Kernel Density Reconstruction for Lagrangian Photochemical Modelling. Part 1: Model Formulation and Preliminary Tests

In this paper a new approach to photochemical modeling is investigated and a lagrangian particle model named Photochemical Lagrangian Particle Model (PLPM) is described. Lagrangian particle models are a consolidated tool to deal with the dispersion of pollutants in the atmosphere. Good results have been obtained dealing with inert pollutants. In recent years, a number of pioneering works have shown as Lagrangian models can be of great interest when dealing with photochemistry, provided that special care is given in the reconstruction of chemicals concentration in the atmosphere. Density reconstruction can be performed through the so called ''box counting'' method: an Eulerian grid for chemistry is introduced and density is computed counting particles in each box. In this way one of the main advantages of the Lagrangian approach, the grid independence, is lost. Photochemical reactions are treated in PLPM by means of the complex chemical mechanism SAPRC90 and four density reconstruction methods have been developed, based on the kernel density estimator approach, in order to obtain grid-free accurate concentrations. These methods are all fully grid-free but they differ each other in considering local or global features of the particles distribution, in treating the Cartesian directions separately or together and in being based on receptors or particles positions in space

Anatomical constraints to C4 evolution: light harvesting capacity in the bundle sheath.

In C4 photosynthesis CO2 assimilation and reduction are typically coordinated across mesophyll (M) and bundle sheath (BS) cells, respectively. This system consequently requires sufficient light to reach BS to generate enough ATP to allow ribulose-1,5-bisphosphate (RuBP) regeneration in BS. Leaf anatomy influences BS light penetration and therefore constrains C4 cycle functionality. Using an absorption scattering model (coded in Excel, and freely downloadable) we simulate light penetration profiles and rates of ATP production in BS across the C3 , C3 -C4 and C4 anatomical continua. We present a trade-off for light absorption between BS pigment concentration and space allocation. C3 BS anatomy limits light absorption and benefits little from high pigment concentrations. Unpigmented BS extensions increase BS light penetration. C4 and C3 -C4 anatomies have the potential to generate sufficient ATP in the BS, whereas typical C3 anatomy does not, except some C3 taxa closely related to C4 groups. Insufficient volume of BS, relative to M, will hamper a C4 cycle via insufficient BS light absorption. Thus, BS ATP production and RuBP regeneration, coupled with increased BS investments, allow greater operational plasticity. We propose that larger BS in C3 lineages may be co-opted for C3 -C4 and C4 biochemistry requirements

Cell density and airspace patterning in the leaf can be manipulated to increase leaf photosynthetic capacity

The pattern of cell division, growth and separation during leaf development determines the pattern and volume of airspace in a leaf. The resulting balance of cellular material and airspace is expected to significantly influence the primary function of the leaf, photosynthesis, and yet the manner and degree to which cell division patterns affect airspace networks and photosynthesis remains largely unexplored. In this paper we investigate the relationship of cell size and patterning, airspace and photosynthesis by promoting and repressing the expression of cell cycle genes in the leaf mesophyll. Using microCT imaging to quantify leaf cellular architecture and fluorescence/gas exchange analysis to measure leaf function, we show that increased cell density in the mesophyll of Arabidopsis can be used to increase leaf photosynthetic capacity. Our analysis suggests that this occurs both by increasing tissue density (decreasing the relative volume of airspace) and by altering the pattern of airspace distribution within the leaf. Our results indicate that cell division patterns influence the photosynthetic performance of a leaf, and that it is possible to engineer improved photosynthesis via this approach

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

© 2016. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited. Ioannis Kioutsioukis, et al, ‘Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data’, Atmospheric Chemistry and Physics, Vol 16(24): 15629-15652, published 20 December 2016, the version of record is available at doi:10.5194/acp-16-15629-2016 Published by Copernicus Publications on behalf of the European Geosciences Union.Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O3), nitrogen dioxide (NO2) and particulate matter (PM10). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O3 than NO2 and PM10, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station's best deterministic model at no more than 60 % of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31 % compared to using the full ensemble in an unconditional way. The skill improvements were higher for O3 and lower for PM10, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion.Peer reviewedFinal Published versio

Modeled deposition of nitrogen and sulfur in Europe estimated by 14 air quality model systems: evaluation, effects of changes in emissions and implications for habitat protection

