dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study we develop a secondary inorganic aerosol (SIA) module for the MOCAGE chemistry transport model developed at CNRM. The aim is to have a module suitable for running at different model resolutions and for operational applications with reasonable computing times. Based on the ISORROPIA II thermodynamic equilibrium module, the new version of the model is presented and evaluated at both the global and regional scales. The results show high concentrations of secondary inorganic aerosols in the most polluted regions: Europe, Asia and the eastern part of North America. Asia shows higher sulfate concentrations than other regions thanks to emission reductions in Europe and North America. Using two simulations, one with and the other without secondary inorganic aerosol formation, the global model outputs are compared to previous studies, to MODIS AOD retrievals, and also to in situ measurements from the HTAP database.
The model shows a better agreement with MODIS AOD retrievals in all geographical regions after introducing the new SIA scheme. It also provides a good statistical agreement with in situ measurements of secondary inorganic aerosol composition: sulfate, nitrate and ammonium. In addition, the simulation with SIA generally gives a better agreement with observations for secondary inorganic aerosol precursors (nitric acid, sulfur dioxide, ammonia), in particular with a reduction of the modified normalized mean bias (MNMB). At the regional scale, over Europe, the model simulation with SIA is compared to the in situ measurements from the EMEP database and shows a good agreement with secondary inorganic aerosol composition. The results at the regional scale are consistent with those obtained from the global sim-ulations.
It is a global model that includes the capability for simulating smaller domains with finer resolutions. MOCAGE is used for simulat-ing stratospheric and tropospheric chemical concentrations (ozone for example) and also for air quality forecasts including ozone, nitrogen oxides and aerosols. Recently, new developments have been made to account for the formation of secondary inorganic aerosols in MOCAGE. This SIA module is based on the gas-aerosol equilibrium assumption like in other models. It aims to be valid at different scales and resolutions since MOCAGE can simulate simultaneously the global and regional scales thanks to grid nesting. These new developments are aimed at being used for research purposes, but also for eventually being incorporated into operational systems. Therefore, choices were made to have, at first, a simple and computationally efficient module.
The aim of this paper is to present and evaluate the MOCAGE SIA module both at the global and regional (European) scales. Section 2 presents the MOCAGE model including the newly developed secondary inorganic aerosol module. Then in Sect. 3 we define the experimental set-up of the simulations and the observations used for the model evaluation. Results are discussed in Sect. 4 for global simulations and Sect. 5 for regional simulations. Finally, Sect. 6 concludes this study.
Current aerosol module The model in its current state is able to represent primary aerosols  (Martet et al. , 2009; Sič et al. , 2015). The latest version of the primary aerosol scheme in MOCAGE has been evaluated by  Sič et al. (2015). Sič et al. (2015)  checked the aerosol physical parameterizations and proposed improvements. Based on simulations including only primary aerosols, they checked the consistency and validated the dry and wet deposition, the sedimentation and the emission processes. Concerning emissions, emission changes produced a strong impact by lowering known biases of sea salt and African dust. The wet deposition scheme changes also have a strong impact, but they are more complex to analyse. Regarding sedimentation, changes produced a less important effect. = 2. 4 × 10 4 , k 3 = 3.
New developments of the aerosol module In  Sič et al. (2015) , they only take into account primary aerosols that do not interact with each other. Therefore external mixing was assumed and each type of aerosol used specific size bins. To introduce SIA into MOCAGE, we assume aerosol internal mixing in order to represent interactions between aerosols. To implement internal mixing, we use a new set of bin sizes that are the same for all types of aerosols, ranging from 2 nm to 50 µm with size bin limits of 2, 10, and 100 nm, and 1, 2. 5, 10 and 50 µm. These new bin limits have been tested on a 1-year global simulation only with primary aerosols and compared to a similar simulation that used the aerosol-specific size bins following  Sič et al. (2015). The use of these new size bins gives similar results to when using the aerosol-dependent ones, with a resulting difference of less than 5 % in the estimation of the PM 10 and PM 2.
