dataesr/scientific-paragraphs-categorization huggingface dataset:
In order to circumvent this difficulty, an innovative methodology has been developed. The Analog Application for Air Quality (A 3 Q) method is based on the comparison of each day of 20 lockdown to a group of analog days having similar meteorological conditions. The A 3 Q method has been successfully evaluated and applied to a comprehensive in-situ dataset of primary and secondary pollutants obtained at the SIRTA observatory, a suburban background site of the Paris megacity (France). The overall slight decrease of PM1 concentrations (-14%) compared to business-as-usual conditions conceals contrasting behaviours. Primary traffic tracers (NOx and traffic-related carbonaceous aerosols) dropped by 42-66% 25 during the lockdown period. Further, the A 3 Q method enabled us to characterize of changes triggered by NOx decreases.
Moreover, Springtime in North-Western Europe is usually associated with high PM pollution episodes dominated by secondary material (mainly ammonium nitrate and sulfate, and secondary organic aerosols) as shown in  Petit et al. (2015) ,  Beekmann et al. (2015) ,  Petit et al. (2017a)  for instance. To this perspective, the complex chemistry of PM must be taken into account, emphasizing the need of in-situ aerosol chemical characterization data. 65 The present study aims at reconciling a robust and innovative methodology with a quasi-comprehensive insitu dataset, acquired within the Paris region (France). The 12-million inhabitants of the region, representing around 20% of the total French population, were placed under lockdown from March 17 th , 2020 to May 10 th , 2020, further designated as LP2020.
Instrumentation In-situ measurement datasets used in this study have been primarily obtained at the SIRTA atmospheric observatory (2. 15°E, 48. 71°N;  Haeffelin et al. , 2005) , a facility which contributes to the EU-research https://doi. org/10. 5194/acp-2021-369 Preprint. Discussion started: 4 May 2021 c Author(s) 2021. CC BY 4. 0 License. infrastructure ACTRIS (https://www. actris. eu), following its Quality assurance / Quality control guidelines. 75 The chemical composition of major submicron non-refractory species has been monitored since the end of 2011 using an Aerosol Chemical Speciation Monitor (ACSM,  Ng et al. , 2011) , constituting the longest ACSM dataset worldwide. Measurement principles of the ACSM are extensively described elsewhere  (Budisulistiorini et al. , 2014; Zhang et al.
, 2019; Poulain et al. , 2020). Here, 30-min concentrations of submicron Organic Aerosols (OA), Nitrate (NO3), Sulfate (SO4), Ammonium (NH4) and Chloride (Cl) were corrected from 80 collection efficiency  (Middlebrook et al. , 2012)   (CE). The ACSM at SIRTA was regularly calibrated using 300nm ammonium nitrate and ammonium sulfate particles to derive Ionization Efficiencies (IE), and successfully participated in ACTRIS intercomparaison exercises  (Crenn et al. , 2015; Freney et al. , 2019). The black carbon dataset consists in Aethalometer measurements  (Drinovec et al. , 2015). It is composed of subsequent and harmonized datasets obtained from AE31 (01/2011-03/2013) and AE33 , 03/2013-06/2020) 85 devices and applying a common validation procedure  (Petit et al. , 2017b). Briefly, for each elementary measurement data point (5-min and 1-min time-base for AE31 and AE33, respectively), BC concentrations were set as invalid when below -LoD (Limit of Detection, 100 ng/m 3 ); for BC950nm ≥ 200 ng/m 3 , the spectral dependence was calculated from the linear regression of ln(λ) versus ln(Batn).
airparif. asso. fr). To this respect, urban NOx concentrations refer here to the average of all urban background stations measuring NOx. 100 As the ozone instrument from SIRTA experienced a major breakdown in 2020, daily ozone (O3) concentrations were obtained between 2012 and 2020 from a peri-urban station in Les Ulis (2. 165°E, 48. 68°N), operated by Airparif. This station is located around 10 km away from SIRTA and O3 from Les Ulis has shown a good comparability with O3 from SIRTA during the period of common measurements. Meteorological variables at SIRTA (wind speed and direction, temperature, relative humidity and pressure) 105 were provided from the ReObs database  (Chiriaco et al. , 2018).
