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Fine particulate matter (PM 2.5 ) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM 2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM 2.5 species from a high-density observation network, satellite PM 2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO 4 2- ), nitrate (NO 3 - ), ammonium (NH 4 + ), and chloride (Cl - ) estimates, with high coefficients of determination (CV-R 2 ) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 μg/m 3 , respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO 4 2- ), 20% (NO 3 - ), and 14% (NH 4 + ) of the total PM 2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM 2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO 4 2- has been the dominant SIA component in eastern China, although it was surpassed by NO 3 - in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM 2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM 2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

The momentum transport and pollutant dispersion in the atmospheric surface layer (ASL) are governed by a broad spectrum of turbulence structures. Whereas, their contributions have not been explicitly investigated in the context of real urban morphology. This paper aims to elucidate the contributions from different types of eddies in the ASL over a dense city to provide the reference of urban planning, realizing more favorable ventilation and pollutant dispersion. The building-resolved large-eddy simulation dataset of winds and pollutants over the Kowloon downtown, Hong Kong, is decomposed into a few intrinsic mode functions (IMFs) via empirical mode decomposition (EMD). EMD is a data-driven algorithm that has been successfully implemented in many research fields. The results show that four IMFs are generally enough to capture most of the turbulence structures in real urban ASL. In particular, the first two IMFs, which are initiated by individual buildings, capture the small-scale vortex packets that populate within the irregular building clusters. On the other hand, the third and fourth IMFs capture the large-scale motions (LSMs) detached to the ground surface that are highly efficient in transport. They collectively contribute to nearly 40% of vertical momentum transport even with relatively low vertical turbulence kinetic energy (TKE). LSMs are long, streaky structures that mainly consist of streamwise TKE components. It is found that the open areas and regular streets promote the portion of streamwise TKE in LSMs, improving the vertical momentum transport and pollutant dispersion. In addition, these streaky LSMs are found to play a crucial role in pollutant dilution in the near field after the pollutant source, while the small-scale vortex packets are more efficient in transport in the mid-field and far-field., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Chun-Ho Liu reports financial support was provided by Hong Kong (HK) Research Grants Council (RGC)., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Increasing surface ozone (O 3 ) concentrations has emerged as a key air pollution problem in many urban regions worldwide in the last decade. A longstanding major issue in tackling ozone pollution is the identification of the O 3 formation regime and its sensitivity to precursor emissions. In this work, we propose a new transformed empirical kinetic modeling approach (EKMA) to diagnose the O 3 formation regime using regulatory O 3 and NO 2 observation datasets, which are easily accessible. We demonstrate that mapping of monitored O 3 and NO 2 data on the modeled regional O 3 -NO 2 relationship diagram can illustrate the ozone formation regime and historical evolution of O 3 precursors of the region. By applying this new approach, we show that for most urban regions of China, the O 3 formation is currently associated with a volatile organic compound (VOC)-limited regime, which is located within the zone of daytime-produced O 3 (DPO 3 ) to an 8h-NO 2 concentration ratio below 8.3 ([DPO 3 ]/[8h-NO 2 ] ≤ 8.3). The ozone production and controlling effects of VOCs and NO x in different cities of China were compared according to their historical O 3 -NO 2 evolution routes. The approach developed herein may have broad application potential for evaluating the efficiency of precursor controls and further mitigating O 3 pollution, in particular, for regions where comprehensive photochemical studies are unavailable.