Composite Dataset Research Articles

Estimation of seasonal base flow contribution to a tropical river using stable isotope analysis

Increase in water demand within the Cauvery River Basin is a cause of serious concern over sustainable utilization and managment of water resources. Sustainable, efficent and equitable managment of Cauvery riverwater requires a thorough understanding of the contributing sources and hydrological processes influencing avaliability of water resources. The study investigates the seasonal contribution of surface runoff and baseflow to the Cauvery streamflow using isotope mass balance approach. Stable Isotope measurements (δ2H, δ18O) of Cauvery river water along with groundwater are carried during seasonal time intervals spanning from 2014 − 2016, covering pre-monsoon (PM), south-west monsoon (SWM) and north-east monsoon (NEM) seasons. Field campaigns yielded seasonal datasets of Cauvery riverwater and groundwater isotopic composition. Stable isotope analysis showed a seasonal shift in the river water isotopic composition (δ2H 8‰, δ18O 0.95‰) between pre- monsoon (PM) and south-west monsoon (SWM) seasons. Isotopically heavier values are recorded during the pre-monsoon season; coinciding with the period of low flow condition, whereas the monsoon season is characterized by high discharge and recorded isotopically lighter values. The Krishna Raja Sagar (KRS) Reservoir controls the isotopic composition of riverwater by evaporative process, during the pre-monsoon season resulting in river water becoming isotopically lighter. We utilized two-component mixing model to estimate the seasonal base flow contribution to the Cauvery River, for which the river is segmented into three sectors. The average base flow contribution to river flow during pre-monsoon season in sector I is 85 ± 5%, whereas in sector II and III the average groundwater contribution drops to 59 ± 3% and 39 ± 3% respectively. Similarly, the average base flow contribution to the river flow during south-west monsoon season drops to 32 ± 7% in sector I, whereas in sector II and III the average base flow contribution is estimated at 42 ± 7% and 47 ± 7% respectively. The base flow contribution is appreciably high in the upper Cauvery Basin comprising the Western Ghats, which plays a crucial role in sustaining stream flow during the pre-monsoon season. Assessment of spatial and temporal variability of surface water and groundwater isotopic composition within the Cauvery River Basin provides an important dataset for developing a sustainable river management plan in this tropical mesoscale riverine system.

Long-term forest vegetation dynamics in Nanda Devi Biosphere Reserve, Indian west Himalaya: evidence from repeat studies on compositional patterns.

It has been established that resurvey of historical vegetation stands, even those not marked permanently, could foster our understanding of vegetation dynamics and changes in structure and composition over time. However, such studies are poorly available, particularly in remote landscapes of the Indian Himalaya. There exists a complete lack of resurveys, which has limited our ability to provide reliable evidence of changes over the decades. This study, for the first time in the Indian Himalaya, considered repeat surveys (nearly after 25years) of vegetation stands in eleven forest communities of the buffer zone of NDBR (Nanda Devi Biosphere Reserve). Thirty historical forest stands, earlier studied in 1988-1990, were revisited during 2012-2014 and investigated using the same survey methods as used in the previous study. We found that previously reported dominant tree species, i.e., Alnus nepalensis, Acer cappadocicum, Quercus floribunda, Quercus semecarpifolia, Hippophae salicifolia, and Betula utilis, in nine out of eleven communities in the study area are continuing to exhibit dominance in the community. However, a significant increase in species richness and density in the seedling and sapling layer in Quercus floribunda, Quercus semecarpifolia, Rhododendron arboreum, and Abies pindrow is indicative of the ongoing process of change in forest composition. The compositional features of plant communities, when analyzed through Community Change Sensitivity (CCS) approach, identified Quercus floribunda, mixed Quercus-deciduous spp., Hippophae salicifolia, and Abies pindrow as the most change-sensitive communities in the study area and thus can be prioritized as the long-term ecological monitoring sites in the west Himalaya to understand intensity and patterns of changes. The potential changes based on the ecological information from two time period compositional data sets, having conservation and management implications, should be accommodated in the long-term perspective plans of the reserve.

