Articles | Volume 25, issue 7
https://doi.org/10.5194/acp-25-4211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-4211-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Apr 2025
Research article |
| 14 Apr 2025
| 14 Apr 2025
Impacts of irrigation on ozone and fine particulate matter (PM2.5) air quality: implications for emission control strategies for intensively irrigated regions in China
Tiangang Yuan
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China
Tzung-May Fu
Coastal Atmosphere and Climate of the Greater Bay Area Observation and Research Station of Guangdong Province, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
Aoxing Zhang
Coastal Atmosphere and Climate of the Greater Bay Area Observation and Research Station of Guangdong Province, Southern University of Science and Technology, Shenzhen, Guangdong, 518055, China
David H. Y. Yung
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China
Jin Wu
School of Biological Sciences, The University of Hong Kong, Hong Kong SAR, China
Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China
State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
Sien Li
Center for Agricultural Water Research in China, China Agricultural University, Beijing, 100083, China
Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China
State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Hong Kong SAR, China
Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong SAR, China
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Jiachen Zhu, Amos P. K. Tai, and Steve Hung Lam Yim
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Xueying Liu, Amos P. K. Tai, and Ka Ming Fung
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Xu Feng, Haipeng Lin, Tzung-May Fu, Melissa P. Sulprizio, Jiawei Zhuang, Daniel J. Jacob, Heng Tian, Yaping Ma, Lijuan Zhang, Xiaolin Wang, Qi Chen, and Zhiwei Han
Geosci. Model Dev., 14, 3741–3768, https://doi.org/10.5194/gmd-14-3741-2021, https://doi.org/10.5194/gmd-14-3741-2021, 2021
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Felix Leung, Karina Williams, Stephen Sitch, Amos P. K. Tai, Andy Wiltshire, Jemma Gornall, Elizabeth A. Ainsworth, Timothy Arkebauer, and David Scoby
Geosci. Model Dev., 13, 6201–6213, https://doi.org/10.5194/gmd-13-6201-2020, https://doi.org/10.5194/gmd-13-6201-2020, 2020
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Lang Wang, Amos P. K. Tai, Chi-Yung Tam, Mehliyar Sadiq, Peng Wang, and Kevin K. W. Cheung
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We investigate the effects of future land use and land cover change (LULCC) on surface ozone air quality worldwide and find that LULCC can significantly influence ozone in North America and Europe via modifying surface energy balance, boundary-layer meteorology, and regional circulation. The strength of such “biogeophysical effects” of LULCC is strongly dependent on forest type and generally greater than the “biogeochemical effects” via changing deposition and emission fluxes alone.
Cited articles
Abramoff, R. Z., Ciais, P., Zhu, P., Hasegawa, T., Wakatsuki, H., and Makowski, D.: Adaptation strategies strongly reduce the future impacts of climate change on simulated crop yields, Earths Future, 11, e2022EF003190, https://doi.org/10.1029/2022EF003190, 2023.
Alexander, B., Sherwen, T., Holmes, C. D., Fisher, J. A., Chen, Q., Evans, M. J., and Kasibhatla, P.: Global inorganic nitrate production mechanisms: comparison of a global model with nitrate isotope observations, Atmos. Chem. Phys., 20, 3859–3877, https://doi.org/10.5194/acp-20-3859-2020, 2020.
An, Z., Huang, R. J., Zhang, R., Tie, X., Li, G., Cao, J., Zhou, W., Shi, Z., Han, Y., Gu, Z., and Ji, Y.: Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes, P. Natl. Acad. Sci. USA, 116, 8657–8666, https://doi.org/10.1073/pnas.1900125116, 2019.
Bavi, A., Kashkuli, H. A., Boroomand, S., Naseri, A., and Albaji, M.: Evaporation losses from sprinkler irrigation systems under various operating conditions, J. Appl. Sci., 9, 597–600, https://doi.org/10.3923/jas.2009.597.600, 2009.
Beguería, S., Vicente-Serrano, S. M., and Angulo-Martínez, M. A.: Multiscalar global drought dataset: the SPEIbase: a new gridded product for the analysis of drought variability and impacts, B. Am. Meteorol. Soc., 91, 1351–1356, https://doi.org/10.1175/2010BAMS2988.1, 2010.
Chen, Z., Chen, D., Zhao, C., Kwan, M.-p., Cai, J., Zhuang, Y., Zhao, B.o., Wang, X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., and Xu, B.: 2020. Influence of meteorological conditions on PM2.5 concentrations across China: a review of methodology and mechanism, Environ. Int. 139, 105558, https://doi.org/10.1016/j.envint.2020.105558, 2020.
