dataesr/scientific-paragraphs-categorization huggingface dataset:
1  where the contributions are represented per country. Local planning authorities (e. g. municipality) represent 25% of the respondents whereas universities, research institutions, environmental agencies represent each, about 20%. The final purpose of this review is to contribute to improved knowledge on integrated assessment for air quality plans on the regional and local scales and to improve the use of scientific knowledge by policy makers and regulatory bodies across member states. In the present paper we focus only on the second topic of the database, i. e. on the air quality assessment. Companion papers  (Rasoloharimahefa et al. , 2016; Carnevale et al. , 2016; Viaene et al. , 2016)  are devoted to the other aspects of the database. Section 2 details the approach followed to review the methodologies used to assess air quality, along 5 subtopics detailed here per section: Section 3 gives an overview of existing integrated assessment tools; Section 4 focuses on their air quality modelling component; A particular attention is given to resolution and downscaling in Section 5, whereas emissions and other model inputs are discussed in Section 6; finally, we assess in Section 7 the way in which measurements and modelling are associated within these methodologies both in terms of data-assimilation and evaluation of the model performance.
The final air quality plan indicators (e. g. mean concentration, percentiles. ) were also addressed. For most questions, a series of options was proposed. The feedback from several air quality planners was asked to improve the questionnaire and make sure all questions and proposed answers could be fully understood. The questionnaire was then made available to all. In the following sections, 5 subtopics are analyzed. For each of them the methodological state of the art is presented first while the database is used in a second step to compare current practices with the state of the art. It is important to note that while the state of the art is organized as a review of best practices, independent of the APPRAISAL database, current practices rely on the APPRAISAL database of methodologies that provides an overview based on the available responses to the questionnaire.
Current practices The APPRAISAL database gives us the possibility to compare current practices with the state of art. From this analysis, it appears that IAM is mainly used for defining mitigation measures and planning (Fig. 2 ). In this respect the main difference between AQP and RP lies in the proportion of IAM used for source apportionment. This application of IAM mainly occurs in "air quality plans" (35% of the AQP) but significantly less in "research projects (roughly 10% of the RP). This might be related to the fact that a quantitative allocation of sources is required in the current legislation while a RP is not necessarily aiming at identifying and/or quantifying the causes of exceedances. 'Scenario analysis' is most frequently used (Fig. 3 ), both in AQP (more than 60% of the cases) and RP (roughly 30% of the cases).
Current practices The APPRAISAL database indicates that National, Regional and Local Authorities use a large variety of air quality models to design their Air Quality Plans and assess their impacts on air quality. In total thirty-three different model names are mentioned. The most popular are Eulerian models (CAMx with 8 citations and CHIMERE with 11), CALPUFF is cited 6 times in our sample, but finally also traffic models are included (IMMIS, PROKAS and OSPM) with more than 5 citations. The many different models that are used today are a clear indication that no standard reference model currently exists. It is also interesting to note that in many AQPs, more than one model is used: three or more are used in more than 30% of the cases, while about 30% of the AQP refer to the use of two models and about 40% to a single model.
Regarding research projects a single model is used in 45% of the cases, two in 15% and three or more in 40% of the cases sampled. In these projects CHIMERE is the most often used chemical transport model. It is important to stress however that in one reported case, no air quality model is used. Information about modelling methodologies is in general available since approximately 70% and 85% of the models referred to by the APPRAISAL database contributors are included in the EEA Model Documentation System, for AQP and RP, respectively. If we analyze the responses in terms of model types, Eulerian models are the most used with 30 and 60% for AQP and RP, respectively (Fig. 5 ) which is not surprising since Eulerian modelling can be applied from the regional down to the local scale (see also Section 4).
