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The datasets present collected data aimed at measuring the state of cultural tourism and economic development and resilience of a select set of potential cultural tourism destinations in Europe, as part of the Horizon 2020 funded project SmartCulTour (www.smartcultour.eu). The data is collected on the level of Local Administrative Units (LAUs) for the following municipalities/cities: Spain: Ainsa, Barbastro, Benasque, Graus, Huesca, Jaca, Sariñena the Netherlands: Rotterdam, Delft, Dordrecht, Molenlanden, Barendrecht, Ridderkerk, Zwijndrecht Finland: Utsjoki Italy: Vicenza, Caldogno, Pojana Maggiore, Grumolo delle Abbadesse, Lonigo, Montagna Croatia: Split, Trogir, Kaštela, Solin, Sinj, Dugopolje, Klis Belgium: Dendermonde, Puurs-Sint-Amands, Bornem, Berlare, Aalst, Denderleeuw, Willebroek The data is presented as panel data and available for the following years: 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019. Please consult the metadata on each dataset for an overview of collected indicators and units of measurement.

Description: The eddy covariance technique was used to record continuous, non-invasive measurements of CO2, H2O and energy exchange between the ecosystem and the atmosphere. The measuring system consists of a semi-open path infrared gas analyser LI-7200 (LI-COR, USA), and a CSAT3 Sonic Anemometer (Campbell Scientific, USA) at a measuring height of 52 m over a canopy height of ~25 m. Data were recorded at a frequency of 20 Hz that was treated using the post-processing software EddyPro® (v.7.0.6; www.licor.com/eddypro) to compute fluxes for each 30-minute averaging period. To treat the raw fluxes, primary data processing steps were applied, including spike removal (Vickers, 1997 J Atmos Ocean Technol), coordinate rotation, block averaging detrending of CO2, H2O and sonic temperature, time lag compensation using covariance maximisation detection method, random uncertainty estimation (Finkelstein et al. 2001 Journal of Geophysical Research Atmospheres), computation of turbulent fluxes and mean fluxes, spectral corrections (Moncrieff et al. 1997 J Hydrol Amst) using correction of low-pass filtering effects, planar fit rotation (Wilczak et al. 2001 Boundary Layer Meteorol) and quality flagging policy (Göckede et al. 2006 Boundary Layer Meteorol). Eddy covariance meteorological data from above and below canopy is available at DOI 10.5281/zenodo.3888374. Cells with -9999 represent not enough data collected, which can be regarded as NA. This data has been collected over a heavily logged landscape between 2012 - 2018, please note 2016 was removed from this dataset. Before 2015, the landscape was ~10 years recovering from it's previous round of logging (four times logged). During 2015 the landscape was salvaged logged, removing 75% of tree stand basal area. The first data sheet, named "Raw_data" contains all raw fluxes that have been treated by EddyPro, which have not been filtered or quality controlled. The second sheet, named "Daily_fluxes" contains daily mean fluxes of net ecosystem CO2 exchange (NEE), ecosystem respirationn (Reco) and gross primary productivity and their associated standard errors. Net ecosystem CO2 exchange (NEE) was calculated by adding the estimated CO2 storage flux to the observed CO2 flux. Data was subjecto quality control including the removal of quality flags 4 and 5 (Göckede et al. 2006 Boundary Layer Meteorol) and the application of a mean u* threshold of >0.29 m s-1 to the dataset, as established using the package "REddyProc" (v.1.2; (Wultzer et al. 2019 Biogeosciences)) in based on the Moving Point Method (Reichstein et al. 2005, GCB). Data was subsequently gap filled and partitioned, as descripted within the variable methods of this sheet. This data was part of an analysis of carbon fluxes within three periods of data collection: in 2012 – 2013, which captured the four-times logged ecosystem ~10 years after its previous round of logging, in 2015 during a new round of active salvage logging, and in 2017 – 2018 when the ecosystem was recovery 2-3 years after the salvage logging. Days with large standard errors for Reco (> ± 5 µmol m−2 s−1) were deemed as bad quality and removed