Chen, Z. Y., Zhuang, Y., Xie, X., Chen, D., Cheng, N., Yang, L., and Li, R.: Understanding long-term variations of meteorological influences on ground ozone concentrations in Beijing during 2006–2016, Environ. Pollut., 245, 29–37, https://doi.org/10.1016/j.envpol.2018.10.117, 2019.
Cheng, J., Su, J., Cui, T., Li, X., Dong, X., Sun, F., Yang, Y., Tong, D., Zheng, Y., Li, Y., Li, J., Zhang, Q., and He, K.: Dominant role of emission reduction in PM2.5 air quality improvement in Beijing during 2013–2017: a model-based decomposition analysis, Atmos. Chem. Phys., 19, 6125–6146, https://doi.org/10.5194/acp-19-6125-2019, 2019.
Cook, B., Shukla, S. P., Puma, M. J., and Nazarenko, L. S.: Irrigation as an historical climate forcing, Clim. Dynam., 44, 1715–1730, https://doi.org/10.1007/s00382-014-2204-7, 2015.
Currell, M. J., Han, D., Chen, Z., and Cartwright, I.: Sustainability of groundwater usage in northern China: dependence on palaeowaters and effects on water quality, quantity and ecosystem health, Hydrol. Processes, 26, 4050–4066, https://doi.org/10.1002/hyp.9208, 2012.
Dang, R., Liao, H., and Fu, Y.: Quantifying the anthropogenic and meteorological influences on summertime surface ozone in China over 2012–2017, Sci. Total Environ., 754, 142394, https://doi.org/10.1016/j.scitotenv.2020.142394, 2021.
Feng, X., Lin, H., Fu, T.-M., Sulprizio, M. P., Zhuang, J., Jacob, D. J., Tian, H., Ma, Y., Zhang, L., Wang, X., Chen, Q., and Han, Z.: WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions, Geosci. Model Dev., 14, 3741–3768, https://doi.org/10.5194/gmd-14-3741-2021, 2021 (code available at: https://github.com/WRF-GC/wrf-gc-release, last access: 1 May 2024).
Grafton, R. Q., Williams, J., Perry., C. J., Molle, F., Rinhler, C., Steduto, P., Udall, B., Wheeler, S. A. Wang, Y., Garrick, D., and Allen, R. G.: The paradox of irrigation efficiency, Science, 361, 748–750, https://doi.org/10.1126/science.aat9314, 2018.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Han, H., Liu, J., Shu, L., Wang, T., and Yuan, H.: Local and synoptic meteorological influences on daily variability in summertime surface ozone in eastern China, Atmos. Chem. Phys., 20, 203–222, https://doi.org/10.5194/acp-20-203-2020, 2020a.
Han, S., Tian, F., and Gao, L.: Current status and recent trend of irrigation water use in China, Irrig. Drain., 69, 25–35, https://doi.org/10.1002/ird.2441, 2020b.
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: The Community Noah-MP Land Surface Modeling System Technical Description Version 5.0, NCAR Technical Note, NCAR/TN-575+STR, https://doi.org/10.5065/ew8g-yr95, 2023.
Hodzic, A. and Jimenez, J. L.: Modeling anthropogenically controlled secondary organic aerosols in a megacity: a simplified framework for global and climate models, Geosci. Model Dev., 4, 901–917, https://doi.org/10.5194/gmd-4-901-2011, 2011.
Huang, X., Ding, A., Gao, J., Zheng, B., Zhou, D., Qi, X., Tang, R., Wang, J., Ren, C., Nie, W., Chi, X., Xu, Z., Chen, L., Li, Y., Che, F., Pang, N., Wang, H., Tong, D., Qin, W., Cheng, W., Liu, W., Fu, Q., Liu, B., Chai, F., Davis, S., Zhang, Q., and He, K.: Enhanced secondary pollution offset reduction of primary emissions during COVID-19 lockdown in China, Natl. Sci. Rev., 8, nwaa137, https://doi.org/10.1093/nsr/nwaa137, 2021.
Hudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R., Valin, L. C., and Cohen, R. C.: Steps towards a mechanistic model of global soil nitric oxide emissions: implementation and space based-constraints, Atmos. Chem. Phys., 12, 7779–7795, https://doi.org/10.5194/acp-12-7779-2012, 2012.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by longlived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp. https://doi.org/10.1017/9781009157896, 2021.