Results obtained here enrich the existing timeseries  (Zhang et al. , 2019)  from June 2011 to March 2018; together they form one of the longest ACSM PMF timeseries published to date. MO-OOA and LO-OOA were summed as OOA. The oxidation properties of secondary organic aerosols were characterized by removing the contribution of primary factors to the OA matrix, as follows: 120 𝑓 𝑖 𝑆𝑂𝐴 = 𝑚/𝑧 𝑖 -(𝑓 𝑖 𝐻𝑂𝐴 • [𝐻𝑂𝐴] + 𝑓 𝑖 𝐵𝐵𝑂𝐴 • [𝐵𝐵𝑂𝐴]) [𝑂𝐴] -([𝐻𝑂𝐴] + [𝐵𝐵𝑂𝐴]) From there, O:CSOA, H:CSOA and OScSOA were calculated from the equations provided in  (Aiken et al. , 2008; Kroll et al. , 2011; Canagaratna et al. , 2014). These equations provide only qualitative information for ACSM data, but are sufficient to characterize a change, since they are uniformly applied throughout the dataset.
125 Fossil-fuel (BCff) and Biomass burning (BCwb) fractions were estimated from aethalometer measurements  (Sandradewi et al. , 2008). Since the choice of ff and wb is critical and given the size of our dataset, their determination was based on the statistical hourly distribution of the AAE (Fig. S1 ). The value of 1. 85 was chosen for wb, corresponding to a maximum frequency during the night. This value is close to the values used previously at SIRTA  (Zhang et al. , 2019) , and from the recommended value of 1. 72  (Zotter et al. , 2016). PMff and 130 PMwb were estimated from BCff and BCwb concentrations, respectively, following the conversion factors of 2 and 10. 3 found for SIRTA during the same season  (Petit et al. , 2014). The full timeseries between 2012 and 2020 is presented in Figure  S2.
Choice of the comparison reference period The assessment of lockdown impact on air quality lies on the use of a reference period, which is assumed to be representative of business-as-usual conditions during LP2020, following: 145 However, it can lead to some variability in the results, depending on the chosen reference period, representing therefore a critical issue. The major danger would be to apply methodologies unquestioningly, without verifying the strong inherent hypothesis that data are comparable. For instance, one could consider 150 the weeks preceding the lockdown period as a satisfactory reference (e. g. ,  Toscano and Murena, 2020; Dantas et al. , 2020; Otmani et al. , 2020). In the case of SIRTA, we tried to compare the pollutant concentrations averaged over the lockdown period (LP 2020) to the ones calculated for the 20 previous days (pLP 2020 : 25/02/2020 -16/03/2020).
Description Analogs of atmospheric circulation  (Yiou et al. , 2013; Lorenz, 1969; Van Den Dool, 1994; Zorita and Storch, 1999; Cattiaux et al. , 2012)  have been widely used for different climatological purposes, notably for atmospheric reconstructions and in the characterization of the role of synoptic circulation in extreme meteorological events  (Yiou et al. , 2013; Vautard et al. , 2018). Circulation analogs are generally computed from 200 daily pressure spatial distributions. Here, we built the analogy based on three successive layers: Synoptic. Circulation analogs are computed similarly to previous studies. We used daily sea-level pressure (SLP) data, to better characterize near-surface atmospheric circulation as our study covers near-surface pollutants. The SLP data is extracted from NCEP/NCAR reanalysis data  (Kalnay et al.
This innovative approach was applied to a comprehensive in-situ dataset, acquired at the SIRTA observatory, a sub-urban station located 20km South-West of Paris. The A 3 Q method provided very satisfactory results over a business-as-usual period, which 475 ensures a robust characterization of concentration changes in the Paris region during lockdown. The unprecedented drop of traffic commuting due to the stay-at-home order led to an expected decrease of primary traffic pollutants, NOx (-42%), HOA (-62%) and BCff (-55%), as well as an increase of ozone concentrations due to the lesser contribution of the O3 sink by NO. Concomitantly, primary particles related to residential wood-burning shows a slight increase (+ 12-20%). The decrease of NOx (-42%) triggered positive 480 feedbacks regarding secondary aerosols especially nitrate and SOA, which are found to drop by 42% and 25%  Finally, the proposed A 3 Q method may be considered as an efficient tool to monitor and quantify more precisely the impact of lockdowns in other urban areas.
https://doi. org/10. 5194/acp-2021-369 Preprint. Discussion started: 4 May 2021 c Author(s) 2021. CC BY 4. 0 License. Because the dataset consists in multi-year observations, long-term trends may impact the estimated 240 concentration change due to lockdown. To that end, a seasonal Mann-Kendall test was performed on each variable on the 01/2012-02/2020 period. The Mann-Kendall R package was used (Collaud Coen et al. , 2020), which includes three pre-whitening approaches in order to reduce the weight of autocorrelation. Results are.
