Therefore, a definition of a brief parameter, affinity coefficient Kf (relating to ion valence and distance between opposite charged ions), is introduced and used to explain the difference in adsorption performance of five titanates for NH4+. The conclusion about cation exchange and ions affinity may provide possible strategies for enhancement of cationic contaminant adsorption from water or wastewater.Environmental pollution, especially because of trace metals, seriously affects ecological safety, and bird feathers are often used as bioindicators to monitor this risk in various environments. However, the feasibility of feathers as bioindicators for trace metals in polymetallic contaminated areas has not been extensively studied. In this study, we used inductively coupled plasma mass spectrometry (ICP-MS) to quantify and compare the contents of nine trace metal(loid)s (V, Cr, Mn, Co, Cu, Zn, As, Cd and Pb) among soil, plants, insects and birds (feathers and internal tissues) sampled in the mining area of Tongling, a polymetallic contaminated area in Anhui Province, eastern China. We detected significant trace metal pollution in the abiotic and biotic materials. The contents of Cr, Cu, Zn, As and Pb in feathers differed among bird species and among sampling sites, with higher contents often recorded in tree sparrows (Passer montanus). The metal(loid)s V, Mn, Co, Zn, and As had higher contents in feathers than in internal tissues including heart, liver, kidneys, muscles and bones. The contents of some elements in feathers were positively correlated with those in internal tissues, for example, Co, As, and Cd in the heart, V and Co in the kidneys, Cd in the liver, Pb in bones, and As in muscles. Furthermore, the contents of V, Cr, As and Pb in feathers were higher than those in other biomaterials, implying an increasing trend from plants, insects, and feathers. Our study indicates that bird feathers can be used as effective, non-destructive bioindicators to monitor trace metal(loid) pollution, especially for V, Co, As, Cd and Pb, in polymetallic contaminated areas, providing reliable information for ecological assessment.Regional ocean models require accurate weather data for atmospheric boundary conditions such as air temperature, wind speed, and direction to simulate the coastal environment. In this study, a numerical modelling framework was developed to simulate different physical, chemical, and biological processes in a semi-enclosed coastal ecosystem by integrating the Weather Research and Forecasting (WRF) model with a 3D hydrodynamic and ecosystem model (Ise Bay Simulator). The final analytic data of the global forecast system released by the National Centers for Environmental Prediction with a 0.25° horizontal resolution was used as an atmospheric boundary condition for the WRF model to dynamically downscale the weather information to a spatial and temporal fine resolution. This modelling framework proved to be an effective tool to simulate the physical and biogeochemical processes in a semi-enclosed coastal embayment.  https://www.selleckchem.com/products/mk-0159.html  The WRF-driven ecosystem simulation and recorded Automated Meteorological Data Acquisition System (AMeDAS)-driven ecosystem simulation results were further compared with the observed data. The performance of both the recorded AMeDAS and WRF generated weather datasets were equally good, and more than 80% of the variation in bottom dissolved oxygen for shallow water and more than 90% for deep water was reproduced.Due to the difference of vertical distribution of algae in lakes, it is necessary to carry out remote sensing estimation of algal biomass based on the vertically heterogeneous distribution of chlorophyll in order to improve the accuracy of biomass inversion. A new algorithm is proposed and validated to measure algal biomass in Lake Chaohu based on the Moderate Resolution Imaging Spectrometer (MODIS) images. The algal biomass index (ABI) is defined as the difference in remote-sensing reflectance (Rrs, sr-1) at 555 nm normalized against two baselines with one formed linearly between Rrs(859) and Rrs(469) and another formed linearly between Rrs(645) and Rrs(469). Both theory and model simulations show that ABI has a good relation with the algal biomass in the euphotic zone (R2 = 0.88, p less then 0.01, N = 50). Field data were further used to estimate the biomass outside the euphotic layer through an empirical algorithm. The ABI algorithm was applied to MODIS Rayleigh-corrected reflectance (Rrc) data after testing the sensitivity to sun glint and thickness of aerosols, which showed an acceptable precision (root mean square error less then 21.31 mg and mean relative error less then 16.08%). Spectral analyses showed that ABI algorithm was immune to concentration of colored dissolved organic matter (CDOM) but relatively sensitive to suspended particulate inorganic matter (SPIM), which can be solved by using Turbid Water Index (TWI) though in such a challenging environment. A long-term (2012-2017) estimation of algal biomass was further calculated based on the robust algorithm, which shows both seasonal and spatial variations in Lake Chaohu. Tests of ABI algorithm on Sentinel-3 OLCI demonstrates the potential for application in other remote sensors, which meets the need of observation using multi-sensor remote sensing in the future.Ammonia-oxidizing archaea (AOA) and bacteria (AOB) play important roles in N cycling in sediments globally. However, little is known about their ammonia oxidation rates along a river-estuary-sea continuum. In this study, we investigated how the potential ammonia oxidation rates (PARs) of AOA and AOB changed spatially along a continuum comprising three habitats the Shanghai urban river network, the Yangtze Estuary, and the adjacent East China Sea, in summer and winter. The AOA and AOB PARs (0.53 ± 0.49 and 0.72 ± 0.69 μg N g-1 d-1, mean ± SD, respectively) and their amoA gene abundance (0.47 ± 0.85 × 106 and 2.4 ± 3.54 × 106 copies g-1, respectively) decreased along the continuum, particularly from the urban river to the estuary, driven by decreasing sediment total organic C and N and other correlated inorganic nutrients (e.g., NH4+) along the gradient of anthropogenic influences. These spatial patterns were consistent between the seasons. The urban river network, where the anthropogenic influences were strongest, saw the largest seasonal differences, as both AOA and AOB had higher PARs and abundance in summer than in winter.