Incorporating oral bioaccessibility into human health risk assessment due to potentially toxic elements in extractive waste and contaminated soils from an abandoned mine site.

The waste rock, tailings and soil around an abandoned mine site in Gorno (northwest Italy) contain elevated concentrations of potentially toxic elements (PTE) exceeding the permissible limits for residential uses. Specifically, the maximum concentrations of As, Cd, Pb, and Zn were 107 mg/kg, 340 mg/kg, 1064 mg/kg, and 148 433 mg/kg, respectively. A site-specific human health risk assessment (HHRA) was conducted for residential and recreational exposure scenarios, using an approach based on Risk Based Corrective Action (RBCA) method, refined by incorporating oral bioaccessibility data. Oral bioaccessibility analyses were performed by simulating the human digestion process in vitro (Unified BARGE Method). Detailed analysis of oral bioaccessible fraction (BAF i.e. ratio of bioaccessible concentrations to total concentrations on <250 µm fraction) indicated BAF of As (5-33%), Cd (72-98%), Co (24-42%), Cr (3-11%), Cu (25-90%), Ni (17-60%), Pb (16-88%) and Zn (73-94%). The solid phase distribution and mineralogical analyses showed that the variation of BAF is attributed to presence of alkaline calcareous rocks and association of PTE with a variety of minerals. The HHRA for ingestion pathway, suggested that bioaccessibility-corrected cancer risk reached up to 2.7 × 10-5 and 0.55 × 10-5 for residential and recreational senarios respectively (acceptable level is 1 × 10-5). The hazard index (HI) recalculated after incorporation of oral bioaccessible concentrations for a residential scenario ranged from 0.02 to 17.9. This was above the acceptable level (>1) for 50% of the samples, indicating potential human health risks. This study provides information for site-specific risk assessments and planning future research.

Fingerprinting ambient air to understand bioaerosol profiles in three different environments in the south east of England.

Molecular and chemical fingerprints from 10 contrasting outdoor air environments, including three agricultural farms, three urban parks and four industrial sites were investigated to advance our understanding of bioaerosol distribution and emissions. Both phospholipid fatty acids (PLFA) and microbial volatile organic compounds (MVOC) profiles showed a different distribution in summer compared to winter. Further to this, a strong positive correlation was found between the total concentration of MVOCs and PLFAs (r = 0.670, p = 0.004 in winter and r = 0.767, p = 0.001 in summer) demonstrating that either chemical or molecular fingerprints of outdoor environments can provide good insights into the sources and distribution of bioaerosols. Environment specific variables and most representative MVOCs were identified and linked to microbial species emissions via a MVOC database and PLFAs taxonomical classification. While similar MVOCs and PLFAs were identified across all the environments suggesting common microbial communities, specific MVOCs were identified for each contrasting environment. Specifically, 3,4-dimethylpent-1-yn-3-ol, ethoxyethane and propanal were identified as key MVOCs for the industrial areas (and were correlated to fungi, Staphylococcus aureus (Gram positive bacteria) and Gram negative bacteria, R = 0.863, R = 0.618 and R = 0.676, respectively) while phthalic acid, propene and isobutane were key for urban environments (correlated to Gram negative bacteria, fungi and bacteria, R = 0.874, R = 0.962 and R = 0.969 respectively); and ethanol, 2-methyl-2-propanol, 2-methyl-1-pentene, butane, isoprene and methyl acetate were key for farms (correlated to fungi, Gram positive bacteria and bacteria, R = 0.690 and 0.783, R = 0.706 and R = 0.790, 0.761 and 0.768). The combination of MVOCs and PLFAs markers can assist in rapid microbial fingerprinting of distinct environmental influences on ambient air quality.

Factors governing the solid phase distribution of Cr, Cu and As in contaminated soil after 40 years of ageing.

The physico-chemical factors affecting the distribution, behavior and speciation of chromium (Cr), copper (Cu) and arsenic (As) was investigated at a former wood impregnation site (Fredensborg, Denmark). Forty soil samples were collected and extracted using a sequential extraction technique known as the Chemometric Identification of Substrates and Element Distributions (CISED) and a multivariate statistical tool (redundancy analysis) was applied. CISED data was linked to water-extractable Cr, Cu and As and bioavailable Cu as determined by a whole-cell bacterial bioreporter assay. Results showed that soil pH significantly affected the solid phase distribution of all three elements on site. Additionally, elements competing for binding sites, Ca, Mg and Mn in the case of Cu, and P, in the case of As, played a major role in the distribution of these elements in soil. Element-specific distributions were observed amongst the six identified soil phases including residual pore salts, exchangeable, carbonates (tentative designation), Mn-Al oxide, amorphous Fe oxide, and crystalline Fe oxide. While Cr was strongly bound to non-extractable crystalline Fe oxide in the oxic top soil, Cu and notably, As were associated with readily extractable phases, suggesting that Cu and As, and not Cr, constitute the highest risk to environmental and human health. However, bioavailable Cu did not significantly correlate with CISED identified soil phases, suggesting that sequential extraction schemes such as CISED may not be ideally suited for inferring bioavailability to microorganisms in soil and supports the integration of receptor-specific bioavailability tests into risk assessments as a complement to chemical methods.

