An appraisal of the principal concerns and controlling factors for Arsenic contamination in Chile

Chemical composition of arsenic-based acid mine drainage in the downstream of a gold mine: Fuzzy regression and clustering analysis

This study employs fuzzy regression and fuzzy multivariate clustering techniques to analyze arsenic-polluted water samples originating from acid rock drainage in waste rock dumps. The research focuses on understanding the complex relationships between variables associated with arsenic contamination, such as water arsenic concentration, pH levels, and soil characteristics. To this end, fuzzy regression models were developed to estimate the relationships between water arsenic concentration and independent variables, thus, incorporating the inherent uncertainties into the analysis. Furthermore, multivariate fuzzy k-means clustering analysis facilitated the identification of fuzzy-based clusters within the dataset, providing insights into spatial patterns and potential sources of arsenic pollution. The pairwise comparisons indicated the strongest correlation of 0.62 between soil total arsenic and pH, while the weakest correlation of 0.13 was observed between soil-soluble arsenic and soil iron, providing valuable insights into their relationships and impact on water arsenic levels. The associated uncertainties in the relationships among the variables were determined based on the degree of belongingness of each data point to various fuzzy sets. Three distinct clusters emerged from the analysis: Cluster 1 comprised Points 5, 6, and 7; Cluster 2 included Points 1, 2, 3, 4, 8, and 9; and Cluster 3 consisted of Points 10, 11, 12, and 13. The findings enhance our understanding of the factors influencing arsenic contamination to provide an effective mitigation strategy in acid rock drainage scenarios. This research also demonstrates the applicability and effectiveness of fuzzy regression and fuzzy multivariate clustering in the analysis of arsenic-polluted water samples.

Photo driven homogeneous advanced oxidation coupled to adsorption process for an effective arsenic removal from drinking water

The presence of arsenic (As) in drinking water is a major concern for human health. As(III) is the most toxic water-soluble form and it is hard to remove by separation methods, including adsorption, while As(V) is less toxic and easily removable by adsorption. In this work homogenous photo driven advanced oxidation processes (HP-AOPs), namely UVC/H2O2 and UVC/NaOCl, have been investigated in the oxidation of As(III) (initial concentration of 0.1 mg/L) to As(V) and commercial available adsorbents (γ-Al2O3, Bayoxide E33, MgAl-LDHs and ZnAl-LDHs) were tested for subsequent As(V) removal. UVC/H2O2 (99% of As removal, 19 mg/L of H2O2, 2 min of treatment time) and UVC/NaOCl (99% of As removal, 5.1 mg/L of NaOCl, 2 min of treatment time) were found to be more effective than H2O2 (2% of As removal in the same condition of UVC/H2O2) and NaOCl (6% of As removal in the same condition of UVC/NaOCl), respectively and the optimum operation conditions were identified by response surface methodology (RSM) in distilled water and subsequently confirmed in real drinking water (with differences of less than 1%). UVC/NaOCl was the most suitable process being a good compromise among oxidation efficiency, oxidant dose and treatment time. The best results in terms of subsequent removal of As(V) by adsorption were obtained using ZnAl-LDH (88% in both distilled and drinking water). Accordingly, UVC/NaOCl advanced oxidation coupled to ZnAl-LDH adsorption is the best combination for an effective removal of arsenic from drinking water.

Heavy Metals in Foods and Beverages: Global Situation, Health Risks and Reduction Methods

Heavy metals are chemical elements with a toxic effect on the human body. The expansion of industries has led to significant increasing levels of these constituents in the environment. Intensive agriculture can also lead to an increased concentration of heavy metals as a result of using different fertilizers and pesticides. Heavy metal accumulation in soil and plants represents a serious issue because of the potential risks to consumers. There are several methods available for the removal of these toxic components from different substrates (chemical precipitation, electrodialysis, coagulation and flocculation, photocatalytic removal, and adsorption-based processes), but most procedures are expensive and difficult to perform. Thus, more research is needed on the development of low-cost methods in foods. This work represents a review on the heavy metal presence in different food substrates (such as fruits and vegetables, milk and dairy products, meat and meat derivatives, oils, and alcoholic beverages) and provides an overview of the current situation worldwide, taking into account the fact that risks for human health are induced by the intensification of industry and the high degree of pollution. Considering that the toxicological quality of food affects its acceptability, this work provides valuable data regarding the actual situation on the proposed topic.