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Implementation increased the diagnosis of type 1 infarction from 12.4% to 17.8%, and from 7.5% to 9.4% in patients with and without kidney impairment (both significant). Patients with kidney impairment and type 1 myocardial infarction were less likely to undergo coronary revascularization (26% versus 53%) or receive dual anti-platelets (40% versus 68%) than those without kidney impairment, and this did not change post-implementation. In patients with hs-cTnI above the 99th centile, the primary outcome occurred twice as often in those with kidney impairment compared to those without (24% versus 12%, hazard ratio 1.53, 95% confidence interval 1.31 to 1.78). Thus, hs-cTnI testing increased the identification of myocardial injury and infarction but failed to address disparities in management and outcomes between those with and without kidney impairment.Printing and dyeing wastewater generally has high pH, high turbidity, poor biodegradability, complex composition, and high chroma, which make it one of the most difficult industrial wastewaters to treat. Herein, heterogeneous ozone oxidation technology is applied to oxidize and degrade printing and dyeing wastewater. A metal oxide catalyst supported on activated carbon (γ-MnO2/AC) was prepared by hydrothermal synthetic method and shown to enable synergistic catalysis involving MnO2 metal sites and N/C sites. A simulated methyl orange solution was used to determine the effects of various preparation and operation parameters. The results confirmed that the γ-MnO2/AC catalyst exhibited good chemical oxygen demand (COD) removal and reusability. Additionally, γ-MnO2/AC demonstrated excellent degradation of the secondary biochemical effluent of printing and dyeing wastewater (COD removal = 72.45% within 120 min). The γ-MnO2/AC catalyst was fully characterized, and the mechanism governing its catalytic ozone oxidation process was investigated experimentally.This work evaluated, for the first time, the performance of an integral microalgae-based domestic wastewater treatment system composed of an anoxic reactor and an aerobic photobioreactor, coupled with an anaerobic digester for converting the produced algal-bacterial biomass into biogas, with regards to the removal of 16 contaminants of emerging concern (CECs) penicillin G, tetracycline, enrofloxacin, ciprofloxacin, sulfamethoxazole, tylosin, trimethoprim, dexamethasone, ibuprofen, naproxen, acetaminophen, diclofenac, progesterone, carbamazepine, triclosan and propylparaben. The influence of the hydraulic retention time (HRT) in the anoxic-aerobic bioreactors (4 and 2.5 days) and in the anaerobic digester (30 and 10 days) on the fate of these CECs was investigated. The most biodegradable contaminants (removal efficiency >80% regardless of HRT) were tetracycline, ciprofloxacin, sulfamethoxazole, tylosin, trimethoprim, dexamethasone, ibuprofen, naproxen, acetaminophen and propylparaben (degraded predominantly in the anoxic-aerobic bioreactors), and tetracycline, sulfamethoxazole, tylosin, trimethoprim and naproxen (degraded predominantly in the anaerobic reactor). The anoxic-aerobic bioreactors provided removal of at least 48% for all CECs tested. The most recalcitrant contaminants in the anaerobic reactor, which were not removed at any of the HRT tested, were enrofloxacin, ciprofloxacin, progesterone and propylparaben.The factors controlling soil organic carbon (SOC) content in wetlands need to be identified to estimate the global stores of SOC. Although there have been a large number of small-scale studies of the local patterns of SOC content, global studies are still required. selleck chemical We used a random forest algorithm and other statistical approaches to determine the controls on the SOC content in wetlands at global, continental, and national scales based on the Harmonized World Soil Database and field data. The results showed that, at the three scales explored, the soil cation exchange capacity and bulk density were the main controls on the SOC content in wetlands. Moreover, equations for estimating global SOC content were established. To assess the universality of SOC content estimation equations, the soil properties were considered as a "community" and the normalized stochasticity ratio (NST) was used to assess the stochasticity in the assembly of soil "communities". The results showed that, globally, the interaction of these factors was stochastic in the "community" composed of the controllers and SOC. The reason for this result might be that microbes were not considered in the equation. Therefore, the weighted abundance of related microbes (WARM) was therefore recommended in the estimation of SOC. With NST and WARM factors, we found that microbes play a key role in increasing the determinacy of SOC estimation equations in wetlands with less anthropogenic contamination. Our findings show that when microbial impacts are taken into account, the patterns of SOC content in pristine wetlands are more universal. Our newly established equations for estimating global SOC content are crucial in projecting changes in wetland SOC, and the two factors indicated in this study favor the universality for SOC content estimation.Antibiotics and arsenic are two frequently detected contaminants in soils and waters, both of which have potential threats to human health. There are few studies focusing on the interaction between these two groups of contaminants in the environment. In this study, we found that the presence of oxytetracycline could significantly promote the oxidation of inorganic As(III) to As(V) with trace Fe(III) (10 μM) and H2O2 (100 μM) at near natural pH, and OTC was degraded simultaneously. The most possible mechanism was that OTC could complex with Fe(III) and reduce Fe(III) to Fe(II), which further induced the Fenton-like reaction. Furthermore, structural Fe(III) of α-FeOOH and adsorbed Fe(III) of montmorillonite could also induce these reactions, and the oxidation rate of As(III) was higher with Fe(III)-montmorillonite than aqueous Fe(III). Based on this study, the transformation