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

Integrating deep learning techniques for effective river water quality monitoring and management

Effective river water quality monitoring is essential for sustainable water resource management. In this study, we established a comprehensive monitoring system along the Kaveri River, capturing real-time data on multiple critical water quality parameters. The parameters collected encompassed water contamination levels, turbidity, pH measurements, temperature, and total dissolved solids (TDS), providing a holistic view of river water quality. The monitoring system was meticulously set up with strategically positioned sensors at various river locations, ensuring data collection at regular 5-min intervals. This data was then transmitted to a cloud-based web portal, facilitating storage and analysis. To assess water quality, we introduced a novel hybrid approach, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The proposed CNN-LSTM model achieved a validation accuracy of 98.40%, surpassing the performance of other state-of-the-art methods. Notably, the practical application of this system includes real-time alerts, promptly notifying stakeholders when water quality parameters exceed predefined thresholds. This feature aids in making informed decisions in water resource management. The study's contributions lie in its effective river water quality monitoring system, which encompassing various parameters, and its potential to positively impact environmental conservation efforts by providing a valuable tool for informed decision-making and timely interventions.

Sediment load assessments under climate change scenarios and a lack of integration between climatologists and environmental modelers

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water quality parameters in which their accurately in-situ measurements are impossible and face difficulties. Using a rich water quality dataset spanned from 1980 to 2023, we employed four machine learning-based models to estimate the British Colombia WQI (BCWQI) in the Lake Päijänne, Finland, without parameters like chemical oxygen demand (COD) and total phosphorus (TP). Measurement of both COD and TP is time-consuming, needs laboratory equipment and labor costs, and faces sampling-related difficulties. Our results suggest the machine learning-based models successfully estimate the BCWQI in Lake Päijänne when TP and COD are omitted from the dataset. The long-short term memory model is the least sensitive model to exclusion of COD and TP from inputs. This model with the coefficient of determination and root-mean squared error of 0.91 and 0.11, respectively, outperforms the support vector regression, random forest, and neural network models in real-time estimation of the BCWQI in Lake Päijänne. Incorporation of BCWQI with the machine learning-based models could enhance assessment of overall quality of inland-waters with a limited database in a more economical and time-saving way. Our proposed method is an effort to replace the traditional offline water quality assessment tools with a real-time model and improve understanding of decision-makers on the effectiveness of management practices on the changes in lake water quality.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Case study on climate change effects and food security in Southeast Asia

Agriculture, a cornerstone of human civilization, faces rising challenges from climate change, resource limitations, and stagnating yields. Precise crop production forecasts are crucial for shaping trade policies, development strategies, and humanitarian initiatives. This study introduces a comprehensive machine learning framework designed to predict crop production. We leverage CMIP5 climate projections under a moderate carbon emission scenario to evaluate the future suitability of agricultural lands and incorporate climatic data, historical agricultural trends, and fertilizer usage to project yield changes. Our integrated approach forecasts significant regional variations in crop production across Southeast Asia by 2028, identifying potential cropland utilization. Specifically, the cropland area in Indonesia, Malaysia, Philippines, and Viet Nam is projected to decline by more than 10% if no action is taken, and there is potential to mitigate that loss. Moreover, rice production is projected to decline by 19% in Viet Nam and 7% in Thailand, while the Philippines may see a 5% increase compared to 2021 levels. Our findings underscore the critical impacts of climate change and human activities on agricultural productivity, offering essential insights for policy-making and fostering international cooperation.