Current practices The APPRAISAL data base allows for an evaluation of current practices concerning model input data, i. e. emission inventory, meteorological data and boundary conditions. From the responses received, it is noted that the scale and resolution of the emission inventory in general is in good agreement with the scale and purpose of the study (and model). Studies at the national level generally use emissions from national official inventories while studies that focus on the regional or urban (1-5 km), to local (up to 1 km) and street level scale use project specific emission data. In principle the resolution of the modelling system should be in line with the resolution of the emission inventory but among the 59 questionnaires, Fig. 6. Main scope for air quality assessment with respect to spatial scale for AQP (blue) and RP (red).
g. public power) which are a disaggregation of macro-sectors activity, -activity levels (SNAP level 3, e. g "combustion plants 300 MW (boilers)") which are a disaggregation of sectors levels. For each disaggregation level, more details can also be added with definition of fuel specification. Emission inventories with disaggregation to the sector activity and activity levels are most commonly used (Fig. 7 ). Together they cover one half of the questionnaires. Only 10% of the studies use a macro-sector disaggregation level. A combination of different level of disaggregation is often used. Fuels specification is used in more than 50% of the cases. According to the database, there is no relation between the category disaggregation level and the spatial scale of the study. Concerning the approach used to set up the inventory, a combined approach using both a bottom-up and top down methodology is most common (58%).
Current practices In about 85% of the assessment studies reported in the APPRAISAL database, measurement data is used as complementary information to the modelling results. In more than half of those studies measurement data are used for model evaluation. Apart from model evaluation, measurement data are used for postprocessing (20%), boundary conditions of local or street canyon models (13%), model calibration (9%) and/or data assimilation (4%). The measurement data used for these applications are collected by monitoring stations in an automated network in 75% of the cases whereas measurement data from specific field campaigns are used only in 20% of the studies. This clearly points out how important continuous and automated monitoring network data are as complementary information to model applications.
Conclusions In this paper we summarized the outcome of the APPRAISAL project with respect to the review of current IAM practices. The database constructed in this project has been used as main support to this analysis. This database was populated on the basis of a survey sent throughout Europe, to which 59 responses from 13 countries were received. For each of the investigated topics a first section dealt with the review of the state of the art while a second part was devoted to the analysis of the current practices identified in the APPRAISAL database. In the phase of design and assessment of Air Quality Plans, integrated assessment modelling is currently mainly performed through scenario analysis while more elaborated methods using optimization methods still remain in the research projects.
Regarding the main IAM component, i. e. air quality modeling, there are many different models reported to be applied but none is a standard or preferred modelling tool. However, most of these models are documented and information is available for most of them through the EEA model database. The variety of models applied for AQP is not limited to variability across groups, as indicated by the number of different modelling tools applied within a given group. Indeed more than half of the sampled cases report the use of more than one model for their AQP. This is clearly related to the fact that at present no single model is capable to describe properly all relevant spatial scales of the air pollution phenomena from the regional down to the street level scale. Most respondents are aware of scale issues in designing and assessing the impacts of their AQP.
This is reflected in the consistency of the input data they use, in particular emissions. The methodology and resolution of emission inventories indeed generally fit well the modelling purposes and scale of interest of the AQP. Emissions are however reported to be the most uncertain input in the IAM modelling chain and the consistency between emissions used at different scales remains a major concern. A recurring theme is the challenge posed by local scale modelling and especially the integration with larger-scale results. This is certainly true for IAM tools which remain few at the local scale. The lack of a proper downscaling procedure in most of the studies however calls for an improvement in this respect with the aim to improve the accuracy of street level assessments. Regarding model evaluation, it is troublesome to note that in 15% of the reported cases, measurement data are not used at all, even not for model evaluation.
4. Air quality modeling: a key component for IAM 4. 1. State of the art Air quality models constitute one of the main components of IAM systems used to assess the effectiveness of control strategies adopted by regulatory agencies. The Model Documentation System (MDS) (EEA, 2011; last updated in 2009) provides a comprehensive overview of the air quality models currently used in Europe (http:// acm. eionet. europa. eu/databases/MDS/index_html). Additional information about a wide range of meso-scale air quality and meteorological models was also produced by the COST 728 and 732 Actions (2011) (http://www. mi. zmaw. de/index. php? id=6295&no_cache=1). In general one can distinguish the following model main sub-categories: (a) Gaussian; (b) Statistical; (c) Lagrangian, and (d) Eulerian including Computational Fluid Dynamic (CFD).
