Microplastics in freshwater systems : dynamic behaviour and transport processes

Freshwater ecosystems are viewed as a key medium for the transport of land-based plastics into oceans. Microplastic (MP) particles in freshwater environments demonstrate high persistence and an extensive range of size and shape distributions, which make their mobility, distribution, and fate vary distinctly depending on the prevailing environmental conditions. The inherent physical properties of different plastic polymers are constantly evolving at different specific reaction rates due to the complex weathering processes in the environment. This continuously alters the underlying mechanisms governing MP dynamics and further complicates their ultimate fate in natural aquatic systems. This paper conducts a comprehensive review of the dynamic behaviour of MPs in freshwater ecosystems, focusing on investigating the settling, aggregation, retention, and suspension processes governing their transport from the source to the sink. We provide an in-depth overview of the key theoretical foundations of MP behaviour in ambient flows and the key influential factors (i.e. size, density, shape, composition). Our findings highlight intricate interplays between MP dynamic behaviours and local hydrodynamics and water chemistry, which lead to the continuous evolution of MP physicochemical properties (e.g., size, surface charge) through interactions with suspended solids, organic natural matter, and microorganisms under light and wind exposure. This dynamic poses significant challenges in predicting MP transport processes and ultimate fate. Gap analysis highlights the discrepancy between current models based on controlled laboratory conditions and complex natural environments, signifying the need for investigating MP dynamic behaviour across a wide range of environmental conditions (e.g. simulating complex flow patterns and solution chemistries of real water bodies). Further research is needed to expand field studies to correlate environment hydrodynamics with MP abundance and to conduct mesoscale experiments that accurately reflect the effects of weathering and flow hydrodynamics on MP behaviours. Integrating detailed physical experiments with numerical modelling tools is essential for a comprehensive understanding of the interactions among various MPs and their overall impact on the environment. This facilitates robust and reliable environmental risk assessment for MP control and pollution management

A SOFTWARE SENSOR FOR IN-SITU MONITORING OF THE 5-DAY BIOCHEMICAL OXYGEN DEMAND

Prikazana su dva senzora temeljena na tehnikama umjetne inteligencije. Namijenjeni su opažanju tijekom petodnevnoga, tj. vremenski zahtjevnoga, postupka određivanja biokemijske potrošnje kisika (BPK5). Vrijednost se tako može, nakon mjerenja, izračunati gotovo trenutačno. Upotrijebljene su mreža usmjerene vrste i mreža temeljena na radijalnoj funkciji (skr. UUNM, RFUNM), kojima se odredila najveća vrijednost te potrošnje (BPK5(maks)). To je funkcija srednje, najveće i najmanje vrijednosti otopljenoga kisika (OKsred, OKnajveći, OKnajmanji). Mjerenje je načinjeno u rijeci Sefidrood. Nadalje, mreža je optimizirana primjenom Levenberg-Marquardtova (LM), elastičnoga, povratnoga (EP) i algoritma skaliranoga, konjugiranoga gradijenta (SKG). Rezultati su pokazali kako je za uvježbavanje i provjeru najbolji Levenberg-Marquadtov algoritam. Također, svaka je mreža ocijenjena uporabom srednje kvadratne pogrješke, koeficijentom korelacije te omjerom odstupanja. Rezultati su pokazali kako su obje mreže, usmjerena i radijalna, postigle približno jednake rezultate.Due to the time-consuming procedure for determining the 5-day biochemical oxygen demand (BOD5), the present study developed two software sensors based on artificial intelligence techniques to estimate this indicator instantaneously. For this purpose, feed-forward and radial basis function neural networks (FFANN and RBFANN, respectively) were tuned to estimate the maximum values of BOD5 (BOD5(max)) as a function of average, maximum and minimum dissolved oxygen in the Sefidrood River. Also, Levenberg-Marquardt (LM), resilient back propagation (RP), and scaled conjugate gradient (SCG) algorithms were used to optimize the FFANN parameters. The results demonstrated that the performance of LM algorithm in tuning the FFANN was better than others, in verification step. Besides, the performance of both FFANN and RBFANN models for prediction of the BOD5(max) was approximately the same

