Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing

Investigation of an Optimal Sampling Resolution to Support Soil Management Decisions for Urban Plots

The main objective of the current study was to use seven lots in Hartford, CT that are planned for community reuse to determine the optimal sampling density that allows for the detection of hotspots of lead pollution while limiting the labor of the sampling process. The sampling density was investigated using soil Pb measured by in situ X-ray Fluorescence as the indicator to evaluate soil health, with a new threshold of 200-mg/kg proposed by the USEPA in January of 2024. Even though this study takes place in an urban setting, where the new USEPA policy requires the use of a 100-mg/kg threshold for Pb due to the fact that there are other identifiable sources of the contaminant, only the 200-mg/kg threshold is discussed because it is evident from the analysis that compliance of a 100 mg/kg threshold in urban plots is highly unlikely (five out of seven sites would require complete site excavation prior to reuse). Using the inverse distance weighted geospatial interpolation of in situ pXRF determined lead measurements, grid sampling resolutions of 3-m, 4-m, 5-m, 6-m, 8-m, 10-m, and 12-m were compared. Ultimately, the case study finds that the largest grid resolution that can be implemented for soil screening to maintain hotspots of pollution to properly inform soil management decisions is a 6-m grid, or a density of approximately 1/36-m2.

A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.) and achieve a more precise quantification. The dataset of soil heavy metals was collected from a typical area in the Chang-Zhu-Tan region, Hunan, China in 2021. The identification of the contribution of soil parent material was accomplished through enrichment factors and crustal reference elements. Meanwhile, the anthropogenic emission was identified with principal component analysis and GeoDetector. GeoDetector was used to accurately point to the pollution source from a spatial differentiation perspective. Subsequently, the pollution pathways linked to the identified sources were determined. Non-metal manufacturing factories were found to be significant anthropogenic sources of local soil contamination, mainly through rivers and atmospheric deposition. Furthermore, the influence of irrigation water on heavy metals showed a more pronounced effect within a distance of 1000 m, became weaker after that, and then gradually disappeared. This model may offer improved technical guidance for practical production and the management of soil heavy metal contamination.

Integrated pollution analysis, pollution area identification and source apportionment of heavy metal contamination in agricultural soil

Given the global prevalence of soil heavy metal contamination, knowledge concerning of soil environmental quality assessment, pollution area identification and source apportionment is critical for implementation of soil pollution prevention and safe utilization strategies. In this study, soil static environmental capacity (QI) for heavy metals was selected to evaluate pollution risks in agricultural soils of Wenzhou, southeast China. Combined with geostatistical methods, the pollution area was identified along with uncertainty analysis. Potential sources were quantitatively apportioned using a positive matrix factorization model (PMF). Results showed that agricultural soils in this study were mainly contaminated by Cd and Pb based on both Nemerow and QI indices. The environmental capacity assessment found more than 90% areas were identified as polluted soils for Qi-Zn, Qi-Cd and Qi-Pb, with minor uncertain areas. Cu was identified as having a high proportion of uncertain pollution area status, which was similar to the results of the integrated environmental capacity for all metals. PMF results indicated that industrial discharge, agrochemicals and parent material accounted for 32.1%, 32.2% and 35.7% of heavy metal accumulation in soils, respectively. Implementation of strict policies to reduce anthropogenic source emissions and remediate soil pollution are crucial to minimize metal pollution inputs, improve agricultural soil quality and enhance food safety.

Online temperature-monitoring technology for grain storage: a three-dimensional visualization method based on an adaptive neighborhood clustering algorithm

BACKGROUND

Post-harvest quality assurance is a crucial link between grain production and end users. It is essential to ensure that grain does not deteriorate due to heating during storage. To visualize the temperature distribution of a grain pile, the present study proposed a three-dimensional (3D) temperature field visualization method based on an adaptive neighborhood clustering algorithm (ANCA). The ANCA-based visualization method contains four calculation modules. First, discrete grain temperature data, obtained by sensors, are collected and interpolated using back propagation (BP) neural networks to model the temperature field. Then a new adaptive neighborhood clustering algorithm is applied to divide interpolation data into different categories by combining spatial characteristics and spatiotemporal information. Next, the Quickhull algorithm is used to compute the boundary points of each cluster. Finally, the polyhedrons determined by boundary points are rendered into different colors and are constructed in a 3D temperature model of the grain pile.

RESULTS

The experimental results show that ANCA is much better than the DBSCAN and MeanShift algorithms on compactness (around 95.7% of tested cases) and separation (approximately 91.3% of tested cases). Moreover, the ANCA-based visualization method for grain pile temperatures has a shorter rendering time and better visual effects.

CONCLUSION

This research provides an efficient 3D visualization method that allows managers of grain depots to obtain temperature field information for bulk grain visually in real time to help them protect grain quality during storage. © 2023 Society of Chemical Industry.

