dataesr/scientific-paragraphs-categorization huggingface dataset:
By applying this approach, Schneider et al. found that data fusion can produce realistic hourly concentration fields of NO 2 and the performance of the approach is dependent on the number of observations, their spatial distribution, the uncertainty of the measurements and the model capability to reproduce urban air quality patterns. They provided the first data fusion results using low-cost sensor data. However, they did not consider the sensor measurement uncertainty in their estimation and they did not include mobile sensor measurements. In this paper, data fusion is performed in Nantes (France) using fixed and mobile low-cost sensor observations of PM 10. The feasibility and the potential added-value with respect to the dispersion model (ADMS-Urban) calculations of using fixed and mobile low-cost sensor observations for air quality mapping via an external drift kriging approach is investigated.
5 concentrations in urban areas to build a database for a better estimation of outdoor air quality. This work focused on the estimation of the PM 10 concentrations using the AtmoTrack low-cost sensor observations in Nantes in November 2018. The reference station measurements (Fig. 1a ) were provided by Air Pays de La Loire (hereafter AirPDL, http://www. airpl. org/) with a 15-min time resolution. Note that the station measurements are not used in the spatial interpolation but only in the sensor data preprocessing as presented in section 3. During the sampling period, AtmoTrack deployed 16 fixed sensors (Fig. 1b ) including 3 replicates at Victor Hugo station (station under the traffic influence) and 3 other replicates at La Bouteillerie station (station under urban background influence). Most of the fixed sensors are in the city center excepted the sensor with the ID 10, which is in the west part of the city.
In addition, 19 mobile sensors were on-board of driving school cars, ambulances and service vehicles to measure PM 10 concentrations over numerous routes each day of the sampling period. The routes are presented for each sensor in Fig. 1c. The vehicle routes ensure a unique spatial coverage over the urban area although they rely on their itineraries and on their drive time (mainly daytime). Fig. 1d  shows that the locations with the largest number of observations are mainly in the center and the north west part of the city. The reference station measurements, the raw fixed and mobile LCS observations have been compared over the sampling period. The fixed sensor observations were aggregated in time (15 min average) to match the time resolution of the reference measurements. The comparisons of the two datasets at Victor Hugo (Fig.
In addition, sporadic pollution events are only measured by the LCS (November 15th at Victor Hugo). As there is no reason for the reference station not to record the right pollutant levels it seems that these events could be associated to malfunction of the devices. The correlation coefficient between the reference measurements and the LCS (Fig. 3 , right panels) is satisfactory for both stations. However, the correlation is higher at La Bouteillerie (R = 0. 85) than at Victor Hugo (R = 0. 62). The very local emissions of PM 10 (due to traffic and other human activities) at Victor Hugo could be an additional complication for sensor measurements. To reduce as much as possible the bias related to the low-cost sensor measurements to be used in the mapping approach, a preprocessing on LCS data has been applied as discussed in section 3.
Preprocessing AtmoTrack sensors were placed next to the Air Pays de la Loire reference stations to be pre-calibrated before being installed on vehicles. No adjustment was made after the sensors were deployed. The preprocessing of the LCS raw dataset proposed in this work consists of two steps. The first step is the elimination of unreliable data based on the repeatability criterion. The second step is the correction of the daily variation of the background concentrations. The concentration threshold based on the repeatability criterion is calculated according to  Spinelle et al. (2013). Their approach is based on laboratory tests for gaseous pollutants, however because there is no reference that defines the repeatability for PM ambient air measurement, this method was adapted. LCS observations which are below this threshold are then removed from the dataset.
