Computational Science and Engineering (CSE) projects are typically developed by multidisciplinary teams. Despite being part of the same project, each team manages its own workflows, using specific execution environments and data processing tools. Analyzing the data processed by all workflows globally is a core task in a CSE project. However, this analysis is hard because the data generated by these workflows are not integrated. In addition, since these workflows may take a long time to execute, data analysis needs to be done at runtime to reduce cost and time of the CSE project. A typical solution in scientific data analysis is to capture and relate the data in a provenance database while the workflows run, thus allowing for data analysis at runtime. However, the main problem is that such data capture competes with the running workflows, adding significant overhead to their execution. To mitigate this problem, we introduce in this paper a system called ProvLake, which adopts design principles for providing efficient distributed data capture from the workflows. While capturing the data, ProvLake logically integrates and ingests them into a provenance database ready for analyses at runtime. We validated ProvLake in a real use case in the O&G industry encompassing four workflows that process 5 TB datasets for a deep learning classifier. Compared with Komadu, the closest solution that meets our goals, our approach enables runtime multiworkflow data analysis with much smaller overhead, such as 0.1%. Geological raw data files Kubernetes VolumeInter-workflow Data RelationshipsMulti-store Data Relationships Legend Deep learning training datasets

Combining unique high-altitude aircraft measurements and detailed regional model simulations, we show that inplant biochemistry plays a central but previously unidentified role in fine particulate-forming processes and atmosphere−biosphere− climate interactions over the Amazon rainforest. Isoprene epoxydiol secondary organic aerosols (IEPOX-SOA) are key components of sub-micrometer aerosol particle mass throughout the troposphere over the Amazon rainforest and are traditionally thought to form by multiphase chemical pathways. Here, we show that these pathways are strongly inhibited by the solid thermodynamic phase state of aerosol particles and lack of particle and cloud liquid water in the upper troposphere. Strong diffusion limitations within organic aerosol coatings prevailing at low temperatures and low relative humidity in the upper troposphere strongly inhibit the reactive uptake of IEPOX to inorganic aerosols. We find that direct emissions of 2-methyltetrol gases formed by in-plant biochemical oxidation and/or oxidation of deposited IEPOX gases on the surfaces of soils and leaves and their transport by cloud updrafts followed by their condensation at low temperatures could explain over 90% of the IEPOX-SOA mass concentrations in the upper troposphere. Our simulations indicate that even near the surface, direct emissions of 2-methyltetrol gases represent a ubiquitous, but previously unaccounted for, source of IEPOX-SOA. Our results provide compelling evidence for new pathways related to land surface−aerosol−cloud interactions that have not been considered previously.

Abstract. This paper evaluates the contributions of the emissions from mobile, stationary and biogenic sources on air pollution in the Amazon rainforest by using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. The analyzed air pollutants were CO, NO x , SO 2 , O 3 , PM 2.5 , PM 10 and volatile organic compounds (VOCs). Five scenarios were defined in order to evaluate the emissions by biogenic, mobile and stationary sources, as well as a future scenario to assess the potential air quality impact of doubled anthropogenic emissions. The stationary sources explain the highest concentrations for all air pollutants evaluated, except for CO, for which the mobile sources are predominant. The anthropogenic sources considered resulted an increasing in the spatial peak-temporal average concentrations of pollutants in 3 to 2780 times in relation to those with only biogenic sources. The future scenario showed an increase in the range of 3 to 62 % in average concentrations and 45 to 109 % in peak concentrations depending on the pollutant. In addition, the spatial distributions of the scenarios has shown that the air pollution plume from the city of Manaus is predominantly transported west and southwest, and it can reach hundreds of kilometers in length.

During the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) campaign, size-resolved cloud condensation nuclei (CCN) spectra were characterized at a research site (T3) 60 km downwind of the city of Manaus, Brazil, in central Amazonia for one year (12 March 2014 to 3 March 2015). Particle hygroscopicity (κCCN) and mixing state were derived from the size-resolved CCN spectra, and the hygroscopicity of the organic component of the aerosol (κorg) was then calculated from κCCN and concurrent chemical composition measurements. The annual average κCCN increased from 0.13 at 75 nm to 0.17 at 171 nm, and the increase was largely due to an increase in sulfate volume fraction. During both wet and dry seasons, κCCN, κorg, and particle composition under background conditions exhibited essentially no diel variations. The constant κorg of ~ 0.15 is consistent with the largely uniform and high O : C value (~ 0.8), indicating that the aerosols under background conditions are dominated by the aged regional aerosol particles consisting of highly oxygenated organic compounds. For air masses strongly influenced by urban pollution and/or local biomass burning, lower values of κorg and organic O : C atomic ratio were observed during night, due to accumulation of freshly emitted particles, dominated by primary organic aerosol (POA) with low hygroscopicity, within a shallow nocturnal boundary layer. The O : C, κorg, and κCCN increased from the early morning hours and peaked around noon, driven by the formation and aging of secondary organic aerosol (SOA) and dilution of POA emissions into a deeper boundary layer, while the development of the boundary layer, which leads to mixing with aged particles from the residual layer aloft, likely also contributed to the increases. The hygroscopicities associated with individual organic factors, derived from PMF analysis of AMS spectra, were estimated through multi-variable linear regression. For the SOA factors, the variation of the κ value with O : C agrees well with the linear relationship reported from earlier laboratory studies of SOA hygroscopicity. On the other hand, the variation in O:C of ambient aerosol organics is largely driven by the variation in the volume fractions of POA and SOA factors, which have very different O : C values. As POA factors have hygroscopicity values well below the linear relationship between SOA hygroscopicity and O:C, mixtures with different POA and SOA fractions exhibit a steeper slope for the increase of κorg with O : C, as observed during this and earlier field studies. This finding helps better understand and reconcile the differences in the relationships between κorg and O : C observed in laboratory and field studies, therefore providing a basis for improved parameterization in global models, especially in a tropical context