A rapid method for quantifying small‐scale vegetation patch structure to complement conventional quadrat surveys

AbstractAimsVegetation sampling typically involves the use of quadrats to estimate species cover abundance. Such surveys do not generally record small‐scale vegetation patch structure at sub‐quadrat scales. Here we test a simple method to quantify patch structure that complements conventional techniques. We compare the two methods, and analyse metrics derived from small‐scale patch surveys with environment/management data.LocationNorthumberland, United Kingdom.MethodsWe recorded cover abundance of all species in an upland moor with 1‐m2 quadrats. These were divided into 100 ‘sub‐quadrats’, 10 cm × 10 cm, and the dominant and subdominant species identified. Patch metrics (number, area and shape) for individual species recorded as dominant or subdominant in the subquadrat survey were analysed using multi‐variate generalised linear models with environmental and management data. Sub‐quadrat data were also aggregated for each quadrat, to create species composition data. The two sets of compositional data, from whole‐quadrat and sub‐quadrat aggregations, were compared via Procrustes rotation of ordination scores.ResultsPatch number, area and shape for dominant and subdominant species were all significantly affected by soil pH, soil water content, slope and elevation. Effects of proximity to sheep tracks and drainage ditches were less consistent amongst species. Ordinations of vegetation data from conventional and sub‐quadrats were similar, with significant Procrustes R2 of 67% and 70% for dominant and subdominant species respectively.ConclusionsSub‐quadrat surveys can easily be used to complement existing whole‐quadrat surveys at little cost in time or resources. Their patch metrics can provide additional insights into the environmental and management drivers that may affect the growth of individual plants or clumps, potentially in relation to plant traits, and thus alter the overall community composition. The methods we describe can readily be adapted to other sizes of quadrats and sub‐quadrats in a wide range of vegetation communities.

Old World Vipers-A Review about Snake Venom Proteomics of Viperinae and Their Variations.

Fine-tuned by millions of years of evolution, snake venoms have frightened but also fascinated humanity and nowadays they constitute potential resources for drug development, therapeutics and antivenoms. The continuous progress of mass spectrometry techniques and latest advances in proteomics workflows enabled toxinologists to decipher venoms by modern omics technologies, so-called ‘venomics’. A tremendous upsurge reporting on snake venom proteomes could be observed. Within this review we focus on the highly venomous and widely distributed subfamily of Viperinae (Serpentes: Viperidae). A detailed public literature database search was performed (2003–2020) and we extensively reviewed all compositional venom studies of the so-called Old-World Vipers. In total, 54 studies resulted in 89 venom proteomes. The Viperinae venoms are dominated by four major, four secondary, six minor and several rare toxin families and peptides, respectively. The multitude of different venomics approaches complicates the comparison of venom composition datasets and therefore we differentiated between non-quantitative and three groups of quantitative workflows. The resulting direct comparisons within these groups show remarkable differences on the intra- and interspecies level across genera with a focus on regional differences. In summary, the present compilation is the first comprehensive up-to-date database on Viperinae venom proteomes and differentiating between analytical methods and workflows.

Intercomparison and Refinement of Surface Complexation Models for U(VI) Adsorption onto Goethite Based on a Metadata Analysis.