Jacob, D. J. and Winner, D. A.: Effect of climate change on air quality, Atmos. Environ., 43, 51–63, https://doi.org/10.1016/j.atmosenv.2008.09.051, 2009.
Jacobson, M.: Studying the effects of soil moisture on ozone, temperatures, and winds in Los Angeles, J. Appl. Meteorol., 38, 607–616, https://doi.org/10.1016/S1352-2310(96)00201-4, 1999.
Jacobson, M.: Short-term effects of agriculture on air pollutant and climate in California, J. Geophys. Res., 113, D23101, https://doi.org/10.1029/2008JD010689, 2008.
Jia, K., Liang, S. L., Wei, X. Q., Yao, Y. J., Yang, L. Q., Zhang, X. T., and Liu, D. Y.: Validation of Global Land Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region, Remote Sens. Lett., 9, 847–856, https://doi.org/10.1080/2150704X.2018.1484958, 2018.
Kanamaru, H. and Kanamitsu, M.: Model diagnosis of nighttime minimum temperature warming during summer due to irrigation in the California Central Valley, J. Hydrometeorol., 9, 1061–1072, https://doi.org/10.1175/2008JHM967.1, 2008.
Kim, P. S., Jacob, D. J., Fisher, J. A., Travis, K., Yu, K., Zhu, L., Yantosca, R. M., Sulprizio, M. P., Jimenez, J. L., Campuzano-Jost, P., Froyd, K. D., Liao, J., Hair, J. W., Fenn, M. A., Butler, C. F., Wagner, N. L., Gordon, T. D., Welti, A., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Teng, A. P., Millet, D. B., Schwarz, J. P., Markovic, M. Z., and Perring, A. E.: Sources, seasonality, and trends of southeast US aerosol: an integrated analysis of surface, aircraft, and satellite observations with the GEOS-Chem chemical transport model, Atmos. Chem. Phys., 15, 10411–10433, https://doi.org/10.5194/acp-15-10411-2015, 2015.
Lawston, P. M., Santanello Jr., J. A., Zaitchik, B. F., and Rodell, M.: Impact of irrigation methods on land surface model spinup and initialization of WRF forecasts, J. Hydrometeorol., 16, 1135–1154, https://doi.org/10.1175/JHM-D-14-0203.1, 2015.
Le, T., Wang, Y., Liu, L., Yang, J., Yung, Y. L., Li, G., and Seinfeld, J. H.: Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China, Science, 369, 702–706, https://doi.org/10.1126/science.abb7431, 2020.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, https://doi.org/10.1038/nature15371, 2015.
Leung, D. M., Tai, A. P. K., Mickley, L. J., Moch, J. M., van Donkelaar, A., Shen, L., and Martin, R. V.: Synoptic meteorological modes of variability for fine particulate matter (PM2.5) air quality in major metropolitan regions of China, Atmos. Chem. Phys., 18, 6733–6748, https://doi.org/10.5194/acp-18-6733-2018, 2018.
Leng, G., Leung, L. R., and Huang, M.: Significant impacts of irrigation water sources and methods on modeling irrigation effects in the ACME Land Model, J. Adv. Model. Earth Syst., 9, 1665–1683, https://doi.org/10.1002/2016MS000885, 2017.
Li, J., Mahalov, A., and Hyde, P.: Impacts of agricultural irrigation on ozone concentrations in the Central Valley of California and in the contiguous United States based on WRF-Chem simulations, Agr. Forest Meteorol., 221, 34–49, https://doi.org/10.1016/j.agrformet.2016.02.004, 2016.
Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., and Bates, K. H.: Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China, P. Natl. Acad. Sci. USA, 116, 422–427, https://doi.org/10.1073/pnas.1812168116, 2019.
Li, K., Jacob, D. J., Shen, L., Lu, X., De Smedt, I., and Liao, H.: Increases in surface ozone pollution in China from 2013 to 2019: anthropogenic and meteorological influences, Atmos. Chem. Phys., 20, 11423–11433, https://doi.org/10.5194/acp-20-11423-2020, 2020.
Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017a.
Li, M., Liu, H., Geng, G., Hong, C., Liu, F., Song, Y., Tong, D., Zheng, B., Cui, H., Man, H., Zhang, Q., and He, K.: Anthropogenic emission inventories in China: a review, Natl. Sci. Rev., 4, 834–866, https://doi.org/10.1093/nsr/nwx150, 2017b.
Li, X. L., Lu, H., Yu, L., and Yang, K.: Comparison of the spatial characteristics of four remotely sensed leaf area index products over China: Direct validation and relative uncertainties, Remote Sens., 10, 26, https://doi.org/10.3390/RS10010148, 2018.