Optimization of spatial prediction and sampling strategy of site contamination based on Thiessen polygon coupling interpolation

Contaminated sites pose a serious threat to the ecological environment and human health. Because of the presence of multiple peaks in the pollution data of some contaminated sites, as well as strong spatial heterogeneity and skewness in their distribution, the accuracy of spatial interpolation prediction is low. This study proposes a method for investigating highly skewed contaminated sites, which uses Thiessen polygons coupled with geostatistics and deterministic interpolation to optimize the spatial prediction and sampling strategy of sites. An industrial site in Luohe is used as an example to validate the proposed method. The results indicate that using 40 × 40 m as the minimum initial sampling unit can obtain data that is representative of the regional pollution situation. Evaluation indexes reveal that the ordinary kriging (OK) method for interpolation prediction accuracy and the radial basis function_inverse distance weighted (RBF_IMQ) method for pollution scope prediction provides the best results, which can effectively improve the spatial prediction accuracy of pollution in the study area. Each accuracy indicator is enhanced by 20-70% after supplementing 11 sampling points in the suspect region, and the identification of the pollution scope approaches 95%. This method offers a novel approach for investigating highly biased contaminated sites, which can optimize the spatial prediction accuracy of pollution and reduce economic costs.

Improving the mapping accuracy of soil heavy metals through an adaptive multi-fidelity interpolation method

Soil heavy metal pollution poses a serious threat to environmental safety and human health. Accurately mapping the soil heavy metal distribution is a prerequisite for soil remediation and restoration at contaminated sites. To improve the accuracy of soil heavy metal mapping, this study proposed an error correction-based multi-fidelity technique to adaptively correct the biases of traditional interpolation methods. The inverse distance weighting (IDW) interpolation method was chosen and combined with the proposed technique to form the adaptive multi-fidelity interpolation framework (AMF-IDW). In AMF-IDW, sampled data were first divided into multiple data groups. Then one data group was used to build the low-fidelity interpolation model through IDW, while the other data groups were treated as high-fidelity data and used for adaptively correcting the low-fidelity model. The capability of AMF-IDW to map the soil heavy metal distribution was evaluated in both hypothetical and real-world scenarios. Results showed that AMF-IDW provided more accurate mapping results compared with IDW and the superiority of AMF-IDW became more evident as the number of adaptive corrections increased. Eventually, after using up all data groups, AMF-IDW improved the R² values for mapping results of different heavy metals by 12.35-24.32%, and decreased the RMSE values by 30.35%-42.86%, indicating a much higher level of mapping accuracy relative to IDW. The proposed adaptive multi-fidelity technique can be equally combined with other interpolation methods and provide promising potential in improving the soil pollution mapping accuracy.

Influencing factors identification and the nested structure analysis of heavy metals in soils in entire city and surrounding the multiple pollution sources

Identifying the sources of pollutants and analyzing the nested structure of heavy metals is vital for the prevention and control of soil pollution. However, there is a lack of research on comparison the main sources and the nested structure at different scales. In this study, two spatial extent scales were taken as the research objects, the results showed that, (1) the point exceeding standard rate of As, Cr, Ni, and Pb is higher at the entire city scale; (2) As and Pb, while Cr, Ni, and Zn, have weaker spatial variability at the entire scale and surrounding the pollution sources, respectively; (3) the contribution of the larger structure of Cr and Ni, while Cr, Ni, and Zn, at the entire scale and surrounding the pollution sources, respectively, is bigger to the total variability. The representation of semivariogram is better when the general spatial variability is weaker and the contribution of the smaller structure is lower; (4) various factors with different influencing distance could lead to nested structure even at a small extent spatial scale. The results provide a basis for the determination of remediation and prevention objectives at different spatial scales.

A new method for estimating ore grade based on sample length weighting

Estimation of ore grade is very important for the value evaluation of ore deposits, and it directly affects the development of mineral resources. To improve the accuracy of the inverse distance weighting (IDW) method in ore grade estimation and reduce the smoothing effect of the IDW method in grade estimation, the weight calculation method involved in the IDW method was improved. The length parameter of the ore sample was used to calculate the weight of the IDW method. The length of the ore samples was used as a new factor of the weighting calculation. A new method of IDW integrated with sample length weighting (IDWW) was proposed. The grade estimation of Li, Al, and Fe in porcelain clay ore was used as a case study. A comparative protocol for grade estimation via the IDWW method was designed and implemented. The number of samples involved in the estimation, sample combination, sample grade distribution, and other factors affecting the grade estimation were considered in the experimental scheme. The grade estimation results of the IDWW and the IDW methods were used for comparative analysis of grades of the original and combined samples. The estimated results of the IDWW method were also compared with those of the IDW method. The deviation analysis of the estimated grade mainly included the minimum, maximum, mean, and coefficient of variation of the ore grade. The estimation effect of IDWW method was verified. The minimum deviations of the estimated grade of Li, Al, and Fe were between 9.129% and 59.554%. The maximum deviations were between 4.210 and 22.375%. The mean deviations were between - 1.068 and 7.187%. The deviations in the coefficient of variation were between 3.076 and 36.186%. The deviations in the maximum, minimum, mean, and coefficients of variation of the IDWW were consistent with those of the IDW, demonstrating the accuracy and stability of the IDWW method. The more the samples involved in the estimation, the greater the estimation deviations of IDW and IDWW methods. The estimated deviations of Li, Al, and Fe were affected by the shape of the grade distribution, when the same estimation parameters were used. The grade distribution pattern of the samples significantly influenced the grade estimation results. The IDWW method offers significant theoretical advantages and addresses the adverse effects of uneven sample lengths on the estimates. The IDWW method can effectively reduce the smoothing effect and improves the utilization efficiency of the original samples.