from the dataset and we used only days that had four or more observed half-hourly values of NEE. Of the final dataset , 29.5% of the half-hourly values are original observed fluxes, and 70.5% gap-filled. Of the 455 days remaining after all filtering processes were applied, 65 days were during the 10-years recovery phase (2012-2013), 100 during the active salvage logging (2015) and 290 during the 2-3 years recovery from active salvage logging phase (2017-2018). Project: This dataset was collected as part of the following SAFE research project: Changing carbon dioxide and water budgets from deforestation and habitat modification XML metadata: GEMINI compliant metadata for this dataset is available here Files: This consists of 1 file: SAFE_EC_byYear.xlsx SAFE_EC_byYear.xlsx This file contains dataset metadata and 6 data tables: Raw_data_2012_2013 (described in worksheet Raw_data_2012_2013) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. Number of fields: 105 Number of data rows: 24213 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_h2o_flux: Random error of CO2 flux (Field type: numeric) H_strg: Estimate of storage sensible heat flux (Field type: numeric) LE_strg: Estimate of storage latent heat flux (Field type: numeric) co2_strg: Estimate of storage CO2 flux (Field type: numeric) h2o_strg: Estimate of storage H20 flux (Field type: numeric) co2_v.adv: Estimate of vertical advection flux of CO2 (Field type: numeric) h2o_v.adv: Estimate of vertical advection flux of H20 (Field type: numeric) co2_molar_density: Measured or estimated molar density of gas (Field type: numeric) co2_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) co2_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) co2_time_lag: Time lag used to synchronize gas time series (Field type: numeric) co2_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) h2o_molar_density: Measured or estimated molar density of gas (Field type: numeric) h2o_mole_fraction: Measured or estimated mole fraction of gas (Field type: numeric) h2o_mixing_ratio: Measured or estimated mixing ratio of gas (Field type: numeric) h2o_time_lag: Time lag used to synchronize gas time series (Field type: numeric) h2o_def_timelag: Flag: whether the reported time lag is the default (1) or calculated (0) (Field type: numeric) sonic_temperature: Mean temperature of ambient air as measured by the anemometer (Field type: numeric) air_temperature: Mean temperature of ambient air, either calculated from high frequency air temperature readings, or estimated from sonic temperature (Field type: numeric) air_pressure: Mean pressure of ambient air, either calculated from high frequency air pressure readings, or estimated based on site altitude (barometric pressure) (Field type: numeric) air_density: Density of ambient air (Field type: numeric) air_heat_capacity: Specific heat at constant pressure of ambient air (Field type: numeric) air_molar_volume: Molar volume of ambient air (Field type: numeric) ET: Evapotranspiration flux (Field type: numeric) water_vapor_density: Ambient mass density of water vapor (Field type: numeric) e: Ambient water vapor partial pressure (Field type: numeric) es: Ambient water vapor partial pressure at saturation (Field type: numeric) specific_humidity: Ambient specific humidity on a mass basis (Field type: numeric) RH: Ambient relative humidity (Field type: numeric) VPD: Ambient water vapor pressure deficit (Field type: numeric) Tdew: Ambient dew point temperature (Field type: numeric) u_unrot: Wind component along the u anemometer axis (Field type: numeric) v_unrot: Wind component along the v anemometer axis (Field type: numeric) w_unrot: Wind component along the w anemometer axis (Field type: numeric) u_rot: Rotated u wind component (mean wind speed) (Field type: numeric) v_rot: Rotated v wind component (should be zero) (Field type: numeric) w_rot: Rotated w wind component (should be zero) (Field type: numeric) wind_speed: Mean wind speed (Field type: numeric) max_wind_speed: Maximum instantaneous wind speed (Field type: numeric) wind_dir: Direction from which the wind blows, with respect to Geographic or Magnetic north (Field type: numeric) yaw: First rotation angle (Field type: numeric) pitch: Second