Integrated application of multiple indicators and geographic information system-based approaches for comprehensive assessment of environmental impacts of toxic metals-contaminated agricultural soils and vegetables

Conventional monitoring and mapping approaches are laborious, expensive, and time-consuming because they need a large number of data and consequently extensive sampling and experimental operations. Therefore, due to the growing concern about the potential of contamination of soils and agricultural products with heavy metals (HMs), a field experiment was conducted on 77 farm lands in an area of 2300 ha in the southeast of Shiraz (Iran) to investigate the source of metal contamination in the soils and vegetables and to model spatial distribution of HMs (iron, Fe; manganese, Mn; copper, Cu; zinc, Zn; cadmium, Cd; nickel, Ni, and lead, Pb) over the region using geographic information system (GIS) and geostatistical (Ordinary Kriging, OK) approaches and compare the results with deterministic approaches (Inverse Distance Weighting, IDW with different weighting power). Furthermore, some ecological and health risks indices including Pollution index (PI), Nemerow integrated pollution index (NIPI), pollution load index (PLI), degree of contamination (Cdeg), modified contamination degree (mCd), PIaverage and PIvector for soil quality, multi-element contamination (MEC), the probability of toxicity (MERMQ), the potential ecological index (RI), total hazard index (THI) and total carcinogenic risk index (TCR) based on ingestion, inhalation, and dermal exposure pathways for adults and children respectively for analyzing the noncarcinogenic and carcinogenic risks were calculated. Experimental semivariogram of the mentioned HMs were calculated and theoretical models (i.e., exponential, spherical, Gaussian, and linear models) were fitted in order to model their spatial structures and to investigate the most representative models. Moreover, principal component analysis (PCA) and cluster analysis (CA) were used to identify sources of HMs in the soils. Results showed that IDW method was more efficient than the OK approach to estimate the properties and HMs contents in the soils and plants. The estimated daily intake of metals (DIM) values of Pb and Ni exceeded their safe limits. In addition, Cd was the main element responsible for ecological risk. The PIave and PIvector indices showed that soil quality in the study area is not suitable. According to mCd values, the soils classified as ultra-high contaminated for Cu and Cd, extremely high for Zn and Pb, very high, high, and very low degree of contamination for Ni, Mn, and Fe, respectively. 36, 60, and 4 % of the sampling sites had high, medium, and low risk levels with 49, 21, and 9 % probability of toxicity, respectively. The maximum health risk index (HRI) value of 20.42 with extremely high risk for children was obtained for Ni and the HI for adults and children were 0.22 and 1.55, respectively. The THI values of Pb and Cd were the highest compared to the other HMs studied, revealing a possible non-cancer risk in children associated with exposure to these metals. The routes of exposure with the greatest influence on the THI and TCR indices were in the order of ingestion > inhalation > dermal. Therefore, ingestion, as the main route of exposure, is the route of greatest contribution to health risks. PCA analysis revealed that Fe, Mn, Cu, and Ni may originate from natural sources, while Fe was appeared to be controlled by fertilizer, and Cu primarily coming from pesticide, while Cd and Pb were mainly associated with the anthropogenic contamination, atmospheric depositions, and terrific in the urban soils. While, Zn mainly originated from fertilization. Findings are vital for developing remediation approaches for controlling the contaminants distribution as well as for monitoring and mapping the quality and health of soil resources.

Spatial variability in herbaceous plant phenology is mostly explained by variability in temperature but also by photoperiod and functional traits

Whereas temporal variability of plant phenology in response to climate change has already been well studied, the spatial variability of phenology is not well understood. Given that phenological shifts may affect biotic interactions, there is a need to investigate how the variability in environmental factors relates to the spatial variability in herbaceous species' phenology by at the same time considering their functional traits to predict their general and species-specific responses to future climate change. In this project, we analysed phenology records of 148 herbaceous species, which were observed for a single year by the PhenObs network in 15 botanical gardens. For each species, we characterised the spatial variability in six different phenological stages across gardens. We used boosted regression trees to link these variabilities in phenology to the variability in environmental parameters (temperature, latitude and local habitat conditions) as well as species traits (seed mass, vegetative height, specific leaf area and temporal niche) hypothesised to be related to phenology variability. We found that spatial variability in the phenology of herbaceous species was mainly driven by the variability in temperature but also photoperiod was an important driving factor for some phenological stages. In addition, we found that early-flowering and less competitive species characterised by small specific leaf area and vegetative height were more variable in their phenology. Our findings contribute to the field of phenology by showing that besides temperature, photoperiod and functional traits are important to be included when spatial variability of herbaceous species is investigated.

Identifying childhood malaria hotspots and risk factors in a Nigerian city using geostatistical modelling approach

Malaria ranks high among prevalent and ravaging infectious diseases in sub-Saharan Africa (SSA). The negative impacts, disease burden, and risk are higher among children and pregnant women as part of the most vulnerable groups to malaria in Nigeria. However, the burden of malaria is not even in space and time. This study explores the spatial variability of malaria prevalence among children under five years (U5) in medium-sized rapidly growing city of Akure, Nigeria using model-based geostatistical modeling (MBG) technique to predict U5 malaria burden at a 100 × 100 m grid, while the parameter estimation was done using Monte Carlo maximum likelihood method. The non-spatial logistic regression model shows that U5 malaria prevalence is significantly influenced by the usage of insecticide-treated nets-ITNs, window protection, and water source. Furthermore, the MBG model shows predicted U5 malaria prevalence in Akure is greater than 35% at certain locations while we were able to ascertain places with U5 prevalence > 10% (i.e. hotspots) using exceedance probability modelling which is a vital tool for policy development. The map provides place-based evidence on the spatial variation of U5 malaria in Akure, and direction on where intensified interventions are crucial for the reduction of U5 malaria burden and improvement of urban health in Akure, Nigeria.

Variability of ambient air ammonia in urban Europe (Finland, France, Italy, Spain, and the UK)

This study addressed the scarcity of NH3 measurements in urban Europe and the diverse monitoring protocols, hindering direct data comparison. Sixty-nine datasets from Finland, France, Italy, Spain, and the UK across various site types, including industrial (IND, 8), traffic (TR, 12), urban (UB, 22), suburban (SUB, 12), and regional background (RB, 15), are analyzed to this study. Among these, 26 sites provided 5, or more, years of data for time series analysis. Despite varied protocols, necessitating future harmonization, the average NH3 concentration across sites reached 8.0 ± 8.9 μg/m3. Excluding farming/agricultural hotspots (FAHs), IND and TR sites had the highest concentrations (4.7 ± 3.2 and 4.5 ± 1.0 μg/m3), followed by UB, SUB, and RB sites (3.3 ± 1.5, 2.7 ± 1.3, and 1.0 ± 0.3 μg/m3, respectively) indicating that industrial, traffic, and other urban sources were primary contributors to NH3 outside FAH regions. When referring exclusively to the FAHs, concentrations ranged from 10.0 ± 2.3 to 15.6 ± 17.2 μg/m3, with the highest concentrations being reached in RB sites close to the farming and agricultural sources, and that, on average for FAHs there is a decreasing NH3 concentration gradient towards the city. Time trends showed that over half of the sites (18/26) observed statistically significant trends. Approximately 50 % of UB and TR sites showed a decreasing trend, while 30 % an increasing one. Meta-analysis revealed a small insignificant decreasing trend for non-FAH RB sites. In FAHs, there was a significant upward trend at a rate of 3.51[0.45,6.57]%/yr. Seasonal patterns of NH3 concentrations varied, with urban areas experiencing fluctuations influenced by surrounding emissions, particularly in FAHs. Diel variation showed differing patterns at urban monitoring sites, all with higher daytime concentrations, but with variations in peak times depending on major emission sources and meteorological patterns. These results offer valuable insights into the spatio-temporal patterns of gas-phase NH3 concentrations in urban Europe, contributing to future efforts in benchmarking NH3 pollution control in urban areas.

Cross-scale spatial variability and associations of carbon pools provide insight into regulating carbon sequestration in tropical montane rainforests

The spatial distribution of plant, soil, and microbial carbon pools, along with their intricate interactions, presents a great challenge for the current carbon cycle research. However, it is not clear what are the characteristics of the spatial variability of these carbon pools, particularly their cross-scale relationships. We investigated the cross-scale spatial variability of microbial necromass carbon (MNC), soil organic carbon (SOC) and plant biomass (PB), as well as their correlation in a tropical montane rainforest using multifractal analysis. The results showed multifractal spatial variations of MNC, SOC, and PB, demonstrating their adherence to power-law scaling. MNC, especially low MNC, exhibited stronger spatial heterogeneity and weaker evenness compared with SOC and PB. The cross-scale correlation between MNC and SOC was stronger than their correlations at the measurement scale. Furthermore, the cross-scale spatial variability of MNC and SOC exhibited stronger and more stable correlations than those with PB. Additionally, this research suggests that when SOC and PB are both low, it is advisable for reforestations to potentiate MNC formation, whereas when both SOC and PB are high some thinning can be advisable to favour MNC formation. Thus, these results support the utilization of management measures such as reforestation or thinning as nature-based solutions to regulate carbon sequestration capacity of tropical forests by affecting the correlations among various carbon pools.