Heavy metal mapping, source identification, and ecological risk assessment in the International Hamoun wetland, Sistan region, Iran

This study is aimed at assessing the ecological risk of heavy metals (HMs) in the International Hamoun wetland, southeastern Iran. Twenty sediment samples were collected from the wetland surface for geochemical analysis of 23 HMs. The inverse distance weighting (IDW) technique was used to map the HMs. The single and multi-element pollution indicators and PER index (PERI) were respectively used to determine the contamination intensity and PER level. The principal components analysis (PCA) was performed to identify the HM source. The mean concentration of cesium (Cs: 5.2 µg/g), selenium (Se: 0.9 µg/g), and tellurium (Te: 0.2 µg/g) was higher than their mean values in the Earth's crust. The enrichment factor (EF) showed the Hamoun was high to extremely enriched by Te, As, and Se. The geo-accumulation index (GeoI) revealed the highest level of contamination caused by As, barium (Ba), cobalt (Co), chromium (Cr), cuprum (Cu), ferrum (Fe), manganese (Mn), nickel (Ni), lead (Pb), rubidium(Rb), titanium (Ti), vanadium(V), yttrium (Y), and zinc (Zn) in most study sites. The sediment contamination factor in more than 55% of the sediment samples was between 8 and 16, indicating very high contamination intensity in the studied wetland. The PER values were between 80 and 160 in more than 60% of the sediment samples, suggesting a considerable risk in the wetland. The PCA showed both anthropogenic and crustal activities were effective in increasing the concentration of HMs in the wetland. The largest ecological risk was due to arsenic (As) and cadmium (Cd). It is recommended to pay more attention to these HMs, which could cause more environmental pollution in the International Hamoun wetland, southeastern Iran.

Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine learning

The ability to control the risk of soil heavy metal pollution is limited by the inability to accurately depict their spatial distributions and to reasonably delineate the risk zones. To overcome this limitation and develop machine learning methods, a hybrid data-driven method supported by random forest (RF) and fuzzy c-means with the aid of inverse distance weighted interpolation was proposed to delineate and further identify risk zones of soil heavy metal pollution on the basis of 577 soil samples and 12 environmental covariates. The results indicated that, compared to multiple linear regression, RF had a better prediction performance for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, with the corresponding R² values of 0.86, 0.85, 0.78, 0.85, 0.84, 0.78, 0.79 and 0.76, respectively. The relative concentrations (predicted concentrations divided by risk screening values) of Cd (17.69), Cr (1.38), Hg (0.31), Pb (6.52), and Zn (8.24) were relatively high in the north central part of the study area. There were large differences in the key influencing factors and their contributions among the eight heavy metals. Overall, industrial enterprises (21.60% for As), soil pH (31.60% for Cd), and population (15.50% for Cr) were the key influencing factors for the heavy metals in soil. Four risk zones, including one high risk zone, one medium risk zone, and two low risk zones were delineated and identified based on the characteristics of the eight heavy metals and their influencing factors, and accordingly discriminated risk control strategies were developed. In the high risk zone, it will be necessary to strictly control the discharge of heavy metals from the various industrial enterprises and mines by the adoption of cleaner production practices, centralizedly treat the domestic wastes from residents, substantially reduce the irrigation of polluted river water, and positively remediate the Cd, Cr, and Ni-polluted soil.

Achieving Carbon Neutrality through Urban Planning and Design

Much of the research on climate change has focused on carbon reduction in cities or countries. However, more attention needs to be paid to how to achieve carbon neutrality in the urban design and planning stage, and the lack of quantitative analysis of carbon related to urban space makes it difficult to locate urban space and provide direct guidance for urban planning and design. This study proposed three optimization paths to achieve carbon neutrality in multi-scale urban building clusters. Firstly, we reconstructed the quantitative calculation system of urban building communities with the goal of carbon neutrality; secondly, we screened the carbon source reduction and carbon sink interventions that are suitable for multi-scale urban building communities; finally, we constructed a carbon emission and carbon sink calculation system of planning and design schemes based on the layout of relevant elements of planning and design schemes with a grid cell of 100 × 100 m. In practice, there was a gap of about 115,000 tons of CO₂ from the carbon-neutral target and 26% of carbon emission was distributed in the Xiajiabian Station TOD. In this study, nine types of carbon reduction measures were adopted to achieve carbon neutrality in the region, among which the highest carbon reduction was achieved by biomass energy measures, accounting for 29% of the total carbon reduction of 33,745.27 T. The objective of this study is to accurately and quantitatively assess the carbon targets of urban spaces at different scales and adopt effective measures to achieve carbon neutrality.

Effects of Ambient Air Pollution on Precocious Puberty: A Case-Crossover Analysis in Nanjing, China

BACKGROUND

Ambient air pollution is closely related to a variety of health outcomes. Few studies have focused on the correlations between air pollution exposure and children's sexual development. In this study, we investigated the potential effects of exposure to air pollution on precocious puberty among children using real-world evidence.

METHODS

We conducted a case-crossover study (n = 2201) to investigate the effect of ambient air pollution exposure on precocious puberty from January 2016 to December 2021. Average exposure levels of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃ before diagnosis were calculated by using the inverse distance weighting (IDW) method. Distributed lag nonlinear model (DLNM) was used to assess the effect of air pollutants exposure on precocious puberty.