rotation angle (Field type: numeric) u.: Friction velocity (Field type: numeric) TKE: Turbulent kinetic energy (Field type: numeric) L: Monin-Obukhov length (Field type: numeric) X.z.d..L: Monin-Obukhov stability parameter - (z-d)/L (Field type: numeric) bowen_ratio: Sensible heat flux to latent heat flux ratio (Field type: numeric) T.: Scaling temperature (Field type: numeric) model: Model for footprint estimation, 1- Kljun et al. (2004): A crosswind integrated parameterization of footprint estimations obtained with a 3D Lagrangian model by means of a scaling procedure.2 - Kormann and Meixner (2001): A crosswind integrated model based on the solution of the two dimensional advection-diffusion equation given by van Ulden (1978) and others for power-law profiles in wind velocity and eddy diffusivity, 3 - Hsieh et al. (2000): A crosswind integrated model based on the former model of Gash (1986) and on simulations with a Lagrangian stochastic model. (Field type: numeric) x_peak: Along-wind distance providing <1% contribution to turbulent fluxes (Field type: numeric) x_offset: Along-wind distance providing the highest (peak) contribution to turbulent fluxes (Field type: numeric) x_10.: Along-wind distance providing 10% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_30.: Along-wind distance providing 30% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_50.: Along-wind distance providing 50% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_70.: Along-wind distance providing 70% (cumulative) contribution to turbulent fluxes (Field type: numeric) x_90.: Along-wind distance providing 90% (cumulative) contribution to turbulent fluxes (Field type: numeric) un_Tau: Uncorrected momentum flux (Field type: numeric) Tau_scf: Spectral correction factor for momentum flux (Field type: numeric) un_H: Uncorrected sensible heat flux (Field type: numeric) H_scf: Spectral correction factor for sensible heat flux (Field type: numeric) un_LE: Uncorrected latent heat flux (Field type: numeric) LE_scf: Spectral correction factor for latent heat flux (Field type: numeric) un_co2_flux: Uncorrected gas flux (Field type: numeric) co2_scf: Spectral correction factor for gas flux (Field type: numeric) un_h2o_flux: Uncorrected gas flux (Field type: numeric) h2o_scf: Spectral correction factor for gas flux (Field type: numeric) spikes_hf: Hard flags for individual variables for spike test (Field type: numeric) amplitude_resolution_hf: Hard flags for individual variables for amplitude resolution (Field type: numeric) drop_out_hf: Hard flags for individual variables for drop-out test (Field type: numeric) absolute_limits_hf: Hard flags for individual variables for absolute limits (Field type: numeric) skewness_kurtosis_hf: Hard flags for individual variables for skewness and kurtosis (Field type: numeric) skewness_kurtosis_sf: Soft flags for individual variables for skewness and kurtosis test (Field type: numeric) discontinuities_hf: Hard flags for individual variables for discontinuities test (Field type: numeric) discontinuities_sf: Soft flags for individual variables for discontinuities test (Field type: numeric) timelag_hf: Hard flags for gas concentration for time lag test (Field type: numeric) timelag_sf: Soft flags for gas concentration for time lag test (Field type: numeric) attack_angle_hf: Hard flags for gas concentration for time lag test (Field type: numeric) non_steady_wind_hf: Soft flags for gas concentration for time lag test (Field type: numeric) u_spikes: Number of spikes detected and eliminated for rotated u wind component (Field type: numeric) v_spikes: Number of spikes detected and eliminated forrotated v wind component (Field type: numeric) w_spikes: Number of spikes detected and eliminated for rotated w wind component (Field type: numeric) ts_spikes: Number of spikes detected and eliminated for ts variable (Field type: numeric) co2_spikes: Number of spikes detected and eliminated for co2 variable (Field type: numeric) h2o_spikes: Number of spikes detected and eliminated for h2o variable (Field type: numeric) Raw_data_2014 (described in worksheet Raw_data_2014) Description: EddyPro output of eddy covariance data collected at 52m at the top of the flux tower. There is a significant