Linking oral bioaccessibility and solid phase distribution of potentially toxic elements in extractive waste and soil from an abandoned mine site: Case study in Campello Monti, NW Italy.

Oral bioaccessibility and solid phase distribution of potentially toxic elements (PTE) from extractive waste streams were investigated to assess the potential human health risk posed by abandoned mines. The solid phase distribution along with micro-X-ray fluorescence (micro-XRF) and scanning electron microscopy (SEM) analysis were also performed. The results showed that the total concentrations of PTE were higher in <250 µm size fractions of waste rock and soil samples in comparison to the <2 mm size fractions. Mean value of total concentrations of chromium(Cr), copper (Cu), and nickel (Ni) in waste rocks (size fractions <250 µm) were found to be 1299, 1570, and 4010 mg/kg respectively due to the parent material. However, only 11% of Ni in this sample was orally bioaccessible. Detailed analysis of the oral bioaccessible fraction (BAF, reported as the ratio of highest bioaccessible concentration compared with the total concentration from the 250 µm fraction) across all samples showed that Cr, Cu, and Ni varied from 1 to 6%, 14 to 47%, and 5 to 21%, respectively. The variation can be attributed to the difference in pH, organic matter content and mineralogical composition of the samples. Non-specific sequential extraction showed that the non-mobile forms of PTE were associated with the clay and Fe oxide components of the environmental matrices. The present study demonstrates how oral bioaccessibility, solid phase distribution and mineralogical analysis can provide insights into the distribution, fate and behaviour of PTE in waste streams from abandoned mine sites and inform human health risk posed by such sites .

Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods.

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study using two different residuals uncertainty methods: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Prediction-interval coverage probability (PICP), the most important of the statistical measures of uncertainty, was used to evaluate uncertainty. Additionally, three state-of-the-art ML models including support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) were selected to spatially model groundwater nitrate concentrations. The models were calibrated with nitrate concentrations from 80 wells (70% of the data) and then validated with nitrate concentrations from 34 wells (30% of the data). Both uncertainty and predictive performance criteria should be considered when comparing and selecting the best model. Results highlight that the kNN model is the best model because not only did it have the lowest uncertainty based on the PICP statistic in both the QR (0.94) and the UNEEC (in all clusters, 0.85-0.91) methods, but it also had predictive performance statistics (RMSE = 10.63, R2 = 0.71) that were relatively similar to RF (RMSE = 10.41, R2 = 0.72) and higher than SVM (RMSE = 13.28, R2 = 0.58). Determining the uncertainty of ML models used for spatially modelling groundwater-nitrate pollution enables managers to achieve better risk-based decision making and consequently increases the reliability and credibility of groundwater-nitrate predictions.

A novel machine learning-based approach for the risk assessment of nitrate groundwater contamination.

This study aimed to develop a novel framework for risk assessment of nitrate groundwater contamination by integrating chemical and statistical analysis for an arid region. A standard method was applied for assessing the vulnerability of groundwater to nitrate pollution in Lenjanat plain, Iran. Nitrate concentration were collected from 102 wells of the plain and used to provide pollution occurrence and probability maps. Three machine learning models including boosted regression trees (BRT), multivariate discriminant analysis (MDA), and support vector machine (SVM) were used for the probability of groundwater pollution occurrence. Afterwards, an ensemble modeling approach was applied for production of the groundwater pollution occurrence probability map. Validation of the models was carried out using area under the receiver operating characteristic curve method (AUC); values above 80% were selected to contribute in ensembling process. Results indicated that accuracy for the three models ranged from 0.81 to 0.87, therefore all models were considered for ensemble modeling process. The resultant groundwater pollution risk (produced by vulnerability, pollution, and probability maps) indicated that the central regions of the plain have high and very high risk of nitrate pollution further confirmed by the exiting landuse map. The findings may provide very helpful information in decision making for groundwater pollution risk management especially in semi-arid regions.