Two simulations, with and without secondary inorganic aerosols, are conducted for the year 2010 and are compared to the EMEP measurement data set. Both simulations have the global domain at 2 • lon×2 • lat and a nested European domain at 0. 5 • lon × 0. 5 • lat resolution. The latter domain covers the western part of the European continent between 16 • W to 36 • E and 32 to 72 • N. The two domains communicate with each other by a two-way grid nesting scheme.
Observations for global simulation evaluation MODIS daily mean AODs were used to evaluate the model simulations. For this purpose, we use the daily MODIS data level 3 (L3, collection 5. 1) for the year 2005 and perform an additional quality control and screening as presented in  Sič et al. (2015). This processing is done to minimize the number of observations that are cloud contaminated and those with statistically low confidence which often artificially increase AOD  (Zhang et al. , 2005; Koren et al. , 2007; Remer et al. , 2008). Moreover, Ruiz-Arias et al. (  2013 ) showed there is a rapid increase of the relative underestimation of AODs when the MODIS' L3 AODs are below 0. 1. We then perform an additional screening by rejecting all AOD values below 0. 05. Below this value, the underestimation of AOD leads to a mean relative error higher than 50 %  (Ruiz-Arias et al.
, 2013). AODs in MOCAGE are calculated at 550 nm using Mie theory with refractive indices taken from the Global Aerosol Data Set  (Köpke et al. , 1997)  and extinction efficiencies derived with Wiscombe's Mie scattering code for homogeneous spherical particles  (Wiscombe, 1980). For the model evaluation, we also use the database made available by HTAP. It includes data from several measurement networks: EMEP, IMPROVE, NAtChem, EANET, CREATE, EUSAAR, NILU and the WMO-PCSAG global assessment precipitation data set (http://www. htap. org/, http: //ebas. nilu. no). We use observations of gaseous concentrations (nitric acid, nitric oxides, sulfur dioxide, ammonia), and the particulate matter composition (sulfate, nitrate, ammonium). The release used here is dated from 1 April 2014. Daily observations and weekly observations are used separately in order to consider comparisons at the same temporal scale.
EMEP database The European Monitoring and Evaluation Programme (EMEP) is a scientifically based and policy-driven programme under the Convention on Long-range Transboundary Air Pollution (CLRTAP) for international co-operation to solve transboundary air pollution problems (http://www. emep. int). Observations were downloaded through the EBAS repository (http://ebas. nilu. no). Daily observations are used to evaluate secondary inorganic aerosol composition (sulfate, nitrate, ammonium) over Europe. Figure  2  represents the location of the stations measuring SIA composition on a daily basis. One can note similar remarks as for the measurements shown in Fig. 1  with a good coverage of sulfate and nitrate measurements and to a lesser extent for ammonium measurements. Nevertheless, some areas, in France for example, are not very well covered.
AIRBASE database To make a complementary evaluation, and because SIA directly affects major regulated air pollutants, we also make AIRBASE measuring stations are located on various sites: urban, periurban, rural, etc. In order to be able to compare the model simulations at the 0. 5 • longitude × 0. 5 • latitude resolution, we select the stations which are representative of the model resolution. Following  Joly and Peuch (2012) , each station is characterized by a class between 1 and 10 according to its statistical characteristics, 1 corresponding to a fully rural behaviour and 10 to a highly polluted station. The selection of stations is done following  Lacressonnière et al. (2012) , who conducted an evaluation of MOCAGE at the regional scale over several years. Only the stations corresponding to classes 1 to 5 are kept for ozone.
Atmospheric chemical composition against observations from the HTAP database In this section, we use the daily observations as one time series to calculate the statistics. This allows us to give the same weight to every observation instead of every measuring station because measuring stations do not always provide the full set of observations for the whole year. Modelled fields are interpolated to the observation location. We take the concentration at the surface, knowing that the altitude difference between the model and the actual station altitude can lead to significant differences. This is why stations with an altitude difference higher than 1000 m with the model orography have been suppressed for the statistics. After this screening, there are 98 stations left on daily observations (104 before screening).
