Distribution of rare earth elements in soils of contrasting geological and pedological settings to support human health assessment and environmental policies

Establishing quality reference values (QRVs) for rare earth elements (REEs) in soils is essential for the screening of these emergent contaminants. Currently, Brazil has the second-largest reserve of REEs, but data regarding background concentrations and distributions in soils remain scarce. The aim of this study was to establish the QRVs and assess the spatial distribution of REEs in soils, including REE fractionations and anomalies in (Piauí) state (251,529.186 km²), northeastern Brazil. This study reports the most detailed data on REE geochemistry in Brazilian soils. A total of 243 composite soil samples was collected at 0-20 cm depth. The mean background concentrations in soils followed the abundance of the earth's upper crust: Ce > La > Nd > Pr > Sm > Dy > Gd > Er > Yb > Eu > Tb > Lu. The ∑REEs (mg kg^-1) showed the following order based on the individual mesoregions of Piauí state: Southeast (262.75) > North and Central-North (89.68) > Southwest (40.33). The highest QRVs were observed in the Southeast mesoregion. The establishment of QRVs based on the mesoregion scale improves data representativeness and the monitoring of natural REE values by identifying hot spots. Geostatistical modeling indicated significant local variability, especially in the Southeast mesoregion. The levels of these elements in this spatial zone are naturally higher than the other values across Piauí state and the mesoregion itself and indicate a high potential to exceed the QRVs. Our approach provides much needed data to help strengthen policies for both human health and environmental protection.

Spatial distribution of heavy metals in soils of the flood plain of the Seversky Donets River (Russia) based on geostatistical methods

Soil contamination by heavy metals (HM) is a worldwide problem for human health. To reduce risk to human health from exposure to toxic chemicals associated with soil contamination, it is necessary to monitor and assess HM concentrations in the soil for places where the concentration exceeds the acceptable levels. Spatial patterning is a necessary tool for assessment of the exposure risk of HM contamination. Soil sampling (n = 65) was carried out in technogenically polluted soils located at Rostov oblast to study the content and spatial distribution of four HM (Cu, Zn, Pb, and Cr) in the surface layer (0-20 cm) of the impact zone of former Lake Atamanskoe (floodplain of the Seversky Donets River valley, Rostov region) with an area of 3.91 km². Extremely high values of HM concentrations were found with the maximum values of 702 mg/kg, 72,886 mg/kg, 2300 mg/kg, 259 mg/kg for Cu, Zn, Pb, and Cr, respectively. Inverse distance-weighted (IDW) interpolation was used to prepare 3D monoelement images of HM. Lognormal kriging and indicator kriging techniques were applied to create elemental spatial distribution maps and HM probability maps. The results showed that the total content of Cu, Zn, Pb, and Cr was moderately spatially dependent (nugget-to-sill ratio ranged from 31 to 38%), whereas the contamination index Zc formed strong spatial dependence patterns (nugget-to-sill ratio ranged from 0 to 21.4%). The obtained results of this study could serve as a guide to the authorities in identifying those areas which need remediation. Moreover, this study provides a tool for assessing the hygienic situation in the vicinity of Kamensk-Shakhtinsky (Rostov region) for decision making that can help to minimize the environmental risk of technogenic soil contamination of HM.

Dataset of five years of in situ and satellite derived chlorophyll a concentrations and its spatiotemporal variability in the Rotorua Te Arawa Lakes, New Zealand

Horizontal patchiness of water quality attributes in lakes substantially influences the ability to accurately determine an average condition of a lake from traditional in situ sampling. Monitoring programmes for lake water quality often rely on water samples from one or few locations but the assumption of representativeness is seldomly tested. Satellite observations can support environmental monitoring by detecting horizontal variability of water quality attributes over entire lakes. This article is a co-submission with Lehmann et al. (2021), who present a method to create a regional calibration of a satellite chlorophyll a algorithm and a spatial analysis of an image time series to detect recurring patchiness. Our method was developed on 13 lakes in the central North Island of New Zealand and this publication makes available the data used in our analysis and the spatial fields of results. These data are immediately valuable for practitioners operating within the region of interest providing a five year archive of synoptic water quality data and spatial fields to help optimize in situ monitoring efforts. In addition, there is value to the wider scientific community as the study lakes are a useful ‘natural lab’ for the development of aquatic remote sensing methods due to the range of trophic conditions and water colour in a single satellite image scene. Together with decades of in situ water quality records, our data is therefore useful for the development and validation of widely applicable methods of water quality retrieval from satellite data.

Soil-air partitioning of semivolatile organic compounds in the Lesser Himalaya region: Influence of soil organic matter, atmospheric transport processes and secondary emissions

After decades of imposed regulations about reducing the primary emissions of persistent organic pollutants (POPs), these pollutants are still present in the environment. Soils are important repositories of such persistent semivolatile organic contaminants (SVOCs), and it is assumed that SVOCs sequestered in these reservoirs are being re-mobilized due to anthropogenic influence. In this study, concentrations of organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) in soil and air, their fugacities, fluxes and the soil-air partition coefficient (K_SA) were determined for three different land cover types (glacial, remote/mountainous and urban) of the Lesser Himalayan Region (LHR). The concentrations of OCPs, PCBs and PBDEs in soils and air ranged between 0.01 and 2.8, 0.81-4.8, 0.089-0.75 ng g^-1; 0.2-106, 0.027-182, and 0.011-7.26 pg m^-3, respectively. The levels of SVOCs in the soil were correlated with soil organic matter (SOM) indicating that SOM is a substrate for the organic pollutants in soils. The Clausius-Clapeyron plots between ln P and inverse of temperature (1000/T) suggested that long range atmospheric transport was the major input source of PBDEs and higher chlorinated PCBs over the LHR. The uneven and wide distribution of local sources in LHR and up-slope enrichment of SVOCs explained the spatial variability and altitudinal patterns. The soils near mountain and urban lakes act as local sinks of SVOCs such as β-HCH, pp΄-DDT, CB-28, -118, -153, BDE-47, -99, and -154, with soil-air exchange fluxes tending more toward deposition. However, the soils near glacial lakes acted as local sources of more volatile congeners of α-HCH, γ-HCH, op'-DDT, pp'-DDE and lower to medium chlorinated PCBs such as CB-18, -28, -53, -42 and BDE-47, -99, with soil-air exchange tending more toward volatilization flux.

Spatial variability and delineation of management zones based on soil micronutrient status in apple orchard soils of Kashmir valley, India

Knowledge and up-scaling of status, availability, and distribution of soil micronutrients are hugely significant for enhancing crop productivity and profitability. However, regional database entailing spatial variability of key micronutrients for a major apple-producing region like the Kashmir Himalayan Region (KHR) is missing. We investigated 588 topsoil samples between 2016 and 2017 (to 30 cm depth) to estimate spatial variability of extractable Zn, Cu, Mn, and Fe; develop spatial maps; and delineate potential management zones (MZs) in apple orchards using geo-statistical methods. Soil sampling was performed post-harvest season. Principal component analysis and fuzzy c-mean clustering were performed to develop MZs. Results exhibited wide variation, with high coefficient of variation (%) for Mn and Cu (123.9 and 114.4, respectively) and low (71.6) and medium (104) variability for Fe and Zn. Mean concentrations followed the order: Fe > Mn > Cu > Zn, with deficiencies evident towards central and northern regions of the study area. The best-fitted model was exponential. The nugget/sill ratio values were 0.41, 0.08, 0.37, and 0.38 for Zn, Cu, Mn, and Fe, respectively, indicating strong spatial dependence for Cu and moderate dependence for Fe, Zn, and Mn. Correlations between elevation, slope, and soil pH with micronutrients were negative, whereas with soil organic carbon and electrical conductivity positive relationships were identified. Three principal components accounted for 67·18% data variance. Based on the fuzzy performance index and modified partition entropy, five MZs were delineated, which exhibited variations from each other. These MZs highlighted the need for specific supplemental provisions in increasing soil fertility and apple productivity.

Soil spatial variability characterization: Delineating index-based management zones in salt-affected agroecosystem of India

Farm level recommendation in salt-affected agricultural landscapes is practically difficult due to spatial variations in inherent soil salinity, diverse farming situations and associated land ownerships with small-scale production systems. This study presents spatial array analysis of 354 geo-referenced soil samples revealing widespread heterogeneity in soil sodicity and fertility status across salt-affected Ghaghar basin of Kaithal district in Haryana, India. Six principal components accounted for 73% of the total variability, and the most important contributors [electrical conductivity (EC_e), sodium adsorption ratio (SAR), DTPA extractable copper (Cu) and boron (B), soil organic carbon (OC) and available phosphorus (AP)] as minimum data set were used to develop the soil quality index (SQI). Geostatistical analysis revealed Circular (EC_e and AP), Exponential (SAR, OC and B) and Gaussian (Cu) as the best fit semivariogram ordinary kriging model with weak to moderate spatial dependence. Three soil management zones (SMZs) were delineated by grouping the entire area based on soil quality index (SQI). Fertilizer recommendations for rice-wheat cropping system in different SMZs were calculated using soil test crop response (STCR) equation to ensure balanced fertilization, resource saving and reducing environmental footprints. Gypsum requirement map was prepared for systematic allocation and distribution, and enabling farmers to precisely use the mineral gypsum in order to reclaim and reduce stresses led by sodic lands. The implications of this study showed zone-specific advocacy for gypsum application (as soil ameliorant) and balanced fertilization in sustainable restoration of sodic lands, improving nutrient use efficiency and stabilizing crop production in salt-affected regions of India and similar ecologies elsewhere.

