Carboxymethyl cellulose stabilized iron sulfide (CMC-FeS) nanoparticles have been shown promising for reductive immobilization of U(VI) in water and soil. This work aimed to fill some critical knowledge gaps on the effects of the stabilizer and water chemistry, reaction mechanisms, and long-term stability of stabilized uranium. The optimal CMC-to-FeS molar ratio was determined to be 0.0010. CMC-FeS performed effectively over pH 6.0–9.0, with the best removal being at pH 7.0 and 8.0. The retarded first-order model adequately interpreted the kinetic data, representing a mechanistically sounder model for heterogeneous reactants of decaying reactivity. The presence of Ca2+ (1 mM) or bicarbonate (1 mM) lowered the initial rate constant by a factor of 1.6 and 9.5, respectively, while 1 mM of Na+ showed negligible effect. Humic acid at 1.0 mg/L (as total organic carbon) doubled the removal rate, but inhibited the removal at elevated concentrations (≥5.0 mg/L). Fourier transform infrared spectroscopy, X-ray diffractometer, X-ray photoelectron spectroscopy, and extraction studies indicated that reductive conversion of UO22+ to UO2(s) was the primary reaction mechanism, accounting for 90% of U removal at pH 7.0. S2− and S22− were the primary electron sources, whereas sorbed and structural Fe(II) acted as supplementary electron donors. The immobilized U remained stable under anoxic conditions after 180 days of aging, while 26% immobilized U was remobilized when exposed to air for 180 days. The long-term stability is attributed to the protective reduction potential of CMC-FeS, the formation of uraninite and associated structural resistance to oxidation, and the high affinity of FeS oxidation products toward U(VI).
Electrokinetic remediation is an effective technology for soil contaminated with heavy metals. However, little is known about the fate of antibiotic resistance in the process under heavy metal stress, since antibiotic resistance genes (ARGs) are widely distributed and can be co-selected with heavy metals. This study focused on antibiotic resistant bacteria and ARGs over different remediation periods (1, 2, and 5 days), voltages (0.4 and 0.8 V cm−1), and initial concentrations (250–1,000 mg kg−1 for Cu, and 1,000–3,000 mg kg−1 for Zn). The application of polarity-reversal maintained a suitable pH, eliminating possible negative effects on soil quality. In addition to a decrease in total metals, the speciation was modified as residual forms decreased while reactive forms increased. Compared with anti-oxytetracycline bacteria, anti-sulfamethoxazole bacteria were more resistant to the electric field, which might be ascribed to greater constraints on their resistance enzymes. The presence of heavy metals accelerated the spread of ARGs, with a 2.67-fold increase for tetG, and a 3.86-fold increase for sul1. Among the ARGs studied, tetM and tetW, as well as sul genes were more easily removed than tetC and tetG genes. Finally, a significant correlation was found between ARGs and Cu, consistent with the relatively stronger toxicity of Cu and its high potential to induce the SOS response. This study advances the understanding of how electrokinetics influences antibiotic resistance in soil with heavy metals, which has important implications for the simultaneous control of these pollutants in soil.
The decarbonisation of energy provision is key to managing global greenhouse gas emissions and hence mitigating climate change. Digital technologies such as big data, machine learning, and the Internet of Things are receiving more and more attention as they can aid the decarbonisation process while requiring limited investments. The orchestration of these novel technologies, so-called cyber-physical systems (CPS), provides further, synergetic effects that increase efficiency of energy provision and industrial production, thereby optimising economic feasibility and environmental impact. This comprehensive review article assesses the current as well as the potential impact of digital technologies within CPS on the decarbonisation of energy systems. Ad hoc calculation for selected applications of CPS and its subsystems estimates not only the economic impact but also the emission reduction potential. This assessment clearly shows that digitalisation of energy systems using CPS completely alters the marginal abatement cost curve (MACC) and creates novel pathways for the transition to a low-carbon energy system. Moreover, the assessment concludes that when CPS are combined with artificial intelligence (AI), decarbonisation could potentially progress at an unforeseeable pace while introducing unpredictable and potentially existential risks. Therefore, the impact of intelligent CPS on systemic resilience and energy security is discussed and policy recommendations are deducted. The assessment shows that the potential benefits clearly outweigh the latent risks as long as these are managed by policy makers.
Abstract In China, irrigation is widespread in 40.7% cropland to sustain crop yields. By its action on water cycle, irrigation affects water resources, and local climate. In this study, a new irrigation module, including flood and paddy irrigation technologies, was developed in the ORCHIDEE-CROP land surface model which describes crop phenology and growth in order to estimate irrigation demands over China from 1982 to 2014. Three simulations were performed including NI: no irrigation; IR: with irrigation limited by local water resources; and FI: with irrigation demand fulfilled. Observations and census data were used to validate the simulations. Results showed that the estimated irrigation water withdrawal (W) based on IR and FI scenarios bracket statistical W with fair spatial agreements (r = 0.68 ± 0.07; p < 0.01). Improving irrigation efficiency was found to be the dominant factor leading to the observed W decrease. By comparing simulated total water storage (TWS) with GRACE observations, we found that simulated TWS with irrigation well explained the TWS variation over China. However, our simulation overestimated the seasonality of TWS in the Yangtze River Basin due to ignoring regulation of artificial reservoirs. The observed TWS decrease in the Yellow River Basin caused by groundwater depletion was not totally captured in our simulation, but it can be inferred by combining simulated TWS with census data. Moreover, we demonstrated that land use change tended to drive W locally, but had little effect on total W over China due to water resources limitation.
{BACKGROUND: Globally, toxic metal exposures are a well-recognized risk factor for many adverse health outcomes. DNA methylation-based measures of biological aging are predictive of disease, but have poorly understood relationships with metal exposures. OBJECTIVE: We performed a pilot study examining the relationships of 24-h urine metal concentrations with three novel DNA methylation-based measures of biological aging: DNAmAge, GrimAge, and PhenoAge. METHODS: We utilized a previously established urine panel of five common metals [arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg)] found in a subset of the elderly US Veterans Affairs Normative Aging Study cohort (N = 48). The measures of DNA methylation-based biological age were calculated using CpG sites on the Illumina HumanMethylation450 BeadChip. Bayesian Kernel Machine Regression (BKMR) was used to determine metals most important to the aging outcomes and the relationship of the cumulative metal mixture with the outcomes. Individual relationships of important metals with the biological aging outcomes were modeled using fully-adjusted linear models controlling for chronological age, renal function, and lifestyle/environmental factors. RESULTS: Mn was selected as important to PhenoAge. A 1 ng/mL increase in urine Mn was associated with a 9.93-year increase in PhenoAge (95%CI: 1.24, 18.61