The weekly cycle of photosynthesis in Europe reveals the negative impact of particulate pollution on ecosystem productivity

Creators: He, Liyin; Rosa, Lorenzo; Lobell, David B.; Wang, Yuan; Yin, Yi; Doughty, Russell; Yao, Yitong; Berry, Joseph A.; Frankenberg, Christian¹

1. California Institute of Technology

Abstract

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality—reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period—we estimate a potential of 41 to 50 Mt net additional annual CO₂ uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity.

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Acknowledgement

This work was supported by the Carnegie Institution for Science. L.H. was partially supported by the fellowship from Resnick Sustainability Institute at California Institute of Technology. Y. Yin and C.F. were supported by the NASA OCO2/3 science team (Grant 80NSSC18K0895). Y.W. was supported by the NSF grant (Award No. AGS-2103714). We thank Drs. Wu Sun (Carnegie Institution for Science), Yujie Wang (California Institute of Technology), and Xinchen Lu (University of California, Berkeley) for helpful discussions.

Contributions

L.H. and D.B.L. conceived the study; L.H., L.R., D.B.L., and C.F. designed the study; L.H. and R.D. collected data; L.H. performed data analysis and wrote the paper; Y.W., Y. Yin, and J.A.B. contributed the discussion of method development; L.H. led the interpretation of the results with input from L.R., D.B.L., Y.W., Y. Yin, Y. Yao, J.A.B., and C.F.; and all authors provided feedback on the manuscript.

Data Availability

The TROPOMI SIF product can be accessed at https://climatesciences.jpl.nasa.gov/sif/download-data/level-2/ (34). Hourly meteorological data from ERA5 is publicly available through the Copernicus Climate Change Service (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form) (78). VIIRS EPS AOD data can be downloaded from https://www.star.nesdis.noaa.gov/pub/smcd/VIIRS_Aerosol/viirs_aerosol_gridded_data/ (79). The MODIS MCD12Q1 v006 landcover data are available at https://lpdaac.usgs.gov/products/mcd12q1v006/ (80). Additionally, the MODIS MOD15A2H v006 fPAR data can be found at https://lpdaac.usgs.gov/products/mod15a2hv006/ (81). The MODIS MCD18C2 v061 PAR data are available at https://lpdaac.usgs.gov/products/mcd18c2v061/ (82). FLUXCOM GPP and NEE are available at https://www.fluxcom.org/CF-Download/ (76). TROPOMI NO2 can be downloaded from http://www.tropomi.eu/data-products/nitrogen-dioxide (83). MODIS cloud optical thickness is available at https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MCD06COSP_D3_MODIS (84). The eddy covariance measurements are obtained from the ICOS (https://meta.icos-cp.eu/collections/-ZrCo_Cousoqvxnlvz83l0K4) (72). The SEM analysis is carried out using the "semopy" package in Python. The regression analysis is conducted by "statsmodels" package in Python. All other data are included in the manuscript and/or SI Appendix.

Conflict of Interest

The authors declare no competing interest.

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