Deforestation drivers in the Brazilian Amazon: assessing new spatial predictors

Researches on the deforestation of the Amazon have gained prominence in the last recent years, mainly with the change in the policy regarding the facing of this phenomenon by the Brazilian government. Therefore, an understanding about the causes that pressure the occurrence of deforestation remains relevant and has a leading role in the world. Therefore, the aim of this study is to perform the analysis of the spatial variability of the reasons for the deforestation in the Amazon Biome, in Brazil, (2010-2019). To achieve this goal, 14 variables were selected, the choice and adjustment of the regression model were determined and a diagnosis was carried out in order to verify the most appropriate model. To achieve this purpose, a geographic database was structured in a geographic information system environment. The main results revealed that the adjusted R² of the Geographically Weighted Regression (GWR) was 0.96, that is, the GWR model explains 96% of the variations in deforestation. Therefore, it was observed a significant gain when using this model. In addition, it was also observed that the average variable of the number of oxen was, among those analyzed, the one that showed the highest correlation with deforestation. Thus, it was found that the livestock sector in southern Amazonia is the main economic agent that pressures large areas of deforestation, since stockfarming is practiced extensively. Finally, it was concluded that the municipalities with the largest areas of deforestation formed a cluster in the southern portion of the Amazon, in the arc of deforestation.

Biodiversity increased both productivity and its spatial stability in temperate forests in northeastern China

Although the relationship between biodiversity and ecosystem functioning has been extensively studied, it remains unclear if the relationships of biodiversity with productivity and its spatial stability vary along productivity gradients in natural ecosystems. Based on a large dataset from 2324 permanent forest inventory plots across northeastern China, we examined the intensity of species richness (SR) and tree size diversity (Hd) effects on aboveground wood productivity (AWP) and its spatial stability among different productivity levels. Structural equation modeling was applied, integrating abiotic (climate and soil) and biotic (stand density) factors. Our results demonstrated that both SR and Hd positively affected AWP and its spatial stability, and the intensity of these positive effects decreased with increasing productivity. At low productivity levels, SR and Hd increased spatial stability by reducing spatial variability and increasing mean AWP. At high productivity levels, stability increased only through mean AWP increase. Moreover, temperature and stand density affected the AWP directly and indirectly via biodiversity, and the strength and direction of these effects varied among different productivity levels. We concluded that biodiversity could simultaneously enhance productivity and its spatial stability in temperate forests, and that the effect intensity was uniform along productivity gradients, which provided a new perspective on relationships within biodiversity-ecosystem functioning.

Variations in coralligenous assemblages from local to biogeographic spatial scale

The present study aims at contributing to the knowledge of the spatial variability of coralligenous reefs through the evaluation of patterns ranging from local to biogeographic scale around the island of Sardinia. The coralligenous reef assemblages of six areas were studied through a hierarchical sampling design: three sites per area were selected, in each site three plots were sampled and in each plot ten photographic samples were collected. The structure of coralligenous reefs across closed biogeographic regions is described, highlighting that nearly pristine assemblages, although characterized by similar high diversity, can be either dominated by animals, such as gorgonians and bryozoans, or macroalgae. The observed variations seem largely related to biogeographic patterns rather than spatial distance, supporting the need to identify specific reference conditions to assess the ecological quality of this habitat depending on the biogeographic area to be monitored.

Main sources controlling atmospheric burdens of persistent organic pollutants on a national scale

National long-term monitoring programs on persistent organic pollutants (POPs) in background air have traditionally relied on active air sampling techniques. Due to limited spatial coverage of active air samplers, questions remain (i) whether active air sampler monitoring sites are representative for atmospheric burdens within the larger geographical area targeted by the monitoring programs, and thus (ii) if the main sources affecting POPs in background air across a nation are understood. The main objective of this study was to explore the utility of spatial and temporal trends in concert with multiple modelling approaches to understand the main sources affecting polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) in background air across a nation. For this purpose, a comprehensive campaign was carried out in summer 2016, measuring POPs in background air across Norway using passive air sampling. Results were compared to a similar campaign in 2006 to assess possible changes over one decade. We furthermore used the Global EMEP Multi-media Modeling System (GLEMOS) and the Flexible Particle dispersion model (FLEXPART) to predict and evaluate the relative importance of primary emissions, secondary emissions, long-range atmospheric transport (LRAT) and national emissions in controlling atmospheric burdens of PCB-153 on a national scale. The concentrations in air of both PCBs and most of the targeted OCPs were generally low, with the exception of hexachlorobenzene (HCB). A limited spatial variability for all POPs in this study, together with predictions by both models, suggest that LRAT dominates atmospheric burdens across Norway. Model predictions by the GLEMOS model, as well as measured isomeric ratios, further suggest that LRAT of some POPs are dictated by secondary emissions. Our results illustrate the utility of combining observations and mechanistic modelling approaches to help identify the main factors affecting atmospheric burdens of POPs across a nation, which, in turn, may be used to inform both national monitoring and control strategies.

Spatial and temporal variability of airborne ultrafine particles in the Greater Montreal area: Results of monitoring campaigns in two seasons

It has been hypothesized that ultrafine particles (UFP) in air pollution may cause lung cancer. In preparation for an epidemiologic case-control study to assess this hypothesis in Montreal, Canada, we conducted a UFP measurement campaign in order to create an exposure surface with which we could assign UFP exposure to subjects corresponding to their residential addresses. The purpose of this paper is to describe the temporal and spatial variability that underlies the creation of an exposure surface in the Montreal area, and to consider the implications for epidemiological exposure assessment. We identified 249 fixed sampling sites, selected to provide a dense spatial representation of the areas of residence of Montreal residents. We conducted a winter campaign and a summer campaign, and each of the sites was visited three times during each seasonal campaign. Each visit entailed a 20-minute measurement period for UFPs with a separate measurement each second. This provided data for temporal comparisons at each site between seasons, between visits and between seconds. The median of UFP measurements was 16,593 particles/cm³ in winter and 8919 particles/cm³ in summer. Across the 249 sampling sites the Spearman correlation coefficient between the UFP measurements of winter and summer was 0.35. Within each visit, correlation was below 0.50 between pairs of UFP measurements taken more than 60 s apart, and there was hardly any correlation among measurements taken more than 300 s apart. When sites were grouped by proximity to certain types of pollution sources, and the seven resulting groups compared, there were modest, albeit statistically significant, differences in UFP levels. There was moderate positive spatial autocorrelation in UFPs over the study area. High temporal variability of UFPs from short-term measurements campaigns will likely compromise the predictive validity of the exposure surface, and will eventually attenuate the epidemiologic risk estimates.

Integrating random forests and propagation models for high-resolution noise mapping

The adverse effects of long-term exposure to environmental noise on human health are of increasing concern. Noise mapping methods such as spatial interpolation and land use regression cannot capture complex relationships between environmental conditions and noise propagation or attenuation in a three-dimension (3D) built environment. In this study, we developed a hybrid approach by combining a traffic propagation model and random forests (RF) machine learning algorithm to map the total environment noise levels for daily average, daytime, nighttime, and day-evening-nighttime at 30 m × 30 m resolution for the island of Montreal, Canada. The propagation model was used to predict traffic noise surfaces using road traffic flow, 3D building information, and a digital elevation model. The traffic noise estimates were compared with ground-based sound-level measurements at 87 points to extract residuals between total environmental noise and traffic noise. Residuals at these points were fit to RF models with multiple environmental and geographic predictor variables (e.g., vegetation index, population density, brightness of nighttime lights, land use types, and distances to noise contour around the airport, bus stops, and road intersections). Using the sound-level measurements as baseline data, the prediction errors, i.e., mean error, mean absolute error, and root mean squared error of daily average noise levels estimated by our hybrid approach was -0.03 dB(A), 2.67 dB(A), and 3.36 dB(A). Combining deterministic and stochastic models can provide accurate total environmental noise estimates for large geographic areas where sound-level measurements are available.

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.