Results and evaluation of the regional simulation The first set of simulations showed that the model was able to simulate correctly SIA on the global scale. The next step is to check the behaviour of the model over a regional domain, Europe, with a better resolution and different emission inventories. This section presents results on the second set of simulations over the year 2010 including two nested domains: the global one (at 2 • lon × 2 • lat) and a regional one (at 0. 5 • lon × 0. 5 • lat). As we have already looked at model results on a global scale (see Sect. 4), the focus in this section is put on the regional European domain. Firstly, we analyse the results before comparing them with measurements from the EMEP database for secondary inorganic aerosol components. Then we make a comparison to AIRBASE measurements from an air quality point of view.
71. Table  13  presents the statistics for the gaseous precursors of SIA. The model has a similar behaviour as on the global scale against the daily observations from the HTAP database. In both simulations, the species with the best performances is NO 2 , while the one with the worse scores is NH 3. The use of SIA mainly affects NH 3 with a very significant improvement of all statistical indicators. The differences between the model results and the observations can partly be explained by uncertainties in the emission inventories used. In  Kuenen et al. (2014) , they report uncertainties in ammonia emis-sion of about 50 %. For NO x , uncertainties are lower but still about 30 %. SO 2 only has about 10 % uncertainty. These uncertainties in emission might explain differences for species ammonia, ammonium and for nitrogen dioxide.
Feedback on the gaseous chemistry Figure  12  represents the annual mean concentrations of surface ozone for the RACM and the RACMSIA experiments. One can see a significant decrease in surface ozone, especially over oceans, between 5 and 10 ppbv. On land, concentrations are nearly the same. By being absorbed into the aerosol phase, nitric acid is not available for forming NO x again, and then the ozone equilibrium is displaced. The effect is less important over the land because of the proximity of NO x sources which drive its concentrations. Table  14  presents the statistics for ozone against hourly observations from the AIRBASE database. The statistics are very similar between the two experiments; only the MNMB is slightly better for the RACMSIA experiment. It is linked to the ozone maps showing a decrease over the ocean, while the field is similar over land.
Conclusions In this study we developed a secondary inorganic aerosol module in the MOCAGE CTM. These developments were made with the objectives of having a simple and computationally efficient module able to give good results while being able to be used in an operational framework and of being valid at different scales. We showed that the model is able to represent secondary inorganic aerosols on both the global scale and the European regional scale. The different constituents of the secondary inorganic aerosols sulfate, nitrate and ammonium simulated by the model fit well against the different observational data sets used. These databases and the AIRBASE database were also used to assess gaseous species concentrations. Comparisons show a neutral impact of SIA on SO 2 and NO 2 , a mixed impact on HNO 3 (with a much better MNMB but slightly worse FGE and correlation) and a large improvement of NH 3.
Simulations with SIA do not show a significant improvement in statistical scores for ozone. Nevertheless, there is an impact on ozone fields at the surface over the sea that is significant but very little change over land as reflected by the scores. The comparison with satellite AODs shows that the global aerosol budget is significantly better when SIA are used in the model. Finally, the model is able to perform generally very well at reproducing daily variations of SIA, as illustrated by the comparison between MOCAGE and observations at a station in Ireland. By comparing the MOCAGE model results to the AIR-BASE data set over Europe in terms of particulate matter concentration, we also showed that the model performs better with the introduction of secondary inorganic aerosols. Especially in spring (March, April, May), the MNMB of the PM 2.
Another aspect to work on is the thermodynamic equilibrium hypothesis which leads to uncertainties. To improve this, it is necessary to account for the kinetics of the transfer between the gas phase and the aerosol phase, especially for big particles  (Wexler and Seinfeld, 1990; Capaldo et al. , 2000). A third improvement would be to take into account the formation of secondary organic aerosols in order to have the complete range of atmospheric particles and be able to represent properly the different interactions and impact of aerosols. One of the final goals is to integrate this module for operational forecasts into the Prev'Air and COPERNICUS programs. The MOCAGE model will also be used to make research studies including long run simulations, for instance, for the CCMI programme (Chemistry-Climate Model initiative) and the analysis of the aerosol budget in the Mediterranean area.
