Low Spatial Variability Research Articles

Stability of the Microbiome of the Sponge Mycale (Oxymycale) acerata in the Western Antarctic Peninsula.

The sponge microbiome, especially in Low Microbial Abundance (LMA) species, is expected to be influenced by the local environment; however, contrasting results exist with evidence showing that host specificity is also important, hence suggesting that the microbiome is influenced by host-specific and environmental factors. Despite sponges being important members of Southern Ocean benthic communities, their relationships with the microbial communities they host remain poorly studied. Here, we studied the spatial and temporal patterns of the microbiota associated with the ecologically important LMA sponge M. acerata at sites along ∼400 km of the Western Antarctic Peninsula (WAP) to assess patterns in the core and variable microbial components of the symbiont communities of this sponge species. The analyses of 31 samples revealed that the microbiome of M. acerata is composed of 35 prokaryotic phyla (3 Archaea, 31 Bacteria, and one unaffiliated), being mainly dominated by Proteobacteria with Gammaproteobacteria as the most dominant class. The core community was composed of six prokaryotic OTUs, with gammaproteobacterial OTU (EC94 Family), showing a mean abundance over 65% of the total abundance. Despite some differences in rare OTUs, the core community did not show clear patterns in diversity and abundance associated with specific sites/environmental conditions, confirming a low variability in community structure of this species along the WAP. The analysis at small scale (Doumer Island, Palmer Archipelago) showed no differences in space and time in the microbiome M. acerata collected at sites around the island, sampled in three consecutive years (2016–2018). Our results highlight the existence of a low spatial and temporal variability in the microbiome of M. acerata, supporting previous suggestions based on limited studies on this and other Antarctic sponges.

Electromagnetic sensing and infiltration measurements to evaluate turfgrass salinity and reclamation

Scarce freshwater resources in arid and semiarid regions means that recreational landscapes should use recycled or low-quality waters for irrigation, increasing the risk of salinity and infiltration problems. We map salinity distribution within turf fields using electromagnetic sensing, evaluate need for leaching and evaluate post leaching results for subsequent management decisions. Electromagnetic measurements were made with two EM38 instruments positioned vertically and horizontally in order to determine salinity distribution. Sensor readings were coupled to GPS data to create spatial salinity maps. Next, optimal calibration point coordinates were determined via Electrical Conductivity Sampling Assessment and Prediction (ESAP) software. Soil samples from 0–15 and 15–30 cm depths were used for each calibration point. Laboratory soil saturation percentage, moisture content, electrical conductivity (ECe) and pHe of saturation extracts were determined for calibration to convert resistivity measurements to ECe. Next, ECe maps were created using ESAP software. Leaching for reclamation was performed by means of sprinkling. Treated municipal wastewater was utilized both for irrigation and for reclamation leaching. Low water content and high spatial variability of soil texture adversely affected the accuracy of the readings. Pre and post leaching surveys indicate that in one fairway there was only a 43% and 58% decrease in soil salinity at 0–15 and 15–30 cm depths, respectively which is very low relative to expected results considering the amount of water applied. This relatively low reduction in salinity and the lack of runoff during irrigation combined with infiltration measurements suggests that aeration techniques for healthier grasses led to water bypassing small pores thus limiting leaching efficiency. In this instance practices to improve infiltration lead paradoxically to less salinity reclamation than expected.

Source-resolved variability of fine particulate matter and human exposure in an urban area

Abstract. Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx (Particulate Matter Comprehensive Air quality Model with Extensions) was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36×36, 12×12, 4×4, and 1×1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12×12 km resolves the urban–rural gradient. Increasing resolution to 4×4 km resolves large stationary sources such as power plants, and the 1×1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12×12 km. Predominantly local pollutants such as elemental carbon and primary organic aerosol have gradients that can only be resolved at the 1×1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population-weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis at the county level.

Online control of rapid target-directed aiming using blurred visual feedback.

