Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

Prediction of soil heavy metal contents in urban residential areas and the strength of deep learning: A case study of Beijing

Predicting soil heavy metal (SHM) content is crucial for understanding SHM pollution levels in urban residential areas and guide efforts to reduce pollution. However, current research indicates low SHM prediction accuracy in urban areas. Therefore, we employed a deep learning method (fully connected deep neural network) alongside four other methods (muti-layer perceptron, radial basis function neural network, multiple stepwise linear regression, and Kriging interpolation) to predict SHM content in the urban residential areas of Beijing and demonstrated the strength of deep learning in improving prediction accuracy. We found the contents of the evaluated heavy metals (Cd, Cu, Pb, and Zn) exhibited significant correlations with numerous other soil physicochemical properties and environmental factors. The prediction accuracy for Cu, Pb, and Zn contents was relatively high across different methods. Notably, deep learning showed considerable strength in predicting the contents of the four heavy metals, with the R2 for the test set of the model ranging from 0.75 to 0.91. Compared to other methods, deep learning achieved markedly higher prediction accuracy according to different accuracy evaluation indicators (e.g., deep learning showed increases in the cumulative R2 of the four heavy metals ranging from 53.16 % to 187.36 % compared to other methods). Our study indicates that deep learning can significantly improve SHM content prediction accuracy in urban areas and is highly applicable in urban residential areas with complex environmental influences.

Evaluation of the prediction effectiveness for geochemical mapping using machine learning methods: A case study from northern Guangdong Province in China

This study compares seven machine learning models to investigate whether they improve the accuracy of geochemical mapping compared to ordinary kriging (OK). Arsenic is widely present in soil due to human activities and soil parent material, posing significant toxicity. Predicting the spatial distribution of elements in soil has become a current research hotspot. Lianzhou City in northern Guangdong Province, China, was chosen as the study area, collecting a total of 2908 surface soil samples from 0 to 20 cm depth. Seven machine learning models were chosen: Random Forest (RF), Support Vector Machine (SVM), Ridge Regression (Ridge), Gradient Boosting Decision Tree (GBDT), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Gaussian Process Regression (GPR). Exploring the advantages and disadvantages of machine learning and traditional geological statistical models in predicting the spatial distribution of heavy metal elements, this study also analyzes factors affecting the accuracy of element prediction. The two best-performing models in the original model, RF (R2 = 0.445) and GBDT (R2 = 0.414), did not outperform OK (R2 = 0.459) in terms of prediction accuracy. Ridge and GPR, the worst-performing methods, have R2 values of only 0.201 and 0.248, respectively. To improve the models' prediction accuracy, a spatial regionalized (SR) covariate index was added. Improvements varied among different methods, with RF and GBDT increasing their R2 values from 0.4 to 0.78 after enhancement. In contrast, the GPR model showed the least significant improvement, with its R2 value only reaching 0.25 in the improved method. This study concluded that choosing the right machine learning model and considering factors that influence prediction accuracy, such as regional variations, the number of sampling points, and their distribution, are crucial for ensuring the accuracy of predictions. This provides valuable insights for future research in this area.

The rapid increase of urban contaminated sites along China's urbanization during the last 30 years

Contaminated sites pose serious threats to the soil environment and human health. However, the location and temporal changes of urban contaminated sites across China remain unknown due to data scarcity. Here, we developed a machine-learning model to identify the contaminated sites using public data. Results show that the trained model with 2,005 surveyed site samples and six variables can achieve a model performance evaluation value of 0.86. 43,676 contaminated sites were identified from 83,498 polluting enterprise plots in China. However, these contaminated sites have significant spatiotemporal heterogeneity, mainly located in economically developed provinces, urban agglomerations, and core urban areas. Moreover, the contaminated sites increased by 325% along with urban expansion from 1990 to 2018. The abandoned contaminated sites increased rapidly, but the contaminated sites in production decreased continuously. This methodological framework and our findings contribute to the precise management of contaminated sites and provide insights into urban sustainable development.

