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Olle Selinus (1943.6.27–2023.11.18) was born in Cardiff, Wales in Great Britain. He studied at Uppsala University where he received his PhD in minerology-petrology in 1971. Then Olle Selinus started his position as a geochemist and State Geologist at Geological Survey of Sweden (SGU), where he continued working for 39 years until his retirement in 2010. The most significant contribution of Olle Selinus to the international scientific community was the development of the scientific discipline "Medical Geology" which was first adopted in 1997 and the establishment of International Medical Geology Association (IMGA) in 2006. Another significant contribution was the establishment of the International Symposium on Environmental Geochemistry (ISEG) conference series started from 1985. • The scientific discipline "Medical Geology" was first adopted in 1997. • International Medical Geology Association (IMGA) was established in 2006. • First International Symposium on Environmental Geochemistry (ISEG) was held in 1985. • Olle Selinus was one of the main founders of IMGA and ISEG. [ABSTRACT FROM AUTHOR]

There is increased stress on soil and crop quality caused by machinery traffic during field operations in tillage systems, especially the area where the machinery turns, known as the headlands. While the influence of traffic on soil physical quality is understood, the impact on soil chemical quality and soil fertility is less so. Furthermore, correcting soil fertility and chemical quality in fields under cereal tillage requires strategic sampling due spatial variability, specifically in headlands. Using a simplified sampling strategy, the objective of this study was to examine the variability in soil chemical quality within traffic-dependent zones in the headland. In a case-study in Ireland, 41 fields under tillage were surveyed, and soil sampling zones delineated within headlands based on typical field traffic patterns. Soil phosphorus (P), potassium (K), lime requirement (LR), pH and organic matter (%OM) were measured at two depths. Elevated values of major nutrients (P, K) were recorded around the main headland tramline. Using a generalized linear mixed model, significant differences in P, K, LR and pH were found between headland zones, whereas %OM values did not vary significantly. In addition to laboratory analyses, predictions from mid-infrared spectroscopy (MIR) models resulted in similar patterns in %OM and LR with comparable statistical significance, which highlights the potential of MIR as an alternative to lab analysis of soil in traffic-dependent headland zones. Sampling headland zones to determine soil chemical quality coupled with the application of MIR for rapid soil analysis will improve decision support for soil management in tillage systems. [ABSTRACT FROM AUTHOR]

Recent development of handheld mid-infrared (MIR) spectrometers holds promising applications for soil analysis. However, problems arise due to water content and other in-situ characteristics, and compatibility with existing spectral libraries and calibrations developed on benchtop instruments. This study examined the potential of handheld MIR for sampling and analysis of field fresh soils. A handheld MIR-DRIFTS instrument (Agilent 4300 handheld FTIR) was used to determine primary soil fertility metrics, specifically, lime requirement (LR) and organic matter (%OM). LR was predicted from handheld collected spectra using calibration models developed from (1) an existing benchtop spectral library and (2) a newly developed model using handheld spectra. Predictions of handheld spectra from the benchtop model exhibited a bias of 2.14 t ha−1, however this was improved (−0.13 t ha−1) when calibrating the model from spectra collected from the handheld instrument. To overcome the adverse effects of moisture on spectral predictions of LR and %OM, moisture correction spectra for seven different moisture classes were applied to soil spectra under field conditions. The moisture correction for the prediction of LR under field moist condition was partially successful and could correct for soils with moisture contents below the permanent wilting point. Prediction of %OM using moisture correction was effective across all moisture categories, and predicted values came within 1% of laboratory values. This research highlights the challenges of handheld MIR in the field, such as finding moisture corrections, practices for field spectra acquisition and the use of spectral libraries and serves as a starting point for future research. [Display omitted] • Benchtop spectral calibration overestimated LR predicted from handheld soil spectra. • Spectral predictions for LR improved when calibrations derived from same instrument. • Spectral difference between wet and dried soil used as moisture correction spectra. • Correction predicted close to lab %OM value, regardless of initial moisture content. • Correction for LR effective only at low initial gravimetric moisture content <15%. [ABSTRACT FROM AUTHOR]

Anaerobic biological phosphorus removal has proposed a new direction for the removal of phosphorus from wastewater, and the discovery of phosphate reduction makes people have a more comprehensive understanding of microbial phosphorus cycling. Here, from the perspective of thermodynamics, the bioreduction reaction of phosphate was analyzed and its mechanism was discussed. The research progress of phosphate reduction and the application prospects of anaerobic biological phosphorus removal from wastewater were introduced, pointing out the situation and guiding the further research in this field. [ABSTRACT FROM AUTHOR]

A new method for simultaneous measurement of fifteen rare earth elements (REEs) [La (Ⅲ), Ce (Ⅲ), Pr (Ⅲ), Nd (Ⅲ), Sm (Ⅲ), Eu (Ⅲ), Gd (Ⅲ), Tb (Ⅲ), Dy (Ⅲ), Ho (Ⅲ), Er (Ⅲ), Tm (Ⅲ), Yb (Ⅲ), Lu (Ⅲ), and Y (Ⅲ)] was established in this study using the diffusive gradients in thin films (DGT) technique with Chelex ® 100 binding gel. Five different types of ion exchange resins (Chelex ® 100, D418, D001-cc, 001 × 7, and HSTY ® -SS) were selected for the initial investigation of their adsorption performance for REEs. The Chelex ® 100 binding gel had the greatest uptake efficiencies of >95% for the fifteen REE ions, which was used for all subsequent experiments. The binding gel exhibited rapid binding dynamics to REEs in mixed solution of the fifteen REE ions. Elution efficiencies ranging from 86.5% to 93.8% for these REEs were obtained based on extraction using 2.0 M HCl. The Chelex ® 100 DGT uptake of the fifteen REE ions increased linearly with the deployment time and found to be independent of pH (3–9) and ionic strength (3 mM–100 mM). The capacities of Chelex ® 100 DGT for measurement of the mixed elements were determined at a range of 5.39–6.75 μg cm −2 . Application of the DGT for soil analysis showed that Chelex ® 100 DGT was a useful tool in simultaneous measurement of the fifteen REE ions, even in a soil with high concentrations of REEs. This study demonstrated the advantage of Chelex ® 100 DGT in simultaneous measurement of the fifteen REE ions due to high uptake efficiencies and a wide tolerance to environmental interference. [ABSTRACT FROM AUTHOR]

White-rot fungi, Phanerochaete chrysosporium was inoculated to sewage sludge composting. Its effect on transformation of organic matter and mobility of heavy metals (Zn, Pb, Cu, and Ni) was studied. Detailed sampling was performed to measure C contents in humic extracts (HE), humic acids (HAs), fulvic acids (FAs), humin and distribution of heavy metals, including acid exchangeable fraction (AE), reducible fraction (RED), oxidization fraction (OXI) and residual fraction (RES). In our study, it is evident that the HE, HAs increased obviously and hydrolyzed humin decreased markedly in inoculation. The stabilization rate ((OXI + RES)/(AE + RED)) of Zn, Pb, Cu, and Ni was 20.31%, 7%, 14.3% and 19.79% higher in inoculating reactor. Additionally, the changes of heavy metals fractions could be explained by the organic variables. The results of this study demonstrated that Phanerochaete chrysosporium passivates the heavy metal by provoking the formation of humus. [ABSTRACT FROM AUTHOR]

In recent decades, magnetic susceptibility monitoring has developed as a useful technique in environmental pollution studies, particularly metal contamination of soil. This study provides the first ever examination of the effects of grass cover on magnetic susceptibility (MS) measurements of underlying urban soils. Magnetic measurements were taken in situ to determine the effects on κ (volume magnetic susceptibility) when the grass layer was present (κ grass ) and after the grass layer was trimmed down to the root (κ no grass ). Height of grass was recorded in situ at each grid point. Soil samples (n=185) were collected and measurements of mass specific magnetic susceptibility (χ) were performed in the laboratory and frequency dependence (χ fd %) calculated. Metal concentrations (Pb, Cu, Zn and Fe) in the soil samples were determined and a gradiometry survey carried out in situ on a section of the study area. Significant correlations were found between each of the MS measurements and the metal content of the soil at the p <0.01 level. Spatial distribution maps were created using Inverse Distance Weighting (IDW) and Local Indicators of Spatial Association (LISA) to identify common patterns. κ grass (ranged from 1.67 to 301.00×10 −5 SI) and κ no grass (ranged from 2.08 to 530.67×10 −5 SI) measured in situ are highly correlated [r=0.966, n=194, p <0.01]. The volume susceptibility datasets in the presence and absence of grass coverage share a similar spatial distribution pattern. This study re-evaluates in situ κ monitoring techniques and the results suggest that the removal of grass coverage prior to obtaining in situ κ measurements of urban soil is unnecessary. This layer does not impede the MS sensor from accurately measuring elevated κ in soils, and therefore κ measurements recorded with grass coverage present can be reliably used to identify areas of urban soil metal contamination. [ABSTRACT FROM AUTHOR]