Surface temperature and pressure have been measured for more than 100 years. Thus, the relationship between surface temperature variations and solar activity can be investigated using a global historical dataset. Because sea surface temperature (SST) is more persistent than the sea-level pressure, long-term variations can be more easily detected in the tem-perature field. Therefore, we investigate mainly surface temperature variation from the historical data, complemented by pressure or geopotential height fields with a modern dataset. Direct measurement of the solar UV is only recent, but a record of the sunspot number, which is a proxy of the solar extreme ultraviolet (EUV), is available from the 18th century. The solar EUV produces the ionization in the Earth's upper atmosphere. Therefore, change in the solar EUV radiation is felt on the Earth's surface as change in geomagnetic field induced by the electric current in the ionosphere.
It is, thus, possible to associate the variation of sunspot number with the solar EUV activity. Comparison of the variation calculated from Earth's magnetic field demonstrates excellent agreement between the 10. 7 cm solar radio flux (F10. 7) and the sunspot number  (Svalgaard, 2007). Therefore, both can be used as a proxy of the solar irradiance variation. Annual mean surface temperature anomalies related to the solar cycle have been studied using various methods and different historical global datasets covering between 120 and 150 years. Lohmann et al. (2004)  calculated the correlation coefficient between the proxy solar irradiance from  Lean et al. (1995)  and band-pass (between 9-and 5-year period) filtered SSTs reconstructed by  Kaplan et al. (1998)  from 1856 to 2000. Lean and Rind (2008)  extracted solar signals by applying a multiple linear regression analysis to surface temperatures reconstructed by the University of East Anglia Climatic Research Unit F  (Brohan et al.
, 2006)  for the period 1889-2006. A similar multiple linear regression analysis was conducted by  Tung and Zhou (2010) , who compared the regression analysis of two different historical datasets, namely NOAA's Extended Reconstructed Sea Surface Temperatures (ERSST) and the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset  (Rayner et al. , 2003) , to confirm consistent features of the solar signal. Gray et al. (2013)  performed a lagged multiple linear regression analysis to investigate delayed components in the solar signal using the HadISST dataset. Despite different reconstructions and analysis methods, common features are seen during high solar activity in the surface temperatures: a midlatitude warming, and a tropical cooling in the southeastern Pacific and the South Atlantic.
Note that this cooling is different from the La Niña-like pattern previously reported  (van Loon et al. , 2007; Meehl et al. , 2008 Meehl et al. , , 2009) )  and will be discussed in more detail below. We first compare the analysis results of a historical surface temperature dataset with those of a modern dataset to identify the fundamental global features of surface temperature variations related to the solar cycle, i. e. the observed surface solar signals. Next, we study the vertical structure of the solar signal with recent data to identify possible physical mechanisms producing the surface solar signals. Identification of the causes and characteristics of solar signals is particularly difficult for decadal-scale periodic variations because strong feedbacks exist on these timescales in the climate system.
Data This study combines the analysis of a historical SST dataset to characterize the surface response to the 11-year solar cycle, with a modern reanalysis dataset to investigate the underlying dynamical processes. For the historical dataset, we use the NOAA Extended Reconstructed SST v3b (ERSST), described by  Smith et al. (2008)  and available at http://www. esrl. noaa. gov/psd/data/gridded/data. noaa. ersst. html. The ERSST dataset spans more than 160 years from 1854 to the present, with monthly resolution, and a spatial resolution of 2 • longitude × 2 • latitude from 88 • N to 88 • S and 0 to 358 • E. Given the sparsity of observations before 1880  (Smith and Reynolds, 2003) , we limited the present study to the period 1880-2010. To examine the tropospheric and stratospheric dynamical response to the solar cycle, we use the ERA-Interim atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF)  (Dee et al.