Adsorption of uranium onto goethite is an important partitioning process that controls uranium mobility in subsurface environments, for which many different surface complexation models (SCMs) have been developed. While individual models can fit the data for which they are parameterized, many perform poorly when compared with experimental data covering a broader range of conditions. There is an imperative need to quantitatively evaluate the variations in the models and to develop a more robust model that can be used with more confidence across the wide range of conditions. We conducted an intercomparison and refinement of the SCMs based on a metadata analysis. By seeking the globally best fit to a composite dataset with wide ranges of pH, solid/sorbate ratios, and carbonate concentrations, we developed a series of models with different levels of complexity following a systematic roadmap. The goethite-uranyl-carbonate ternary surface complexes were required in every model. For the spectroscopically informed models, a triple-plane model was found to provide the best fit, but the performance of the double-layer model with bidentate goethite-uranyl and goethite-uranyl-carbonate complexes was also comparable. Nevertheless, the models that ignore the bidentate feature of uranyl surface complexation consistently performed poorly. The goodness of fitting for the models that ignore adsorption of carbonate and the charge distributions was not significantly compromised compared with that of their counterparts that considered those. This approach of model development for a large and varied dataset improved our understanding of U(VI)-goethite surface reactions and can lead to a path for generating a single set of reactions and equilibrium constants for including U(VI) adsorption onto goethite in reactive transport models.

Estimating uncertainties of source contributions to PM2.5 using moving window evolving dispersion normalized PMF

Conventional factor analyses can present problems in cases with changing numbers of sources and/or time-dependent source compositions. There is also lack of a reliable method to estimate uncertainties in the source contributions derived by positive matrix factorization (PMF). Applying a moving window evolving PMF to hourly PM2.5 composition dataset from a field campaign in Tianjin China that included the Spring and Lantern Festivals and the start of COVID-19 pandemic has substantially improved the apportionment compared to a conventional analysis using the entire data. Festival-related sources (e.g., fireworks and residential burning here) have been clearly identified and estimated during both the Spring and Lantern Festivals. During this period, the sources changed because the time period overlaps with the outbreak of COVID-19 and related reductions in activity during the lockdown that began on Lunar New Year. Multiple PMF runs providing source contribution estimates made it possible to estimate the uncertainties in these values. Our results show that winds-dependent sources like dust and distant point sources have larger uncertainties than the other sources. Compared with conventional PMF analyses, the current method may better reflect the actual emissions as well as being able to estimate uncertainties. Thus, this approach appears to be an improvement if the appropriate data are available.

Making the most of expert knowledge to analyse archaeological data: a case study on Parthian and Sasanian glazed pottery

Chemical compositional data sets of archaeological artefacts are often analysed using standard statistical procedures. Adopting a different approach, we examine the major element oxides found in Parthian and Sasanian glazed pottery by identifying statistically important ratios of oxides in conjunction with the expert knowledge of the archaeological scientist during, rather than after, the identification process. This results in meaningful ratios, both statistically and archaeologically, which help identify the recipes and production practices used by Mesopotamian glass and glaze producers. The application of logratio analysis to interrogate the chronological groups suggests that the silica sources used for glazes were significantly purer in later periods and exhibited less variation. Comparing the expert-assisted selection of ratios derived from a legacy data set to the ratios from compositional data of Parthian glazed pottery recovered at the early 3rd century CE Roman military outpost of Ain Sinu in northern Iraq supports the hypothesis that there was a shift to more standardised recipes in the production of glass used for glazes. If this translates to a centralised glass-making industry, it would suggest that there are, as yet, undiscovered glass production centres, potentially in areas associated with increased urbanisation in southern Mesopotamia around the time of the Parthian–Sasanian transition.

Historical Changes in Seasonal Aerosol Acidity in the Po Valley (Italy) as Inferred from Fog Water and Aerosol Measurements.

Acidity profoundly affects almost every aspect that shapes the composition of ambient particles and their environmental impact. Thermodynamic analysis of gas-particle composition datasets offers robust estimates of acidity, but they are not available for long periods of time. Fog composition datasets, however, are available for many decades; we develop a thermodynamic analysis to estimate the ammonia in equilibrium with fog water and to infer the pre-fog aerosol pH starting from fog chemical composition and pH. The acidity values from the new method agree with the results of thermodynamic analysis of the available gas-particle composition data. Applying the new method to historical (25 years) fog water composition at the rural station of San Pietro Capofiume (SPC) in the Po Valley (Italy) suggests that the aerosol has been mildly acidic, with its pH decreasing by 0.5–1.5 pH units over the last decades. The observed pH of the fog water also increased 1 unit over the same period. Analysis of the simulated aerosol pH reveals that the aerosol acidity trend is driven by a decrease in aerosol precursor concentrations, and changes in temperature and relative humidity. Currently, NOx controls would be most effective for PM2.5 reduction in the Po valley both during summer and winter. In the future, however, seasonal transitions to the NH3-sensitive region may occur, meaning that the NH3 reduction policy may become increasingly necessary.