The accuracy and precision of target-directed aiming is contingent upon the availability of online visual feedback. The present study aimed to examine the visual regulation of aiming with blurred vision. The aiming task was executed using a stylus on a graphics digitizing board, which was translated onto a screen in the form of a cursor (representing the moving limb) and target. The vision conditions involved the complete disappearance or blur of the cursor alone, target alone, and cursor+target. These conditions involved leaving the screen uncovered or covering with a diffusing sheet to induce blur. The distance between the screen and sheet was increased to make the blur progressively more severe (0cm, 3cm). Results showed significantly less radial and variable error under blurred compared to no vision of the cursor and cursor+target. These findings were corroborated by the movement kinematics including a shorter proportion of time to peak velocity, more negative within-participant correlation between the distances travelled to and after peak velocity, and lower spatial variability from peak velocity to the end of the movement under blurred vision. The superior accuracy and precision under the blurred compared to no vision conditions is consistent with functioning visual regulation of aiming, which is primarily contingent upon the online visual feedback of the moving limb. This outcome may be attributed to the processing of low spatial-high temporal frequencies. Potential implications for low vision diagnostics are discussed.

Dynamic reconfiguration of frequency-specific cortical coactivation patterns during psychedelic and anesthetized states induced by ketamine

Recent neuroimaging studies have demonstrated that spontaneous brain activity exhibits rich spatiotemporal structure that can be characterized as the exploration of a repertoire of spatially distributed patterns that recur over time. The repertoire of brain states may reflect the capacity for consciousness, since general anesthetics suppress and psychedelic drugs enhance such dynamics. However, the modulation of brain activity repertoire across varying states of consciousness has not yet been studied in a systematic and unified framework. As a unique drug that has both psychedelic and anesthetic properties depending on the dose, ketamine offers an opportunity to examine brain reconfiguration dynamics along a continuum of consciousness. Here we investigated the dynamic organization of cortical activity during wakefulness and during altered states of consciousness induced by different doses of ketamine. Through k-means clustering analysis of the envelope data of source-localized electroencephalographic (EEG) signals, we identified a set of recurring states that represent frequency-specific spatial coactivation patterns. We quantified the effect of ketamine on individual brain states in terms of fractional occupancy and transition probabilities and found that ketamine anesthesia tends to shift the configuration toward brain states with low spatial variability. Furthermore, by assessing the temporal dynamics of the occurrence and transitions of brain states, we showed that subanesthetic ketamine is associated with a richer repertoire, while anesthetic ketamine induces dynamic changes in brain state organization, with the repertoire richness evolving from a reduced level to one comparable to that of normal wakefulness before recovery of consciousness. These results provide a novel description of ketamine's modulation of the dynamic configuration of cortical activity and advance understanding of the neurophysiological mechanism of ketamine in terms of the spatial, temporal, and spectral structures of underlying whole-brain dynamics.

Tree Diversity and Soil Characteristics in a Tea–Forest Interface in Southwest Sri Lanka

Fragmented and degraded ecosystems should be restored to ensure the biological connectivity among fragmented forest landscapes. The tropical rainforests of Southwestern Sri Lanka are highly degraded and fragmented due to plantation agriculture and human settlements. However, the high spatial variation in environmental factors and ecosystem functions challenge the success rate of restoration interventions. The aim of this study was to assess the vegetation composition and stand structure in relation to the spatial variation in key soil physicochemical parameters in the Endane Biodiversity Corridor that links peripheral forest reserve to the Sinharaja Rainforest Complex (SRC). The site that extends over 24 ha was classified into five land-use categories (productive tea lands, marginal tea lands, scrub—abandoned three years ago, and two woodlands—abandoned 15 years ago) in which the vegetation composition, stand structure, and physicochemical parameters of soil were assessed and mapped. Results revealed that the Shannon diversity index in the scrub and the woodlands were higher than in the tea lands. The diversity among the secondary forest patches was similar. However, with a mean record of 14 species, the species richness was high in sites close to the SRC. In comparison to the SRC (358 Mg ha−1), there was a substantial potential to sequester more carbon in the restoration sites (12–108 Mg ha−1). While explaining 31% of abundance and species distribution, the ordination results revealed a close relationship of the soil parameters to vegetation composition and species abundance. The calculated coefficient variation values for soil parameters (TN, EC, Av.P, Ex.K, OC, and BD) were beyond 12%, indicating high or moderate soil spatial variability among the land use categories. Coefficient of variation for soil pH was estimated to be 9%, revealing low soil spatial variability among the land use categories. The maps of these soil parameters corresponded with the type of land use and fertilizer application to tea fields. The highest and the lowest total N contents were observed in the scrub and woodlands, respectively, which appears to be mediated by the relative composition of N-fixing trees between the two groups. Our results facilitate effective matching of sites to species for restoration of the Endane Biodiversity Corridor that may be replicated in similar restoration contexts in tropical Asia.

