dataesr/scientific-paragraphs-categorization huggingface dataset:
Air Pollution Estimation Air pollution estimation estimates the concentration of pollutants in the air at a given location and time. This section discusses interpolation using traditional and deep learning methods (CNN-LSTM). We are using sensory data, and the estimation is conducted on top of pollution maps generated from fixed stations and enriched using opportunistic MPM. This paper proposes an approach to estimate air pollution using sensory data only. We use the enriched maps created in the previous step 4. 3 to do this. We apply three methods for interpolation: Inverse distance weighting (IDW), ordinary kriging, and CNN-LSTM to expand the spatial and temporal coverage. IDW is a simple and commonly used method for interpolation. It works by assigning a weight to each observation based on its distance to the location is estimated.
Versailles Experiment The experiment was carried out on real-life data collected in Versailles City as described in 4. 1. Firstly, we loaded data from all available stations, precisely the PM2. 5 dimension. Secondly, we removed all missing values and kept only records with measurements from all available stations. Finally, we normalized the data using min-max normalization. Once the data was preprocessed and prepared, we utilized K-means clustering to partition it into distinct clusters. Using the different approaches for choosing the best K for the clustering, we set K = 3 in our experiment, forming 3 clusters. Figure  6  shows the mean of each station per the three clusters. The records with low pollution levels correspond to cluster 0, those with medium pollution levels were grouped in cluster 2, and those with high pollution levels fall in cluster 1.
Chicago Experiments In order to validate our approach, we applied our methodology to data collected from open datasets such as OpenAQ and Aircasting as described in 4. 1 section. This experiment has two types of opportunistic sensing data besides the fixed station measures. We have low-cost fixed sensors that provide measures  Once the data was preprocessed and prepared, we utilized K-means clustering to partition it into distinct clusters. For each record, three measures were associated with the three reference grade stations in the area of interest. Using the different approaches for choosing the best K for the clustering, we set K = 4 in our experiment, forming 4 clusters. Figure  19  shows the mean of each station per the four clusters. Records with low pollution levels correspond to cluster 2, records between low and medium pollution levels are grouped in cluster 1, records with medium pollution levels correspond to cluster 3, and records with high pollution levels fall in cluster 0.
The results show a significant improvement in using CNN-LSTM to estimate pollution levels. Figures  22  and 23  show the plots of applying IDW and Ordinary Kriging on random samples in the dataset for 1km x 1km and 500m x 500m granularity respectively. On the other side, figures 24 and 25 plots the estimation using the CNN-LSTM method for the two granularities 1km x 1km and 500m x 500m respectively. The plots correspond to different samples from different clusters. Our primary goal in this study is to expand the spatiotemporal coverage. We are enhancing air monitoring fixed stations by incorporating mobile sensor data collected from the public. Previous projects have typically conducted In contrast, our study utilizes opportunistic MPM data to supplement fixed station data. Our initial challenge was to figure out how to combine opportunistic MPM data with fixed-station data for estimating air pollution.
We hypothesized that periods of air pollution where fixed stations' measurements fall within the same cluster could share similar MPM data. We clustered the fixed stations' data to test our hypothesis and merged them with MPM data. Our experiments  confirmed the validity of our hypothesis, and this methodology could improve the accuracy of fixed station data. This section will analyze and interpret the experiments' results of estimating air pollution using fixed and opportunistic Mobile Participatory Monitoring (MPM) data. The experiments aimed to evaluate the effectiveness of the proposed approach of enriching the fixed stations generated air pollution maps with opportunistic MPM data. We used two basic interpolation techniques, Inverse Distance Weighting (IDW) and Ordinary Kriging; we then utilized CNN-LSTM to enhance the estimation accuracy.
Even using advanced techniques such as CNN-LSTM, we still had a high MAE and RMSE. Hence, the model couldn't learn the correct patterns while using only fixed stations. On the other side, in table 3, MAE and RMSE decreased significantly for all the used methods, and CNN-LSTM has shown the best results among the used techniques. Moreover, we used data from open datasets such as OpenAQ and Aircasting to better validate our approach Chicago experiment. We ran our approach on top of the available data in the area of interest. The reported results in section 5. 2 illustrate the effectiveness of our approach in better-estimating air pollution when following the proposed approach. Results in table  4  show acceptable results in terms of MAE and RMSE, especially for the CNN-LSTM model. Table  5  summarizes the Mobile Participatory Monitoring data statistics in the two experiments for 1KM x 1KM granularity.