Multiple Probe Measurements at Uranus Motivated by Spatial Variability

A major motivation for multiple atmospheric probe measurements at Uranus is the understanding of dynamic processes that create and maintain spatial variation in thermal structure, composition, and horizontal winds. But origin questions-regarding the planet's formation and evolution, and conditions in the protoplanetary disk-are also major science drivers for multiprobe exploration. Spatial variation in thermal structure reveals how the atmosphere transports heat from the interior, and measuring compositional variability in the atmosphere is key to ultimately gaining an understanding of the bulk abundances of several heavy elements. We review the current knowledge of spatial variability in Uranus' atmosphere, and we outline how multiple probe exploration would advance our understanding of this variability. The other giant planets are discussed, both to connect multiprobe exploration of those atmospheres to open questions at Uranus, and to demonstrate how multiprobe exploration of Uranus itself is motivated by lessons learned about the spatial variation at Jupiter, Saturn, and Neptune. We outline the measurements of highest value from miniature secondary probes (which would complement more detailed investigation by a larger flagship probe), and present the path toward overcoming current challenges and uncertainties in areas including mission design, cost, trajectory, instrument maturity, power, and timeline.

Spatial and temporal variations of microclimate and outdoor thermal comfort in informal settlements of warm humid Dar es Salaam, Tanzania

In developing countries, urbanization is dominated by the growth of informal settlements which represents 40-80% of major cities. The challenges brought up by the growth of informal settlements spans from social-economic to environmental. Previously, upgrading of the informal settlements focused on social-economic aspects such as provision of necessary services for the residents, whereas the quality of the outdoor thermal environment has not received much attention. This paper entails to investigate the potential of upgrading the outdoor thermal environment in informal settlements in the warm humid city of Dar es Salaam, Tanzania through examining the influence of addition of trees with different Leaf Area Index (LAI) and incremental increase of buildings heights. The study uses simulation as a method for analysis of the warm season and calculates the Physiological Equivalent Temperature (PET) as a thermal index. Results show substantial improvement of both microclimate and outdoor thermal comfort. Incremental increase of buildings heights in a street canyon to 12, 18, and 24 m leads to the reduction of PET by 2.5, 2.8, and 3.8 °C respectively at 2:00 p.m. Similarly, applying LAI's of 2, 4, and 6 m2/m2 leads to reduction of the mean radiant temperature by 7.9, 10.1, and 12.2 °C; while PET was reduced by 3.9, 4.7, and 5.6 °C respectively at 2:00 p.m. Nonetheless, upgrading of informal settlements shows marginal influence on the reduction of air temperature. Despite the noted thermal improvement in the studied area, the thermal comfort limits of the warm season were difficult to reach. The findings suggest that addition of vegetation is the economically most effective way for upgrading thermal conditions in informal urban fabric areas.

Impacts of recent rainfall changes on agricultural productivity and water resources within the Southern Western Ghats of Kerala, India

Significant changes in rainfall patterns are critical to agriculture, and the dependency of cropping systems on rainfall variability would engender appropriate farming practices and agriculture policies for a climate-resilient agriculture system. This study analyses the significance of rainfall variability on agriculture productivity in the Wayanad district of Kerala (India) using time series data on rainfall (1989-2019) and crop yield (2000-2019). The spatial variability of rainfall patterns reveals a dichotomy between the rain gauge stations in the northern and southern parts of the region. Despite the absence of statistically significant trends in the monthly, seasonal and annual rainfall, based on the Mann-Kendall trend analysis, an increase in the yield of many crops (e.g., winter paddy, banana) is evident, which emphasises the critical role of irrigation in driving the crop productivity. As an adaptation strategy to changing rainfall patterns, irrigation would meet the additional crop water requirement for sustainable agricultural production under the varying rainfall distributions. However, the increase in the area under irrigation in recent years has had significant implications for both surface water and groundwater resources. The conclusive findings suggest that the region requires climate-resilient agriculture, focusing on optimising irrigation and developing sustainable agriculture and water conservation strategies.

Spatial variations in the difference in elevational shifts between greenness and temperature isolines across the Tibetan Plateau grasslands under warming

Climate warming has induced widespread isotherm shifts toward higher elevations on the Tibetan Plateau, but elevational shifts of vegetation greenness (indicated by Normalized Difference Vegetation Index, NDVI) do not necessarily keep pace with the isotherm shifts. Thus, there should be spatial variations in the difference between the velocities of vertical movement of greenness isolines (VNDVI) and isotherms (VT) across the Tibetan Plateau grasslands. Using satellite-observed NDVI and gridded climate data during 2000-2017, we found uphill shifts of the isotherms in 81.8 % of the surveyed areas, mainly in the eastern, central, southwestern, and northeastern parts, whereas upward shifts of the greenness isolines were observed only in 49.7 % of these areas, mainly in the southeastern, west-central, and southwestern edge of Tibetan Plateau grasslands. In the areas where both the greenness isolines and isotherms shifted uphill, VDNVI was faster than VT in the west-central and northeastern parts, and VNDVI was smaller than VT in the western, south-central, central, and southeastern regions; the difference between VNDVI and VT was positively related with elevational gradient of NDVI (NDVIEG) in the areas where NDVIEG was negative and the temporal trend of NDVI was positive, and was negatively related with NDVIEG and temporal trends of NDVI and temperature in the areas where NDVIEG was positive and temporal trend of NDVI was negative. Our results revealed spatial heterogeneity in the difference in the elevational shifts between the isotherm and vegetation greenness isoline across the Tibetan Plateau grasslands, which is related with both diverse adaptation to local environment (NDVIEG) and complex responses of vegetation greenness to warming in terms of both direction and magnitude. These findings have important implications for the prediction of vegetation production and carbon cycle and the adaptive management of alpine grasslands under climate change.

Groundwater quality index development using the ANN model of Delhi Metropolitan City, India

Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of groundwater. Changes in the local topography and drainage systems in an area have negative impacts on both the quality and quantity of groundwater. This underscores the critical need to assess the susceptibility of groundwater to pollution and implement measures to mitigate these risks. The water quality index (WQI) is an approach that simulates the water quality at peculiar locations for a particular period of time. The artificial neural network (ANN) model approach is such an idealistic methodology that can be utilized for WQI development and provides better results for specific locations in optimum time. Therefore, the goal of the current study is to provide a unique way for using artificial neural networks (ANN) to characterize the groundwater quality of Delhi Metropolitan City, India. In order to make the water fit for residential and drinking use, the research also pinpoints the geographical variability and spots where the contaminated region has to be sufficiently cleaned. A minimum WQI of 41.51 was obtained at the Jagatpur location while a maximum value of 779.01 was at the Peeragarhi location. During the training phase, the results obtained using the ANN model were highly favorable, demonstrating a strong association with an R-value of 98.10%, thus highlighting the program's exceptional efficiency. However, in accordance with the correlation regression findings, the prediction outcomes of the ANN model in testing are observed to be an R-value of 99.99-100%. This study confirms the promise and advantages of employing advanced artificial intelligence in managing groundwater quality in the studied area.

Temporal and spatial distribution of soil water repellency in grassland soils and its relation to soil moisture, hydrophobic matter, and particle size

In grassland soils, soil water repellency (SWR) may be one of the triggers of soil erosion and degradation as it can reduce water infiltration and penetration into the soil. Few studies were focusing on the evaluation of soil hydro-physical properties, such as hydrophobicity, and their relation to soil moisture, hydrophobic matter, and particle size in grassland soils. In this study, 800 soil samples were collected from the Xilingol grassland in Inner Mongolia, China, using the water droplet penetration time (WDPT) test to evaluate water repellency and we aimed to investigate the temporal and spatial distribution of SWR in grassland soils using the Kriging and Inverse Distance Weighting (IDW) interpolation methods and determine the physical-chemical properties that trigger the SWR. The results showed that the grassland soils in the studied area were slightly water-repellent and a few portions of the area exhibited strong water-repellency. In April, areas of soils with a depth of 0-5 cm and slight to strong SWR accounted for 80 % of the total studied area, of which 5 % had strong water repellency. Moreover, in August, 90 % of the studied area consisted of soils with slight to strong SWR, of which 60 % accounted for soils with strong SWR. With a soil water content of 10.95 %, the SWR reached its peak, with an average value of 60.32 s. The SWR was positively correlated with total N, available N, and soil organic matter (SOM) contents, and therein the hydrophobic acid matter and the hydrophobic basic matter content had a positive contribution to SWR, and the hydrophilic basic matter and the hydrophilic acidic matter had a negative contribution on SWR. In addition, SWR was found to be negatively related to the soil particle size (r = -0.672). A slight SWR was also observed in the majority of the studied area, particularly in the topsoil and fine soils, especially during the monsoon period; hence, SWR must be also considered to reduce the risk of occurrence of soil erosion and degradation in grasslands.

