Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning

Modeling arsenic in European topsoils with a coupled semiparametric (GAMLSS-RF) model for censored data

Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Organization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection.

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Performance and Mechanism of As(III/V) Removal from Aqueous Solution by Fe3O4-Sunflower Straw Biochar

Humans and ecosystems are severely damaged by the existence of As(III/V) in the aquatic environment. Herein, an advanced Fe₃O₄@SFBC (Fe₃O₄-sunflower straw biochar) adsorbent was fabricated by co-precipitation method with sunflower straw biochar (SFBC) prepared at different calcination temperatures and different SFBC/Fe mass ratios as templates. The optimal pH for As(III/V) removal was investigated, and Fe₃O₄@SFBC shows removal efficiency of 86.43% and 95.94% for As(III) and As(V), respectively, at pH 6 and 4. The adsorption effect of calcining and casting the biochar-bound Fe₃O₄ obtained at different temperatures and different SFBC/Fe mass ratios were analyzed by batch experiments. The results show that when the SFBC biochar is calcined at 450 °C with an SFBC/Fe mass ratio of 1:5, the adsorption of As(III) and As(V) reaches the maximum, which are 121.347 and 188.753 mg/g, respectively. Fe₃O₄@SFBC morphology, structure, surface functional groups, magnetic moment, and internal morphology were observed by XRD, FTIR, SEM, TEM, and VSM under optimal working conditions. The material shows a small particle size in the range of 12-14 nm with better magnetic properties (54.52 emu/g), which is suitable for arsenic removal. The adsorption mechanism of As(III/V) by Fe₃O₄@SFBC indicates the presence of chemisorption, electrostatic, and complexation. Finally, the material was used for five consecutive cycles of adsorption-desorption experiments, and no significant decrease in removal efficiency was observed. Therefore, the new adsorbent Fe₃O₄@SFBC can be efficiently used for arsenic removal in the aqueous system.