Using the fast-repetition-rate fluorometry (FRRF) technique to monitor the variable yield of chlorophyll fluorescence emission (Fv = Fm − Fo), we develop a pulse sequence to measure flux bottlenecks that limit the transport of electrons and protons from their water oxidation source through the photosynthetic electron transport chain (PETC) into CO ₂ carboxylation and triose phosphate production within the Calvin–Benson–Bassham (CBB) cycle of living organisms. FRRF uses a single source of microsecond flashes to excite photochemistry and produce fluorescence, thus removing slower non-photochemical quenching contributions to total Fv (pFv = total Fv − NPQ). pFv is the fraction of total Fv that originates from net charge separation and recombination in photosystem II: P ↔ P∗ ↔ P ⁺Q _A ⁻ ↔.. Subsequent charge-transfer steps are monitored, revealing fluxes from water oxidation (reducing P ⁺) into downstream carrier pools of the PETC (oxidizing Q _A ⁻). As these pools fill in sequence, Q _A/Q _A ⁻ repopulates at characteristic lag times as measured by pFv dynamics. Three temporal phases of pFv are observable in various phototrophs performing C ₃ photosynthesis that reveal five flux bottlenecks between redox carriers in the PETC and CBB cycle. pFv dynamics reveal that the PETC and CBB cycles operate synchronously, governed by the law of mass action and microscopic reversibility, with CO ₂ accelerating upstream reactions in both linear and cyclic electron pathways. pFv fluxes reliably monitor species-dependent phenotypic changes in PETC/CBB bottlenecks that determine carboxylation rate and influence cellular growth rate. This versatile technique can be applied to monitor the effects of transgene or environmental changes on light-use efficiency in diverse photosynthetic organisms.

Introduction – While CO₂ enrichment studies have provided valuable insights into how plants respond to elevated CO₂ levels expected under future climate conditions (up to ~700 ppm), if and how photosynthesis can adapt to more extreme CO₂ levels in nature remains yet unknown. Here, we investigate changes in photosynthetic metabolism in a C3 plant growing near high CO₂ emission sources in Yellowstone National Park, at levels up to 6000 ppm CO₂. Methods – We analyze chlorophyll variable fluorescence emission kinetics to identify changes in the photosynthetic electron transport chain (PETC) between plants adapted to high CO₂ versus control plants growing at ambient CO₂. By applying short flashes using the Fast Repetition Rate Fluorometry (FRRF) technique we separate photochemical from non-photochemical quenching events. This enables monitoring electron transport from water oxidation within PSII into the downstream carrier pools (Plastoquinone (PQ), Ferrodoxin, and NADP⁺) of the PETC and into CO₂ carboxylation within the Calvin Benson-Bassham (CBB) cycle. Results – We find that plants growing near the extreme CO₂ sources in Yellowstone are much slower to overcome bottlenecks in the PETC and reach lower photochemical Fv/Fm values indicative of lower photosynthetic efficiency. We observe that these plants require higher CO₂ levels to activate the reactions in the CBB cycle. Discussion – We conclude that plants growing near high CO₂ sources are metabolically poised in Cyclic Electron Flow (CEF) at ambient CO₂ concentrations and switch to Linear Electron Flow (LEF) only in high CO₂. Second, after a brief period of dark time (3–4 minutes), the near-source plants more rapidly restore the PETC bottlenecks, slowing LEF and recovering CEF. This outcome retains electrons in the PQH₂ pool slowing flux into the NADP⁺ pool through LEF and blocking utilization of CO₂ by reactions in the CBB cycle. We explain this adaptive response as a coping mechanism against acidification at high CO₂ growth conditions, which compromises enzymatic activity and proton motive force generation. We conclude that plants adapt to extreme atmospheric CO₂ by further compromising the inefficient CBB cycle and by rerouting electron flux through the PETC from LEF into CEF to stimulate more proton pumping and ATP production.

Wind machines are increasingly used to mitigate spring frost damage in agricultural sectors. Complementing quasi-3D temperature measurements to quantify the warming effects of wind machines (Dai et al., 2023), this study develops a numerical model to quantify warming effects on air and plant tissues and resolve the dynamic interplay between turbulent rotating plumes and canopy structure. We implement an integrated model in a large-eddy simulation and validate the model against field observations. Simulation results show remarkable agreement with the air mixing and warming effects observed during wind machine operation in Dai et al. (2023). Simulation results reveal significant air and leaf warming near the wind machine due to direct jet-mixing. Beyond 20 m from the machine (3–4 rotor diameters), while wind velocities drop rapidly, the warming is sustained and gradually decreases over distance. This sustained warming, without direct jet mixing, likely results from the advection of jet-entrained warm air. The warming extends 150 m upstream and 550 m downstream, influenced by the background wind. This difference is attributed to the interaction between the machine-induced jet and the background wind, forming convergence patterns when jets oppose the wind and extended warming plumes in wave-like patterns when jets align with the wind. Cross-stream warming symmetrically extends about 250 m. Within these warming regions, leaf temperatures closely follow air temperatures due to strong turbulent heat exchanges. Outside the warming zone, radiative cooling prevails, bringing the leaf–air temperature difference back to approximately 1 degree. These findings collectively give new insights into interactions between the induced warming plumes and air flows within the canopy and provide a useful tool to optimize operational wind machine deployment. This integrated model uniquely provides a full, multi-process representation of outdoor reality with respect to wind machine operation in orchards.

Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.

