Enhancement of hydrogenotrophic methanogenesis for methane production by nano zero-valent iron in soils.

Nanoscale zero-valent iron (nZVI) is attracting increasing attention as the most commonly used environmental remediation material. However, given the high surface area and strong reducing capabilities of nZVI, there is a lack of understanding regarding its effects on the complex anaerobic methane production process in flooded soils. To elucidate the mechanism of CH4 production in soil exposed to nZVI, paddy soil was collected and subjected to anaerobic culture under continuous flooding conditions, with various dosages of nZVI applied. The results showed that the introduction of nZVI into anaerobic flooded rice paddy systems promoted microbial utilization of acetate and carbon dioxide as carbon sources for methane production, ultimately leading to increased methane production. Following the introduction of nZVI into the soil, there was a rapid increase in hydrogen levels in the headspace, surpassing that of the control group. The hydrogen levels in both the experimental and control groups were depleted by the 29th day of culture. These findings suggest that nZVI exposure facilitates the enrichment of hydrogenotrophic methanogens, providing them with a favorable environment for growth. Additionally, it affected soil physicochemical properties by increasing pH and electrical conductivity. The metagenomic analysis further indicates that under exposure to nZVI, hydrogenotrophic methanogens, particularly Methanobacteriaceae and Methanocellaceae, were enriched. The relative abundance of genes such as mcrA and mcrB associated with methane production was increased. This study provides important theoretical insights into the response of key microbes, functional genes, and methane production pathways to nZVI during anaerobic methane production in rice paddy soils, offering fundamental insights into the long-term fate and risks associated with the introduction of nZVI into soils.

Prediction of Photochemical Properties of Dissolved Organic Matter Using Machine Learning.

Apparent quantum yields (Φ) of photochemically produced reactive intermediates (PPRIs) formed by dissolved organic matter (DOM) are vital to element cycles and contaminant fates in surface water. Simultaneous determination of ΦPPRI values from numerous water samples through existing experimental methods is time consuming and ineffective. Herein, machine learning models were developed with a systematic data set including 1329 data points to predict the values of three ΦPPRIs (Φ3DOM*, Φ1O2, and Φ·OH) based on DOM spectral parameters, experimental conditions, and calculation parameters. The best predictive performances for Φ3DOM*, Φ1O2, and Φ·OH were achieved using the CatBoost model, which outperformed the traditional linear regression models. The significances of the wavelength range and spectral parameters on the three ΦPPRI predictions were revealed, suggesting that DOM with lower molecular weight, lower aromatic content, and a more autochthonous portion possessed higher ΦPPRIs. Chain models were constructed by adding the predicted Φ3DOM* as a new feature into the Φ1O2 and Φ·OH models, which consequently improved the predictive performance of Φ1O2 but worsened the Φ·OH prediction likely due to the complex formation pathways of ·OH. Overall, this study offered robust ΦPPRI prediction across interlaboratory differences and provided new insights into the relationship between PPRIs formation and DOM properties.

Bioaccessibility of arsenic, lead, and cadmium in contaminated mining/smelting soils: Assessment, modeling, and application for soil environment criteria derivation.

Soil environment criteria (SEC) are commonly derived from the total concentration of pollutants in soils, resulting in overly stringent values. Herein, we examined the feasibility of deriving the SEC by using the bioaccessibility of pollutants. In this regard, soil samples from 33 locations at 12 mining/smelting sites in China were collected and examined in terms of soil properties, chemical fraction distributions, and bioaccessibilities of cadmium (Cd), lead (Pb), and arsenic (As). The gastric (GP) and intestinal phases (IP) of the potentially hazardous trace elements (PHEs) were measured by in vitro assays, showing that these values varied from 11 % to 72 %, 1-79 %, and 2-27 % for Cd, Pb and As, respectively. Pearson analysis showed that the GP and IP bioaccessibilities of these PHEs were mainly influenced by soil pH, CEC, and clay fraction and positively correlated with the sequential extraction form. The random forest regression (RF) model showed excellent performance in predicting the gastric phase (GP) bioaccessibilities of Cd, Pb, and As, with a mean R2 and RMSE of 0.86 and 0.31, respectively. Both the measured and predicted bioaccessibilities were feasible to be used to derive SEC. This work will contribute to the development of regional soil environmental standards based on bioaccessibility for Cd-, Pb-, and As-contaminated mining/smelting soils.

Photochemistry of dissolved organic matter in water from the Pearl river (China): Seasonal patterns and predictive modelling.

Photochemical properties of dissolved organic matter (DOM) vary widely in natural and engineered water systems due to the different dominant compositions. However, seasonal patterns of DOM photochemical properties in urban rivers remain unclear. In this study, two seasons (wet and dry) of water samples were collected from eleven sites throughout the Pearl River (China) to investigate the spatiotemporal variability of DOM optical and photochemical properties. The optical properties of DOM in the Pearl River were characterized by UV-vis and fluorescence spectroscopies, which showed the substantial decrease in absorption coefficient and fluorescence intensity and increase in absorbence ratio (E2/E3) and specific absorption coefficient (SUVA) from the wet to dry season. The photochemical properties in terms of the apparent quantum yields of 3DOM*, 1O2 and ·OH from DOM (Φ3DOM*, Φ1O2 and Φ·OH, DOM) under illumination also displayed a significant decrease from the wet to the dry season. Spearman's rank correlation analysis revealed the strongest relationships between Φ3DOM*, Φ1O2 and Φ·OH, DOM and the relative abundance of microbial humic-like component (C2%) derived from parallel factor analysis (PARAFAC). Partial least squares regression (PLSR) modelling exhibited an excellent prediction strength for steady-state concentrations of 1O2 ([1O2]ss) and ·OH ([·OH]ss) with adjusted R2 values of 0.85 and 0.91, respectively, by using DOC concentration ([DOC]), optical properties, nitrate and nitrite concentrations as the response variables. In addition, the model identified that the Fmax of humic-like component C4 (Fmax-C4) was the most effective predictor amongst the used response variables. This study provides an approach to describe and predict the seasonal patterns of DOM photochemical properties in urbanized rivers, offering a good understanding of the formation mechanism of reactive species from river DOM.