data gap, with some intermittent records available during the daytime, between 17/2/2014-17/06/2014 due to the problems in the power supply. Number of fields: 105 Number of data rows: 17520 Fields: Location: SAFE flux tower location name, as in the SAFE Gazetteer (Field type: location) date: Date of the end of the averaging period (Field type: date) time: Time of the end of the averaging period (Field type: time) DOY: decimal day of year (Field type: numeric) daytime: Daytime or nightime, 1 = daytime, 0 = nighttime (Field type: numeric) file_records: Number of valid records found in the raw file (or set of raw files) (Field type: numeric) used_records: Number of valid records used for current the averaging period (Field type: numeric) Tau: Corrected momentum flux (Field type: numeric) qc_Tau: Quality flag for momentum flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_Tau: Random error for momentum flux, if selected (Field type: numeric) H: Corrected sensible heat flux (Field type: numeric) qc_H: Quality flag for sensible heat flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_H: Random error for momentum flux, if selected (Field type: numeric) LE: Corrected latent heat flux (Field type: numeric) qc_LE: Quality flag of latent heat flux based on Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_LE: Random error for latent heat flux, if selected (Field type: numeric) co2_flux: CO2 flux (Field type: numeric) qc_co2_flux: Quality flag for CO2 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best, "5" is worst (Field type: numeric) rand_err_co2_flux: Random error of CO2 flux (Field type: numeric) h2o_flux: H2O flux (Field type: numeric) qc_h2o_flux: Quality flag of H20 flux, Göckede et al., 2006: A system based on 5 quality grades. "0" is best,

This repository contains some of the scripts and datasets used for the zEPHYR Complex terrain benchmark

Dataset associated with the publication "Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy" by Sebastiano C. D'Angelo, Antonio J. Martín, Selene Cobo, Diego Freire-Ordóñez, Gonzalo Guillén-Gosálbez, and Javier Pérez-Ramírez, available at https://doi.org/10.1039/D2EE02683J. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript and in the Electronic Supplementary Information (ESI). The structure of the dataset is here elucidated sheet by sheet: GeneralParameters: numerical values for the scaled functional unit used in the study, the world population value adopted, and the three voltage efficiencies assumed in different parts of the study. AL_BaseCase_SensECE: numerical values associated with the results for the ammonia leaf scenarios adopting a voltage efficiency of 63% (base case) and a Faradaic efficiency varying from 1% to 100%; highest, average, and lowest capacity factors for the solar power production were here used. The ammonia leaf configuration here assessed is the one including solar panels, electrolyzer, and fuel cell as key components. The results report all the ReCiPe 2016 (hierarchical approach) midpoints and endpoints and the values for the assessed planetary boundaries; the levelised cost of ammonia (LCOA) is reported, as well. AL_EtaV75_SensECE: this sheet has the structure as the previous one, but includes the results for the ammonia leaf scenario using 75% voltage efficiency, instead of 63%. The remaining assumptions do not deviate from the base case. AL_Eta100_SensECE: this sheet has the structure as the previous one, but includes the results for the ammonia leaf scenario using 100% voltage efficiency, instead of 63%. The remaining assumptions do not deviate from the base case. AL_NoFC_H2Vented_SensECE: this sheet has the same structure as the sheet "AL_BaseCase_SensECE", but includes the ammonia leaf scenario using a configuration with no fuel cell. The hydrogen by-product was here considered vented to the air. The remaining assumptions do not deviate from the base case. AL_NoFC_H2Subst_SensECE: this sheet has the same structure as the sheet "AL_BaseCase_SensECE", but includes the ammonia leaf scenario using a configuration with no fuel cell. The hydrogen by-product was here considered substituting the production of an equivalent quantity from a water electrolyzer deployed in the same