Soil moisture downscaling using multiple modes of the DISPATCH algorithm in a semi-humid/humid region

Remotely sensed soil moisture (SM) with the enhanced spatial resolution (several kilometers or even tens of meters) is of value for important local applications like irrigation scheduling and farm-scale water management. The Disaggregation based on Physical and Theoretical scale Change (DISPATCH) algorithm is an established evaporation-based tool for passive-SM downscaling. However, its performance in areas where evapotranspiration is generally energy-limited has not been adequately evaluated. Here, DISPATCH is applied to coarse-scale L-band passive-SM to evaluate its applicability to produce a 1-km downscaled product in the semi-humid/humid Huai River Basin (HRB), which has been identified as challenging for DISPATCH in previous studies. The influence of different calibration modes of linear SEE (soil evaporative efficiency) model on disaggregation results and LST (land surface temperature) inputs at different temporal resolutions are also investigated. The performances of DISPATCH were evaluated using a spatio-temporal statistical analysis against available in-situ SM. Globally,DISPATCH performs poorly in HRB with low correlation and least-square regression slopes (significantly different to unity) between downscaled-SM and in-situ observations. This relatively poor performance is attributed to low background SM spatial variability and weak moisture-evaporation coupling within HRB. However, in spite of those global poor performances, an improvement in the SM spatio-temporal representation was observed under summer-dry conditions over HRB. The uncertainty of DISPATCH estimates can be reduced by the multi-date calibration of linear SEE model within HRB. Likewise, downscaled SM based on merging multiple LST products for three consecutive days has better temporal coverage and lower uncertainty than comparable products only using daily LST retrievals. These configurations are expected to further enlarge the applicability of DISPATCH in more humid climates.

Radon Spatial Variations in University’s Buildings Located in an Italian Karst Region

In the framework of a collaboration between INAIL and University of Salento, an indoor radon survey in 54 buildings belonging to the UniSalento University (Southeast Italy) was carried out. The monitored buildings differ by type, construction period, materials, etc., and are located in an area with a morphology characterized mainly by marls, calcareous marls, and calcarenites (karst area). The sample of the survey includes 963 rooms at different floors: it consists in rooms mainly located at ground floor (67%), first floor (12%), and below ground (12%). SSNTD passive dosimeters measured the average radon activity concentration for two consecutive semesters (spring/summer and autumn/winter) from which annual radon averages were estimated for each room. The spatial variability within buildings was investigated in terms of variation between floors and among rooms at the same floor. Data analysis provides evidence that the distributions (in terms of arithmetic mean, standard deviation, median, and geometric mean) of indoor radon annual averages at ground floor and at first floor within building are very similar. This highlights that the karstic characteristics of soil and building materials affect radon levels not only below ground and at ground floor, but also at first floor. Moreover, to evaluate the spatial variability of radon among buildings or floors, the analysis of the distribution of coefficient of variation (CV) was carried out: the results show a low spatial variability with median and average values of CVs ≤ 30% both for the whole building and at different floor levels.

The use of artificial neural network techniques to predict selected parameters as a supplement to the input data set in the construction of 3D parametric models

