Data quality oriented procedure, for detailed mapping of heavy metals in urban topsoil as an approach to human health risk assessment

Świdwa-Urbańska, Joanna; Batlle-Sales, Jorge

doi:10.1016/j.jenvman.2021.113019

Cited by 12 publications

(5 citation statements)

References 39 publications

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“…In Equation (5) ,

denotes the standard deviation, and

signifies the center of the membership function associated with the output variable ( 35 ).…”

Section: Fuzzy Intelligent Computing Model Based On Fismentioning

confidence: 99%

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

Wang,

Guan,

Peng

et al. 2024

Front. Public Health

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IntroductionTo enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data.MethodsThree cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment.Results(1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model’s high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model’s p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model’s credibility in estimating health risks under varied urban environments.DiscussionThese results showcase the model’s ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

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“…In Equation (5) ,

denotes the standard deviation, and

signifies the center of the membership function associated with the output variable ( 35 ).…”

Section: Fuzzy Intelligent Computing Model Based On Fismentioning

confidence: 99%

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

Wang,

Guan,

Peng

et al. 2024

Front. Public Health

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“…Heavy metal content is an important indicator of soil quality [62]. The content of heavy metals in soil is not only affected by a variety of environmental factors, but is also related to the cropping system, fertilization, and other human activities [63].…”

Section: Spatial Distribution Characteristics Of Soils Of Different G...mentioning

confidence: 99%

Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

Fan,

Gao,

Zuo

et al. 2023

Land

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Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.

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“…Unfortunately, many nations lack their own national health risk assessment procedures and most of the studies conducted to date have instead relied heavily on the US Environmental Protection Agency (EPA) model and parameters [16]. Many studies have effectively assessed the human health risk posed by HMs in polluted soils using the USEPA approach, including those conducted in Poland [17], China [18], Chad, Africa [19], India [20], Turkey [21] and Brazil [22]. In addition, spatial distribution and heterogeneity of pollution status can also be assessed in great detail when these data are integrated with geographic information system technology (GIS) [23,24].…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment and Spatial Distribution of Heavy Metals with an Emphasis on Antimony (Sb) in Urban Soil in Bojnourd, Iran

et al. 2023

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Recently, one of the major environmental issues is the pollution of soil with Antimony (Sb), which is ecologically detrimental and potentially carcinogenic to humans. In developing countries such as Iran, Sb concentrations in soils have not yet been accurately determined. Therefore, the purpose of this study was to examine the levels of Sb and the other HMs (Pb, Cd, As, Ni, Hg and Cr) in the surface soils of Bojnourd, Iran, as well as their distribution and potential risks to the environment and human health. A total of 37 soil samples (0–20 cm) were taken from different sampling stations: 900 × 900 m regular grid for traffic areas (TA), residential areas (RA) and suburb areas (SA). The contamination factor (CF) and geo-accumulation index (Igeo) are two indices that were used to reflect the potential ecological risk from HMs. Geographic information system (GIS), Spearman correlation matrix and health risk indexes were utilized to investigate the source and potential hazard of HMs. The results showed that the average concentration of HMs in TA was highly enriched compared to other areas. Most soil samples were identified to have low levels of Sb and Ni pollution, while having moderate to high levels of Pb, Cr, As, Hg and Cd pollution, as determined by the pollution indices (Igeo and CF). Geostatistical analysis and GIS mapping of the spatial distribution of HM concentrations showed that there have been similar patterns of spatial distribution for Cd, Cr, Ni and Sb and their hot spots were in the southeast, west and center of the city. Neither the hazard quotient (HQ) nor the hazard index (HI) of the examined HMs indicated any non-carcinogenic risk to adults or children. However, carcinogenic risk assessment revealed that cancer risk was raised from Cr and Cd contents for children, while these elements showed an acceptable risk for adults. Furthermore, children’s carcinogenic and non-carcinogenic values were greater than adults’, indicating more potential health hazards associated with these HMs. Therefore, assessing the risk posed by HM pollution in urban surface soil is vital and urgent for children. A more detailed investigation is also required to identify the spatial distribution of soil pollution in areas recognized as enriched in Sb. A proper assessment of the environmental risk and the corresponding risk to humans from HM in a study area can be critical to developing an appropriate remediation method.

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Data quality oriented procedure, for detailed mapping of heavy metals in urban topsoil as an approach to human health risk assessment

Cited by 12 publications

References 39 publications

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model

Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

Risk Assessment and Spatial Distribution of Heavy Metals with an Emphasis on Antimony (Sb) in Urban Soil in Bojnourd, Iran

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