A 2018 correspondence by Zhao, Gao, Yang, and Ni argued from a holistic, black-box comparison of national emission trends with haze occurrence that vehicle exhaust is an overstated cause of haze in China, and closed by noting that the genuine interacting causes of haze remain undetermined. The question that correspondence left open is to determine, quantitatively and mechanistically, the interacting causes of severe urban haze in China — decomposing the combined effects of vehicle-exhaust, industrial, residential/non-fossil (biomass-burning and biogenic), and other emissions, together with the secondary aerosol-forming atmospheric transformations and meteorological processes that convert these precursors into haze — sufficiently to establish causal mechanisms. This question remains open: the vehicle-versus-industry attribution debate was still described as unresolved in 2020, authoritative syntheses frame severe haze as a synergy of emissions and atmospheric processes whose causes and mechanisms still need unraveling, and the formation mechanisms of sulfate, nitrate, secondary organic aerosol, and nitrous acid (HONO) during haze remain subjects of active, incomplete research through 2024–2025.

Background

In China, a haze event is an episode of severely reduced visibility caused by fine particulate matter (PM2.5, particles with aerodynamic diameter below 2.5 micrometers) accumulating in the lower atmosphere, typically in winter over the North China Plain and other urbanized basins. PM2.5 mass is either primary (emitted directly as particles, e.g., from vehicles, coal burning, industry, or dust) or secondary (formed in the atmosphere when emitted gases — sulfur dioxide (SO2), nitrogen oxides (NOx), ammonia (NH3), and volatile organic compounds (VOCs) — react to produce sulfate, nitrate, and secondary organic aerosol (SOA)). Two landmark 2014 studies established that secondary aerosol dominates the particulate mass during severe Chinese haze episodes: Huang et al. (Nature, 2014) found secondary aerosol contributing roughly 30–77% of PM2.5 in four megacities, and Guo et al. (PNAS, 2014) showed that gaseous precursor emissions, rather than primary particles, drive the growth of Beijing haze. Subsequent work (Cheng et al., Science Advances, 2016; Wang et al., PNAS, 2016) proposed multiphase chemistry — the oxidation of SO2 by NO2 inside deliquesced aerosol water (liquid water absorbed by airborne particles from humid air) and fog droplets, enabled by ammonia neutralization — as the mechanism for the rapid sulfate production that chemical-transport models (three-dimensional simulations of emissions, chemistry, and transport) historically failed to reproduce. Source apportionment — partitioning observed PM2.5 among its emitters, whether by receptor models that analyze measured chemical fingerprints or by chemical-transport models that tag emissions by source — is the standard tool for attribution, and its methods routinely disagree. A separate line of work attributes haze causation statistically: a fault-tree analysis (a deductive diagram that decomposes a top event into its contributing causes) for Guangzhou (Huang et al., Chemosphere, 2016) traced haze causation to vehicle emission, while a holistic "black-box" style correlation approach (in the spirit of the pollution-tree method of Zheng et al., 2012, which correlates whole pollutant mixtures with effects without modeling mechanisms) led Zhao, Gao, Yang, and Ni (Science of the Total Environment, 612:490–491, 2018) to the opposite conclusion: China's total vehicular emissions of NOx, VOCs, and PM declined even as the vehicle fleet and haze burden grew, so vehicle exhaust is, in their words, an overstated cause of haze in China. Their correspondence closes by conceding that the holistic black-box approach cannot resolve the interacting chemical species, meteorological conditions, atmospheric processes, and emission sources behind haze, and that the genuine interacting causes — the combined effects of industrial, biogenic/non-fossil, and atmospheric transformations beyond vehicle exhaust — remain undetermined. Determining those causes is complicated by feedbacks: heavy aerosol loading cools the surface, suppresses boundary-layer (the lowest atmospheric layer, typically capped by a temperature inversion) growth, and thereby traps pollutants in a shallower volume, while aerosol water and fog provide reaction volumes that accelerate secondary formation — mechanisms that make causes and effects mutually reinforcing and non-additive. At the mitigation-policy level, Zhang et al. (PNAS, 2019) quantified that China's 2013–2017 PM2.5 improvement was driven mainly by industrial emission standards, boiler upgrades, capacity retirement, and residential clean-fuel switches, and An et al. (PNAS, 2019), in the authoritative synthesis of the field, state that improved understanding of emission sources, of the physical and chemical processes during haze evolution, and of interactions with meteorology and climate is still necessary to unravel the causes, mechanisms, and trends of severe haze.