5 basis as it is detailed in the supplementary material document as S1. Therefore, we consider both PM categories for this first step of the preprocessing. Rp is 4 µg/m 3 for PM 10 and 2 µg/m 3 for PM 2. 5. Both thresholds are applied to the dataset. Note that the repeatability criterion is calculated using the fixed LCS data because the comparison with the reference station is necessary. The repeatability threshold based on fixed data is then applied to the mobile observations. Indeed, because of the spatial and temporal representativity issues illustrated by Figs. 1d  and 2d , the repeatability can't be estimated using mobile observations. This could imply a bias on the mobile data filtering. Considering PM 2. 5 and PM 10 for the entire dataset, we obtained a 6. 5% removal of mobile LCS data and a 14% removal of fixed LCS data.
PM concentrations measured on road by mobile LCS are higher in average than those measured by fixed LCS installed close to the stations. Therefore, less data with low PM concentration values is eliminated from the dataset from mobile LCS data. Once the thresholding step is complete, the correction of the daily variation of the background  (Hankey et al. , 2015)  is applied. The corrected concentration is determined from Eq. (  3 ). = - - C C Bg Bg ( ) r i LCS stations (3) With C r the corrected concentration value, C i the initial concentration value, Bg LCS is the background calculated from all the LCS mea- surements and Bg stations the background calculated from all reference stations. Bg LCS corresponds to the median value in a moving-window of 15 min on a continuous run of mobile measurements.
A continuous run is defined as the measurements from a mobile sensor without interruption of measurements greater than 5 min. Bg stations is the daily average of the reference station measurements. The PM 10 observation distribution of the fixed and mobile LCS estimated by the preprocessing are presented in Fig. 4. As presented in Fig. 2 , the preprocessed fixed sensor observations have been aggregated in time to match the time resolution of the reference measurements, i. e. 15 min (Fig. 4 ). The comparisons of the datasets at Victor Hugo (Fig. 4a ) and La Bouteillerie (Fig. 4b ) show that the two modes that characterize the distributions of the fixed LCS raw data are not present after the preprocessing. Thus, the fixed LCS and reference observation distributions are more comparable. Fig. 4c  shows the distributions of the preprocessed fixed and mobile LCS observations with the initial time resolution (10 s) and the reference observation distribution from all stations.
The three distributions show similar mean and dispersion implying a consistency over the estimation domain of the pollutant measurements for the 3 networks. Because of the elimination of observations from the raw dataset, the sampling time density (Fig. 4d ) of the preprocessed LCS observations has been decreased especially for the fixed sensors. To assess the accuracy of the preprocessed dataset, the hourly average of the raw and preprocessed PM 10 observations from mobile and fixed LCS are compared to the ADMS-Urban dispersion model calculations from 7 am to 7 pm in November 2018. ADMS-Urban is a modeling platform which is developed by the Cambridge Environmental Research Consultant (CERC;  Carruthers et al. , 1997). It includes several models for the calculation of the pollutant concentrations at the urban scale.
5 µg/m 3 at 5 pm) with the model calculations (Fig. 5a ). In addition, the PM 10 concentration distributions of the preprocessed LCS data are more in agreement with the model output distributions unlike the raw LCS data (Fig. 5b ). These results demonstrate the relevance of applying a preprocessing on LCS raw data. We are then more confident in using sensor observations for air quality mapping. The approach used in this work to estimate the PM 10 concentrations in Nantes is the data fusion. This is described in the following section.
6 ). The modeled annual average is representative of the main patterns of the pollutant concentrations and it was interpolated over the modelling domain by AirPDL. The hourly model outputs for November 2018 were provided at grid points. We assessed the feasibility to use them as external drift in kriging. We interpolated the hourly outputs and calculated the correlation with sensor observations. Results did not show better correlation than using the annual average. Thus, to avoid additional bias by interpolating ADMS-Urban hourly outputs, we defined the annual average as the drift. This variable is displayed in Fig. 6  for the estimation  domain which corresponds to Nantes and its surroundings. To consider the measurement uncertainty and the variability of the LCS data in the kriging, the variance of measurement errors (hereafter VME) is defined as an input of the calculation (the VME of each sample data is added to the corresponding diagonal term of the covariance matrix before solving the kriging system).