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017-2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00-8:00 UTC) and late evening (19:00-22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed significant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.

Identifying emissions hotspots and strategies to reduce real-world fuel use and emissions for passenger rail: A spatially resolved approach

The objectives of this work are to model spatially resolved passenger locomotive fuel use and emission rates, locate emissions hotspots, and identify strategies to reduce trip train fuel use and emissions. Train fuel use and emission rates, speed, acceleration, track grade, and track curvature were quantified based on over-the-rail measurements, using portable emission measurement systems, for diesel and biodiesel passenger rail service on the Amtrak-operated Piedmont route. Measurements included 66 one-way trips and 12 combinations of locomotives, consists, and fuels. A locomotive power demand (LPD) based emissions model was developed based on the physics of resistive forces opposing train motion, taking into account factors such as speed, acceleration, track grade, and curvature. The model was applied to locate spatially-resolved locomotive emissions hotspots on a passenger rail route, and also identify train speed trajectories with low trip fuel use and emissions. Results show that acceleration, grade, and drag are the major resistive forces affecting LPD. Hotspot track segments have 3 to 10 times higher emission rates than non-hotspot segments. Real-world trajectories are identified that reduce trip fuel use and emissions by 13 % to 49 % compared to the average. Strategies for reducing trip fuel use and emissions include dispatching energy-efficient and low-emitting locomotives, using a 20 % blend of biodiesel, and operating on low-LPD trajectories. Implementing these strategies will not only decrease trip fuel use and emissions but reduce the number and intensity of hotspots and, thus, lowering the potential for exposure to train-generated pollution near railroad tracks. This work provides insights on reducing railroad energy use and emissions, which would lead to a more sustainable and environmental-friendly rail transportation system.

Pyrolysis temperature and biochar redox activity on arsenic availability and speciation in a sediment

Biochar is widely used for water and soil remediation in part because of its local availability and low production cost. However, its effectiveness depends on physicochemical properties related to its feedstock and pyrolysis temperature, as well as the environmental conditions of its use site. Furthermore, biochar is susceptible to natural aging caused by changes in soil or sediment moisture, which can alter its redox properties and interactions with contaminants such as arsenic (As). In this study, we investigated the effect of pyrolysis temperature and biochar application on the release and transformations of As in contaminated sediments subjected to redox fluctuations. Biochar application and pyrolysis temperature played an important role in As species availability, As methylation, and dissolved organic carbon concentration. Furthermore, successive flooding cycles that induced reductive conditions in sediments increased the As content in the solution by up to seven times. In the solid phase, the application of biochar and the flooding cycle altered the spatial distribution and speciation of carbon, iron (Fe) and As. In general, the application of biochar decreased the reduction of Fe(III) and As(V) after the first cycle of flooding. Our results demonstrate that the flooding cycle plays an important role in the reoxidation of biochar to the point of enhancing the immobilization of As.

A coupled geostatistical and machine learning approach to address spatial prediction of trace metals and pollution indices in sediments of the abandoned gold mining site of Bekao, Adamawa, Cameroon

Trace metals present in high amounts in aquatic systems are a perpetual concern. This study applied geostatistical and machine learning models namely Ordinary Kriging (OK), Ordinary Cokriging (OCK) and Artificial Neural Network (ANN) to assess the spatial variability of trace metals and pollution indices in surface sediments along the Lom River in an abandoned gold mining site at Bekao (Adamawa Cameroon). For this purpose, thirty-one (31) surface sediment samples are collected in order to determine the total concentrations of As, Cr, Cu, Fe, Mn, Ni, Pb, Sn and Zn. These trace metals are used to compute pollution indices as the sediment pollution index (SPI), the Nemerow index (NI), the modified contamination degree (mCD), and the potential ecological risk assessment (RI). OK, OCK and ANN models are compared to determine the best model performance. The best models are selected based on the values of the root mean square error (RMSE), the coefficient of determination (R2), the scatter index (SI) and the BIAS. Results showed that the sequence of trace metal mean concentrations in the sediments is Fe > Mn > Cu > Ni > Sn > Cr > Zn > Pb > As. The mean concentrations of Ni, Cu, Zn and Sn are above the average shale values (ASV) and the pollution status is globally moderate to significant with a low potential ecological risk. The spatial dependency obtained with semivariogram models are moderate to weak for Mn, Fe, Ni, Pb, SPI, NI, mCD, RI As, Cr, and Sn and strong for Cu and Zn. According to cross-validation parameters, ANN model is the best method for the prediction on trace metal concentrations and pollution indices in surface sediments along the Lom River in the abandoned gold mining site of Bekao.

Estimating the daily average concentration variations of PCDD/Fs in Taiwan using a novel Geo-AI based ensemble mixed spatial model

It is generally established that PCDD/Fs is harmful to human health and therefore extensive field research is necessary. This study is the first to use a novel geospatial-artificial intelligence (Geo-AI) based ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and geographic predictor variables selected using SHapley Additive exPlanations (SHAP) values to predict spatial-temporal fluctuations in PCDD/Fs concentrations across the entire island of Taiwan. Daily PCDD/F I-TEQ levels from 2006 to 2016 were used for model construction, while external data was used for validating model dependability. We utilized Geo-AI, incorporating kriging, five machine learning, and ensemble methods (combinations of the aforementioned five models) to develop EMSMs. The EMSMs were used to estimate long-term spatiotemporal variations in PCDD/F I-TEQ levels, considering in-situ measurements, meteorological factors, geospatial predictors, social and seasonal influences over a 10-year period. The findings demonstrated that the EMSM was superior to all other models, with an increase in explanatory power reaching 87 %. The results of spatial-temporal resolution show that the temporal fluctuation of PCDD/F concentrations can be a result of weather circumstances, while geographical variance can be the result of urbanization and industrialization. These results provide accurate estimates that support pollution control measures and epidemiological studies.

Spatial variability, source identification and risks assessment of antibiotics in multimedia of North China's largest freshwater lake using positive matrix factorization and Monte Carlo simulation

Antibiotics are widely found in aquatic ecosystems and pose a serious threat to human and the ecological system. Samples of surface water (SW), overlying water (OW), pore water (PW) and sediments (Sedi) were collected to investigate the spatial variability, potential sources, ecological risk (RQs) and health risks (HQs) of nine common antibiotics in Baiyangdian Lake using positive matrix factorization (PMF), and Monte Carlo simulation. Significant spatial autocorrelation of most antibiotics were observed in PW and Sedi samples rather than in SW and OW samples, and higher antibiotic levels were found in the northwest of waters and the southwest of sediments. Livestock (26.74-35.57%) and aquaculture (21.62-37.70%) were identified as primary sources of antibiotics in the water and sediments. Norfloxacin and roxithromycin showed high levels of RQ and HQ in more than 50% of samples, respectively. The combined RQ (ΣRQ) in the PW can be used as a sign of across multimedia risk. Notably, appreciable health risks were observed for the combined HQ (ΣHQ) in about 80% of samples, indicating the importance of taking health risk of antibiotics into consideration. The findings of this work provides a reference for antibiotics pollution control and risk management in shallow lake.

Spatial variability of arsenic fractionation in an abandoned arsenic-containing mine: Insights into soil particle sizes and quantitative mineralogical analysis

Soil particle sizes and mineral phases play a major role in the migration of arsenic (As) in mine. In this study, soil As fractionation and mineralogical composition in different particle sizes soil at naturally mineralized and anthropogenically disturbed zones from an abandoned mine were comprehensively studied. The results showed that soil As contents in anthropogenically disturbed mining zone (MZ), processing zone (PZ), and smelting zone (SZ) were increased with decreasing of soil particle sizes. The contents of As in the fine soil particles (0.45-2 μm) reached to 850-4800 mg·kg^-1, which mainly existed at readily soluble, specifically sorbed, and Al-oxide fractions, and contributed to 25.9-62.6 % of the total As contents in soil. Conversely, soil As contents in naturally mineralized zone (NZ) were decreased with decreasing of soil particle sizes and As was mainly accumulated in the coarse fraction of soil (0.075-2 mm). Despite the speciation of As in 0.075-2 mm soil mainly existed as residual fraction, the content of non-residual As fraction reached up to 1636 mg·kg^-1, indicating a high potential risk of As in naturally mineralized soil. The utilization of scanning electron microscopy, fourier transform infrared spectroscopy combined with mineral liberation analyzer revealed that soil As in NZ and PZ was mainly retained by iron (hydrogen)oxide, while whereas the dominant host minerals for soil As in MZ and SZ were the surrounding rocks of calcite and the iron-rich silicate mineral biotite. Notably, both of the calcite and biotite exhibited high mineral liberation, which was partly contributed to a significant portion of mobile As fraction in MZ and SZ soil. The results suggested that the potential risks of soil As from SZ and MZ at abandoned mine, particularly in the fine soil particles, should be a prior concern.

