dataesr/scientific-paragraphs-categorization huggingface dataset:
In the present work, we performed oxidation experiments with limonene-oxygen-nitrogen and α-pinene-oxygennitrogen mixtures in a jet-stirred reactor (JSR) at 590 K, a residence time of 2 s, and atmospheric pressure. Oxidation products were analyzed by liquid chromatography, flow injection, and soft ionization-high resolution mass spectrometry. H/D exchange and 2,4-dinitrophenyl hydrazine derivatization were used to assess the presence of OOH and C=O groups in oxidation products, respectively. We probed the effects of the type of ionization used in mass spectrometry analyses on the detection of oxidation products. Heated electrospray ionization (HESI) and atmospheric pressure chemical ionization (APCI), in positive and negative modes were used. We built an experimental database consisting of literature data for atmospheric oxidation and presently obtained combustion data for the oxidation of the two selected terpenes.
, 2021; Belhadj et al. , 2021b). The exhaustive analysis of chemical species remains, under the current instrumental limitations, impossible. Indeed, this would consist in analyzing several thousands of molecules using separative techniques such as ultra-highpressure liquid chromatography (UHPLC) or ion mobility spectrometry (IMS)  (Krechmer et al. , 2016; Kristensen et al. , 2016). Nevertheless, it is possible to classify these molecular species, considering only CxHyOz compounds, according to criteria accessible via graphic tools representation such as van Krevelen diagrams, double bond equivalent number (DBE), and average carbon oxidation state (OSc) versus the number of carbon atoms  (Kourtchev et al. , 2015; Nozière et al. , 2015). Such postprocessing of large datasets has the advantage of immediately highlighting classes of compounds or physicochemical properties such as the condensation of molecules (vapor pressure), the large variety of oxidized products (CxHyO1+15 in the present experiments) and the formation of oligomers  (Kroll et al.
Autoxidation products detected in a JSR In order to compare the oxidation of α-pinene and limonene, we compiled the positive and negative ionization data obtained with APCI (Table  S1 ) and HESI (Table  S2 ) ionization sources to obtain a more exhaustive database. For the APCI and HESI sources, we distinguished three datasets, two of which are specific to the oxidation of α-pinene and limonene and one which is common to both isomers. In the following text, "only" will be used to describe the molecules specific to the oxidation of one of the isomeric terpenes. This common dataset represents more than 77% of the chemical formulas identified in the α-pinene oxidation samples detected with APCI. For limonene, for which the number of identified chemical formulas is greater, this common dataset represents over 93% of the chemical formulas detected after APCI ionization.
325 Only one study  (Meusinger et al. , 2017)  was performed in positive mode and none of the studies reported data 326 https://doi. org/10. 5194/acp-2022-635 Preprint. Discussion started: 11 October 2022 c Author(s) 2022. CC BY 4. 0 License. were obtained with two ionization modes (+/-). For limonene oxidation, the four studies selected identified 1434 chemical formulas. Only one study  (Walser et al. , 2008)  used (+) and (-) ionization modes. In the case of limonene oxidation, for which accretion is more important than for α-pinene, and for which a greater number of chemical formulas were identified, the similarities are more important. These results are presented in Figure  4  where the ionization polarity used in each study is specified. (see conditions in Table  1 ). Each digit of the binary numbers in parentheses identifies the datasets being compared.
For α-pinene, no chemical formula is common to all datasets. Different hypotheses can be offered to explain this result. Among them, the number of chemical formulas identified per study remains limited (a few dozen to several hundred) and these small datasets are sometimes restricted to specific mass ranges, e. g. C10 to C20  (Quéléver et al. , 2019 ). In the case of studies carried out with an NO3 -source, sensitive to HOMS, produced preferentially by autoxidation, we note that nearly 50% of the chemical formulas (10/22; (1010)) are linked by a simple difference of 2 oxygen atoms. For limonene, 78 chemical formulas are common to the four studies selected here. In this data set, a large majority of chemical formulas show a similar relationship to autoxidation: 62%  (Tomaz et al. , 2021) , 54%  (Walser et al.