Documentation of aggregated/compiled values in food composition databases; EuroFIR default to improve harmonization

Documentation of all information concerning foods, components, values and references is essential to maintain a food composition database. To users, it provides information on origin and quality of the data. Nevertheless, recent evaluation of value documentation of macronutrients according to the EuroFIR Standard in 26 European food composition datasets showed inconsistencies. The documentation of aggregated (calculated from initial values) and compiled (created within the database to complete missing data) values was not fully comparable, since different thesauri terms were used in different countries for similar situations. In this light, it was recognized that automated value documentation linked to automated procedures to calculate values from means, recipes and formulae could contribute to better harmonisation between databases.A working group thus developed guidelines for default value documentation of aggregated/compiled values, using the current EuroFIR Standard and thesauri. Options for aggregation/compilation in the FoodCASE data management system were taken as starting point. The guidelines were implemented in the FoodCASE software (version 5.5.0), resulting in automated and harmonized value documentation for aggregated/compiled values when using FoodCASE. Default documentation can also be implemented in other software. Applying this default for harmonized documentation of aggregated/compiled values will assist compilers and increase transparency and comparability of (European) food composition data.

Meltwaters dominate groundwater recharge in cold arid desert of Upper Indus River Basin (UIRB), western Himalayas

Groundwater resources play a key role in sustaining irrigated agriculture and providing domestic water supplies in the trans-boundary Upper Indus River Basin. Understanding groundwater recharge and flow pathways in Upper Indus Basin is critical to good groundwater management, yet groundwater recharge sources remain poorly quantified. Here new dataset of stable oxygen and hydrogen isotope compositions and geochemical tracers of rain (n = 110), groundwater (n = 140), glacier melt (n = 56) and snowmelt (n = 46) collected during 2015–2018 has been analysed to investigate the spatio-temporal variation of δ18O and δ2H in source waters and groundwater and to quantify and estimate the sources of recharging water. We found microclimate in sub-basins is dominantly controlling stable water isotopic composition of source waters and groundwater. It is shown that meltwaters supply up to 83% of groundwater recharge, emphasizing the importance of the cryosphere in sustaining groundwater resources in the Upper Indus River Basin. Meltwater-derived recharge is split evenly among glacial meltwaters (44% of annual recharge) and snowmelt (39%); by contrast, rainfall contributes only 17% of annual recharge. Our data highlight the dependence of groundwater recharge on meltwaters derived from glaciers and snowpacks, suggesting that changes to the pattern, form, timing and amount of precipitation as well as glacial retreat may have disproportionate impacts on groundwater recharge, with potential to alter water supplies and affect local and regional economies.

Predicting Mental Health Problems with Automatic Identification of Metaphors.

Mental health problems are prevalent and an important issue in medicine. However, clinical diagnosis of mental health problems is costly, time-consuming, and often significantly delayed, which highlights the need for novel methods to identify them. Previous psycholinguistic and psychiatry research has suggested that the use of metaphors in texts is linked to the mental health status of the authors. In this paper, we propose a method for automatically detecting metaphors in texts to predict various mental health problems, specifically anxiety, depression, inferiority, sensitivity, social phobias, and obsession. We perform experiments on a composition dataset collected from second-language students and on the eRisk2017 dataset collected from Social Media. The experimental results show that our approach can help predict mental health problems in authors of written texts, and our algorithm performs better than other state-of-the-art methods. In addition, we report that the use of metaphors even in nonnative languages can be indicative of various mental health problems.