Adsorbent based on MOF-5/cellulose aerogel composite for adsorption of organic dyes from wastewater

Industries persistently contribute to environmental pollution by releasing a multitude of harmful substances, including organic dyes, which represent a significant hazard to human health. As a result, the demand for effective adsorbents in wastewater treatment technology is steadily increasing so as to mitigate or eradicate these environmental risks. In response to this challenge, we have developed an advanced composite known as MOF-5/Cellulose aerogel, utilizing the Pampas plant as a natural material in the production of cellulose aerogel. Our investigation focused on analyzing the adsorption and flexibility characteristics of this novel composite for organic dye removal. Additionally, we conducted tests to assess the aerogel's reusability and determined that its absorption rate remained consistent, with the adsorption capacity of the MOF-5/cellulose aerogel composite only experiencing a marginal 5% reduction. Characterization of the material was conducted through XRD analysis, revealing the cubic structure of MOF aerogel particles under scanning electron microscopy. Our study unequivocally demonstrates the superior adsorption capabilities of the MOF-5/cellulose aerogel composite, particularly evident in its efficient removal of acid blue dye, as evaluated meticulously using UV-Vis spectrophotometric techniques. Notably, our findings revealed an impressive 96% absorption rate for the anionic dye under acidic pH conditions. Furthermore, the synthesized MOF-5/cellulose aerogel composite exhibited Langmuir isotherm behavior and followed pseudo-second-order kinetics during the absorption process. With its remarkable absorption efficiency, MOF-5/cellulose aerogel composites are poised to emerge as leading adsorbents for water purification and various other applications.

Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina

Evaluating the forthcoming impacts of climate change is important for formulating efficient and flexible approaches to water resource management. General Circulation Models (GCMs) are primary tools that enable scientists to study both past and potential future climate changes, as well as their impacts on policies and actions. In this work, we quantify the future projected impacts of hydroclimatic extremes on the coastal, risk-prone Tar-Pamlico River basin in North Carolina using GCMs from the Sixth International Coupled Model Intercomparison Project (CMIP6). These models incorporate projected future societal development scenarios (Shared Socioeconomic Pathways, SSPs) as defined in the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Specifically, we have utilized historical residential expansion data, the Soil and Water Assessment Tool Plus (SWAT+), the Standardized Precipitation Index (SPI), and the Interquartile Range (IQR) method for analyzing extremes from 2024 to 2100. Our findings include: (1) a trend toward wetter conditions is identified with an increase in flood events toward 2100; (2) projected increases in the severity of flood peaks are found, quantified by a rise of 21% compared to the 2000-2020 period; (3) downstream regions are forecast to experience severe droughts up to 2044; and (4) low-lying and coastal regions are found as particularly susceptible to higher flood peaks and more frequent drought events between 2045 and 2100. This work provides valuable insights into the anticipated shifts in natural disaster patterns and supports decision-makers and authorities in promoting adaptive strategies and sustainable policies to address challenges posed by future climate changes in the Tar-Pamlico region and throughout the state of North Carolina, United States.

Performance evaluation of a new sponge-based moving bed biofilm reactor for the removal of pharmaceutical pollutants from real wastewater

Pharmaceutical pollutants, a group of emerging contaminants, have attracted outstanding attention in recent years, and their removal from aquatic environments has been addressed. In the current study, a new sponge-based moving bed biofilm reactor (MBBR) was developed to remove chemical oxygen demand (COD) and the pharmaceutical compound Ibuprofen (IBU). A 30-L pilot scale MBBR was constructed, which was continuously fed from the effluent of the first clarifier of the Southern Tehran wastewater treatment plant. The controlled operational parameters were pH in the natural range, Dissolved Oxygen of 1.5-2 mg/L, average suspended mixed liquor suspended solids (MLSS), and mixed liquor volatile suspended solids (MLVSS) of 1.68 ± 0.1 g/L and 1.48 ± 0.1 g/L, respectively. The effect of hydraulic retention time (HRT) (5 h, 10 h, 15 h), filling ratio (10%, 20%, 30%), and initial IBU concentration (2 mg/L, 5 mg/L, 10 mg/L) on removal efficiencies was assessed. The findings of this study revealed a COD removal efficiency ranging from 48.9 to 96.7%, with the best removal efficiency observed at an HRT of 10 h, a filling ratio of 20%, and an initial IBU concentration of 2 mg/L. Simultaneously, the IBU removal rate ranged from 25 to 92.7%, with the highest removal efficiency observed under the same HRT and filling ratio, albeit with an initial IBU concentration of 5 mg/L. An extension of HRT from 5 to 10 h significantly improved both COD and IBU removal. However, further extension from 10 to 15 h slightly enhanced the removal efficiency of COD and IBU, and even in some cases, removal efficiency decreased. Based on the obtained results, 20% of the filling ratio was chosen as the optimum state. Increasing the initial concentration of IBU from 2 to 5 mg/L generally improved COD and IBU removal, whereas an increase from 5 to 10 mg/L caused a decline in COD and IBU removal. This study also optimized the reactor's efficiency for COD and IBU removal by using response surface methodology (RSM) with independent variables of HRT, filling ratio, and initial IBU concentration. In this regard, the quadratic model was found to be significant. Utilizing the central composite design (CCD), the optimal operating parameters at an HRT of 10 h, a filling ratio of 21%, and an initial IBU concentration of 3 mg/L were pinpointed, achieving the highest COD and IBU removal efficiencies. The present study demonstrated that sponge-based MBBR stands out as a promising technology for COD and IBU removal.