Ecological risk characteristics of sediment-bound heavy metals in large shallow lakes for aquatic organisms: The case of Taihu Lake, China

Heavy metal contamination in the surface sediments of large shallow lakes in China is becoming increasingly serious. However, more attention has been paid to the human health risk of heavy metals in the past, while little consideration has been given to aquatic organisms. Taking Taihu Lake as an example, we explored the spatial and temporal heterogeneity of the potential ecological risks of seven heavy metals (Cd, As, Cu, Pb, Cr, Ni, and Zn) to species at different taxonomic scales using an improved species sensitivity distribution (SSD) method. The results showed that all six heavy metals, except Cr, were exceeded to some extent compared to background levels, with Cd being the most severe exceedance. Based on the hazardous concentration for 5% of the species (HC₅), Cd had the lowest HC₅ value, implying the highest ecological risk of toxicity. Ni and Pb had the highest HC₅ values and the lowest risk. Cu, Cr, As and Zn were at a relatively moderate levels. For the different groups of aquatic organisms, the ecological risk of most heavy metals was generally lower for vertebrates than for the whole species. The risk for invertebrates and algae was higher than that for all species. Zn and Cu had the highest potentially affected fractions (PAFs) for all classification cases, with mean PAFs of 30.25% and 47.2%, respectively. Spatially, the high ecological risk of sediment heavy metals was significantly related to the spatial characteristics of the type and intensity of human activities in the catchment. Administratively, the environmental quality standards for freshwater sediments proposed by America and Canada are insufficient to protected against the ecological risks of heavy metals in Taihu Lake. In the absence of such standards, China urgently needs to establish an approptiate system of environmental quality standards for heavy metals in lake sediments.

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.

Ecotoxicology and geostatistical techniques employed in subtropical reservoirs sediments after decades of copper sulfate application

Spatial distribution linked to geostatistical techniques contributes to sum up information into an easier-to-comprehend knowledge. This study compares copper spatial distribution in surface sediments and subsequent categorization according to its toxicological potential in two reservoirs, Rio Grande (RG) and Itupararanga (ITU) (São Paulo-Brazil), where copper sulfate is applied and not applied, respectively. Sediments from 47 sites in RG and 52 sites in ITU were collected, and then, copper concentrations were interpolated using geostatistical techniques (kriging). The resulting sediment distributions were classified in categories based on sediment quality guides: threshold effect level and probable effect level; regional reference values (RRVs) and enrichment factor (EF). Copper presented a heterogenic distribution and higher concentrations in RG (2283.00 ± 1308.75 mg/kg) especially on the upstream downstream, associated with algicide application as well as the sediment grain size, contrary to ITU (21.81 ± 8.28 mg/kg) where a no-clear pattern of distribution was observed. Sediments in RG are predominantly categorized as "Very Bad", whereas sediments in ITU are mainly categorized as "Good", showing values higher than RRV. The classification is supported by the EF categorization, which in RG is primarily categorized as "Very High" contrasting to ITU classified as "Absent/Very Low". Copper total stock in superficial sediment estimated for RG is 4515.35 Ton of Cu and for ITU is 27.45 Ton of Cu.

Quantitative Assessment and Spatial Analysis of Metals and Metalloids in Soil Using the Geo-Accumulation Index in the Capital Town of Romblon Province, Philippines

The municipality of Romblon in the Philippines is an island known for its marble industry. The subsurface of the Philippines is known for its limestone. The production of marble into slab, tiles, and novelty items requires heavy equipment to cut rocks and boulders. The finishing of marble requires polishing to smoothen the surface. During the manufacturing process, massive amounts of particulates and slurry are produced, and with a lack of technology and human expertise, the environment can be adversely affected. Hence, this study assessed and monitored the environmental conditions in the municipality of Romblon, particularly the soils and sediments, which were affected due to uncontrolled discharges and particulates deposition. A total of fifty-six soil and twenty-three sediment samples were collected and used to estimate the metal and metalloid (MM) concentrations in the whole area using a neural network-particle swarm optimization inverse distance weighting model (NN-PSO). There were nine MMs; e.g., As, Cr, Ni, Pb, Cu, Ba, Mn, Zn and Fe, with significant concentrations detected in the area in both soils and sediments. The geo-accumulation index was computed to assess the level of contamination in the area, and only the soil exhibited contamination with zinc, while others were still on a safe level. Nemerow's pollution index (NPI) was calculated for the samples collected, and soil was evaluated and seen to have a light pollution level, while sediment was considered as "clean". Furthermore, the single ecological risk (Er) index for both soil and sediment samples was considered to be a low pollution risk because all values of Er were less than 40.