Min MPM and Max MPM columns refer to the minimum and maximum number of sensors available in a time period. The percentiles show that we have a very low contribution of MPM data to the map. Coverage Before Enrichment column shows the percentage of the monitored area. Coverage After Enrichment shows the percentage of the monitored area after we applied our enrichment process. Each cluster has a percentage as the number of MPM sensors varies between clusters. In Versailles Experiment, the coverage after enrichment is 83. 3%, 75%, and 58. 3% for clusters one, two, and three, respectively. While for Chicago Experiment 8. 3%, 9. 7%, 9%, 10. 1% are the coverage percentages in clusters one, two, three, and four, respectively. Moreover, figure  26  plots the map of the monitoring coverage before and after enrichment for one sample of the maps in Versailles.
However, even while using advanced techniques, we still have some concerns. Indeed, in some areas where no observations are found, the model tends to overestimate values, such as in the left corner of the plots in figure  25. This suggests to investigate the explainability of these models. A possible solution to work around the estimation near the border is to retain only the results within the convex hull of the dataset. Moreover, there are still opportunities for further improvement. Augmenting the approach with additional features can result in better estimation. We believe that enrichment with air pollution features such as land use, traffic, and meteorological data has significant improvements. These features can give more insights and provide valuable context into the factors influencing air pollution levels.
Conclusion Air pollution monitoring using fixed stations and low-cost and mobile sensors has been a trendy topic over the last few years. Air quality is a permanent concern in urban areas, as improving the air quality index can help face urbanization challenges. Several studies tried to interpolate pollution measures from fixed stations, mobile sensors, or a combination. They use different methods and may require additional features. In this study, we present an approach that combines fixed station data with mobile participatory sensing data collected by individuals during their daily activities rather than at specific outdoor locations. This type of data collection presents a challenge, as only 10% of the time is spent outdoors, resulting in a scarcity of MPM data. The mobile sensing data, in our case, have different characteristics than the data used in previous approaches.
MPM data is not collected initially to monitor air pollution outdoors and in specific places. We were interested in using the opportunistic MPM data in enriching fixed stations' data to better estimate air pollution in uncovered spots. The primary objective was to leverage the opportunistic MPM data and utilize it to enhance the fixed stations' measures to generate enriched air pollution maps to estimate air pollution better. Our approach has been validated using two real-world datasets from Versailles and Chicago cities. The reported results show our proposed approach's applicability and efficiency in estimating air pollution in unmonitored spots. For now, we have validated the feasibility of our approach using sensory data. However, we believe involving more air pollution-related features such as land use, meteorological, and traffic features can significantly improve the estimation performance, especially when utilizing deep learning models such as the CNN-LSTM model.
Acknowledgements This work has supported by the  H2020 EU   GO GREEN ROUTES  funded under the research and innovation programme  H2020-EU. 3. 5. 2 grant agreement No  869764. We would like to express our sincere gratitude to all the participants who contributed in the data collection in Versailles. We are thankful to all volunteers as their invaluable contributions were instrumental in enriching the quality and depth of our research.
Thus observations targeting QSOs in the lead up to or during the blowout phase may better illuminate the role and impact QSOs play in removing cool gas from their host galaxies. This paper makes use of the following ALMA data: ADS/ JAO. ALMA#2018. 1. 01790. S. ALMA is a partnership of ESO (representing its member states), NSF (USA) and NINS (Japan), together with NRC (Canada), MOST and ASIAA (Taiwan), and KASI (Republic of Korea), in cooperation with the Republic of Chile. The Joint ALMA Observatory is operated by ESO, AUI/NRAO, and NAOJ. This work benefited from the support of the project Z-GAL ANR-AAPG2019 of the French National Research Agency (ANR). Finally, we thank the anonymous referee for their feedback which helped improve this paper. Facility: ALMA. Software:  CASA (v4. 53; McMullin et al.
Discussion To comprehensively describe the variability of the important WUE ecosystem function along recent climate change, we conducted analyses on the summer-time trends, drought response and the causes of variability across the European continent using RS derived and reanalysis datasets. Our results show agreement of the declining trend for all the WUE indices in Central Europe, indicating that plants photosynthesize less per water used and did optimize stomatal conductance for higher IWUE, suggesting declining health and performance of ecosystems there  17. Furthermore we found increasing forest IWUE across the continent which is an agreement to past findings  13, 45, 47. The increase of grassland IWUE is more pronounced, and can be ascribed to positive relationship of grassland GPP to VPD and T (Supplementary Fig.