Mass balance of metals during the phytoremediation process using Noccaea caerulescens: a pot study

There are two widely used methods to estimate the time taken for phytoremediation for the removal of the target pollutants, i.e., using the data of metal uptake by the harvested parts of the selected plant or using the decrement in average element content between the beginning and end of the remediation. The latter not only depends on sampling points but is also determined by sampling time because even if the soil is initially perfectly homogenized, plant growth itself heterogenizes the soil as time goes by. In this study, phytoremediation was tested on one homogenized soil obtained from various soil samples taken within an e-waste dismantling and recycling site, and the remediation time for different points of bulk and rhizosphere soil was estimated using the two methods. Phytoremediation efficiency, as assessed by the change in soil metal concentrations over 100 days, widely varied depending on which of the six soil compartments of the pot was sampled, and the standard deviations of Cd, Zn, Pb, and Cu increased as the experiment proceeded, indicating the inaccuracy of this method. When applied to rhizosphere soil, this method led to a large overestimation of phytoremediation efficiency for Cd and Zn, which was 81- and 77-fold that was obtained by measuring the actual amount of metals taken up by Noccaea caerulescens. The significant difference between the two methods indicated that the blended soil became heterogeneous during the phytoremediation process because the species extracted metals from different soil parts, manifested by the variation in the metal content. The gap between these two estimation methods decreased when the soil was mixed thoroughly at the end of the experiment. This work shows that calculating the metal decontamination efficiency based on the measurement of the actual amount of metal taken by the plant is more robust than estimating it based on the evolution of soil metal concentration over time. In addition, our study reveals that using N. caerulescens may not be appropriate in Pb- or Cu-polluted soil, since this species mobilized these metals but did not extract them.

Is the source of domestic water associated with the risk of malaria infection? Spatial variability and a mixed-effects multilevel analysis

BACKGROUND

There is a dearth of information on the relationship between domestic water source and malaria infection in malaria-endemic regions such as Tanzania. This study examined the geospatial variability and association between domestic water source and malaria prevalence in Tanzania.

METHODS

We analyzed data from a sample of 6707 children, aged 6-59 months, from the 2017 Tanzania Malaria Indicator Survey. The outcome variable was the result of malaria testing (positive or negative) and the main explanatory variable was domestic water source (piped or non-piped). Random effect variables were administrative region and geographical zone. ArcGIS 10.7 was used to create geospatial distribution maps. A STATA MP 14.0 was used to fit a mixed-effects multilevel logistic regression to examine the factors associated with malaria prevalence.

RESULTS

The prevalence of malaria and non-piped domestic water source was respectively 7.3% and 59.6%. The regions and zones with a higher prevalence of malaria also had a higher percentage of non-piped water. There was a statistically significant variation in the risk of malaria across the regions (variance = 1.27; 95% CI, 0.40-4.07) and zones (variance = 4.75; 95% CI, 1.46-15.46). The final fixed-effects model showed that non-piped domestic water was significantly associated with malaria prevalence (adjusted odds ratio (AOR) = 2.18; 95% CI, 1.64-2.89; P < 0.001).

CONCLUSIONS

A non-piped source of domestic water was independently associated with positive testing for malaria. Moreover, regions with a high percentage of non-piped domestic water had a correspondingly high prevalence of malaria.

Composition and diversity of soil microbial communities in the alpine wetland and alpine forest ecosystems on the Tibetan Plateau

While the composition and diversity of soil microbial communities play a central and essential role in biogeochemical cycling of nutrients, they are known to be shaped by the physical and chemical properties of soils and various environmental factors. This study investigated the composition and diversity of microbial communities in 48 samples of seasonally frozen soils collected from 16 sites in an alpine wetland region (Lhasa River basin) and an alpine forest region (Nyang River basin) on the Tibetan Plateau using high-throughput sequencing that targeted the V3-V4 region of 16S rRNA gene. The dominant soil microbial phyla included Proteobacteria, Acidobacteria, and Actinobacteria in the alpine wetland and alpine forest ecosystems, and no significant difference was observed for their microbial composition. Linear discriminant analysis Effect Size (LEfSe) analysis showed that significant enrichment of Hymenobacteraceae and Cytophagales (belonging to Bacteroidetes) existed in the alpine wetland soils, while the alpine forest soils were enriched with Alphaproteobacteria (belonging to Proteobacteria), suggesting that these species could be potential biomarkers for alpine wetland and alpine forest ecosystems. Results of redundancy analysis (RDA) suggest that the microbial community diversity and abundance in the seasonally frozen soils on the Tibetan Plateau were mainly related to the total potassium in the alpine wetland ecosystem, and available potassium and soil moisture in the alpine forest ecosystem, respectively. In addition, function prediction analysis by Tax4Fun revealed the existence of potential functional pathways involved in human diseases in all soil samples. These results provide insights on the structure and function of soil microbial communities in the alpine wetland and alpine forest ecosystems on the Tibetan Plateau, while the potential risk to human health from the pathogenic microbes in the seasonally frozen soils deserves attention.

Uncovering the spatial variability of recent deforestation drivers in the Brazilian Cerrado

In recent years, the Cerrado deforestation has increased considerably, reaching rates higher than in the Amazonian realm. Although the effects of deforestation are well known, the understanding of its drives at regional levels is incipient. Most studies consider that a driver influences deforestation likewise in all regions. However, deforestation has a strong spatial structure that can lead drivers to vary their influence on deforestation in different regions. Here, we evaluated the spatial variability in the relationship between the recent Cerrado deforestation and socioeconomic, environmental, and structural drivers at a regional scale. We used a geographically weighted regression (GWR) to assess the spatial variability of predictor variables. We identified regions that respond similarly to the drivers by grouping municipalities, considering their GWR coefficients through hierarchical clustering. The analyses that consider the spatial variability of predictors are more appropriated to assess the causes of recent deforestation. Remnant natural vegetation influenced the recent deforestation in all defined regions. Greater access to rural credit concession was the main driving force of deforestation in the northeast region defined here. Distance to roads increased deforestation in the northeast and north regions, while it inhibited deforestation in the central-east and southeast regions. Rainfall inhibited deforestation in the northeast, north, and southwest regions. Steep slope prevented deforestation mainly in the northeast, north, and southwest regions. Our results highlight that, to effectively reduce Cerrado deforestation, public policies should integrate strategies focusing not only at national and biome levels but also at the regional spatial level.

Spatial variability of inhalable fungal communities in airborne PM2.5 across Nanchang, China

Although fungi are ubiquitous in the atmosphere and have important effect on human health, their spatial variability about diversity and taxonomic composition, remain poorly understood. Considering the differences of environmental conditions, distinct airborne fungal communities were expected in the urban, suburban and forest areas. To test this hypothesis, PM_2.5 samples were consecutively collected for 14 days across Nanchang, China. The results showed that fungal diversities of samples from forest were higher than that from the other two areas. Even though the airborne fungal communities at one sampling site fluctuated during the short-term period, the compositions in the forest significantly diverged from the other two areas (Anosim and Adonis: p < 0.01). Canonical correspondence analysis (CCA) and mantel test (p < 0.01) indicated that these disparities among sampling sites were partly drove by air pollutants. High concentrations of air particles (PM_2.5 and PM₁₀) and gaseous pollutants (NO₂ and CO) associated with human activities were accompanied by high relative abundances of several genera such as Alternaria, Penicillium and Coprinellus. Likewise, Pearson correlation analysis showed that rainfall and relative humidity enhanced the relative abundances of 13 genera like Malassezia and Schizophyllum. Notably, part of these genera was potential allergens and pathogens to human, and it seemed that there were higher health risks in urban and suburban. This study furthers our understanding of the variation of airborne fungal community in different land-use types and different treatments may be applied to deal with the potential threat of airborne fungi.

Composition, uniqueness and connectivity across tropical coastal lagoon habitats in the Red Sea

Background

Tropical habitats and their associated environmental characteristics play a critical role in shaping macroinvertebrate communities. Assessing patterns of diversity over space and time and investigating the factors that control and generate those patterns is critical for conservation efforts. However, these factors are still poorly understood in sub-tropical and tropical regions. The present study applied a combination of uni- and multivariate techniques to test whether patterns of biodiversity, composition, and structure of macrobenthic assemblages change across different lagoon habitats (two mangrove sites; two seagrass meadows with varying levels of vegetation cover; and an unvegetated subtidal area) and between seasons and years.

Results

In total, 4771 invertebrates were identified belonging to 272 operational taxonomic units (OTUs). We observed that macrobenthic lagoon assemblages are diverse, heterogeneous and that the most evident biological pattern was spatial rather than temporal. To investigate whether macrofaunal patterns within the lagoon habitats (mangrove, seagrass, unvegetated area) changed through the time, we analysed each habitat separately. The results showed high seasonal and inter-annual variability in the macrofaunal patterns. However, the seagrass beds that are characterized by variable vegetation cover, through time, showed comparatively higher stability (with the lowest values of inter-annual variability and a high number of resident taxa). These results support the theory that seagrass habitat complexity promotes diversity and density of macrobenthic assemblages. Despite the structural and functional importance of seagrass beds documented in this study, the results also highlighted the small-scale heterogeneity of tropical habitats that may serve as biodiversity repositories.

Conclusions

Comprehensive approaches at the “seascape” level are required for improved ecosystem management and to maintain connectivity patterns amongst habitats. This is particularly true along the Saudi Arabian coast of the Red Sea, which is currently experiencing rapid coastal development. Also, considering the high temporal variability (seasonal and inter-annual) of tropical shallow-water habitats, monitoring and management plans must include temporal scales.