Reassessing the greenhouse effect of biogenic carbon emissions in constructed wetlands

Biogenic carbon emissions, including carbon dioxide (CO2) and methane (CH4), have emerged as a major concern during organic pollutant degradation within constructed wetlands (CWs). Since these organic compounds primarily originate from the photosynthetic fixation of atmospheric CO2, it potentially introduces uncertainty when assessing the greenhouse effect of biogenic carbon emissions in CWs based on direct field observations. To objectively assessing this effect, this study proposed a new strategy by quantifying CO2-equivalent (CO2-eq) changes as carbon passes through CWs and tested it in various types of CWs based on 64 literature records. The findings reveal that CWs can contribute to CO2-eq additions, yet are only responsible for 15.6% derived from direct field observations. The type of CWs plays a crucial role in these CO2-eq additions, with vertical flow CWs causing the lowest levels (6.8%), followed by surface flow CWs (14.2%). In contrast, horizontal flow CWs are associated with the strongest CO2-eq addition (25.7%). The findings provide new insights for the objective assessment of the greenhouse effect of biogenic carbon emissions in CWs, which will be beneficial for future life cycle assessment.

Water quality's responses to water energy variability of the Yangtze River

River energy serves as an indicator of pollutant-carrying capacity (PCC), influencing regional water quality dynamics. In this study, MIKE21 hydrodynamics-water quality models were developed for two scenarios, and grid-by-grid numerical integration of energy was conducted for the Yangtze River's mainstream. Comparison of predicted and measured values at monitoring points revealed a close fit, with average relative errors ranging from 5.17 to 8.37%. The concept of PCC was introduced to assess water flow's ability to transport pollutants during its course, elucidating the relationship between river energy and water quality. A relationship model between Unit Area Energy (UAE) and PCC was fitted (R2 = 0.8184). Temporally, reservoir construction enhanced the smoothness of UAE distribution by 74.47%, attributable to peak shaving and flow regulation. While this flood-drought season energy transfer reduced PCC differences, it concurrently amplified pollutant retention by 40.95%. Spatially, energy distribution fine-tuned PCC values, showcasing binary variation with energy changes and a critical threshold. Peak PCC values for TP, NH3-N, and COD were 2.46, 2.26, and 54.09 t/(km·a), respectively. These insights support local utility regulators and decision-makers in navigating low-carrying capacity, sensitive areas, enhancing targeted water protection measures for increased effectiveness and specificity.