location as the ammonia leaf. The remaining assumptions do not deviate from the base case. AL_BaseCase_SpatAnal_BreakFEff: numerical results for the ammonia leaf base case scenario stemming from the spatial analysis performed on a global grid of 1140 points. The yearly average capacity factors for the solar panels at each location are included, and the results portraying the breakeven Faradaic efficiency for the indicators climate change - CO2 concentration, global warming, human health, and levelised cost of ammonia were included. The assumptions for the voltage efficiency and the other parameters correspond to the base case. AL_BaseCase_SpatAnal_AbsValues: numerical results for the ammonia leaf scenarios using the base case state-of-the-art (34%) and 100% Faradaic efficiency, as well as the base case voltage efficiency of 63%. The same metrics as the previous sheet are reported. The structure of the sheet is the same as the previous one. AL_BaseCase_Breakdowns: breakdown of the same four indicators as the previous sheet for the best and worst combination of Faradaic efficiency and solar panels capacity factors, i.e., 34% Faradaic efficiency and 6% capacity factor on one side and 100% Faradaic efficiency and 26% capacity factor on the other side. The breakdown is divided into solar panels, electrolyser, fuel cell, and other elements. A further breakdown of the levelised cost of ammonia (LCOA) into capital expenditure (CAPEX) and operating expenditure (OPEX) is provided, as well. The voltage efficiency is the same as the base case, as well as the other parameters. AL_BaseCase_CAPEXSens: numerical results for the levelised cost of ammonia (LCOA) in dependence of the sensitivity on the capital expenditure (CAPEX) for the ammonia leaf configuration assessed in the base case. Two cases assuming state-of-the-art (34%) and 100% Faradaic efficiency were assumed, and lowest, average, and highest capacity factor are included. The remaining parameters do not deviate from the base case configuration. AL_gHB_BestMap: numerical results to produce the map showing the best technology between ammonia leaf (AL) and green Haber-Bosch (gHB) in the category climate change - CO2 concentration for all the assessed locations. column D shows the share of safe operating space (%SOS) for each location, while column E shows which technology was selected, where 1 is ammonia leaf and 2 is green HB. AL_BaseCase_Sensitivity: percentual variation of the results obtained assuming the base configuration ammonia leaf for a state-of-the-art Faradaic efficiency and an average capacity factor for the solar panels. The varied parameters include the voltage efficiency (columns C-D-E), the levelised cost of electricity (columns G-H-I), the electrolyser cost (columns K-L-M), the fuel cell cost (columns O-P-Q), the electrolyser environmental impact (columns S-T-U), and the fuel cell environmental impact (columns W-X-Y). CompTech_BaseCase: environmental and economic metrics characterizing the assessed Haber-Bosch scenarios (business as usual, BAU; blue Haber-Bosch; green Haber-Bosch for lowest, average, and highest solar panels capacity factor; BAU assuming natural gas spot prices in Europe in August 2022). The reported metrics are the ReCiPe 2016 (hierarchical approach) midpoints and endpoints, the planetary boundaries, and the levelised cost of ammonia (LCOA). CompTech_EtaV75: this sheet has the same structure as the previous one, but the hydrogen electrolyser used for the green Haber-Bosch scenarios was assumed to have a 10% stack efficiency improvement. The remaining parameters are the same. CompTech_EtaV100: this sheet has the same structure as the previous one, but the hydrogen electrolyser used for the green Haber-Bosch scenarios was assumed to have a 100% stack efficiency. The remaining parameters are the same. CompValues_Fig1: numerical values for yearly global warming impacts of a selection of countries, as well as for the yearly human health impacts of selected diseases and catastrophic events.

This dataset supports the findings of the article "Nature-based solutions are critical for putting Brazil on track towards net-zero emissions by 2050" from Soterroni et al. (2023) accepted in Global Change Biology.

