dataesr/scientific-paragraphs-categorization huggingface dataset:
Software Architecture Several software such as FME, 3DCityDB, pgAdminIII and Eclipse are deployed to perform the 3D and solar irradiance analysis  (Murshed et al. , 2017). The algorithms are written in Python scripts, considering the object oriented approach (Figure  2 ). Figure  2 : Software architecture and data flow  (Murshed et al. , 2017). The LoD2 data of the CityGML format are imported in a PostgreSQL database provided with PostGIS extension, which allows treating spatial objects by creating a special structure in the database. The original CityGML file is adapted to the relational schema by transforming through 3DCityDB, which reorganizes files into specific tables. Finally, the data can be retrieved using the Python code for further calculations, analysis, and saving of results.
3D Analysis Part The 3D analysis part involves treating of CityGML data and performing necessary geometric calculations. The main steps are: a. Surface analysis, b. Definition of the objects: building, surface and surface points, c. Creation of the grid of surface points, d. Shading characterization and sky view factor calculation, e. Solar position and characterization. Figure  3  explains the different processing steps, inputs and calculated results. a. Surface analysis is the main module from which all other 3D analysis parts are called. It determines which points are "shared" i. e. , in-between two surfaces. This is done by spatial analysis and by checking for an intersection of a buffer (0. 1 m) around the point, if any other wall surface is nearby. Such an assumption helps to avoid unnecessary b.
Therefore, the calculation of irradiance on such tilted surfaces (analogous to the PV installation surfaces) is very important. The second method can be adapted to calculate radiation on such surfaces. For example, we found that the irradiance on a tilted surface is 140 W/m², which is higher than the radiation calculated earlier on the actual roof surface (127 W/m²), which has a slope less than <20° (Figure  10 ). Both models were run on the same virtual machine with standard configurations. The run time of both models with 95 buildings was almost the same (approximately 4 minutes). The multi-processing functions of python helped to improve the model run time. Based on the results, it is difficult to justify which method is more accurate and suitable for PV potential analyses, although both of them considered similar input datasets and assumptions.
CONCLUSIONS Two solar irradiation algorithms were tested in this study to perform a comparative evaluation of irradiation results with a view to assess PV potential in a city or district. For this reason, CityGML data and an open source software infrastructure was used to perform a quick but robust calculation of direct and diffuse radiation of different building surfaces. Moreover, with the semantic relationship among the points, surfaces and buildings within the CityGML data, it was possible to aggregate the results on different spatial and/or temporal (hourly, monthly, yearly, etc. ) resolutions. The testing of the model with about 12000 LoD2 buildings in the city of Karlsruhe showed the applicability of these algorithms at a city scale. Several limitations are also evident in this study.
After the air quality simulation, individual urban travels provided by OLYMPUS will be processed with the AQM-simulated concentration fields in order to create a space-time exposure budget for all individuals. Although this is beyond the scope of this article, improvement of exposure in our modeling platform is one of the great innovations brought by the development of the OLYMPUS model. In this paper, the operation and main features of the OLYMPUS model are described. The different modules will be presented individually. An application to greater Paris will be presented in the last section. The model results for this case study will be presented, evaluated and discussed.
It is thus able to take into account the possible "changes of practices" related to mobility. The same can be considered for domestic heating practices. Finally, the emission data produced by OLYMPUS allow us to build much more advanced emission scenarios than the air quality models have simulated so far (taking into account only a regulatory factor for emission abatement). From this point of view, OLYMPUS is a fairly comprehensive and innovative tool. OLYMPUS is integrated into an urban modeling platform connecting in series several urban models. It has been designed to collect city-specific input data such as morphology, population distribution and employment centers, road transport networks and public transport, and climate variables that affect emissions. Climate data are provided by a meteorological model.
In the current situation, land use, population and urban services can be obtained from surveys. When simulating a public policy scenario, these data can be pro-vided by the outputs of the NEDUM-2D model (http://www. rgte. centre-cired. fr/Rubrique-de-services/Archive-Equipe/ Vincent-Viguie/article/NEDUM-2D-model, last access: 24 October 2018) included in the platform and simulating an urban organization corresponding to an economic, environmental and urban planning scenario on a horizon given time. The first step in OLYMPUS is to simulate a synthetic population, its properties (such as age, type of household) and its spatial distribution in order to describe, count and then spatialize individual activities within the area. Then, OLYMPUS uses activity-based emission factors to produce a spatially based emission inventory for non-methane volatile organic compounds (NMVOCs), nitrogen oxides (NO x ), carbon oxides (CO and CO 2 ), SO 2 and primary particles.