Multiple linear regression model Following numerous earlier studies (e. g. Lean and Rind, 2008; Frame and Gray, 2010; Chiodo et al. , 2014; Mitchell et al. , 2015a, b) , the ocean and atmosphere responses to solar variations are examined using a multiple linear regression (MLR) model. This technique can isolate the effects of different forcings, represented by explanatory variables (or regressors), on the variance of a time-dependent variable (or predictand). Annual signals are extracted by applying the MLR to continuous monthly resolved time series. Monthly or sea-sonal signals (2 to 3 consecutive months) are diagnosed by applying the MLR to time series of the individual month or season (i. e. , the seasonal average is performed prior to the MLR), respectively. All data time series have the seasonal cycle removed before the MLR, as well as before any seasonalaverage calculations.
, 2011 ) (available at http://climate. uvic. ca/EMICAR5/ forcing_data/RCP85_MIDYR_CONC. DAT) to account for the increase in anthropogenic forcing; the Niño 3. 4 index derived from the ERSST v3b dataset; the F10. 7 cm solar radio flux index (available at http://lasp. colorado. edu/lisird/tss/ noaa_radio_flux. html); and the global aerosol optical depth (AOD) at 550 nm updated from  Sato et al. (1993)  to represent volcanic effects and two stratospheric quasi-biennial oscillation (QBO) orthogonal indices (QBOa and QBOb), defined as the first two principal components of the ERA-I zonal mean zonal wind in the latitude interval (10 • S, 10 • N) and pressure-height interval (70-5) hPa, respectively. QBO regressors and F10. 7 index are only available from the mid-20th century, so that QBO regressors are not included in the MLR and the F10.
7 index is replaced by the sunspot numbers when the long-term historical SST dataset is analysed. Sensitivity tests of the MLR model revealed that including or removing the stratospheric QBO regressors for the period 1979-2010 negligibly affects the solar regression coefficients and their statistical significance, in particular in the troposphere. Although the F10. 7 cm index more directly represents the irradiance variability in the UV band than the sunspot number  (Tapping, 2013) , both indices concur at annual timescales: a correlation coefficient of 0. 997 between the annually averaged F10. 7 and sunspot number time series is found for the period 1965-2012. The solar regression coefficient used in our study assumes that a difference of 130 solar flux units (1 sfu = 10 -22 W m -2 Hz -1 ) or 100 sunspots represents the difference between the 11-year solar cycle maximum and minimum.
Surface temperature signal As mentioned in the Introduction,  Zhou and Tung (2010; hereafter ZT2010)  calculated CMDs between high-and lowactivity periods of the 11-year solar cycle using the ERSST dataset. In their analysis, data near the World War II period  (1942) (1943) (1944) (1945) (1946) (1947) (1948) (1949) (1950)  were excluded. We performed the same CMD analysis using the same dataset as ZT2010, but starting in 1880 instead of 1854. We confirm the results of ZT2010 in Fig. 1a. The correlation coefficient between the expansion coefficients of the extracted pattern and the solar index shows a similar high correlation (0. 69). To assess the stability of the relationship between SSTs and the solar cycle, the use of a long dataset is crucial. However, historical datasets have problems with spatial coverage and inhomogeneity of the observing systems.
This drawback may be compensated by a comparison with a recent global dataset assimilating satellite observations. Figure  1b  shows the surface solar signal extracted by MLR using the ERA-I and F10. 7 cm radio flux time series (solar index) as one of the explanatory variables for the period from 1979 to 2010. Despite the short time period of only three solar cycles, the results show a similar pattern in surface temperature to those obtained from longer historical datasets. Common features in the spatial structure of the solar signal in surface temperatures include (i) (subpolar regions): warming around 45-60 • N over the Eurasian continent and cooling over the west of Greenland; (ii) (mid-latitudes): warming over the ocean basins around 30-45 • latitudes in the Northern Hemisphere (NH) as well as in the Southern Hemisphere (SH); (iii) (tropics): warming over the Indian Ocean and the central Pacific, and cooling in the eastern Pacific and the Atlantic, particularly in the SH.