RESULTS

The mean age of the children who were diagnosed with precocious puberty was 7.47 ± 1.24 years. The average concentration of PM_2.5 and PM₁₀ were 38.81 ± 26.36 μg/m³ and 69.77 ± 41.07 μg/m³, respectively. We found that exposure to high concentrations of PM_2.5 and PM₁₀ might increase the risk of precocious puberty using the DLNM model adjusted for the age, SO₂, NO₂, CO, and O₃ levels. The strongest effects of the PM_2.5 and PM₁₀ on precocious puberty were observed in lag 27 (OR = 1.72, 95% CI: 1.01-2.92) and lag 16 (OR = 1.95, 95% CI: 1.33-2.85), respectively.

CONCLUSION

Our findings supported that short-term exposure to air pollution was the risk factor for precocious puberty. Every effort should be made to protect children from air pollution.

Optimizing spatial interpolation method and sampling number for predicting cadmium distribution in the largest shallow lake of North China

Cadmium (Cd) pollution has been widely recognized in lake ecosystems. Although the accurate prediction of the spatial distributions of Cd in lakes is important for controlling Cd pollution, the traditional monitoring methods of setting discrete and limited sampling points cannot actually reflect the continuous spatial distribution characteristics of Cd. In this study, we set up 93 sampling points in Baiyangdian Lake (BYDL), and collected surface water, overlying water and sediment samples from each sampling point. Cd contents were measured to predict their spatial distributions in different environmental components by three interpolation methods, inverse distance weighted (IDW), radial basis function (RBF) and ordinary kriging (OK), and the effects of different sampling numbers on the interpolation accuracy were also assessed to optimize the interpolation method and sampling number. The results showed that the interpolation accuracy of IDW decreased with increasing power values. The best basis function for RBF was IMQ, and the best semivariogram models for OK were the spherical model and stable model. The best interpolation method for the waters and sediments was RBF-IMQ compared with OK and IDW. Within the sampling number range of 50-93, the interpolation accuracy for Cd in surface water increased with the increase in sampling number. Comparatively, the interpolation accuracy was the highest for overlying water and sediments when the sampling number was 60. The findings of this work provide a combined sampling and spatial interpolation method for monitoring the spatial distribution and pollution levels of Cd in the waters and sediments of shallow lakes.

Low-complexity mapping of soil temperature for thermal treatment follow-up

Thermal desorption is a method of soil treatment that heats soil in order to vaporize and extract contaminants. It relies on temperature measurements to assess the progress of the remediation, but these measurements are generally not numerous because of cost constraints. This paper proposes a low-complexity method to interpolate sparse temperature data over the whole site to generate visual representations that ease the treatment follow-up. The temperatures of the points that are not monitored are approximated by a weighted average of the 3 closest measurements, then a third-degree polynomial is fitted to the data via a finite element method. The resulting approximations yield an overall Root Mean Square Error (RMSE) of the temperature estimation of 35 K, which allows for realistic representations of the temperature at each point of the map with reduced sensor deployment.

Evaluation and Spatial Variability of Cryogenic Soil Properties (Yamal-Nenets Autonomous District, Russia)

Agricultural development in northern polar areas has potential as a result of global warming. Such expansion requires modern soil surveys and large-scale maps. In this study, the abandoned arable experimental field founded by I.G. Eichfeld one century ago in Salekhard city (Russian Arctic), located in the polar circle, was investigated. Our aims were to assess the nutritional soil properties and their spatial variability. For spatial assessment and mapping, ordinary kriging (OK) and inverse distance-weighted (IDW) methods were employed. We found that due to long-term agriculture use, the soil cover was represented by a unique Plaggic Podzol (Turbic) that is not typical of the region. The soil was characterized by relatively low soil organic carbon (SOC) content, high acidity and a high content of plant-available forms of phosphorus in the humus-accumulative horizon. The results showed that some properties (pH H2O, pH CaCl2) were characterized by large-scale heterogeneity and showed clear spatial dependence. However, some properties (ammonium and nitrate nitrogen, basal respiration) showed a pure-nugget effect, presumably due to experimentation with fertilizer over a long period of time.

Effectiveness of predicting the spatial distributions of target contaminants of a coking plant based on their related pollutants

The prediction accuracy of the spatial distribution of soil pollutants at a site is relatively low. Related pollutants can be used as auxiliary variables to improve the prediction accuracy. However, little relevant research has been conducted on site soil pollution. To analyze the prediction accuracy of target pollutants combined with auxiliary pollutants, Cu, toluene, and phenanthrene were selected as the target pollutants for this study. Based on geostatistical analysis and spatial analysis, the following results were obtained. (1) The reduction in the root mean square errors (RMSEs) for Cu, toluene, and phenanthrene with multivariable cokriging was 68.4%, 81.6%, and 81.2%, respectively, which are proportional to the correlation coefficient of the relationship between the auxiliary pollutants and the target pollutants. (2) The RMSEs calculated for the multivariable cokriging were lower than those obtained by only combining one related pollutants, and two co-variables should be better. (3) The predicted results for Cu, phenanthrene, and toluene and their corresponding related pollutants are more accurate than the results obtained not using the related pollutants. (4) In the interpolation process, the RMSEs for Cu, toluene, and phenanthrene with multivariable cokriging basically increase as the neighborhood sample data increases, and then they become stable. (5) When 84, 61, and 34 sample points were removed, the RMSEs for Cu, toluene, and phenanthrene, respectively, with multivariable cokriging were close to the RMSEs of the target pollutants based on the total samples. The results are of great significance to improving the prediction accuracy of the spatial distribution of soil pollutants at coking plant sites.

