Integration of tree source and sink activities.

Tree crop modeling could be instrumental in facilitating integration of numerous aspects of the development, growth and physiology of fruit tree crops and provide a valuable tool for testing concepts for understanding how fruit trees work, if it could be achieved. This chapter presents a synopsis of how modeling of fruit trees was approached. It focuses on the development of a mechanistic, compartmental model of mature peach tree carbon partitioning over a growing season. The model was termed a compartmental model because carbohydrates were only distributed to the collective compartments of fruits, leaves, stems and large branches, and the trunk according to their relative demand functions as the season progressed. Roots were only given carbohydrates when the demands of all of the other organs were fulfilled. This model demonstrated that carbohydrate partitioning in trees could be modeled without deterministic, empirically derived, partitioning coefficients and was useful for indicating periods of the growing season when calculated photosynthetic assimilation was not adequate to supply calculated carbohydrate demands of growing organs. The development of the described model is so complex that the modeling work will never be fully completed. However, to demonstrate the utility of this modeling approach, it was decided to develop an L-Almond model using the same approach.

Microplastics and nanoplastics barely enhance contaminant mobility in agricultural soils

Farmland soils are prone to contamination with micro- and nanoplastics through a variety of agricultural practices. Concerns are recurrently raised that micro- and nanoplastics act as vector for organic contaminants to deeper soil layers and endanger groundwater resources. Whether and to what extent micro- and nanoplastics facilitate the transport of organic contaminants in soil remains unclear. Here we calculated the ratio between transport and desorption time scales using two diffusion models for micro- and nanoplastics between 100 nm and 1 mm. To identify micro- and nanoplastics bound contaminant transport we evaluated diffusion and partitioning coefficients of prominent agrochemicals and additives and of frequently used polymers e.g., polyethylene and tire material. Our findings suggest that the desorption of most organic contaminants is too fast for micro- and nanoplastics to act as transport facilitators in soil. Contaminant transport enabled by microplastics was found to be relevant only for very hydrophobic contaminants (logKow >5) under preferential flow conditions. While micro- and nanoplastics might be a source of potentially harmful contaminants in farmland soils this study suggests that they do not considerably enhance contaminant mobility.

Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning

The formation, properties, and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas–particle partitioning coefficients of the participating organic vapours. Since these coefficients are often difficult to measure and to compute, we developed a machine learning model to predict them given molecular structure as input. Our data-driven approach is based on the dataset by Wang et al. (2017), who computed the partitioning coefficients and saturation vapour pressures of 3414 atmospheric oxidation products from the Master Chemical Mechanism using the COSMOtherm programme. We trained a kernel ridge regression (KRR) machine learning model on the saturation vapour pressure (Psat) and on two equilibrium partitioning coefficients: between a water-insoluble organic matter phase and the gas phase (KWIOM/G) and between an infinitely dilute solution with pure water and the gas phase (KW/G). For the input representation of the atomic structure of each organic molecule to the machine, we tested different descriptors. We find that the many-body tensor representation (MBTR) works best for our application, but the topological fingerprint (TopFP) approach is almost as good and computationally cheaper to evaluate. Our best machine learning model (KRR with a Gaussian kernel + MBTR) predicts Psat and KWIOM/G to within 0.3 logarithmic units and KW/G to within 0.4 logarithmic units of the original COSMOtherm calculations. This is equal to or better than the typical accuracy of COSMOtherm predictions compared to experimental data (where available). We then applied our machine learning model to a dataset of 35 383 molecules that we generated based on a carbon-10 backbone functionalized with zero to six carboxyl, carbonyl, or hydroxyl groups to evaluate its performance for polyfunctional compounds with potentially low Psat. The resulting saturation vapour pressure and partitioning coefficient distributions were physico-chemically reasonable, for example, in terms of the average effects of the addition of single functional groups. The volatility predictions for the most highly oxidized compounds were in qualitative agreement with experimentally inferred volatilities of, for example, α-pinene oxidation products with as yet unknown structures but similar elemental compositions.