Spatial mapping and size distribution of oxidative potential of particulate matter released by spatially disaggregated sources

The ability of particulate matter (PM) to induce oxidative stress is frequently estimated by acellular oxidative potential (OP) assays, such as ascorbic acid (AA) and 1,4-dithiothreitol (DTT), used as proxy of reactive oxygen species (ROS) generation in biological systems, and particle-bound ROS measurement, such as 2',7'-dichlorodihydrofluorescein (DCFH) assay. In this study, we evaluated the spatial and size distribution of OP results obtained by three OP assays (OP^AA, OP^DCFH and OP^DTT), to qualitative identify the relative relevance of single source contributions in building up OP values and to map the PM potential to induce oxidative stress in living organisms. To this aim, AA, DCFH and DTT assays were applied to size-segregated PM samples, collected by low-pressure cascade impactors, and to PM₁₀ samples collected at 23 different sampling sites (about 1 km between each other) in Terni, an urban and industrial hot-spot of Central Italy, by using recently developed high spatial resolution samplers of PM, which worked in parallel during three monitoring periods (February, April and December 2017). The sampling sites were chosen for representing the main spatially disaggregated sources of PM (vehicular traffic, rail network, domestic heating, power plant for waste treatment, steel plant) present in the study area. The obtained results clearly showed a very different sensitivity of the three assays toward each local PM source. OP^AA was particularly sensitive toward coarse particles released from the railway, OP^DCFH was sensible to fine particles released from the steel plant and domestic biomass heating, and OP^DTT was quite selectively sensitive toward the fine fraction of PM released by industrial and biomass burning sources.

Two decades of trends in urban particulate matter concentrations across Australia

Australia is a highly developed country with low population density. Capital cities are situated mainly around the coastline and are subjected to different meteorological conditions. This complex set of drivers is expected to result in varying trends in particulate matter (PM) mass concentrations in urban ambient air across the country. Thus, the aim of this study was to determine the long-term trends in PM₁₀ and PM_2.5 concentrations in capital cities, and to analyse the factors that influenced such trends. The spatial variability of PM concentrations within the capital cities was first established to identify representative stations. Then trends were determined using the Mann-Kendall trend test, Sen's slope, and the generalised additive model. The results show that, in general, the PM concentrations in Australian cities are relatively low (12.1-21.7 μg m^-3 mean daily PM₁₀ and 4.6-8.7 μg m^-3 mean daily PM_2.5) and within the WHO daily limit 95% of the time. Over the past two decades, very small declines of 8.0 × 10^-5-1.1 × 10^-3 μg m^-3.yr^-1 for PM₁₀ and 7.7 × 10^-5-2.6 × 10^-3 μg m^-3.yr^-1 for PM_2.5 were observed while some stations exhibited increase in concentration based on available data; more stations showed a significant monotonic decline for PM₁₀ than PM_2.5. This is attributed to the effectiveness of the implemented emission reduction policies particularly for vehicle exhaust and power generation, given the simultaneous increase in the demand for energy and the number of vehicles over the last two decades. Regarding climate, in the coastal cities of Sydney and Brisbane, high rainfall and strong winds aid in maintaining low PM concentrations despite the significant anthropogenic emissions, while higher PM levels in Darwin can be attributed to its tropical savannah climate, which makes it prone to bushfires and necessitates regular prescribed burnings. PM concentrations increase when exceptional events such as bushfires and dust storms are induced by the extreme climate variability. Further reduction of PM concentrations in Australian cities is unlikely, considering the expanding urbanisation and the changing climate.

Topography and plant community structure contribute to spatial heterogeneity of soil respiration in a subtropical forest

Soil respiration is the largest carbon (C) flux from terrestrial ecosystems into the atmosphere. Accurate estimates of the magnitude and distribution of soil respiration are critically important to models of global C cycling and predictions of future climate change. One of the greatest challenges to accurate large-scale estimation of soil respiration is its great spatial heterogeneity at the site level. Our study explored how soil respiration varies in space and the drivers that lead to this variance in a natural subtropical evergreen broadleaf forest in Southern China. We conducted a two-year soil respiration measurement for 168 randomly selected sampling points in a 4 ha plot. We measured the spatial variance of soil respiration and tested its correlation to a variety of abiotic and biotic factors including topography, aboveground plant community structure, soil environmental factors, soil organic matter, and microbial community structure. We found that soil respiration was highly varied across the study plot, with a spatial variation coefficient (CV) of 32.75%. The structural equation modeling (SEM) analysis showed that elevation influenced tree species diversity, productivity, and soil water content, which in turn affected soil respiration via soil C content, clay content, fungal:bacterial ratio, annual litterfall, and fine root biomass. 31% of the total spatial variation of soil respiration was accounted for in the SEM, mostly by elevation, soil C content, annual litterfall biomass, tree species diversity as estimated by the Simpson's index, and soil water content, with standardized total effects of 0.31, -0.31, 0.29, 0.19, and -0.18, respectively. Our data demonstrated that soil respiration was highly spatially varied at the fine scale, and was primarily regulated by factors of topography and plant community structure. More studies investigating the spatial variation of soil respiration are therefore needed to better understand and assess terrestrial ecosystem C cycling.

Fluoride occurrence in United States groundwater

Data from 38,105 wells were used to characterize fluoride (F) occurrence in untreated United States (U.S.) groundwater. For domestic wells (n = 11,032), water from which is generally not purposely fluoridated or monitored for quality, 10.9% of the samples have F concentrations >0.7 mg/L (U.S. Public Health Service recommended optimal F concentration in drinking water for preventing tooth decay) (87% are <0.7 mg/L); 2.6% have F > 2 mg/L (EPA Secondary Maximum Contaminant Level, SMCL); and 0.6% have F > 4 mg/L (EPA MCL). The data indicate the biggest concern with F in domestic wells at the national scale could be one of under consumption of F with respect to the oral-health benchmark (0.7 mg/L). Elevated F concentrations relative to the SMCL and MCL are regionally important, particularly in the western U.S. Statistical comparisons of potentially important controlling factors in four F-concentration categories (<0.1-0.7 mg/L; >0.7-2 mg/L; >2-4 mg/L; >4 mg/L) at the national scale indicate the highest F-concentration category is associated with groundwater that has significantly greater pH values, TDS and alkalinity concentrations, and well depths, and lower Ca/Na ratios and mean annual precipitation, than the lowest F-concentration category. The relative importance of the controlling factors appears to be regionally variable. Three case studies illustrate the spatial variability in controlling factors using groundwater-age (groundwater residence time), water-isotope (evaporative concentration), and water-temperature (geothermal processes) data. Populations potentially served by domestic wells with F concentrations <0.7, >0.7, >2, and >4 mg/L are estimated to be ~28,200,000, ~3,110,000; ~522,000; and ~172,000 people, respectively, in 40 principal aquifers with at least 25 F analyses per aquifer.

Cd and Pb bioaccumulation in Eurasian watermilfoil (Myriophyllum spicatum) in relation to the role of metal contents in wetland sediments

The Choghakhor Wetland in Chaharmahal and Bakhtiari Province (Iran) has a significant role in maintaining water and sediment quality, because the wetland acts as a sink for contaminants that can pollute the aquatic ecosystem and affect human health. The present study uses a simple geostatistical technique to investigate the spatial variability of Cd and Pb in the Choghakhor Wetland to link the spatial variations of sediments to heavy metal contents in Myriophyllum spicatum. It was hypothesized that the heavy metal contents in the Choghakhor Wetland sediments impacted the concentration values in macrophytes. The value of heavy metals in sediments ranges from 0.54- to 1.84-μg/g dry weight (dw) for Cd, and from 1.32- to 2.46-μg/g dw for Pb, with a mean value of 1.12- and 1.82-μg/g dw, respectively. The mean value of Cd and Pb was 1.14- and 1.67-μg/g dw, respectively, in the aquatic macrophyte Myriophyllum spicatum, with corresponding ranges of 0.9-1.83-μg/g dw and 1.33-1.95-μg/g dw. There was a spatial structure in the Cd and Pb contents in the wetland sediments, with lower contents in the northern zone despite the values quantified in the south. We observed a direct spatial relationship between the Cd content in sediments and the Cd concentration in M. spicatum, and confirmed an effect of plant uptake for Cd. However, there were no significant differences between Pb in sediments and Pb in samples of M. spicatum, which implied that other factors like discharged industrial waste could also affect the accumulation of metals in plants. It can be concluded that spatial patterns indicated differences in the territory of the sediment Cd content at the Choghakhor Wetland localization in association with sediment enrichment. However, it was surprising that Pb did not show this pattern despite anthropic pressure.