Data-sets We created a dataset consisting of the variables included in this study from 1995 -2018 in an 8daily time resolution and a 3 km spatial resolution over the Euro-CORDEX 36 domain. The time period was limited by the necessary overlap of different data products, and the temporal resolution by the lowest one among the data products. The spatial extent and resolution were chosen to match upcoming land-surface model outputs. Therefore, where needed, the variables were interpolated in time to daily mean across 8 days and in space to the 3 km Euro-CORDEX grid. We used GLASS12B02 V40 (http://www. glass. umd. edu/introduction. html)  32 GPP data, which was created with the revised EC-LUE model 33,  49  which, importantly, includes long-term effects of CO 2 , temperature (T) and VPD on GPP.
The ET data used in this work is from GLASS11B02 V41, calculated by a multimodel ensemble approach merging five process-based ET data-sets  32, 50. Importantly, we compared GPP and ET from FLUXNET2015 (https://fluxnet. org/data/fluxnet2015dataset/)  51 and ICOS Drought-2018 (https://www. icos-cp. eu/data-products/YVR0-4898 ) 52 station data-sets to corresponding grid cells of GLASS, ERA5-Land (https://cds. climate. copernicus. eu/cdsapp#!/dataset/reanalysis-era5-land)  53 and GLEAM3. 5a (https://www. gleam. eu/)  54 and opted to use GLASS ET for the analyses based on the outcome (Supplementary Figure  S6 , S11 and S12, Tables  S3  and S4 ). Moreover, the T r data is from GLEAM3. 5a and was also compared to upscaled sap flux data from the SAPFLUXNET 0. 1. 5 dataset (http://sapfluxnet. creaf.
cat/)  55 (Supplementary Fig. S6 , Table  S5 ). To determine SSI droughts we acquired SM data from ERA5-Land for two soil layers, 0-7 cm and 7-28 cm. P data to identify hydro-climates (with mean annual P, Supplementary Table  S1 ) and SPI droughts, as well as other meteorological variables used here, temperature (T), relative humidity (RH), shortwave incoming radiation (R s ) and wind speed (W), were taken from the COSMO-REA6 data-set (https://reanalysis. meteo. uni-bonn. de/?COSMO-REA6)  48.
Estimation of Water-Use Efficiency Indices Using the above datasets, VPD and G c were calculated as follows: E S =0. 6108 × e 17. 27 ×T T +237. 3 E A = E S × RH 100 VPD=E S -E A [kPa] G c = T r VPD [mm kPa -1 ] and the WUE indices as: EWUE= GPP ET [gC mm -1 ] TWUE= GPP T r [gC mm -1 ] IWUE= GPP T r ×VPD= GPP G c [gC kPa mm -1 ] Where E S is the saturated vapour pressure and E A the actual vapour pressure. At last, we used IGBP-Modified MODIS 20-category land cover data (https://www2. mmm. ucar. edu/wrf/users/docs/user_guide_V3/user_guide_V3. 8/ users_guide_chap3. htm#_Land_Use_and) for determination of land cover in the study domain.
Drought Analysis First, we calculated standardized drought indices SSI and SPI of SM and P to identify monthly dry and wet conditions using the climate indices python package  57  with a gamma distribution for each grid cell in the 1995 -2018 study period. We then identified the drought response in each location or grid cell as the difference of the median WUE in drought years to the overall median WUE in that grid cell. For this, we defined a drought where the monthly SSI or SPI is less than -1. Similarly, drought severity categories are defined over the monthly SPI and SSI values based on thresholds from Supplementary Table  S2. Respective anomalies refer to the difference between the median WUE when the drought index is within the given threshold and the overall median WUE at this location.
Causal Network Discovery Analysis PCMCI+  37, 38  is a causal network discovery method, which can estimate contemporary and lagged dependencies between time series. Two important assumptions to entitle causality in PCMCI+ are causal sufficiency and time series stationarity. To fulfill causal sufficiency, we included as many as 11 time series of variables related to ecosystem process variability (i. e. GPP, ET, T r , T, RH, VPD, G c , R s , W, P and SM) and conducted three analyses whereby we added one respective WUE index to the input variables. Time series stationarity is satisfied by detrending and using seasonal anomalies of the time-series of each variable before inputting into the PCMCI+ algorithm of the python package tigramite (https://github. com/jakobrunge/tigramite). Directed causal links between variables are iteratively found with partial correlation tests.