Environmental risk assessment of wetland ecosystems using Bayesian belief networks

Wetlands are valuable natural capital and sensitive ecosystems facing significant risks from anthropogenic and climatic stressors. An assessment of the environmental risk levels for wetlands’ dynamic ecosystems can provide a better understanding of their current ecosystem health and functions. Different levels of environmental risk are defined by considering the categories of risk and the probability and severity of each in the environment. Determining environmental risk levels provides a general overview of ecosystem function. This mechanism increases the visibility of risk levels and their values in three distinct states (i.e., low, moderate, and high) associated with ecosystem function. The Bayesian belief network (BBN) is a novel tool for determining environmental risk levels and monitoring the effectiveness of environmental planning and management measures in reducing the levels of risk. This study develops a robust methodological framework for determining the overall level of risks based on a combination of varied environmental risk factors using the BBN model. The proposed model is adopted for a case study of Shadegan International Wetlands (SIWs), which consist of a series of Ramsar wetlands in the southwest of Iran with international ecological significance. A comprehensive list of parameters and variables contributing to the environmental risk for the wetlands and their relationships were identified through a review of literature and expert judgment to develop an influence diagram. The BBN model is adopted for the case study location by determining the states of variables in the network and filling the probability distribution tables. The environmental risk levels for the SIWs are determined based on the results obtained at the output node of the BBN. A sensitivity analysis is performed for the BBN model. We proposed model-informed management strategies for wetland risk control. According to the BBN model results, the SIWs ecosystems are under threat from a high level of environmental risk. Prolonged drought has been identified as the primary contributor to the SIWs’ environmental risk levels

Strong Warming Rates in the Surface and Bottom Layers of a Boreal Lake: Results From Approximately Six Decades of Measurements (1964–2020)

High-latitude lakes are warming faster than the global average with deep implications for life on Earth. Using an approximately six-decade long in situ data set, we explored the changes in lake surface-water temperature (LST), lake deep-water temperature (LDT), lake depth-weighted mean water temperature (LDMT), and ice-free days in Lake Kallavesi, a boreal lake in central Finland, when the lake was stratified (June–August). Our results suggest that the LST is warming faster than the local air temperature (AT). As for the LST, fast warming was also observed in the LDT and LDMT, but at rates slower than those in the LST. The number of ice-free days also shows an upward trend, with a rate of about 7 days per decade during the study period. The corresponding local AT is the main driver of the LST, followed by the ice-free days and annual mean AT. Air temperature and ice-free days also mainly contribute to the changes in the LDMT. The LDT is affected more by the North Atlantic Oscillation signals in this freshwater lake. The AT in the prior months does not affect the LDT in Lake Kallavesi although the AT during the prior season, that is, spring, is the main driver of summer LDT. This highlights the local AT impact on the LDT at time scales longer than a month. The warming rates in the lake water are at a minimum in June because the lake is not yet strongly stratified in this month when compared to July and August. These findings improve our knowledge of long-term changes in the lake water temperature in a high-latitude lake, a region with severe environmental consequences due to fast changes in the AT and lake ice phenology

Modelling impacts of climate change and anthropogenic activities on inflows and sediment loads of wetlands : case study of the Anzali wetland