New Evidence of Semi-Mangrove Plant Barringtonia racemosa in Soil Clean-Up: Tolerance and Absorption of Lead and Cadmium

Mangrove plants play an important role in the remediation of heavy-metal-contaminated estuarine and coastal areas; Barringtonia racemosa is a typical semi-mangrove plant. However, the effect of heavy metal stress on this plant has not been explored. In this study, tolerance characteristics and the accumulation profile of cadmium (Cd) and lead (Pb) in B. racemosa were evaluated. The results indicated that B. racemosa exhibited a high tolerance in single Cd/Pb and Cd + Pb stress, with a significant increase in biomass yield in all treatment groups, a significant increase in plant height, leaf area, chlorophyll and carotenoid content in most treatment groups and without significant reduction of SOD, POD, MDA, proline content, Chl a, Chl b, Chl a + b, Car, ratio of Chl a:b and ratio of Car:Chl (a + b). Cd and Pb mainly accumulated in the root (≥93.43%) and the content of Cd and Pb in B. racemosa was root > stem > leaf. Pb showed antagonistic effects on the Cd accumulation in the roots and Cd showed antagonistic or synergistic effects on the Pb accumulation in the roots, which depended on the concentration of Cd and Pb. There was a significant synergistic effect of Cd and Pb enrichment under a low Cd and Pb concentration treatment. Thus, phytoremediation could potentially use B. racemosa for Cd and Pb.

Determining the Spatial Distribution Characteristics of Urban Regeneration Projects in China on the City Scale: The Case of Shenzhen

Urban regeneration (UR) has been a leading concern in urban studies globally. China’s rapid urbanization has undergone profound urban decay and social contestation, for which UR has emerged as a viable solution. However, UR is not without its drawbacks. It has caused emerging spatial and planning problems; however, few studies have explored the characteristics and issues of UR from the view of spatial analytics on the city scale. This study aims to depict the distribution characteristics of UR projects in Chinese cities and to reveal whether it meets the requirements of urban development from the planning perspective. The nearest neighbor index and its hierarchical clustering, as well as kernel density estimation are used in conjunction to investigate the spatial distribution characteristics; and the relationship between project distribution and each urban development indicator is explored using mixed spatial characteristics analyses, such as buffer analysis, space syntax, and heat mapping. Considering Shenzhen as the empirical study city, this research is based on all officially released data of implemented UR projects between 2010 and 2021. The findings imply that the UR projects in Shenzhen are mostly located in areas with higher economic development levels and accessibility with areas witnessing industrial restructuring and severe urban decay being prone to be designated for UR initiatives. The spatial distribution characteristics disclose the challenges inherent in the mix of top-down and market-driven UR approaches as well as the dilemma of the center-periphery pattern in UR implementation. Furthermore, the contradiction between the growing population and limited land resources as well as the barriers to industrial clustering formation are also revealed. This study enriches the methodological framework for spatial and visualization studies of urban regeneration in worldwide cities and sheds light on how to promote UR in regard to urban sustainability with ramifications for future urban development in other Chinese cities.

Trends in the impact of socioeconomic developments on polycyclic aromatic hydrocarbon concentrations in Dianchi Lake

An analysis of the correlation between polycyclic aromatic hydrocarbons (PAHs) and economic parameters demonstrates that the total population, gross domestic product, coal consumption, petroleum, temperature, and day consumption significantly affect PAH concentrations in Dianchi Lake, Yunnan province, China. An artificial neural network (ANN) model was developed to predict the trend in PAH concentrations in the sediments of Dianchi Lake over the next 10 years based on current indicators of economic development. The ANN model estimated the concentration of PAHs from 1980 to 2014. The model was evaluated using available observations for the historical trends; concentrations of PAHs in the sediments of Dianchi Lake are calculated to be at 2128.1 ng/g in 2025 and are expected to decline up to 1044.3 ng/g by 2030. These concentrations are considered relatively high because of their impacts on the health of people and aquatic organisms and the development of surrounding industries. We show the importance of the socioeconomic and climate factors in increasing the pollution levels. Our results could support the local government to formulate effective measures to reduce the pollution levels in the lake.

