The physiological basis for estimating photosynthesis from Chla fluorescence.

Solar-induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII ) as an input (MLR-SIF). Utilizing 29 C3 and C4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level. The MLR-SIF model is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of the MLR-SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIFPSII . Once inferred from direct observables of SIF, SIFPSII provides 'parameter savings' to the MLR-SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions.

Direct tracing of NH3 and N2O emissions associated with urea fertilization approaches, using static incubation cells.

Nitrogen fertilization contributes significantly to crop production globally. However, low efficiency application management approaches lead to substantial N losses of which ammonia and nitrous oxide are known as environmental threats. Urea, the largest N fertilization source globally, is associated with high ammonia losses. A large variety of application modes are practiced under different environmental conditions worldwide. Yet, the complexity of N-processes in different soils, under changing agro-environmental conditions, challenges the evaluation of fertilization approaches efficiency in reducing N-gaseous losses. In this research a simply designed static incubation cell was connected to a Long-Path gas cell and a Fourier Transform IR spectrometer (LP-FTIR), allowing online determination of ammonia and nitrous oxide emissions in parallel to tracking mineral N-dynamics in soil samples. The static chamber was used to evaluate different application approaches of urea (i.e., incorporation or surface application with or without wetting) in a Sandy Loam and to compare surface applied regular urea vs. urea amended with the urease inhibitors NPPT+NBPT [N-(n-butyl) thiophosphoric triamide and N-(n-propyl) thiophosphoric triamide, respectively] in four different representative soils. Ammonia emissions peaked few days after application, where highest losses were observed for surface application mode. Highest emissions, up to 5% (w/w) of applied Urea-N, were obtained with the lighter and more basic soils (Sandy Loam and Loess; pH > 7.9). Nitrous oxide emissions showed a lag period of ~1 week and were higher under lower urea application rates, and when nitrification was faster (~1-1.3% (w/w) of applied N). Urease inhibitors significantly reduced ammonia losses in all tested soils and particularly in the Sandy Loam and Loess. Their effect on nitrous oxide losses were observed with the Sandy Loam and particularly after 2 weeks. The static system may underestimate realistic ammonia losses, but it offers a rather simply operated system, providing information about N-gaseous losses for improving N-fertilization management.

Detection and quantification of water-based aerosols using active open-path FTIR.

Aerosols have a leading role in many eco-systems and knowledge of their properties is critical for many applications. This study suggests using active Open-Path Fourier Transform Infra-Red (OP-FTIR) spectroscopy for quantifying water droplets and solutes load in the atmosphere. The OP-FTIR was used to measure water droplets, with and without solutes, in a 20 m spray tunnel. Three sets of spraying experiments generated different hydrosols clouds: (1) tap water only, (2) aqueous ammonium sulfate (0.25-3.6%wt) and (3) aqueous ethylene glycol (0.47-2.38%wt). Experiment (1) yielded a linear relationship between the shift of the extinction spectrum baseline and the water load in the line-of-sight (LOS) (R(2) = 0.984). Experiment (2) also yielded a linear relationship between the integrated extinction in the range of 880-1150 cm(-1) and the ammonium sulfate load in the LOS (R(2) = 0.972). For the semi-volatile ethylene glycol (experiment 3), present in the gas and condense phases, quantification was much more complex and two spectral approaches were developed: (1) according to the linear relationship from the first experiment (determination error of 8%), and (2) inverse modeling (determination error of 57%). This work demonstrates the potential of the OP-FTIR for detecting clouds of water-based aerosols and for quantifying water droplets and solutes at relatively low concentrations.

Reconstruction of passive open-path FTIR ambient spectra using meteorological measurements and its application for detection of aerosol cloud drift.

Remote sensing of atmospheric aerosols is of great importance to public and environmental health. This research promotes a simple way of detecting an aerosol cloud using a passive Open Path FTIR (OP-FTIR) system, without utilizing radiative transfer models and without relying on an artificial light source. Meteorological measurements (temperature, relative humidity and solar irradiance), and chemometric methods (multiple linear regression and artificial neural networks) together with previous cloud-free OP-FTIR measurements were used to estimate the ambient spectrum in real time. The cloud detection process included a statistical comparison between the estimated cloud-free signal and the measured OP-FTIR signal. During the study we were able to successfully detect several aerosol clouds (water spray) in controlled conditions as well as during agricultural pesticide spraying in an orchard.