of As(III) and OTC could occur in four natural water samples, including river water, groundwater and livestock wastewaters. The results of this study revealed the overlooked effect of residual tetracyclines antibiotics on the transformation of co-existing As(III) in natural waters and soils, which might greatly reduce the toxicity of As(III) in the environment.Louisiana, located in the southeast United States, is home to 40% of the continental US's coastal wetlands yet accounts for 80% of the nation's coastal wetland loss. This loss is generally attributed to decreased sediment supply, hydrologic alteration from levees, channelization, subsidence, sea-level rise, and wave and tidal induced marsh edge erosion. The Mid-Barataria Sediment Diversion is a US $1.3 billion coastal restoration project that will divert up to 2100 m3 s-1 of sediment-laden Mississippi River water directly into Barataria Basin. The influx of colder, nutrient-rich, springtime river water could negatively impact water quality of the receiving basin. We quantified the effects of colder, surface water temperature on the nitrate (NO3-) reduction rate in vegetated marsh and open water bay sediments. Colder water limited NO3- removal processes averaging 17.1 mg N m-2 d-1 in the range of 5-14 °C, before increasing almost 3-fold in the 20 °C treatments at 50.6 mg N m-2 d-1. Low N removal rates, especially near the project inflow where temperatures will be coldest will favor transport of NO3- further into Barataria Basin where eutrophic conditions could become expressed. These results will inform coastal managers around the world of the potential ecosystem response to coastal restoration aimed at river reconnection where colder waters enter warmer, shallow basins.The synergetic control of carbon and air pollutant emissions will be an unflagging effort for China in its dual pursuit of air quality improvement and carbon neutrality. The shared features of sectoral emissions from network and supply chain perspectives, as well as the evolution of these features under policy intervention remain to be investigated. This study develops four ecological networks for CO2 and SO2 emissions targeting the period 2010-2015 with strengthened emission control implemented. By fusing input-output analysis, Ecological Network Analysis and Structural Path Analysis, the shared intersectoral linkages of emissions are examined, and the key supply chains are identified. The results indicate that most sectors have control over Transportation Equipment, Electronic Equipment, and Construction, and almost all sectors have dependence on Power and Heat. Exploitative relationships induced by emission flows along supply chains are predominant, accounting for over 60% for four emission flow networks. Eight shared supply chains are identified among the top 20 that generally induce larger than 50% emissions in both 2010 and 2015. The one with the largest emissions is "Total capital formation → Construction → Nonmetals". During 2010-2015, the critical evolution of network features is the decrease in the economy's control over Construction, dependence on Fossil Energy Mining, and emissions contained in the paths associated with exports. The findings help to more pertinently strategize on prescient regulation of key supply chains for a more effective carbon-pollution synergetic control.Accurate air quality prediction can help cope with air pollution and improve the life quality. With the development of the deployments of low-cost air quality sensors, increasing data related to air quality has provided chances to find out more accurate prediction methods. Air quality is affected by many external factors such as the position, wind, meteorological information, and so on. Meanwhile, these factors are spatio-temporal dynamic and there are many dynamic contextual relationships between them. Many methods for air quality prediction do not consider these complex spatio-temporal correlations and dynamic contextual relationships. In this paper, we propose a dual-path dynamic directed graph convolutional network (DP-DDGCN) for air quality prediction. We first create a dual-path transposed dynamic directed graph according to static distance relationships of stations and the dynamic relationships generated by wind speed and directions. Then based on the dual-path dynamic directed graph, we can capture the dynamic spatial dependencies more comprehensively. After that we apply gated recurrent units (GRUs) and add the future meteorological features, to extract the complex temporal dependencies of historical air quality data. Using dual-path dynamic directed graph blocks and the GRUs, we finally construct a dynamic spatio-temporal gated recurrent block to capture the dynamic spatio-temporal contextual correlations. Based on real-world datasets, which record a large amount of PM2.5 concentration data, we compare the proposed model with the benchmark models. The experimental results show that our proposed model has the best performance in predicting the PM2.5 concentrations.Formation of non-extractable residues (NERs) is the major fate of most environmental organic contaminants in soil, however, there is no direct evidence yet to support the assumed physical entrapment of NERs (i.e., type I NERs) inside soil humic substances. Here, we used 14C-radiotracer and silylation techniques to analyze NERs of six emerging and traditional organic contaminants formed in a suspension of humic acids (HA) under catalysis of the oxidative enzyme laccase. Laccase induced formation of both type I and covalently bound NERs (i.e., type II NERs) of bisphenol A, bisphenol F, and tetrabromobisphenol A to a large extent, and of bisphenol S (BPS) and sulfamethoxazole (SMX) to a less extent, while no induction for phenanthrene. The type I NERs were formed supposedly owing to laccase-induced alteration of primary (active groups) and secondary (conformation) structure of humic supramolecules, contributing surprisingly to large extents (23.5%-65.7%) to the total NERs, particularly for BPS and SMX, which both were otherwise not transformed by laccase catalysis.