, 2008) , 69%  (Kundu et al. , 2012) , 66%  (Bateman et al. , 2009)  and 72% (this study), simple difference of two oxygen atoms). This result seems to indicate that autoxidation dominates. One can then ask if reaction mechanisms common to atmospheric and combustion chemistry can generate, despite radically different experimental conditions, a set of common chemical formulas and if in this common dataset, a common link, characteristic of autoxidation, is observable? To address that question, we compared all the previous results, for each of these terpenes to those obtained under the present combustion study. The comparisons were made using HESI data. One should remember that the oxidation conditions in a JSR were chosen in order to maximize low-temperature autoxidation. Again, we used Venn diagrams to analyze these datasets composed of 1590 chemical formulas in the case of α-pinene and 5184 chemical formulas in the case of limonene.
The results of these analyses are presented in Figure  5. 1 ). For limonene, the similarities with combustion are more important and less spread out. They represent for the different studies: 65%  (Kundu et al. , 2012) , 88%  (Walser et al. , 2008) , 81%  (Tomaz et al. , 2021) , 57%  (Bateman et al. , 2009). Moreover, there is a common dataset of 78 chemical formulas which can derive from autoxidation mechanisms. It is necessary to specify again that different reaction mechanisms can cause the observed similarities. However, the preponderance of autoxidation in so-called cool flame combustion is obvious, and in atmospheric chemistry, this reaction mechanism is competitive or dominates  (Crounse et al. , 2013; Jokinen et al. , 2014). If we search for an autoxidation link between these 78 chemical formulas, we observe that 45% of these chemical formulas meet this condition: difference of two oxygen atoms between formulas, at constant number of carbon and hydrogen atoms.
More precisely, these molecules are centered in a van Krevelen diagram on the ratios O/C=0. 6 and H/C=1. 6, in the range 0. 29 < O/C < 0. 77 and 1. 33 < H/C < 1. 8. All oxidized molecules associated with this dataset are presented in Figure  6. The dispersion of the chemical formulas, far from being random, remains consistent with an autoxidation mechanism where the numbers of carbon and hydrogen atoms are constant. A 3-D representation of all limonene oxidation data is given in Supplement (Fig. S2 ) where DBE is used as third dimension. From that figure, one can note that products with higher DBE (DBE>10) are preferably formed under JSR conditions, i. e. at elevated temperature. The corresponding chemical formulas could correspond to carbonyls and / or cyclic ethers ( • QOOH → carbonyl + alkene + OH and / or cyclic ether + OH • ).
Conclusion The oxidation of limonene-oxygen-nitrogen and α-pinene-oxygen-nitrogen mixtures was carried out using a jetstirred reactor at elevated temperature (590 K), a residence time of 2 s, and atmospheric pressure. The products were analyzed by liquid chromatography, flow injection, and soft ionization-high resolution mass spectrometry. H/D exchange and 2,4-dinitrophenyl hydrazine derivatization were used to assess the presence of OOH and C=O groups in products, respectively. We probed the effects of the type of ionization used in mass spectrometry analyses on the detection of oxidation products. Heated electrospray ionization (HESI +/-) and atmospheric pressure chemical ionization (APCI +/-) were used. A large dataset was obtained and compared with literature data obtained during the oxidation of limonene and α-pinene under simulated tropospheric and low-temperature oxidation conditions.
This work showed a surprisingly similar set of chemical formulas of products, including oligomers, formed under the two rather different conditions, i. e. , cool flames and simulated atmospheric oxidation. Data analysis involving van Krevelen diagrams, oxygen number distribution, oxidation state of carbon, and chemical relationship between molecules, indicated that a subset of chemical formulas is common to all experiments independently of experimental conditions. More than 35% of the chemical formulas detected in combustion chemistry experiments using a JSR have been detected in the studies carried out under atmospheric conditions. Finally, we have outlined the existence of a substantial common dataset of autoxidation products. This result tends to show that autoxidation is indeed inducing similarity between atmospheric and combustion products.