The impact of spatial variance on precision estimates in an auditory oddball paradigm

Predictive processing theories suggest that a principal function of the brain is to reduce the surprise of incoming sensory information by creating accurate and precise models of the environment. These models are commonly explored by looking at the prediction errors elicited when experience departs from predictions. One such prediction error is the mismatch negativity (MMN). Using this component, it is possible to examine the effect of external noise on the precision of the developed model. Recent studies have shown that the brain may not update its model every time there is a change in the environment, rather it will only update it when doing so will increase precision and or accuracy of the model. The current study examined this process using oddball sound sequences with high and low spatial variability and examining how this affected the elicited MMN to a duration deviant sound. The results showed a strong null effect of spatial variance both at a local and sequence levels. These results indicate that variability in the sound sequence will not invariably affect model precision estimates and thus the amplitude of the MMN component.

Spatial variability and source analysis of typical soil trace elements at permafrost section along national highway 214 in the eastern Qinghai-Tibet Plateau

This paper attempts to reveal the enrichment status, spatial characteristics and material sources of typical soil trace elements at permafrost section along National Highway 214 on the Qinghai-Tibet Plateau. Therefore, the samples of typical trace elements in surface soil, being located at the northern slope of Bayan Kara Mountains, were collected and tested. The concentrations of typical trace elements in soil were analysed by mathematical statistics, spatial analysis and ecological assessment. The results show that: (1) the concentrations of As, Cd and Hg in the soil are higher than the local background values, and their degrees of variation were high. There was a certain degree of accumulation. Soil As and Hg elements constitute "slight pollution", indicating there is a none-to-slight ecological hazard. (2) The distributions of soil As, Cd, Pb and Zn concentrations are lower near the highway and increase with distance from it and then become relatively low further away. The distributions of Cr, Cu, Hg and Ni concentrations show no obvious trends in any direction. (3) The spatial heterogeneity of typical trace elements in soil is affected by soil organic matter (SOM), cation exchange capacity (CEC), pH, slope curvature and aspect. At the local scale, soil texture and topography were the main affecting factors. Concentrations of Cd, Cr, Cu, Ni, Pb and Zn were mainly affected by natural factors, while those of As and Hg were affected by both natural and human factors.

Spatial variability of source contributions to nitrate in regional groundwater based on the positive matrix factorization and Bayesian model

Groundwater nitrate (NO₃^-) pollution has attracted widespread attention; however, accurately evaluating the sources of NO₃^- and their contribution patterns in regional groundwater is difficult in areas with multiple sources and complex hydrogeological conditions. In this study, 161 groundwater samples were collected from the Poyang Lake Basin for hydrochemical and dual NO₃^- isotope analyses to explore the sources of NO₃^- and their spatial contribution using the Positive Matrix Factorization (PMF) and Bayesian stable isotope mixing (MixSIAR) models. The results revealed that the enrichment of NO₃^- in groundwater was primarily attributed to sewage/manure (SM), which accounted for more than 50 %. The contributions of nitrogen fertilizer and soil organic nitrogen should also be considered. Groundwater NO₃^- sources showed obvious spatial differences in contributions. Regions with large contributions of SM (>90 %) were located in the southeastern part of the study area and downstream of Nanchang, which are areas with relatively high population density. Nitrogen fertilizer and soil organic nitrogen showed concentrated contributions in paddy soil in the lower reaches of the Gan and Rao Rivers, and these accumulations were mainly driven by the soil type, land use type, and topography. This study provides insight into groundwater NO₃^- contamination on a regional scale.

Variability analysis of soil properties, mapping, and crop test responses in Southern Ethiopia

Agricultural productivity is significantly impacted by soil properties, which vary spatially from a small to a larger area. This variation may be caused by a combination of intrinsic and extrinsic factors, including human activities like soil management practices. The aim of the current study was to analyze soil spatial variability, create a Digital Soil Map (DSM), and test map information with crop in Southern Ethiopia. A total of 18 geo-referenced surface soil samples at depth of 20 cm were collected. Selected soil Physico-chemical properties such as soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (av. P), sulfur (S), exchangeable bases [calcium (Ca), magnesium (Mg), and potassium (K)], soil micronutrients [boron (B), copper (Cu), iron (Fe), manganese (Mn) and zinc (Zn)] and cation exchange capacity (CEC) were analyzed. The results revealed clay texture with a mean pH value of 4.6 (strong acidity). About 50% of essential nutrients [N, P, S, Ca, Mg, B & Fe] were deficient. The geostatistical analysis has shown that the best-fitted models were exponential for (OC, TN, available P, S, Mg, CEC, B, Fe, and Zn), spherical for (pH, Ca, Cu and Mn), and Gaussian for (C:N, K, K:Mg, and PBS). The range of all soil properties varied from 50 m to 84 m which was above the actual distance between soil samples (i.e., 46 m). The result showed that the spatial dependence values for soil properties of [OC, TN, CEC, PBS, ESP, and Cu]; [pH, C: N ratio, available P, S, Ca, Mg, K, Na, K: Mg ratio and Zn] and [B, Fe, M n] were strong (<25%]; weak (>75%) and moderate (25%-75%), respectively. Model performance using indicators such as prediction mean error (PME), root mean square standardized error (RMSSE), mean standard error (MSE), and root-mean-square error (RMSE) also confirmed the acceptable prediction. The DSM demonstrated the limitation of N, P, S, and B nutrients for intervention. The DSM information was tested under field conditions using haricot bean (Phaseolus vulgaris) with lime and organic fertilizers as treatments. The experiment consists of lime rates (0, 3, 6 t/ha), rhizobium inoculation (inoculated and non-inoculated), and fertilizer types (0, 150 kg ha^-1 NPSB, 5 t/ha vermi compost, 10t/ha farmyard manure (FYM)) in Randomized Complete Block Design with three replications. The result exhibited interaction effects of lime, inoculation, and fertilizer types significantly influenced (p < 0.05) biomass and grain yield of haricot beans. Rhizobium inoculation x 6t/ha lime x 150 kg ha^-1 NPSB recorded the maximum grain yield (3186.1 kg/ha) which was 26.3 fold over the non-treated soil (117 kg ha^-1). In conclusion, the DSM classified the area into distinct management zones which were tested with a crop trial. The results of the trial confirm the importance of site-specific nutrients/amendment application for sustainable soil management.

Does global warming increase the risk of liver cancer in Australia? Perspectives based on spatial variability

Australia has experienced an astonishing increase in liver cancer over the past few decades and the epidemiological reasons behind this are puzzling. The existing recognized risk factors for liver cancer, viral hepatitis, and alcohol consumption, are inconsistent with the trend in liver cancer. Behind the effects of migration and metabolic disease lies a potential contribution of climate change to an increase in liver cancer. This study explored the climate-associated distribution of high-risk areas for liver cancer by comparing liver cancer to lung cancer and finds that the incidence of liver cancer is more pronounced in hot and humid areas. This study showed the risk of liver cancer was higher in the equatorial region and tropical regions. These results will extend the study on the health consequences of climate change and provide more ideas and directions for future researchers.

CO2 dynamics in a small and old subtropical reservoir in East Asia: Environmental controls driving seasonal and spatial variability

Inland waters have been increasingly viewed as hotspots for greenhouse gas (GHG) emissions owing to their strong capability to intercept and mineralize carbon from the terrestrial environment. Although small waterbodies in humid subtropical climates have the potential to emit considerable amounts of GHG, their emission patterns have remained understudied. This study involved intensive measurements of carbon dioxide (CO₂) emissions from a small reservoir and its upstream and downstream reaches located in subtropical Hong Kong. Our results revealed that a variety of metabolic, hydrological, and hydrochemical processes play a critical role in regulating its CO₂ dynamics. The reservoir was an overall source of CO₂ to the atmosphere with an average areal flux of 24.6 mmol m^-2 d^-1, and it occasionally functioned as a sink for atmospheric CO₂ under intense solar radiation when primary productivity was high. This flux is on the low side relative to that of global (sub)tropical reservoirs, which was likely attributable to the prolonged history of the reservoir (>150 years) and the occasional undersaturation of CO₂ in the water column. We also noticed pronounced differences in the underlying controls of CO₂ dynamics between the reservoir and its upstream and downstream reaches, emphasizing the importance of taking into account the distinct characteristics of both lentic and lotic waters when evaluating catchment-scale CO₂ fluxes.