Problem Statement

What are the genuine, interacting causes of severe urban haze in China? The task, as posed by Zhao, Gao, Yang and Ni (2018) at the close of their argument that vehicle exhaust is an overstated cause, is to decompose the combined effects of the candidate causes — vehicle-exhaust emissions, industrial emissions (SO2, NOx, particulate precursors), residential and non-fossil/biogenic (biomass-burning and biogenic) emissions, and the atmospheric transformations and meteorological processes (secondary sulfate/nitrate/organic aerosol formation, aerosol-water and cloud/fog multiphase chemistry, boundary-layer stagnation and feedback) that act on them — into a quantitative, mechanism-based causal account of haze episodes. The decomposition concerns a well-defined severe-haze episode class (for example the winter North China Plain episodes described in the Background) and comprises two explicitly separate accounts that must not be merged into a single set of terms. First, a sector-resolved apportionment of that episode's PM2.5 in which the vehicle-exhaust, industrial, residential/non-fossil (biomass-burning and biogenic), and other sectors are each split into a primary and a secondary fraction, so that the secondary sulfate, nitrate, and organic aerosol formed from a sector's precursors is counted within that sector and never as a second, independent set of terms. Second, an independent attribution of the episode's PM2.5 burden between emissions and meteorology, kept separate from the sector shares rather than summed with them as though meteorology supplied PM2.5 mass. In both accounts the mechanism chain by which the precursors become the observed secondary aerosol must be identified, and both must be consistent with the episode's observed precursor, oxidant, aerosol-water, and meteorological measurements. The decomposition must quantitatively account for the dominant observed components of the episode's PM2.5 through identified mechanism chains rather than through qualitative correlation.

