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Excess nitrogen in water bodies is associated with a number of environmental problems, including hypoxia and eutrophication. Originating from anthropogenic activities such as fertilizer application, and influenced by watershed characteristics such as the structure of the drainage network, stream discharge, temperature, and soil moisture, factors influencing nitrogen transport and transformation are many and interconnected. This paper describes the development and application of a process-oriented nitrogen model based on the modeling framework of PAWS (Process-based Adaptive Watershed Simulator) that can describe coupled hydrologic, thermal and nutrient processes. The integrated model was tested for an agricultural watershed with complex land use, namely the Kalamazoo River watershed in Michigan, USA. Nitrogen transport and transformations on the landscape were modeled by representing multiple sources and processes (fertilizer/manure application, point sources, atmospheric deposition, nitrogen retention and removal in wetlands and other lowland storage, etc.) across multiple hydrologic domains (streams, groundwater, soil water). The coupled model provides a tool to examine nitrogen budgets and to quantify the impacts of human activities and agricultural practices on the riverine export of nitrogen species. Model results indicate that the river network removed approximately 5.96 % of the total anthropogenic nitrogen input to the watershed, and that the riverine export of nitrogen accounted for 29.22 % of the total anthropogenic inputs during 2004-2009 while the groundwater contribution of nitrogen to the rivers during the same period was found to be 18.53 % highlighting the important role of groundwater within the watershed.

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The partitioning coefficient Kd for a specific compound and location is not only a key input parameter of fate and transport models, but also critical in estimating the safety environmental concentration threshold. In order to reduce the uncertainty caused by non-linear interactions among environmental factors, machine learning based models for predicting Kd were developed in this work based on literature datasets of nonionic pesticides including molecular descriptors, soil properties, and experimental settings. The equilibrium concentration (Ce) values were specifically included for the reason that a varied range of Kd corresponding to a given Ce occurred in a real environment. By transforming 466 isotherms reported in the literature, 2618 paired equilibrium concentrations of liquid-solid (Ce-Qe) data points were obtained. Results of SHapley Additive exPlanations revealed that soil organic carbon, Ce, and cavity formation were the most important. The distance-based applicability domain analysis was conducted for the 27 most frequently used pesticides with 15952 pieces of soil information from the HWSD-China dataset by setting three Ce scenarios (i.e., 10, 100, and 1000 µg L-1). It was revealed the groups of compounds showing log Kd < 0.06 and log Kd > 1.19 were composed mostly of those with log Kow of -0.800 and 5.50, respectively. When log Kd varied between 0.100 and 1.00, it was impacted by interactions among soil types, molecular descriptors, and Ce comprehensively, which accounted for 55% of the total 2618 calculations. It could be concluded that site-specific models developed in this work are necessary and practicable for the environmental risk assessment and management of nonionic organic compounds.

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Proliferation of microplastics in rivers, lakes, estuaries, coastal waters and oceans is a major global challenge and threat to the environment, livelihoods and human health. Reliable predictive tools can play an essential role in developing an improved understanding of microplastics behaviour, exposure and risk in water bodies, and facilitate identification of sources and accumulation hot spots, thereby enabling informed decision-making for targeted prevention and clean-up activities. This study presents a new numerical framework (CaMPSim-3D) for predicting microplastics fate and transport in different aquatic settings, which consists of a Lagrangian, three-dimensional (3D) particle-tracking model (PTM) coupled with an Eulerian-based hydrodynamic modeling system (TELEMAC). The 3D PTM has several innovative features that enable accurate simulation and efficient coupling with TELEMAC, which utilizes an unstructured computational mesh. The PTM is capable of considering spatio-temporally varying diffusivity, and uses an innovative algorithm to locate particles within the Eulerian mesh. Model accuracy associated with different advection schemes was verified by comparing numerical predictions to known analytical solutions for several test cases. The implications of choosing different advection schemes for modeling microplastics transport was then investigated by applying the PTM to simulate particle transport in the lower Saint John River Estuary in eastern Canada. The sensitivity of the PTM predictions to the advection scheme was investigated using six numerical schemes with different levels of complexity. Predicted particle distributions and residence times based on the fourth-order Runge-Kutta (RK4) scheme differed significantly (residence times by up to 100 %) from those computed using the traditional first-order (Euler) method. The Third Order Total Variation Diminishing (TVD3) Runge-Kutta method was found to be optimal, providing the closest results to RK4 with approximately 27 % lower computational cost.

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Surface water quality is among the significant challenges in the Sutlej River basin, passing through Pakistan's most densely populated province. Currently, the overall surface water quality is grossly polluted, mainly due to the direct discharge of wastewater from the urban areas to the Sutlej River directly or through stream networks. Escherichia coli concentrations vary under extreme weather events like floods and droughts and socioeconomic circumstances like urbanization, population growth, and treatment options. This paper assesses the future E. coli load and concentrations using the Soil and Water Assessment Tool (SWAT) along with scenarios based on Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) developed by the Intergovernmental Panel on Climate Change (IPCC). E. coli concentrations according to a more polluted scenario disclose a near and mid future increase by 108% and 173%, and far future increases up to 251% compared to the reference period (baseline) concentrations. The E. coli concentration is reduced by - 54%, - 68%, and - 81% for all the projected time steps compared to the baseline concentrations. While highly improved sewerage and manure management options are adapted, the concentration is further reduced by - 96%, - 101%, and - 105%, respectively, compared to the baseline. Our modeling and scenario matrix study shows that reducing microbiological concentrations in the surface water is possible. Still, it requires rigorous sanitation and treatment options, and socioeconomic variables play an essential role besides climate change to determine the microbiological concentration of water resources and be included in future studies whenever water quality and health risks are considered.