The article presents the possibilities of using artificial neural networks for parametric prediction in borehole profiles, the application of which supplemented the set of information in all boreholes located within the analyzed area. The approach presented in the article will be used when there is no possibility of specialized interpretation of the drilling geophysics curves, supplementing the missing data. The set of data used in the study included solutions in the profiles of 10 boreholes, four of which were characterized by the availability of the full data set analyzed in this article, including compressional wave velocity, effective porosity, hydrocarbon saturation, Young’s modulus and Poisson’s ratio. Using the technique of the operation of artificial neural networks, a prediction of missing information was carried out based on the relationships between the analyzed parameters in the wells, where the estimated data was available. In recent years, there has been a dynamic development of machine learning technology and the so-called artificial intelligence. There are very few fields of science in which they find no application. The hydrocarbon saturation parameter, despite the challenges posed by the interpretation of this parameter, was also subjected to an estimation attempt, confirming the low correlation values between the analyzed parameters and requiring much more advanced work of an individual nature. The results of parametric prediction, previously validated by characterizing the R and RMSE parameters, were applied in the next step in the spatial modeling process of all analyzed parameters. Finally, as part of the visualization of the differences between the use of an incomplete and partially estimated data set in spatial analysis, a map of mean values of the selected parameter within the analyzed interval was presented. The set of data prepared in this way allowed for a more reliable spatial reconstruction of the distribution of parameters important in the context of the characteristics of the hydrocarbon reservoir, on the basis of which, in the subsequent stages, it is possible to more fully assess the deposit potential of the analyzed object. The methodology presented in the article, supported by a real case study, is an alternative to geophysical interpretations that require financial and time resources, sometimes large numbers of boreholes, especially for areas characterized by relatively low spatial variability and tectonic complexity. The condition is the availability of the interpretation in at least several boreholes, constituting a pattern for recreating the variability of the tested parameter / parameters in the remaining profiles of the boreholes.

Soil Sampling Strategies for the Characterization of Spatial Variability Under Two Distinct Land Uses

ABSTRACT Spatial heterogeneity is a universal phenomenon existing in ecological systems at all scales. In the humid tropics, soil sampling and characterization are complex due to high spatial variability. The inability of researchers to capture this spatial variability inhibits the effective management of resources in terrestrial ecosystems. Hence, simple sampling approaches that are labor and cost-efficient but generate representative samples are required to optimize soil sampling and characterization. In this paper, we used a combination of descriptive statistics and geostatistics to characterize the spatial variability of soil under two land uses. One-way ANOVA and a Student t-test were also used to assess the effectiveness of three distinct sampling designs at capturing the variation in the soil’s physicochemical properties. The wetland soils generally exhibited low spatial variability, whereas the variability of the agroforest ranged from low (coefficient of variance (CV) < 25%) to highly variable (CV > 75%). Results show that in the wetland, CVs of samples obtained using the random sampling design (RSD) and response surface directed design (RSDD) were highly correlated (pearson correlation coefficient (r) = 0.98). Similarly, in the agroforest, the CVs of the stratified random sampling design (SRSD) and RSDD gave almost the same accuracy (r = 0.97). Thus, it is recommended that in this and similar humid-tropical wetland and agroforest ecosystems, the RSD and the RSDD, which require the least samples, can be used, respectively. However, more comprehensive testing is required on a broader range of soils before its more widespread application in other climatic conditions.

Advective structure functions in anisotropic two-dimensional turbulence

Abstract

Sequential aiming in pairs: the multiple levels of joint action

The task constraints imposed upon a co-actor can often influence our own actions. Likewise, the observation of somebody else’s movements can involuntarily contaminate the execution of our own movements. These joint action outcomes have rarely been considered in unison. The aim of the present study was to simultaneously examine the underlying processes contributing to joint action. We had pairs of participants work together to execute sequential aiming movements between two targets—the first person’s movement was contingent upon the anticipation of the second person’s movement (leader), while the second person’s movement was contingent upon the direct observation of the first person’s movement (follower). Participants executed separate blocks of two-target aiming movements under different contexts; that is, solely on their own using one (2T1L) and two (2T2L) of their upper limbs, or with another person (2T2P). The first movement segment generally indicated a more abrupt approach (shorter time after peak velocity, greater displacement and magnitude of peak velocity), which surprisingly coincided with lower spatial variability, for the 2T2P context. Meanwhile, the second segment indicated a similar kinematic profile as the first segment for the 2T2P context. The first movement of the leader appeared to accommodate the follower for their movement, while the second movement of the follower was primed by the observation of the leader’s movement. These findings collectively advocate two distinct levels of joint action including the anticipation (top–down) and mapping (bottom–up) of other people’s actions.

