In this study, the biodegradation of endocrine-disrupting chemicals (EDCs) (namely the natural and synthetic estrogens 17β-estradiol (E2) and 17α-ethinylestradiol (EE2), respectively) was assessed in an aerobic granular sludge (AGS) sequencing batch reactor (SBR) treating simulated domestic sewage. To better understand the fate of these compounds, their concentrations were determined in both liquid and solid (biomass) samples. Throughout the operation of the reactor, subjected to alternating anaerobic and aerated conditions, the removal of the hormones, both present in the influent at a concentration of 20 μg L-1, amounted to 99% (for E2) and 93% (for EE2), with the latter showing higher resistance to biodegradation. Through yeast estrogen screen assays, an average moderate residual estrogenic activity (0.09 μg L-1 EQ-E2) was found in the samples analysed. E2 and EE2 profiles over the SBR cycle suggest a rapid initial adsorption of these compounds on the granular biomass occurring anaerobically, followed by biodegradation under aeration. A possible sequence of steps for the removal of the micropollutants, including the key microbial players, was proposed. Besides the good capability of the AGS on EDCs removal, the results revealed high removal efficiencies (>90%) of COD, ammonium and phosphate. Most of the incoming organics (>80%) were consumed under anaerobic conditions, when phosphate was released (75.2 mgP L-1). Nitrification and phosphate uptake took place along the aeration phase, with effluent ammonium and phosphate levels around 2 mg L-1. Although nitrite accumulation took place over the cycle, nitrate consisted of the main oxidized nitrogen form in the effluent. The specific ammonium and phosphate uptake rates attained in the SBR were found to be 3.3 mgNH4+-N gVSS-1.h-1 and 6.7 mgPO43--P gVSS-1 h-1, respectively, while the specific denitrification rate corresponded to 1.0 mgNOx--N gVSS-1 h-1.Cresyl diphenyl phosphate (CDP), as a kind of aryl substituted organophosphate esters (OPEs), is commonly used as emerging flame retardants and plasticizers detected in environmental media. Due to the accumulation of CDP in organisms, it is very important to discover the toxicological mechanism and metabolic process of CDP. Hence, liver microsomes of crucian carps (Carassius carassius) were prepared for in vitro metabolism kinetics assay to estimate metabolism rates of CDP. After 140 min incubation, the depletion of CDP accounted for 58.1%-77.1% (expect 0.5 and 2 μM) of the administrated concentrations. The depletion rates were best fitted to the Michaelis-Menten model (R2 = 0.995), where maximum velocity (Vmax) and Michaelis-Menten constant (Km) were 12,700 ± 2120 pmol min-1·mg-1 protein and 1030 ± 212 μM, respectively. Moreover, the in vitro hepatic clearance (CLint) of CDP was 12.3 μL min-1·mg-1 protein. Log Kow and bioconcentration factor (BCF) of aryl-OPEs were both higher than those of alkyl- and chlorinated-OPEs, indicating that CDP may easily accumulate in aquatic organisms. The results made clear that the metabolism rate of CDP was greater than those of other OPEs detected in liver microsomes in previous research. This paper was first of its kind to comprehensively investigate the in vitro metabolic kinetics of CDP in fish liver microsomes. The present study might provide useful information to understand the environmental fate and metabolic processes of these kinds of substances, and also provide a theoretical basis for the ecological risk assessment of emerging contaminants.Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM2.5) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM2.5 predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM2.5 prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM2.5 predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R2 of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM2.5 predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM2.5 prediction models.  https://www.selleckchem.com/products/nvl-655.html  We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM2.5 prediction is necessary for accurate and reliable PM2.5 predictions.Understanding the arsenic (As) aging process is important for predicting the environmental behavior of exogenous As in paddy soils. In this work, samples of sixteen paddy soils with various soil properties were spiked with two concentrations (30 and 100 mg kg-1) of arsenate and subjected to a 360 day-long incubation under continuous flooding condition. Soil available As extracted by 0.05 M NH4H2PO4 was monitored through the aging process. Results showed that the available As%, the percentage of remaining available As in aged soils to added total As, fell from 44.2% to 41.9% on the 1st day to 22.0% and 23.0% on the 115th day for the low and high As spiked soils, respectively, then it remained basically unchanged after the 115th day. The pseudo-second order equation could adequately describe the aging kinetics of exogenous As in paddy soils. There was no significant difference in As aging parameters between the two spiked concentrations. Contents of soil free Al and Mn oxides, clay and cation exchange capacity strongly affected the aging rate of exogenous As. An empirical model, incorporating soil pH, cation exchange capacity, Olsen-P and flooding time, was developed to predict well the change of soil available As% during aging process (R2 = 0.711). The model could be potentially utilized to manage As-contaminated paddy fields and normalize ecotoxicity and bioaccumulation datasets in attempt to derive more widely applicable soil environmental quality criteria for As.