In the following, a maximum of 50% of uncertainty is considered for the fixed LCS and 75% for the mobile LCS. After all, the VME allows to allocate more weight to the data points characterized by low uncertainty and variability in the estimation of PM 10 concentrations. Note that kriging is performed by way of a R computing language code that uses the RGeostats package (a package for geostatistical applications, MINES ParisTech / ARMINES (2019), RGeostats: The Geostatistical R Package. Version: 11. 2. 3, Free download from: http:// cg. ensmp. fr/rgeostats). The data fusion results are presented in the following section.
Data fusion results Because this is computationally expensive to apply the data fusion with a large amount of observations, we performed the estimation on 11/29/2018. This day is one of the most sampled in November 2018 with more than 15,000 observations and it does not show any unexplained variations of PM 10 concentrations. The hourly PM 10 map is estimated using the fixed and mobile LCS observations collected during each hour. Thus, at each measurement position, the average of the observations is considered, and the external drift kriging approach is applied.
The residuals between the drift and the LCS data are calculated still considering the nearest model output point grid to the data position. Then the variogram of the residuals is determined (Fig. 8b ), given the variance of the residuals in space over the domain. Data fusion is performed based on this information. As a result, at 9 am the fused map shows that PM 10 concentrations range from 19. 9 to 30. 5 µg/m 3. The maximums of concentrations are on the roads similarly to the drift. The average PM 10 concentrations over the domain calculated by the data fusion is 1. 5 times higher than the drift (20. 6 and 13. 7 µg/m 3 , respectively). The annual mean may underestimate the potential pollution occurring at hourly resolution. At 9 am, the average PM 10 concentrations of the reference stations is higher than the annual mean over the domain (16 µg/m 3 ), so the pollutant concentrations are raised to a level which is representative of this specific time with a greater or lesser magnitude depending on sensor observations.
This is especially clear in the hourly comparison presented in the supplementary materials as S8. This could be explained by the predominance of the drift over the data when the related VME is high. Note that because we consider the mean of the observations at the measurement positions over the estimation period (an hour), a smoothing of the pollution events is expected for this period. In fact, local pollution events are minimized in the estimation. This is remarkable, especially at Victor Hugo station, which is under the influence of traffic. In addition, the raw LCS data were preprocessed using the observations from all the reference stations (especially the daily mean of all the reference observations was used, cf. Section 3). This way the data fusion implies a slight overestimate of the PM 10 concentrations at urban background stations and a slight underestimate at the traffic station compared to the.
Impact of the sensor measurement uncertainty on the estimation The main constraint to use sensor data in air quality mapping remains their measurement uncertainty. It has a significant effect on the data fusion approach by determining the weight allocated to the data. Sensitivity tests were carried out to quantify the effect of the measurement uncertainty on the estimation. In the previous results, a relative uncertainty was defined (75% and 50% for mobile and fixed LCS data, respectively, hereafter R1) based on the European directive and the exploratory analysis of the dataset. The sensitivity study was performed for a reduction of the initial uncertainty to the reference station uncertainty (i. e. 25%, hereafter R2) and for the doubling of the initial uncertainty (i. e. 150% and 100% for fixed and mobile sensors, respectively and hereafter R3).
Conclusion The recent technological developments in miniaturizing the instruments to measure outdoor ambient air pollution offer new possibilities for air quality monitoring and mapping. Since the reference station network is sparse and expensive, the new portable and low-cost devices could provide observations of pollutants with a higher spatial and temporal resolution. In this work, we investigated the potential added value of these data with respect to the dispersion model (ADMS-Urban) calculations for air quality mapping by applying a data fusion technique. This methodology consists in combining sensor data and dispersion model calculation to provide an estimation of pollutant concentration fields at the urban scale. The estimation of PM 10 concentration levels is performed in Nantes (France) on 11/29/2018 at hourly time resolution.
