Search

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]

Soils in the vicinity of roads are recipients of contaminants from traffic emissions. In order to obtain a better understanding of the impacts of traffic on soils, a total of 225 surface soil samples were collected from an urban park (Phoenix Park, Dublin, Ireland) in a grid system. Metal (Pb, Cu and Zn) concentrations were determined using a portable X-ray fluorescence analyzer. Strong spatial variations for the concentrations of Pb, Cu and Zn were observed. The spatial distribution maps created using geographical information system techniques revealed elevated metal concentrations close to the main traffic route in the park. The relationships between the accumulation of Pb, Cu and Zn in the roadside soils and the distance from the road were well fitted with an exponential model. Elevated metal concentrations from traffic pollution extended to a distance of approximately 40 m from the roadside. The results of this study provide useful information for the management of urban parks particularly in relation to policies aimed at reducing the impact of traffic related pollution on soils. [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]

Assessing the environmental risk of metal contamination in soils requires the determination of both total (TCs) and bioavailable (BCs) element concentrations. A total of 200 surface (0-10 cm) soil samples were collected from an urban sports ground (South Park) in Galway, Ireland, a former landfill and dumping site, which is currently under remediation. The potential BCs of metals were measured using ethylene-diamine-tetra-acetic acid (EDTA) extraction followed by inductively coupled plasma-optical emission spectrometry analysis, while the TCs were determined using portable X-ray fluorescence spectrometry. It was found that Zn was primarily present in the insoluble residue (EDTA un-extractable) fraction in soils, with the median ratio of BCs/TCs 0.27. However, Pb and Cu had higher ratios of BCs/TCs (median values of 0.60 and 0.39, respectively) suggesting that they are potentially more bioavailable in the soils. The spatial distribution maps showed that both TCs and BCs for Cu, Pb and Zn in the study area were spatially heterogeneous. It was found that the BCs exhibited generally similar spatial patterns as their TCs of Cu, Pb and Zn: high values were mainly located in the west, north-east and south-east portions of the study area, where only a thin layer of topsoil existed. It was recommended that the current remediation action for this site needs to be carried out on an urgent basis. [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]

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran''s I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with. [Copyright &y& Elsevier]

Geochemical mapping requires a good understanding of geochemical variables and proper use of mathematical and mapping techniques. In this study, GIS mapping for the geochemical variables in Irish soils was achieved using trans-Gaussian kriging following investigation of probability features, outlier identification, optimal power transformation by the Box–Cox method and spatial structure modelling. Relatively high nugget effect values for the variogram models were observed for most variables. The assumption of spatial autocorrelation was seriously breached by two variables, available P and available K, and spatial interpolation was not performed for these. Two or even three nested theoretical models were needed to model variogram structures for most variables reflecting their variation at different spatial scales. The spatial distribution patterns for most elements illustrated the influences of underlying bedrock and soil type, while some of the heavy metals (e.g. Pb, Zn and Cu) were also affected by mining activities and urban sprawl. The available nutrients (P, K and Mg) appeared to be most affected by agricultural activities. [ABSTRACT FROM AUTHOR]

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]

Heavy metals in urban soils continue to attract attention because of their potential long-term effects on human health. During a previous investigation of urban soils in Galway City, Ireland, a pollution hotspot of Pb, Cu, Zn and As was identified in the sports ground of South Park in the Claddagh. The sports ground was formerly a rubbish dumping site for both municipal and industrial wastes. In the present study, a portable X-ray fluorescence (PXRF) analyser was used to obtain rapid in-situ elemental analyses of the topsoil (depth: about 5–10 cm) at 200 locations on a 20 × 20-m grid in South Park. Extremely high values of the pollutants were found, with maximum values of Pb, Zn, Cu and As of 10,297, 24,716, 2224 and 744 mg/kg soil, respectively. High values occur particularly where the topsoil cover is thin, whereas lower values were found in areas where imported topsoil covers the polluted substrate. Geographic Information Systems (GIS) techniques were applied to the dataset to create elemental spatial distribution maps, three-dimensional images and interpretive hazard maps of the pollutants in the study area. Immediate action to remediate the contaminated topsoil is recommended to safeguard the health of children who play at the sports ground. [ABSTRACT FROM AUTHOR]

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]

[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]

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]

The presence of arsenic in groundwater has become a global concern due to the health risks from drinking water with elevated concentrations. The Water Framework Directive (WFD) of the European Union calls for drinking water risk assessment for member states. The present study amalgamates readily available national and sub-national scale datasets on arsenic in groundwater in the Republic of Ireland. However, due to the presence of high levels of left censoring (i.e. arsenic values below an analytical detection limit) and changes in detection limits over time, the application of conventional statistical methods would inhibit the generation of meaningful results. In order to handle these issues several arsenic databases were integrated and the data modelled using statistical methods appropriate for non-detect data. In addition, geostatistical methods were used to assess principal risk components of elevated arsenic related to lithology, aquifer type and groundwater vulnerability. Geographic statistical methods were used to overcome some of the geographical limitations of the Irish Environmental Protection Agency (EPA) sample database. Nearest-neighbour inverse distance weighting (IDW) and local indicator of spatial association (LISA) methods were used to estimate risk in non-sampled areas. Significant differences were also noted between different aquifer lithologies, indicating that Rhyolite, Sandstone and Shale (Greywackes), and Impure Limestone potentially presented a greater risk of elevated arsenic in groundwaters. Significant differences also occurred among aquifer types with poorly productive aquifers, locally important fractured bedrock aquifers and regionally important fissured bedrock aquifers presenting the highest potential risk of elevated arsenic. No significant differences were detected among different groundwater vulnerability groups as defined by the Geological Survey of Ireland. This research will assist management and future policy directions of groundwater resources at EU level and guide future research focused on understanding arsenic mobilisation processes to facilitate in guiding future development, testing and treatment requirements of groundwater resources. [ABSTRACT FROM AUTHOR]

Potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) harm the ecosystem and human health, especially in urban areas. Identifying and understanding their potential sources and underlying interactions in urban soils are critical for informed management and risk assessment. This study investigated the potential sources and the spatially varying relationships between 9 PTEs and PAHs in the topsoil of Dublin by combining positive matrix factorisation (PMF) and geographically weighted regression (GWR). The PMF model allocated four possible sources based on species concentrations and uncertainties. The factor profiles indicated the associations with high-temperature combustion (PAHs), natural lithologic factors (As, Cd, Co, Cr, Ni), mineralisation and mining (Zn), as well as anthropogenic inputs (Cu, Hg, Pb), respectively. In addition, selected representative elements Cr, Zn, and Pb showed distinct spatial interactions with PAHs in the GWR model. Negative relationships between PAHs and Cr were observed in all samples, suggesting the control of Cr concentrations by natural factors. Negative relationships between PAHs and Zn in the eastern and north-eastern regions were related to mineralisation and anthropogenic Zn-Pb mining. In contrast, the surrounding regions exhibited a natural relationship between these two variables with positive coefficients. Increasing positive coefficients from west to east were observed between PAHs and Pb in the study area. This special pattern was consistent with prevailing south-westerly wind direction in Dublin, highlighting the predominant influences on PAHs and Pb concentrations from vehicle and coal combustion through atmospheric deposition. Our results provided a better understanding of geochemical features for PTEs and PAHs in the topsoil of Dublin, demonstrating the efficiency of combined approaches of receptor models and spatial analysis in environmental studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)