Liang, S., Li, X., Teng, Y., Fu, H., Chen, L., Mao, J., Zhang, H., Gao, S., Sun, Y., Ma, Z., and Azzi, M: Estimation of health and economic benefits based on ozone exposure level with high spatial-temporal resolution by fusing satellite and station observations, Environ Pollut., 255, 113267, https://doi.org/10.1016/j.envpol.2019.113267, 2019.
Liang, S., Cheng, J., Jia, K., Jiang, B., Liu, Q., Xiao, Z., Yao, Y., Yuan, W., Zhang, X., Zhao, X., and Zhou, J.: The global land surface satellite (GLASS) product suite, B. Am. Meteorol. Soc., 102, E323–E337, https://doi.org/10.1175/BAMS-D-18-0341.1, 2021.
Liao, D., Niu, J., Ciais, P., Du, T., Zhang, B., and Kang, S.: Changing climate threatens irrigation benefits of maize gross primary productivity in China, Earths Future, 12, e2022EF003474, https://doi.org/10.1029/2022EF003474, 2024.
Lin, H., Feng, X., Fu, T.-M., Tian, H., Ma, Y., Zhang, L., Jacob, D. J., Yantosca, R. M., Sulprizio, M. P., Lundgren, E. W., Zhuang, J., Zhang, Q., Lu, X., Zhang, L., Shen, L., Guo, J., Eastham, S. D., and Keller, C. A.: WRF-GC (v1.0): online coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.2.1) for regional atmospheric chemistry modeling – Part 1: Description of the one-way model, Geosci. Model Dev., 13, 3241–3265, https://doi.org/10.5194/gmd-13-3241-2020, 2020.
Liu, G., Wang, W., Shao, Q., Wei, J., Zheng, J., Liu, B., and Chen, Z.: Simulating the climatic effects of irrigation over China by using the WRF-Noah model system with mosaic approach, J. Geophys. Res.-Atmos., 126, e2020JD034428, https://doi.org/10.1029/2020JD034428, 2021a.
Liu, J., Kuang, W., Zhang, Z., Xu, X., Qin, X., Qin, Y., Ning, J., Zhou, W., Zhang, S., Li, R., Yan, C., Wu, S., Shi, X., Jiang, N., Yu, D., Pan, X., and Chi, W.: Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s, J. Geog. Sci., 24, 195–210, https://doi.org/10.1007/s11442-014-1082-6, 2014.
Liu, J., Jin, J., and Niu, G.-Y.: Effects of irrigation on seasonal and annual temperature and precipitation over China simulated by the WRF model, J. Geophys. Res.-Atmos., 126, e2020JD034222, https://doi.org/10.1029/2020JD034222, 2021b.
Liu, K., Bo, Y., Li, X., Wang, S., and Zhou, G.: Uncovering current and future variations of irrigation water use across China using machine learning, Earths Future, 12, e2023EF003562, https://doi.org/10.1029/2023EF003562, 2024a.
Liu, X., Chu, B., Tang, R., Liu, Y., Qiu, B., Gao, M., Li, X., Xiao, J., Sun Zhe, H., Huang, X., Desai, A. R., Ding, A., and Wang, H.: Air quality improvements can strengthen China's food security, Nat. Food, 5, 158–170, https://doi.org/10.1038/s43016-023-00882-y, 2024b.
Liu, Z., Zhou, M., Chen, Y., Chen, D., Pan, Y., Song, T., Ji, D., Chen, Q., and Zhang, L.: The nonlinear response of fine particulate matter pollution to ammonia emission reductions in North China, Environ. Res. Lett., 16, 034014, https://doi.org/10.1088/1748-9326/abdf86, 2021c.
Lobell, D. B., Bonfils, C. J., Kueppers, L. M., and Snyder, M. A.: Irrigation cooling effect on temperature and heat index extremes, Geophys. Res. Lett., 35, L09705, https://doi.org/10.1029/2008GL034145, 2008.
Lu, X., Hong, J., Zhang, L., Cooper, O. R., Schultz, M. G., Xu, X., Wang, T., Gao, M., Zhao, Y., and Zhang, Y.: Severe surface ozone pollution in China: a global perspective. Environ. Sci. Technol. Lett., 5, 487–494, https://doi.org/10.1021/acs.estlett.8b00366, 2018.
Lu, X., Zhang, L., and Shen, L.: Meteorology and climate influences on tropospheric ozone: a review of natural sources, chemistry, and transport patterns, Curr. Pollut. Rep., 5, 238–260, https://doi.org/10.1007/s40726-019-00118-3, 2019.