Comprehensive Insights Into the Occurrence, Source, Distribution and Risk Assessment of Polycyclic Aromatic Hydrocarbons in a Large Drinking Reservoir System

The resource, environment, and ecological value of drinking reservoirs have received widespread concerns due to the pollution of persistent organic pollutants such as polycyclic aromatic hydrocarbons (PAHs). Therefore, we comprehensively studied the occurrence, source, distribution and risk assessment of representative PAHs in Fengshuba Reservoir (FSBR) (large drinking reservoir, China). The total concentrations of 16 USEPA PAHs in the water phase, porewater phase, sediment phase and soil phase were in ranges of 109.72-393.19 ng/L, 5.75-35.15 μg/L, 364.4-743.71 μg/kg and 367.81-639.89 μg/kg, respectively. The naphthalene (Nap) was the dominant PAHs in the water phase, while it was Nap and phenanthrene (Phe) in porewater, sediment and soil phase. The main sources of PAHs in FSBR were biomass combustion. Redundancy analysis indicated that, the NTU, NO2-, NH4+, Chl-α and IC were the dominant factor influencing the PAHs distribution in water phase and the PAHs in sediment phase was affected by T and NO3-. Pseudo-partitioning coefficients indicated that the PAHs in the porewater phase was more likely to migrate to the sediment phase. Risk assessment indicated that the PAHs both in the water and sediment phases were generally in a Low-risk state, while the PAHs in the soil phase were in a Moderate-risk state and the Nap was in a High-risk state, and exposure to the PAHs in FSBR through drinking and skin exposure had little impact on consumers' health. In summary, Nap could be used as a key indicator to evaluate the existence and potential risk of PAHs in FSBR.

Molecular characterization of gaseous and particulate oxygenated compounds at a remote site in Cape Corsica in the western Mediterranean Basin

The characterization of the molecular composition of organic carbon in both gaseous and aerosol is key to understanding the processes involved in the formation and aging of secondary organic aerosol. Therefore a technique using active sampling on cartridges and filters and derivatization followed by analysis using a thermal desorption–gas chromatography–mass spectrometer (TD–GC–MS) has been used. It is aimed at studying the molecular composition of organic carbon in both gaseous and aerosol phases (PM2.5) during an intensive field campaign which took place in Corsica (France) during the summer of 2013: the ChArMEx (Chemistry and Aerosol Mediterranean Experiment) SOP1b (Special Observation Period 1B) campaign. These measurements led to the identification of 51 oxygenated (carbonyl and or hydroxyl) compounds in the gaseous phase with concentrations between 21 and 3900 ng m−3 and of 85 compounds in the particulate phase with concentrations between 0.3 and 277 ng m−3. Comparisons of these measurements with collocated data using other techniques have been conducted, showing fair agreement in general for most species except for glyoxal in the gas phase and malonic, tartaric, malic and succinic acids in the particle phase, with disagreements that can reach up to a factor of 8 and 20 on average, respectively, for the latter two acids. Comparison between the sum of all compounds identified by TD–GC–MS in the particle phase and the total organic matter (OM) mass reveals that on average 18 % of the total OM mass can be explained by the compounds measured by TD–GC–MS. This number increases to 24 % of the total water-soluble OM (WSOM) measured by coupling the Particle Into Liquid Sampler (PILS)-TOC (total organic carbon) if we consider only the sum of the soluble compounds measured by TD–GC–MS. This highlights the important fraction of the OM mass identified by these measurements but also the relative important fraction of OM mass remaining unidentified during the campaign and therefore the complexity of characterizing exhaustively the organic aerosol (OA) molecular chemical composition. The fraction of OM measured by TD–GC–MS is largely dominated by di-carboxylic acids, which represent 49 % of the PM2.5 content detected and quantified by this technique. Other contributions to PM2.5 composition measured by TD–GC–MS are then represented by tri-carboxylic acids (15 %), alcohols (13 %), aldehydes (10 %), di-hydroxy-carboxylic acids (5 %), monocarboxylic acids and ketones (3 % each), and hydroxyl-carboxylic acids (2 %). These results highlight the importance of polyfunctionalized carboxylic acids for OM, while the chemical processes responsible for their formation in both phases remain uncertain. While not measured by the TD–GC–MS technique, humic-like substances (HULISs) represent the most abundant identified species in the aerosol, contributing for 59 % of the total OM mass on average during the campaign. A total of 14 compounds were detected and quantified in both phases, allowing the calculation of experimental partitioning coefficients for these species. The comparison of these experimental partitioning coefficients with theoretical ones, estimated by three different models, reveals large discrepancies varying from 2 to 7 orders of magnitude. These results suggest that the supposed instantaneous equilibrium being established between gaseous and particulate phases assuming a homogeneous non-viscous particle phase is questionable.