Impacts of urbanization on soil organic carbon stocks in the northeast coastal agricultural areas of China

Dynamic changes in soil organic carbon pools have significant impacts on regional and global carbon balance. Due to rapid development in urbanized areas, the land use changes dramatically, impacting soil organic carbon (SOC) stocks in topsoil. This study aimed to document the impacts of urbanization on SOC stocks in a rapidly urbanized area from northeastern China. A total of 12 auxiliary variables were as SOC predictors including elevation, slope aspect, slope gradient, topographic wetness index, Landsat TM band3, Landsat TM band4, Landsat TM5, and normalized difference vegetation index. Urban-specific variables including population (POP), gross domestic product (GDP), distance to the socio-economic center, and distance to the roads are also considered. A set of 523 (in 1990) and 847 (in 2015) top soil samples with SOC measurement were collected. Two random forest (RF) models, one with all auxiliary variables except urban-specific variable (MA) and the other with all auxiliary variables (MB) were used to map the spatial distribution of SOC stocks in the two periods. Ten-fold cross-validation was conducted to evaluate the performance of RF models. We find that the full auxiliary variables model had a better performance for the both periods. POP and GDP were key auxiliary variables affecting spatial variability of SOC stocks in 2015. Over a 25-year period, SOC stocks decreased from 2.77 ± 1.09 kg m^-2 to 2.16 ± 0.93 kg m^-2, resulting in 3.78 Tg SOC loss in this region. Rapid urbanization led to drastic land- use change, which was the main reason for the decrease of SOC stocks. Additionally, urban-specific variables should be used as the main auxiliary variables when predicting SOC stocks in the areas that experience rapid urbanization. We believe that accurate prediction and mapping of SOC stocks will help manage land use and facilitate soil quality assessment so as to increase soil carbon sequestration in the region.

An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China

Soil organic carbon (SOC) and soil total nitrogen (STN) are major soil indicators for soil quality and fertility. Accurate mapping SOC and STN in soils would help both managed and natural soils and ecosystem management. This study developed an improved similarity-based approach (ISA) to predicting and mapping topsoil (0-20 cm soil depth) SOC and STN in a coastal region of northeastern China. Six environmental variables including elevation, slope gradient, topographic wetness index, the mean annual temperature, the mean annual temperature, and normalized difference vegetation index were used as predictors. Soil survey data in 2012 was designed based on the clustering of the study area into six climatic vegetation landscape units. In each landscape unit, 20-25 sampling points were determined at different landform positions considering local climate, soil type, elevation and other environmental factors, and finally 126 sampling points were obtained. Soil sampling from the depth of 0-20 cm were used for model prediction and validation. The ISA model performance was compared with the geographically weighted regression (GWR), regression kriging (RK), boosted regression trees (BRT) considering mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R ²), and maximum relative difference (RD) indices. We found that the ISA method performed best with the highest R² and lowest MAE, RMSE compared to GWR, RK, and BRT methods. The ISA method could explain 76% and 83% of the total SOC and STN variability, respectively, 12-40% higher than other models in the study area. Elevation had the largest influence on SOC and STN distribution. We conclude that the developed ISA model is robust and effective in mapping SOC and STN, particularly in the areas with complex vegetation-landscape when limited samples are available. The method needs to be tested for other regions in our future research.

Assessment of the NO2 distribution and relationship with traffic load in the Caribbean coastal city

NO₂ ambient concentrations were measured in a coastal Caribbean city. Barranquilla is a Caribbean city located in the North of Colombia that has approximately 1.200.000 inhabitants and possesses a warm, humid climate. In order to obtain the concentration of the contaminant in an adequate resolution, 137 Passive diffusion tubes from Gradko© were installed. Tubes prepared with 20% Triethanolamine/De-ionised water were located at the roadside between 1 and 5 m from the kerb edge. The sampling period was two weeks, from 3/16/2019 to 3/30/2019. Samples were analyzed on the UV CARY1 spectrophotometer by Gradko©. Results showed an average of 19.92 ± 11.50 μg/m³, with a maximum and minimum value of 70.27 and 0.57 μg/m³, respectively. NO₂ correlation with load traffic load was higher than with maximum traffic. The expected results include the analysis of the areas of the city with high concentrations of this pollutant that exceed the limit values established by the WHO in six (6) points; however, they never exceed the local legal limit for annual exposure, which is significantly less restrictive. Overall, the multiregression analysis is a very effective method to enrich the understanding of NO₂ distributions. It can provide scientific evidence for the relationship between NO₂ and traffic, beneficial for developing the targeted policies and measures to reduce NO₂ pollution levels in hot spots. This research may subsidize knowledge to serve as a tool for environmental and health authorities.

Spatial dependency of arsenic, antimony, boron and other trace elements in the shallow groundwater systems of the Lower Katari Basin, Bolivian Altiplano

Spatial patterns, cluster or dispersion trends are statistically different from random patterns of trace elements (TEs), which are essential to recognize, e.g., how they are distributed and change their behavior in different environmental processes and/or in the polluted/contaminated areas caused by urban and industrial pollutant located in upstream basins and/or by different natural geological conditions. The present study focused on a statistical approach to obtain the spatial variability of TEs (As, B and Sb) in shallow groundwater (GW) in a high-altitude arid region (Lower Katari Basin, Bolivian Altiplano), using multivariate analysis (PCA and HCA), geochemical modeling (PHREEQC, MINTEQ) and spatial analyses (Moran's I and LISA), considering the community supply wells. The results indicate that despite of the outliers there is a good autocorrelation in all cases, since Moran's I values are positive. The global spatial dependence analysis indicated a positive and statistically significant spatial autocorrelation (SA) for all cases and TEs are not randomly distributed at 99% confidence level. The results of hydrochemical modeling suggested the precipitation and stability of Fe (III) phases such as goethite. The re-adsorption of As and Sb on the mineral surface in the aquifer could be limiting the concentrations of both metalloids in southern regions. Spatial autocorrelation was positive (High-High) in northwestern (arsenic), southeastern (boron) and northeastern (antimony) region. The results reflected that the As and Sb are the main pollutants linked to the natural geological conditions, but B is a main pollutant due to the anthropogenic activities. Furthermore, >50% shallow groundwater exceeded the WHO limit and NB-512 guideline values for Sb (87%), B (56%) and As (50%); therefore the spatial distribution and concentrations of these TEs in GW raise a significant concern about drinking water quality in the study area.

Spatial-temporal variability of PM2.5 air quality in Beijing, China during 2013-2018

This study investigates spatial-temporal variability and trends of ambient PM_2.5 in Beijing, China, using data collected from eight urban and four suburban stations. During 2013-2018, the city-wide annual PM_2.5 concentrations decreased significantly by 40% (84 μg/m³ in 2013 vs. 50 μg/m³ in 2018). The decreasing PM_2.5 trend is more pronounced in winter and during the heating season (November-March), in urban areas, and at the median and upper percentiles of PM_2.5 concentrations. The 95th percentile PM_2.5 concentrations had decreased by 20 μg/m³/yr in the heating season and 16 μg/m³/yr in the non-heating season. During the six-year study period, there was a significant increase in excellent air quality days (PM_2.5 concentration < 35 μg/m³) and a significant decrease in heavy pollution days (PM_2.5 concentration > 150 μg/m³). PM_2.5 concentrations were strongly correlated across the 12 stations. Urban areas in south Beijing experienced higher PM_2.5 levels than suburban sites at every hour-of-day, day-of-week, and month-of-year. PM_2.5 levels were higher during winter and the heating season, when PM_2.5 emission was high due to space heating and mixing layer heights were low. PM_2.5 was higher at weekends than during weekdays, when 20% of private passenger vehicles are prohibited, and higher at night than during the day, when heavy duty delivery vehicles are not permitted. These temporal and spatial trends suggest that Beijing's PM_2.5 is strongly impacted by local emissions. Our results indicate, control strategies implemented were successful in Beijing's air quality improvement, but further reduction of PM_2.5 concentrations in Beijing could be challenging due to significant contribution from its neighboring cities, calling for comprehensive and collaborative efforts in regional/national scale.

Spatial distribution of soil phosphorous fractions following 1-year farrowing sows in an outdoor hog-rearing farm in Eastern Canada

Outdoor hog-rearing operations are of interest for both producers and consumers due to high product quality, animal welfare status, and low input and potential environmental risks. However, hog manure is rich in phosphorus (P), an environmentally sensitive nutrient, and distribution of different P fractions down the soil profile in these production systems is not well understood. The objective of this study was to determine the spatial variability of soil P in different soil depth intervals following 1-year outdoor farrowing sows in a 0.5-ha paddock in cold-temperate climate of Eastern Canada. Soil samples were collected with 0-15, 15-30, and > 30 cm depth intervals (up to 60 cm or the depth that sampling was possible) in grazing/rooting, feeding, wallow, and farrowing hut areas. Soil samples were analyzed for Olsen P (P_ol), organic P (P_O), and total P (P_T). Areas with more frequent presence of hogs showed 45-80% greater P_ol concentrations, and movement of soluble P_O down the soil profile was higher in these areas compared with the grazing/rooting area. The P_O formed 80% of P_T throughout the paddock, and the spatial distribution of P_O was similar to P_T in all soil depth intervals but different from P_ol. Results also showed that P_O concentrations in the paddock decreased at > 30 cm depth except for the feeding area. Findings of this study reveal that farrowing sow cycle in an outdoor hog-rearing farm setting can result in hot spots of P, which enhance the risk of environmental pollution.