Heavy metals prevent the growing amount of sewage sludge from being disposed as fertilizeron land. The electrokinetic remediation and bioleaching technology are the promising methods to remove heavy metals. In recent years, some innovation has been made to achieve better efficiency, including the innovation of processes and agents. This paper reviews the development of the electrokinetic remediation and bioleaching technology and analyses their advantages and limitation, pointing out the need of the future research for the heavy metals-contaminated sewage sludge. [ABSTRACT FROM AUTHOR]

The effect of dissolved oxygen concentration (DO) on simultaneous nitrification and denitrification was studied in a moving bed sequencing batch reactor (MBSBR) by microelectrode measurements and by real-time PCR. In this system, the biofilm grew on polyurethane foam carriers used to treat municipal sewage at five DO concentrations (1.5, 2.5, 3.5, 4.5 and 5.5 mg/L). The results indicated that the MBSBR exhibited good removal of chemical oxygen demand (92.43%) and nitrogen (83.73%) when DO concentration was 2.5 mg/L. Increasing the oxygen concentration in the reactor was inhibitory to denitrification. Microelectrode measurements showed that the thickness of oxygen penetration increased from 1.2 to 2.6 mm when the DO concentration (from 1.5 mg/L to 5.5 mg/L) in the system increased. Oxygen diffusion was not significantly limited by the boundary layer surrounding the carrier and had the largest slope when DO concentration was 2.5 mg/L. The real-time PCR analysis indicated that the amount of the ammonia-oxidizing bacteria and nitrite-oxidizing bacteria increased slowly as DO concentration increased. The proportions of ammonia-oxidizing bacteria and nitrite-oxidizing bacteria, as a percentage of the total bacteria, were low with average values of 0.063% and 0.67%, respectively. When the DO concentration was 2.5 mg/L, oxygen diffusion was optimal and ensured the optimal bacterial community structure and activity; under these conditions, the MBSBR was efficient for total inorganic nitrogen removal. Changing the DO concentration could alter the aerobic zone and the bacterial community structure in the biofilm, directly influencing the simultaneous nitrification and denitrification activity in MBSBRs. [ABSTRACT FROM AUTHOR]

The health risks associated with rice consumption may decrease if consumers use cooking practices which can reduce the bioaccessibility of metal(loid)s. The effects of cooking on the Cd and As bioaccessibility, at three contamination levels of rice, were studied. Results indicated that cooking reduced bioaccessibility of Cd and As in rice. Cooking resulted in a significant increase (p < 0.01) of Cd and As concentrations in the residual fraction. Low volume water-cooking of rice to dryness reduced total Cd by about 10% for rices A and B, while medium or high volume water-cooking had no effect on Cd bioaccessibility in all rice types. In contrast, low volume cooking did not remove As, but a significant decrease (p < 0.05) was observed when cooking with higher volumes of water. This study provides information for a better understanding of more realistic estimation of metal(loid)s exposure from rice and the possible health risks. [ABSTRACT FROM AUTHOR]

In recent years, the problems of high cadmium content and low Cd mobility in karst areas compared with non-karst areas have attracted extensive attention. Identifying the difference between the net Cd flux in karst and non-karst areas is crucial for sustainable land management. Basically, rock weathering is a natural source of Cd. While Cd accumulation in soil has increased due to both anthropogenic and natural sources, the contributions of anthropogenic input and rock weathering to soil Cd concentration in karst and non-karst areas remain unclear. In this study, the input (e.g., atmospheric deposition, fertilizer and irrigation) and output fluxes (e.g., leaching and early and later rice harvest) of Cd in farmland soils in karst and non-karst areas of Guangxi from 2017 to 2018 were monitored. The results showed no significant difference (P = 0.289) in the contributions of various input pathways between karst area and non-karst area, and atmospheric deposition (64–55 %) played an important role in the input fluxes. Nevertheless, the low soil pH value and high rainfall in non-karst areas increase Cd mobility and plant availability in soil, causing the output flux in non-karst areas (5.30 g/ha/y) to be greater than that in karst areas (3.17 g/ha/y). According to the national standard, the exceedance rate of Cd in rice grains in non-karst area (17 %) was higher than that in karst area (15 %). Therefore, ecological risk assessment in non-karst areas with high Cd bioavailability should be prioritized. Furthermore, the annual increase rate of Cd concentration in topsoil of karst and non-karst areas was −0.27 μg/kg/y and −1.23 μg/kg/y, respectively. However, the soil Cd concentration in karst and some non-karst areas still exceeded the guideline value. Given that the net input flux of Cd was negative, the accumulation of Cd in soil may be determined by rock weathering. [Display omitted] • Atmospheric deposition was the primary pathway for inputting Cd in karst and non-karst area. • Leaching water was the main output pathway of soil Cd. • The output fluxes of Cd in non-karst area were significantly higher than those in karst area. • The environmental health risks of Cd in non-karst areas should be monitored as a priority. • Annual balance indicated that Cd in the soil was mainly controlled by geological background. [ABSTRACT FROM AUTHOR]

This study examines the growth, metabolism of Phanerochaete chrysosporium ( P. chrysosporium ) and route of lignin degradation in response to cadmium (Cd) stress in solid-state fermentation of rice straw. Less living fungi biomass was found under Cd exposure, suggesting that Cd had strong toxicity to P. chrysosporium . The maximum values of lignin peroxidase and manganese peroxidase were 0.34 and 5.21 U g −1 at the Cd concentration of 32 mg kg −1 , respectively, lower than that in control, which indicated Cd stress would inhibit ligninolytic enzymes. The production of reactive oxygen species (ROS) including hydroxyl radicals ( OH), superoxide anion radical (O 2 − ) and hydrogen peroxide (H 2 O 2 ) increased after Cd exposure. Higher concentration of oxalate was detected at high Cd concentrations. Cd stress also had influence on the rates of lignocelluloses degradation and the route of lignin degradation. Partial Cd could be removed by P. chrysosporium . [ABSTRACT FROM AUTHOR]

On the basis of achieving shortcut nitrification in a lab-scale SBR, the effects of constant pH and unsteady pH at different free ammonia concentrations on shortcut nitrification for landfill leachate treatment was investigated. The results indicate that under the condition of DO of 0.5 ± 0.2 mg/L and temperature of 30 ± 2 °C, the absolute value of nitrite accumulation increased significantly with the increase in free ammonia (FA) concentration from 5.30 to 48.67 mg/L; however, the nitrite accumulation rate remained almost constant at a constant pH of 8.0 ± 0.1. Ammonia oxidation and the nitrite accumulation become slow with the pH decreased from 8.0 ± 0.1 to 7.5 ± 0.2, and the activities of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) were severely inhibited when the pH further decreased to 6.5. More importantly, this study confirmed that the pH decrease from 8.0 to 6.5 within a short time exhibited significant negative effect on the ammonia oxidation rather than the FA concentration. [ABSTRACT FROM AUTHOR]

To reveal the succession procedure of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) community structure in sequencing biofilm batch reactor (SBBR), the molecular biological techniques of denaturing gradient gel electrophoresis (DGGE), cloning, and real-time PCR were applied. DGGE showed that the structural diversity of the bacterial community increased during the biofilm formation period, and some kinds of populations had been highly preponderant consistently. The results of cloning and sequencing revealed that Nitrosomonas was the dominant species. The real-time PCR analysis indicated that the amount of the AOB increased significantly after the cultivation period, and the NOB gradually decreased. The AOB content on the 25th day was 17 times that of the 6th day. It also showed the biofilm formed successfully with accumulating nitrite and prepared to achieve the achievement of simultaneous nitrification and denitrification in SBBR. Furthermore, the ammonia-oxidizing rate was in correspondence with the NH-N removal efficiency. [ABSTRACT FROM AUTHOR]

