Global modeling of the nitrate radical (NO3) for present and pre-industrial scenarios

AbstractIncreasing the complexity of the chemistry scheme in the global chemistry transport model STOCHEM to STOCHEM-CRI (Utembe et al., 2010) leads to an increase in NOx as well as ozone resulting in higher NO3 production over forested regions and regions impacted by anthropogenic emission. Peak NO3 is located over the continents near NOx emission sources. NO3 is formed in the main by the reaction of NO2 with O3, and the significant losses of NO3 are due to the photolysis and the reactions with NO and VOCs. Isoprene is an important biogenic VOC, and the possibility of HOx recycling via isoprene chemistry and other mechanisms such as the reaction of RO2 with HO2 has been investigated previously (Archibald et al., 2010a). The importance of including HOx recycling processes on the global budget of NO3 for present and pre-industrial scenarios has been studied using STOCHEM-CRI, and the results are compared. The large increase (up to 60% for present and up to 80% for pre-industrial) in NO3 is driven by the reduced lifetime of emitted VOCs because of the increase in the HOx concentration. The maximum concentration changes (up to 15ppt) for NO3 from pre-industrial to present day are found at the surface between 30oN and 60oN because of the increase in NOx concentrations in the present day integrations

Airborne observations of trace gases over boreal Canada during BORTAS: campaign climatology, air mass analysis and enhancement ratios

In situ airborne measurements were made over eastern Canada in summer 2011 as part of the BORTAS experiment (Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites). In this paper we present observations of greenhouse gases (CO2 and CH4) and other biomass burning tracers (CO, HCN and CH3CN), both climatologically and through case studies, as recorded on board the FAAM BAe-146 research aircraft.Vertical profiles of CO2 were generally characterised by depleted boundary layer concentrations relative to the free troposphere, consistent with terrestrial biospheric uptake. In contrast, CH4 concentrations were found to rise with decreasing altitude due to strong local and regional surface sources. BORTAS observations were found to be broadly comparable with both previous measurements in the region during the regional burning season and with reanalysed composition fields from the EU Monitoring Atmospheric Composition and Change (MACC) project. We use coincident tracer–tracer correlations and a Lagrangian trajectory model to characterise and differentiate air mass history of intercepted plumes. In particular, CO, HCN and CH3CN were used to identify air masses that have been recently influenced by biomass burning.Examining individual cases we were able to quantify emissions from biomass burning. Using both near-field ( 1 day) sampling, boreal forest fire plumes were identified throughout the troposphere. Fresh plumes from fires in northwestern Ontario yield emission factors for CH4 and CO2 of 8.5 ± 0.9 g (kg dry matter)−1 and 1512 ± 185 g (kg dry matter)−1, respectively. We have also investigated the efficacy of calculating emission factors from far-field sampling, in which there might be expected to be limited mixing with background and other characteristic air masses, and we provide guidance on best practice and limitations in such analysis. We have found that for measurements within plumes that originated from fires in northwestern Ontario 2–4 days upwind, emission factors can be calculated that range between 1618 ± 216 and 1702 ± 173 g (kg dry matter)−1 for CO2 and 1.8 ± 0.2 and 6.1 ± 1 g (kg dry matter)−1 for CH4

The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing

Microbial ecology is plagued by problems of an abstract nature. Cell sizes are so small and population sizes so large that both are virtually incomprehensible. Niches are so far from our everyday experience as to make their very definition elusive. Organisms that may be abundant and critical to our survival are little understood, seldom described and/or cultured, and sometimes yet to be even seen. One way to confront these problems is to use data of an even more abstract nature: molecular sequence data. Massive environmental nucleic acid sequencing, such as metagenomics or metatranscriptomics, promises functional analysis of microbial communities as a whole, without prior knowledge of which organisms are in the environment or exactly how they are interacting. But sequence-based ecological studies nearly always use a comparative approach, and that requires relevant reference sequences, which are an extremely limited resource when it comes to microbial eukaryotes

SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism