dataesr/scientific-paragraphs-categorization huggingface dataset:
who. int/ phe/health topics/outdoorair/databases/en/index. html). Road traffic is the most important source of particles in Stockholm's inner city; in particular, vehicle exhaust emissions produce ultrafine particles as a result of imperfect combustion of diesel and petrol  (Johansson et al. , 2004 (Johansson et al. , , 2007;; Norman and Johansson, 2006). The observed PM 10 concentrations are relatively low at Birkenes, a rural, coastal site ∼30 km from Kristiansand in southern Norway with a monthly observed mean concentration of 8. 4 µg m -3 and the peak daily concentration of 28. 9 µg m -3. WRF/Polyphemus underpredicts PM 10 concentrations at all sites, whereas WRF/Chem-MADRID overpredicts them, in particular, at Södermalm. Such differences may be attributed to a different emission module of sea salt with higher sea-salt emissions from WRF/Chem-MADRID and the inclusion of emissions of soil dust.
Figures  7 8 compare observed and simulated hourly PM 2. 5 and PM 10 concentrations at 3 sites and 24 h average concentrations of PM 10 at 10 sites that fall into the D02 domain (i. e. , Tremblay-en-France and Ternay, France; Deuselbach and Langenbrugge, Germany; Payerne and Chaumont, Switzerland; Ispra, Italy; Celje, Slovenia; Harwell, Rochester Stoke, and London Bloomsbury, UK; and Cabo de Creus and Niembro, Spain). For hourly PM 2. 5 and PM 10 , WRF/Polyphemus shows less sensitivity to grid resolutions than WRF/Chem-MADRID at Harwell, Rochester Stoke, and London Bloomsbury, with better agreement with observations. WRF/Chem-MADRID at 0. 125 • gives higher  1 WP -WRF/Polyphemus; WC-S -WRF/Chem-MADRID simulations with the offline BVOC emissions of  Simpson et al. (1999) ; 2 the data pairs are based on the evaluation of predictions at a horizontal grid resolution of 0.
A comprehensive model evaluation is performed to evaluate the model's performance using three surface monitoring stations including EMEP, AirBase, and BDQA and several satellite databases including MOPPIT, GOME, TOMS, and MODIS. For domainwide statistical performance at 0. 5 • , compared with WRF/Polyphemus, WRF/Chem-MADRID gives higher domainwide mean values for all surface concentrations and column variables except for hourly O 3 and TOR (which differs substantially due to the use of different upper layer boundary conditions). Compared with observations, WRF/Polyphemus gives better statistical performance for daily HNO 3 , SO 2 , and NO 2 at the EMEP sites, maximum 1 h O 3 at the AirBase sites, maximum 8 h O 3 at all sites, PM 2. 5 Y. Zhang et al. : Evaluation of chemical concentrations and sensitivity simulations at the AirBase sites, PM 10 composition, column abundance of CO, NO 2 , and TOR, and AOD, whereas WRF/Chem-MADRID outperforms for NH 3 , hourly SO 2 , NO 2 , and O 3 at the AirBase and BDQA sites, maximum 1 h O 3 at the BDQA and EMEP sites, and PM 10 at all sites.
Acknowledgements. This project is sponsored by the  EPA STAR # R83337601 , the  NSF/USDA EaSM program   AGS-1049200 , and the fellowship award # 704389J , the  Atmospheric Environment Center (CEREA)/ École des Ponts ParisTech, France , through a visiting professorship of  Y. Zhang  at  CEREA , the  Joint Laboratory of École des Ponts ParisTech and EDF R&D, Paris, France , and  COST ES1004. Thanks are due to contributions of former members of the air quality forecasting laboratory at  NCSU , including  Yao-Sheng Chen ,  Xu-Yan Liu , and  Ryan Cassidy  for preparing some inputs for WRF/Chem-MADRID, processing data, and making plots. Thanks are also due to  Yiguo Wang , CEREA, for his help in using the AirBase data. Edited by:  F. Yu.
) Time (local standard time) Sommet Puy de Dome (FR07015, (45. 77N, 2. 96E)), Rural, France OBS WRF/Chem-Madrid-D01 WRF/Chem-Madrid-D02 Polyphemus-D01 Polyphemus-D02 m-3) (µg SO Concentration Fig. 3. Simulated and observed hourly or daily concentrations of SO 2 in July 2001 at selected sites over D01 and D02 in four latitude bands: 57-60 • N, 48-52 • N, 45-48 • N, and 38-43 • N (rows 1, 2, 3, and 4, respectively). located with the selected meteorological sites from the NCEP or the ECA&D databases (see Table 5 in Part 1) includ- ing 2 AirBase/BDQA sites (Melun and Tremblay-en-France) in France and 4 AirBase sites (Düsseldorf-Lörick in Ger- many, Avenida Gasteiz in Spain, Södermalm in Sweden, and London Bloomsbury in the UK). Melun (FR04069) is co-located with the NCEP site (Melun, 7153) and the ECA&D site (Brétigny-sur-Orge, 000764).
Carbon fluxes Carbon fluxes were measured in situ, in August 2010, during low tide (air-sediment interface), with a benthic chamber  [20]. The chamber covered a 0. 07 m 2 sediment area and consisted of a stainless-steel ring pushed into the emersed sediment to about 10 cm depth. It was hermetically sealed by either a transparent or dark Perspex® dome. The chamber was then connected to a closed gas circuit for about 15-30 min in order to record changes in CO 2 partial pressure (data logger LiCor Li-1400, with a 30s logging frequency). CO 2 was monitored with an infrared gas analyzer (LiCor Li 800) in ppm. From recorded data of either light or dark incubations, CO 2 partial pressure was regressed against time. Slopes of the corresponding regression curves were used to calculate respectively the net primary production and respiration.
Introduction Comparative studies of the above-ground biomass (AGB) of tropical forests exist for South America  [1] [2] [3]  and Asia  [4]  but not for Africa. Thus, some ostensibly simple questions remain unanswered: how much AGB does an average structurally intact African tropical forest store? Where in Africa is biomass lower or higher; and what controls this spatial variation? How do African forest AGB values compare with those on other continents? Here, we collate standardized AGB data from across tropical Africa to provide a first answer to these broad questions. Understanding the spatial patterns of biomass in African forests is important on at least four counts. First, to provide insights into how tropical forests function. Africa provides a useful contrast with Amazonia in terms of separating possible causal factors underlying AGB variation, as unlike Amazonia, Africa does not possess a strong east-west gradient in soil fertility that coincides with other gradients such as mean annual air temperature  [1, 3, 5].
Methods (a) Data collection and processing Forest inventory plot data, collected and collated as part of the African Tropical Rainforest Observatory Network (AfriTRON; www. afritron. org), were selected for analysis when conforming to the following criteria: closed-canopy tropical forest; georeferenced; all trees greater than or equal to 100 mm diameter measured; greater than or equal to 0. 2 ha; majority of stems identified to species; old-growth and structurally intact, i. e. not impacted by recent selective logging or fire; mean annual air temperature greater than or equal to 208C and greater than or equal to 1000 mm mean annual precipitation (from WorldClim  [23] ). Three remaining plots previously characterized by researchers as 'montane' forest were excluded. In all plots, tree diameter was measured at 1.
3 m along the stem from the ground, or above buttresses, if present. The 260 plots (total, 312. 5 ha) that conformed to the criteria comprised 132 899 stems, of which 85% were identified to species and 96% to genera. Further details are given in the electronic supplementary material. For each plot, we calculated (i) stem density greater than or equal to 100 mm diameter per ha; (ii) the BA (sum of the crosssectional area at 1. 3 m, or above buttresses, of all live trees) in m 2 ha 21 ; (iii) BA-weighted wood mass density (WMD BA ), i. e. the mean of the WMD of each stem weighted by its BA, where WMD is dry mass/fresh volume in g cm 23. The best taxonomic match wood density of each stem was extracted from a global database  [24, 25]  following a well-established procedure  [26] ; (iv) AGB (including stem, branches and leaves) was calculated using the Chave et al.
[15]  'moist forest' equation to estimate the AGB of each tree in the plot, using diameter, WMD and tree height, with height estimated from diameter using the recommended regional equations for West (region west of the Dahomey gap), Central (Congo-Ogouee Basin and contiguous forest) and East (east of Congo Basin) Africa, as defined in  [7] , rstb. royalsocietypublishing. org Phil Trans R Soc B 368: 20120295 and expressed dry mass as Mg ha 21 (¼ metric tonnes ha 21 ). The stem density BA, WMD, WMD BA and AGB values were calculated using the http://www. forestplots. net/ data management facility  [27] ; version 13 April 2013  [28]. The locations of the study plots are shown in figure  1. Average mean annual temperature (T A ), mean monthly maximum air temperature (T max ), mean monthly minimum air temperature (T min ), mean temperature in the warmest and coldest quarters (T WARMQ , T COLDQ ), temperature seasonality (coefficient of variation; T CV ) and average mean annual precipitation (P A ), mean monthly maximum precipitation (P max ), mean monthly minimum precipitation (P min ), precipitation in the wettest and driest quarters (P WETQ , P DRYQ ) and precipitation seasonality (coefficient of variation; P CV ) were extracted from the WorldClim database at the finest resolution available (30 0 ;  [23] ), giving mean long-term climate data  (approx.
1950 -2000)  for each plot location (see the electronic supplementary material for further details). Detailed information on soils was not available for most plots, but the soil class or type was often known or estimated from data outside the plot, local knowledge, local soil or geology  [29]. For each plot, we therefore had a notional soil type, and where necessary this information was converted to a standard classification and soil variables extracted (for 0-30 cm and 30-100 cm depth) for the corresponding soil type at or closest to the plot location from the FAO Digital Soil Map of the World dataset  [29]. This provides a method of incorporating consistent soil information, while avoiding the possible problem of incorrectly assigning plots overlying non-dominant soil types, or averaging data from plots on differing soil types within the same interpolated soil map grid square.
Hence, plots within the same landscape on differing soil types are assigned corresponding differing soil parameters. The soil data are to be treated with caution, as they are not in situ data, particularly as soil geographers sometimes use vegetation characteristics themselves as an aid to their mapping of soil  [30] , giving rise to a potential tautology. Nevertheless, our approach taken here incorporates the in situ data available and avoids some common pitfalls of using gridded soil data allowing for a first-order analysis of any likely edaphic effects on the studied stand properties. To test for soil-related effects, we used (i) principal components analysis (PCA) on the soil-structure-related data (0-100 cm), giving a sand-clay axis (PC1 sand; low values are high sand content) and a silt axis (PC2 clay-silt; high values are clay-rich, low values silt-rich; loadings in the electronic supplementary material); (ii) sum of exchangeable bases (0-30 cm), in cmol kg 21 ( P B), the most relevant to tree growth cation-related plant nutrition variable in the FAO dataset; (iii) C : N ratios as a surrogate for plant available phosphorus.
Phosphorus availability is likely to be very important for tree growth but is not reported in the FAO or other large-scale soil datasets. However, soil C : N ratio (0-30 cm) has been shown to be strongly negatively correlated with total extractable phosphorus across in Amazonia  [5] , and unpublished African in situ soil data also support this notion (S. Lewis et al. , unpublished data). Additionally, we also define soil classes based on pedogenic development, following the scheme in reference  [31] : all soils younger than alisols (in this dataset cambisols and histosols), score 1; all soils younger than ferralsols but older than alisols, score 2; all ferralsols, score 3. Habitat fragmentation indices were devised using Google Earth Pro. We measured the distance from the plot centre to (i) the nearest forest edge (any absence of forest cover greater than or equal to 1 ha), giving a distance to edge (fragment edge in km, F E ) and (ii) the nearest edge of a clearing greater than or equal to 1 ha in eight directions every 458 from north, from which we estimated fragment size by summing the areas of the eight triangles generated (fragment area in km 2 ; F A ).
(b) Statistical analysis The dataset is complex with explanatory variables spatially autocorrelated. Furthermore, some of the soil types are rare, and temperature-and precipitation-related variables also correlate. As there is no single statistical method that can account for all of these aspects of the dataset, our approach was to use a series of statistical techniques, each with its own limitations, to build a synthetic understanding of the controls on AGB. We first investigate the continuous variables, presenting Spearman's correlation coefficients, accounting for spatial autocorrelation using Dutilleul's method  [32]. For categorical soil variables, we use ANOVA to assess their potential impacts on response variables. We then take an information-theoretic approach, testing all possible combinations of the climate, fragmentation and soil variables, selecting the best model on the basis of the lowest Akaike's information criterion, corrected for finite sample sizes (AIC C ).
We assume all of the ordinary leastsquares (OLS) models within two AIC C units of the lowest AIC C model are plausible alternatives in terms of explaining variation in the dataset  [33, 34]. Extensive preliminary analysis showed which pairs of variables had the most explanatory power T min or T COLDQ , T max or T WARMQ , P min or P DRYQ , P max or P WETQ. We selected T min , T WARMQ , P min and P WETQ for inclusion in the models to better allow comparisons of models across response variables. Following this, the low AIC C models were checked for parameter redundancy by removing redundant variables that are the same sign (i. e. if T A and T WARMQ are included and of the same sign, then one is removed based on importance values), and the full suite of models was run again, minus these redundant terms (see the electronic supplementary material for further details).
Removing redundant terms aids the interpretation of the results and avoids the possible problem of over-fitting sometimes associated with larger datasets  [34]. We then account for spatial autocorrelation in our OLS models. As there is no definitive technique to account for spatial autocorrelation  [35] , we follow the recent example of Quesada et al. [3]  who used eigenvector-based spatial filtering (extracted by principle component of neighbour matrices  [36, 37] ) on a similar dataset from Amazonia, which aides cross-continental comparisons. We identify the spatial filters significantly correlated with the residuals from the OLS model, and re-run the identical explanatory variables as in the OLS model plus the selected filters, termed spatial eigenvector mapping (SEVM) models. We computed other less stringent filtering methods, but as these inform more on the underlying structure of the variables rather than addressing our specific hypotheses we omit them for brevity (see  [3] ).
(c) Relationships considering all variables The lowest AIC C OLS model for AGB included P A , P WETQ , T A , T WARMQ , C : N, P B and PC2 (silt -clay continuum) soil variables and explained 32. 4% of the variation in the dataset (table  1 ). P A was positively related to AGB, higher by 1. 3 Mg dry mass ha 21 for each 10 mm increment of rainfall, unless precipitation in the wettest quarter was higher, when this would reduce AGB. Put another way, precipitation in the nine drier months is positively related to AGB, whereas it is negatively related in the wettest three months. Similarly, T A was positively related to AGB and T WARMQ negatively related. Taken together, this implies a net AGB difference of approximately 211. 7 Mg dry mass ha 21 (approx. 3% of AGB) for each degree Celsius of higher temperature.
Overall, among the warmest African forests, if temperature variation has an impact on AGB variation, then it is negative. The lowest AIC C OLS model for BA was similar to the AGB OLS models, but with the two soil fertility terms not included, and an added negative F E term; this model explained 24. 6% of the variation in the dataset (table  1 ). Twelve other low AIC C models were identified, adding to the best model negative P B and/or C : N terms, adding a positive T A term or removing P WETQ or F E in some combination. Thus, the low AIC C BA and AGB models were collectively similar. Adding the SEVM filters retained similar results, but removed the precipitation terms and reduced the magnitude of both the negative T WARMQ and positive PC2 terms (table  2 ). The five alternative low AIC C models include the missing P A and P WETQ terms and/or the C : N term.
Hence, for BA, the temperature relationship is negative and larger than that for AGB (approx. 3-5% lower BA in forests growing under higher air temperature). The spatial residuals were improved using the SEVM filters over the OLS models. The lowest AIC C OLS model for WMD BA included positive effects of P min , positive T A impact, positive PC2 (clay) plus negative C : N relationship, PC1 (sand) and F A terms. The model explained 15. 0% of the variation in the dataset (table  1 ). There were three alternative low AIC C models, involving an additional negative term P B, negative T min or without the F A term, respectively. The lowest AIC C SEVM model retained only a strong positive relationship with temperature, PC2 and the negative F A terms. Seven alternative low AIC C models included an additional PC1, C : N, and/or T A term or dropped PC2 in various combinations.
Spatially, our results show clear patterns such as the relationship with latitude, with the highest AGB forests near the equator. Here, we briefly consider the impact of soil parameters, rainfall, temperature and forest fragmentation, in turn, followed by conclusions on the possible causes of difference among the sampled plots in Central, West and East Africa. The soil data derive from a gridded global database rather than from the plots themselves and thus must be treated cautiously. Furthermore, the analyses were sensitive to outlier soil types (leading to the exclusion of swamp plots on histosols from the latter analyses). The AGB-soil fertility results were, however, partially consistent with both our stated hypotheses. First, we hypothesized that higher resource availability increases NPP increasing AGB.
By contrast, only one of eight SEVM low AIC C models included a negative net temperature term. This suggests that after accounting for the spatial structure in the temperature data the negative effect of temperature is removed (but note that the SEVM filters did not substantially improve the residuals in the model, see electronic supplementary material). The cause of the difference is due to filter 1 in the SEVM analyses, which is deeply concave with distance. This is driven by a preponderance of higher elevation plot locations around the eastern and western periphery of the Congo Basin, giving long-distance temperature symmetry in the dataset. Thus, when plots from only Central Africa are retained the same shaped SEVM filter 1 is retained, whereas when only plots less than or equal to 500 m are retained in the analysis (i.