25 μM each primer, 3. 2 μg of bovine serum albumin (Roche Diagnostic, Basel, Switzerland) and 2 μl of DNA template that was diluted 10-fold to reduce PCR inhibition by humic substances. The thermal profile of the PCR amplification was 40 cycles of denaturation at 95 • C (30 s), annealing at 49 • C (30 s) and elongation at 72 • C (60 s), with a final elongation step at 72 • C for min. Tags had at least five differences between them to minimize ambiguities  [31]. The sequenced multiplexes comprised extractions/PCR blank controls, unused tag combinations and positive controls  [29]. The PCR products were then sequenced using the MiSeq platform (Illumina Inc. , San Diego, USA), with the expected sequencing depth set at 000 reads per sample. The sequences were processed using ObiTools software  [32].
Low-quality sequences (containing Ns, alignment scores <50, lengths <140 bp or >320 bp and singletons) were excluded. The remaining sequences were clustered into MOTUs using SUMACLUST  [33]  at a threshold of sequence similarity of 97%. The final number of MOTUs after curation was 11785. The hexapod MOTUs were taxonomically assigned using Basic Local Alignment Search Tool, (BLAST), with a query coverage criterion of 95%. MOTUs showing <80% similarity with either the local or the EMBL reference databases were removed, leading to 590 MOTUs  [34]. These retained MOTUs included taxa from classes Insecta and Entognatha, which both belong to the subphylum Hexapoda. All sequences with a frequency of occurrence below 0. 05 per MOTU and per run were discarded. This threshold was empirically determined to clear the negative sequencing controls (non-used tag combinations) included in our global data production procedure.
Data analysis All data handling, visualization and statistical analyses were conducted using R v4. 0. 6  [37]. We first identified the relationships between all environmental variables (the amounts of DOC and DON, and Cmic and Nmic) across temperatures using principal component analyses (PCAs) and simple general linear models (GLMs), with warming as categorical fixed-effect terms. We then built simple GLMs, with temperature as fixed-effects terms, to identify differences in rarefied mOTU abundance and richness due to warming. Changes in the composition of the hexapod communities for each pair of temperature levels were assessed using sparse partial least squares discriminant analysis (sPLS-DA) as implemented in the mixOmics package  [38]. sPLS-DA selects variables and classifies the most discriminative taxa in the community matrices.
The optimal number of components was chosen based on the error rate of t-tests, which suggested the use of single-component sPLS-DAs in both warming levels. The mixOmics package also delivers P values based on the area under the receiver operating characteristic curve to complement the sPLS-DA performance results. The main sPLS-DA variates (the x-variate, capturing the compositional similarity of soil hexapod communities) were then subjected to simple GLMs against the set of environmental variables: the amounts of soil DOC and DON, Cmic and Nmic.
2c ), where increases in bi-decadal precipitation during 1980-2020 occur over 65 and 73% of tropical land based on the two precipitation datasets, respectively (Supplementary Fig. 8 ). On the contrary, a sharp increase in γ T CGR is probably associated with a decrease in precipitation during the 2015-2016 extreme El Niño, suggesting that severe water stress in a single year causes the increase in γ T CGR calculated over a window of 20 y (Supplementary Fig. 3c  and Fig. 2a ). These findings, collectively, suggest that changes in water availability in the tropics may be the primary driver of the variations in γ T CGR. Additionally, we detect changes in γ T CGR in the four dryness categories separated by the standardized deviation of the mean annual precipitation, soil moisture, scPDSI, SPEI and terrestrial water storage in the spatial domain (see Supplementary Fig.
Atmospheric CO 2 The monthly mean atmospheric carbon dioxide concentration (CO 2 ) at the Mauna Loa Observatory for 1959-2020 and the South Pole station for 1980-2020 were accessed from the US National Oceanic and Atmospheric Administration (http://www. esrl. noaa. gov/gmd/ccgg/ trends/). The Mauna Loa Observatory CO 2 dataset represents the longest record of direct measurements of CO 2 in the atmosphere. There are many CO 2 observations missing in the South Pole station dataset before 1980, and we have thus chosen the time series of CO 2 observations from 1980 to 2020, which allowed us to detect the changes CGR sensitivity for the recent period.
