 1.29% to 2.76% reduction in annual PM2.5 averages with January, March, and December having more than 4% reduction in the 3 years. Thus, the meteorological change in North China had helped alleviate PM2.5 to certain extent and should not be held responsible for the regional air pollution problem.Key PointsMeteorological changes led to 1.9% to 2.7 percent% reduction in annual PM2.5 averages over North China 2014 to 2016 driven by temperature warmingSignificant increases are detected in the surface temperature, boundary layer height, and dissipation and decreases in relative humidityThe meteorological change should not be held responsible for the regional air pollution problem in North China Peer Reviewed ... Article in Journal/Newspaper Arctic University of Michigan: Deep Blue Journal of Geophysical Research: Atmospheres 125 16

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