The raw sensor dataset has been preprocessed following 2 steps. First, the unreliable data were removed from the dataset (6. 5% of the mobile sensor data and 14% of the fixed sensor data were eliminated) by applying a thresholding method based on the repeatability criterion. Then, the bias related to the daily variations of the background was corrected based on the daily average of the reference station observations (AirPDL network). The corrected data were then used in the spatial interpolation to estimate the PM 10 concentrations in Nantes. A data fusion technique was applied combining the fixed and mobile sensor data and the 2016 annual average of the PM 10 concentrations calculated by the ADMS-Urban dispersion model (provided by AirPDL). Especially, an external drift kriging approach is used, and the 2016 annual average from the model is defined as the drift.
In kriging, the variance of the measurement error (VME) has been considered to deal with the measurement uncertainty and the variability of the observations in the spatial interpolation. In this study, a relative measurement uncertainty is defined such as 75% and 50% of error is used for mobile and fixed sensors, respectively. The data fusion was performed hourly from 7 am to 7 pm on 11/29/2018 using the fixed and mobile sensor data. Results show that fused maps are closely related to the relative variability associated with the data points. In fact, the higher the variability is, the less the weight of the data point will be on the estimation. Thus, the estimated PM 10 concentrations are following the drift patterns. The estimation is also lead by the correlation between the sensor data and the drift.
Also, the large number of data points represents a real challenge and introduces the big data science for air quality mapping. With more than 2000 data points for an estimation period (1 h in this study), kriging calculation becomes very consuming in time and could take a lot of calculation resources. To answer this difficulty, geostatistical approaches specifically developed for large datasets such as Rivoirard and Romary, 2011, could be explored. Also, machine learning techniques, as applied in Hu and Rahman, 2017, could be investigated. These approaches allow to learn about historical observations and environmental variables and reduce significantly the computing time of the calculations. Eventually, the resulting air quality maps will be a unique support for various applications such as the estimation of the individual exposure, the communication to the citizens, the air quality monitoring, and the improvement of the pollutant emission inventories and pollution modeling.
Acknowledgments This work was supported by the  French Ministry of Ecological and Solidarity Transition  in the framework of the  Central Laboratory  for Air Quality Monitoring activities. We thank  Air Pays de La Loire  for providing the PM 10 reference station observations and the outputs of the ADMS-Urban model simulations for the estimation period. We also thank  Laurent Spinelle ,  INERIS , for sharing his expertise on low cost sensors and their measurement capabilities and for his contribution in the data preprocessing development. Valuable support on the issue of measurement uncertainty was provided by  Cécile Raventos , INERIS.
Managing such variations through accurate predictions is thus crucial. Continental hydroclimate services have recently received attention to address various user needs. However, predictions for months ahead can be limited at catchment scale, highlighting the need for data tailoring. Here, we compare the predictions from two hydrologic setups at catchment scale. One setup (E-HYPE) is used in a European hydroclimate service, whereas the other (GR6J) is used for local water-related risk assessment. Our results show that predictions from the continental setup can be as accurate as the predictions from the local model when predicting streamflow averaged over several months and when looking at changes in streamflow rather than absolute values. A good estimation of the hydrologic states, such as soil moisture or lake levels, prior to the prediction is the most important factor in obtaining accurate streamflow predictions in both setups.
Data, Methodology, and Comparison Framework In this section, we present the data used, the modeling conceptualizations, and the common framework developed to compare and evaluate two hydrologic forecasting chains. The chains are based on two distinct hydrologic models: the GR6J model, calibrated and run locally, and the HYPE model, calibrated and run continentally. The models differ from one another in terms of data requirements, model structure, conceptualization and setup.