Potential of GPM IMERG Precipitation Estimates to Monitor Natural Disaster Triggers in Urban Areas: The Case of Rio de Janeiro, Brazil

Extreme rainfall can be a catastrophic trigger for natural disaster events at urban scales. However, there remains large uncertainties as to how satellite precipitation can identify these triggers at a city scale. The objective of this study is to evaluate the potential of satellite-based rainfall estimates to monitor natural disaster triggers in urban areas. Rainfall estimates from the Global Precipitation Measurement (GPM) mission are evaluated over the city of Rio de Janeiro, Brazil, where urban floods and landslides occur periodically as a result of extreme rainfall events. Two rainfall products derived from the Integrated Multi-satellite Retrievals for GPM (IMERG), the IMERG Early and IMERG Final products, are integrated into the Noah Multi-Parameterization (Noah-MP) land surface model in order to simulate the spatial and temporal dynamics of two key hydrometeorological disaster triggers across the city over the wet seasons during 2001–2019. Here, total runoff (TR) and rootzone soil moisture (RZSM) are considered as flood and landslide triggers, respectively. Ground-based observations at 33 pluviometric stations are interpolated, and the resulting rainfall fields are used in an in-situ precipitation-based simulation, considered as the reference for evaluating the IMERG-driven simulations. The evaluation is performed during the wet seasons (November-April), when average rainfall over the city is 4.4 mm/day. Results show that IMERG products show low spatial variability at the city scale, generally overestimate rainfall rates by 12–35%, and impacts on TR and RZSM vary spatially mostly as a function of land cover and soil types. Results based on statistical and categorical metrics show that IMERG skill in detecting extreme events is moderate, with IMERG Final performing slightly better for most metrics. By analyzing two recent storms, we observe that IMERG detects mostly hourly extreme events, but underestimates rainfall rates, resulting in underestimated TR and RZSM. An evaluation of normalized time series using percentiles shows that both satellite products have significantly improved skill in detecting extreme events when compared to the evaluation using absolute values, indicating that IMERG precipitation could be potentially used as a predictor for natural disasters in urban areas.

Resource potential and variability assessment of solar and wind energy in India

With the growing concerns over climate change and energy security, the Government of India aims to enhance the share of solar and wind based Renewable Energy (RE) in energy mix. However, current official estimates of RE potential undervalue the actual scale of potential and they also fall way short of meeting the future low-carbon demand. These estimates are generally available at coarse spatial resolution of regions/states and do not provide information of RE temporal variability. Considering low resolution values in system planning may result in over/underestimation of other required resources like grid infrastructure and flexibility options. This calls for re-assessing India’s RE potential by a thorough examination of land suitability, and quantification of its spatial and temporal variability to support grid integration and system planning activities.In this context, this article performs a Geographical Information System (GIS) based multi-criteria-assessment of ground-mounted solar Photovoltaic (PV) and on-shore wind energy potential in India. Geo-spatial and multi-year hourly meteorological data sets are utilized to estimate geographical and techno-economic RE potential. Calculated supply curve outlines the availability of estimated whole solar energy within the Levelized Cost of Energy (LCOE) range of 51.6 $/MWh to 89 $/MWh. 3102 GW of wind capacity is available below 115 $/MWh LCOE. Variability assessment shows that the western region of the country has highest RE potential with low spatial variability. A complementary relationship between solar and wind energy in various states shows the feasibility of hybrid solar-wind plants to ensure reliable green energy supply. By performing these assessments for 1° by 1° geographical grid cells, this study generates a granular data set paving the path for policymakers to setup feasible policies.

Recent changes in temperature extremes across the north-eastern region of Italy and their relationship with large-scale circulation

The present study is an analysis of recent changes (1981-2016) in air temperature across the north-eastern region of Italy (NERI), located in a transition zone influenced by the Mediterranean and continental climatological regimes. Annual and seasonal trends in mean maximum and minimum temperatures as well as in selected extreme indices are investigated using high-quality and homogenised daily data deriving from a distributed network of 50 weather stations. The results reveal an overall widespread warming, particularly intense in spring and summer, with significant positive trends in mean and daytime (maximum-related) temperature extremes, especially at lower altitudes. No substantial elevation dependency is observed for trends in minimum-related temperature indices, which are generally characterised by lower spatial variability. Comparable positive and negative trends are identified in the frequency and duration of warm- and cold-related extremes indices. Finally, the influence on temperature extremes of dominant large-scale circulation modes in southern Europe (North Atlantic Oscillation [NAO] and East-Atlantic [EA] pattern) is examined. The results indicate that especially NAO-induced changes can explain observed temperature trends over the NERI, while the impact of the EA pattern is slightly weaker.

