The plastisphere may act as reservoir of antibiotic resistome, accelerating global antimicrobial resistance dissemination. However, the environmental risks in the plastisphere of field microplastics (MPs) in farmland remain largely unknown. Here, antibiotic resistance genes (ARGs) and virulence factors (VFs) on polyethylene microplastics (PE-MPs) and polybutylene adipate terephthalate and polylactic acid microplastics (PBAT/PLA-MPs) from residues were investigated using metagenomic analysis. The results suggested that the profiles of ARG and VF in the plastisphere of PBAT/PLA-MPs had greater number of detected genes with statistically higher values of diversity and abundance than soil and PE-MP. Procrustes analysis indicated a good fitting correlation between ARG/VF profiles and bacterial community composition. Actinobacteria was the major host for tetracycline and glycopeptide resistance genes in the soil and PE-MP plastisphere, whereas the primary host for multidrug resistance genes changed to Proteobacteria in PBAT/PLA-MP plastisphere. Besides, three human pathogens, Sphingomonas paucimobilis, Lactobacillus plantarum and Pseudomonas aeruginosa were identified in the plastisphere. The PE-MP plastisphere exhibited a higher transfer potential of ARGs than PBAT/PLA-MP plastisphere. This work enhances our knowledge of potential environmental risks posed by microplastic in farmland and provides valuable insights for risk assessment and management of agricultural mulching applications.
Mineral scale refers to the hard inorganic solids nucleated on substrates or deposited from the aqueous phase. The formation and deposition of barium sulfate and strontium sulfate in various industries, such as water treatment and oilfield operations, can significantly impact facility operations, posing serious threats. Machine learning (ML) approaches have been adopted recently in scale threat predictions to address the limitations of conventional scaling prediction models. However, there are few reports on collecting sulfate mineral scaling data, employing ML methods for data analysis, and evaluating the modeling results to gain deeper insights of sulfate mineral scaling process and to improve the accuracy of sulfate scaling threat prediction. Despite comprehensive experimental studies, the literature does not provide adequate guidance for identifying the influence on the solubility of barium sulfate and strontium sulfate under different aqueous environments and actual operating conditions. To this end, this study collected 1600 experimental datasets of barium/strontium sulfate from the literature to construct and evaluate the reliability and versatility of a ML-based model for sulfate solubility calculations. Single neural networks, hybrid neural networks, and optimization algorithms were employed to build solubility prediction models for barium sulfate and strontium sulfate across a wide range of temperatures, pressures, and different ions. The model's applicability in predicting sulfate scaling threats in various actual operating environments demonstrated its broad usability, consistent with its actual performance. This study marks the first stride towards constructing a reliable model for identifying the scaling trends of barium sulfate and strontium sulfate across various operating conditions, underscoring the importance of developing robust and accurate prediction models to address challenges in various industrial systems.
Isoprene hydroxy peroxy radicals (ISOPOO), derived from isoprene oxidation by hydroxy radicals (OH), are key intermediates for ozone and secondary organic aerosol (SOA) formation in the atmosphere. Although ISOPOO-water complexes are ubiquitous, their impacts on ISOPOO chemistry remain obscure. Here the previously overlooked water effect on the bimolecular reaction kinetics of ISOPOO was investigated in an oxidative flow reactor. The major first-generation products of ISOPOO, isoprene hydroxy hydroperoxides (ISOPOOH), methacrolein (MACR), and methyl vinyl ketone (MVK), were measured simultaneously at various relative humidity (RH) with the help of a cold trap to avoid potential losses in direct gas sampling. We found that ISOPOO reactions were accelerated significantly under wet conditions, with a greater enhancement on 1,2-ISOPOO than 4,3-ISOPOO. 1,2-ISOPOOH yield appeared faster growth with RH than 4,3-ISOPOOH. MVK yield showed an upward-downward trend with RH, while MACR yield plateaued from 30% RH. To explain the enhancement in the ISOPOOH yield from 3% to 80% RH, the overall rate constants of 1,2-ISOPOO + HO2 and 4,3-ISOPOO + HO2 reactions at 80% RH should be 13 times and twice those at 3% RH, respectively. The empirical formulas were proposed for the first time to parameterize the water effect on ISOPOO + HO2 reactions. The updated kinetics of ISOPOO reactions were incorporated in a box model to simulate the RH-dependent ISOPOOH and C4 carbonyl yields under typical atmospheric conditions. High RH can enhance the ISOPOOH yield in urban, rural, and forest areas, and promote SOA formation correspondingly. Our findings shed light on the critical role of humidity in the reactions of ISOPOO and benefit evaluating the fate of isoprene and its impacts on air quality more accurately in the ambient atmosphere.
We study the equilibrium effects of innovation subsidies that reduce firms' innovation costs in a monopolistic competition model with firm heterogeneity in innovation capabilities and an industry-level resource constraint. Subsidies change product market competition and resource price, and further affect firms' innovation. We show a counterintuitive result: though subsidies lower innovation costs, high-capability firms may reduce their innovation. This finding implies that the demand curve for innovation investments of certain firms in equilibrium can be locally upward-sloping. We show that at the industry level, both average innovation input and output demonstrate inverted-U shaped responses to increasing subsidies but with differing turning points. Notably, an increase in average innovation input may be accompanied by a decrease in average innovation output. These findings cast doubts on the interpretation of existing empirical evidences on firm and industry responses to innovation subsidies, most of which assume away treatment effect heterogeneity and equilibrium feedbacks.
Harmful algal blooms pose tremendous threats to ecological safety and human health. In this study, simulated solar light (SSL) irradiation was used to activate periodate (PI) for the inactivation of Microcystis aeruginosa and degradation of microcystin-LR (MC-LR). We found that PI-SSL system could effectively inactivate 5 × 106 cells·mL−1 algal cells below the limit of detection within 180 min. ·OH and iodine (IO3· and IO4·) radicals generated in PI-SSL system could rupture cell membranes, releasing intracellular substances including MC-LR into the reaction system. However, the released MC-LR could be degraded into non-toxic small molecules via hydroxylation and ring cleavage processes in PI-SSL system, reducing their environmental risks. High algae inactivation performance of PI-SSL system in solution with a wide pH range (3–9), with the coexisting anions (Cl−, NO3− and SO42−) and the copresence of natural organic matters (humic acid and fulvic acid), real water (lake water and river water), as well as in continuous-flow reactor (14 h) were also achieved. In addition, under natural sunlight irradiation, effective algae inactivation could also be achieved in an enlarged reactor (1 L). Overall, our study showed that PI-SSL system could avoid the inference by the background substances and could be employed as a feasible technique to treat algal bloom water.