Synergies and trade-offs of ecosystem services affected by land use structures of small watershed in the Loess Plateau

The Chinese government has implemented a series of ecological restoration projects in the Loess Plateau (LP), and the surface cover changed dramatically, impacting the ecosystem services (ESs) greatly. In this study, we used K-means clustering to classify the land use structures (LUSs) of the LP from 1990 to 2015 at the small watershed scale, and investigated the effects of LUS on water supply (WS), soil conservation (SC), and carbon sequestration (CS, expressed as NPP) with constraint lines. The values of WS and SC were obtained from the InVEST simulation, validated by the hydrographic station data. The results showed that the LUSs in LP were cropland structure (CLS, distinguished with CS), forest structure (FS), grassland structure (GS), crop-grassland structure (CGS), crop-forest-grassland structure (CFGS) and a very few areas of barren structure (BS). The proportion of dominant land use in those LUSs with a balance of WS, SC, and CS was 0.6-0.7 (cropland in CLS), 0.5 (forest in FS), 0.45/0.4 (cropland/grassland in CGS), 0.75 to 0.85 (grassland in GS), and 0.15/0.4/0.25 to 0.35 (cropland/forest/grassland in CFGS), respectively. The types of constraint curves of ESs for those LUSs involves hump-shaped curve, negative convex, half-concave-waved curve and concave-waved curve. This study proposed a method to objectively delineate LUS and improved the constraint line method to make it suitable for cases with less data, innovatively presenting the variation of ESs inside LUSs, which may provide a reference for optimal land planning and sustainable development of social-ecological systems.

Investigating the applicability and assumptions of the regression relationship between flow discharge and nitrogen concentrations for load estimation

The regression relationship between water discharge rates and nutrient concentrations can provide a quick and straightforward way to estimate nutrient loads. However, recent studies indicated that the relationship might produce large biases in load estimates and, therefore, may not be applicable in certain types of cases. The goal of this study is to explore the theoretical reasons behind the selective applicability of the regression relationship between flow rates and nitrate + nitrite concentrations. For this study, we examined daily flow and nitrate + nitrite concentration observations made at the outlets of 22 watersheds monitored by the Heidelberg Tributary Loading Program (HTLP). The statistical relationship between the flow rates and concentrations was explored using regression equations offered by the LOAD ESTimator (LOADEST). Results demonstrated that the use of the regression equations provided nitrate + nitrite load estimates at acceptable accuracy levels (N S E ≥ 0.35 and | P B I A S | ≤ 30.0 %) in 14 watersheds (64 % of 22 study watersheds). The regression relationships provided highly biased results at eight watersheds (36 %), implying their limited applicability. The heteroscedasticity of the residuals led to the high bias and resulting inaccurate regression, which was commonly found in watersheds where low flow had high nitrate + nitrite concentration variations. Conversely, the regression relationships provided acceptable accuracy for watersheds that had a relatively constant variance of the nitrate + nitrite concentrations. The results indicate that the homoscedasticity of residuals is the key assumption to be satisfied to estimate nitrate + nitrite loads from a statistical regression between flow discharge and nitrate + nitrite concentrations. The transport capacity (capacity-limited) concept implicitly assumed in the regression relationship between flow discharge and nitrate + nitrite concentrations is not always applicable, especially to agricultural areas in which nitrate + nitrite loads are highly variable depending on management practices (supply-limited). The findings suggest that the regression relationship should be carefully applied to areas in which intensive agricultural activities, including crop management and conservation practices, are implemented. Thus, the transport capacity concept is reasonably regarded to contribute to the homoscedasticity of residuals.

Quantifying the consequences of unsustainable sand mining and cascade dams on aspects in a tropical river basin