Evaluation of surface water quality after mine closure in the coal-mining region of Guizhou, China

In this study, we used pollution evaluation indices, principal component analysis (PCA), and hierarchical cluster analysis (HCA) to assess the pollution status of surface water systems after mine closure in an intensive coal-mining area of Guizhou. The concentrations of Fe, Al, Mn, Hg, total nitrogen, volatile phenols, sulfide, and petroleum in most of the water samples exceeded the limits set by China and the World Health Organization. The surface water in this region was characterised as near-neutral-high metals and near-neutral-low metals. The metal elements showed high spatial variability as inferred from their high variation coefficients. In contrast, most non-metallic pollution elements were not considered dominant polluters and generally showed low spatial variability. Modifications to the existing heavy metal pollution index and degree of contamination scheme showed comparable results to that of the heavy metal evaluation index, where 76.92%, 7.69%, and 15.38% of the water samples showed low, medium, and high pollution, respectively. Combined, the results of the HCA, PCA, and pollution indices inferred highest surface water contamination at sites closest to the coal mines (G1, G4, G9), while distal sites were less contaminated. Our results highlight the need to assess the pollution status of surface water systems after mine closure in intensive coal-mining areas in order to prevent severe ecological and environmental degradation.

Spatial distribution, fractionation and ecological risk assessment of potentially toxic metals in bottom sediments of the Zarivar freshwater Lake (Northwestern Iran)

Freshwater lakes are one of the most vulnerable ecosystems to environmental contamination. This study was initiated to assess the spatial distribution, fractionation, ecological risk of selected potentially toxic metals (Pb, Zn, Cu, Cr, and Ni) in bottom sediments of the Zarivar lake, the second largest freshwater lake in Iran. The results revealed that Pb, Zn and Cu had the high spatial variability (coefficient of variation >50) across the sampling sites and their maximum concentrations (197.5 for Pb, 198.7 for Zn and 185.6 mg/kg for Cu) were observed in sampling sites from the northern, western and eastern margins of the lake. Cr and Ni with average concentrations of 28.3 and 31.38 mg/kg respectively, exhibited low spatial variability (coefficient of variation <20) and their concentrations did not vary significantly among the sampling sites. Based on the redundancy analysis (RDA), sediment organic matter was strongly correlated with Pb, Zn and Cu while Fe2O3 and Al2O3 showed a positive correlation with Ni and Cr. The calculated average enrichment factor (EF) and geoaccumulation index (Igeo) showed that the contamination level of metals can be arranged in the following order of Pb> Cu > Zn > Cr > Ni. Results from the modified five-step sequential extraction analysis indicated that 40 % of total Pb and Zn were associated with the reducible fraction, 45 % of Cu with the oxidizable fraction and more than 80 % of total Ni and Cr were retrieved from the residual fraction. It was also noticed that Pb, Zn and Cu were more incorporated into the non-residual fractions in the sites with a higher total concentration of these metals, suggesting that both total concentration and fractionation behavior of metals were influenced by their potential sources in the study area. Ecological risk assessment using the potential ecological risk index (PERI) and the modified potential ecological risk index (MPERI) showed that sediments from the eight sampling sites pose a moderate to considerable risk whereas the other sites had low ecological risk level. In comparison to sediment quality guidelines (SQGs), the effects range low (ERL) and probable effect level (PEL) values for Pb, Cu and Zn were exceeded at some sampling sites while Ni and Cr concentrations were found to be below or close to their SQGs values at all the sampling sites. Pb was generally identified as the contaminant of most concern in the study area. Taking into account the results obtained from the fractionation study and the source contribution estimate, it can be inferred that the Pb, Zn and Cu with the average contribution of 79, 54 and 64 % respectively, were mainly derived from anthropogenic sources whereas Ni and Cr with the estimated contribution of 80 and 89 % were predominately from the lithogenic source.