Climatic data We extracted monthly temperature, precipitation and solar radiation (cloud cover in CRU-4) for 1960-2020 from both the CRU-4 and ERA5 datasets. Monthly climatic data at a spatial resolution of 0. 5°from the Climate Research Unit (CRU TS 4. 05), University of East Anglia, are generated by scaling up rain-gauge observations  31. We resampled the data to a spatial resolution of 0. 25°using bilinear interpolation. The ERA5 data were downloaded from the website of the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5)  32  , which are the fifth generation ECMWF reanalysis data for the predictions of global climate and weather replacing the ERA-Interim reanalysis. The data are provided at a spatial resolution of 0. 1°and a temporal coverage from 1950 to the present.
We also extracted monthly gauges-based precipitation data for 1960-2019 from the Global Precipitation Climatology Centre (GPCC)  33. We further used the self-calibrating Palmer Drought Severity Index (scPDSI) for the detection of changes in water stress conditions  34  , where negative values of scPDSI indicate drier conditions and vice versa. scPDSI is estimated based on precipitation, potential evapotranspiration (PET) and temperature, and the Penman-Monteith method is used to calculate PET. Likewise, the Standardized Precipitation and Evaporation Index (SPEI) data (using a three-month integration period) for 1960-2018 were extracted to indicate water availability conditions  35. We used monthly root-zone soil-moisture data from the dataset of the Global Land Evaporation Amsterdam Model version 3 (GLEAM-v3) produced at a spatial resolution of 0.
Other data We extracted monthly NBP data under scenario 3 for 1960-2019 from 15 dynamic global vegetation models (DGVMs) from TRENDY version-9  41  being part of the Global Carbon Project  42. DGVMs used in this study included CLASSIC, CLM5. 0, DLEM, IBIS, ISBA-CTRIP, JSBACH, JULE-ES, LPJ-GUESS, LPX-Bern, OCN, ORCHIDEE-CNP, ORCHIDEEv3, SDGVM, VISIT and YIBs. We further collected data for ESA CCI land cover to exclude water bodies and built-up land in the tropical regions. We also extracted data from Multivariate ENSO Index Version 2 (MEI. v2) to indicate El Niño and La Niña during 1979-2021.
The relative importance is assessed using the "lmg" approach, which is calculated based on a sequential R 2 but considers the dependence on ordering of explanatory variables by averaging over the total number of combinations of ordering in a multiple regression. The Theil-Sen slope and Mann-Kendall trend test were used to identify trends and their significance (P < 0. 05) in γ T CGR or γ T NBP for the early and recent periods. We used the function 'zyp. trend. vector' provided in the R package 'zyp', including the Yue-Pilon prewhitening method applied to remove serial autocorrelation  23  , to conduct the trend test. The slope of a linear regression between trends in γ T NBP and each of γ T NPP , γ T Rh , and γ T FIRE (carbon flux components) were used to detect the sensitivity of changes between variables.
Data availability All data used to support the findings of this study are publicly available. Atmospheric CO 2 concentration at the Mauna Loa Observatory and the South Pole station are available from http://www. esrl. noaa. gov/gmd/ ccgg/trends/. CRU climatic data are available from https://crudata. uea. ac. uk/cru/data/hrg/. GPCC precipitation are available from https:// www. dwd. de/EN/ourservices/gpcc/gpcc. html ERA5 climatic data are available from https://cds. climate. copernicus. eu/cdsapp#!/dataset/ reanalysis-era5-land-monthly-means?tab=overview. CMIP6 outputs are available from https://esgf-node. llnl. gov/search/cmip6/. scPDSI and SPEI data are available from https://crudata. uea. ac. uk/cru/data/ drought/ and https://spei. csic. es/database. html. GRACE data are available from https://figshare.
com/articles/dataset/GRACE-REC_A_ reconstruction_of_climate-driven_water_storage_changes_over_the_ last_century/7670849. LPJ-GUESS model simulations are available from Guy Schurgers (gusc@ign. ku. dk) upon request. The outputs of DGVMs from TRENDY are available from Hui Yang (huiyang. pku@gmail. com) upon request.