Fundings. This research has been supported by the  EUFP7  and  H2020   ACTRIS  projects (grant nos. 262254  and  654109 ), by the  National Center for Scientific Research (CNRS) , by the  French alternatives energies and Atomic Energy Commission (CEA) , by the  French Ministry of Environment , and by the  DIM-R2DS program  from the  Ile-de-France region. The authors gratefully acknowledge  CNRS-INSU  for supporting measurements performed at the  SI-SIRTA , and those 520 within the long-term monitoring aerosol program SNO-CLAP, both of which are components of the  ACTRIS French Research Instructure , and whose data is hosted at the AERIS data center (https://www. aeris-data. fr/). 525 https://doi. org/10. 5194/acp-2021-369Preprint. Discussion started: 4 May 2021 c Author(s)  2021. CC BY 4. 0 License.
Here, to overcome this problem, we proposed and evaluated a new retrieval approach from AMSR2 observations at X-band to produce a new X-VOD product. The X-VOD retrievals were based on the inversion of the X-MEB model, an extension of the L-MEB model (L-band microwave emission of the biosphere) to the X-band. The main originality in comparison to previous algorithms is that (i) only VOD was retrieved while SM was estimated from a reanalysis data set (ERA5-Land); (ii) model inversion was based on an innovative approach to initialize the cost function; and (iii) new values for the soil and vegetation X-MEB model parameters were calibrated. To evaluate the methodology, we performed the VOD retrievals over the whole African continent over 2014-2016, including a dry (2015) and a wet (2014) year.
In a first step, we calibrated a set of three parameters: effective scattering albedo (ω), soil roughness (H R ) and VOD first guess (VOD ini ). Several datasets of vegetation indices as Above-Ground Biomass (AGB), Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) were chosen as reference data to optimize these model parameters. Globally-constant values (ω = 0. 06 and H R = 0. 6) were found to achieve high spatial and temporal correlations between retrieved X-VOD and the reference vegetation parameters. Comparison with other X-VOD products suggested IB X-VOD had competitive advantages in terms of both spatial and temporal performances. In particular, spatial correlation with three biomass datasets was found to be higher than for previous X-VOD products (R 2 ~ 0. 76-0.
, 2012) ; detecting a recent reversal in loss of global terrestrial biomass from 2003 onwards  (Liu et al. , 2015) ; evaluating the degree of isohydricity at the ecosystem scale  (Konings and Gentine, 2017) ; significantly improving the simulation of evapotranspiration and GPP  (Kumar et al. , 2020). These potential applications strengthen our motivations to extend the L-MEB (L-band microwave emission of the biosphere) model to X-band which offers a long-term observation records (AMSR-E & AMSR2). The X-MEB model is very similar to the L-MEB model, except that the soil and vegetation parameters of X-MEB need to be calibrated at X-band  (Pellarin et al. , 2006). X-VOD data sets have been mainly retrieved from the LPDR algorithm (the Land Parameter Data Record) based on the inversion of the land-water emissivity slope index  (Du et al.
, 2017; Brandt et al. , 2018a). To overcome the first issue, several methods, such as use of a priori information (constraints) and multi-temporal algorithms, have been applied  (Konings et al. , 2016; Baur et al. , 2019). As well, a recent study used the self-constrain relationship between soil and vegetation parameters as constraints  (Zhao et al. , 2021). In this study, we evaluated the possibility to retrieve VOD from AMSR2 using an approach similar to that applied in  Baur et al. (2019) , which consisted in retrieving only vegetation parameters while soil moisture (SM) was estimated from ancillary data. The observation time of the SM data derived from other Earth observation (EO) sensors (such as SMOS, SMAP) is different from that of AMSR2 and the time period of those products is too short (for instance, SMOS was launched in the end of 2009).
Therefore, in the present study, we used model-based SM simulations from ECMWF ERA5-Land (C3S, 2019) as a known input. ERA5-Land is a state-of-theart global reanalysis product that provides a long-term time series record at high spatial (~10 km) and temporal (hourly) resolutions. The use of modelled SM from ERA5-Land allowed having SM (and temperatures) data at exactly the same time as the AMSR2 observations. Note that we evaluated in this study a new retrieval approach from AMSR2 observations at X-band, but our final objective is to compute eventually a long-term VOD time series from the AMSR-E/AMSR2 observations at both the C-and X-bands from 2002 to present. In order to decrease the uncertainty on the retrieved VOD values, especially for dense vegetation, it is necessary to account for the effects of vegetation scattering, as parameterized by the effective scattering albedo (ω) and of soil roughness, as parameterized by the H R parameter  (Wigneron et al.