• PMF was more specific in source apportionment of HMs in soil samples. • Three sources were identified by PCA-APCS compared to four sources detected by PMF. • Hg was the only element influenced by LOI regarding the results of RDA. • Cu and Zn were influenced by fertilizer application based on RDA. The main objectives of the current research were source identification and quantification of the relationship between land use pattern and heavy metals (HMs) (Cr, Ni, Cd, Hg, Pb, Co, Zn, Cu, As) in soil samples collected in the border of Republic of Ireland and Northern Ireland. For the first goal, positive matrix factorization (PMF), principal component analysis with absolute principal component scores (PCA/APCS) and Unmix were utilized whereas, for the second objective, redundancy analysis (RDA) was employed. The results of source apportionment indicated that the geological formations (e.g. parent rocks), mineral explorations along with application of fertilizers in agriculture were the most influential contributing factors for the elevated levels of HMs. In this context, PCA/APCS and Unmix identified 3 sources compared to 4 sources detected by PMF with R2 values larger than 0.7, except for As and Hg, indicating the reasonable accuracy of these receptor models for source identification. Among the 9 HMs considered, the performance of both PMF and PCA/APCS for As and Hg were poor with R2 values equal to 0.23 and 0.51 for PMF versus 0.71 and 0.48 yielded by PCA-APCS. According to the findings of RDA; Cr, Co, As, Ni and Cu appeared to be the primary elements having strong correlations with pH and land use types. Additionally, the results of RDA demonstrated that Zn and Cu are the most probable elements that may be influenced by the amount of phosphorus in soil whereas Hg, Pb, Cr, Co and Ni are less likely to be affected. [ABSTRACT FROM AUTHOR]

Abstract: It is challenging to perform spatial geochemical modelling due to the spatial heterogeneity features of geochemical variables. Meanwhile, high quality geochemical maps are needed for better environmental management. Soil organic C (SOC) distribution maps are required for improvements in soil management and for the estimation of C stocks at regional scales. This study investigates the use of a geographically weighted regression (GWR) method for the spatial modelling of SOC in Ireland. A total of 1310 samples of SOC data were extracted from the National Soil Database of Ireland. Environmental factors of rainfall, land cover and soil type were investigated and included as the independent variables to establish the GWR model. The GWR provided comparable and reasonable results with the other chosen methods of ordinary kriging (OK), inverse distance weighted (IDW) and multiple linear regression (MLR). The SOC map produced using the GWR model showed clear spatial patterns influenced by environmental factors and the smoothing effect of spatial interpolation was reduced. This study has demonstrated that GWR provides a promising method for spatial geochemical modelling of SOC and potentially other geochemical parameters. [Copyright &y& Elsevier]

Abstract: Geochemical variables in soils are affected by multiple factors, and different variables have different responses to these factors, causing complicated multivariate relationships between them. In this study, the relationships between a total of 45 geochemical variables in soils and in particular mineral soils of Ireland were investigated using multivariate analyses following an investigation into their probability features and data transformation based on a total of 1310 surface soil samples. There was strong variation in the values of geochemical variables. Multi-modal features in the histograms and multi-kink features in the normal quantile–quantile (Q–Q) plots were observed for many variables implying the existence of multiple populations. Obvious outliers were identified using the normal Q–Q plots. Most of the variables did not pass a Kolmogorov–Smirnov test for either normal or lognormal distribution, and the Box–Cox power transformation was effective in transposing data to a form suited to further parametric statistical analyses. There were generally good correlations between most metals, but relatively poor correlations between extractable nutrients (available P, K, and Mg) and metals. The extractable nutrients were more affected by agricultural activities that were more spatially variable compared with natural factors such as geology and soil type. Cluster analysis classified the variables into groups of metals of mainly natural origin, mobile elements, and variables that are affected by agricultural activities and pollution. Among the three factors of rock type, soil type and land use investigated, land use was found to be the least important influencing factor for most of the geochemical variables studied. [Copyright &y& Elsevier]

Pollution hotspots in urban soils need to be identified for better environmental management. It is important to know if there are hotspots and if the hotspots are statistically significant. In this study identification of pollution hotspots was investigated using Pb concentrations in urban soils of Galway City in Ireland as an example, and the influencing factors on results of hotspot identification were investigated. The index of local Moran's I is a useful tool for identifying pollution hotspots of Pb pollution in urban soils, and for classifying them into spatial clusters and spatial outliers. The results were affected by the definition of weight function, data transformation and existence of extreme values. Compared with the results for the positively skewed raw data, the transformed data and data with extreme values excluded revealed a larger area for the high value spatial clusters in the city centre. While it is hard to decide the best way of using this index, it is suggested that all these influencing factors should be considered until reasonable and reliable results are obtained. GIS mapping can be applied to help evaluate the results via visualization of the spatial patterns. Meanwhile, selected pollution hotspots (extreme values) in this study were confirmed by re-analyses and re-sampling. [Copyright &y& Elsevier]

Abstract: The probability features of non-normality and non-lognormality are widely observed in geochemistry due to the influences of multiple factors that are difficult to quantify and model. In Northern Ireland, the pseudo-total concentrations of 14 elements (Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, Pb and Zn) from 6138 topsoils were measured, and GIS mapping showed that the spatial distribution of these data were in line with the spatial distribution of geology in the area. Investigations into the influences of geology on the concentration data and their probability features were carried out using GIS and statistics in this study. The whole raw data sets for each element were positively skewed and none of them followed either normal or lognormal distributions. Logarithmic transformation was found to have “over-transformed” most of the data sets, changing their skewness from positive to negative values. When soil samples were classified by rock type using a GIS overlay function, obvious differences were observed in the chemical concentrations of soils derived from different rock types. Soils in basalt areas displayed the highest concentrations for most elements under study (Ca, Co, Cr, Cu, Fe, Mg, Mn, Na, Ni, P and Zn) but the lowest concentrations for K, while the highest levels for Cd and Pb occurred in the shale areas. Classifying soils by rock type produced more normally distributed data sets, especially for the igneous rock areas. To restrain the influence of soil type and land cover, samples from both gleys and pastures were extracted via a GIS and it was found the data sets then showed generally greater tendencies towards normality. However, many of the data sets would still not pass a test for normality unless the sample size was small (e.g. of the order of a couple of hundreds). Geology, soil type, land cover and sample size all played important roles in determining soil chemical concentrations and their probability features. However, the influences from other factors were still evident. Attempts made in this study show that it remains a challenging task in geochemistry to separate all the factors and to model their influence at the regional scale. [Copyright &y& Elsevier]

Abstract: Spatial variation is a typical feature of geochemical variables, providing a challenge for sampling design and environmental monitoring. It is generally qualitatively but not quantitatively described using spatial distribution maps. In this study, the feasibility of quantifying spatial variation is investigated using neighbourhood statistics within a GIS environment, using, as an example, near-total Ni concentrations in the surface soils of Northern Ireland. A total of 6138 topsoil samples were collected at an average sampling density close to 1 sample per km2. At this sampling density it was possible to calculate neighbourhood statistics directly from the raw data. Neighbourhood statistics of local mean, local standard deviation and local coefficient of variation were calculated using window sizes of 3 km×3 km, 6 km×6 km, 9 km×9 km, 12 km×12 km, 24 km×24 km and 48 km×48 km and visualized using GIS mapping techniques. The results showed that the highest soil Ni concentrations were located in the northern part of Northern Ireland where basalt is the main rock type. Lowest soil Ni concentrations were found in the western region of the Province on schist and limestone geologies. The granite area in the south-eastern region of Northern Ireland also displayed low soil Ni values. In terms of assessing the degree of spatial variation, high local standard deviation values were found to be associated with high local mean values thereby limiting the usefulness of local standard deviation as an indicator of spatial variation. This effect did not occur when local coefficient of variation values were used in place of local standard deviation so the coefficient of variation values are recommended as a more appropriate indicator to quantify spatial variation. The strongest spatial variations were observed on the western edge of the basalt area along the boundary of the basalt–sandstone areas and the schist area. Within each rock type, spatial variations were relatively weak and this was most clearly demonstrated in the basalt area. As the window size used for calculation of neighbourhood statistics was increased, so too was the resulting smoothing effect which led to clearer patterns but with loss of detail in the spatial variation observed. Neighbourhood statistics, coupled to a GIS, were found to be an effective way of quantifying and visualizing spatial variation in environmental geochemistry. [Copyright &y& Elsevier]

