Groundwater Contamination in a Rural Municipality of Northeastern Brazil: Application of Geostatistics, Geoprocessing, and Geochemistry Techniques

Santos, Raiany Sandhy Souza; Filho, Milton Santos Cardoso; Marques, Isadora Machado; de Oliveira, Anderson Gomes; de Oliveira, Olívia Maria Cordeiro; Moreira, Ícaro Thiago Andrade

doi:10.1007/s11270-024-06972-2

Cited by 3 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The progression has intensified the pressure on atmospheric and aquatic resources, exerting a significant impact on the health and quality of life of city dwellers [6][7][8]. Moreover, because of widespread and complex human interventions in urban soils, their ecological functions and biodiversity encounter significant challenges.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration

Chen,

Zhang,

Wong

et al. 2024

Processes

View full text Add to dashboard Cite

As urbanization accelerates, the contamination of urban soil and the consequent health implications stemming from urban expansion are increasingly salient. In recent years, a plethora of cities and regions nationwide have embarked on rigorous soil geological surveys with a focus on environmental quality, yielding invaluable foundational data. This research aims to develop scientifically robust and rational land-use planning strategies while assessing the levels of heavy metal pollution and associated risks. The urban agglomeration encompassing Zhengzhou, Luoyang, and Kaifeng (referred to as Zheng–Bian–Luo Urban Agglomeration) in Henan Province was designated as the study area. Leveraging the Nemerow comprehensive index method alongside the Hakanson potential ecological risk assessment method, this study delved into the pollution levels and potential ecological ramifications of nine heavy metals, namely Cr, Mn, Ni, Cu, Zn, As, Cd, Pb, and Co. Research indicates that the hierarchy of individual potential ecological risks ranges from most to least significant as follows: Cd > Pb > Cr > Ni > Cu > Zn > As > Mn > Co. The concentrations of Cd in both Zhengzhou and Kaifeng surpassed the established background levels. Furthermore, the mean single-factor pollution index values for the heavy metals Cd and Zn exceeded 1, signifying a state of minor pollution. The Nemerow comprehensive index P of Cd and Zn is between 1 < Pcomp ≤ 2, which is considered mild pollution. The comprehensive P values of the other seven metal elements are all less than 0.7, reaching a clean (alert) level. Predominantly, the primary potential risk factor in the superficial soil of the Zheng–Bian–Luo urban agglomeration is Cd, while the ecological risk implications associated with other heavy metal elements are comparatively minimal. The soil environmental quality within the designated study area remains secure, although certain localized areas pose potential risks of heavy metal pollution. A comprehensive assessment of the current state of soil heavy metal pollution is essential to establish a theoretical foundation and provide technical support for soil environmental protection, pollution mitigation, and sustainable utilization.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration

Chen,

Zhang,

Wong

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

Using of high spatial resolution images to evaluate the thematic accuracy of land use and occupation maps with the Kappa index

Carvalho,

Oliveira,

Araújo

et al. 2024

MOJES

View full text Add to dashboard Cite

The objective from this article evaluates the thematic quality of automatic mappings with supervised classification for land use and land cover, using high spatial resolution satellite images as "ground truth". This consider the advancement of remote sensing technologies has enabled the acquisition of satellite images with various spatial resolutions, which are essential for thematic mapping and automatic classifiers in the context of land use and land cover mapping. In fact, the wide availability of high, medium, and low spatial resolution satellite images has significantly optimized the time and resources required by using more accurate classifiers during data processing. The image used for verification of this paper was GeoEye, with a spatial resolution of 0.5m, dated October 2023. The images submitted to the automatic classifier were Sentinel-2A with a spatial resolution of 10m and Planet with a spatial resolution of 5m, both from the same satellite pass period (October 2023) over the study area, aiming to avoid seasonal and phenological variations in vegetation, as well as changes in the environment due to anthropogenic intervention. The classification method adopted was Maximum Likelihood (MAXVER). The classification accuracy was rigorously evaluated to ensure the reliability of the results using the Kappa index, assessing the agreement between the observed and expected classifications. Based on the methods presented, the set of mapped classes in this study showed good accuracy for the Planet image and very good accuracy for the Sentinel image.

show abstract

Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran

Tavakoli,

Motlagh,

Sayadi

et al. 2024

Water

View full text Add to dashboard Cite

Groundwater salinization poses a critical threat to sustainable development in arid and semi-arid rurbanizing regions, exemplified by Kerman Province, Iran. This region experiences groundwater ecosystem degradation as a result of the rapid conversion of rural agricultural land to urban areas under chronic drought conditions. This study aims to enhance Groundwater Pollution Risk (GwPR) mapping by integrating the DRASTIC index with machine learning (ML) models, including Random Forest (RF), Boosted Regression Trees (BRT), Generalized Linear Model (GLM), Support Vector Machine (SVM), and Multivariate Adaptive Regression Splines (MARS), alongside hydrogeochemical investigations, to promote sustainable water management in Kerman Province. The RF model achieved the highest accuracy with an Area Under the Curve (AUC) of 0.995 in predicting GwPR, outperforming BRT (0.988), SVM (0.977), MARS (0.951), and GLM (0.887). The RF-based map identified new high-vulnerability zones in the northeast and northwest and showed an expanded moderate vulnerability zone, covering 48.46% of the study area. Analysis revealed exceedances of WHO standards for total hardness (TH), sodium, sulfates, chlorides, and electrical conductivity (EC) in these high-vulnerability areas, indicating contamination from mineralized aquifers and unsustainable agricultural practices. The findings underscore the RF model’s effectiveness in groundwater prediction and highlight the need for stricter monitoring and management, including regulating groundwater extraction and improving water use efficiency in riverine aquifers.

show abstract

Groundwater Contamination in a Rural Municipality of Northeastern Brazil: Application of Geostatistics, Geoprocessing, and Geochemistry Techniques

Cited by 3 publications

References 50 publications

Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration

Assessment of Heavy Metal Contamination and Ecological Risk in Soil within the Zheng–Bian–Luo Urban Agglomeration

Using of high spatial resolution images to evaluate the thematic accuracy of land use and occupation maps with the Kappa index

Sustainable Groundwater Management Using Machine Learning-Based DRASTIC Model in Rurbanizing Riverine Region: A Case Study of Kerman Province, Iran

Contact Info

Product

Resources

About