Comparison of ATR-FTIR and NIR spectroscopy for identification of microplastics in biosolids

Biosolids are considered a potentially major input of microplastics (MPs) to agricultural soils. Our study aims to identify the polymeric origin of MPs extracted from biosolid samples by comparing their Attenuated Total Reflection (ATR) - Fourier-transform infrared (FTIR) spectra with the corresponding near-infrared (NIR) spectra. The reflectance spectra were preprocessed by Savitzky-Golay (SG), first derivative (FD) and compared with analogous spectra acquired on a set of fifty-two selected commercial plastic (SCP) materials collected from readily available products. According to the results portrayed in radar chart and built from both ATR-FTIR and NIR spectral datasets, the MPs showed high correlations with polymers such as polyethylene (LDPE, HDPE), polyethylene terephthalate (PET), polystyrene (PS), polypropylene (PP) and polyamide (PA), determined in SCP samples. Each unknown MP sample had on average three or more links to several types of SCP, according to the correlation coefficients for each polymer ranging from 0.7 up to 1. The comparison analysis classified the majority of MPs as composed mainly by LDPE/HDPE, according to the top correlation coefficients (r > 0.90). PP and PET were better identified with NIR than ATR-FTIR. In contrast to ATR-FTIR analysis, NIR was unable to identify PS. Based on these results, the primary sources of MPs in the biosolids could be identified as discarded consumer packaging (containers, bags, bottles) and fibers from laundry, disposable glove, and cleaning cloth. SYNOPSIS: Microplastics (MPs) are considered contaminants of emerging concern. This study compares two simple and fast spectroscopy techniques to identify microplastics in the biosolid matrix.

Anthropic impact on soil heavy metal contamination in riparian ecosystems of northern Algeria

The aim of this research was to highlight the impact of urbanization and agriculture on soil quality, mainly by focusing on heavy metal accumulation (Cd, Cu, Fe, Mn, Ni, Pb and Zn) in the fragile riparian ecosystems of the Medjerda river (Souk-Ahras, Algeria). This study was performed in three cultivated soils (0-10, 10-20, 20-60 cm depth) along an increasing gradient of anthropogenic pressure in non-urban (NU), peri-urban (PU) and urban areas (U). Geo-accumulation index (Igeo), Enrichment factor (EF), Pollution load index (PLI) and Potential ecological risk index (RI) were calculated, as well as the potential non-carcinogenic risk for humans (HI). Additionally, to corroborate the role of Bufo spinosus D. as biosentinel, a skin biopsy was performed to quantify the concentration of heavy metals in both terrestrial and aquatic ecosystems. The results showed that when compared to NU, U and PU areas were richer in heavy metals. In particular, Igeo-Cd displayed strongly contaminated soil in U (>3), EF showed high enrichment of heavy metals (>2) for all the soils except for Ni, PLI presented no contamination for all (<1) while RI was significantly higher in U (>300), denoting a strong impact of heavy metals on soil quality. However, HI was below 1 for all the studied areas, although the highest values were related to U and PU. The skin biopsy showed the highest values for Cu, Fe and Pb in PU (0.328, 0.713 and 0.524 mg kg^-1, respectively) similarly to trends observed in the soil of that area. This study shed light on the rising pollution of heavy metals due to urbanization and agricultural input in these fragile ecosystems where Bufo spinosus D. plays the role of potential bio-indicator for environmental pollution.

Environmental implications of interaction between humic substances and iron oxide nanoparticles: A review

Goethite, hematite, ferrihydrite, and other iron oxides bind through various sorption reactions with humic substances (HS) in soils creating nano-, micro-, and macro-aggregates with a specific nature and stability. Long residence times of soil organic matter (SOM) have been attributed to iron-humic substance (Fe-HS) complexes due to physical protection and chemical stabilization at the organic-mineral interface. Humic acids (HA) and fulvic acids (FA) contain many acidic functional groups that interact with Fe oxides through different mechanisms. Due to the numerous interactions between mineral Fe and natural SOM, much research has led into a better identification and definition of HS. In this review, we first focus on the surface colloidal properties of Fe oxides and their reactivity toward HS. These minerals can be efficiently identified by usual techniques, such as XRD, FTIR spectroscopy, XAS, Mössbauer, diffuse reflectance spectroscopies (DRS), HRTEM, ATM, NanoSIMS. Second, we present the recent state of art regarding the adsorption/precipitation of HS onto iron mineral surfaces and their effects on binding metalloid and trace elements. Finally, we consider future research directions based on recent scientific literature, with particular focus on the ability of Fe nano-particles to increase Fe bioavailability, improve carbon sequestration, reduce greenhouse gas emissions, and decrease the impact of persistent organic and inorganic pollutants. The methodology in this field has rapidly developed over the last decade. However, new procedures to estimate the nature of Fe-HA bonds will be important contributions in clarifying the role of natural iron oxides in soil for carbon stabilization.

Prediction of bioaccessible lead in urban and suburban soils with Vis-NIR diffuse reflectance spectroscopy

The successful use of visible and near-infrared (Vis-NIR) reflectance spectroscopy analysis requires selecting an optimal procedure of data acquisition and an accurate modeling approach. In this study, Vis-NIR with 350-2500 nm wavelengths were applied to detect different forms of lead (Pb) through the spectrally active soil constituents combining principal component regression (PCR) and Partial least-square regression (PLSR) for the Vis-NIR model calibration. Three clouds with different soil spectral properties were divided by the Linear discriminant analysis (LDA) in categories of Pb contamination risks: "low," "health," "ecological," ranging from 200 to 750 mg kg^-1. Farm soils were used for calibration (n = 26), and more polluted garden soils (n = 36) from New York City were used for validation. Total and bioaccessible Pb concentrations were examined with PLSR models and compared with Support Vector Machine (SVM) Regression and Boosting Regression Tree (BRT) models. Performances of all models' predictions were qualitatively evaluated by the Root Mean Square Error (RMSE), Residual Prediction Deviation (RPD), and coefficient of determination (R²). For total Pb, the best predictive models were obtained with BRT (R² = 0.82 and RMSE 341.80 mg kg^-1) followed by SVM (validation, R² = 0.77 and RMSE 337.96 mg kg^-1), and lastly by PLSR (validation, R² = 0.74 and RMSE 499.04 mg kg^-1). The PLSR technique is the most accurate calibration model for bioaccessible Pb with an R² value of 0.91 and RMSE of 68.27 mg kg^-1. The regression analysis indicated that bioaccessible Pb is strongly influenced by organic content, and to a lesser extent, by Fe concentrations. Although PLSR obtained lower accuracy, the model selected many characteristic bands and, thus, provided accurate approach for Pb pollution monitoring.