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil

Increasing concentrations of potentially toxic elements (PTE) in agricultural soils remain a major source of public concern. Monitoring PTEs in an agricultural field with no history of contaminants necessitate adequate analysis utilizing a robust model to accurately uncover hidden PTEs. Detecting and mapping the distribution of soil properties using portable X-ray fluorescence (pXRF) and proximal sensing techniques is not only rapid, but also relatively inexpensive. In this study, an ensemble model, consisting of partial least square regression (PLSR), support vector machine (SVM), random forest (RF) and cubist, was used for the prediction and mapping of soil As content in an agricultural field with no history of pollution. The datasets were collected using pXRF and field spectroscopy techniques. The main goal was to compare the ensemble model to each of the calibration techniques in terms of prediction accuracy of As content in such a field. Other components [e.g., soil organic carbon (SOC), Mn, S, soil pH, Fe] that are known to influence As levels in the soil were also retrieved to assess their correlation with soil As. The models were evaluated using the root mean squared error (RMSE_CV), the coefficient of determination (R²_CV) and the ratio of performance to interquartile range (RPIQ). In terms of prediction accuracy, the ensemble model outperformed each of the individual techniques (R²_CV = 0.80/0.75) and obtained the least error margin (RMSE_CV = 1.91/2.16). Overall, all the predictive techniques were able to detect both low and high estimated values of soil As within the study field, but with the ensemble model resembling the measurements better. The ensemble model, a promising tool as demonstrated by the current study, is highly recommended to be included in future studies for more accurate estimation of As and other PTEs in other agricultural fields.

Association of short-term exposure to air pollution with recurrent ischemic cerebrovascular events in older adults

The acute effects of ambient air pollution on recurrence of ischemic cerebrovascular events (ICEs) remains largely unknown. We therefore conducted a time-stratified case-crossover study of 43,896 patients who were 60 years or older and were admitted to hospital for recurrent ICEs including ischemic stroke and transient ischemic attack in Guangzhou, China during 2016-2019. Based on each patient's home address and pollutant data from its neighboring air quality monitoring stations, we used an inverse distance weighting method to assess exposures to particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5), particulate matter with an aerodynamic diameter ≤10 μm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO) and ozone (O₃). Conditional logistic regression models were used to quantify exposure-response associations. During the study period, there were 43,896 case days and 149,131 control days. In single-pollutant models, each 10 μg/m³ increase in exposure to PM₁₀, NO₂ and CO (mean exposure on date of admission and 1 day prior) was significantly associated with a 0.74% (95% confidence interval [CI]: 0.13-1.36%), 2.15% (1.38-2.93%) and 0.14% (0.07-0.21%) increase in odds of hospital admissions for recurrent ICEs, respectively, and no significant departures from linearity were detected. The association for NO₂ exposure remained consistent in 2-pollutant models, while the associations for PM₁₀ and CO disappeared or changed materially with adjustment for other pollutants. Stronger association for NO₂ exposure was observed in cool season than that in warm season. We found that short-term exposure to ambient air pollutants, especially NO₂, was associated with increased risk of hospital admissions for recurrent ICEs in older adults.

A Hybrid Neural Network-Particle Swarm Optimization Informed Spatial Interpolation Technique for Groundwater Quality Mapping in a Small Island Province of the Philippines

Water quality monitoring demands the use of spatial interpolation techniques due to on-ground challenges. The implementation of various spatial interpolation methods results in significant variations from the true spatial distribution of water quality in a specific location. The aim of this research is to improve mapping prediction capabilities of spatial interpolation algorithms by using a neural network with the particle swarm optimization (NN-PSO) technique. Hybrid interpolation approaches were evaluated and compared by cross-validation using mean absolute error (MAE) and Pearson’s correlation coefficient (R). The governing interpolation techniques for the physicochemical parameters of groundwater (GW) and heavy metal concentrations were the geostatistical approaches combined with NN-PSO. The best methods for physicochemical characteristics and heavy metal concentrations were observed to have the least MAE and R values, ranging from 1.7 to 4.3 times and 1.2 to 5.6 times higher than the interpolation technique without the NN-PSO for the dry and wet season, respectively. The hybrid interpolation methods exhibit an improved performance as compared to the non-hybrid methods. The application of NN-PSO technique to spatial interpolation methods was found to be a promising approach for improving the accuracy of spatial maps for GW quality.

Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA

Inorganic contaminants, including potentially toxic metals (PTMs), originating from un-reclaimed abandoned mine areas may accumulate in soils and present significant distress to environmental and public health. The ability to generate realistic spatial distribution models of such contamination is important for risk assessment and remedial planning of sites where this has occurred. This study evaluated the prediction accuracy of optimized ordinary kriging compared to spatial regression-informed cokriging for PTMs (Zn, Mn, Cu, Pb, and Cd) in soils near abandoned mines in Bumpus Cove, Tennessee, USA. Cokriging variables and neighborhood sizes were systematically selected from prior statistical analyses based on the association with PTM transport and soil physico-chemical properties (soil texture, moisture content, bulk density, pH, cation exchange capacity (CEC), and total organic carbon (TOC)). A log transform was applied to fit the frequency histograms to a normal distribution. Superior models were chosen based on six diagnostics (ME, RMS, MES, RMSS, ASE, and ASE-RMS), which produced mixed results. Cokriging models were preferred for Mn, Zn, Cu, and Cd, whereas ordinary kriging yielded better model results for Pb. This study determined that the preliminary process of developing spatial regression models, thus enabling the selection of contributing soil properties, can improve the interpolation accuracy of PTMs in abandoned mine sites.

