12 citations as recorded by crossref.

1. Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4 A. Martin et al. https://doi.org/10.5194/gmd-17-5705-2024
2. Contrasting Biogenic Isoprene Emission Responses to La Niña and El Niño Driven by Temperature: Insights from HCHO-Based Global Inversion H. Li et al. https://doi.org/10.1021/acs.est.5c12927
3. Intense El Niño provokes production of new reactive volatiles as stress defences in Amazon rainforest J. Byron et al. https://doi.org/10.1038/s43247-026-03597-7
4. Changes in global atmospheric oxidant chemistry from land cover conversion R. Vella et al. https://doi.org/10.5194/acp-25-9885-2025
5. Inferring drivers of tropical isoprene: competing effects of emissions and chemistry J. Yoon et al. https://doi.org/10.5194/acp-26-4509-2026
6. Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide Y. Chi et al. https://doi.org/10.1016/j.jenvman.2024.122725
7. Influence of land cover change on atmospheric organic gases, aerosols, and radiative effects R. Vella et al. https://doi.org/10.5194/acp-25-243-2025
8. An Investigation of Benzene, Toluene, Ethylbenzene, m,p-xylene; Biogenic Volatile Organic Compounds; and Carbonyl Compounds in Chiang Mai’s Atmosphere and Estimation of Their Emission Sources During the Episode Period D. Baek et al. https://doi.org/10.3390/atmos16030342
9. Tropospheric ozone responses to the El Niño–Southern Oscillation (ENSO): quantification of individual processes and future projections from multiple chemical models J. Li et al. https://doi.org/10.5194/acp-25-12983-2025
10. Influence of the Super El Niño event during 2015/2016 on the aerosol properties and lightning activity in subtropical South America V. Tur et al. https://doi.org/10.1016/j.atmosres.2024.107383
11. Chiral volatile organic compound fluxes from soil in the Amazon Rainforest across seasons J. Schüttler et al. https://doi.org/10.5194/bg-23-3467-2026
12. Multi-stress interaction effects on BVOC emission fingerprints from Oak and Beech: A cross-investigation using Machine Learning and Positive Matrix Factorization B. Dey et al. https://doi.org/10.5194/bg-23-1423-2026