Mao, J., Tai, A. P. K., Yung, D. H. Y., Yuan, T., Chau, K. T., and Feng, Z.: Multidecadal ozone trends in China and implications for human health and crop yields: a hybrid approach combining a chemical transport model and machine learning, Atmos. Chem. Phys., 24, 345–366, https://doi.org/10.5194/acp-24-345-2024, 2024.
McDermid, S., Nocco, M., Lawston-Parker, P., Keune, J., Pokhrel, Y., Jain, M., Jägermeyr, J., Brocca, L., Massari, C., Jones, A. D., Vahmani, P., Thiery, W., Yao, Y., Bell, A., Chen, L., Dorigo, W., Hanasaki, N., Jasechko, S., Lo, M.-H., Mahmood, R., Mishra, V., Muller, N. D., Niyogi, D., Rabin, S. S., Sloat, L., Wada, Y., Zappa, L., Chen, F., Cook, B. I., Kim, H., Lombardozzi, D., Polcher, J., Ryu, D., Santanello, J., Satoh, Y., Seneviratne, S., Singh, D., and Yokohata, T.: Irrigation in the Earth system, Nat. Rev. Earth Environ., 4, 435–453, https://doi.org/10.1038/s43017-023-00438-5, 2023.
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009.
Nakanishi, M. and Niino, H.: An improved mellor-yamada level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local–scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Pye, H. O. T., Liao, H., Wu, S., Mickley, L. J., Jacob, D. J., Henze, D. K., and Seinfeld, J. H.: Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States, J. Geophys. Res.-Atmos., 114, D01205, https://doi.org/10.1029/2008JD010701, 2009.
Qi, X., Feng, K., Sun, L., Zhao, D., Huang, X., Zhang, D., Liu, Z., and Baiocchi, G.: Rising agricultural water scarcity in China is driven by expansion of irrigated cropland in water scarce regions, One Earth, 5, 1139–1152, https://doi.org/10.1016/j.oneear.2022.09.008, 2022.
Qian, J., Liao, H., Yang, Y., Li, K., Chen, L., and Zhu, J.: Meteorological influences on daily variation and trend of summertime surface ozone over years of 2015–2020: Quantification for cities in the Yangtze River Delta, Sci. Total Environ., 834, 155107, https://doi.org/10.1016/j.scitotenv.2022.155107, 2022.
Qian, Y., Yang, Z., Feng, Z., Liu, Y., Gustafson, W. I., Berg, L. K., Huang, M., Yang, B., and Ma, H. Y.: Neglecting irrigation contributes to the simulated summertime warm-and-dry bias in the central United States, npj Clim. Atmos. Sci., 3, 31, https://doi.org/10.1038/s41612-020-00135-w, 2020.
Randerson, J. T., van der Werf, G. R., Giglio, L., Collatz, G. J., and Kasibhatla, P. S.: Global Fire Emissions Database, Version 4, (GFEDv4), ORNL DAAC, Oak Ridge, Tennessee, USA., https://doi.org/10.3334/ORNLDAAC/1293, 2018.
Ren, J., Guo, F., and Xie, S.: Diagnosing ozone–NOx–VOC sensitivity and revealing causes of ozone increases in China based on 2013–2021 satellite retrievals, Atmos. Chem. Phys., 22, 15035–15047, https://doi.org/10.5194/acp-22-15035-2022, 2022.
Sacks, W. J., Cook, B. I., Buenning, N., Levis, S., and Helkowski, J. H.: Effects of global irrigation on the near-surface climate, Clim. Dynam., 33, 159–175, https://doi.org/10.1007/s00382-008-0445-z, 2009.
Shen, Y., Xiao, Z., Wang, Y., Xiao, W., Yao, L., and Zhou, C.: Impacts of agricultural soil NOx emissions on O3 over mainland China. J. Geophys. Res.-Atmos., 128, e2022JD037986, https://doi.org/10.1029/2022JD037986, 2023.
Shi, Z., Huang, L., Li, J., Ying, Q., Zhang, H., and Hu, J.: Sensitivity analysis of the surface ozone and fine particulate matter to meteorological parameters in China, Atmos. Chem. Phys., 20, 13455–13466, https://doi.org/10.5194/acp-20-13455-2020, 2020.
