Global Flux‐Based Assessment Reveals Declining Ozone Risk for Wheat in Future Climate Change Scenarios
Bibliographic record
Abstract
ABSTRACT Tropospheric ozone (O 3 ) is a widespread air pollutant that impairs crop physiology and threatens global food security. Most global‐scale assessments have relied on exposure‐based metrics, which overlook plant–environment interactions that control O 3 uptake. This study presents a global flux‐based assessment of future O 3 risk for wheat ( Triticum aestivum ) using a dual‐sink dry deposition model driven by Earth System Models from the Coupled Model Intercomparison Project 6 (CMIP6) under three Shared Socioeconomic Pathways (SSP1‐2.6, SSP3‐7.0, and SSP5‐8.5). We quantify phytotoxic O 3 dose (POD 6 ) and production losses from 2000 to 2100, analyze regional trends, and perform multiple simulations to assess the influence of soil water availability and atmospheric CO 2 on O 3 risk. Finally, we explore the roles of radiative forcing (RF), emission policies on O 3 precursors (EP), and their interaction, in determining O 3 risk changes. We find a general decline in O 3 risk, although regional disparities remain. Under SSP1‐2.6 (strong EP, low RF) POD 6 declines throughout the century, leading global mean production losses to decrease from 3.3% to 5.0% at the beginning of the century to less than 1.4% at its end. In contrast, SSP3‐7.0 (weak EP, high RF) shows end‐century losses between 1.3% and 4.9% and may exacerbate risks in several regions (South and East Asia, South America, Sub‐Saharan Africa). SSP5‐8.5 displays intermediate outcomes: O 3 risk increases until mid‐century in many regions, and then declines by 2100 (0.5%–2.6%), due to delayed EP adoption. Increasing atmospheric CO 2 concentrations will likely hinder future O 3 risk due to reduced stomatal conductance, but some hotspots will persist near the Southern and Eastern edges of the Tibetan Plateau. These findings provide a basis for prioritizing region‐specific mitigation strategies to reduce O 3 damage to wheat under future climate conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".