Abstract: The authors investigated statistical distributions for concentrations of chemical elements from the National Geochemical Survey (NGS) database of the U.S. Geological Survey. At the time of this study, the NGS data set encompasses 48,544 stream sediment and soil samples from the conterminous United States analyzed by ICP-AES following a 4-acid near-total digestion. This report includes 27 elements: Al, Ca, Fe, K, Mg, Na, P, Ti, Ba, Ce, Co, Cr, Cu, Ga, La, Li, Mn, Nb, Nd, Ni, Pb, Sc, Sr, Th, V, Y and Zn. The goal and challenge for the statistical overview was to delineate chemical distributions in a complex, heterogeneous data set spanning a large geographic range (the conterminous United States), and many different geological provinces and rock types. After declustering to create a uniform spatial sample distribution with 16,511 samples, histograms and quantile–quantile (Q–Q) plots were employed to delineate subpopulations that have coherent chemical and mineral affinities. Probability groupings are discerned by changes in slope (kinks) on the plots. Major rock-forming elements, e.g., Al, Ca, K and Na, tend to display linear segments on normal Q–Q plots. These segments can commonly be linked to petrologic or mineralogical associations. For example, linear segments on K and Na plots reflect dilution of clay minerals by quartz sand (low in K and Na). Minor and trace element relationships are best displayed on lognormal Q–Q plots. These sensitively reflect discrete relationships in subpopulations within the wide range of the data. For example, small but distinctly log-linear subpopulations for Pb, Cu, Zn and Ag are interpreted to represent ore-grade enrichment of naturally occurring minerals such as sulfides. None of the 27 chemical elements could pass the test for either normal or lognormal distribution on the declustered data set. Part of the reasons relate to the presence of mixtures of subpopulations and outliers. Random samples of the data set with successively smaller numbers of data points showed that few elements passed standard statistical tests for normality or log-normality until sample size decreased to a few hundred data points. Large sample size enhances the power of statistical tests, and leads to rejection of most statistical hypotheses for real data sets. For large sample sizes (e.g., n >1000), graphical methods such as histogram, stem-and-leaf, and probability plots are recommended for rough judgement of probability distribution if needed. [Copyright &y& Elsevier]

Abstract: Chemical components in geological samples are often measured by more than one method to be able to measure high concentrations in resistant minerals as well as low concentrations in other mineral phases. For quality control, it is necessary to evaluate if the results from these methods are consistent or not. A total of 1884 sediment samples taken from the Leinster area of Ireland were analysed by both instrumental neutron activation analysis (INAA) and atomic absorption spectrometry (AAS) for 5 chemical elements (Co, Cr, Fe, Ni, and Zn). It is well known that INAA detects the total concentrations of chemical elements, but it has much higher detection limits than AAS. In this study, descriptive statistics, scatter plots, statistical tests and GIS mapping techniques were employed to compare the differences between the results from the two analytical methods, and to evaluate the impact of detection limits on data quality. Descriptive statistics and scatter plots were effective in showing the discrepancies between values of the two methods. Scatter plots showed outliers and the poor accuracy of INAA for concentrations close to or below the detection limits. It is suggested that errors may have happened during both INAA and AAS processes for some samples. Statistical results of both nonparametric sign and Wilcoxon signed rank tests showed that the differences between concentrations measured with the two methods were significant at the level of p &#60;0.001, and the average differences were 20–30% for the 5 elements. Meanwhile, more than 25% of all samples showed differences greater than 40–60% for these elements. After removing values lower than 3 times detection limits of both methods, only a slight improvement was achieved for the consistency between the two methods. Besides detection limits, the incomplete digestion prior to AAS may have played an important role. The higher AAS values of Ni than its INAA values may have been caused by multiple detection limits in different batches of laboratory analyses. The high detection limits of INAA also caused spatial patterns on the GIS maps showing differences between the two methods. The current geochemical database may be sufficient for some aspects of mineral exploration, but not for other geochemical studies, such as baseline database construction and environmental investigations. Better results could have been achieved by initial error checking and correction, improvement on the detection limits of INAA, and improvement on the acid digestion for AAS. It is possible that other geochemical databases contain similar data quality issues demonstrated in this one. This study demonstrates how straightforward statistical methods can be applied to check/verify the quality of geochemical databases. [Copyright &y& Elsevier]

Castromil is one of the Au mining areas in Portugal that has been abandoned since 1940. Due to the lack of regulations and environmental education, Castromil is now a residential area suffering from the considerable consequences of poorly regulated mining activities; tailings, shafts and adits are present. Geochemical data related to environmental studies in old mining areas frequently show extremely high values and very skewed distributions that need to be properly addressed. Agricultural soils from this region have high concentrations of As and Pb. In this study, the Box–Cox transformation and geostatistics were applied to study heavy element (As and Pb) concentrations in soils in order to characterize the hazard posed by them in the area. This provides a decision support tool to define the areas where remedial action is needed in light of the risks to humans and ecosystems and for contaminant migration. The results discussed here take into account the hazard-based standards for soils as target and intervention values. [Copyright &y& Elsevier]

A total of 191 soil samples, taken in 1964 from grassland of southeastern Ireland, and 220 samples, taken in 1996 from the same area, were examined for soil organic carbon (SOC). Temporal and spatial changes in SOC concentration after the ca. 30-year interval were evaluated using conventional statistics, geostatistics and geographic information system (GIS) analyses.A lognormal distribution feature was observed for both data sets, and the results of a t-test showed that the difference between them was not significant. Spatial outliers were detected with the index of local Moran''s I in order to obtain a robust variogram. Kriging was used for spatial prediction for both data sets using the same grid system. The difference between the two maps was determined using map algebraic functions and showed considerable spatial change. The analysis has enabled the delineation of two discrete sectors in the study area, which have clearly behaved differently: mean increase of SOC concentration in the eastern coastal sector was 30.2% compared to a mean decrease of 16.0% in the inland sector. Changes in land use or cultivation did not adequately account for the spatial difference. The division between the sectors appeared to coincide approximately with and to reflect both hill land and geological patterns. A satisfactory explanation for the difference has not been identified.Thus, a combination of geostatistics and GIS map algebra provides a useful tool for the examination of spatio-temporal changes in the environmental sciences and may detect features that are not discernible when only conventional statistics are used. [Copyright &y& Elsevier]

Soil organic C (SOC) concentrations in topsoil samples taken at 678 sites in the grassland of Ireland were investigated using statistics and geostatistics. SOC concentrations (Walkley–Black method) follow a lognormal distribution, with a median and geometric mean of 5.0%, and an arithmetic mean of 5.3%. Statistically significant (P<0.01) positive correlation between SOC and silt-plus-clay, and negative correlation between SOC and sand were observed, with lower correlation (P=0.17) between SOC and pH. Lower SOC concentrations were associated with higher percentages of land in tillage. In order to obtain a robust measurement of spatial structure, spatial outliers were detected, and subsequently eliminated, using the local Moran''s I index. The spatial distribution of SOC concentrations based on kriging interpolation showed coherent spatial patterns, with the highest values in the western coastal area, and relatively low values in the inland and southeastern coastal areas; soils at higher elevation were also found to contain higher SOC concentrations. These patterns are consistent with the distribution of rainfall within the country. [Copyright &y& Elsevier]

Presents a paper which attempts to demonstrate the application of geostatistics in spatial analysis of environmental geochemcial, using the copper content in sediments of the Yangtze River. Details on the spatial distribution pattern of an element; Information on the proportional effect for the variogram.