Data For the setup of GR6J, daily precipitation and temperature were obtained from the SAFRAN reanalysis from Météo-France  (Quintana-Seguí et al. , 2008; Vidal et al. , 2010). This reanalysis covers France at the 8 × 8-km grid resolution. It combines climate model outputs and local (gauge) data. These precipitation and temperature data were used in the calibration of GR6J, as well as in the initialization of the forecast (i. e. , a model run forced with observations up to the forecast issue date). Local streamflow observations were also used. They are available from the French national streamflow database (Hydro database: http://www. hydro. eaufrance. fr/). They were used in the calibration of GR6J and as observed reference in the evaluation carried out in this study. For the setup of E-HYPE, gridded precipitation and temperature were obtained from two global adjusted reanalyzes: Hydro-GFD  (Berg et al.
, 2018)  and the WATCH Forcing Data (WFDEI;  Weedon et al. , 2014). Both are based on the ERA-Interim reanalysis data and are available at a 0. 5 × 0. 5°resolution (~55 × 55 km). The WFDEI data set was used in the first calibration phase of E-HYPE carried out at the Swedish Meteorological and Hydrological Institute (SMHI) prior to this study. Hydro-GFD was used in a second calibration phase to adjust the water balance. In this study, Hydro-GFD was also used to force E-HYPE up to the forecast issue dates and thus create initial hydrologic states. Streamflow observations used in the calibration of E-HYPE were obtained from the Global Runoff Data Centre (GRDC) Reference Dataset (http://www. bafg. de/ GRDC/), which has a global coverage and whose river stations across France come from the Hydro database.
For both models, precipitation and temperature forecasts are obtained from the System 4 European Centre for Medium-Range Weather Forecasts (ECMWF) Global Circulation Model (GCM). System 4 reforecasts for the period 1981-2009 are available at a spatial resolution of about 0. 7°and are initialized at the beginning of each month  (Molteni et al. , 2011). Each forecast ensemble consists of 15 members covering the 7 months ahead. Forecasts were previously bias adjusted based on the Hydro-GFD data set and using the Distribution-based Scaling method  (Yang et al. , 2010). Both models run with daily weather input at the catchment scale and generate daily hydrological outputs. Gridded forecast weather variables are associated to catchments based on the shortest distance between grid cells and catchment centroids.
Model validation is carried out over the set of European catchments for which observed streamflow data are available. In this study, we used the parameter sets identified by  Hundecha et al. (2016)  over the calibration period  (1980) (1981) (1982) (1983) (1984) (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999)  to run the forecasts over the study period. Despite the time overlap between the calibration of E-HYPE and the study period, none of the catchments in this study were directly used to calibrate E-HYPE. The model structures of GR6J and HYPE also differ despite fundamental similarities. In GR6J (cf. Figure  1a ), daily precipitation for the upstream area contributing to the outlet is first intercepted before either contributing directly to surface runoff or reaching a production store and then percolating.
cf. Appendix A). In calibration and validation in • "Observed climatology": An ensemble based on observed streamflow data from the local Hydro database is used. For each forecast date, traces are randomly selected within the available streamflow time series for the catchment. Fifteen streamflow traces are selected to match the number of members available from System 4. Candidate time series start on the same day as the forecast date in all available historical years, excluding the forecast year. • "Simulated climatology": Historical simulated streamflow time series for each hydrologic model are considered. At each forecast date, 15 traces are randomly selected from historical time series of streamflow simulations of each model in order to build an ensemble of possible outcomes that matches the number of System 4 outcomes.
Water Resources Research to depart at the initial conditions (ICs) obtained at the time of forecast, then we evaluate the "ESP" ensemble using the "simulated climatology" ensemble as benchmark, and, finally, in order to evaluate the gain or loss in skill from using GCM seasonal forecasts, we evaluate the "SYS4" system against the "ESP" ensemble. Note that when the "observed climatology" ensemble is used as benchmark in the computation of skill, the reference used is observed streamflow from the Hydro database. When the "simulated climatology" ensemble or "ESP" is used as benchmark, the streamflow simulated with each model is used as reference. In this case, since the simulations depend on the model, different references are used to evaluate the systems depending on the model used. This could lead to pitfalls in our analysis if one of the models failed to represent dominant hydrologic processes and their dynamics.