Soil quality cannot be improved after thirty years of land use change from forest to rangeland

Soil quality can be assessed by measuring its physical, chemical and biological properties. In terrestrial ecosystems, the knowledge of the status of soil quality under different land use/cover can increase our understanding of processes related to soil functioning and help to properly managing ecosystems and increase their services. Conversion of the forest to rangelands is one of the most common forms of land use change having a significant effect on soil quality indicators. Here, we addressed the following objectives: (ii) to study the current status of soil physical, chemical and biological characteristics after more than thirty years of land use change from forest (dominated by Carpinus betulus and Parrotia persica) to rangeland, and (ii) to provide an overview of the spatial distributions of soil properties in forest and rangeland covers using a geostatistical method. For this, two sites (i.e., forest and rangeland) were selected in northern Iran. Within each site, 50 soil samples were collected at 0-10 cm depth along two sampling lines (250 m length) with a total of 100 soil samples for each site. Results showed that following the change of land use from forest to rangeland soil porosity, aggregate stability, pH, electrical conductivity and nutrient (i.e., total N and available P, K, Ca and Mg) contents increased, whereas soil bulk density and C/N ratio decreased. In addition, the population of soil biota (i.e., earthworms, acarina, collembola, nematode, protozoa, bacteria and fungi), microbial and enzyme activities decreased after more than thirty years of land use change from forest to rangeland. Principal component analysis confirmed that forest site had a more fertile soil and a higher biological activity than rangeland cover. Based on heat plots of soil properties, forest ecosystems created hot spots of soil quality indicators in the study area. Based on the geostatistical approach, most of the soil variables in the rangeland site followed a linear model, while in the forest site, most models were exponential and spherical. The fractal dimension values of the soil properties in the forest (1.62-1.99) had larger variations than in the rangeland (1.75-1.99) site. As a general conclusion, soil quality was not improved after more than thirty years of land use change from forest to rangeland, suggesting that degraded forest habitats should be restored by native tree species rather than converted to other land uses.

Seasonal occurrence of multiple classes of antibiotics in East China rivers and their association with suspended particulate matter

Understanding the occurrence and fate of antibiotics from different categories is vital to predict their environmental exposure and risks. This study presents the spatiotemporal occurrence of 45 multi-class antibiotics and their associations with suspended particulate matter (SPM) in Xiaoqing River (XRB) and Yellow River (YRB) via 10-month monitoring in East China. Thirty-five and 31 antibiotics were detected in XRB and YRB, respectively. Among them, fluoroquinolones (FQs) had the highest total mean concentration (up to 24.8 μg/L in XRB and 15.4 μg/L in YRB), followed by sulfonamides (SAs) (14.0 μg/L and 15.4 μg/L) and macrolides (MLs) (1.1 μg/L and 1.6 μg/L). Significant spatial-temporal variations were found in both rivers where higher concentrations of antibiotics were observed in urban and densely populated areas during winter and spring. Hydrological factors such as river flow and water volume, instream attenuation and antibiotic usage may cause the observed variabilities in the seasonal patterns of antibiotic pollution. Using linear regression analysis, for the first time, this study confirmed that the total concentrations of MLs (p < 0.05), FQs (p < 0.001) and SAs (p < 0.001) were strongly correlated with the turbidity/total suspended solids in the studied rivers (except MLs in YRB). It is thus suggested that partitioning processes onto SPM might affect the distribution of detected antibiotics in rivers, which are largely dependent on SPM composition and characteristics. The risk quotient (RQ) determined for up to 87 % of individual compound was below 0.1 in both rivers; however, the high joint toxicity reflected by the mixed RQs of detected antibiotics may rise risk alarm for aquatic species. Further aspects regarding active mechanisms of SPM-antibiotic interactions and ecological risks of coexistence of multiple antibiotics need to be investigated.

Contrasting effects of native and exotic vegetation on soil infiltrability in the Sonoran Desert

Invasion by exotic grasses is transforming drylands across the planet, but the ecohydrological feedbacks of such invasions are not fully understood. For example, in the Sonoran Desert, previous studies have shown that buffelgrass (Cenchrus ciliaris) alters the spatial patterns of soil moisture, leading researchers to hypothesize that such alterations are related to the plants' effects on soil infiltrability. To evaluate this hypothesis, we compared field-saturated hydraulic conductivity (K_fs) in a native shrubland with that in a neighboring savanna extensively dominated by exotic buffelgrass. We measured K_fs during the dormant and growing seasons in both canopy and intercanopy zones. We found that K_fs was generally lower during the dormant season than during the growing season. There were no significant differences between sites during the dormant season, and at both sites, K_fs was 6-7 times higher under shrubs than in the intercanopies. During the growing season, K_fs for the exotic intercanopy was comparable to that for shrub cluster edges (140 mm h^-1) and was more than twice that for the native intercanopy. Both shrubs and buffelgrass improved K_fs by reducing soil bulk density (thus increasing porosity). Additionally, surface roughness in the exotic intercanopy was nearly 3 times higher than in the native intercanopy. The combination of greater surface roughness and higher infiltration rates during the growing season most likely alters hydrological connectivity in savannas invaded by exotic grasses such as buffelgrass. By capturing portions of the runoff generated in the intercanopy, these grasses reduce runon into shrub patches, with potentially substantial impacts on native vegetation dynamics and stability.

Spatial and temporal assessment of crack cocaine use in 13 European cities through wastewater-based epidemiology

Already in early 2000s, concerns have been growing in the EU about increasing use of cocaine and it is estimated that below 1 % of the population administer the drug by smoking crack cocaine. New available data suggests an increase in the use of crack cocaine and an increase in the number of crack cocaine users entering treatment has been reported in several European countries. Robust estimations of crack cocaine use are however not available yet. The use of crack cocaine has long been associated with severe adverse socio-economic conditions as well as mental health problems, such as suicide ideation and depression. The aim of this study was to assess spatial trends in population-normalized mass loads of crack cocaine biomarkers (i.e., anhydroecgonine and anhydroecgonine methyl ester) in 13 European cities in six countries (the Netherlands, Belgium, Ireland, Portugal, Spain and Italy). Furthermore, temporal trends over a five-year period were evaluated through the analysis of historic samples collected in the Netherlands. Finally, the stability of the crack cocaine biomarkers in wastewater was investigated through batch experiments. The samples were analyzed with a new developed and validated hydrophilic interaction liquid chromatography coupled to mass spectrometry method. Targeted crack cocaine biomarkers were found in all cities. Also, crack cocaine biomarker was detected in wastewater from 2017 to 2021 in the Netherlands, but no significance between the years were found. With respect to biomarker in-sample stability, AEME was found to be stable in wastewater. This study assessed crack cocaine use for the first time on a broad scale, both temporal and in cities across Europe, with wastewater-based epidemiology and it shows the importance of wastewater analysis to monitor community loads of crack cocaine use.

Spatial variability and risk assessment of heavy metals in the soil surrounding solid waste from coking plants in Shanxi, China

Heavy metal pollution in the soil surrounding solid wastes from coking plants poses potential threats to human health and has attracted widespread attention. This study is the first to assess the spatial variability and risks of heavy metals in the soil surrounding solid waste from coking plants. The results showed that the concentrations of Cu, Ni, Pb, and Cd in the soil were much higher than the background value of the soil. Solid waste had a clear influence on the contents of Ni, Cd, Mn, Pb, and Cr in the soil. The ecological risk assessment of heavy metal pollution demonstrated that the pollution degree of Cu, Pb, and Cd was more serious than others, and the ecological risk of heavy metals was mainly caused by Cd in the soil. The human health risk assessment showed that adults and children near coking plants might face carcinogenic risk from exposure to Cr. This study can provide a theoretical basis for the prevention and management of soil heavy metal pollution surrounding solid waste in coking plants.

Assessment of the soil-erosion-sediment for sustainable development of South America

One of the greatest threats to maintaining sustainable agro-ecosystems is mitigating the episodic soil loss from farm operations, further exacerbated by meteorological extremes. The Revised Universal Soil Loss Equation (RUSLE) is a model that combines the effects of rain, soil erodibility, topography, land cover, and conservation practices for estimating the annual average soil losses. This study aims to quantify soil water erosion to continental South America (S.A.) through RUSLE using available datasets and characterizing the average sediment delivery rate (SDR) to the major S.A. basins. Soil erodibility was estimated from the Global Gridded Soil Information soil database. LS-factor's topographical parameter was derived from Digital Elevation Models using the "Shuttle Radar Topography Mission" dataset. The R-factor was estimated from a previous study developed for S.A. and the C-factor from the Global Land Cover (Copernicus Global Land Services) database. We used a modeling study for SDR that simulated the annual average sediment transport in 27 basins in S.A. RUSLE set up presented a satisfactory performance compared to other applications on a continental scale with an estimated averaged soil loss for S.A. of 3.8 t ha^-1 year^-1. Chile (>20.0 t ha^-1 year^-1) and Colombia (8.1 t ha^-1 year^-1) showed the highest soil loss. Regarding SDR, Suriname, French Guyana, and Guyana presented the lowest values (<1.0 t ha^-1 year^-1). The highest soil losses were found in the Andes Cordillera of Colombia and the Center-South Region of Chile. In the former, the combination of "high" K-factor, "very high" C-factor, and "very high" LS-factor were the leading causes. In the latter, agriculture, livestock, deforestation, and aggressive R-factor explained the high soil loss. Basins with the highest SDR were located in the North Argentina - South Atlantic basin (27.73%), Mar Chiquitita (2.66%), Amazon River basin (2.32%), Magdalena (2.14%) (in Andes Cordillera), and Orinoco (1.83%).