Total organic carbon (TOC) has received increased attention in recent years, not only as an important indicator in soil fertility, but also due to its close relationship with the atmosphere. Generally, soil TOC and pH values follow a negative correlation, which was revealed by traditional statistical methods. However, the conventional global models lack the ability to capture the spatial variation locally. In this study, spatially varying local relationships between TOC and pH values are studied by geographically weighted regression (GWR) on continental-scale data of European agricultural soil from the project 'Geochemical Mapping of Agricultural and Grazing land Soil' (GEMAS). In this study, TOC is the dependent and pH the independent variable. Both negative and positive local correlation coefficients are observed, showing the existence of 'special' spatially varying relationships between TOC and pH values. Original negative relationships change to positive values in more than 50% of the study area. Novel finding of significant positive correlations is observed in central-eastern Europe, while negative correlations are found mainly in northern Europe. Mixed relationships occur in southern Europe. These special patterns are strongly associated with specific natural factors, especially the extensive occurrence of quartz-rich soil in the central-eastern part of Europe. Anthropogenic inputs may have also played a role in the mixed southern European areas. The GWR technique is powerful and effective for revealing spatially varying relationships at the local level. Thus, it provides a new way to further explore the related influencing factors on the TOC and pH spatial distribution. Unlabelled Image • Spatially varying relationships between TOC and pH are revealed by GWR. • Positive correlations between TOC and pH cluster in central-eastern Europe. • Negative and mixed correlations are observed in northern and southern Europe. • Quartz-rich soil is the main contributing factor to the positive relationship. [ABSTRACT FROM AUTHOR]

• MIR-DRIFTS calibration for SMP-buffer lime requirement built for tillage fields. • Model results 0.2 < R2 < 0.8, 0.7 < RMSEP < 3.2 and 0.5 < RPIQ < 2.9 in validation. • Loading weights indicate organic matter, clay and carbonates as model drivers. • Prediction of field variability not precise enough. • MIR predicted field averages lie close to laboratory averages. Lime is a crucial soil conditioner to bring agricultural soils to optimum pH values for nutrient availability. Lime recommendations are typically determined in laboratory extractions, the most common being the "Shoemaker-McLean and Pratt" (SMP) buffer method, that requires carcinogenic reagents soon to be abolished under the EU legislation. As an alternative to wet chemistry, mid-infrared (MIR) spectroscopy has shown to be a cost-and time effective method at predicting soil properties. The capability and feasibility of diffuse reflectance infrared spectroscopy (DRIFTS) to predict lime requirement (LR) in tillage fields is examined. Samples from 41 cereal tillage fields (n = 655) are used to build a calibration for DRIFTS using partial least squares regression (PLSR). The samples were split into calibration set (31 fields, n = 495) and validation set (10 fields, n = 160). After pre-processing with trim, smoothing and standard normal variate, a calibration model using 6 latent variables, provided R2 of 0.89 and root mean square error of cross-validation (RMSECV) of 1.56 t/ha. Prediction of all fields from the validation set resulted in R2 of 0.76 and root mean square error of prediction (RMSEP) of 1.68 t/ha. The predictions of the single fields ranged from R2 values of 0.41 to 0.72, RMSEP of 0.48 to 4.2 t/ha and ratios of performance to inter-quartile distance (RPIQ) of 0.45 to 3.56. It was shown that the signals of soil constituents having an influence on the LR were picked up in the spectra and were identified in the loading weights of the PLSR. While the error is too high to predict the variability of LR within the field, MIR prediction using field averages provided a viable alternative to current laboratory methods for blanket spreading of lime on tillage fields. [ABSTRACT FROM AUTHOR]

• Electroplating wastewater was treated using SWDIM freezing technology. • The technology was effective for treating strongly acidic and strongly alkaline wastewaters. • Primary SWDIM freezing allowed the ice melt water to be reused in the primary washing tank. • Secondary SWDIM treatment enabled the concentrated water to be reused for electroplating bath. • After secondary treatment, only 12.6% of pollutants from the wastewater remained. Wastewater produced from washing can be concentrated and reused in an electroplating bath to produce a cleaner product. Predominant methods of concentrating wastewater from electroplating washing include reverse osmosis and evaporation concentration. However, reverse osmosis cannot treat strongly acidic (pH < 2) or strongly alkaline (pH > 12) wastewaters, and the scope of its application is narrow, while evaporation uses high quantities of energy and is costly. In contrast, freezing electroplating washing wastewater incurs low cost and can be used for treating washing wastewater from various electroplating processes. In this study, supercooled water dynamic ice making (SWDIM) technology was applied for the first time to freeze and crystalize electroplating washing wastewater, followed by centrifugal separation to separate solid and liquid phases. Freezing treatment was performed on the washing water from the acid copper process (pH = 1.15), nickel plating process (pH = 5.1), and zincate zinc plating process (pH = 13.2). The results show that after primary freezing and centrifugal dehydration, the solute removal rate of ice melt water exceeded 90%, which can meet the requirements of the primary washing tank to supplement the washing water. After secondary freezing and centrifugal dehydration, the concentrated wastewater (approximately 0.14 vol of the original wastewater) can be produced, further evaporated and concentrated 2.8 times, and reused for the electroplating bath. [ABSTRACT FROM AUTHOR]

The existing methods for seawater freezing desalination include direct crystal suspension freezing and indirect progressive contact freezing (IPCF). However, freeze desalination is rarely used because of the low rates of desalination and freshwater production along with the relatively high treatment costs. In this study, we evaluated a new freeze desalination method using supercooled water dynamic ice making. The proposed method had a theoretical energy consumption of only 58% of that needed for IPCF. With gravity filtration separation of the produced ice–water mixture for 20 min or centrifugal dehydration for 1 min, a 0.5% salt content standard for freshwater and a 60% freshwater yield were attained. In addition, the method produced ice crystals with minimal impurities, after brine attached to the surface was removed through centrifugal dehydration, a 0.05% salt content standard for drinking water was achieved. • A new seawater freezing desalination method using SWDIM • The method had a theoretical energy consumption of only 58% of that needed for IPCF • A 0.5% salt content standard for freshwater was achieved with gravity filtration separation for 20 min • A 0.05% salt content standard for drinking water was achieved with centrifugal dehydration for 8 min [ABSTRACT FROM AUTHOR]

In recent years, the biogeochemical behavior and environmental impact of Selenium (Se) on soil-plant systems have received widespread attention, and traditional statistical methods reveal generally positive correlations between rice Se and soil Se. However, that initial positive relationship may have been obscured by local external factors. Using local scale data from the geochemical evaluation of land quality project, this work employed geographically weighted regression (GWR) to examine the spatial variation of rice Se (as the dependent variable) and soil Se (as the independent variable) in Guangxi. Strong and weak correlation coefficients occur between rice Se and soil Se, thereby indicating that their relationships are spatially varying. Guangxi is characterized by significantly positive correlations in most areas, with weak correlations mostly found in the south-western and central-eastern regions. Areas with weak correlation can be divided into two patterns: high soil Se with low rice Se and high rice Se with low soil Se. The unique patterns are correlated with distinct natural factors, particularly the abundance of Fe-rich soils in the carbonate area; by contrast, sandstone areas in central Guangxi may have been affected by anthropogenic activities. To reveal the spatially varying relationships at the local scale, we employed GWR, an effective tool that allowed us to identify the association between environmental variables and influencing factors and explore spatially varying relationships between them. This study breaks through the existing understanding that soil Se is completely positively correlated with rice Se for the first time, and concludes that their correlation is spatially variable, providing an effective approach for the study of complex relationships. [Display omitted] • Spatially varying relationships between rice Se and soil Se are revealed by GWR. • Weak correlations are observed in north-western and central-eastern Guangxi. • Fe-enriched soil is the main influencing factor of the weak relationship. • The utility of Se-enriched land is proposed based on the spatial relationships. [ABSTRACT FROM AUTHOR]

Soil lead (Pb) provides an important exposure pathway to the human body through soil ingestion and dust inhalation and is closely associated with human health as well as social behaviour. The challenge of transforming different spatial supports arises when linking point data (Pb concentration) to areal data (health status or social behaviour). A detailed review of methodologies for integrating point and areal data has been carried out. Among a number of methodologies, eight methods: (1) average, (2) median, (3) centroids inverse distance weighted (IDW), (4) average block IDW, (5) median block IDW, (6) centroids ordinary kriging (OK), (7) average block OK and (8) median block OK, have been compared using Pb data set in the Greater London Authority (GLA) area. The results indicated that the method of median block IDW was recommended for further investigation of the relationship between Pb concentration and socio-economic factors in the ward-level of the GLA area. The reasons were (i) spatial interpolations were useful for predicting unobserved values when simple average and median could not work in the locations where there were no samples collected in some areal units; (ii) the median value was more suitable than the average value for a skewed data set; (iii) the block method reduced estimation error and provided more representative values of areal units than the centroid method; (iv) IDW reserved more spatial variation than OK, containing more local maxima (hotspot) and local minima. Despite that it is still hard to decide the optimal method, this study has highlighted the point-to-area transformation issue and provided valuable examples to compare the different methods. Unlabelled Image • Methods for integrating point and areal data have been reviewed. • The advantages and disadvantages of 8 methods were pointed out. • Median block IDW is recommended to calculate Pb level in each ward of London. [ABSTRACT FROM AUTHOR]