The creation of this synthetic population is based on the reconstitution of surveys in the GAIA module (panel a of the flowchart in Fig. 1 ). The second task of OLYMPUS is to provide a transportation database built by taking into account the lifestyles of individuals. This database is obtained from successive diagnoses on the generation of individual trips -zonal attractiveness, spatial and temporal distribution of activities, transport supply, and choice of routes. In the OLYMPUS modeling process, this task is based on three modules. -A first module, THEMIS (b), defines the accessibility and attractiveness of the different administrative units of the city, as well as the average time travels between them. -An activity-based travel demand (ABTD) module called MOIRAI (c) computes the daily activity patterns of all agents.
For buildings, emission calculation methodologies are also taken from the EEA guidebook (European Environment Agency, 2013). The computation of pollutant emissions is carried out by the VULCAN module (f). All running OLYMPUS scripts are shell based, Python 2. 7 programmed, and C compiled for faster execution speed. For network graphs and spatialized data analysis, NetworkX  (Hagberg et al. , 2008) , as well as GeoPandas and NetCDF, libraries were included in the Python interpreter. Due to the large number of computation loops, the model applies data parallelism that consists of partitioning the data with a multithreading approach. 3 The synthetic population generator (GAIA) The GAIA synthetic population generator is the first OLYM-PUS module to be run. It allows for the generation of a synthetic population representative of a given urban area.
The synthetic population generator uses mainly urban census data to assign each agent in this population an age, gender and main activity, as well as socioeconomic parameters such as possession of a driver's license. The module distributes this synthetic population over the modeled territory through an urban zoning based on household densities in the urban area -an exogenous variable provided to the model. In the end, we obtain a synthetic population based on census data or demographic scenarios, with an individual description of its agents as the specific contribution of GAIA. There are several statistical techniques for estimating the characteristics of a population in a restricted area, as reported in  Rahman (2017). The most common method is the iterative proportional fitting (IPF) procedure  (Deming and Stephan, 1940; Beckman et al.
This approach makes it possible to create (on the basis of a representative sample) a database that distinguishes each individual (disaggregated data) as well as each household and dwelling by assigning their own characteristics. These two methods differ in how to generate a built-in population but the results are recognized as quite comparable, although one of the main interests of the IPF procedure is its ability to generate greater variability than conditional probabilities in the population. Despite this, the use of the IPFP in a region such as the Île-de-France would require considerable work to structure the input data. For this reason, we decided to use the conditional probability approach, which has been widely used in the field of transport demand modeling  (Antoni et al. , 2010; Banos et al.
Urban structure and population properties The prerequisites for population generation are the definition of the domain and the classification of urban areas on the basis of an urban density index (UDI). GAIA discretizes the type of urban area on a scale of 1 to 3 (SUBURBAN -UR-BAN -CENTER) according to the population density. The UDI is defined in Eq. (  1 ). It is the result of the classification of the dataset into three large sets of urban density by applying a linear division of the domain from the sparsely populated areas to the very dense areas. It is based solely on real population density and the population is digitized ac- cording to the logarithm of population density. UDI(z) =          CENTER, if log 1 + n hh A > ∝ 2 URBAN, if ∝ 1 < log 1 + n hh A < ∝ 2 SUBURB, if log 1 + n hh A < ∝ 1 , (1) where ∝ 1 and ∝ 2 are key classification values depending on the logarithm of household density, n hh is the number of households, z is a specific area of the domain and A is the surface area.
All of these data will be analyzed at the finest accessible spatial scale, called the transport analysis zone (TAZ), which can be a district, a subdistrict, a municipality or any other division of the city. The resolution of the data foreshadows the refinement of the mobility of the population. The modeling of urban road transport is organized in three stages: 1. determination of the attractiveness and accessibility of the different zones that make up the domain; 2. restitution of the agent travels on the basis of the realization of the programmed activities; and 3. assignment of motorized trips on the road network.
TAZ accessibility and attractiveness (THEMIS) The operating diagram of THEMIS is presented in Fig. 3a. The main steps are definition of accessibility and computation of attractiveness. One of the key forcing data of the ABTD model (ABTDM) is the accessibility of the TAZ, which provides the basis for A. Elessa Etuman and I. Coll: OLYMPUS: an air pollutant and GHG urban emissions model mobility choices. Accessibility is calculated from all the activities considered useful for the agents within a given radius, its value also taking into account the public transport service in this area. For this purpose, THEMIS analyzes the population density, road network and public transport network of the TAZ. The result is an index with five levels of accessibility accounting for public and individual transport to the area and called the urban transport accessibility index (UTAI).