, H/D exchange with D2O and 2,4-dinitrophenyl hydrazine derivation were used, respectively 3 Data Processing High resolution mass spectrometry (HR-MS) generates large datasets which are difficult to fully analyze by sequential methods. When the study requires the processing of several thousands of molecules, the use of statistical tools and graphical representation means becomes necessary. In this study, we have chosen to use the van Krevelen diagram (Van Krevelen, 1950) by adding an additional dimension, the double bond equivalent (DBE). The DBE number represents the sum of unsaturation and rings present in a chemical compound (Melendez-Perez et al. , https://doi. org/10. 5194/acp-2022-635 Preprint. Discussion started: 11 October 2022 c Author(s) 2022. CC BY 4. 0 License. 2016). The interest of this type of representation is to be able to identify more easily the clusters (increase of the DBE number at constant O/C and H/C ratios).
Acknowledgements The authors gratefully acknowledge funding from the  Labex Caprysses  ( ANR-11-LABX-0006-01 ), the  Labex Voltaire  ( ANR-10-LABX-100-01 ),  CPER , and EFRD (PROMESTOCK and APPROPOR-e projects) and the  French MESRI  for a Ph. D. grant. We also thank  Matthieu Riva  for sharing his experimental data on limonene oxidation. https://doi. org/10. 5194/acp-2022-635Preprint. Discussion started: 11 October 2022 c Author(s)  2022. CC BY 4. 0 License.
Lakes and reservoirs are important for environmental anthropogenic functions in terms of agriculture and/or settlements. Here we present a first global overview of their chemistry by considering , water bodies, with data from peerreviewed publications from to and further five online databases. This work focusses on major ions (Ca + , Mg + , Na + , K + , HCO -&CO -, Cl -, SO -) and investigates analogies as well as di erences between lakes and reservoirs. We applied a Principal Component Analysis (PCA) to group both types of water bodies and to find di erences and similarities. The PCA identified fewer variabilities for major ions in reservoirs than in lakes. Moreover, our analyses showed that lakes generally have more total dissolved solids (TDS). Such higher TDS loads in lakes could result from more diverse (and less controlled) inputs from larger catchments and from longer lasting interactions with thicker internal sediment layers.
Methods Data were retrieved from 485 peer-reviewed publications from 1868 to 2020. The locations of all water bodies used for aqueous chemistry investigations (663 lakes and 106 reservoirs) are provided in Figure  1. All peer-reviewed publications used in this study are listed in the Supplementary material. In addition, freely available online databases with data up to 2020 were researched (Table  1 ). Because our intention was to describe lakes and reservoirs on the global scale, we gathered literature about water bodies in different geographical regions and with diverse physical and chemical characteristics. The lakes and reservoirs included in these considerations are located in North America, Asia, Africa, Europe, Australia, South America, and Antarctica. They cover an altitude range from 430 m below sea level (Dead Sea) to 5,140 m above sea level  (Garig Co, China).
Note that most surface water samples were collected during the warm season and during daytime, mostly close to the ouflow of the water bodies. Therefore, influences of seasonal or diel influences and stratification in standing water bodies cannot be interpreted by these data. Moreover, in the work presented here we focused on major ion chemistry (Ca 2+ , Mg 2+ , Na + , K + , HCO - 3 & CO 2- 3 , Cl -, SO 2- 4 ). These are among the most frequently reported variables in our database, and basic hydrochemical classifications of waters traditionally rely on these ions. Our analyses were based on data in mmol L -1. When ion concentrations were provided in other units, they were converted accordingly. When DIC concentrations were supplied as different variables than concentrations of hydrogencarbonate, they were converted into HCO - 3 & CO 2- 3.
Data used for our investigations refer to selected analyses with errors of ion balances smaller than 10%. This corresponds to 247 lakes and 66 reservoirs. The threshold of a 10%-ion balance seems a reasonable quality indicator. This holds especially true when considering that most analyses were carried out with different methods including titrations, ion chromatography and spectrophotometry. We also considered total dissolved solids (TDS) in water. This parameter was often provided in the literature or else it was calculated based on cations and anions contents. The selected major ion data were analyzed by the PCA. None of the major ions investigated followed a Gaussian distribution. They were therefore treated according to the optimal box-cox transformation  (Box and Cox, 1964). The PCA was conducted according to  Jolliffe (2011)  and used major ions as independent variables with the aim to separate reservoirs and lakes from a hydrochemical point of view.