Shi, Z., Song, C., Liu, B., Lu, G., Xu, J., Van Vu, T., Elliott, R. J. R., Li, W., Bloss, W. J., and Harrison, R. M.: Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns, Sci. Adv., 7, eabd6696, https://doi.org/10.1126/sciadv.abd6696, 2021.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda, M. G., Hunag, X., Wang, W., and Powers, J. G.: A Description of the Advanced Research WRF Model Version 3, NCAR Tech. Note NCAR/TN-475+STR, https://doi.org/10.5065/D68S4MVH, 2008.
Song, C., Jiao, Y., Yang, W., Yu, Y., Zhang, J., and Liu, Y.: Research progress on the influence of irrigation methods on ammonia volatilization in farmland, IOP C. Ser. Earth Env., 647, 012170, https://doi.org/10.1088/1755-1315/647/1/012170, 2021.
Sorooshian, S., Li, J., Hsu, K., and Gao, X.: Influence of irrigation schemes used in RCMs on ET estimation: results and comparative studies from California's Central Valley agricultural regions, J. Geophys. Res., 117, D06107, https://doi.org/10.1029/2011JD016978, 2012.
Sorooshian, S., AghaKouchak, A., and Li, J.: Influence of irrigation on land hydrological processes over California, J. Geophys. Res.-Atmos., 119, 13137–13152, https://doi.org/10.1002/2014JD022232, 2014.
Sun, J., Liu, L., Xu, L., Wang, Y., Wu, Z., Hu, M., Shi, Z., Li, Y., Zhang, X., Chen, J., and Li, W.: Key role of nitrate in phase transitions of urban particles: Implications of important reactive surfaces for secondary aerosol formation. J. Geophys. Res.-Atmos., 123, 1234–1243, https://doi.org/10.1002/2017JD027264, 2018.
Sun, J., Qin, M., Xie, X., Fu, W., Qin, Y., Sheng, L., Li, L., Li, J., Sulaymon, I. D., Jiang, L., Huang, L., Yu, X., and Hu, J.: Seasonal modeling analysis of nitrate formation pathways in Yangtze River Delta region, China, Atmos. Chem. Phys., 22, 12629–12646, https://doi.org/10.5194/acp-22-12629-2022, 2022.
Tai, A. P., Mickley, L. J., and Jacob, D. J.: Correlations between fine particulate matter (PM2.5) and meteorological variables in the United States: Implications for the sensitivity of PM2.5 to climate change, Atmos. Environ., 44, 3976–3984, https://doi.org/10.1016/j.atmosenv.2010.06.060, 2010.
Tai, A. P., Martin, M. V., and Heald, C. L.: Threat to future global food security from climate change and ozone air pollution, Nat. Clim. Change, 4, 817–821, https://doi.org/10.1038/NCLIMATE2317, 2014.
Tai, A. P. K., Mickley, L. J., Jacob, D. J., Leibensperger, E. M., Zhang, L., Fisher, J. A., and Pye, H. O. T.: Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change, Atmos. Chem. Phys., 12, 3131–3145, https://doi.org/10.5194/acp-12-3131-2012, 2012.
Tie, X., Huang, R. J., Cao, J., Zhang, Q., Cheng, Y., Su, H., Chang, D., Pöschl, U., Hoffmann, T., Dusek, U., Li, G., Worsnop, D. R., and O'Dowd, C. D.: Severe Pollution in China Amplified by Atmospheric Moisture, Sci. Rep., 7, 15760, https://doi.org/10.1038/s41598-017-15909-1, 2017.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather. Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989.
Travis, K. R., Crawford, J. H., Chen, G., Jordan, C. E., Nault, B. A., Kim, H., Jimenez, J. L., Campuzano-Jost, P., Dibb, J. E., Woo, J.-H., Kim, Y., Zhai, S., Wang, X., McDuffie, E. E., Luo, G., Yu, F., Kim, S., Simpson, I. J., Blake, D. R., Chang, L., and Kim, M. J.: Limitations in representation of physical processes prevent successful simulation of PM2.5 during KORUS-AQ, Atmos. Chem. Phys., 22, 7933–7958, https://doi.org/10.5194/acp-22-7933-2022, 2022.
Wang, H., Huang, C., Tao, W., Gao, Y., Wang, S., Jing, S., Wang, W., Yan, R., Wang, Q., An, J., Tian, J., Hu, Q., Lou, S., Pöschl, U., Cheng, Y., and Su, H.: Seasonality and reduced nitric oxide titration dominated ozone increase during COVID-19 lockdown in eastern China, npj Clim. Atmos. Sci., 5, 24, https://doi.org/10.1038/s41612-022-00249-3, 2022a.