Comparison of environmental controls on soil moisture spatial patterns at mesoscales: Observational evidence from two regions in China

Soil moisture plays a vital role in terrestrial water cycles, which requires the understanding of the spatial characteristics of soil moisture patterns and their influencing factors across different scales; however, inconsistent findings have been reported in the past with regards to the relative importance of meteorological (e.g., precipitation and evapotranspiration) and local (e.g., soil texture) factors in controlling soil moisture spatial variability at mesoscales. To further elucidate this issue, long-term soil moisture data from East and Northwest China were examined in this study, using two statistical methods of temporal stability analysis (TSA) and empirical orthogonal function (EOF) analysis. The TSA results showed that the spatial distributions of mean relative difference (MRD; an indicator of the relative wetness condition at a location as compared to the areal average wetness condition) of soil moisture had statistically significant correlations with mean annual precipitation (P-) and soil texture in both study regions; however, the spatial correlation between MRD and P- was much stronger in the Northwest region, suggesting that precipitation played a more important role in determining the spatial distribution of soil moisture in Northwest China. Moreover, the EOF analysis revealed that the primary soil moisture spatial structure was mostly related with soil texture in the East region while with P- in the Northwest region, which was also consistent with the TSA results. The stronger impact of precipitation on the soil moisture spatial distribution in the Northwest region could be largely attributed to the greater spatial variability in P- that varied by ~4.6 times from 171.2 to 791.0 mm/year across the region. By comparison, in the East region characterized by lower spatial variability in P- that varied by ~2.6 times from 484.2 to 1236.0 mm/year, soil texture exerted a stronger impact on the primary soil moisture spatial structure. Therefore, the observational evidence from this study demonstrated that the relative importance of meteorological and local factors in controlling mesoscale soil moisture spatial variability might vary considerably across regions, primarily depending on the spatial variability in those influencing factors across the region under consideration.

Assessing the association and predictability of heavy metals in avian organs, feathers, and bones using crowdsourced samples

Birds are bioindicators for research on the relationship between environmental heavy metal concentration levels and accumulation levels in bird tissues. We use roadkill samples, collected by citizen science participants, to investigate the accumulation levels and associations of seven heavy metals in internal organs (heart, liver, and kidney), feathers (primary and breast), and bones (sternum and femur) of two focal species, Amaurornis phoenicurus and Gallinula chloropus. We found that heavy metal accumulation varied by target tissue, and that variables are associated with bird species and heavy metal type. Although Zn and Cu were highest by concentration among both species, Cu was mostly accumulated in internal organs, As in feathers, and Pb in bones. Concentrations of As, Ni, and Pb in feathers of both focal species were lower than those reported in literature, whereas Cd and Cr were above toxic levels. The results also showed that spatial correlation for heavy metal concentration among bird tissues were weaker than non-spatial correlation, suggesting low spatial autocorrelations and variability. In addition, multiple regression analysis revealed significant correlation for Cr, As, and Pb estimations in A. phoenicurus heart, sternum, and kidney, respectively; and potentially Cr in G. chloropus femur by using feathers. These results support the feasibility of using feathers as indicators of As, Cr, and Pb heavy metal contamination to enhance our understanding of heavy metal accumulation in birds, although caution is required for feather-based estimations of Cd, Cu, and Ni concentration.

Multi-exponential Transverse Relaxation Times Estimation from Magnetic Resonance Images under Rician Noise and Spatial Regularization.

Relaxation signal inside each voxel of magnetic resonance images (MRI) is commonly fitted by a multi-exponential decay curve. The estimation of a discrete multi-component relaxation model parameters from magnitude MRI data is a challenging nonlinear inverse problem since it should be conducted on the entire image voxels under non-Gaussian noise statistics. This paper proposes an efficient algorithm allowing the joint estimation of relaxation time values and their amplitudes using different criteria taking into account a Rician noise model, combined with a spatial regularization accounting for low spatial variability of relaxation time constants and amplitudes between neighboring voxels. The Rician noise hypothesis is accounted for either by an adapted nonlinear least squares algorithm applied to a corrected least squares criterion or by a majorization-minimization approach applied to the maximum likelihood criterion. In order to solve the resulting large-scale non-negativity constrained optimization problem with a reduced numerical complexity and computing time, an optimization algorithm based on a majorization approach ensuring separability of variables between voxels is proposed. The minimization is carried out iteratively using an adapted Levenberg-Marquardt algorithm that ensures convergence by imposing a sufficient decrease of the objective function and the non-negativity of the parameters. The importance of the regularization alongside the Rician noise incorporation is shown both visually and numerically on a simulated phantom and on magnitude MRI images acquired on fruit samples.