In our models, we refused the null hypothesis of a t-test when the p-value was higher than 0. 01. We always chose the models with the lowest Akaike Information Criterion (AIC)  (Akaike, 1974). Subsequently, a Kruskal-Wallis test was performed in order to predict if lakes and reservoirs had the same distribution  (Kruskal and Wallis, 1952 ; Table  2 ). The Bartlett test was also applied to evaluate the variance of each dataset (Table  3 ;  Bartlett and Fowler, 1937). Based on major ion compositions from the same suite of samples, we determined global average and median lake-and reservoir-compositions. For this purpose, we used the volume of each water body to calculate their weighted values. Volumes were either retrieved from the literature data or else they were evaluated by the areas of the respective water bodies multiplied by their average depth.
Ranking of the most important variables Our literature search yielded a total of 33 chemical variables (Supplementary Table  S2 ). Several of these parameters were measured only occasionally. The most frequently observed variables include carbon species (DIC, DOC, and POC) and major ions (Ca 2+ , Mg 2+ , Na + , K + , SO 2- 4 , Cl -, HCO - 3 and NO - 3 ). Chlorophyll was available for ∼25% of the water bodies studied. We did not further consider nutrients, chlorophyll, organic carbon and dissolved gases because they were most frequently measured in lakes. Note that the water bodies in our database are not equally distributed over climate zones, with continental and temperate climates accounting for ∼75% of the whole dataset (Supplementary Table  S1 ). The investigated water bodies differ largely by volume, with the smallest one (Lake Blake Mere in the UK) being around ten orders of magnitude smaller than the biggest one (Caspian Sea).
Geochemical characterization with major ions: Principal component analysis Our PCA was performed on the first four components (PC I to PC IV). They covered 88% of the total variance of the dataset. This led to the following observations: -The increase of PC I associates with increases of all variables -The increase of PC II associates with increases of Ca 2+ and Mg 2+ , while K + decreases -The increase of PC III associates with increases of K + , while HCO - 3 & CO 2- 3 decreases -The increase of PC IV associates with increases of K + and HCO - 3 & CO 2- 3 , while SO 2- 4 and Cl -decrease All PCA loadings are outlined in Supplementary Table  S3  and variances are available in Figure  2A. The Kruskal-Wallis test on the four components (Table  2 ) showed that lakes and reservoirs had different distributions over PCII and PCIII.
We named this field "RA96. " The Kruskal-Wallis test also confirmed that the dataset of reservoirs and the one of lakes do not belong to the same population and have different distributions. In order to confirm that reservoirs are more concentrated in the RA96-field of Figure  2C , we also performed different statistical tests under two hypotheses: -The null hypothesis that assumes reservoirs and lakes to have the same distribution and variance -The alternative hypothesis that assumes reservoirs to be more concentrated in the RA96-field rather than in the remaining area The hypothesis was tested by a binomial analysis  (Dodge, 2008). The p-value of the test is: p (X ≥ 96 | X ∼ Bin (N, p)) = 0. 003 This result shows that the water bodies are unlikely to be randomly distributed within the plot.
Major ion trends Global average and mean compositions of lake and reservoir waters were weighted by their volumes (Table  4  and Figure  3 ). According to  Piper (1944) , the weighted average composition for lakes is a sodium-chloride-type water characterized by high contents in Cl -and Na + & K + and lower contents in HCO - TABLE Global volume-weighted compositions of lake and reservoir waters reported as averages and medians (the latter in brackets). Values (in mmol L -) refer to analyses in our database with ion balances < % ( lakes, reservoirs). HCO - 3 & CO 2- 3. In terms of their cations, they rank with Na + & K + > Mg 2+ > Ca 2+. In comparison, the average chemical composition of reservoirs is lower in Cl -but has roughly equal average concentrations of HCO - 3 & CO 2- 3 and SO 2- 4.