In this study, we present an extension to the Monin–Obukov similarity theory (MOST) for the roughness sublayer (RSL) over short vegetation. We test our theory using temperature measurements from fiber optic cables in an array-shaped set-up. This provides a high vertical measurement resolution that enables us to measure the sharp temperature gradients near the surface. It is well-known that MOST is invalid in the RSL as the flow is distorted by roughness elements. However, to derive the surface temperature, it is common practice to extrapolate the logarithmic profiles down to the surface through the RSL. Instead of logarithmic behaviour defined by MOST near the surface, our observations show near-linear temperature profiles. This log-to-linear transition is described over an aerodynamically smooth surface by the Van Driest equation in classical turbulence literature. Here we propose that the Van Driest equation can also be used to describe this transition over a rough surface, by replacing the viscous length scale with a surface length scale L_s that represents the size of the smallest eddies near the grass structures. We show that L_s scales with the geometry of the vegetation and that the model shows the potential to be scaled up to tall canopies. The adapted Van Driest model outperforms the roughness length concept in describing the temperature profiles near the surface and predicting the surface temperature.

Wind machines have been increasingly used for frost damage mitigation in the agricultural community. During radiative frost nights, wind machines are used to erode near-surface thermal inversion by air mixing. The underlying mixing processes remain poorly understood. A full picture of warming effects caused by air mixing requires measurements with wide coverage and high resolution. Our study aimed to quantify the magnitude and area of warming by air mixing and identify the characteristic mixing processes downwind and upwind. We installed 9 km of fiber optic cables in a 6.75 ha orchard block, creating two horizontal planes and three vertical profiles. Quasi-3D temperature responses with spatial sampling and temporal resolution of 25 cm and 10 s, respectively, were obtained before and during machine operation. We found a 50% reduction of the local inversion strength (8 K) over 0.42 ha at 1 m and 0.46 ha at 2 m height. The warming area for a 30% reduction extends to 2.81 and 2.52 ha, respectively. As the propeller rotates 360°, the weak background wind substantially impacts the air mixing processes downwind and upwind. When jets blow along with background wind, the warming plumes arrive earlier than the jet due to horizontal advection from earlier warmed sections. The warming plumes consequently accumulate downwind and penetrate deep into the canopy. In contrast, in upwind direction, wind drag resistance causes warming plumes arrive later than the jet. Quadrant analysis reveals that flux transport during the machine operation is dominated by sweeping and ejection motions. Intermittent downdrafts of warm air and updrafts of cool air result in efficient vertical heat exchange. This feature makes wind machines highly effective in raising canopy airspace temperature to mitigate frost damage.

Norovirus infections are among the major causes of acute gastroenteritis worldwide. In Germany, norovirus infections are the most frequently reported cause of gastroenteritis, although only laboratory confirmed cases are officially counted. The high infectivity and environmental persistence of norovirus, makes the virus a relevant pathogen for water related infections. In the 2017 guidelines for potable water reuse, the World Health Organization proposes Norovirus as a reference pathogen for viral pathogens for quantitative microbial risk assessment (QMRA). A challenge for QMRA is, that norovirus data are rarely available over long monitoring periods to assess inter-annual variability of the associated health risk, raising the question about the relevance of this source of variability regarding potential risk management alternatives. Moreover, norovirus infections show high prevalence during winter and early spring and lower incidence during summer. Therefore, our objective is to derive risk scenarios for assessing the potential relevance of the within and between year variability of norovirus concentrations in municipal wastewater for the assessment of health risks of fieldworkers, if treated wastewater is used for irrigation in agriculture. To this end, we use the correlation between norovirus influent concentration and reported epidemiological incidence (R²=0.93), found at a large city in Germany. Risk scenarios are subsequently derived from long-term reported epidemiological data, by applying a Bayesian regression approach. For assessing the practical relevance for wastewater reuse we apply the risk scenarios to different irrigation patterns under various treatment options, namely “status-quo” and “irrigation on demand”. While status-quo refers to an almost all-year irrigation, the latter assumes that irrigation only takes place during the vegetation period from May - September. Our results indicate that the log-difference of infection risks between scenarios may vary between 0.8 and 1.7 log given the same level of pre-treatment. They also indicate that under the same exposure scenario the between-year variability of norovirus infection risk may be > 1log, which makes it a relevant factor to consider in future QMRA studies and studies which aim at evaluating safe water reuse applications. The predictive power and wider use of epidemiological data as a suitable predictor variable should be further validated with paired multi-year data.

Plant transpiration accounts for about half of all terrestrial evaporation. Plants need water for many vital functions including nutrient uptake, growth and leaf cooling. The regulation of plant water transport by stomata in the leaves leads to the loss of 97% of the water that is taken up via their roots, to the atmosphere. Measuring plant-water dynamics is essential to gain better insight into its roles in the terrestrial water cycle and plant productivity. It can be measured at different levels of integration, from the single cell micro-scale to the ecosystem macro-scale, on time scales from minutes to months. In this contribution, we give an overview of state-of-the-art techniques for plant-water dynamics measurement and highlight several promising innovations for future monitoring. Some of the techniques we will cover include: gas exchange for stomatal conductance and transpiration monitoring, lysimetry, thermometry, heat-based sap flow monitoring, reflectance monitoring including satellite remote sensing, ultrasound spectroscopy, dendrometry, accelometry, scintillometry, stable water isotope analysis and eddy covariance. To fully assess water transport within the soil-plant-atmosphere continuum, a variety of techniques are required to monitor environmental variables in combination with biological responses at different scales. Yet this is not sufficient: to truly account for spatial heterogeneity, a dense network sampling is needed.