References

  1. Zhao, Yi-Bo; Gao, Pan-Pan; Yang, Wan-Dong; Ni, Hong-Gang (2018). Vehicle exhaust: An overstated cause of haze in China. Science of the Total Environment 612, 490-491. doi:10.1016/j.scitotenv.2017.08.255, https://doi.org/10.1016/j.scitotenv.2017.08.255
  2. Guo, Song; Hu, Min; Zamora, Misti L.; Peng, Jianfei; Shang, Dongjie; Zheng, Jing; Du, Zhuofei; Wu, Zhijun; Shao, Min; Zeng, Limin; Molina, Mario J.; Zhang, Renyi (2014). Elucidating severe urban haze formation in China. Proceedings of the National Academy of Sciences 111, 17373-17378. doi:10.1073/pnas.1419604111, https://doi.org/10.1073/pnas.1419604111
  3. Huang, Ru-Jin; Zhang, Yanlin; Bozzetti, Carlo; Ho, Kin-Fai; Cao, Jun-Ji; Han, Yongming; Daellenbach, Kaspar R.; Slowik, Jay G.; Platt, Stephen M.; Canonaco, Francesco; Zotter, Peter; Wolf, Robert; Pieber, Simone M.; Bruns, Emily A.; Crippa, Monica; Ciarelli, Giancarlo; Piazzalunga, Andrea; Schwikowski, Margit; Abbaszade, Gülcin; Schnelle-Kreis, Jürgen; Zimmermann, Ralf; An, Zhisheng; Szidat, Sönke; Baltensperger, Urs; Haddad, Imad El; Prévôt, André S. H. (2014). High secondary aerosol contribution to particulate pollution during haze events in China. Nature 514, 218-222. doi:10.1038/nature13774, https://doi.org/10.1038/nature13774
  4. Cheng, Yafang; Zheng, Guangjie; Wei, Chao; Mu, Qing; Zheng, Bo; Wang, Zhibin; Gao, Meng; Zhang, Qiang; He, Kebin; Carmichael, Gregory; Pöschl, Ulrich; Su, Hang (2016). Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Science Advances 2, e1601530. doi:10.1126/sciadv.1601530, https://doi.org/10.1126/sciadv.1601530
  5. Wang, Gehui; Zhang, Renyi; Gomez, Mario E.; Yang, Lingxiao; Levy Zamora, Misti; Hu, Min; Lin, Yun; Peng, Jianfei; Guo, Song; Meng, Jingjing; Li, Jianjun; Cheng, Chunlei; Hu, Tafeng; Ren, Yanqin; Wang, Yuesi; Gao, Jian; Cao, Junji; An, Zhisheng; Zhou, Weijian; Li, Guohui; Wang, Jiayuan; Tian, Pengfei; Marrero-Ortiz, Wilmarie; Secrest, Jeremiah; Du, Zhuofei; Zheng, Jing; Shang, Dongjie; Zeng, Limin; Shao, Min; Wang, Weigang; Huang, Yao; Wang, Yuan; Zhu, Yujiao; Li, Yixin; Hu, Jiaxi; Pan, Bowen; Cai, Li; Cheng, Yuting; Ji, Yuemeng; Zhang, Fang; Rosenfeld, Daniel; Liss, Peter S.; Duce, Robert A.; Kolb, Charles E.; Molina, Mario J. (2016). Persistent sulfate formation from London Fog to Chinese haze. Proceedings of the National Academy of Sciences 113, 13630-13635. doi:10.1073/pnas.1616540113, https://doi.org/10.1073/pnas.1616540113
  6. An, Zhisheng; Huang, Ru-Jin; Zhang, Renyi; Tie, Xuexi; Li, Guohui; Cao, Junji; Zhou, Weijian; Shi, Zhengguo; Han, Yongming; Gu, Zhaolin; Ji, Yuemeng (2019). Severe haze in northern China: A synergy of anthropogenic emissions and atmospheric processes. Proceedings of the National Academy of Sciences 116, 8657-8666. doi:10.1073/pnas.1900125116, https://doi.org/10.1073/pnas.1900125116
  7. Zhang, Qiang; Zheng, Yixuan; Tong, Dan; Shao, Min; Wang, Shuxiao; Zhang, Yuanhang; Xu, Xiangde; Wang, Jinnan; He, Hong; Liu, Wenqing; Ding, Yihui; Lei, Yu; Li, Junhua; Wang, Zifa; Zhang, Xiaoye; Wang, Yuesi; Cheng, Jing; Liu, Yang; Shi, Qinren; Yan, Liu; Geng, Guannan; Hong, Chaopeng; Li, Meng; Liu, Fei; Zheng, Bo; Cao, Junji; Ding, Aijun; Gao, Jian; Fu, Qingyan; Huo, Juntao; Liu, Baoxian; Liu, Zirui; Yang, Fumo; He, Kebin; Hao, Jiming (2019). Drivers of improved PM2.5 air quality in China from 2013 to 2017. Proceedings of the National Academy of Sciences 116, 24463-24469. doi:10.1073/pnas.1907956116, https://doi.org/10.1073/pnas.1907956116
  8. Qiang, Wei; Lee, Harry F.; Lin, Ziwei; Wong, David W.H. (2020). Revisiting the impact of vehicle emissions and other contributors to air pollution in urban built-up areas: A dynamic spatial econometric analysis. Science of the Total Environment 740, 140098. doi:10.1016/j.scitotenv.2020.140098, https://doi.org/10.1016/j.scitotenv.2020.140098
  9. Wen, Yifan; Liu, Min; Zhang, Shaojun; Wu, Xiaomeng; Wu, Ye; Hao, Jiming (2023). Updating On-Road Vehicle Emissions for China: Spatial Patterns, Temporal Trends, and Mitigation Drivers. Environmental Science & Technology 57, 14299-14309. doi:10.1021/acs.est.3c04909, https://doi.org/10.1021/acs.est.3c04909
  10. Peng, Jianfei; Hu, Min; Shang, Dongjie; Wu, Zhijun; Du, Zhuofei; Tan, Tianyi; Wang, Yanan; Zhang, Fang; Zhang, Renyi (2021). Explosive Secondary Aerosol Formation during Severe Haze in the North China Plain. Environmental Science & Technology 55, 2189-2207. doi:10.1021/acs.est.0c07204, https://doi.org/10.1021/acs.est.0c07204
  11. Cai, Suyi; Liu, Tengyu; Huang, Xin; Song, Yu; Wang, Tiantian; Sun, Zhaobin; Gao, Jian; Ding, Aijun (2024). Important Role of Low Cloud and Fog in Sulfate Aerosol Formation During Winter Haze Over the North China Plain. Geophysical Research Letters 51, e2023GL106597. doi:10.1029/2023GL106597, https://doi.org/10.1029/2023GL106597
  12. Zhang, Xinran; Tong, Shengrui; Jia, Chenhui; Zhang, Wenqian; Wang, Zhen; Tang, Guiqian; Hu, Bo; Liu, Zirui; Wang, Lili; Zhao, Pusheng; Pan, Yuepeng; Ge, Maofa (2023). Elucidating HONO formation mechanism and its essential contribution to OH during haze events. npj Climate and Atmospheric Science 6, 55. doi:10.1038/s41612-023-00371-w, https://doi.org/10.1038/s41612-023-00371-w
  13. Huang, Weiqing; Fan, Hongbo; Qiu, Yongfu; Cheng, Zhiyu; Xu, Pingru; Qian, Yu (2016). Causation mechanism analysis for haze pollution related to vehicle emission in Guangzhou, China by employing the fault tree approach. Chemosphere 151, 9-16. doi:10.1016/j.chemosphere.2016.02.024, https://doi.org/10.1016/j.chemosphere.2016.02.024
  14. Zheng, Weiwei; Wang, Xia; Tian, Dajun; Zhang, Hao; Tian, Weidong; Andersen, Melvin E.; Zheng, Yuxin; Sun, Xin; Jiang, Songhui; Cao, Zhaojin; He, Gengsheng; Qu, Weidong (2012). Pollution Trees: Identifying Similarities among Complex Pollutant Mixtures in Water and Correlating Them to Mutagenicity. Environmental Science & Technology 46, 7274-7282. doi:10.1021/es300728q, https://doi.org/10.1021/es300728q