Acknowledgements. We are indebted to the many organizations and people who made the numerous expeditions to collect the data analysed possible. We thank  Christian Amani  ( University of Bukavu ),  M. Sindani  ( University of Kisangani ),  Olivier J. Hardy ,  Jean Lejoly  ( Brussels Free University ) and  Commission Universitaire pour le Developpement  grant to  J. V. (Kisangani  plots),  Pierre Ploton  and  Narcisse Kamdem  ( Dend Deng  plots),  Mike D. Swaine  ( Kade plots ),  Sophie Allain  (Dja plots),  Jeanette Sonke(Dja, MDC ,  NGI , CAM, BIS plots),  Raymond Lumbuenamo ,  Bruno Perodeau ,  Americain Mopanga ,  Ngamba Mongama  and  Pala Eyiano  (WWF-DRC; Malebo plots), and  FRIA grant  ( FNRS ) to J. F. B. (Malebo plots), C. E. B. and  Armand Boubady  (C. E. B. , Miole plots),  Rougier Gabon  ( Haut-Abanga ,  Leke/Moyabi  plots),  C.
Funding statement. USAID/CIFOR  (to S. L. L. and T. S. ), GEOCARBON (to S. L. L. and B. S. ) and  European Research Council  (to O. L. P. , S. L. L. and Y. M. ),  NERC (New Investigators  award to S. L. ;  TROBIT Consortium  award to J. L. and S. L. L. ),  Gordon  and  Betty Moore and David and Lucile Packard Foundations  (to L. J. T. W. , S. L. L. and Y. M. ),  Leverhulme Trust  (to S. L. L. ),  Missouri Botanical Garden  (to M. L. ),  Royal Society  joint project (to T. R. F and E. F. ) and  Royal Society University Research Fellowship  (to S. L. L. ) grants all part-funded AfriTRON (www. afritron. net) and this work. ForestPlots. netdevelopment is funded by the  Gordon and Betty Moore Foundation ,  NERC ,  The University of Leeds ,  European Research Council  and the  Royal Society.
Morphometric analysis Thyroid tissue sections (5 lm) were cut from paraffin embedded fetal tissue. Every 10th section was stained with hematoxylin-eosin (HE) and imaged with a Zeiss Axio Imager A2 microscope using Axiovision 4. 8 software (Zeiss, Jena, Germany). For quantitative analysis, the number and the size of follicles, the epithelial height and the area of blood vessels were determined using the Axiovision AutoMeasure quantitative software (Zeiss). Results from male and female fetuses were considered both separately and together. The follicles were assessed in a semi-automated system and manual correction was performed for each slide. Due to the irregularity in size and shape, the blood vessel area was traced manually, and the samples from 10 animals from each treatment group (five males and five females) were analyzed quantitatively.
8 software for male and female thyroids separately. For quantification of Ki67-positive nuclei, five non-overlapping images from each thyroid section were taken at 200Â magnification. The number of Ki67-positive and the total number of hematoxylin-stained nuclei was determined semi-quantitatively (Zeiss Axiovision AutoMeasure software with manual correction) and analyzed separately for male and female fetal thyroids.
Statistical analysis Normality of data distribution was tested with the Shapiro-Wilk test and non-normally distributed data were log-transformed prior to analysis using one-way ANOVA. For the Ki67 immunodetection and the relative thyroid organ weights, statistical analysis was performed with the ANOVA test. For the morphometric analysis of the follicle number, follicle size and blood vessel area, a two-factor analysis of variance and a chi-square analysis were employed to determine differences for treatment and gender. Analyses of endocrine data were performed using JMP (5. 1, Thomson Learning, London, UK).
The follicles were categorized in three size groups: (1) <150 lm 2 , (2) 150-500 lm 2 , and (3) >500 lm 2 using the Axiovision AutoMeasure quantitative software (Zeiss). Statistical significance was determined with the Chi-Square analysis. Only the 150-500 lm 2 category of thyroid follicle area showed significant differences between treatment groups: the percent distribution of 150-500 lm 2 follicles in the TC and TT treatment groups was different from controls (CC). We observed a trend for controls (CC) vs. CT-treated animals. Means ± SEM are given. p = 0. 06; TC vs. TT: p = 0. 16. No significant correlation of any kind was found between maternal TSH and fetal thyroid hormone levels.