Methamphetamine use in typical Chinese cities evaluated by wastewater-based epidemiology

Methamphetamine has become one of the most widely used illicit drugs in China. To understand the current situation in China, the prevalence and consumption of methamphetamine were estimated through wastewater-based epidemiology (WBE) in the present study. Methamphetamine concentrations ranged from 42.6 ng/L (Harbin) to 700 ng/L (Xi'an) in influent wastewater samples collected from 27 wastewater treatment plants (WWTPs) in 22 Chinese cities. The estimated consumption of methamphetamine was 23.0 (Dingxi) to 376 (Xi'an) mg/day/1000 inhabitants with a mean value of 157 mg/day/1000 inhabitants. The annual consumption in 2018 was estimated to be 84 tons (95% confidence interval, 44-136), which was 26% lower than that in 2014. The prevalence of methamphetamine use was 0.64% (95% confidence interval, 0.18-1.25), indicating that more than five million people used methamphetamine in 2018. Although drug abuse is common in the country, the consumption showed a different spatial pattern, with the highest values in Central China and the lowest use in Northeast China, so drug use is still considered a geographic and culture-dependent behaviour. The results indicated that WBE can not only be used to assess the trends of illicit drug use, but also to analyse the spatial differences in the whole country, which will provide complementary evidence for the prevention and control of methamphetamine use.

Energy minimization within target-directed aiming: the mediating influence of the number of movements and target size

In target-directed aiming, performers tend to more greatly undershoot targets when aiming down compared to up because they try to avoid an overshoot error and subsequently minimize the time and energy expenditure that is required to suddenly combat gravitational forces. The present study aims to further examine this principle of time and energy minimization by directly mediating the perceived cost of potential errors as well as the likelihood of their occurrence by manipulating the number of movements and target size, respectively. Participants executed rapid aiming movements in the up/down direction as part of a one-/two-target movement towards a small/large target. Primary movement endpoints showed greater undershooting when aiming in the downward compared to upward direction and small compared to large targets. Meanwhile, the overall movement time showed that slower movements were generated for down compared to up, but only when aiming toward large targets. The failure to mediate the central tendency as a function of the number of movements and target size indicates that the feature of minimization is highly prominent within the performers’ pre-response planning. However, the continued minimization of energy in the presence of large targets may inadvertently cost the movement time.

Spatial homogeneity and heterogeneity of ambient air pollutants in Tehran

To investigate spatial inequality of ambient air pollutants and comparison of their heterogeneity and homogeneity across Tehran, the following quantitative indicators were utilized: coefficient of divergence (COD), the 90^th percentile of the absolute differences between ambient air pollutant concentrations and coefficient of variation (CV). Real-time hourly concentrations of particulate matter (PM) and gaseous air pollutants (GAPs) of twenty-two air quality monitoring stations (AQMSs) were obtained from Tehran Air Quality Control Company (TAQCC) in 2017. Annual mean concentrations of PM_2.5, PM_10-2.5, and PM₁₀ (PM_X) ranged from 21.7 to 40.5, 37.3 to 75.0 and 58.0 to 110.4 μg m^-3, respectively. Annual mean PM_2.5 and PM₁₀ concentrations were higher than the World Health Organization air quality guideline (WHO AQG) and national standard levels. NO₂, O₃, SO₂ and CO annual mean concentrations ranged from 27.0 to 76.8, 15.5 to 25.1, 4.6 to 12.2 ppb, and 1.9 to 3.8 ppm over AQMSs, respectively. Our generated spatial maps exhibited that ambient PM_X concentrations increased from the north into south and south-western areas as the hotspots of ambient PM_X in Tehran. O₃ hotspots were observed in the north and south-west, while NO₂ hotspots were in the west and south. COD values of PM_X demonstrated more results lower than the 0.2 cut off compared to GAPs; indicating high to moderate spatial homogeneity for PM_X and moderate to high spatial heterogeneity for GAPs. Regarding CV approach, the spatial variabilities of air pollutants followed in the order of O₃ (87.3%) > SO₂ (65.2%) > CO (61.8%) > PM_10-2.5 (52.5%) > PM_2.5 (48.9%) > NO₂ (48.1%) > PM₁₀ (42.9%), which were mainly in agreement with COD results, except for NO₂. COD values observed a statistically (P < 0.05) positive correlation with the values of the 90^th percentile across AQMSs. Our study, for the first time, highlights spatial inequality of ambient PM_X and GAPs in Tehran in detail to better facilitate establishing new intra-urban control policies.

Sampling optimization for biomonitoring metal contamination with marine macroalgae

The aim of the present study was to optimize the protocol for sampling marine macroalgae to be used to biomonitor heavy metal contamination in marine ecosystems. For this purpose, we collected 50 subsamples of the brown seaweed Fucus vesiculosus at random in each of three sampling sites (SS) and determined the concentrations of Al, As, Cd, Co, Cr, Cu, Fe, Hg, N, Ni, Pb, Zn and δ15N. We used semivariograms to explore the possible existence of spatial structure in the concentrations of the elements. Spatial structure was observed in 88% of the semivariograms studied, with element concentrations varying longitudinally and transversally along the SS. Using randomization techniques, we estimated that in each SS, a minimum of 30 evenly distributed subsamples should be collected within three bands parallel to the coastline (and also at different heights on the rocks if necessary), and analyzed in a single composite sample representative of the intra-SS variability.

Ground surface settlement analysis of shield tunneling under spatial variability of multiple geotechnical parameters

This paper presents an efficient method of shield tunneling reliability analysis using spatial random fields. We introduced two stochastic methods into numerical simulation. The first one computes the maximal ground surface settlement using classical statistics, in which the response surface method is utilized to calculate the failure probability by first-order second moment. Cohesion, internal friction angle, Young's modulus and mechanical model factor are considered as random variables. The second method is the spatial random fields of aforementioned three key geotechnical parameters. Using these two methods, similar multiple soil layers are converted into a stationary random field by local regression as the first step, and then the process is followed by the spatially conditional discretization of multivariate. Failure probability of maximal ground surface settlement is calculated by a subset Monte-Carlo Algorithm. This approach is applied into the four-overlapping shield tunnels of the 5^th and 6^th metro lines intersecting at Huanhu W Rd station, Tianjin China. The failure analysis results indicated that classical statistics of geotechnical parameters showing higher variability than spatial random fields, which substantially support the complex shield tunneling project.

Investigation of the spatial variability of poly- and perfluoroalkyl substance trophic magnification in selected riverine ecosystems

The occurrence at different trophic levels of 17 poly- and perfluoroalkyl substances (PFASs), including perfluoroalkyl acids (PFAAs) and some of their precursors (e.g., perfluoroalkane sulfonamides, 6:2 fluorotelomer sulfonate (6:2 FTSA)), was investigated in riverine freshwater food webs in Southeastern France. Two fish species (Barbus barbus and Squalius cephalus) and various invertebrate taxa were collected in five rivers to assess the spatial variability of trophic magnification factors (TMFs). Particular attention was devoted to sample and data processing to minimize potential biases associated with the TMF determination. Fish were significantly more contaminated than invertebrates (ΣPFAS = 7-1811 vs. 0.9-213 ng g^-1 wet weight (ww)). Those from the Rhône River presented significantly higher levels due to high concentrations of perfluoroundecanoic acid (406 ng g^-1 ww) and perfluorotridecanoic acid (566 ng g^-1 ww) ascribed to an industrial point source. Perfluorooctane sulfonate (PFOS) was dominant at the other sites (concentration range = 3.6-134 ng g^-1 ww). Two linear regression models were compared (i.e., Kendall regression vs. Generalized Linear Mixed-Effect Model, GLMM). Results showed that TMFs calculated using the non-weighted Kendall regression were higher than those obtained using the GLMM approach. GLMM-based TMFs were consistently >1 for C₉-C₁₄ perfluorocarboxylic acids (PFCAs), PFOS and perfluorodecane sulfonate (PFDS), indicating their apparent biomagnification in the investigated food webs. Comparatively, 6:2 FTSA and N-ethylperfluorooctane sulfonamidoacetic acid (N-EtFOSAA) were less often detected and were not significantly biomagnified, probably because of metabolization. TMF estimates were generally consistent across sites although some PFASs (in particular C₉, C₁₀ and C₁₃ PFCAs) displayed higher variability, due to a unique extreme value that may have resulted from the contribution of unattributed precursor biotransformation.

The individuality index: a measure to quantify the degree of inter-individual, spatial variability in intra-cerebral brain electric and metabolic activity

Contemporary neuroscience research primarily focuses on the identification of brain activation patterns commonly deviant across participant groups or experimental conditions. This approach inherently underestimates potentially meaningful intra- and inter-individual variability present in brain physiological measures. We propose a parameter referred to as 'individuality index (II)' that takes individual variability into account. It quantifies the degree of individual variance of brain activation patterns for different brain regions and participants. IIs can be computed based on intra-cerebral source strength values such as the ones derived from the exact low resolution electromagnetic tomography source localization software. We exemplary estimated IIs for simulated datasets. Our results illustrate how IIs are affected by different spatial activation patterns across participants and quantify their distributional properties. They suggest that the proposed indices can meaningfully quantify inter- and intra-individuality of brain activation patterns. Their application to realistic datasets will allow the identification of (1) those brain regions that show particularly heterogeneous activation patterns, the contribution of which is particularly likely to be underestimated by conventional group statistics, (2) those brain regions that can alternatively be recruited by different participants for the same tasks, and (3) their associations with potentially decisive behavioral variables such as individually applied mental strategy.

