Combining human and snail indicators for an integrative risk assessment of metal(loid)-contaminated soils

Prediction models and major controlling factors of antibiotics bioavailability in hyporheic zone

Recently, antibiotics have been frequently detected in the hyporheic zone (HZ) as a novel contaminant. Bioavailability assessment has gradually attracted more attention in order to provide a more realistic assessment of human health risks. In this study, two typical antibiotics, oxytetracycline (OTC) and sulfamethoxazole (SMZ), were used as target pollutants in the HZ of the Zaohe-Weihe River, and the polar organics integrated sampler was used to analyze the variation of antibiotics bioavailability. According to the characteristics of the HZ, the total concentration of pollutants, pH, and dissolved oxygen (DO) were selected as major predictive factors to analyze their correlation with the antibiotics bioavailability. Then the predictive antibiotic bioavailability models were constructed by stepwise multiple linear regression method. The results showed that there was a highly significant negative correlation between OTC bioavailability and DO (P < 0.001), while SMZ bioavailability showed a highly significant negative correlation with total concentration of pollutants (P < 0.001) and a significant negative correlation with DO (P < 0.01). The results of correlation analysis were further verified by Principal Component Analysis. Based on the experimental data, we constructed eight prediction models for the bioavailability of two antibiotics and verified them. The data points of the six prediction models were distributed in the 95% prediction band, indicating that the models were more reliable and accurate. The prediction models in this study provide reference for the accurate ecological risk assessment of the bioavailability of pollutants in the HZ, and also provide a new idea for predicting the bioavailability of pollutants in practical applications.

Assessment of soil heavy metal pollution in provinces of China based on different soil types: From normalization to soil quality criteria and ecological risk assessment

Ecological risks can vary dramatically depending on abiotic factors, such as soil properties and the background values of elements. This study developed a framework for an integrated risk assessment system to derive soil quality criteria (SQC) for heavy metals (HMs) applicable to different soil types and to assess ecological risks at a multi-regional scale. Through the construction of normalization and species sensitivity distribution models, 248 SQC values for Cd, Pb, Zn, As, Cu, Cr, Sb, and Ni in 31 Chinese provinces were derived. These SQC considered the soil types and background values of the elements and effectively reduced the uncertainty caused by spatial heterogeneity. Using the derived SQC values, the qualitative and quantitative ecological risks of HMs in the terrestrial environment of China were comprehensively assessed using a three-level ecological risk assessment (ERA) approach. Compared to traditional ERA methods, the new methodology reached a more quantitative conclusion. The mean overall probabilities of ecological risk in China were 2.42 % (Cd), 2.82 % (Pb), 12.17 % (Zn), 14.89 % (As), 10.42 % (Cu), 32.20 %(Cr), and 8.88 % (Ni). The new framework could be useful for the ERA of various soil types.

Bioavailability Evaluation of Perchlorate in Different Foods In Vivo: Comparison with In Vitro Assays and Implications for Human Health Risk Assessment

Perchlorate in various foods continuously arouses public health concern. Bioavailability is a critical parameter to better estimate perchlorate exposure from diets. In this study, perchlorate bioavailability in five foods was determined in an in vivo mouse model and compared with in vitro bioaccessibility/bioavailability. The estimated in vivo perchlorate bioavailability for different foods ranged from 18.01 ± 4.53% to 45.60 ± 7.11%, with the order lettuce > pork > rice > milk powder > soybean. Moisture, fiber, and fat in foods were identified as critical factors affecting perchlorate bioavailability (correlation r = 0.71, 0.52, and -0.67, respectively). Linear regression analysis revealed that the in vitro perchlorate bioavailability determined using the Caco-2 cell model has the potential to estimate the in vivo perchlorate bioavailability in foods (R² = 0.67, slope = 1.33, and y intercept = 4.99). These findings provide insights into the effects of the food matrices on perchlorate bioavailability and could contribute to decrease the uncertainty regarding perchlorate dietary exposure risk assessment.