Activity-based travel demand (MOIRAI) The ABTD module MOIRAI simulates the mobility choices for each agent in the synthetic population during the day. One of the challenges of the module is to represent mobility in the most realistic way possible, taking into account the social constraints of each agent in space and time. Several ABTDMs exist in the literature. Malayath and Verma (2013)  proposed a review of existing models and their uses. Based on this review, we decided to use random utility theory to simulate the choice of individuals in MOIRAI. In this theory, a stochastic approach makes it possible to take into account the rationality of agent decisions. That is, the decision is described as the choice of the agent to do what is most useful, depending on the opportunities available. In this process, utility is generally divided into two components, one describing the observed practices and the other describing the random component.
The probability P trips (x) is derived from a specification of the number of trips per day based on age and priorities. This number of daily trips varies from country to country. It can be provided by local surveys or estimated from an aggregated survey database. We used information from household travel surveys, which indicates that the mobility of children and the elderly is lower than that of the population aged 20-60 years. After determining the number of activities of the agent and establishing an order of priority, MOIRAI defines the sequence of trips. Sequences can start from home (home-based sequence, HBS), include multiple home returns (multiple  The probability of making one or more circuits depends on the number of daily trips. After generating the agent schedule, the module locates each activity.
Assignment The transport demand previously generated by the ABTD module MOIRAI generates travel matrices providing information only about the origins and destinations of the flows. The next step is to project on our modeling grid the paths taken by the agents in order to provide spatialized pollutant emissions from transport. For this purpose, we only take into account flows related to private vehicle use. There are three ways to handle traffic assignment. One is the microscopic approach, which manages the traffic at the scale of each vehicle, as proposed by models such as VISSIM  (Gomes and May, 2004) , AIMSUN  (Barcelo et al. , 1989)  and PARAMICS  (Cameron et al. , 1994). A second approach is that of mesoscopic models, which are interested in the evolution of sets of vehicles, like the models CON-TRAM  (Taylor, 2003)  and DYNASMART  (Mahmassani et al.
, 2005). Both approaches are not very compatible with the scale of the city, on which we focus. Indeed, although the use of instant-emission models like PHEM  (Rexeis et al. , 2013)  and MOVES (U. S. Environmental Protection Agency, 2013) can provide added value, obtaining input traffic data describing each cycle of acceleration and deceleration of the vehicle is quite difficult, and their consideration requires high computational time. These constraints make the microscopic approach somewhat precarious. We must therefore rely on a macroscopic description of the traffic in the form of a vehicle flow and using global variables such as the average speed on each section of a traffic axis, as in the DAVISUM (Broquereau, 1999) and TransCAD (Caliper Corporation, 2010) models. As most of these transport models are not open source, we opted for the development of our own traffic assignment model within the OLYMPUS ensemble: HERMES.
Road emissions Road transport emissions -referred to as mobile emissions in the inventory -are calculated on linear road sections where traffic properties at a given time are homogeneous (driving cycle, average speed). For passenger vehicles, the traffic flows are derived from the travel matrices of the assignment module. From a quantitative point of view, emission factors based on traffic characteristics are applied to each section of road in order to obtain the quantities of pollutants emitted into the atmosphere per unit of time. In the literature, three main databases provide exhaust emission factors. These are HBEFA  (Keller et al. , 2017) , COPERT  (Ntziachristos et al. , 2009)  and MOBILE6  (US EPA, 2003). They differ in that some depend on instantaneous speeds, while others consider average driving speeds or apply to specific driving cycles such as standard highway traffic.
In this area, the quality of the available input data would allow for greater robustness and better reliability of the simulations. We conducted a 1-year simulation with OLYMPUS in the Paris region. The input data selection, working assumptions and configurations selected for the OLYMPUS model are described below. The results of the emissions calculations are then analyzed. The source code can be obtained from the LISA website at http://www. lisa. u-pec. fr/~aelessa/OLP (Elessa Etuman, 2018)  or upon request to the authors.
Configuration The simulations were carried out for the year 2009, for which we had a large database of input data (surveys, censuses, inventories). The simulation domain is the Île-de-France region (greater Paris). It is a monocentric urban area with a population density of 21 000 inhabitants km -2 in the city center and a density that decreases radially to the remote suburbs, which are predominantly rural (Fig. 10 ). The population in this territory is greater than 11 million inhabitants. In terms of transport infrastructure, Paris is considered by the Institute for Transportation and Development Policy as the city with the most efficient network  (Marks, 2016). Individual mobility is 3. 87 trips per person per day on average, with 41 million trips made each day in the region. The majority of trips (70 %) do not include travel to the center of the metropolis: trips in Île-de-France are mostly short (4.
4 km on average) and close to home. In OLYMPUS, the computing space unit is TAZ. Here, it comes from the National Institute of Statistics (INSEE), which has set up a specific division of the territory called IRIS that gathers between 1800 and 5000 inhabitants. For our domain, this choice leads to the constitution of 1300 TAZs. Figure  11  illustrates the division of the region into IRIS, as used by OLYMPUS. The modeling of anthropogenic combustion emissions resulting from individual activities requires a very large amount of data. The main sources of these data are shown in Table  1. To generate the synthetic population in GAIA, we used aggregated data from the census of the city, mainly derived from INSEE. They include the distribution of the population in the territory by age and gender, the number of households by IRIS, and the average distribution of households by type (single, couples, family, single-parent family).