Microscale spatial variability of Beryllium-7 at a reference site in southwest China

Quantitative assessment of soil erosion and deposition rates using fallout radionuclides (FRNs), including Beryllium-7 (⁷Be), requires establishment of a reliable reference inventory i.e. the inventory of a non-eroding point. Little information, however, is currently available on the microscale spatial variability of ⁷Be inventory within reference sites. This is important information to inform sample design and replication, and in addition, to evaluate the uncertainty of derived soil redistribution data. In this study, soil samples were taken systematically at grid points on a 5 m × 12 m experimental reference plot with a bare soil surface, at two sampling occasions (2019 and 2021) in southwest China. ⁷Be activities were measured to explore the potential variability of ⁷Be inventory at the microscale. To determine possible causes of ⁷Be inventory variation, physicochemical characteristics including organic matter content (OM), pH, cation exchange capacity (CEC) and grain size compositions were analyzed at each sample location. ⁷Be inventories for the two periods were estimated at 211.1 ± 20.0 and 456.1 ± 43.8 Bq m^-2 (mean ± 2 SEM, n = 44), with coefficients of variation of 31.4 and 31.9% for the 2019 and 2021 sampling cases, respectively. No significant correlations were observed between ⁷Be activity and the measured soil compositional properties, suggesting observed spatial variability is primarily a result of random variation due to rainsplash and other processes, although sampling and measuring processes may contribute some uncertainties. Using the traditional method, ca. 40 independent reference samples are required to estimate the mean ⁷Be inventory, i.e. to represent input across the site, with an allowable error of 10% at 95% confidence, while application of a bootstrap approach suggests that ca. 28 would be adequate under similar accuracy. Overall, results of this study emphasize that the simple assumption of uniform distribution of ⁷Be across the reference area needs detailed examination on a case-by-case basis, if this radionuclide is to be used effectively to assess patterns and rates of soil redistribution from field to hillslope scale.

A mesoscale analysis of relations between fish species richness and environmental and anthropogenic pressures in the Mediterranean Sea

Although there is a great knowledge about individual anthropogenic threats to different fish species in the Mediterranean Sea, little is known about how these threats accumulate and interact to affect fish species richness in conjunction with environmental dynamics. This study assesses the role of these threats in the fish richness component and identifies the main areas where the interaction between fish species richness and threats is highest. Our results show that fish richness seems to be higher in saltier and colder areas where the chlorophyll-a and phosphate concentrations are lower. Among the anthropogenic threats analyzed, the costal impact and the fishing effort seems to be the more relevant ones. Overall areas with high fish richness are mainly located along the western and northern shores, with lower values in the south-eastern regions. Areas of potential high cumulative threats are widespread in both the western and eastern basins, with fewer areas located in the south-eastern region. By describing the spatial patterns of the fish richness and which drivers explain these patterns we can also identify which anthropogenic activities can be managed more effectively to maintain and restore marine fish biodiversity in the basin.

Effects of vegetation and terrain changes on spatial heterogeneity of soil C-N-P in the coastal zone protected forests at northern China

Soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) are important indicators reflecting soil quality, and they can be used to effectively evaluate the effect of soil remediation. Many studies have evaluated the content of SOC, TN and TP in different ecosystems. However, after constructing protected forests for ecological restoration in the ecologically fragile coastal zone, the spatial distribution and influencing mechanism of SOC, TN and TP content is still uncertain. In this study, the spatial heterogeneity and influencing factors of SOC, TN and TP in surface (0-20 cm) soil were analyzed by traditional analysis and geostatistics. A total of 39 soil samples were collected under the coastal zone protected forest types including Quercus acutissima Carruth (QAC), Pinus thunbergii Parl (PTP), mixed PTP and QAC (QP) and Castanea mollissima BL (CMB) in the coastal zone protected forests in northern China. The results show that SOC, TN and TP content were defined as moderate variation, and they also show significant changes under different protected forest types (P < 0.05). The semivariance results indicate that SOC, TN and TP all exhibited strong spatial dependence class, with Range of 224 m, 229 m and 282 m respectively, which were more than the sampling scale of 200 m. The spatial prediction results showed that SOC, TN and TP content all appear in large areas of extremely low value in CMB, and its cross validation results showed that using vegetation and terrain factors as covariates in the spatial prediction of SOC, TN and TP can improve the prediction accuracy. The results of correlation analysis showed that the influencing factor for SOC and TN, and TP were NDVI and topographical changes, respectively. In general, vegetation and terrain factors as auxiliary factors can improved the accuracy of soil C-N-P spatial distribution prediction after afforestation in coastal zone.

Assessing spatial variability of selected soil properties in Upper Kabete Campus coffee farm, University of Nairobi, Kenya

This study aimed to evaluate spatial variability of selected soil parameters as a smart agricultural technology guide to precise fertilizer application. A farm designated as Field 3 which is under Arabica coffee within a bigger Soil Mapping Unit (SMU) was selected for a more detailed soil observation at a scale of 1:5000. Soil samples were taken at depths of 0–15 and 15–30 cm across 20 sample locations in grids and selected properties analysed in the laboratory. Kriging interpolation method was used to estimate the accuracy of interpolation through cross-validation of the top soil parameters. In 0 to 15 and 15–30 cm depth, soil reaction, percentage organic carbon and percent nitrogen showed low variability of 5.1% and 5.8%, 10.4% and 12.7%, 14.5% and 17.6% respectively. Phosphorus was deficient in both depths and showed moderate variability of 36.2% and 42.3% in 0–15 and 15–30 cm respectively. Calcium and Magnesium ranged from sufficient to rich and showed moderate and low variability in top and bottom depths, respectively. All micronutrients were sufficient in the soil. The soils were classified as Mollic Nitisols. Results showed that soil parameters varied spatially within the field therefore, there is need for variable input application depending on the levels of these elements and purchasing of fertilizer blends that are suitable for nutrient deficiencies. Precision agriculture is highly recommended in the field to capitalize on soil heterogeneity.

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There is need for variable agricultural input application in farms.

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Field spatial variability is a panacea to economically sound soil management.

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Precision agriculture is recommended for profits and environmental protection.

Distribution of radiocesium and its controlling factors under the Japanese cedar canopies

This study investigated the spatial distribution of radiocesium deposited by the Fukushima Daiichi Nuclear Power Plant accident in a densely planted Japanese cedar stand. Systematic grid sampling was conducted to determine ¹³⁷Cs inventories in the layers of deposited organic material and mineral soil at two different spatial scales (hillslope [60 m²] and small [1 m²]). The results showed that ¹³⁷Cs inventories along the hillslope were heterogeneously distributed, with coefficients of variation for the deposited organic material and mineral soil layers of 46.4% and 48.9%, respectively. The ¹³⁷Cs inventory in each layer tended to show a lognormal distribution. The correlation between the ¹³⁷Cs inventories in deposited organic material and mineral soil in the same sampling grid was weak. The controlling mechanisms of the ¹³⁷Cs inventories in the litter and mineral soil layers differed due to differences in the underlying key processes, such as canopy-forest floor transfer due to hydrological and biological processes. No significant correlation was found between the distance from the nearest tree trunk and the ¹³⁷Cs inventory in the deposited organic layer at each sampling point. In contrast, the ¹³⁷Cs inventory in the soil tended to increase as the distance from the nearest tree trunk increased at both the hillslope and small scales. It was found that the initial spatial patterns of ¹³⁷Cs in the soil layer due to atmospheric deposition were preserved in the cedar stand. Finally, we tested the effects of soil sampling density on the reliability of mean soil ¹³⁷Cs inventory estimations in the cedar stand. The results indicated that a soil sampling area greater than 0.06 m² at the hillslope scale and 0.008 m² at the small scale enabled the mean ¹³⁷Cs inventory to be estimated with an uncertainty of less than 20% in the cedar stand.

Spatial distribution of soil nutrients and evaluation of cultivated land in Xuwen county

It is of great significance to promote the quantitative research of soil science and the implementation of precision agriculture. On this basis, taking Xuwen County as the research object, this paper comprehensively analyzed the soil characteristics of cultivated land in Xuwen County and clarify the soil nutrient content and spatial distribution characteristics of cultivated land in Xuwen County, this paper comprehensively applied the methods of geostatistics, geographic information system (GIS) and fuzzy mathematics, and referred to the cultivated land quality grade standard (GB/T 33469-2016), to analyze the soil characteristics and evaluate the soil fertility of this region. The results show that the optimal interpolation model of soil pH and available phosphorus (AP) is a Gaussian model, and the optimal interpolation model of soil organic matter (SOM), available nitrogen (AN) and available potassium (AK) is a J-Bessel model. In addition, the spatial correlation of AK is weak, whereas pH, SOM, AN and AP show moderate spatial correlation. The proportion of excellent, good, average, medium and poor comprehensive fertility index are 26.00%, 32.67%, 19.33%, 19.00% and 3.00%, respectively. The overall level of soil fertility in Xuwen County is above the average, and the fertility quality presents an obvious trend of high in the South and low in the North. Areas that above average fertility are mainly distributed in Maichen Town, Qujie Town, Nanshan Town and Chengbei Town. The results can provide theoretical basis for improving the utilization rate of chemical fertilizer, fine management of cultivated land and ecological environment in this region, which can help in decision-making of precision fertilization.