Reconciling metal–silicate partitioning and late accretion in the Earth

Highly siderophile elements (HSE), including platinum, provide powerful geochemical tools for studying planet formation. Late accretion of chondritic components to Earth after core formation has been invoked as the main source of mantle HSE. However, core formation could also have contributed to the mantle’s HSE content. Here we present measurements of platinum metal-silicate partitioning coefficients, obtained from laser-heated diamond anvil cell experiments, which demonstrate that platinum partitioning into metal is lower at high pressures and temperatures. Consequently, the mantle was likely enriched in platinum immediately following core-mantle differentiation. Core formation models that incorporate these results and simultaneously account for collateral geochemical constraints, lead to excess platinum in the mantle. A subsequent process such as iron exsolution or sulfide segregation is therefore required to remove excess platinum and to explain the mantle’s modern HSE signature. A vestige of this platinum-enriched mantle can potentially account for 186Os-enriched ocean island basalt lavas.

Thermodynamic Properties Investigation of Process Volatile Organic Compounds (VOCs) and Its Transport Impact Factor in Oil Sands Management

This paper presents a new approach for the determination of volatile organic compounds (VOCs) characteristics and their migration influencing factors in oil sands management processes and reveals the relationship between different asphaltene content and different solvents. Specifically, thermodynamic (i.e., partitioning coefficients, Kr, specific retention volume, Vg, the activity coefficients, γ and enthalpy of solution, ΔH0) and their impact factors are discussed. Gas-liquid chromatography (GLC) experimental measurements were used as the test data. A range of solvents (nC5, iC5, nC6, nC7, and Toluene) has been tested in different asphalt contents (0, 2.56, 9.93, 36.86, 53.67 wt%). There are temperatures in the range of 333.2–393.2 K (with 10 K increase) were conducted, respectively. The dynamics properties of asphalt mixture are calculated, and the relation between dynamics properties of asphalt mixture and absolute temperature, asphalt content and solvent type is discussed. The results show that within the acceptable error range, partitioning coefficients, Kr, specific retention volume, Vg, and enthalpy of solution, ΔH0 and other thermodynamic properties have a good tendency to predict, they decrease with the increase in asphaltene content and temperature and increase with the increase in solute carbon number.

Predicting Gas-Particle Partitioning Coefficients of Atmospheric Molecules with Machine Learning

The formation, properties and lifetime of secondary organic aerosols in the atmosphere are largely determined by gas-particle partitioning coefficients of the participating organic vapours. Since these coefficients are often difficult to measure and to compute, we developed a machine learning model to predict them given molecular structure as input. Our data-driven approach is based on the dataset by Wang et al. (Atmos. Chem. Phys., 17, 7529 (2017)), who computed the partitioning coefficients and saturation vapour pressures of 3414 atmospheric oxidation products from the master chemical mechanism using the COSMOtherm program. We trained a kernel ridge regression (KRR) machine learning model on the saturation vapour pressure (Psat), and on two equilibrium partitioning coefficients: between a water-insoluble organic matter phase and the gas phase (KWIOM/G), and between an infinitely dilute solution with pure water and the gas phase (KW/G). For the input representation of the atomic structure of each organic molecule to the machine, we tested different descriptors. We find that the many-body tensor representation (MBTR) works best for our application, but the topological fingerprint (TopFP) approach is almost as good, and is significantly more cost effective. Our best machine learning model (KRR with a Gaussian kernel + MBTR) predicts Psat and KWIOM/G to within 0.3 logarithmic units and KW/G to within 0.4 logarithmic units of the original COSMOtherm calculations. This is equal or better than the typical accuracy of COSMOtherm predictions compared to experimental data (where available). We then applied our machine learning model to a dataset of 35,383 molecules that we generated based on a carbon 10 backbone functionalized with 0 to 6 carboxyl, carbonyl or hydroxyl groups to evaluate its performance for polyfunctional compounds with potentially low Psat. The resulting saturation vapor pressure and partitioning coefficient distributions were physico-chemically reasonable, and the volatility predictions for the most highly oxidized compounds were in qualitative agreement with experimentally inferred volatilities of atmospheric oxidation products with similar elemental composition.

Temperature dependence of the gas-particle partitioning of selected VOCs

The gas-particle partitioning coefficients for volatile organic compounds (VOCs) are difficult to acquire because discriminating the small mass fraction of the VOCs in the aerosol particle relative to that in...