Identifying Influencing Factors of Agricultural Soil Heavy Metals Using a Geographical Detector: A Case Study in Shunyi District, China

Identifying influencing factors of heavy metals is essential for soil evaluation and protection. This study investigates the use of a geographical detector to identify influencing factors of agricultural soil heavy metals from natural and anthropogenic aspects. We focused on six variables of soil heavy metals, i.e., As, Cd, Hg, Cu, Pb, Zn, and four influencing factors, i.e., soil properties (soil type and soil texture), digital elevation model (DEM), land use, and annual deposition fluxes. Experiments were conducted in Shunyi District, China. We studied the spatial correlations between variables of soil heavy metals and influencing factors at both single-object and multi-object levels. A geographical detector was directly used at the single-object level, while principal component analysis (PCA) and geographical detector were sequentially integrated at the multi-object level to identify influencing factors of heavy metals. Results showed that the concentrations of Cd, Cu, and Zn were mainly influenced by DEM (p = 0.008) and land use (p = 0.033) factors, while annual deposition fluxes were the main factors of the concentrations of Hg, Cd, and Pb (p = 0.000). Moreover, the concentration of As was primarily influenced by soil properties (p = 0.026), DEM (p = 0.000), and annual deposition flux (p = 0.000). The multi-object identification results between heavy metals and influencing factors included single object identification in this study. Compared with the results using the PCA and correlation analysis (CA) methods, the identification method developed at different levels can identify much more influencing factors of heavy metals. Due to its promising performance, identification at different levels can be widely employed for soil protection and pollution restoration.

An effective method for determining the optimal sampling scale based on the purposes of soil pollution investigations and the factors influencing the pollutants

There is a lack of a systematic method for determining the optimal sampling scale based on the purposes of soil pollution investigations (purpose_invest) and the factors influencing of pollutants, which could affect the accuracy of determining pollution scope of the pollution. Therefore, in this study, both the purpose_invest and the influencing factors were considered to determine the optimal sampling scale. The conclusions were obtained through geostatistical and spatial analysis. (1) The optimal sampling scale should account for 3% of the range of the pollutants, which can identify pollution information and minimize sampling costs. (2) The optimal sampling scale should be set to 3% of the range of the main factor influencing the pollutants in the absence of prior pollution information. (3) The greater the influences of the factors on the pollutants, the closer the optimal sampling scale calculated according to the influencing factors will be to that calculated based on the purpose_invest. (4) The method of determination based on both the purpose_invest and the influencing factors was concluded to be rational and reliable based on validation and advantage analysis. These results provide a method for soil pollution investigation that can minimize costs and improve the representativeness of the sample sites.

The assessment of annual rainfall field by applying different interpolation methods in the state of Rio Grande do Sul, Brazil

An accurate analysis of spatial rainfall distribution is of great importance for managing watershed water resources, in addition to giving support to meteorological studies and agricultural planning. This work compares the performance of two interpolation methods: Inverse distance weighted (IDW) and Kriging, in the analysis of annual rainfall spatial distribution. We use annual rainfall data for the state of Rio Grande do Sul (Brazil) from 1961 to 2017. To determine which proportion of the sample results in more accurate rainfall distribution maps, we use a certain amount of points close to the estimated point. We use mean squared error (MSE), coefficient of determination (R2), root mean squared error (RMSE) and modified Willmott's concordance index (md). We conduct random fields simulations study, and the performance of the geostatistics and classic methods for the exposed case was evaluated in terms of precision and accuracy obtained by Monte Carlo simulation to support the results. The results indicate that the co-ordinary Kriging interpolator showed better goodness of fit, assuming altitude as a covariate. We concluded that the geostatistical method of Kriging using nine closer points (50% of nearest neighbors) was the one that better represented annual rainfall spatial distribution in the state of Rio Grande do Sul.

An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects.

Concentration and Spatial Distribution of Potentially Toxic Elements in Surface Soil of a Peak-Cluster Depression, Babao Town, Yunnan Province, China

Potentially toxic elements (PTEs) in Chinese agricultural soils, including those in some heritage protection zones, are serious and threaten food safety. Many scientists think that these PTEs may come from parent rock. Hence, at a karst rice-growing agricultural heritage area, Babao town, Guangnan County, Yunnan Province, China, the concentrations of eight PTEs (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) were determined in 148 surface soil, 25 rock, and 52 rice grain samples. A principal component analysis (PCA) and hierarchical cluster analysis were used to divide the surface soil into groups, and inverse distance weighting (IDW) was used to analyze the spatial distribution of PTEs. Soil pollution was assessed with the geoaccumulation index (Igeo). The results show that Cd, Hg, Zn, and Cr were polluting the soil (average Igeo > 0). The highest concentration of PTEs was distributed in the southwest of Babao town in the carbonate rock area, which had the highest pH and soil total organic carbon (Corg), Mn, and TFe₂O₃ contents. PCA biplots of soil samples showed that the carbonate rock area was associated with the most species of PTEs in the study area including Pb, Cd, Hg, As, and Zn. The clastic rock area was associated with Cu and Ni, and the lime and cement plants were associated with CaO, pH, Corg, TC, and aggravated PTE pollution around factories. In high-level PTE areas, rice was planted. Two out of 52 rice grain samples contained Cd and 4 out of 52 rice grain samples had Cr concentrations above the Chinese food safety standard pollutant limit (Cd 0.2 mg/kg; Cr 1 mg/kg). Therefore, the PTEs from parent rocks are already threatening rice safety. The government should therefore plan rice cultivation areas accordingly.