The evaluation and intercomparison of air quality models is key to reducing model errors and uncertainty. The projects AQMEII3 and EURODELTA-Trends, in the framework of the Task Force on Hemispheric Transport of Air Pollutants and the Task Force on Measurements and Modelling, respectively (both task forces under the UNECE Convention on the Long Range Transport of Air Pollution, LTRAP), have brought together various regional air quality models to analyze their performance in terms of air concentrations and wet deposition, as well as to address other specific objectives. This paper jointly examines the results from both project communities by intercomparing and evaluating the deposition estimates of reduced and oxidized nitrogen (N) and sulfur (S) in Europe simulated by 14 air quality model systems for the year 2010. An accurate estimate of deposition is key to an accurate simulation of atmospheric concentrations. In addition, deposition fluxes are increasingly being used to estimate ecological impacts. It is therefore important to know by how much model results differ and how well they agree with observed values, at least when comparison with observations is possible, such as in the case of wet deposition. This study reveals a large variability between the wet deposition estimates of the models, with some performing acceptably (according to previously defined criteria) and others underestimating wet deposition rates. For dry deposition, there are also considerable differences between the model estimates. An ensemble of the models with the best performance for N wet deposition was made and used to explore the implications of N deposition in the conservation of protected European habitats. Exceedances of empirical critical loads were calculated for the most common habitats at a resolution of 100  ×  100 m2 within the Natura 2000 network, and the habitats with the largest areas showing exceedances are determined. Moreover, simulations with reduced emissions in selected source areas indicated a fairly linear relationship between reductions in emissions and changes in the deposition rates of N and S. An approximate 20 % reduction in N and S deposition in Europe is found when emissions at a global scale are reduced by the same amount. European emissions are by far the main contributor to deposition in Europe, whereas the reduction in deposition due to a decrease in emissions in North America is very small and confined to the western part of the domain. Reductions in European emissions led to substantial decreases in the protected habitat areas with critical load exceedances (halving the exceeded area for certain habitats), whereas no change was found, on average, when reducing North American emissions in terms of average values per habitat

Insights into the deterministic skill of air quality ensembles from the analysis of AQMEII data

Simulations from chemical weather models are subject to uncertainties in the input data (e.g. emission inventory, initial and boundary conditions) as well as those intrinsic to the model (e.g. physical parameterization, chemical mechanism). Multi-model ensembles can improve the forecast skill, provided that certain mathematical conditions are fulfilled. In this work, four ensemble methods were applied to two different datasets, and their performance was compared for ozone (O$_{3}$), nitrogen dioxide (NO$_{2}$) and particulate matter (PM$_{10}$). Apart from the unconditional ensemble average, the approach behind the other three methods relies on adding optimum weights to members or constraining the ensemble to those members that meet certain conditions in time or frequency domain. The two different datasets were created for the first and second phase of the Air Quality Model Evaluation International Initiative (AQMEII). The methods are evaluated against ground level observations collected from the EMEP (European Monitoring and Evaluation Programme) and AirBase databases. The goal of the study is to quantify to what extent we can extract predictable signals from an ensemble with superior skill over the single models and the ensemble mean. Verification statistics show that the deterministic models simulate better O$_{3}$ than NO$_{2}$ and PM$_{10}$, linked to different levels of complexity in the represented processes. The unconditional ensemble mean achieves higher skill compared to each station’s best deterministic model at no more than 60% of the sites, indicating a combination of members with unbalanced skill difference and error dependence for the rest. The promotion of the right amount of accuracy and diversity within the ensemble results in an average additional skill of up to 31% compared to using the full ensemble in an unconditional way. The skill improvements were higher for O$_{3}$ and lower for PM$_{10}$, associated with the extent of potential changes in the joint distribution of accuracy and diversity in the ensembles. The skill enhancement was superior using the weighting scheme, but the training period required to acquire representative weights was longer compared to the sub-selecting schemes. Further development of the method is discussed in the conclusion

Systems biology and metabolic modelling unveils limitations to polyhydroxybutyrate accumulation in sugarcane leaves; lessons for C4 engineering

In planta production of the bioplastic polyhydroxybutyrate (PHB) is one important way in which plant biotechnology can address environmental problems and emerging issues related to peak oil. However, high biomass C4 plants such as maize, switch grass and sugarcane develop adverse phenotypes including stunting, chlorosis and reduced biomass as PHB levels in leaves increase. In this study, we explore limitations to PHB accumulation in sugarcane chloroplasts using a systems biology approach, coupled with a metabolic model of C4 photosynthesis. Decreased assimilation was evident in high PHB-producing sugarcane plants, which also showed a dramatic decrease in sucrose and starch content of leaves. A subtle decrease in the C/N ratio was found which was not associated with a decrease in total protein content. An increase in amino acids used for nitrogen recapture was also observed. Based on the accumulation of substrates of ATP-dependent reactions, we hypothesized ATP starvation in bundle sheath chloroplasts. This was supported by mRNA differential expression patterns. The disruption in ATP supply in bundle sheath cells appears to be linked to the physical presence of the PHB polymer which may disrupt photosynthesis by scattering photosynthetically active radiation and/or physically disrupting thylakoid membranes

A reporting format for leaf-level gas exchange data and metadata

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system