Plant responses to gaseous pollutants, biochemical and transcriptomic insights

Atmospheric gaseous pollutants sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), ozone (O 3 ), and carbon monoxide (CO) increasingly co-occur in crop canopies and cause damage that spans atmospheric chemistry, redox signaling, and whole-leaf function. Prior work is often fragmented by single pollutant, single endpoints, or single scale, which limits mechanistic comparability and makes it difficult to build computationally useful models that generalize across environments. This synthesis integrates the atmospheric-to-cellular continuum in a form tended for quantitative plant science and computational researchers. We connect pollutant formation and microclimate-driven exposure to stomatal uptake, apoplastic speciation, subcellular targets, and downstream impacts on photosynthesis, respiration, and stomatal regulation. At the biochemical level, we unify key reaction routes and control points, SO 2 hydration to bisulfite and sulfite and the associated detoxification demands, NO x driven redox interconversion and nitrosative stress with protein modification, O 3 decomposition to reactive oxygen species (ROS) and membrane/chloroplast injury with guard-cell dysfunction. We also clarify the agronomic relevance of CO as a heme-centered modifier that can reshape respiration-linked redox balance and stress signaling, particularly under multi-pollutant mixtures. Beyond summarizing mechanisms, our novelty in this synthesis, is not to repeat well-known single-gas mechanisms, but to bring together key studies that are rarely discussed side by side and show how their results can be used in a practical, quantitative way. Specifically, we organize evidence across SO 2 , NO x , O 3 , and CO around shared convergence nodes (ROS and RNS buffering, antioxidant cycling, and electron-transport constraints), and we translate those mechanisms into discriminative mechanistic fingerprints that can be treated as measurable biomarkers or model features. To support translation, we summarize how prior studies typically quantify dose and outcomes using open-top chambers, FACE, and flux-based datasets that connect stomatal uptake to redox status and yield-related traits. This enables more consistent dataset design, model constraints for machine learning, and interpretable prediction of tolerance and yield risk under realistic multi-pollutant atmospheres.