A Bayesian assessment of occupational health surveillance in workers exposed to silica in the energy and construction industry

Medical records generated during occupational health surveillance processes have large amounts of unexploited information that can help to reduce silica-related health risks and many occupational diseases. The methodology applied in this study consists in analyzing through machine learning techniques a database with 70,000 medical examinations from workers in the energy and construction industry in Spain. First, a general unsupervised Bayesian model is built and node force analysis is used to identify the factors with the greatest impact on the worker's health surveillance process. Second, a predictive Bayesian model is created and mutual information is employed to assess the more relevant factors affecting the medical capability of workers exposed to silica dust. The lung auscultation and the breathing exploration are the two factors that influence the most the medical capability of silica-exposed employees. Probabilistic inference shows a remarkable gender effect, where women present more resilience towards occupational diseases than men showing a higher proportion of normal results in certain key factors, such as body mass index (♀49.73%, ♂25.17%) or spirometry (♀53.73%, ♂48.91%). Finally, environmental conditions demonstrate to have a major influence on spatial variability of occupational diseases. The design of health prevention programs based on geographical variations can be crucial to the attainment of an ongoing and sustained healthier workforce with a reduction in the number of chronic workplace illnesses.

Forest vulnerability to extreme climatic events in Romanian Scots pine forests

In the last years, large-scale mass forest withering and dieback have been reported for Scots pine (Pinus sylvestris) across eastern Europe, particularly in Romania. In these regions, the climate models forecast an increase in intensity and frequency of extreme climate events such as drought. Taking into account these aspects, the exact identification of the influences of drought on the loss of radial growth and vitality in Scots pine stands becomes mandatory. To achieve this aim, we developed the first country-wide Scots pine dendrochronological network in Romania consisting of 34 chronologies of basal area increment (BAI), and including 1401 individual tree-ring width series. Romanian Scots pine forests were severely impacted by the 2000 and 2012 droughts. The high temperatures and low precipitation from April to August were the main climatic causes of radial-growth reduction and large-scale withering in some areas. By mapping post-drought growth resilience, we identified locations where resilience was low and could identify foci of future forest dieback and high tree mortality. The projected appearance of similar prolonged and severe droughts in the future will lead to the damage or local extinction of some Scots pine forests in Romania, regardless of their age, composition or spatial location. The elaboration of adaptive forest management strategies to the impact of climate changes, specifically designed for the Scots pine stands, is not possible without knowing and understanding these aspects.

A Local View of Informal Urban Environments: a Mobile Phone-Based Neighborhood Audit of Street-Level Factors in a Brazilian Informal Community

Street-level environment characteristics influence the health behaviors and safety of urban residents, and may particularly threaten health within informal communities. However, available data on how such characteristics vary within and among informal communities is limited. We sought to adapt street audit strategies designed to characterize the physical environment for use in a large informal community, Rio das Pedras (RdP) located in Rio de Janeiro, Brazil. A smartphone-based systematic observation protocol was used to gather street-level information for a high-density convenience sample of street segments (N = 630, estimated as 86% of all street segments in the community). We adapted items related to physical disorder and physical deterioration. Measures selected to illustrate the approach include the presence of the following: (1) low-hanging or tangled wires, (2) litter, (3) structural evidence of sinking, and (4) an unpleasant odor. Intercept-only spatial generalized additive models (GAM) were used to evaluate and visualize spatial variation within the RdP community. We also examined how our estimates and conclusions about spatial variation might have been affected by lower-density sampling from random subsets street observations. Random subsets were selected to determine the robustness of study results in scenarios with sparser street sampling. Selected characteristics were estimated to be present for between 18% (unpleasant odor) to 59% (low-hanging or tangled wires) of the street segments in RdP; estimates remain similar (± 6%) when relying on a random subset created to simulate lower-density spatial sampling. Spatial patterns of variation based on predicted probabilities across RdP differed by indicator. Structural sinking and low-hanging or tangled wires demonstrated relatively consistent spatial distribution patterns across full and random subset sample sizes. Smartphone-based systematic observations represent an efficient and potentially feasible approach to systematically studying neighborhood environments within informal communities. Future deployment of such tools will benefit from incorporating data collection across multiple time points to explore reliability and quantify neighborhood change. These tools can prove useful means to assess street-level exposures that can be modifiable health determinants across a wide range of informal urban settings. Findings can contribute to improved urban planning and provide useful information for identifying potential locations for neighborhood-scaled interventions that can improve living conditions for residents in Rio das Pedras.

Comparison of IDW, cokriging and ARMA for predicting spatiotemporal variability of soil salinity in a gravel-sand mulched jujube orchard

Information about the spatiotemporal variability of soil salinity is important for managing salinization in gravel-sand mulched fields. We used inverse distance weighting (IDW) and cokriging to model the spatial variability of soil salinity from 2013 to 2016 and used an autoregressive moving-average (ARMA) model time series to analyze the temporal variability. The objectives of this paper are (a) to compare IDW and cokriging for predicting salinity in deep soil layers from surface data, thus finding a more appropriate method to model the spatial variability of soil salinity, and, using ARMA time series, (b) to identify one or a few sampling points, where soil salt content is the most temporally stable, to increase sampling efficiency or decrease cost and to estimate the overall soil salt content of a field. The IDW interpolation was more accurate than cokriging when using surface salt content to estimate the content in deep layers; so, we used IDW to interpolate the data and draw spatial distribution maps of salt content. Salinity in the 0-10 cm layer gradually decreased with the amount of gravel-sand mulching, from 1.02 to 0.7 g/kg over four years, and increased with depth. ARMA was accurate when using sample dates to predict soil salinity in the time series, and the model was more stable. The stability of the salt spatial patterns over time and along the soil profile allowed us to identify a location representative of the field-mean salt content, with mean relative error ranging between 0.56 and 2.19%. The monitoring of soil salt from a few observations is thus a valuable tool for practitioners and will aid the management of soil salt in gravel-sand-mulched fields in arid regions, with a range of potential applications beyond the framework of monitoring salinity.

Wastewater analysis reveals spatial pattern in consumption of anti-diabetes drug metformin in China

Metformin has been widely used as an oral drug for the treatment of diabetes mellitus. However, its consumption can be influenced by many economic and social factors. In this study, we investigated the spatial consumption pattern of metformin in China through wastewater-based epidemiology (WBE) approach. Influent wastewater samples were collected from 21 wastewater treatment plants (WWTPs) in 19 cities of the northeast China. A method using solid-phase extraction combined with N-Methyl-bis (trifluoroacetamide) derivatization for GC-MS detection was applied for metformin analysis. In 21 days, metformin showed high stability in wastewater at 24 °C and -20 °C. The mean concentrations of metformin in all WWTPs ranged from 2.42 μg L^-1 to 53.6 μg L^-1. The consumption of metformin was 0.66-15.6 mg d^-1 capita^-1 with the mean value of 5.54 ± 4.28 mg d^-1 capita^-1. The prevalence of metformin ranged from 0.09% to 2.10% with an average of 0.74%. Both the consumption and prevalence of metformin displays significant spatial variations in northeast China. A statistical correlation analysis indicated that the consumption of metformin increases with the decrease of per capita disposable income of urban residents. To further predict the use of metformin in China, we developed a regress model and depicted a consumption map. The annual consumption of urban residents in Chinese provinces range from 1085-63,828 kg yr^-1 with mean value of 25,347 kg yr^-1, which would provide a certain reference value for public health care and diabetes control.

Climatic determinants impacting the distribution of greenness in China: regional differentiation and spatial variability

This study investigated climatic determinants for regional greenness in China and spatially variable correlations between climatic determinants and vegetation in specific regions using the geographical detector and geographically weighted regression (GWR) methodologies. The analyses were based on normalized difference vegetation index (NDVI) and interpolations of climatic determinants from 652 Chinese meteorological stations. The study period (1982-2013) was divided into two stages (T1-T2) before and after the inflection year identified by the accumulative anomaly of NDVI. Three typical regions (R1-R3) were then selected according to the same NDVI variation trend as China in the two periods. Precipitation was the dominant climatic factor of NDVI in China, and the effect of temperature on greenness increased with warming from T1 to T2. In a relatively arid region (R1), the effect of precipitation in T2 was further strengthened compared to T1. Meanwhile, the effect of minimum temperature in T2 also increased compared with T1 in a relatively humid region (R2), becoming the major climatic determinant. In addition to the regional differentiation, spatial variability was investigated by comparing normalized coefficients of GWR for climatic determinants; this showed significant spatial heterogeneity within each region. Temperature impact areas also existed within precipitation-dominated regions (R1 and R3), where areas of precipitation impact expanded from T1 to T2. Furthermore, regression coefficients between NDVI dynamics and climate variability revealed relationships between regional differentiation and spatial variability. For example, the increasing precipitation rate could mediate the adverse impacts on greenness caused by the higher warming rate in relatively arid regions (R1).