Use of a Portable Rapid Analysis System to Measure Nitrate Concentration of Nutrient and Soil Solution, and Plant Sap in Greenhouse Vegetable Production

A rapid analysis ion-selective electrode (ISE) system for measurement of [NO3−] in nutrient solution (NS), soil solution (SS) and petiole sap (PS) was evaluated. For each material, there were 797–2010 samples from 5 to 6 different crops, and from 2 to 4 different species. Accuracy was evaluated by linear regression (LR) with laboratory analysis (automated colorimetry, Cd reduction), and by relative error (RE), the average percentage deviation from laboratory analysis. For NS, the LR was y = 0.982x + 0.76, R² = 0.962 (n = 2010), for combined data from 5 crops (3 pepper, 2 cucumber). For SS, the LR was y = 0.975x + 1.13, R² = 0.965 (n = 797), for combined data from 5 crops (3 pepper, 2 cucumber). For undiluted PS, the LR relationship was y = 0.742x + 168.02, R² = 0.892 (n = 1425), for combined data from 6 crops (3 pepper, 2 cucumber, 1 melon). The underestimation was most pronounced at [NO3−] of >1500 mg NO3−–N L−1. For diluted petiole sap (dilution by 10 for pepper and melon, 5 for other species), the LR relationship was y = 1.010x + 99.26, R² = 0.927 (n = 1182), for combined data from 6 crops (2 pepper, 2 cucumber, 1 melon, 1 tomato). RE values for all measurements in composite datasets were 14%, 22%, 24% and 25% for NS, SS, undiluted PS and diluted PS respectively, and they were lower in concentrations most likely to be measured in practical on-farm work. The ISE system measured [NO3−] in NS, SS and diluted PS with sufficient accuracy to effectively guide on-farm decision making.

Stock delineation of striped snakehead, Channa striata using multivariate generalised linear models with otolith shape and chemistry data

Otoliths are commonly used to discriminate between fish stocks, through both elemental composition and otolith shape. Typical studies also have a large number of elemental compositions and shape measures relative to the number of otolith samples, with these measures exhibiting strong mean–variance relationships. These properties make otolith composition and shape data highly suitable for use within a multivariate generalised linear model (MGLM) framework, yet MGLMs have never been applied to otolith data. Here we apply both a traditional distance based permutational multivariate analysis of variance (PERMANOVA) and MGLMs to a case study of striped snakehead (Channa striata) in India. We also introduce the Tweedie and gamma distributions as suitable error structures for the MGLMs, drawing similarities to the properties of Biomass data. We demonstrate that otolith elemental data and combined otolith elemental and shape data violate the assumption of homogeneity of variance of PERMANOVA and may give misleading results, while the assumptions of the MGLM with Tweedie and gamma distributions are shown to be satisfied and are appropriate for both otolith shape and elemental composition data. Consistent differences between three groups of C. striata were identified using otolith shape, otolith chemistry and a combined otolith shape and chemistry dataset. This suggests that future research should be conducted into whether there are demographic differences between these groups which may influence management considerations. The MGLM method is widely applicable and could be applied to any multivariate otolith shape or elemental composition dataset.

Determination of glass forming ability of bulk metallic glasses based on machine learning

Nowadays, the development of new bulk metallic glasses (BMGs) is still subject to repeated testing. To address this challenging problem, this paper proposes the random forest (RF) regression model for predicting glass forming ability (GFA) based on the 810 datapoints of the metal alloy composition dataset (including Tg, Tx, Tl, and Dmax, where Tg is the glass transition temperature, Tx the onset crystallization temperature, Tl the liquidus temperature or the offset temperature of melting, Dmax is the critical diameter). Various types of feature parameters related to GFA were first screened to identify the optimal features. Grid search was then used to optimize hyperparameters of the machine-learning (ML). The research suggests that the random forest (RF) regression model's accuracy has been improved, and our proposed approach has great potential in predicting the formation of the BMGs. Furthermore, this study also suggests that both characteristic temperature (Tg, Tx, and Tl) and topological structure parameters play an important role in describing the glass formation of alloys.