Abstract Median kriging with robust estimators of the variogram has been proposed in literature to reduce the influences of outliers in spatial data of soil pollution, because median kriging can utilize outliers in spatial prediction and robust estimators can overcome the bias caused by outliers. However, performance of the method at a field scale remains unknown. This study compared the method in two case studies of soil Pb pollution with two other commonly used methods for outlier identification, including box-plot and standardized kriging prediction error (SKPE), and with two classical geostatistical approaches for spatial prediction, including kriging with and without outliers. One case was based on data with 359 samples collected in an area of 14.5 km2 in Jura, Swiss. The other was based on data with 242 samples collected in an area of 2.8 km2 in Zhuzhou, China. Results showed that the method identified both global and local outliers, while the method did not identify all global outliers based on the box-plot. For the Jura data which were more seriously affected by outliers than the Zhuzhou data, the method identified 49 outliers, sharing 39 with SKPE which identified a total of 46 outliers. For the Zhuzhou data, the method found just three outliers, much fewer than the 12 outliers identified by SKPE. In the case of Jura, kriging prediction with outliers winsorized by the method was negligibly more accurate than prediction without outliers identified by SKPE, e.g., 0.15% in terms of root mean square error (RMSE). However, in the case of Zhuzhou, the former prediction was slightly less accurate than the latter, e.g., 2.39% in terms of RMSE. This study suggested that the method performed well for data which were seriously affected by outliers, but not so well for data slightly affected by outliers. Graphical abstract Outliers and spatial prediction of soil Pb concentration in the Jura study area (left) and Zhuzhou study area (right) using median kriging. Unlabelled Image Highlights • Median kriging with robust estimators of the variogram was applied at a field scale. • Median kriging identified more outliers for data having more outliers & vice versa. • Median kriging mapped more accurately for data having more outliers & vice versa. [ABSTRACT FROM AUTHOR]

Soils are the main sink of pollutants in the terrestrial environment, reflecting the historical fingerprint of urban development. To provide scientifically grounded urban management and reclamation strategies for polluted sites, the spatial patterns of pollutants should be studied first by drawing a special focus on the identification of pollution hot spots. In this paper, Pb, Mo and Ti contents in Yerevan urban soils determined by the combination of X-ray fluorescence and atomic absorption spectrometry were studied using the Local Moran's I index (LMI). These elements (Pb, Mo and Ti) have different sources and a number of distance bands (650, 800, 1000, 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000 m and 15,000 m) were tested. This allows to reveal the optimal distance used for the identification of statistically significant spatial clusters and spatial outliers. According to the results the use of the raw data had to be excluded for elements that demonstrated highly skewed distribution whereas transformed data allowed to identify larger areas of high and low value spatial clusters. Moreover, the results of the different distance bands showed that the high and low spatial clusters became stable near the optimal distance band of 7000 m in the study area. The latter constituted the 1/3 of the maximum distance between all sample pairs. At this distance, Ti LMI results reflected the natural local geology composition whereas Mo distribution was in line with the known sources of industrial pollution ("Plant of Pure Iron"). In the case of Pb, the high-value spatial clusters were in line with the built-up urban areas and spatially correlated with the prevailing northeast winds. This study confirmed the applicability of LMI in the identification of spatial clusters and outliers at the optimal distance band which might be considered also as a break point of spatial clusters expansion and a guidance for spatial boundary of possible remediation and pollution reduction applications. Image 1 • Hot spot analysis is effective in revealing patterns of chemicals in soils. • Optimal distance for LMI is the 1/3 of the maximum length among all sampling sites. • Ti spatial clusters reflect the natural geology composition. • Mo spatial clusters were in line with the known sources of Mo emissions. • Pb spatial clusters were in line with the built-up urban areas. [ABSTRACT FROM AUTHOR]

The effect of dissolved oxygen concentration (DO) during simultaneous nitrification and denitrification (SND) was investigated in a sequencing batch biofilm reactor (SBBR). In addition, the removal rates of nitrogen and bacterial communities were investigated under different concentrations of DO (1.5, 3.5, and 4.5 mg/L). When the SND rate was 95.22%, the chemical oxygen demand and nitrogen removal was 92.22% and 84.15%, respectively, at 2.5 mg/L DO. The denitrification was inhibited by the increase of oxygen concentration. Microelectrode measurements showed that the thickness of oxygen penetration increased from 1.0 mm to 2.7 mm when the DO concentration increased from 1.5 mg/L to 5.5 mg/L. The current location of the aerobic and anaerobic layers in the biofilm was determined for analysis of the microbial community. High-throughput sequencing analysis revealed the communities of the biofilm approached similar structure and composition. Uliginosibacterium species, biofilm-forming bacteria Zoogloea species and Acinetobacter species were dominant. In the aerobic layer, phyla Betaproteobacteria and Saprospirae were predominant, the major phyla were shifted from Proteobacteria followed by Firmicutes and Bacteroidetes , which comprised 82% of the total sequences during the SND period. Anaerolineae was dominated in the anaerobic layer. The high abundance of Nitrospira in the aerobic biofilm provides evidence of the SND system performing better at ammonia oxidization. In addition, real-time PCR indicated that the amount of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) matched the Nitrospirales and Nitrosomonadales abundance well. Collectively, this study demonstrated the dynamics of key bacterial communities in the SND system were highly influenced by the DO concentration. [ABSTRACT FROM AUTHOR]

Rare earth elements (REEs) in urban soils are receiving more attention because of their increased use in modern technology. Up to now, there is little information about the spatial patterns of REEs and their influencing factors in urban soils. In this study, based on the British Geological Survey “London Earth” geochemical survey data containing a total of 6467 soil samples collected from the urban area of London, the spatial distributions of Ce, La, Nd, Sc, Sm, Yb and Y, and their influencing factors were investigated. The element Sc is included in this study as related element. Urban samples were collected at a depth of (0–20 cm), and total concentrations of 48 chemical elements were determined in the <2 mm size fraction of the topsoils using X-ray Fluorescence Spectrometry (XRFS). Spatial clusters and spatial outliers were identified using the index of local Moran's I, showing elevated concentrations of REEs in the northwest, northeast and southern parts of the study areas while all the 7 REEs exhibited generally similar spatial patterns: high values were mainly located in soils over Alluvium, Glacial till and Clay-with-flints deposits, demonstrating the close relationships between REEs and the lithology. Meanwhile, hotspot analysis revealed high values of REEs in public parks, private golf clubs, wetlands and agricultural lands especially in Hillingdon, Enfield, Brent, Harrow, Croydon and Bromley. Soil parent material was identified as the primary source for larger spatial patterns of natural origin, while smaller spatial patterns were associated with anthropogenic sources including agricultural practices, industrial activities, vehicular emission and urbanization. The REE hotspots revealed in urban soils may imply potential contamination requiring further attention. [ABSTRACT FROM AUTHOR]