Assessing the current water clarity status of ~100,000 lakes across southern Canada: A remote sensing approach

Canada has more lakes than any other country, making comprehensive monitoring a huge challenge. As more and more satellite data become readily available, and as faster data processing systems make massive satellite data operations possible, new opportunities exist to use remote sensing to develop comprehensive assessments of water quality at very large spatial scales. In this study, we use a published empirical algorithm to estimate Secchi depth from Landsat 8 reflectance data in order to estimate water clarity in lakes across southern Canada. Combined with ancillary information on lake morphological, hydrological, and watershed geological and landuse characteristics, we were able to assess broad spatial patterns in water clarity for the first time. Ecological zones, underlying geological substrate, and lake depth had particularly strong influences on clarity across the whole country. Lakes in western mountain ecozones had significantly clearer waters than those in the prairies and plains, while lakes in sedimentary rock formations tended to have lower clarity than lakes in intrusive rock. Deep lakes were significantly clearer than shallow lakes over most of the country. Water clarity was also significantly influenced by human impact (urbanization, agriculture, and industry) in the watershed, with most lakes in high impact areas having low clarity or very low clarity. Finally, we used in situ measured data to help interpret the underlying optical water column constituents influencing clarity across Canada, and found that chlorophyll-a, total suspended solids, and color dissolved organic matter all had strong but varying underlying effects on water clarity across different ecozones. This research provides an important step towards further research on the relationship between water column optical properties and the health and vulnerability status of lakes across the country.

Assessment of spatiotemporal variability and trend analysis of reference crop evapotranspiration for the southern region of Peninsular India

Accurate estimation of reference evapotranspiration (ET₀) is an essential requirement for water resource management and scheduling agricultural activities. Several empirical methods have been employed in estimating ET₀ across diverse climate regimes over the past decades. In this study, the Python implementation for estimation of daily and monthly ET₀ values of representative stations of ten agro-climatic zones of Karnataka from 1979 to 2014 using the standard FAO Penman-Monteith method was carried out. The assessment of temporal and spatial variability of monthly ET₀ values across the various agro-climatic zones done by the various statistical measures revealed that the variation in spatial ET₀ values was higher than temporal variation, indicating major difference in ET₀ values was with respect to the stations rather than years under study. The nonparametric Mann-Kendall test conducted at 1% significance level on the annual ET₀ values revealed a statistically significant increasing trend for all the ten stations during the study period. The trend test conducted on the climate variables like mean air temperature, wind speed, relative humidity, and solar radiation signifies their influence on the annual ET₀ values. The magnitude changes in the trends detected by the Theil Sen's slope indicated that increasing values of mean temperature, solar radiation, and decreasing values of relative humidity predominantly contributed to the annual upward trend in ET₀ values for the 10 stations. A trivial impact of wind speed on annual ET₀ values was observed for the stations. Kalburgi and Udupi stations exhibited a positive ET₀ trend with the highest and lowest annual values among ten stations.

Progress in research on site-specific nutrient management for smallholder farmers in sub-Saharan Africa

Increasing fertilizer access and use is an essential component for improving crop production and food security in sub-Saharan Africa (SSA). However, given the heterogeneous nature of smallholder farms, fertilizer application needs to be tailored to specific farming conditions to increase yield, profitability, and nutrient use efficiency. The site-specific nutrient management (SSNM) approach initially developed in the 1990 s for generating field-specific fertilizer recommendations for rice in Asia, has also been introduced to rice, maize and cassava cropping systems in SSA. The SSNM approach has been shown to increase yield, profitability, and nutrient use efficiency. Yield gains of rice and maize with SSNM in SSA were on average 24% and 69% when compared to the farmer practice, respectively, or 11% and 4% when compared to local blanket fertilizer recommendations. However, there is need for more extensive field evaluation to quantify the broader benefits of the SSNM approach in diverse farming systems and environments. Especially for rice, the SSNM approach should be expanded to rainfed systems, which are dominant in SSA and further developed to take into account soil texture and soil water availability. Digital decision support tools such as RiceAdvice and Nutrient Expert can enable wider dissemination of locally relevant SSNM recommendations to reach large numbers of farmers at scale. One of the major limitations of the currently available SSNM decision support tools is the requirement of acquiring a significant amount of farm-specific information needed to formulate SSNM recommendations. The scaling potential of SSNM will be greatly enhanced by integration with other agronomic advisory platforms and seamless integration of digital soil, climate and crop information to improve predictions of SSNM recommendations with reduced need for on-farm data collection. Uncertainty should also be included in future solutions, primarily to also better account for varying prices and economic outcomes.

Spatial heterogeneity of soil nutrients in Yili River Valley

Soil nutrients are a vital reference index of soil fertility and are essential in studying spatial variability for the development of land resources. The traditional statistical methods including correlation analysis and geostatistical analysis, were used to explore the spatial variability of nutrients and its influencing factors in the Yili River Valley. The results showed that soil total potassium (STK) had a weak variation, soil organic carbon (SOC), soil total nitrogen (STN) and soil total phosphorus (STP) showed a moderate degree of variation. Correlation analysis showed that SOC had a significant correlation with STN, STP, STK, silt, soil water content (SWC), Cos a and altitude (p < 0.01). In contrast, negative correlations were found between the SOC and sand, soil bulk density (SBD) and pH (p < 0.01), the same as STN. STP had a significant correlation with STK, silt (p < 0.01) and Cos a (p < 0.05), whereas negative correlations were found between the STP, sand and SBD (p < 0.01). STK had a significant correlation with silt, whereas negative correlations were found between the STK, sand and SBD. Ordinary Kriging interpolation showed that the distribution of SOC and STN had a high value in the northeast, northwest and southeast, and a low value in the central and southwest. STP was high in the northwest and southeast and low in the northeast and southwest. STK was high in the northwest and northeast and low in the central and southeast regions. This is helpful for the rational exploitation of land resources in ecological economy development in the Yili River Basin.

Occurrence of some rare earth elements in vineyard soils under semiarid Mediterranean environment

A comprehensive investigation has been carried out into the concentrations of a range of REEs (neodymium Nd, cerium Ce, lanthanum La, yttrium Y, scandium Sc) in soils of vineyards belonging to the protected denomination of origin (PDO) Valdepeñas (Central Spain). The mean concentrations (expressed in mg kg^-1) are Ce 70.6, Nd 32.9, La 36.2, Y 21.6, and Sc 13.7 in surface horizons (Ap), while in subsurface horizons (Bt or Bw and some Ck), the values are Ce 67.6, Nd 31.8, La 34.4, Y 19.6, and Sc 13.9. The relative abundance in these soils is Ce > La > Nd > Y > Sc in both the surface and subsurface horizons. These values are close to, or slightly higher than, the regional levels but similar to national and global averages, although relatively high values have been detected at certain sampling points. Another aim was to explain the spatial variations in these elements within the territory under study. It was found that the spatial variations are due to the nature of the parent materials and the pedogenetic processes, although the sparse spatial distribution patterns with prominent anomalies are interpreted arising from anthropogenic sources (fertilization). However, these anomalies did not present any environmental risk in the studied zone.

Sources of airborne particulate matter-bound metals and spatial-seasonal variability of health risk potentials in four large cities, South Korea

Fifteen airborne particulate matter-bound metals were analyzed at 14 sites in four large cities (Seoul, Incheon, Busan, Daegu) in South Korea, between August 2013 and June 2017. Among the seven sources resolved by positive matrix factorization, soil dust and marine aerosol accounted for the largest and second largest portions in the three cities; however, in Seoul, soil dust and traffic occupied the largest and the second largest, respectively. Non-carcinogenic risk assessed by inhalation of eight metals (Cd, Co, Ni, Pb, As, Al, Mn, and V) was greater than the hazard index (HI) of 1 at four sites located at or near the industrial complexes. Cumulative incremental lifetime cancer risk (ILCR) due to exposure to five metals (Cd, Co, Ni, Pb, and As) exceeded the 10^-6 cancer benchmark at 14 sites and 10^-5 at six sites, which includes four sites with HI greater than 1. The largest contributor to ILCR was coal combustion in Seoul, Incheon, and Daegu, and industry sources in Busan. Moreover, industry sources were the largest contributors to non-carcinogenic risk in Seoul, Busan, and Daegu, and soil dust was in Incheon. Incheon had the highest HI in spring because of the higher contribution of soil dust sources than in other seasons. The higher ILCR in Incheon in spring and winter and higher ILCR and HI in Daegu in autumn were mainly due to the influence of industry or coal combustion sources. Statistically significant differences in the ILCR and HI values among the sampling sites in Busan and Daegu resulted from the higher contribution of industry sources at a certain site in the respective city.