Atmospheric conditions and composition that influence PM2.5 oxidative potential in Beijing, China.

Epidemiological studies have consistently linked exposure to PM2.5 with adverse health effects. The oxidative potential (OP) of aerosol particles has been widely suggested as a measure of their potential toxicity. Several acellular chemical assays are now readily employed to measure OP; however, uncertainty remains regarding the atmospheric conditions and specific chemical components of PM2.5 that drive OP. A limited number of studies have simultaneously utilised multiple OP assays with a wide range of concurrent measurements and investigated the seasonality of PM2.5 OP. In this work, filter samples were collected in winter 2016 and summer 2017 during the atmospheric pollution and human health in a Chinese megacity campaign (APHH-Beijing), and PM2.5 OP was analysed using four acellular methods: ascorbic acid (AA), dithiothreitol (DTT), 2,7-dichlorofluorescin/hydrogen peroxidase (DCFH) and electron paramagnetic resonance spectroscopy (EPR). Each assay reflects different oxidising properties of PM2.5, including particle-bound reactive oxygen species (DCFH), superoxide radical production (EPR) and catalytic redox chemistry (DTT/AA), and a combination of these four assays provided a detailed overall picture of the oxidising properties of PM2.5 at a central site in Beijing. Positive correlations of OP (normalised per volume of air) of all four assays with overall PM2.5 mass were observed, with stronger correlations in winter compared to summer. In contrast, when OP assay values were normalised for particle mass, days with higher PM2.5 mass concentrations (μgm−3) were found to have lower mass-normalised OP values as measured by AA and DTT. This finding supports that total PM2.5 mass concentrations alone may not always be the best indicator for particle toxicity. Univariate analysis of OP values and an extensive range of additional measurements, 107 in total, including PM2.5 composition, gas-phase composition and meteorological data, provided detailed insight into the chemical components and atmospheric processes that determine PM2.5 OP variability. Multivariate statistical analyses highlighted associations of OP assay responses with varying chemical components in PM2.5 for both mass- and volume-normalised data. AA and DTT assays were well predicted by a small set of measurements in multiple linear regression (MLR) models and indicated fossil fuel combustion, vehicle emissions and biogenic secondary organic aerosol (SOA) as influential particle sources in the assay response. Mass MLR models of OP associated with compositional source profiles predicted OP almost as well as volume MLR models, illustrating the influence of mass composition on both particle-level OP and total volume OP. Univariate and multivariate analysis showed that different assays cover different chemical spaces, and through comparison of mass- and volume-normalised data we demonstrate that mass-normalised OP provides a more nuanced picture of compositional drivers and sources of OP compared to volume-normalised analysis. This study constitutes one of the most extensive and comprehensive composition datasets currently available and provides a unique opportunity to explore chemical variations in PM2.5 and how they affect both PM2.5 OP and the concentrations of particle-bound reactive oxygen species.

Rethinking the use of finite element simulations in comparative biomechanics research.

In the past 15 years, the finite element (FE) method has become a ubiquitous tool in the repertoire of evolutionary biologists. The method is used to estimate and compare biomechanical performance implicated as selective factors in the evolution of morphological structures. A feature common to many comparative studies using 3D FE simulations is small taxonomic sample sizes. The time-consuming nature of FE model construction is considered a main limiting factor in taxonomic breadth of comparative FE analyses. Using a composite FE model dataset, I show that the combination of small taxonomic sample sizes and comparative FE data in analyses of evolutionary associations of biomechanical performance to feeding ecology generates artificially elevated correlations. Such biases introduce false positives into interpretations of clade-level trends. Considering this potential pitfall, recommendations are provided to consider the ways FE analyses are best used to address both taxon-specific and clade-level evolutionary questions.

High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging invivo.

The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio invivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages invivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 invivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.