Improving the tolerance of Li‐ion batteries (LIBs) to extreme temperatures and climates worldwide is vital to their global uptake. However, LIBs call for more strict requirements for the key components when operated in a wide temperature range, especially synchronously desirable interfacial kinetics and thermal stability. Here, a novel multifunctional electrolyte additive, N‐tert‐butyl‐2‐thiophenesulfonamide (NTSA), to fabricate stable LIBs under wide‐temperature conditions, is reported. The Li‐ion solvation structure in the electrolyte is regulated and involves less coordinated solvents (particularly fluoroethylene carbonate), leading to superior Li+ transportation. The effective NTSA additive is preferentially decomposed to form a uniform electrode/electrolyte interface with abundant multiphase inorganic LiF, Li3N, and LiS species simultaneously on the cathode and anode surface. The resulting inorganic‐rich interface can not only boost the interfacial Li‐ion transfer kinetics at low temperatures but also protect the active material and enhance the thermal stability of the interface and LIB devices at high temperatures. By adopting the NTSA‐containing electrolyte, LiCoO2||ω‐Li3V2O5 LIBs can be stably cycled in a wide temperature range between −30 °C and 80 °C, delivering a high capacity of ≈100.1 mAh g−1 (0.2 A g−1) at −20 °C and high capacity retention of 94.5% after 200 cycles (0.5 A g−1) at 55 °C. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic provides an opportunity to investigate how a new and long-lasting threat affects public risk perception and social distancing behavior, which is important for pandemic risk management and recovery of the tertiary industry. We have found that the mechanism that perception decides behavior changes over time. At the beginning of the pandemic, risk directly shapes people's willingness of going out. But under a persistent threat, perception no longer plays the direct role of shape people's willingness. Instead, perception indirectly influences the willingness by shaping people's judgment about the necessity of traveling. Switching from direct to indirect influence, perception's effect is enlarged, which partially prevents people from returning to normal life even if the governmental ban is removed in a zero-COVID community. [ABSTRACT FROM AUTHOR]

Chinese liquors come from a variety of different regions in China. This diversity can be attributed to the difference in microorganisms, but also to the biophysical environment. The latter constitutes a significant basis for the fermentation of products, but has not been adequately understood. Using spatial statistics analysis, we studied the causes of regional differences of Chinese liquors in the perspective of climatic and socio-economic environment. Temporal changes in the density of Chinese liquor manufacturers in each prefecture-level administrative unit from1995 to 2004 revealed no changes in spatio-temporal distribution patterns of Chinese liquors. Most manufacturers were distributed in moderate climatic regions in both developed areas and less developed areas. Socio-economic factors had a significantly positive impact on the development of the Chinese liquor industry, while the climatic environment plays a crucial role in determining their difference a regional scale. Overall, this study provided scientific evidence for the importance of the biophysical environments, especially climate, to determine the geographical patterns of fermentation products. [ABSTRACT FROM AUTHOR]

With the development of researches, the treatments of wastewater have reached a certain level. Whereas, heavy metals in wastewater cause special concern in recent times due to their recalcitrance and persistence in the environment. Therefore, it is important to get rid of the heavy metals in wastewater. The previous studies have provided many alternative processes in removing heavy metals from wastewater. This paper reviews the recent developments and various methods for the removal of heavy metals from wastewater. It also evaluates the advantages and limitations in application of these techniques. A particular focus is given to innovative removal processes including adsorption on abiological adsorbents, biosorption, and photocatalysis. Because these processes have leaded the new trends and attracted more and more researches in removing heavy metals from wastewater due to their high efficency, pluripotency and availability in a copious amount. In general, the applicability, characteristic of wastewater, cost-effectiveness, and plant simplicity are the key factors in selecting the most suitable method for the contaminated wastewater. [ABSTRACT FROM AUTHOR]

Outliers are classified as global outliers and spatial outliers. Up to now, there is little information about the outliers especially the spatial outliers and their influence on the spatial structure modeling of soil properties. A total of 537 soil samples were collected based on a 30 × 30 m grid in a permanent dairy farm in southeast Ireland. Graphic methods of histogram and box plot combined with Moran's I were applied to detect the outliers of soil phosphorus (P). Sixteen outliers (5 global outliers and 11 spatial outliers) of soil P were found in the study area. Compared to the raw data, the data with global outliers excluded always had the larger global Moran's I value indicating a stronger spatial autocorrelation. Clear spatial clusters (High-High and Low-Low clusters) were observed based on local Moran's I. The High-High spatial clusters were located around the main farm yard and near the traffic route due to more intensive management by farmers. The Low-Low spatial clusters were mainly close to the river. For these areas, P fertilizer or slurry should be applied for healthy grass growth. The dataset with outliers excluded had a reliable semi-variogram model with a low nugget/sill ratio (32.4 %), which was closed to its corresponding transformed data (30.5 %). The cross-validation results revealed that the dataset without outliers had the strongest linear regression model ( r = 0.768), indicating that the outliers played an important role in the spatial structure modeling. [ABSTRACT FROM AUTHOR]

• Colocalization of Fe and P was observed in the root Fe plaque. • Localized rhizospheric acidification facilitated the Ca-bound P solubilization. • An increase in phosphatase activity promoted the organic P mineralization. • P-cycling microbial communities improved rhizospheric P mobilization. Rhizoremediation of wetland plants is an environmentally friendly strategy for sediment phosphorous (P) removal, the basic underlying principle of which is the complex interactions between roots and microorganisms. This study investigated the immobilization and mobilization mechanisms of P in the rhizosphere of wetland plants using high-resolution spatial visualization techniques and metagenomic sequencing. Two-dimensional visualization of the spatial distribution of P, iron (Fe) and manganese (Mn) indicated that the sequestration of Fe-oxides rather than Mn-oxides caused the depletion of labile P, resulting in an increase in the Fe-adsorbed P fraction. Plants altered the rhizospheric environments and P-cycling microbial community to mobilize low-availability P from sediments. Mineral P solubilization and organic P mineralization were enhanced by local acidification and increased phosphatase activity, respectively. Microbial P mobilization also increased with increasing relative abundances of P solubilization and mineralization genes (gcd and phnW) and decreasing P transportation genes (ugpA, ugpB , and pit) genes in the rhizosphere. These processes led to the remobilization of 10.04 % of inorganic P, and 15.23 % of organic P, in the rhizosphere during the incubation period. However, the resupply of P via the above processes did not compensate for the depletion of rhizospheric P via root uptake and mineral sequestration. Our results provide novel insights into the mechanisms of rhizospheric P cycling, which will help to inform future phytoremediation strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • GWPCC revealed the spatially varying relationships between Pb and Al in soils. • Negative correlations mainly occurred in western and north-eastern areas. • Positive correlations clustered in central-western and midland areas. • Atmospheric deposition contributed to the negative correlations on blanket peat. • Anthropogenic factors weakened the relationships in eastern coastal regions. The investigation of spatially varying relationships provides a novel and effective way to identify the influencing factors and potential sources of potentially toxic elements (PTEs). The varying relationships for PTEs at different sampling locations can be associated with the different controlling factors from geological units or pollution sources, which benefits environmental studies and management for local government. Generally, the concentrations of lead (Pb) and aluminium (Al) in the soil are reported as being positively correlated under most natural conditions, due to Pb and Al being enriched in clay minerals. However, the original positive relationship can be masked by external influences at the local level, thus the resulting varying relationships can be observed and associated with the potential pollution sources. In this study, Geographically Weighted Pearson Correlation Coefficient (GWPCC) was applied to explore the spatial relationships between Pb and Al in soils. Both positive and negative correlation coefficients were observed from the results of GWPCC at different scales with bandwidths ranging between 43 and 300 nearest neighbours, suggesting the relationships between Pb and Al are spatially varying at the regional scale. Negative correlations were observed in more than 35% of the study area, mainly clustered in the western and north-eastern of Ireland. The positive correlations were found in the central-western and midlands, while comparatively mixed correlations occurred in the eastern coastal areas. The spatial patterns for the majority of negative correlations showed a clear association with blanket peat, which likely can be attributed to long-distance transportation of Pb through atmospheric deposition. Moreover, anthropogenic activities weakened the relationships in the eastern coastal areas. These results demonstrated the efficiency of GWPCC in exploring the spatially varying relationships between environmental variables and identifying associations with related influencing factors, which could be hardly achieved by traditional techniques. [ABSTRACT FROM AUTHOR]

Previous studies have found that Cadmium (Cd) may be condensed during the processes of the weathering and soil formation over carbonate bedrock, which is a typical phenomenon in the southwest region of China. However, the extent of the high value background and the geographical factors influencing its spatial distribution are still unknown. This study collected data from the Multi-Purpose Regional Geochemical Survey (MPRGS) and the Regional Geochemistry-National Reconnaissance Program (RGNR) to investigate the extent and its controlling factors based on spatial analysis. It was found that the RGNR is an ideal data source for regional Cd background studies. Spatially, the high Cd area (Cd > 1.5 m/kg with reference of the GB15618-2018 soil environmental quality in China, 68.9 × 103 km2) coincide well with the carbonate distribution. In the total area (68.9 × 103 km2) with high Cd concentration, 89.8% is related to carbonates. The composition of parent rock, climate conditions, and landuse are controlling factors of the enrichment. The high Cd background effect of limestone is higher than that of dolomite and their effects decease with increasing detritus component they contain. Warm and humid climate in forest land helps to enrich Cd during weathering. Soil Cd concentration related to carbonate background can be predicted by major element concentrations, which is useful for differentiating Cd background from human pollution in the future soil pollution monitoring. Modelling by artificial neural network (ANN) is recommended rather than tradition multiple linear methods (LM) as the latter may have the effect of collinearity. [Display omitted] • ∼90% of soil high Cd (>1.5 mg/kg) is related to carbonate bedrock. • Proportion of calcite, dolomite, and detritus influences the degree of enrichment. • Dolostone tends to have relatively low effect of Cd enrichment than limestone. • Climate and landuse type also influence the enrichment. • Cd background value can be predicted by major elements. [ABSTRACT FROM AUTHOR]

Abstract: Soils are recipients of contaminants from the burning of metal-containing materials in bonfires. In order to obtain a better understanding of the impacts of bonfires on soils, a total of 218 surface soil samples were collected from a traditional bonfire site in Galway City, Ireland. Concentrations of 14 elements including Al, Ca, Co, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, Sc, Ti and Zn in 30 selected samples were determined using ICP-OES. Strong variations were observed for these elements. Concentrations of Zn, Cu and Pb were elevated, indicating that these metals may be influenced by human activities. Therefore, concentrations of these three metals were further measured using a portable X-ray fluorescence (P-XRF) analyser (n =218) and their spatial distribution was investigated using a Geographical Information System (GIS). Soils with elevated metal concentrations, as evident from the spatial distribution maps, coincided with the locations of traditional festival bonfires. The results of this study provide useful information for the management of bonfire sites including the enforcement of a control policy regarding the burning of materials in bonfires. [Copyright &y& Elsevier]

Abstract: Characterization of spatial variation of urban soil geochemistry especially heavy metal pollution is essential for a better understanding of pollution sources and potential risks. A total of 294 surface soil samples were collected from a roadside sports ground in Galway, Ireland, and were analysed by ICP-OES for 23 chemical elements (Al, Ca, Ce, Co, Cu, Fe, K, La, Li, Mg, Mn, Na, Ni, P, Pb, S, Sc, Sr, Th, Ti, V, Y and Zn). Strong variations in soil geochemistry were observed and most elements, with the exception of Cu, Pb, P, S and Zn, showed multi-modal features, indicating the existence of mixed populations which proved difficult to separate. To evaluate the pollution level of the study area, the pollution index (PI) values were calculated based on a comparison with the Dutch target and intervention values. None of the concentrations of metal pollutants exceeded their intervention values, indicating the absence of serious contaminated soil, and the ratios to target values were therefore employed to produce the hazard maps. The spatial distribution and hazard maps for Cu, Pb and Zn indicated relatively high levels of pollution along the southern roadside extending almost 30m into the sports ground, revealing the strong influence of pollution from local traffic. However, heavy metal pollution was alleviated along the eastern roadside of the study area by the presence of a belt of shrubs. Therefore, in order to prevent further contamination from traffic emissions, the planting of hedging or erection of low walls should be considered as shields against traffic pollution for roadside parks. The results in this study are useful for management practices in sports and parks in urban areas. [Copyright &y& Elsevier]

Abstract: The spatial variation of extractable (Morgan''s) soil phosphorus (P), potassium (K), magnesium (Mg), pH and lime requirement (LR) in a permanent dairy farm in southeastern Ireland, was investigated using conventional statistics, geostatistics and a geographical information system (GIS) to produce nutrient maps and to provide information for site-specific fertilizer application. A total of 537 soil samples were collected based on a 30m×30m grid in the study area. Soil P was very variable, ranging from 1.3mg/l to 113.5mg/l, with an average of 6.19mg/l. Soil Mg ranged from 134.7mg/l to 685.2mg/l, with an average of 354.4mg/l. Soil K ranged from 20.1mg/l to 433mg/l, with an average of 174.5mg/l. Soil Mg followed a normal distribution, while the other soil properties followed neither a normal nor a log-normal distribution. Box–Cox transformation was applied to achieve normality, followed by geostatistical analyses, including variogram surface analysis, calculation of experimental variograms and model fitting. Variograms were markedly anisotropic for the variables of soil Mg, K, pH and LR, implying the directional features of their spatial distribution patterns. For soil P, an omni-directional spherical model was used to describe the spatial autocorrelation. For soil Mg, a nested model (an exponential model combined with a Gaussian model) was used to fit the variogram. GIS mapping for the studied soil variables was achieved using trans-Gaussian kriging. Soil nutrient index information was applied to classify nutrient concentrations in GIS maps to provide guidance for site-specific fertilizer or slurry application. High soil P concentrations (>8mg/l) were found at the areas surrounding the main farmyard and close to the traffic route, due to high applications of cattle and pig slurry. For these places, no more P fertilizer or slurry was needed to avoid economic loss and environmental pollution. While at the places where soil P concentrations were lower than 5mg/l, appropriate P fertilizer or slurry was needed to obtain optimal grass output. For soil Mg, pH and LR, the general east–west directional spatial patterns were observed, with soil pH playing an important role. These maps provide useful information for agronomic and environmental management. [Copyright &y& Elsevier]

Abstract: Previous studies have revealed the fractionation processes of rare earth elements (REEs) in hydroponic plants, with a heavy REE (HREE, the elements from Gd to Lu) enrichment in leaves. In this study, effects on the HREE enrichment in soybean leaves with additions of carboxylic acids (acetate, malate, citrate, NTA, EDTA and DTPA) and two soil humic acids (HAs) were investigated. REE speciation in carboxylic acid and HA solutions was simulated using Visual MINTEQ and Model V, respectively. The results showed that the effects caused by carboxylic acids were strongly dependent on the differences between their binding strengths for light REEs (LREEs, the elements from La to Eu) and those for HREEs. A good correlation existed between these effects and the changes of free REE ions in solutions. This relationship was also observed for the HA treatments, provided that the intrinsic equilibrium constants of REEs for cation–proton exchange with HA (i.e., pK MHA) in Model V were estimated using a free-energy relationship with the stability constants for REE complexation with lactic acid. It is suggested that this set of pK MHA values is more suitable for use in Model V for the simulation of REE complexation with HA. [Copyright &y& Elsevier]

[Display omitted] • Hot spot analysis identified the spatial clustering patterns for topsoil PTEs. • K-means clustering analysis is effective in revealing hidden patterns of soil samples. • Consistent spatial patterns were revealed between PTEs and soil samples. • Peat was associated with high concentrations of Bi, Pb, Sb and Sn. • Basalt was associated with high concentrations of Cr, Co, Cu, Mn, Ni, V and Zn. The understanding of sources and controlling factors of potentially toxic elements (PTEs) in soils plays an important role in the improvement of environmental management. With the rapid growth of data volume, effective methods are required for data analytics for the large geochemical data sets. In recent years, spatial machine learning technologies have been proven to have the potential to reveal hidden spatial patterns in order to extract geochemical information. In this study, two spatial clustering techniques of Getis-Ord G i * statistic and K-means clustering analysis were performed on 15 PTEs in 6,862 topsoil samples from the Tellus datasets of Northern Ireland to investigate the hidden spatial patterns and association with their controlling factors. The spatial clustering patterns of hot spots (high values) and cold spots (low values) for the 15 PTEs were revealed, showing clear association with geological features, especially peat and basalt. Peat was associated with high concentrations of Bi, Pb, Sb and Sn, while basalt was associated with high concentrations of Co, Cr, Cu, Mn, Ni, V and Zn. The high concentrations of As, Ba, Mo and U were associated with mixture of various lithologies, indicating the complicated influences on them. In addition, three hidden patterns in the 6,862 soil samples were revealed by K-means clustering analysis. The soil samples in the first and second clusters were overlaid on the peatland and basalt formation, respectively, while the samples in the third cluster were overlaid on the mixture of the other lithologies. These hidden patterns of soil samples were consistent with the spatial clustering patterns for PTEs, highlighting the dominant control of peat and basalt in the topsoil of Northern Ireland. This study demonstrates the power of spatial machine learning techniques in identifying hidden spatial patterns, providing evidences to extract geochemical knowledge in environmental studies. [ABSTRACT FROM AUTHOR]