Human health risk visualization of potentially toxic elements in farmland soil: A combined method of source and probability

Human health is adversely affected by potentially toxic elements (PTEs) in the topsoil, entering the bodies via inhalation, ingestion, and dermal contact. To visualize human health risks, we investigated five PTEs (Cd, As, Pb, Hg, and Cr) in 72 farmland topsoil samples from a town in Chongqing City, southwest China. Based on the human health risk assessment model, sequential indicator simulation (SIS) and the positive matrix factorization model (PMF) were used to construct the spatial health risks and to analyze the sources of PTEs; finally, health risks were combined with the source by ArcGIS. Based on our results, the use of SIS is feasible for the prediction of the spatial distribution of PTEs. Among the risks, the non-cancer risk of As for children most likely exceeded the accepted level in some areas, making As a priority pollutant. Although the health risks of soil Cd were acceptable in the region, the spatial probability distribution of Cd> 0.3 mg/kg represents a threat as Cd enters the human food chain. Even if the industrial discharge was the lowest individual contributor (29.33%), due to the impact of industrial discharge, the total non-cancer risk with a high probability (>0.85) for children still exceeded the accepted level in the northwestern area, which should be regarded as the priority pollution source. The combined method was useful to reduce efforts in environmental management, thus providing a basis for soil remediation and pollution source control.

An Adaptive Inverse-Distance Weighting Interpolation Method Considering Spatial Differentiation in 3D Geological Modeling

The inverse-distance weighting interpolation is widely used in 3D geological modeling and directly affects the accuracy of models. With the development of “smart” or “intelligent” geology, classical inverse-distance weighting interpolation cannot meet the accuracy, reliability, and efficiency requirements of large-scale 3D geological models in these fields. Although the improved inverse-distance weighting interpolation can basically meet the requirements of accuracy and reliability, it cannot meet the requirements of efficiency at the same time. In response to these limitations, the adaptive inverse-distance weighting interpolation method based on geological attribute spatial differentiation and geological attribute feature adaptation was proposed. This method takes into account the spatial differentiation of geological attributes to improve the accuracy and considers the first-order neighborhood selection strategy to adaptively improve efficiency to meet above requirements of large-scale geological modeling. The proposed method was applied to an area in eastern China, and the results of the proposed method, compared to the results of classical inverse-distance weighting interpolation and improved inverse-distance weighting interpolation, suggest that the problems encountered above in large-scale geological modeling can be solved with the proposed method. The method can provide effective support for large-scale 3D geological modeling in smart geology.

Quantitative analysis of the main sources of pollutants in the soils around key areas based on the positive matrix factorization method

Quantitative identification of the main sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in soils around multiple types of key areas is of great significance for blocking pollution sources. However, there is a lack of more comprehensive relevant research. In this study, Beijing was taken as the research area and four main sources were identified using the positive matrix factorization (PMF) method. The concentration of Pb, PAHs, Cr, and Hg in soils was significantly affected by the presence of landuse type, road traffic, natural factor, and industrial production, respectively, and the farmland, distance to main road, Proterozoic Changcheng-Jixian parent material and cinnamon soil type, and the gross industrial production make greater contributions to these four factors respectively than other variables. Moreover, the uncertainty of the PMF indicates that this four-factor PMF solution is stable and appropriate. These results provide support for the comprehensive control of soil environmental risks.

Nighttime environmental noise and semen quality: A single fertility center cohort study

With increased population and urban development, there are growing concerns regarding health impacts of environmental noise. We assessed the relationship between nighttime environmental noise and semen quality of men who visited for fertility evaluation. This is a retrospective cohort study of 1,972 male patient who had undertaken semen analysis between 2016-2018 at a single fertility center of Seoul, South Korea. We used environmental noise data of National Noise Information System (NNIS), Korea. Using semiannual nighttime noise measurement closest to the time of semen sampling, individual noise exposures at each patient's geocoded address were estimated with empirical Bayesian kriging method. We explored the association between environmental noise and semen quality indicators (volume, concentration, % of progressive motility, vitality, normal morphology, total motile sperm count, oligozoospermia, asthenozoospermia, and severe teratozoospermia) using multivariable regression and generalized additive models. Estimated exposure to nighttime environmental noise level in the study population was 58.3±2.2 Leq. Prevalence of oligozoospermia, asthenozoospermia, and severe teratozoospermia were 3.3%, 14.0%, and 10.1%. Highest quartile nighttime noise was associated with 3.5 times higher odds of oligozoospermia (95% CI: 1.18, 10.17) compared to lowest quartile. In men whose noise exposure is in 3rd quartile, odds ratio (OR) of severe teratozoospermia was 0.57 (95% CI: 0.33, 0.98). The OR for 4th quartile noise were toward null. In generalized additive model, the risk of oligozoospermia increases when the nighttime noise is 55 Leq dB or higher. Our study adds an evidence of potential impact of environmental noise on semen quality in men living in Seoul. Additional studies with more refined noise measurement will confirm the finding.

