Mapping soil pollution by spatial analysis and fuzzy classification

Abstract

Spatial analysis and fuzzy classification techniques were used to estimate the spatial distributions of heavy metals in soil. The work was applied to soils in a coastal region that is characterized by intense urban occupation and large numbers of different industries. Concentrations of heavy metals were determined using geostatistical techniques and classes of risk were defined using fuzzy classification. The resulting prediction mappings identify the locations of high concentrations of Pb, Zn, Ni, and Cu in topsoils of the study area. The maps show that areas of high pollution of Ni and Cu are located at the northeast, where there is a predominance of industrial and agricultural activities; Pb and Zn also occur in high concentrations in the northeast, but the maps also show significant concentrations of Pb and Zn in other areas, mainly in the central and southeastern parts, where there are urban leisure activities and trade centers. Maps were also prepared showing levels of pollution risk. These maps show that (1) Cu presents a large pollution risk in the north-northwest, midwest, and southeast sectors, (2) Pb represents a moderate risk in most areas, (3) Zn generally exhibits low risk, and (4) Ni represents either low risk or no risk in the studied area. This study shows that combining geostatistics with fuzzy theory can provide results that offer insight into risk assessment for environmental pollution.

The effects of soil pollution and its relation with morbidity in shoreline areas

“…Soil samples were collected from different locations of the study area at distances between sampling locations ranging from 95 to 650m ( Figure 2) and it has been determined the total metal concentrations Cd, Pb and Hg in accordance to the standardized procedure [10]. Samples area (read point is soils samples).…”

“…The spatial analysis of certain environment pollutants and contaminants related to the occurrence of epidemiological morbidity events could be an important implement to assist in planning and management the resources that would meet such demand [8][9][10][11] According to Assunção et al [12] the analysis of the dispersion of a disease risk is mainly done through maps of incidence rates or some other measure of epidemiological risk. These maps are valuable tools in epidemiological studies and they can present three main objectives from its production: researches that relate environment and public health and also identify patterns of morbidity and mortality in the region of known sources of pollution; the exploratory ones, in order to suggest disease and unknown etiologic factors locations which may be formulated in terms of hypotheses to be further investigated; and finally those who aim to point out associations between potential sources of contamination and areas of high risk.…”

Risk Assessment of Heavy Metal Pollution in Sediments of the Fenghe River by the Fuzzy Synthetic Evaluation Model and Multivariate Statistical Methods

“…Heavy metals are continuously introduced into fluvial environments through mining, combustion residues, vehicle emissions, transportation, and some other human activities with rapid urbanization and industrialization all over the world (Subida et al, 2013;Wang et al, 2013;Zhang et al, 2014). Based on the enrichment factor (EF) (Lourenço et al, 2010), Viers (2009) revealed that the majority of the world river systems are seriously suffering from metal pollution, particularly in Asia (China and India), Europe, and North America. In the case of metals such as Pb and Cd, the EF can be higher than 60 and 100, respectively (Viers, 2009;Varol, 2011).…”

Utilization of Fuzzy Set Theory for Interpretation of Data of Investigations of Soil Contamination by Heavy Metals

“…For example concentrations of heavy metals were determined using geostatistical techniques and classes of risk were defined using fuzzy classification. The resulting prediction mappings identify the locations of high concentrations of Pb, Zn, Ni, and Cu in topsoils of the study area (Lourenço et al, 2010). Another reported application is the acquiring and representing of knowledge on soil-landscape relationships and applying that knowledge to digital soil mapping (Nanos & Rodríguez, 2012;Feng et al, 2006;Amini et al, 2005;Cattle et al, 2002).…”

“…Suitable way for evaluation of soil contamination by heavy metals is application of fuzzy set theory (Kurtener & Badenko, 2000;Amini et al, 2005;Feng et al, 2006;Pop et al, 2009;Lourenço et al, 2010;Xu et al, 2011) Objective of this study is utilization of fuzzy set theory for interpretation of data of investigations of soil contamination by heavy metals. In section 2 simple fuzzy indicator models are outlined.…”