Air pollution is one of the growing pressures on terrestrial ecosystems. Ground-level ozone (O₃) is among the most impactful pollutants affecting vegetation function. This dissertation explores how vegetation responds to O₃ stress across scales – from leaf physiology to regional satellite observations – by integrating atmospheric modelling, plant experiments, and remote sensing. First, we trained machine learning models using Sentinel-5P TROPOMI and ERA5-Land data. The models showed strong agreement with in-situ observations (R2 ≈ 0.9) and produced spatially continuous O₃ maps, revealing regional and seasonal patterns, especially in areas lacking ground stations. Second, we explored the potential of solar-induced chlorophyll fluorescence (SIF) as a proxy for vegetation productivity. SIF correlated more strongly with gross primary productivity (GPP) than traditional greenness indices across most of observed climates and vegetation types. Third, combining soybean experiments and satellite data we analyzed how the timing of O₃ exposure within the growing season affects plant responses. Results showed that early-season O₃ episodes suppressed photosynthesis and fluorescence before visible injury occurred, identifying a reversible “strain phase”. Finally, citrus experiments under contrasting humidity regimes revealed stomatal and non-stomatal O₃ deposition pathways. In humid air, high stomatal conductance led to rapid fluorescence decline, whereas in dry air, reduced conductance delayed but did not prevent oxidative damage. Regional SIF patterns mirrored these dynamics, confirming that climate modulates both O₃ uptake and vegetation response. With these studies we establish a cross-scale framework linking O₃ modelling, plant physiology, and satellite fluorescence for assessing vegetation resilience under atmospheric stress. We demonstrate SIF as a functional, rather than structural, indicator of photosynthetic function and show that subtle O₃ episodes can have significant, yet often undetected, impacts on crops. The results advocate for revising current O₃ risk thresholds, and emphasize the need for interdisciplinary collaboration – from remote sensing to atmospheric chemistry and plant physiology – to sustain ecosystem productivity in a changing atmosphere.

Mechanisms and data-driven detection of ozone-induced vegetation disturbances: from photosynthetic function to satellite-based monitoring

MAMIC, LUKA
2026

Abstract

Air pollution is one of the growing pressures on terrestrial ecosystems. Ground-level ozone (O₃) is among the most impactful pollutants affecting vegetation function. This dissertation explores how vegetation responds to O₃ stress across scales – from leaf physiology to regional satellite observations – by integrating atmospheric modelling, plant experiments, and remote sensing. First, we trained machine learning models using Sentinel-5P TROPOMI and ERA5-Land data. The models showed strong agreement with in-situ observations (R2 ≈ 0.9) and produced spatially continuous O₃ maps, revealing regional and seasonal patterns, especially in areas lacking ground stations. Second, we explored the potential of solar-induced chlorophyll fluorescence (SIF) as a proxy for vegetation productivity. SIF correlated more strongly with gross primary productivity (GPP) than traditional greenness indices across most of observed climates and vegetation types. Third, combining soybean experiments and satellite data we analyzed how the timing of O₃ exposure within the growing season affects plant responses. Results showed that early-season O₃ episodes suppressed photosynthesis and fluorescence before visible injury occurred, identifying a reversible “strain phase”. Finally, citrus experiments under contrasting humidity regimes revealed stomatal and non-stomatal O₃ deposition pathways. In humid air, high stomatal conductance led to rapid fluorescence decline, whereas in dry air, reduced conductance delayed but did not prevent oxidative damage. Regional SIF patterns mirrored these dynamics, confirming that climate modulates both O₃ uptake and vegetation response. With these studies we establish a cross-scale framework linking O₃ modelling, plant physiology, and satellite fluorescence for assessing vegetation resilience under atmospheric stress. We demonstrate SIF as a functional, rather than structural, indicator of photosynthetic function and show that subtle O₃ episodes can have significant, yet often undetected, impacts on crops. The results advocate for revising current O₃ risk thresholds, and emphasize the need for interdisciplinary collaboration – from remote sensing to atmospheric chemistry and plant physiology – to sustain ecosystem productivity in a changing atmosphere.
29-gen-2026
Inglese
CRESPI, Mattia Giovanni
CRESPI, Mattia Giovanni
Università degli Studi di Roma "La Sapienza"
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/357563
Il codice NBN di questa tesi è URN:NBN:IT:UNIROMA1-357563