The performance of the X-VOD parameter retrieved in this study (referred to as IB X-VOD, corresponding to AMSR2 INRAE Bordeaux X-Band VOD) was assessed by inter-comparing with other X-VOD products, including LPDR X-VOD  (Du et al. , 2015) , VODCA X-VOD (VOD Climate Archive,  Moesinger et al. , 2020)  and LPRM X-VOD  (Owe et al. , 2008). The study is structured as follows: Section 2 introduces the datasets used in this study and the methodology presenting the new X-MEB model developed for the inversion of VOD at X-band, the calibration and inter-comparison steps. Section 3 presents the calibration results and the performances of the IB, LPDR, VODCA and LPRM X-VOD by comparison to vegetation proxies in time and space. Section 4 and 5 conclude with some remarks and perspectives.
ERA5-Land ERA5-Land is a global land surface reanalysis dataset, released by the European Centre for Medium-Range Weather Forecasts (ECMWF, https ://cds. climate. copernicus. eu/), describing the land water and energy cycles. It is a downscaled land product from ERA5  (Hersbach et al. , 2020)  with a spatial resolution of 0. 1 • × 0. 1 • (9 km) and a temporal resolution of 1 h. Evaluation of ERA5-Land soil moisture against in situ measurements have suggested an overall good performance  (Beck et al. , 2021) , and an acceptable uncertainty (ubRMSE = 0. 05 m 3 /m 3 ,  Chen et al. , 2021). In addition to SM, skin temperature, soil temperatures in 2 layers (0-7 cm and 28-100 cm under the surface) were also used in X-MEB model. In order to keep datasets spatially consistent, ERA5-Land data was re-sampled to the grid of AMSR2 and to keep it temporally consistent with AMSR2 observations, ERA5-Land data simulated at 1:00 am was selected.
Vegetation variables used for validation We selected three vegetation parameters, including AGB  (Bouvet et al. , 2018; Mermoz et al. , 2015; Saatchi et al. , 2011; Santoro and Cartus, 2019) , LAI, NDVI  (Baret et al. , 2007)  to evaluate the retrieved IB X-VOD data. All the five vegetation parameters used here are based on optical, light detection and ranging (LiDAR) observations and Synthetic Aperture Radar (SAR) data from multiple Earth observation satellites and inventory datasets. They are thus completely independent of IB X-VOD, as the X-MEB model does not use any ancillary information on vegetation as presented above. 2. 1. 3. 1. Saatchi AGB. Saatchi AGB was estimated from the Lorey's height (the basal area weighted height of all trees with a diameter of more than 10 cm). It was extracted based on Geoscience Laser Altimeter System (GLAS) (LiDAR) signal on NASA Ice, Cloud, and land Elevation (ICESat) satellite and spatially extrapolated with Moderate Resolution Imaging Spectroradiometer (MODIS) and Quick Scatterometer (QuikS-CAT) data through maximum entropy (MaxEnt) modelling.
Bouvet-Mermoz AGB. Bouvet-Mermoz AGB (hereafter referred to as Bouvet AGB) consists of the merging of the Bouvet  (Bouvet et al. , 2018)  and Mermoz  (Mermoz et al. , 2015)  AGB datasets. Bouvet AGB was produced using a Bayesian inversion of Phased Array L-band SAR (PALSAR) HH and HV polarizations on Advanced Land Observing Satellite (ALOS) and evaluated with 144 field plots collected between 2006 and 2012. Therefore, the resulting biomass dataset is representative of AGB circa 2010. This dataset only focuses on savannahs and woodlands (low biomass) in Africa at a spatial resolution of 25 m × 25 m. On the contrary,  Mermoz et al. (2015)  paid more attention on the dense forest. Mermoz et al. used both theoretical and experimental approaches to build a relationship between L-band SAR backscatter and dense tropical forest biomass.
Bouvet AGB has an overall good accuracy (RMSD = 17 Mg⋅ha -1 ) for the cross-validation with the field plots  (Bouvet et al. , 2018). 2. 1. 3. 3. CCI AGB. CCI AGB was produced by the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme (http://da ta. ceda. ac. uk/neodc/esacci/biomass/data/agb/maps/2017/v1. 0/). This data was derived based on L-band SAR observations (PALSAR-2) on ALOS-2 satellite and C-band SAR observations on Sentinel-1 satellite  (Santoro and Cartus, 2019). The algorithm first obtained growing stock volume (GSV), then converted to AGB with auxiliary datasets describing canopy density, microwave transmissivity, maximum biomass etc. CCI AGB used in this study is a global map for the year 2017. 2. 1. 3. 4. LAI and NDVI. LAI and NDVI were downloaded from the Copernicus Global Land Service (CGLS) website (https://land.
cope rnicus. eu/global/). CGLS provides a series of bio-geophysical parameters describing the vegetation dynamics at the global scale. These two products were derived from PROBA-V since January 2014 onwards. LAI and NDVI both have a temporal resolution of 10 days and spatial resolution of 1 km. Quality control was conducted according to the reference documents  (Buchhorn et al. , 2017; Swinnen and Toté, 2017). These datasets were resampled to a spatial resolution of 25 km to keep them consistent with X-VOD.