Discussion Our PCA outlined clear geochemical differences between lakes and reservoirs (Figure  2 ). With the sufficiently large sample populations, these differences are unlikely due to uneven numbers of lakes and reservoirs in the database. One explanation for lower variabilities and narrower ranges of major ion compositions in reservoirs may be their purpose for drinking water supply that foresees higher water quality standards. Additionally, reservoirs usually have more frequent water exchanges  (Hayes et al. , 2017). Such shorter residence times may contribute to a narrower distribution of reservoirs in our PCA analysis and may have caused the reservoir samples to fall within the RA96 field. Among the major ions presented, K + is a common component of commercial fertilizers and can also serve as a good reflector of agricultural influences  (Scherer, 2005).
, 2017). Deeper water bodies result in a smaller surface area with respect to their volume, which may cause less evaporation. In contrast, lakes with shallower water bodies are more affected by evaporation, which can influence TDS. Regarding global chemical compositions, weighted averages are more influenced by larger water bodies, which make up only a small group of our database. We therefore consider the weighted medians as the better choice to represent global geochemical compositions of lakes and reservoirs. Weighted median values highlight HCO - 3 & CO 2-  3   as the major anions in both lakes and reservoirs. This finding is consistent with that of  Wetzel (1983). The strongest differences between both water bodies was found in their cation concentrations. In particular, K + contents are smaller in reservoirs when compared to those of lakes.
Again, this likely reflects less agricultural inputs that adhere to stricter rules for drinking water storage. Next to the overall positive trend in Figure  4 , the studied lakes showed higher TDS values. In field "1, " the majority of lakes analyzed have no surface outflows and are characterized by bicarbonate waters and/or high alkali contents (e. g. , Lake Bogoria, Mono Lake, Alkali Lake and Borax Lake). This may be related to the fact that this field hosts lakes that are subject to strong evaporation with resulting higher TDS. Field "2" in Figure  4  groups samples with low TDS that characterizes water bodies with moderate to low ion contents. These waters were likely subject to less evaporation. Note that field "2" hosts all reservoirs of our database. Here the data are also close to a 1:1 ratio between Ca 2+ & Mg 2+ and HCO - 3 & CO 2- 3.
Conclusions In this study, we analyzed a total of 485 publications and five online databases on lakes and reservoirs. A PCA yielded an ion distribution plot that concentrates reservoirs in a narrower field than lakes. Moreover, the presented database also provides a first evaluation of global average and median water compositions for lakes and reservoirs as weighted by their water volumes. Weighted medians revealed calcium-bicarbonate type waters for both types of water bodies. We generally recorded smaller values of total dissolved solids in reservoirs when compared to those of lakes. Associated larger variances of TDS in lakes likely reflect the fact that they have more diverse and less controlled inputs from larger catchments. This finding also implies that lakes may be more at risk to undergo environmental change by a larger variety of possible environmental influences.
For instance, agricultural activities play an important role in anthropogenic and land use influences. These were best reflected by K + that serves as a good indicator of fertilizers. The data presented here reveal that reservoirs show lower K + concentrations. This seems plausible, because they are often located in higher altitude areas with less pronounced agricultural activities. Further investigations on fertilizers and pesticides may reveal better relationships to agricultural influences and possible risks of eutrophication and algae blooms. Our study offers a first global overview of chemical characteristics of lakes and reservoirs. It offers a platform for future global-scale biogeochemical studies, and may help to establish links to other global databases such as the Global Reservoir and Dam database (http://globaldamwatch.
org/grand/) or the GLORICH database on rivers (https://www. geo. uni-hamburg. de/en/geologie/ forschung/aquatische-geochemie/glorich). Future considerations should also include other environmental parameters such as dissolved gases, nutrients or stable and radio isotopes that can act as useful tools to outline origins of water and its dissolved constituents. The presented database could additionally expand to closer investigation of climate, geology and land use. Future work could also include information on the water column and depth profiles, as they can provide seasonal insights linked to stratification. This approach would not only provide more information but would also help to further investigate observed differences between lakes and reservoirs.
Acknowledgments. We appreciate support from the  ANR-JPOC EUREC4A-OA ,  NOAA ATOPIC ,  CNRS-INSU LEFE VENUS , and  CNES   TOSCA I-CASCADE  and  CARAMBA  projects. This work used the  GENCI A0050107298  and  A005013051  computing resources. Data availability statement. Data can be downloaded online (https://figshare. com/s/374c96dc0ca373ea1949).