Wang, J., Xing, J., Mathur, R., Pleim, J. E., Wang, S., Hogrefe, C., Gan, C. M., Wong, D. C., and Hao, J.: Historical trends in PM2.5-related premature mortality during 1990–2010 across the northern hemisphere, Environ. Health Perspect., 125, 400–408, https://doi.org/10.1289/EHP298, 2016.
Wang, L., Zhao, B., Zhang, Y., and Hu, H.: Correlation between surface PM2.5 and O3 in eastern China during 2015–2019: Spatiotemporal variations and meteorological impacts, Atmos. Environ., 294, 119520, https://doi.org/10.1016/j.atmosenv.2022.119520, 2023.
Wang, T., Xue, L., Brimblecombe, P., Lam, Y. F., Li, L., and Zhang, L.: Ozone pollution in China: A review of concentrations, meteorological influences, chemical precursors, and effects, Sci. Total Environ., 575, 1582–1596, https://doi.org/10.1016/j.scitotenv.2016.10.081, 2017.
Wang, T., Xue, L., Feng, Z., Dai, J., Zhang, Y., and Tan, Y.: Ground-level ozone pollution in China: a synthesis of recent findings on influencing factors and impacts, Environ. Res. Lett., 17, 063003, https://doi.org/10.1088/1748-9326/ac69fe, 2022b.
Wang, W., Parrish, D. D., Wang, S., Bao, F., Ni, R., Li, X., Yang, S., Wang, H., Cheng, Y., and Su, H.: Long-term trend of ozone pollution in China during 2014–2020: distinct seasonal and spatial characteristics and ozone sensitivity, Atmos. Chem. Phys., 22, 8935–8949, https://doi.org/10.5194/acp-22-8935-2022, 2022c.
Wang, Y., Gao, W., Wang, S., Song, T., Gong, Z., Ji, D., Wang, L., Liu, Z., Tang, G., Huo, Y., Tian, S., Li, J., Li, M., Yang, Y., Chu, B., Petäjä, T., Kerminen, V.-M., He, H., Hao, J., Kulmala, M., Wang, Y., and Zhang, Y.: Contrasting trends of PM2.5 and surface-ozone concentrations in China from 2013 to 2017, Natl. Sci. Rev., 7, 1331–1339, https://doi.org/10.1093/nsr/nwaa032, 2020.
Xue, L. K., Wang, T., Gao, J., Ding, A. J., Zhou, X. H., Blake, D. R., Wang, X. F., Saunders, S. M., Fan, S. J., Zuo, H. C., Zhang, Q. Z., and Wang, W. X.: Ground-level ozone in four Chinese cities: precursors, regional transport and heterogeneous processes, Atmos. Chem. Phys., 14, 13175–13188, https://doi.org/10.5194/acp-14-13175-2014, 2014.
Yang, B., Zhang, Y., Qian, Y., Tang, J., and Liu, D.: Climatic effects of irrigation over the Huang-Huai-Hai Plain in China simulated by the weather research and forecasting model, J. Geophys. Res.-Atmos., 5, 2246–2264, https://doi.org/10.1002/2015JD023736, 2015.
Yang, J. and Zhao, Y.: Performance and application of air quality models on ozone simulation in China – A review, Atmos. Environ., 293, 119446, https://doi.org/10.1016/j.atmosenv.2022.119446, 2023.
Yang, Y., Jin, Z., Mueller, N. D., Driscoll, A. W., Hernandez, R. R., Grodsky, S. M., Sloat, L. L., Chester, M. V., Zhu, Y.-G., and Lobell, D. B.: Sustainable irrigation and climate feedbacks, Nat. Food, 4, 654–663, https://doi.org/10.1038/s43016-023-00821-x, 2023.
Ye, X., Wang, X., and Zhang, L.: Diagnosing the model bias in simulating daily surface ozone variability using a machine learning method: The effects of dry deposition and cloud optical depth, Environ. Sci. Technol., 56, 16665–16675, https://doi.org/10.1021/acs.est.2c05712, 2022.
Yin, H., Liu, C., Hu, Q. H., Liu, T., Wang, S. T., Gao, M., Xu, S. Q., Zhang, C. X., and Su, W. J.: Opposite impact of emission reduction during the COVID-19 lockdown period on the surface concentrations of PM2.5 and O3 in Wuhan, China, Environ. Pollut., 289, 117899, https://doi.org/10.1016/j.envpol.2021.117899, 2021.
Yuan, T., Tai, A P. K., Mao, J., Tam, O., H. F., Li, R. K. K., Wu, J., and Li, S.: Effects of different irrigation methods on regional climate in North China Plain: A modeling study, Agr. Forest Meteorol., 342, 109728, https://doi.org/10.1016/j.agrformet.2023.109728, 2023.
Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys. Res.-Atmos., 108, 4416, https://doi.org/10.1029/2002JD002775, 2003.
Zhai, S., Jacob, D. J., Wang, X., Shen, L., Li, K., Zhang, Y., Gui, K., Zhao, T., and Liao, H.: Fine particulate matter (PM2.5) trends in China, 2013–2018: separating contributions from anthropogenic emissions and meteorology, Atmos. Chem. Phys., 19, 11031–11041, https://doi.org/10.5194/acp-19-11031-2019, 2019.
Zhai, S., Jacob, D. J., Wang, X., Liu, Z., Wen, T., Shah, V., Li, K., Moch, J. M., Bates, K. H., Song, S., Shen, L., Zhang, Y., Luo, G., Yu, F., Sun, Y., Wang, L., Qi, M., Tao, J., Gui, K., Xu, H., Zhang, Q., Zhao, T., Wang, Y., Lee, H. C., Choi, H., and Liao, H.: Control of particulate nitrate air pollution in China, Nat. Geosci., 14, 389–395, https://doi.org/10.1038/s41561-021-00726-z, 2021.
Zhang, C., Wang, Y., and Hamilton, K.: Improved representation of boundary layer clouds over the southeast Pacific in ARW-WRF using a modified Tiedtke cumulus parameterization scheme, Mon. Weather Rev., 139, 3489–3513, https://doi.org/10.1175/MWR-D-10-05091.1, 2011.
Zhang, C., Dong, J., and Ge, Q.: Mapping 20 years of irrigated croplands in China using MODIS and statistics and existing irrigation products, Sci. Data, 9, 407, https://doi.org/10.1038/s41597-022-01522-z, 2022a.
Zhang, L., Zheng, D., Zhang, K., Chen, H., Ge, Y., and Li, X.: Divergent trends in irrigation-water withdrawal and consumption over mainland China, Environ. Res. Lett., 17, 094001, https://doi.org/10.1088/1748-9326/ac8606, 2022b.
Zhang, Y. L. and Cao, F.: Fine particulate matter (PM2.5) in China at a city level, Sci. Rep., 5, 1–12, https://doi.org/10.1038/srep14884, 2015.
Zhao, H., Chen, K., Liu, Z., Zhang, Y., Shao, T., and Zhang, H.: Coordinated control of PM2.5 and O3 is urgently needed in China after implementation of the “Air pollution prevention and control action plan”, Chemosphere, 270, 129441, https://doi.org/10.1016/j.chemosphere.2020.129441, 2021.
Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X., Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang, Q.: Trends in China's anthropogenic emissions since 2010 as the consequence of clean air actions, Atmos. Chem. Phys., 18, 14095–14111, https://doi.org/10.5194/acp-18-14095-2018, 2018.
Zhou, F., Bo, Y., Ciais, P., Dumas, P., Tang, Q., Wang, X., Liu, J., Zheng, C., Polcher, J., Yin, Z., Guimberteau, M., Peng, S., Ottle, C., Zhao, X., Zhao, J., Tan, Q., Chen, L., Shen, H., Yang, H., Piao, S., Wang, H., and Wada, Y.: Deceleration of China's human water use and its key drivers, P. Natl. Acad. Sci. USA, 117, 7702–7711, https://doi.org/10.1073/pnas.1909902117, 2020.
Zhou, S. S., Tai, A. P. K., Sun, S., Sadiq, M., Heald, C. L., and Geddes, J. A.: Coupling between surface ozone and leaf area index in a chemical transport model: strength of feedback and implications for ozone air quality and vegetation health, Atmos. Chem. Phys., 18, 14133–14148, https://doi.org/10.5194/acp-18-14133-2018, 2018.
Zhu, J., Tai, A. P. K., and Hung Lam Yim, S.: Effects of ozone–vegetation interactions on meteorology and air quality in China using a two-way coupled land–atmosphere model, Atmos. Chem. Phys., 22, 765–782, https://doi.org/10.5194/acp-22-765-2022, 2022.
Short summary
This study utilizes a regional climate–air quality coupled model to first investigate the complex interaction between irrigation, climate and air quality in China. We found that large-scale irrigation practices reduce summertime surface ozone while raising secondary inorganic aerosol concentration via complicated physical and chemical processes. Our results emphasize the importance of making a tradeoff between air pollution controls and sustainable agricultural development.
This study utilizes a regional climate–air quality coupled model to first investigate the...
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