Microscopic Evaluation of Trace Metals in Cloud Droplets in an Acid Precipitation Region

Mass concentrations of soluble trace metals and size, number, and mixing properties of nanometal particles in clouds determine their toxicity to ecosystems. Cloud water was found to be acidic, with a pH of 3.52, at Mt. Lu (elevation 1,165 m) in an acid precipitation region in South China. A combination of Inductively Coupled Plasma Mass Spectrometry (ICPMS) and Transmission Electron Microscopy (TEM) for the first time demonstrates that the soluble metal concentrations and solid metal particle number are surprisingly high in acid clouds at Mt. Lu, where daily concentrations of SO₂, NO₂, and PM₁₀ are 18 μg m^–3, 7 μg m^–3, and 22 μg m^–3. The soluble metals in cloudwater with the highest concentrations were zinc (Zn, 200 μg L^–1), iron (Fe, 88 μg L^–1), and lead (Pb, 77 μg L^–1). TEM reveals that 76% of cloud residues include metal particles that range from 50 nm to 1 μm diameter with a median diameter of 250 nm. Four major metal-associated particle types are Pb-rich (35%), fly ash (27%), Fe-rich (23%), and Zn-rich (15%). Elemental mapping shows that minor soluble metals are distributed within sulfates of cloud residues. Emissions of fine metal particles from large, nonferrous industries and coal-fired power plants with tall stacks were transported upward to this high elevation. Our results suggest that the abundant trace metals in clouds aggravate the impacts of acid clouds or associated precipitation on the ecosystem and human health

<b>A new value-added and long-term aerosol component global dataset derived by GRASP/AERONET</b>

Aerosol component retrievals at 899 AERONET sites from 1993 to 2021 are publicly available for download.Black carbon (BC) in fine mode indicates wavelength-independent strong absorption.Brown carbon (BrC) in fine mode indicates wavelength-dependent absorption.Coarse-mode absorbing insoluble (CAI) indicates coarse mode wavelength-dependent absorption mainly representing iron oxides in dust.Coarse-mode non-absorbing insoluble (CNAI) component indicates coarse non-absorbing dust and aged carbonaceous aerosols.Fine-mode non-absorbing insoluble (FNAI) component indicates fine non-absorbing dust and organic carbon.Quality (*) indicates the aerosol component retrievals with high uncertainty for the low aerosol loading (AOD_440 nm ≤ 0.2).Quality (**) indicates the aerosol component retrievals with medium uncertainty for the aerosol loading (0.2 Quality (***) indicates the aerosol component retrievals with low uncertainty for the aerosol loading (0.4 Users are required to fully cite the relevant publications when using this dataset. Details can be found in the manuscript submit to BAMS Journal:(1) Xindan Zhang, Lei Li, Huizheng Che, Oleg Dubovik, Yevgeny Derimian, Brent Holben, Pawan Gupta, Thomas F. Eck, Elena S. Lind, Carlos Toledano, Xiangao Xia, Yu Zheng, Ke Gui, Xiaoye Zhang. Aerosol components derived from global AERONET measurements by GRASP: A new value-added aerosol component global dataset and its application. Submitted to BAMS Journal, 2024.(2) Li Lei, Dubovik Oleg, Derimian, Yevgeny, Schuster L. Gregory, Lapyonok Tatyana, Litvinov Pavel, Ducos Fabrice, Fuertes David, Chen Cheng, Li Zhengqiang, Lopatin Anton, Torres Benjamin, Che Huizheng. Retrieval of aerosol components directly from satellite and ground-based measurements, Atmospheric Chemistry and Physics, 2019, 19, 13409–13443.</p

Toward an Operational Machine-Learning-Based Model for Deriving the Real-Time Gapless Diurnal Cycle of Ozone Pollution in China with CLDAS Data

An operational real-time surface ozone (O3) retrieval (RT-SOR) model was developed that can provide a gapless diurnal cycle of O3 retrievals with a spatial resolution of 6.25 km by integrating Chinese Land Data Assimilation System (CLDAS) data and multisource auxiliary information. The model robustly captures the hourly O3 variability, with a sample-based (station-based) cross-validation R2 of 0.88 (0.85) and RMSE of 14.3 μg/m3 (16.1 μg/m3). An additional hindcast-validation experiment demonstrated that the generalization ability of the model is robust (R2 = 0.75; RMSE = 21.9 μg/m3). Compared with previous studies, the model performs comparably or even better at the daily scale and fills the gaps in terms of missing hourly O3 data within the 24-hour cycle. More importantly, underpinned by the RT availability of CLDAS data, the hourly concentration of O3 can be updated in RT, which is expected to advance our understanding of the diurnal cycle of O3 pollution in China