Astrocladistics of the Jovian Trojan Swarms

ABSTRACT The Jovian Trojans are two swarms of small objects that share Jupiter’s orbit, clustered around the leading and trailing Lagrange points, L4 and L5. In this work, we investigate the Jovian Trojan population using the technique of astrocladistics, an adaptation of the ‘tree of life’ approach used in biology. We combine colour data from WISE, SDSS, Gaia DR2, and MOVIS surveys with knowledge of the physical and orbital characteristics of the Trojans, to generate a classification tree composed of clans with distinctive characteristics. We identify 48 clans, indicating groups of objects that possibly share a common origin. Amongst these are several that contain members of the known collisional families, though our work identifies subtleties in that classification that bear future investigation. Our clans are often broken into subclans, and most can be grouped into 10 superclans, reflecting the hierarchical nature of the population. Outcomes from this project include the identification of several high priority objects for additional observations and as well as providing context for the objects to be visited by the forthcoming Lucy mission. Our results demonstrate the ability of astrocladistics to classify multiple large and heterogeneous composite survey data sets into groupings useful for studies of the origins and evolution of our Solar system.

Evaluations of Surface PM10 Concentration and Chemical Compositions in MERRA-2 Aerosol Reanalysis over Central and Eastern China

The chemical composition dataset of Aerosol Reanalysis of NASA’s Modern-Era Retrospective Analysis for Research and Application, version 2 (MERRAero) has not been thoroughly evaluated with observation data in mainland China due to the lack of long-term chemical components data. Using the 5-year data of PM10 mass concentrations and chemical compositions obtained from the routine sampling measurements at the World Meteorological Organization the Global Atmosphere Watch Programme regional background stations, Jing Sha (JS) and Lin’An (LA), in central and eastern China, we comprehensively evaluate the surface PM10 concentrations and chemical compositions such as sulfate (SO42−), organic carbon (OC) and black carbon (BC) derived from MERRAero. Overall, the concentrations of PM10, SO42−, OC and BC from the MERRAero agreed well with the measurements, despite a slight and consistent overestimation of BC concentrations and a moderate and persistent underestimation of PM10 concentrations throughout the study period. The MERRAero reanalysis of aerosol compositions performs better during the summertime than wintertime. By considering the nitrate particles in PM10 reconstruction, MERRAero performance can be significantly improved. The unreasonable seasonal variations of PM10 chemical compositions at station LA by MERRAero could be causative factors for the larger MERRAero discrepancies during 2016–2017 than the period of 2011–2013.

NRLMSIS 2.0: A Whole‐Atmosphere Empirical Model of Temperature and Neutral Species Densities

AbstractNRLMSIS® 2.0 is an empirical atmospheric model that extends from the ground to the exobase and describes the average observed behavior of temperature, eight species densities, and mass density via a parametric analytic formulation. The model inputs are location, day of year, time of day, solar activity, and geomagnetic activity. NRLMSIS 2.0 is a major, reformulated upgrade of the previous version, NRLMSISE‐00. The model now couples thermospheric species densities to the entire column, via an effective mass profile that transitions each species from the fully mixed region below ~70 km altitude to the diffusively separated region above ~200 km. Other changes include the extension of atomic oxygen down to 50 km and the use of geopotential height as the internal vertical coordinate. We assimilated extensive new lower and middle atmosphere temperature, O, and H data, along with global average thermospheric mass density derived from satellite orbits, and we validated the model against independent samples of these data. In the mesosphere and below, residual biases and standard deviations are considerably lower than NRLMSISE‐00. The new model is warmer in the upper troposphere and cooler in the stratosphere and mesosphere. In the thermosphere, N2 and O densities are lower in NRLMSIS 2.0; otherwise, the NRLMSISE‐00 thermosphere is largely retained. Future advances in thermospheric specification will likely require new in situ mass spectrometer measurements, new techniques for species density measurement between 100 and 200 km, and the reconciliation of systematic biases among thermospheric temperature and composition data sets, including biases attributable to long‐term changes.