comparisons with air quality indicators monitored over Europe. For this we use AIRBASE, which is a dense measurement network used for air quality issues. It is managed by the European Topic Centre on Air Pollution and Climate Change Mitigation on behalf of the European Environment Agency. For this study, we use the latest version (version 8) of the AIRBASE database (http://acm. eionet. europa. eu/databases/ airbase). AIRBASE data are used in this study to evaluate the performance of the model for PM 10 , PM 2. 5 , ozone and nitrogen dioxide. For 2010, a total of 38 countries, including the 27 European Union countries, have provided air quality data.
Acknowledgements. We would like to thank  Athanasios Nenes  for providing us with the ISORROPIA II source code (http://www. isorropia. eas. gatech. edu/). This work has been possible thanks to the AIRBASE, EMEP and HTAP databases and the EBAS database infrastructure. We also acknowledge the MODIS mission team and scientists for the production of the data used in this study. Edited by:  A. Colette.
The main reason is the companies' lack of confidence in the following points: (i) the emerging technologies,(ii) the respectful efficiency of heavy virtualization,(iii) the complete isolation of the environment between guests and host OS. Nowadays, the Docker technology offers multiple capabilities of resource isolation. It reaches an adequate level of maturity and it can be tested with big data tools. Hadoop software is a big data environment, it is based on the MapReduce Model, which was introduced by Google in 2004  (Dean and Ghemawat, 2008)  as a parallel and distributed computation model. It is largely adopted in companies and data centers; for example Facebook  (JoshBaer, 2015)  and Amazon  (JoshBaer, 2015)  use it to answer the computation needs. In this work, we study and compare the two cate-gories of virtualization.
RELATED WORKS Since the last decade, Hadoop has been interesting the scientists community. Many benchmarks and evaluation tests have been done in order to evaluate its performances or to compare it to other softwares. There are mainly two levels in the benchmark of the software Hadoop. The first one focuses on the comparison of Hadoop with the existing engine in the big data computing. For example, Fadika et al. (Fadika et al. , 2012)  benchmark Hadoop with three data-intensive operations to evaluate the impact of the file system, network and programming model on performances. Stonebraker et al. (Stonebraker et al. , 2010)  compare Mapreduce model to parallel database, they focus on the performance aspect. Pavlo et al. (Pavlo et al. , 2009a)  prove that Hadoop is slower than two state-of-theart parallel database systems, in performing a variety of analytical tasks, by a factor of 3.
1 to 6. 5. Jiang et al. (Jiang et al. , 2010)  give an in-depth study of MapReduce performance to identify bottleneck factors that affect the performance on Hadoop, they show that the best tuning of these factors improves the same benchmark used in  (Pavlo et al. , 2009a)  and  (Pavlo et al. , 2009b ) by a factor of 2. 5 to 3. 5. Zechariah et al. (Fadika et al. , 2011)  compare Hadoop, LEMO-MR and twister (three implementations of the MapReduce model). Gu et al. (Gu and Grossman, 2009)  compare Hadoop software (HDFS/ MapReduce) to the softwares Sector/Sphere. Jefrey et al. (Shafer et al. , 2010)  focus on the file system to identify bottlenecks, they identify the weaknesses of the Hadoop file system to solve and the best practices to follow in the cluster deployment. The second one focuses on the performances and the energetic consumption of Hadoop using different deployment architectures.
For example, Kontagora et al. (Kontagora and Gonzalez-Velez, 2010)  benchmark Hadoop performances using full-virtualization (using VMware Workstation). The paper  (Xu et al. , 2012)  evaluates Hadoop's performances using open-Stack, KVM and XEN. It compares performances using openStack deployment with the physical  deployment. (Gomes Xavier et al. , 2014)  compare the Hadoop software using different tools of container technology, however, neither Docker technology nor heavy technology are considered in the comparison. The Docker technology is benchmarked in other contexts as the HPC technology. For example, Xavier et al. (Xavier et al. , 2013)  present an in-depth performance evaluation of the containers based on the virtualization for HPC. They present the evaluation of the tradeoff between performance and isolation.