Zonal mean vertical structure Since the surface solar signals during the recent  period is very similar to that of the longer historical period (1880-2010) (Fig. 1 ), we may gain further insight into the processes responsible for the solar signal transfer from the stratosphere to the troposphere and the ocean by analysing the modern dataset in more detail. First of all, it should be noted that there are two kinds of westerly jets in the middle atmosphere. Climatological poleward temperature gradient during winter solstice (June in the SH, and December in the NH) is displayed in Fig. 4. The meridional temperature gradient is large in the subtropics of the upper stratosphere due to solar UV heating, while in the lower stratosphere, the gradient is strong around the polar-night region due to longwave cooling.
Conversely, temperature anomalies over the ocean basins east of the continents not only persist from winter, but also continue to develop. The positive temperature anomalies over the North Pacific, east of Japan, extend along 40 • N. In the Atlantic sector, positive temperature anoma- lies are located at lower latitudes along the southeastern US coastal region. A similar SST response in spring has been confirmed with a longer historical SST dataset from 1882 to 2008 (see Fig. 4  of  Tung and Zhou, 2010). Note that temperature anomalies in the Pacific sector are found around ocean frontal zones, where meridional temperature gradients are strong (Fig. 8c ), but in the Atlantic sector, temperature anomalies are located at lower latitudes. As noted by  Zhou and Tang (2010) , mid-latitudes of the Northwestern Atlantic are cold during periods of high solar activity.
The leading and the second EOFs represent canonical ENSO and secular trends, respectively. The third EOF shows decadal variations, and its spatial pattern is illustrated in Fig. 10b. The solar signal (Fig. 10a ) agrees well with the spatial structure of EOF3, which is characterized by a cooling over the cold tongue regions and a warming over the warm pool region in the central Pacific. This pattern of tropical SSTs, known as El Niño Modoki, has been extracted as EOF2 with a shorter dataset from 1979 through 2004 (Fig. 2b  in  Ashok et al. , 2007). Unlike a canonical ENSO, there is a substantial meridional asymmetry in the SST field such that there is warming in the NH and cooling in the SH in EOF3 as well as in the solar signal. Note that the solar signal has greater spatial extent, from the Pacific to Atlantic sectors, while that of EOF3 is confined mainly to the Pacific sector.
Tropospheric processes Annual mean surface temperature variation related to the solar cycle has been studied by several authors using long historical data as indicated in the Introduction. In spite of different methods of analysis, the results present many similarities. The studies, however, focused on specific regions. Lohmann et al. (2004)  indicate that significant solar signals are located in the Pacific sector, while  Lean and Rind (2008)  notice zonal warming signal along around 40 • latitudes in both hemispheres. ZT2010 describe solar signal in each oceanic basin: in the tropical Atlantic, the SST anomalies are cold south of the Equator during periods of high solar activity, but warm directly north of it. The northwestern Atlantic SST anomalies are cold, while those of the northwestern Pacific are warm except off the west coast of North America.
Data availability Meteorological reanalysis, historical datasets and indices used in this paper are all publicly available. NOAA Extended Reconstructed SST v3b (ERSST) is available at http://www. esrl. noaa. gov/psd/data/gridded/data. noaa. ersst. html (NOAA, 2016). The ERA-Interim dataset (ECMWF, 2016) is available at http://apps. ecmwf. int/datasets/ after registration. The F10. 7 cm solar radio flux dataset is available at http://lasp. colorado. edu/lisird/tss/noaa_radio_flux. html (LASP, 2016). Solar sunspot numbers are available at http://www. sidc. be/silso/datafiles  (SIDC, 2016). Aerosol optical depths are available at http://data. giss. nasa. gov/modelforce/strataer/ tau. line_2012. 12. txt  (NASA, 2016). CO 2 concentration dataset (UVIC, 2016) is available at http://climate. uvic.