Other X-VOD products To assess the performance of the new X-VOD product retrieved in this study, we compared it with three other X-VOD products, namely LPDR X-VOD (version 2), VOD Climate Archive (VODCA X-band version 6), LPRM X-VOD (version 5), which are freely available to the public. LPDR X-VOD was designed by University of Montana  (Du et al. , 2015)  based on LPDR algorithm and is available at the National Snow & Ice Data Center (NSIDC) website (https://nsidc. org/data/). LPDR provides a long-term (June 2002-present) global record of VOD and other climate parameters at a 25 km grid cell resolution  (Du et al. , 2017). The LPDR algorithm is based on the (τ-ω) radiative transfer equation  (Mo et al. , 1982)  and VOD is retrieved by inverting a land-water emissivity slope index. It should be noted that LPDR regards the soil as dry bare soil, therefore, LPDR X-VOD also incorporates surface roughness effects.
Importantly, note that the LPDR water fraction dataset was also used in the present study to filter out pixels with large open water. LPRM X-VOD was developed by Vrije Universiteit Amsterdam and NASA  (Owe et al. , 2008; Owe et al. , 2005; Owe et al. , 2001)  based on the Land Parameter Retrieval Model and is available at the Goddard Earth Sciences Data and Information Services Center (GES DISC) website (https://disc. gsfc. nasa. gov/datasets/). As for LPDR, the theoretical background is the τ-ω model, but using MPDI to estimate the vegetation effects. LPRM relies on a nonlinear iterative procedure to divide the emission signal into the soil part and the canopy part, and VOD is retrieved in a second step. VODCA was produced by Technische Universität Wien  (Moesinger et al. , 2020)  and is available at doi:https://doi.
org/10. 5281/zenodo. 2575599. This product combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat and AMSR2) based on the LPRM model. To obtain a long-term and amplitude-consistent X-VOD, VODCA chose AMSR-E as the scaling reference and then applied a CDF-matching technique to adjust VOD from the other sensors. AMSR2 does not share any temporal overlap with the reference sensor, therefore, the scaled VOD from TMI was used to bridge this gap  (Moesinger et al. , 2020).
Fig. 2  illustrates the flow chart of the whole methodology, including the input data (blue rectangles), the calibration step (green rectangles) and the comparison step (red rectangles). In the evaluation part, we extended the study period to 3 years (from year 2014 to 2016) including a dry year (2015) and a wet year (2014) to ensure making the calibration for a large range of climatic conditions. We compared IB X-VOD with the other X-VOD products (LPDR X-VOD, VODCA X-VOD and LPRM X-VOD) by calculating the spatial correlation with the Bouvet/Saatchi/CCI AGB datasets, LAI and NDVI and the temporal correlation with LAI and NDVI (as we did in the calibration part). Only the pixels where these four VOD products were available were considered in the VOD inter-comparison step. Fig. 2. Flow chart presenting the retrieval algorithm of IB X-VOD from AMSR2.
First step: calibration of the effective vegetation scattering albedo and soil roughness This section presents the results from the first step which focused on the calibration of the effective vegetation scattering albedo (ω) and soil roughness (H R ). Fig. 3  presents the spatial and temporal correlation with independent datasets (Bouvet/Saatchi/CCI AGB, LAI and NDVI) obtained for several combinations of the values of ω and H R , assumed to be constant over the whole study area (Africa). It can be seen that the values of ω and H R which produced the highest spatial correlation values are not quite consistent with those producing the highest temporal correlation values. It can also be seen that for a given value of ω, changes in H R have a relatively small impacts on the results. The values ω = 0.
27 in EBF, while other products showed a relatively poor performance with very low or even negative correlations (Fig. 11a ). The potential reason could be the use of modelled SM from ERA5-Land in computing IB X-VOD. It is well known that it is hard to accurately estimate SM in densely vegetated areas from microwave observations since the signal emitted from the soil is significantly attenuated by vegetation, particularly at Xband; while the errors of model simulations are not significantly affected by the dense vegetation. Thus, the ERA5-Land dataset may provide a more reliable SM input in dense forests than that obtained from remote sensing observations. Based on the statistics of dominant products producing the highest temporal correlation with LAI or NDVI for each pixel in Africa (Fig.