A Machine Learning Approach for Spatial Mapping of the Health Risk Associated with Arsenic-Contaminated Groundwater in Taiwan's Lanyang Plain

Groundwater resources are abundant and widely used in Taiwan’s Lanyang Plain. However, in some places the groundwater arsenic (As) concentrations far exceed the World Health Organization’s standards for drinking water quality. Measurements of the As concentrations in groundwater show considerable spatial variability, which means that the associated risk to human health would also vary from region to region. This study aims to adapt a back-propagation neural network (BPNN) method to carry out more reliable spatial mapping of the As concentrations in the groundwater for comparison with the geostatistical ordinary kriging (OK) method results. Cross validation is performed to evaluate the prediction performance by dividing the As monitoring data into three sets. The cross-validation results show that the average determination coefficients (R²) for the As concentrations obtained with BPNN and OK are 0.55 and 0.49, whereas the average root mean square errors (RMSE) are 0.49 and 0.54, respectively. Given the better prediction performance of the BPNN, it is recommended as a more reliable tool for the spatial mapping of the groundwater As concentration. Subsequently, the As concentrations estimated obtained using the BPNN are applied to develop a spatial map illustrating the risk to human health associated with the ingestion of As-containing groundwater based on the noncarcinogenic hazard quotient (HQ) and carcinogenic target risk (TR) standards established by the U.S. Environmental Protection Agency. Such maps can be used to demarcate the areas where residents are at higher risk due to the ingestion of As-containing groundwater, and prioritize the areas where more intensive monitoring of groundwater quality is required. The spatial mapping of As concentrations from the BPNN was also used to demarcate the regions where the groundwater is suitable for farmland and fishponds based on the water quality standards for As for irrigation and aquaculture.

Evaluation of Contamination and Ecological Risk of Heavy Metals Associated with Cement Production in Ewekoro, Southwest Nigeria

BACKGROUND

Exposure to heavy metals emanating from cement production and other anthropogenic activities can pose ecological risks.

OBJECTIVES

A detailed investigation was carried out to assess the contamination and ecological risk of heavy metals associated with dust released during cement production.

METHODS

Sixty samples, including 30 soils and 30 plants, were collected around Lafarge Cement Production Company. Control samples of soil and plants were collected in areas where human activities are limited. Samples were dried, sieved (for soil; 65 μm), packaged and analyzed using inductively coupled plasma mass spectrometry at Acme Laboratory in Canada.

RESULTS

The average concentration of heavy metals in soils of the area are: copper (Cu): 41.63 mg/kg; lead (Pb): 35.43 mg/kg; zinc (Zn): 213.64 mg/kg; chromium (Cr): 35.60 mg/kg; cobalt (Co): 3.84 mg/kg and nickel (Ni): 5.13 mg/kg. Concentrations of Cr in soils were above the recommended standards, while other metals were below recommended limits. The average concentrations of heavy metals in plants were: Cu: 26.32 mg/kg; Pb: 15.46 mg/kg; Zn: 213.94 mg/kg; Cr: 30.62 mg/kg; Co: 0.45 mg/kg and Ni: 3.77 mg/kg. Levels of heavy metals in plants were all above international limits. Geo-accumulation of metals in soils ranged between -0.15 and 6.32, while the contamination factor ranged between 0.53 and 119.59. Ecological risk index of heavy metals in soils ranged between 49.71 and 749.

DISCUSSION

All metals in soils of the study area except for Cr were below the allowable limits, while the levels of metals in plants were above the permissible limits. Levels of heavy metals reported in this study were higher than those from similar cement production areas. Soils around the Ewekoro cement production area were low to extremely contaminated by toxic metals. Cement production, processing, transportation in conjunction with the abandoned railway track in the area greatly contribute to the high degree of contamination observed in the area. Metal transfers from soil to plant are a common phenomenon. The metals pose low to considerable ecological risk.

CONCLUSIONS

Anthropogenic sources, especially cement processing activities, release heavy metals which leads to progressive pollution of the environment and poses high ecological risk.

COMPETING INTERESTS

The authors declare no competing financial interests.

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.