Impacts of air pollution on COVID-19 case fatality rate: a global analysis

The coronavirus disease 2019 (COVID-19) pandemic is still rapidly spreading globally. To probe high-risk cities and the impacts of air pollution on public health, this study explores the relationship between the long-term average concentration of air pollution and the city-level case fatality rate (CFR) of COVID-19 globally. Then, geographically weighted regression (GWR) is applied to examine the spatial variability of the relationships. Six air pollution factors, including nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), PM_2.5 (particles with diameter ≤2.5 μm), PM₁₀ (particles with diameter ≤10 μm), and air quality index (AQI), are positively associated with the city-level COVID-19 CFR. Our results indicate that a 1-unit increase in NO₂ (part per billion, PPB), SO₂ (PPB), O₃ (PPB), PM_2.5 (microgram per cubic meter, μg/m³), PM₁₀ (μg/m³), AQI (score), is related to a 1.450%, 1.005%, 0.992%, 0.860%, 0.568%, and 0.776% increase in the city-level COVID-19 CFR, respectively. Additionally, the effects of NO₂, O₃, PM_2.5, AQI, and probability of living with poor AQI on COVID-19 spatially vary in view of the estimation of the GWR. In other words, the adverse impacts of air pollution on health are different among the cities. In summary, long-term exposure to air pollution is negatively related to the COVID-19 health outcome, and the relationship is spatially non-stationary. Our research sheds light on the impacts of slashing air pollution on public health in the COVID-19 pandemic to help governments formulate air pollution policies in light of the local situations.

Spatial variability of soil nutrients in apple orchards and agricultural areas in Kinnaur region of cold desert, Trans-Himalaya, India

The mountain ecosystem is highly vulnerable to climate changes fraught with a multitude of problems related to environment, food, and nutritional security. Quantification of the soil fertility status can provide an efficient way to devise strategies for sustainable crop production. The lack of information on the soil fertility status prompted us to delineate the spatial variability of the soil attributes, viz., pH, electrical conductivity (EC), soil organic carbon (OC), and the macronutrients (nitrogen (N), phosphorus (P), and potassium (K)). The extensive soil sampling was carried out from the apple orchards (AO) and potential areas under agricultural land (AL) in Kinnaur region of cold desert, Trans-Himalaya, India. Descriptive statistics was employed for the exploratory analysis of data representing a wide variation (coefficient of variation, CV = 5.70-58.62%). The available N and P, categorized as low (< 280 kg ha^-1) to medium (280-560 kg ha^-1) and low (4-10 kg ha^-1) to high (> 25 kg ha^-1), respectively, were the main limiting factors in crop production. The availability of the K was categorized as medium (118-280 kg ha^-1) to high (> 280 kg ha^-1). The geostatistical analysis was carried out to check the spatial dependency in the dataset. The principal component analysis (PCA) was carried out and the dominant PCs were used in fuzzy c-means clustering for the delineation of management zones (MZs). The management zones highlight the need for area-specific interventions for ameliorating soil degradation and increasing apple productivity. The soil nutrient maps in spatial scale would help to provide precise fertilizer recommendations for sustainable production and environmental conservation.

Potential years of life lost due to PM2.5-bound toxic metal exposure: Spatial patterns across 60 cities in China

To clarify the spatial patterns of disease burden caused by toxic metals in fine particulate matter (PM_2.5) across China, annual concentration levels of typical toxic metals in PM_2.5 over 60 cities of China were retrieved. Then, potential years of life lost (PYLL) attributable to toxic metal (As, Cd, Cr (VI), Mn, and Ni) exposure was calculated from health risk assessments and lifetable estimates. The results show that Cr(VI) and As were the most polluted metals and greatly exceeded the recommended annual values in the National Ambient Air Quality Standard of China. PYLL for each death (mean ± standard deviation) of 19.8 ± 4.5 years was observed for lung cancer, followed closely by COPD and pneumonia. Furthermore, the PYLL rate (years per 100,000 people) attributable to exposure to these toxic metals was 457 (male: 505, female: 402) years for different cities; therein, Cr(VI) contributed the highest PYLL among these toxic metals, with a proportion of 72.7% (male: 75.3%, female: 69.5%), followed by As of 16.4% (male: 13.8%, female: 19.8%). The concentration level and PYLL both showed large spatial variability, of which the top-ranking cities were observed to be affected by well-developed metal-related industries and coal-powered industrial sectors.

Intracity occurrence and distribution of airborne PCB congeners in Chicago

To evaluate the magnitude and extent of airborne PCBs in an urban area, we measured and investigated the temporal and spatial behavior of atmospheric concentrations of individual polychlorinated biphenyl (PCB) congeners as well as the sum of all congeners (ΣPCB) in both gas and particle phases at 27 locations across the City of Chicago in a single year (2009). In total, 141 gas-phase air samples were collected, including 22 pairs (44 samples) deployed at the same time but at two different locations, and 46 particle-phase samples. ΣPCB in the gas-phase ranged from 80 to 3000 pg/m³, with a geometric mean (GM) of 530 pg/m³, whereas particle-phase ranged from 8 to 160 pg/m³, with a GM of 28 pg/m³. We found the temporal variability to be about three times larger than the variability over space for all gas-phase congeners and ΣPCB. Around 50% of the sample PCB profiles resembled a mixture of a 1:1 vapor Aroclor mixture of 1016 + 1254, with most of the rest (30%) showing enrichment of PCB 3 (>0.1), which did not match any Aroclor profiles. PCB 11 contributed to ~5% in all samples. The fractions of PCB congeners bound to particles ranged from 0.001 to 0.97. Our analysis shows that airborne PCBs are widely distributed across Chicago and confirms that most locations have a similar PCB distribution, but differ in the concentration levels. Volatilization continues to be the main release process of PCBs into the atmosphere, including both Aroclor and non-Aroclor congeners.

Factors dominating the horizontal and vertical variability of soil water vary with climate and plant type in loess deposits

Identifying the variability and predominant factors affecting soil water (SW) is essential in regions with thick vadose zones and deep-rooted plants. This information is needed to clarify the balance between water availability and plant water demand. We collected 9263 soil samples from 128 profiles of 7-25 m deep soil under different climates (arid, semiarid and subhumid), soil textures and plant types (shallow or deep roots) in China's Loess Plateau. The factors dominating the horizontal and vertical variability of SW were identified using a multimodel inference approach and stepwise regression analysis. Horizontally, the mean water content and storage increased while the water deficits decreased from the northwest to the southeast. Vertically, mean water content and storage are highest in the relatively stable layer, followed by rapidly changing layers and active layers. Plant age and soil clay content dominate the horizontally varied SW, while plant age and normalized difference vegetation index (NDVI) dominate the vertical variability of SW. However, the dominant factors appeared to differ with climate and plant type. It was determined that for climate, soil clay content and plant age in arid regions, precipitation and plant age in semiarid regions, NDVI and plant age in subhumid regions were important factors. For plants, the dominant factors are NDVI and precipitation under shallow-rooted plants; however, NDVI and plant age were dominant under deep-rooted plants. The dominance of plant age highlighted the impact of vegetation patterns on SW, especially for deep-rooted plants, which should be taken into account when managing water resources and ecosystem rehabilitation in degraded regions.

Spatial variability of the sources and distribution of fluoride in groundwater of the Sanya alluvial plain aquifers in northern Tanzania

Groundwater contamination from geogenic sources poses challenges to many countries, especially in the developing world. In Tanzania, the elevated fluoride (F^-) concentration and related chronic fluorosis associated with drinking F^- rich water are common in the East African Rift Valley regions. In these regions, F^- concentration is space dependence which poses much uncertainty when targeting safe source for drinking water. To account for the spatial effects, integrated exploratory spatial data analysis, regression analysis, and geographical information systems tools were used to associate the distribution of F^- in groundwater with spatial variability in terrain slopes, volcanic deposits, recharge water/vadose materials contact time, groundwater resource development for irrigated agriculture in the Sanya alluvial plain (SAP) of northern Tanzania. The F^- concentration increased with distance from steep slopes where the high scale of variation was recorded in the gentle sloping and flat grounds within the SAP. The areas covered with debris avalanche deposits in the gentle sloping and flat grounds correlated with the high spatial variability in F^- concentration. Furthermore, the high spatial variability in F^- correlated positively with depth to groundwater in the Sanya flood plain. In contrast, a negative correlation between F^- and borehole depth was observed. The current irrigation practices in the Sanya alluvial plain contribute to the high spatial variability in F^- concentration, particularly within the perched shallow aquifers in the volcanic river valleys. The findings of this study are important to the overall chain of safe water supply process in historically fluorotic regions. They provide new insights into the well-known F^- contamination through the use of modern geospatial methods and technologies. In Tanzania's context, the findings can improve the current process of drilling permits issuance by the authority and guide the local borehole drillers to be precise in siting safe source for drinking water.

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.