Estimates of shifts in ecosystem service values due to changes in key factors in the Manas River basin, northwest China

The ecosystem service value (ESV) can be quantitatively assessed based on changes in ecosystem services. However, few studies have considered two scientific problems: (1) how to quantify the contributions of factors that drive ecosystem services, and (2) how to improve the spatial variability of ESV distributions. In this study, a geographic information system was used to investigate spatial and temporal changes in three factors, i.e., vegetation cover (VC), human disturbance index (HDI), and temperature vegetation drought index (TVDI), and their effects on the ESV in the Manas River basin. We found that high VC areas were expanding and the HDI was significantly enhanced. The contributions of VC, HDI, and TVDI to the total ESV were 38.0%, 31.6%, and 30.4%, respectively, and the total ESV increased by 3.7%. The cold spots were mainly distributed in the desert area, mountains, and the southern parts of the oasis area, whereas the hot spots occurred mainly to the north of the oasis area and southwest of the desert. VC, HDI, and TVDI reflected the spatial and temporal differences in the ESV determined in this river basin ecosystem. Furtherly, it may be possible to formulate appropriate ecological compensations by analyzing the changes in ESV cold/hot spots. The results of this study provide a scientific basis to facilitate the selection of the key factors that drive changes in the ESV in arid land areas, as well as for achieving a sustainable basin management in similar regions around the world.

Analysis of the spatial variation of indoor radon concentrations (national survey in Bulgaria)

This paper presents the methodology and results of the national radon survey in Bulgaria and its spatial variability. The measurements were carried out in 2778 dwellings using CR-39 track detectors over two successive 9 and 3-month periods from April 2015 to March 2016. The arithmetic (AM) and geometric (GM) means of annual indoor radon concentration were 111 ± 105 Bq/m³ and 81 Bq/m³ (GSD = 2.15), respectively. The distribution of data has been accepted to be log-normal. Two hypotheses have been investigated in the paper. The first one was a spatial variation of indoor radon concentration and the second was spatiality of the factor that influences radon variation. The indoor radon concentrations in the 28 districts have been significantly different, which prove the first hypothesis. The influence of the factors, geology (geotectonic unit, type of rock, and faults distance of the measuring site), type of the region, and the presence of the basement in the building on radon spatial variation, was examined. The analyses have been shown that they significantly affect radon variations but with a relatively small contribution in comparison to the radon variation between district. Furthermore, the significance and contribution of the investigated factors were different in each district, which confirmed the second hypothesis for their spatiality.

Velocity characteristics of atrial fibrillation sources determined by electrographic flow mapping before and after catheter ablation

BACKGROUND

Electrographic-Flow-(EGF)-Mapping is a novel method to identify Atrial Fibrillation (AF) drivers. Sources of excitation during AF can be characterized and monitored.

OBJECTIVE

The aim of this study was to evaluate the correlation between velocity of EGF around a respective AF source and its spatial variability (SV) and stability (SST).

METHODS

25 patients with AF were included in this study (persistent: n = 24, long-standing persistent: n = 1; mean age 70 ± 8.3 years, male: n = 17). Focal impulse and Rotor-Mapping (FIRM) was performed in addition to pulmonary vein isolation. One-minute epochs of unipolar electrograms recorded via a 64-pole basket catheter in both atria were re-analyzed with EGF-Mapping. SST was calculated as the percentage of time in which a source was detected.

RESULTS

AF sources identified with EGF-Mapping show a wide range of SV during 1 min covering between 0.12% and 38% of the recorded basket-catheter surface. The 12 atria where the sources showed highest temporal stability (TS; between 34% and 97% of 1 min recorded) and those 12 with the lowest TS (between 11 and 20%) differed significantly in their velocities (17.8 el/s vs 12.2 el/s; p < 0.01). In 11 atria ablation caused an average decrease of TS by 47% and of velocity by 27% while SV more than doubled.

CONCLUSION

Less stable AF-sources with high spatial variability showed reduced excitation propagation velocity while stable AF sources displayed a high average velocity in their vicinity. Importantly, catheter ablation reduced stability of sources and velocity suggesting a role of these parameters in guidance of ablation.

CONDENSED ABSTRACT

Electrographic Flow (EGF)-Mapping is a novel method to identify Atrial Fibrillation (AF) drivers based on modeling of an electrical potential surface and subsequent flow analysis. Sources of excitation during AF can be characterized and monitored. The aim of this study was to evaluate the correlation between velocity of EGF around a respective AF source and its spatial variability and stability. Less stable AF sources with high spatial variability showed reduced excitation propagation velocity while very stable AF sources displayed a high average velocity in their vicinity. Catheter ablation reduced stability of sources and velocity.

Spatial variability in soil pH and land use as the main influential factor in the red beds of the Nanxiong Basin, China

Soil pH is the main factor affecting soil nutrient availability and chemical substances in soil. It is of great significance to study the spatial variability of soil pH for the management of soil nutrients and the prediction of soil pollution. In order to explore the causes of spatial variability in soil pH in red-bed areas, the Nanxiong Basin in south China was selected as an example, and soil pH was measured in the topsoil by nested sampling (0-20 cm depth). The spatial variability characteristics of soil pH were analyzed by geostatistics and classical statistical methods, and the main factors influencing spatial variability in soil pH are discussed. The coefficient of variation in the red-bed areas of Nanxiong Basin was 17.18%, indicating moderate variability. Geostatistical analysis showed that the spherical model is the optimal theoretical model for explaining variability in soil pH, which is influenced by both structural and random factors. Analysis of the spatial distribution and pattern showed that soil pH is relatively high in the northeast and southwest, and is lower in the northwest. These results indicate that land use patterns and topographic factors are the main and secondary influencing factors, respectively.

Spatial Variations of Soil Heavy Metal Potential Ecological Risks in Typical Moso Bamboo Forests of Southeast China

Spatial patterns of soil heavy metal potential ecological risk are important for the scientific pollution management of bamboo forests. A total of 160 soil samples were collected from typical moso bamboo forests in the southeastern region of China. Ecological risk index and GIS-based kriging methodologies were applied to analyze spatial variations of analyzed metals and were compared to background levels in samples collected from Zhejiang Province. The results show that the exceeded background rate of the single-factor pollution index of Pb, Cd and Cu in all soil samples is greater than 50%, with the highest overbackground rate of Pb being 99.38%. The potential ecological risk of Hg, Pb and Cd reaches a stronger risk level, while other heavy metals such as As, Cu and Cr are associated with a slight risk level. Different spatial patterns across the whole study area indicate that the potential ecological risk in the northwest and southeast areas is high, but is relatively low in the north-central area.

Statistical analysis of estimated and observed soil moisture in sub-humid climate in north-western Jordan

In this study, soil water balance model was implemented to estimation moisture in two sub-humid areas located in north-western part of Jordan namely Irbid and Ras Muneef. In addition, in situ observations of soil moisture were collected from 16 randomly distributed sampling locations and used for monitoring the spatiotemporal variability of soil moisture in the study area. Sampling was performed during the growing season in the study area with a total of seven sampling occasions from 11 March to 12 April 2017. The results showed that the estimated soil moisture in Ras Muneef was slightly higher than Irbid. This might be referred to variations of, for instance, Ras Muneef receiving higher precipitation and lower temperature values comparing to Irbid. Also, we noticed that the spatiotemporal variability of the observed soil moisture is directly linked with the precipitation in our study area. The coefficients of the spatial and temporal variabilities were in the range 3 to 40% and 8 to 15%, respectively. Due to the high Spearman's rank correlation values which range from 0.42 to 0.73, soil moisture is temporarily stable. Strong and positive relationship was found between the estimated and observed soil moisture with determination coefficients (r²) and root mean square error (RMSE) around 0.7 and 0.41, respectively, whereas negative relationship between evapotranspiration and observed soil moisture was shown with R3and RMSE are 0.34 and 0.24 respectively. This indicates that actual soil moisture can be predicted from soil water balance model.

Spatial distribution of heavy metal concentrations in peri-urban soils in eastern China

Complex land use patterns and intense human activities significantly affect the spatial distribution of heavy metals in soils. This is especially true in peri-urban areas. The land use in peri-urban areas is complex and the risk of heavy metal pollution is relatively high. Identifying the correlations between land use patterns and spatial distribution of heavy metals in peri-urban soils is important for enhancing soil security and sustaining soil ecosystem services in areas undergoing rapid urbanisation. In this study, soil samples were collected from 82 experimental sites in a typical peri-urban watershed in eastern China. Copper (Cu), zinc (Zn), cadmium (Cd), nickel (Ni), arsenic (As), chromium (Cr), lead (Pb) and mercury (Hg) concentrations at different soil depths were analysed. Results showed that heavy metal concentrations in peri-urban soils were significantly affected by land use type and varied with soil depth. Farmland had the highest heavy metal concentrations, whereas forestland had the lowest concentrations; the concentrations in soils decreased with increasing soil depth. Spatial analysis of heavy metals in soils showed that their concentrations rapidly increased with the increasing percentage of town areas in buffer zones. This indicated that land use structure influenced the heavy metal concentrations in peri-urban soils and the influences were correlated to the locations of towns and villages. Correlation analysis showed that Cu, Zn, Cd, Pb and Hg concentrations in soils were significantly affected by altitude, distance from roads, distance from towns and villages and soil clay content. Interestingly, historic land use was also found to affect heavy metal concentrations in forestland. These results can provide scientific guidance for designing effective soil management practices for peri-urban areas.