Use of GIS Tools in Sustainable Heritage Management—The Importance of Data Generalization in Spatial Modeling

Cultural heritage is a very important element affecting the sustainable development. To analyze the various forms of spatial management inscribed into sustainable development, information on the location of objects and their concentration at specific areas is necessary. The main goal of the article was to show the possibility of using various GIS tools in modeling the distribution of historical objects. For spatial analysis, it is optimal to use the point location of objects. Often, however, it is extremely difficult, laborious, expensive, and sometimes impossible to obtain. Thus, various map content generalizations were analyzed in the article; the main goal was to find the level for which the data with an acceptable loss of accuracy can be generalized. Such analyses can be extremely useful in sustainable heritage management. Article also shows how cultural heritage fits into the sustainable heritage management. The research included non-movable monuments in Poland. The obtained results showed the universality of this type of research both in the thematic sense (can be used for various types of objects) and spatial sense (can be performed locally, at the country level, or even at the continental level).

Research on Distributed 5G Signal Coverage Detection Algorithm Based on PSO-BP-Kriging

In order to overcome the limitations of traditional road test methods in 5G mobile communication network signal coverage detection, a signal coverage detection algorithm based on distributed sensor network for 5G mobile communication network is proposed. First, the received signal strength of the communication base station is collected and pre-processed by randomly deploying distributed sensor nodes. Then, the neural network objective function is modified by using the variogram function, and the initial weight coefficient of the neural network is optimized by using the improved particle swarm optimization algorithm. Next, the trained network model is used to interpolate the perceptual blind zone. Finally, the sensor node sampling data and the interpolation estimation result are combined to generate an effective coverage of the 5G mobile communication network signal. Simulation results indicate that the proposed algorithm can detect the real situation of 5G mobile communication network signal coverage better than other algorithms, and has certain feasibility and application prospects.

Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China

In order to quantitatively study the effect of environmental protection in China since the twenty-first century and the environmental pollution projected for the next ten years (under the model of extensive economic development), this paper establishes a Bayesian regulation back propagation neural network (BRBPNN) to analyze the typical pollutants (i.e., cadmium (Cd) and benzopyrene (BaP)) for Taihu Lake, a typical Chinese freshwater lake. For the periods 1950–2003 and 1950–2015, the neural network model estimated the BaP concentration for the database with Nash-Sutcliffe model efficiency (NS) = 0.99 and 0.99 and root-mean-square error (RMSE) = 3.1 and 9.3 for the total database and the Cd concentration for the database with NS = 0.93 and 0.98 and RMSE = 45.4 and 65.7 for the total database, respectively. In the model of extensive economic development, the concentration of pollutants in the sediments of Taihu reached the maximum value at the end of the twentieth century and early twenty-first century, and there was an inflection point. After the early twenty-first century, the concentration of pollutants was controlled under various environmental policies and measures. In 2015, the environmental protection ratio of Cd and BaP reached 52% and 89%, respectively. Without environmental protection measures, the concentrations of Cd and BaP obtained from the neural network model is projected to reach 2015.5 μg kg⁻¹ and 407.8 ng g⁻¹, respectively, in 2030. Based on the results of this study, the Chinese government will need to invest more money and energy to clean up the environment.

Impact of the Coal Mining on the Spatial Distribution of Potentially Toxic Metals in Farmland Tillage Soil

Coal mining areas are prone to hazardous element contamination because of mining activities and the resulting wastes, mainly including Cr, Ni, Cu, Zn, Cd and Pb. This study collected 103 samples of farmland tillage soil surrounding a coal mine in southwestern Shandong province and monitored the heavy metal concentrations of each sample by inductively coupled plasma mass spectrometer (ICP-MS). Statistics, geostatistics, and geographical information systems (GIS) were used to determine the spatial pattern of the potentially toxic metals above in the coal mining area. The results show that the toxic metal concentrations have wide ranges, but the average values for Cr, Ni, Cu, Zn, Cd and Pb are 72.16, 29.53, 23.07, 66.30, 0.14 and 23.71 mg Kg-1, which mostly exceed the natural soil background contents of Shandong Province. The element pairs Ni-Cu, Ni-Zn, and Cu-Zn have relatively high correlation coefficients (0.805, 0.505, 0.613, respectively). The Kriging interpolation results show that the contents of soil toxic metals are influenced by coal mining activities. Moreover, micro-domain variation analysis revealed the toxic metals in the typical area of the coal transportation line. These findings offer systematic insight into the influence of coal mining activities on toxic metals in farmland tillage soil.