Understanding the effects of climate change and anthropogenic activities on the hydrogeomorpholgical parameters in wetlands ecosystems is vital for designing effective environmental protection and control protocols for these natural capitals. This study develops methodological approach to model the streamflow and sediment inputs to wetlands under the combined effects of climate and land use / land cover (LULC) changes using the Soil and Water Assessment Tool (SWAT). The precipitation and temperature data from General Circulation Models (GCMs) for different Shared Socio-economic Pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5) are downscaled and bias-corrected with Euclidean distance method and quantile delta mapping (QDM) for the case of the Anzali wetland watershed (AWW) in Iran. The Land Change Modeler (LCM) is adopted to project the future LULC at the AWW. The results indicate that the precipitation and air temperature across the AWW will decrease and increase, respectively, under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Streamflow and sediment loads will reduce under the sole influence of SSP2-4.5 and SSP5-8.5 climate scenarios. An increase in sediment load and inflow was observed under the combined effects of climate and LULC changes, this is mainly due to the projected increased deforestation and urbanization across the AWW. The findings suggest that the densely vegetated regions, mainly located in the zones with steep slope, significantly prevents large sediment load and high streamflow input to the AWW. Under the combined effects of the climate and LULC changes, by 2100, the projected total sediment input to the wetland will reach 22.66, 20.83, and 19.93 million tons under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. The results highlight that without any robust environmental interventions, the large sediment inputs will significantly degrade the Anzali wetland ecosystem and partly-fill the wetland basin, resulting in resigning the wetland from the Montreux record list and the Ramsar Convention on Wetlands of International Importance

Uncertainty quantification of granular computing‑neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams

Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion (Dx), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of Dx and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor = 0.56) that brackets the highest percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Considering the significant inherent uncertainty reported in the previous Dx models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing (Dx) in turbulent environmental flow systems

Decline in Iran’s groundwater recharge

Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran—a global hotspot for groundwater depletion. Using an extended database comprising abstractions from over one million groundwater wells, springs, and qanats, from 2002 to 2017, here we show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge. This decline is primarily attributed to unsustainable water and environmental resources management, exacerbated by decadal changes in climatic conditions. However, it is important to note that the former’s contribution outweighs the latter. Our results show the average annual amount of nationwide groundwater recharge (i.e., ~40 mm/yr) is more than the reported average annual runoff in Iran (i.e., ~32 mm/yr), suggesting the surface water is the main contributor to groundwater recharge. Such a decline in groundwater recharge could further exacerbate the already dire aquifer depletion situation in Iran, with devastating consequences for the country’s natural environment and socio-economic development

An efficient data driven-based model for prediction of the total sediment load in rivers

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance

An Efficient Data Driven-Based Model for Prediction of the Total Sediment Load in Rivers

Sediment load in fluvial systems is one of the critical factors shaping the river geomorphological and hydraulic characteristics. A detailed understanding of the total sediment load (TSL) is required for the protection of physical, environmental, and ecological functions of rivers. This study develops a robust methodological approach based on multiple linear regression (MLR) and support vector regression (SVR) models modified by principal component analysis (PCA) to predict the TSL in rivers. A database of sediment measurement from large-scale physical modelling tests with 4759 datapoints were used to develop the predictive model. A dimensional analysis was performed based on the literature, and ten dimensionless parameters were identified as the key drivers of the TSL in rivers. These drivers were converted to uncorrelated principal components to feed the MLR and SVR models (PCA-based MLR and PCA-based SVR models) developed within this study. A stepwise PCA-based MLR and a 10-fold PCA-based SVR model with different kernel-type functions were tuned to derive an accurate TSL predictive model. Our findings suggest that the PCA-based SVR model with the kernel-type radial basis function has the best predictive performance in terms of statistical error measures including the root-mean-square error normalized with the standard deviation (RMSE/StD) and the Nash–Sutcliffe coefficient of efficiency (NSE), for the estimation of the TSL in rivers. The PCA-based MLR and PCA-based SVR models, with an overall RMSE/StD of 0.45 and 0.35, respectively, outperform the existing well-established empirical formulae for TSL estimation. The analysis of the results confirms the robustness of the proposed PCA-based SVR model for prediction of the cases with high concentration of sediments (NSE = 0.68), where the existing sediment estimation models usually have poor performance