BACKGROUND This section contains definitions of various concepts and terms used in this work. It presents the Hadoop software characteristics and it defines the heavy and light virtualization technologies. Google introduced the model MapReduce as a distributed and parallel Model for data intensive computing. Every job generates a set of "map" and "reduce" tasks, which is executed in a distributed fashion over a cluster of machines. "Map" tasks have to be executed before "reduce" tasks. Tasks have to be executed the nearest to the needed data input. Data outputs of tasks map are transferred from the machine where tasks "map" run to the machines where "reduce" tasks run using the network.
The experiments are based on two levels, the first one considers two slave machines and the second one considers four slave machines. The slave machines can be virtual machines or containers. All experiments are repeated 5 times. The Ganglia software is used to recuperate LOAD, CPU, RAM metrics. It overloads the Hadoop cluster with 2 per cent  (Intel, 2015). The software hsflow is combined to Ganglia to retrieve I/O bound of the hard disk access on the slave machines. These metrics offer the possibility of an in-depth study in the variation in resource utilization during experiments. In order to measure energetic consumption, we use a specific engine mounted to the electrical outlet, it measures overall the energy consumption of the cluster machines every 2 seconds and save it on an external memory card.
Evaluation of the Energy Consumption The energy consumption is an important issue in the big data context i. e. Yahoo deploys a Hadoop cluster over more than 2000 servers; Facebook deploys Hadoop over 600 servers; General Electric deploys Hadoop on a cluster of 1700 servers. At this scale, the energy is a critical aspect which influences considerably the cost of cluster exploitation. A research realized by the U. S. Environmental Protection Agency  (Agency, 2007)  and the Natural Resources Defense Council  (Council, 2014)  announced in 2007 that the cost of energy consumption for cluster management was very high. The commission in the European Union defined the code of conduct on datacenter energy efficiency since 2008 (for Energy and (IET), 2015). Through the experiences, it is clear that the load and the energy consumption are proportional, when we run 4 slave machines.
CONCLUSIONS This paper has four objectives: (i) The analysis and the study of many assumptions concerning the configurations of big data platforms. (ii) The comparison of performances of the Hadoop platform with the two technologies of virtualization. (iii) The study of the variation of the performance for the case of homogeneous and heterogeneous platforms and (iv) The deals with the energy consumption on the Hadoop cluster. (refer to Section 5. 2). We can confirm that assumptions performed from experiments in the HPC domain and focus on the container technology. The deployment of the Hadoop cluster either by using traditional virtualization or containers technology, optimizes the resource exploitation and minimizes idle resources. However, using the two technologies decreases the efficiency and the cluster's performance.
The Hardware and network bandwidth are shared between guests that are localized on the host. We only consider the resource isolation, the other kinds of isolation (like user or session isolation) are not targeted in this work. The two technologies used in this paper can isolate CPU, memory and Hard disk resources. However, despite the evolution of the hard disk resource isolation (as blkio controller), the access rate to hard disks remains an open problem. The main reasons are: (i) the overload due to the workload execution or due to the number of slave machines per host. (ii) The I/O scheduling, when tasks are running; a big quantity of data is transferred between the slave machines. It ensures data replication and merging between tasks. Thus, the I/O scheduling has a direct influence on the Hadoop cluster's efficiency.
The I/O environment considers the network bandwidth and harddisk access. The Hadoop software is adapted to be used with homogeneous platforms. However, hardware technologies are changing continuously and it is not possible to ensure the same Hardware configurations when the cluster is evolving. On the other hand, our experiments argue that heterogeneous platforms degrade the performances, the main reason is being the scheduling policies because the scheduler in Hadoop is performed to work on homogenuous cluster and the only policy used to speed up the processing of the applications is to run a copy of delayed tasks on other machines. The energy consumption is directly related to the load of resources on the physical host i. e. the higher is the load of physical host, the higher is the energy consumption.