Optimal Soybean (Glycine max L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images

Soybean is regarded as one of the most produced crops in the world, presenting a source of high-quality protein for human and animal diets. The general objective of the study was to determine the optimal soybean land suitability and conduct its mapping based on the multicriteria analysis. The multicriteria analysis was based on Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) integration, using Sentinel-2 multitemporal images for suitability validation. The study area covered Osijek-Baranja County, a 4155 km2 area located in eastern Croatia. Three criteria standardization methods (fuzzy, stepwise and linear) were evaluated for soybean land suitability calculation. The delineation of soybean land suitability classes was performed by k-means unsupervised classification. An independent accuracy assessment of calculated suitability values was performed by a novel approach with peak Normalized Difference Vegetation Index (NDVI) values, derived from four Sentinel-2 multispectral satellite images. Fuzzy standardization with the combination of soil and climate criteria produced the most accurate suitability values, having the top coefficient of determination of 0.8438. A total of 14.5% of the study area (602 km2) was determined as the most suitable class for soybean cultivation based on k-means classification results, while 64.3% resulted in some degree of suitability.

Edible weeds: Are urban environments fit for foraging?

Foraging wild-growing edible plants (WEPs) is a re-emerging practice with increasing popularity worldwide, including in urban areas. However, in cities, this practice raises questions about the safety of foraging these plants for human consumption, due to the potential exposure of plants to higher levels of pollutants. In this study, the concentration of 12 elements (Al, V, Cr, Mn, Co, Ni, Zn, As, Rb, Cd, Ba and Pb) in three different WEPs (Amaranthus spp., Plantago tomentosa and Taraxacum officinale) were determined according to different traffic categories in the municipality of São Paulo. Additionally, plants were sampled within the inner areas of three municipal parks in the same study region. Different gradients of elemental concentrations were obtained according to the traffic categories. Freeways presented higher concentrations of several elements than local roads or parks. For the WEPs collected along freeways and some plants along arterial roads, the concentrations of Pb exceeded safety levels for human consumption. Our data suggest that foraging in large urban centres should be performed preferentially in low-traffic areas.

Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated sites

Accurate prediction of the spatial distribution of pollutants in soils based on applicable interpolation methods is often the basis for soil remediation in contaminated sites. However, the applicable interpolation method has not been determined for contaminated sites due to the complex spatial distribution characteristics and stronger local spatial variability of pollutants. In this research, the prediction accuracies of three interpolation methods (including the different values of their parameters) for the spatial distribution of benzo[b]fluoranthene (BbF) in four soil layers were compared. These included inverse distance weighting (IDW), radial basis function (RBF), ordinary kriging (OK). The results indicated: (1) IDW1 is applicable for the first layer, RBF-IMQ is applicable to the second, third, and fourth layers. (2) For IDW, the prediction error is bigger with high weight where high values and low values intersect, while the prediction error is smaller where high (or low) values aggregated distribution. (3) For RBF, if the pollutant concentration trend at the predicted location is consistent with the known points in its neighborhood, the prediction accuracy is higher. (4) IDW is suitable for fitting more drastic curved surfaces, while RBF is more effective for relatively gentle curved surfaces and OK is reasonable for curved surfaces without local outliers. (5) The interpolation uncertainty is positively associated with the contaminant concentration and local spatial variability. Therefore, we suggest the selection of the applicable interpolation model must be based on the principle of the model and the spatial distribution characteristics of the pollutants.

Spatial and Vertical Variations and Heavy Metal Enrichments in Irrigated Soils of the Syr Darya River Watershed, Aral Sea Basin, Kazakhstan

In the Syr Darya River watershed, 225 samples from three different layers in 75 soil profiles were collected from irrigated areas in three different spatial regions (I: n = 29; II: n = 17; III: n = 29), and the spatial and vertical variation characteristics of potentially toxic elements (Cd, Co, Cu, Ni, and Zn) and a metallic element (Mn) were studied. The human health risks and enrichment factors were also evaluated in the Syr Darya River watershed of the Aral Sea Basin in Kazakhstan. There were significant differences in the contents of heavy metals in the different soil layers in the different sampling regions. Based on element variation similarity revealed by hierarchical cluster analysis, the elemental groupings were consistent in the different layers only in region I. For regions II and III, the clustered elemental groups were the same between surface layer A and B, but differed from those in the deep layer C. In sampling region I, the heavy metals in surface soils were significantly correlated with the ones in deep layers, reflecting that they were mainly affected by the elemental composition of parent materials. In region II, the significant correlations only existed for Cu, Mn, and Zn between the surface and deep layers. The similar phenomenon with significant correlation was also observed for heavy metals in sampling region III, except for Cd. Finally, enrichment factor was used to study the mobilization and enrichment of potentially toxic elements. The enrichment factors of Zn, Cu, and Cd in surface layer A that were greater than 1.5 accounted for 1.16%, 6.79%, and 24.36% of sampling region I, respectively. In sampling region II, the enrichment factors of Zn, Cu, Cd, and Co that were greater than 1.5 accounted for 0.03%, 4.76%, 0.54%, and 9.03% of the total area, respectively. In sampling region III, only the enrichment factors of Zn, Cu, and Cd that exceeded 1.5 accounted for 0.24%, 4.90%, and 6.89% of the total area, respectively. Although the contents of the heavy metals were not harmful to human health, the effects of human activities on the heavy metals in the irrigated soils revealed by enrichment factors have been shown in this study area.