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Polybrominated diphenyl ethers (PBDEs) are a family where each congener possesses different physicochemical properties, persistence and/or toxicity. Biodegradation can selectively change the abundance of congeners. These warrant modeling of individual congeners by considering biodegradation pathways together with fate and transport (F&T) mechanisms. Accordingly, this study aims to develop a F&T model (Fate and Transport model for Hydrophobic Pollutants - FTHP) that integrates congener specific biodegradation of PBDEs in sediments. The model is tested using sediment data from a location representing the Lower South Bay of San Francisco. Results demonstrated settling, resuspension, and biodegradation as important mechanisms. FTHP is then used to predict congener concentrations in a period of 20 years for two cases (constant and time-dependent water column concentrations) and four alternative scenarios: no intervention (i.e., natural attenuation, also serves as the base case), no degradation, dredging and biostimulation. The greatest impact on the reduction of total PBDE concentrations was achieved by a reduction in water column concentrations, i.e. source control, and dredging. On the other hand, biostimulation coupled with source control was the most effective in reducing bioaccumulative PBDE congener concentrations and almost as effective as dredging for the rest of congeners. Proposed FTHP model can distinguish between congeners and help devise informed management plans which focus on decreasing risks associated with persistent and bioaccumulative compounds in contaminated sediments.

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The construction of Akkuyu Nuclear Power Plant (NPP) was launched in 2018 and the plant is expected to be operative by the year 2023. Being situated in the Mediterranean coastline, Akkuyu NPP will be the first nuclear power generation facility in Turkey. The plant will have four Russian VVER-1200 type pressurized water reactors with a total installed capacity of 4800 MW. In this study, atmospheric dispersion and ground level deposition of Cs-137 and I-131 released from a possible accident in Akkuyu NPP was estimated using a Lagrangian particle dispersion model, FLEXPART, for different time periods representing relatively extreme meteorological conditions for Mersin. The source term used in simulations was assumed the same with that of the Chernobyl NPP accident that occurred in 1986. In addition, cumulative dose and risk values were calculated from FLEXPART output datasets considering potential exposure pathways such as inhalation, ground-shine exposure and cloud-shine exposure. The results were further analyzed with python codes and dose and risk maps were created for local and regional scales. According to results of the study, it was found that the vicinity of Mersin and Central Anatolia were simulated to be the most significantly affected areas from the accident under both scenario conditions. The northern and western parts and all coastlines of Turkey were simulated to be more contaminated in the simulations conducted under December 2009 conditions, whereas southern and western parts of Turkey and some parts of Middle East countries like Syria, Iraq and Lebanon were simulated to be comparatively more contaminated under August 2010 conditions. The results indicated that radioactivity levels exceeding 100 kBq/m2 were observed near the accident site under both scenario conditions. Values exceeding 10 kBq/m2 level were simulated in western Turkey in the first scenario whereas similar values were found in eastern Turkey in the second scenario. Furthermore, the results indicated 7-day thyroid dose values ranging between 0.10 mSv and 10.0 mSv in western and eastern parts of Mediterranean region for the first and the second scenario, respectively. Similarly, 1-year effective dose of only Cs-137 ranged between 0.1 mSv and 1.0 mSv around Akkuyu NPP site in both scenarios. The results revealed that meteorological conditions were among the most important parameter for the fate and transport of radioactivity originating from such a catastrophic event.

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Acid rock drainage (ARD) is a major environmental problem that poses significant environmental risks during and after mining activities. A new methodology for environmental risk assessment based on probability bounds and a geochemical speciation model (PHREEQC) is presented. The methodology provides conservative and non-conservative ways of estimating risk of heavy metals posed to selected endpoints probabilistically, while propagating data and parameter uncertainties throughout the risk assessment steps. The methodology is demonstrated at a minesite located in British Columbia, Canada. The result of the methodology for the case study minesite shows the fate-and-transport of heavy metals is well simulated in the mine environment. In addition, the results of risk characterization for the case study show that there is risk due to transport of heavy metals into the environment.

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A major obstacle in the development of accurate cellular models for investigating nanobio interactions in vitro is determination of physiologically relevant measures of dose. Comparison of biological responses to nanoparticle exposure typically relies on administered dose metrics such as mass concentration of suspended particles, rather than the effective dose of particles that actually comes in contact with the cells over the time of exposure. Adoption of recently developed dosimetric methodologies will facilitate determination of effective dose delivered to cells in vitro, thereby improving the accuracy and reliability of in vitro screening data, validation of in vitro with in vivo data, and comparison across multiple datasets for the large variety of nanomaterials currently in the market.