The ozone (O3) susceptibility of cv. Williams was tested in nine independently controlled and monitored open-top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia (www.tropoz.org). The plants (27 cv. Williams) were grown under O3 fumigation in OTCs for about three months. At the end of the O3 fumigation period and when the plants were on average 97 cm in height, two leaves were collected from every plant, specifically the third most recently expanded and therefore newly mature leaf (new leaf) and the eighth-most recently expanded (old leaf) both new and old leaves having fully developed under O3 fumigation. From each leaf, two mid-lamina leaf sections ~300 cm2 from both sides of the midrib were taken and measured for total fresh weight. After weighing, and scanning to determine area, one section was, wrapped in tinfoil, snap-frozen in liquid N2 and stored at –20°C before freeze-drying for biochemical analyses; the other was dried at 70 °C to account for leaf mass lost to sampling. At the end of the experiment, leaves, midrib, pseudostem, corm and small suckers were harvested separately, and dried in an oven at 70 °C until constant weight for biomass determination. For all lamina samples collected from the OTC experiment, leaf mass per area (LMA) was calculated using the leaf dry mass (DM) obtained on freeze-dried samples and the leaf area (LA) determined by image analyser software (Image-J, NIH, Bethesda, Maryland, USA). LMA was calculated as LMA= DM/LA in units of g m−2. Freeze-dried leaf samples were subsequently ground into fine powder (Rocklabs Bench Top Ring Mill) and stored in airtight vials until determination of leaf biochemistry and stable isotope concentrations. Powdered leaf samples (~30 mg) were extracted in cold 50% acetone (Ritmejerytė et al. 2019). Total antioxidant capacity (TAC) was determined in the leaf extract by the ferric reducing antioxidant power (FRAP) assay. The assay was carried out according to Benzie and Strain (1996) with some modifications. Ascorbic acid was used as the standard and TAC was expressed as ascorbic acid equivalents (mg AAE g−1 dry weight). Total phenolic content (TPC) was measured in the same leaf extract by the Folin–Ciocalteau method (Cork and Krockenberger 1991; Singleton and Rossi 1965) with some modifications (Ritmejerytė et al. 2019). Gallic acid was used as a standard and TPC was expressed as Gallic acid equivalents (mg GAE g–1 dry weight). The carbon stable isotope ratio (δ13C, ‰) and weight percent (%C) were determined using a Costech Elemental Analyser fitted with a zero-blank auto-sampler coupled via a ConFloIV to a ThermoFinnigan DeltaVPLUS using Continuous-Flow Isotope Ratio Mass Spectrometry (EA-IRMS) at James Cook University’s Advanced Analytical Centre. Stable isotope results are reported as per mil (‰) deviations from the VPDB reference. Precisions (S.D.) on internal standards were better than 0.1‰ for δ13C. The iWUE was calculated from δ13C according to the equation of Farquhar et al. (1989). Environmental variables such as air temperature (T), air relative humidity (RH), shortwave radiation and photosynthetically active radiation (PAR) were monitored using a single meteorological monitoring station (Campbell Scientific, Logan, UT, USA) established in the central OTC. Hourly values of O3 and meteorological conditions were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. The DO3SE model was used to estimate the O3 flux into leaves. References Benzie IF, Strain JJ (1996). The ferric reducing ability of plasma (FRAP) as a measure of “antioxidant power”: the FRAP assay. Analytical biochemistry, 239(1), 70-76. Cork SJ, Krockenberger AK (1991). Methods and pitfalls of extracting condensed tannins and other phenolics from plants: insights from investigations on Eucalyptus leaves. Journal of chemical ecology, 17(1), 123-134. Farquhar GD, Ehleringer JR, Hubick KT (1989). Carbon isotope discrimination and photosynthesis. Annual review of plant biology, 40(1), 503-537. Ritmejerytė E, Boughton BA, Bayly MJ, Miller RE (2019). Divergent responses of above-and below-ground chemical defence to nitrogen and phosphorus supply in waratahs (Telopea speciosissima). Functional Plant Biology, 46(12), 1134-1145. Singleton VL, Rossi JA (1965). Colorimetry of total phenolics with phosphomolybdic-phosphotungstic acid reagents. American Journal of Enology and Viticulture, 16(3), 144-158.  # Examining ozone susceptibility in the genus Musa (bananas) [https://doi.org/10.5061/dryad.fbg79cp26](https://doi.org/10.5061/dryad.fbg79cp26) The ozone (O3) susceptibility of cv. Williams was tested in nine independently controlled and monitored open top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia ([www.tropoz.org](https://tropoz.org/)). The plants (27 cv. Williams) were grown under O3 fumigation in OTCs for about three months. At the end of the O3 fumigation period, two leaves were collected from every plant, specifically the third most recently expanded and therefore newly mature leaf (new leaf) and the eighth-most recently expanded (old leaf) both new and old leaves having fully developed under O3 fumigation from every plant for the determination of leaf functional traits such as leaf mass per area (LMA), intrinsic water-use-efficiency (iWUE), total antioxidant capacity (TAC), and total phenolic contents (TPC). LMA was calculated using the leaf dry mass (DM) obtained on freeze-dried samples and the leaf area (LA) determined by image analyser software (Image-J, NIH, [Bethesda, Maryland](https://en.wikipedia.org/wiki/Bethesda,_Maryland), USA). LMA was calculated as LMA= DM/LA in units of g m−2. TAC was determined in the leaf extract by the ferric reducing antioxidant power (FRAP) assay. TAC was expressed as ascorbic acid equivalents (mg AAE g−1 dry weight). Ascorbic acid was used as the standard. TPC was measured in the same leaf extract by the Folin–Ciocalteau method with some modifications. Gallic acid was used as a standard and TPC was expressed as Gallic acid equivalents (mg GAE g–1 dry weight). The carbon stable isotope ratio (δ13C, ‰) was determined using a Costech Elemental Analyser fitted with a zero-blank auto-sampler coupled via a ConFloIV to a ThermoFinnigan DeltaVPLUS using Continuous-Flow Isotope Ratio Mass Spectrometry (EA-IRMS) at James Cook University’s Advanced Analytical Centre. The δ13C values were used to calculate the iWUE. At the end of the experiment, leaves, midrib, pseudostem, corm and small suckers were harvested separately, and dried in an oven at 70 °C until constant weight for biomass determination. Values (dataset 1) represent OTC of three plants (n=3). Environmental variables such as air temperature (T), air relative humidity (RH), shortwave radiation and photosynthetically active radiation (PAR) were monitored using a single meteorological monitoring station (Campbell Scientific, Logan, UT, USA) established in the central OTC. Hourly values of O3 concentration and meteorological conditions were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. The DO3SE model was used to estimate the O3 flux into leaves. ## Description of the data and file structure Dataset was uploaded in three different excel sheets, Data1, Data2 and Data3 with their metadata (Data1\_Metadata, Data2\_Metadata, and Data3\_Metadata). Metadata sheets represents the parameter names, description, and units. Data1 sheet contains open top chamber averages data. Data2 sheet contains environmental variables during experimental period. Data3 sheet contains hourly values of O3 concentration and meteorological conditions that were measured for the DO3SE (Deposition of O3 for Stomatal Exchange) model. ## Sharing/Access information Data was produced from our own experimental open top chambers (OTC) built at the UK University of Exeter’s TropOz Research facility located at James Cook University’s Environmental Research Complex (ERC) on the Nguma-bada campus in far-north Queensland, Australia ([www.tropoz.org](https://tropoz.org/)). ## Code/Software Tropospheric ozone (O3) is a global air pollutant that adversely affects plant growth and productivity. While the impacts of O3 have previously been examined for some tropical commodity crops, no information is available for the pantropical crop, banana (Musa spp.). In this study, we exposed Australia’s major banana cultivar, Williams, to a range of [O3] in open-top chambers. In addition, we examined 46 diverse Musa lines growing in a common garden for variation in traits that are hypothesized to shape responses to O3: leaf mass per area, intrinsic water-use-efficiency, and total antioxidant capacity. Banana cv. Williams showed substantial susceptibility to O3. Combined our results from open-top chambers and common garden conditions suggest a substantial risk of O3 to banana production and food security throughout the tropics.

Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now. The dataset is a synthesis of literature values selected based on criteria described in the parent paper’s narrative. The files can be opened in Microsoft Excel or any other spreadsheet that can load Excel-format files.

This Upload contains the source data sets of all measurements shown in the Nature Catalysis manuscript NATCATAL-20033655A. All folder and file names correspond to the respective figure numbers of the manuscript.