Mobility calculations in MOIRAI are based on several types of data, mainly surveys or national statistical databases. First, the accessibility of the area by public transport was assessed on the basis of the density of the public transport networks provided by the STIF regional transport agency. Regarding the attractiveness of urban subareas, the creation of attractive WORK zones is based on INSEE census data, which provide the number of jobs per municipality. The average distance at which agents can be attracted to an occupational zone is then deduced from the city's overall transport survey  (STIF, 2012). The total mobility of the agents is also conditioned by the total number of trips per day weighted according to the number of priorities of the agents. Here, the average number of daily trips was estimated from local surveys, and we assumed that trips other than those related to commuting were characterized by the same average distance, which is not the case in reality.
Thus, based on the low distribution error (in %) of   the labor force, we consider the model to generate an acceptable synthetic population for transport modeling. Finally, the average attributes of the agents used as forcing in an aggregated form are correctly represented in the synthetic population: gender data, unemployment rate, distribution of household types and average household size. Because OLYMPUS relies on Bayesian statistics to generate a synthetic population, to obtain representative results it is necessary to initially have a large database containing specific information on the distribution of agent characteristics in the population. Thus, thanks to the transcription of stochastic variables, the synthetic population has great similarities with the population studied. Nevertheless, this approach produces limited variability in socioeconomic parameters within the distribution, offering a simulated population too close to the average characteristics of the actual population.
In this simulation, we limited ourselves to the use of a three-level UDI and the division of the domain into 1300 TAZ. In order to define the most sensitive components of the urban system modeled with OLYMPUS, it will be interesting to test the sensitivity of the outputs of the model to the increase in the spatial variability of the properties of the agents, to the use of a wider range of indices and to a greater number of TAZs. Agent mobility modeling was carried out using the OD matrices of home-work trips generated by MORAI based on data from the employment survey in greater Paris. Figure  12a  and b illustrate the mobility of the synthetic population modeled by OLYMPUS. Figure  12a  shows the complete set of routes built by OLYMPUS, while Fig. 12b  shows the saturation of the road network more specifically in terms of volume on capacity (VOC).
The total emissions of OLYM-PUS are then compared to two reference emission inventories: the AIRPARIF air quality network inventory and that of the European network EMEP. To this end, we have extracted for each inventory the activity sectors corresponding to the emissions calculated by OLYMPUS. The comparison is presented as histograms in Fig. 13  for NO x and for two size sections of particulate matter: PM 2. 5 and PM 10. It should be noted that the calculation methods differ between the two "reference" inventories: AIRPARIF develops bottom-up approaches from local data collection, while EMEP inventories are derived from national totals by species, which are spatially disaggregated using top-down approaches. In addition, the comparison with the EMEP inventory cannot be done in detail due to the lack of information on the subsectors of activity in the EMEP data.
An earlier study by  Timmermans et al. (2013)  confirms that the observed discrepancies in emissions are consistent with the fact that different approaches are used. Indeed, the authors indicate that the expected gap between emission inventories based on different modeling assumptions (choices on the cold-start fraction, fuel evaporation emissions modeling, engine fleet) is expected to be 20 % at the minimum. By contrast, particulate matter emissions related to abrasion seem to be more severely underestimated by OLYMPUS compared with the AIRPARIF database (-30 %), but there are currently very few ways to estimate real emission values. Deviations from the EMEP inventory are greater, which can be explained by the fact that the EMEP approach is coarse and strongly overestimates some emissions compared with the AIRPARIF inventory.
Finally, among the developments envisaged for the model, it would also be interesting to test the implementation of time-dependent attractiveness classes, which may modify the spatial distribution of mobility outside working hours. In addition, the sequencing of activities that influence the temporal variability of the activities is a parameter that can be improved by adding a specific program according to the characteristics of the agents in the synthetic population. In addition to OLYMPUS developments, one of our main prospective goals is to implement OLYMPUS in other cities with different morphologies in order to test the transposability of the model and to have a broader vision of city responses to political forcing.
Acknowledgements. This work was performed using HPC resources from  GENCI-CCRT  (grant  2017-t2015017232 ). This work also received support from the  French National Agency for Research  ( ANR-14-CE22-0013 ) and the  Île-de-France region  ( DIM R2DS ). We also acknowledge AIRPARIF and the  LABEX Urban Futures. Edited by: Christoph Knote Reviewed by: two anonymous referees.