Human interventions at the river basin scale, such as sand mining and hydropower dam construction, have profoundly affected hydrological and hydraulic alteration regimes, sediment budgets, and morphological changes worldwide. Quantifying the consequences of unsustainable ongoing sand mining and hydropower is crucial for obtaining sediment load data and managing hydrogeomorphology. In this study, comprehensive long-term consecutive four-field monitoring, statistical methods, and hydrological models (SWAT) were applied to quantify the spatiotemporal changes in long-term discharge and sediment load from 1996 to 2020 for the tropical river of the Vu Gia Thu Bon (VGTB) in the central region of Vietnam. The SWAT model was calibrated from 1996 to 2010, validated from 2011 to 2020 and showed good performance for daily discharge and monthly sediment. The evolution of river bathymetric data (2010, 2015, 2018, and 2021) was analysed to clarify the upstream sediment supply trapped in the riverbed and how the sand mining volume was removed. The results showed that the mean annual sediment in the Vu Gia and Thu Bon Rivers decreased by 57.3% and 23.8%, respectively, in the postdam period compared with the predam period. The thalweg elevation decreased at the Ai Nghia and Giao Thuy stations from 2010 to 2021 by 1.8 m and 3.9 m, respectively. The water level decreased by 21.1% at Ai Nghia and 44.3% at Giao Thuy. Dam development, sand mining, and changes in land use are the main factors responsible for flow discharge and sediment morphodynamic alterations. Morphological change have increased the water transfer rate from the Vu Gia River to the Thu Bon River through the Quang Hue channel. Downstream of the Vu Gia River, water transfer and riverbed incision have decreased flow discharge and water level and increased saltwater intrusion in recent years. As a result, water shortages induced by saltwater intrusion during drought periods have emerged as a significant constraint in hindering the domestic water supply and agricultural production.

The fate of nitrogen in the Zarin-Gol River receiving trout farm effluent

This study investigated the Zarrin-Gol River ecosystem in Iran to trace organic matter in the food web and evaluate the impact of aquaculture farm effluent using stable isotopes of nitrogen (δ15N) and carbon (δ13C). Using a previously-developed model (Islam 2005), we estimated that a trout farm in the vicinity released 1.4 tons of nitrogen into the river. This was comparable to an estimated total nutrient load of 2.1 tons of nitrogen for the six-month fish-rearing period based on a web-based constituent load estimator (LOADEST). A model estimate of river nitrogen concentration at the time of minimum river discharge (100 L/s) was 2.74 mg/L. Despite relatively high nitrogen loading from the farm, isotope data showed typical food web structure. Several biological groups had elevated δ13C or δ15N values, but there was limited evidence for the entry of organic matter from the trout farm into the food web, with sites above and below trout farms having inconsistent patterns in 15N enrichment. By coupling nitrogen load modeling with stable isotope analysis we showed that stable isotopes might not be effective tracers of organic matter into food webs, depending on surrounding land use and other point sources of nutrients. The Zarrin-Gol River ecosystem, like other basins with high human population density, remains vulnerable to eutrophication in part due to trout farm effluent.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

Investigating biodegradation of polyethylene and polypropylene microplastics in Tehran DWTPs

Microplastic (MP) pollution is a growing concern and various methods are being sought to alleviate the level of pollution worldwide. This study investigates the biodegradation capacity of MPs by indigenous microorganisms of raw water from Tehran drinking water treatment plants. By exposing polypropylene (PP) and polyethylene (PE) MPs to selected microbial colonies, structural, morphological, and chemical changes were detected by scanning electron microscope (SEM), cell weight measurement, Fourier transform infrared (FTIR), Raman spectroscopy test, and thermal gravimetric analysis (TGA). Selected bacterial strains include Pseudomonas protegens strain (A), Bacillus cereus strain (B), and Pseudomonas protegens strain (C). SEM analysis showed roughness and cracks on PP MPs exposed to strains A and C. However, PE MPs exposed to strain B faced limited degradation. In samples related to strain A, the Raman spectrum was completely changed, and a new chemical structure was created. Both TGA and FTIR analysis confirmed changes detected by Raman analysis of PP and PE MPs in chemical changes in this study. The results of cell dry weight loss for microbial strains A, B, and C were 13.5, 38.6, and 25.6%, respectively. Moreover, MPs weight loss was recorded at 32.6% for PP MPs with strain A, 13.3% for PE MPs with strain B, and 25.6% for PP MPs with strain C.

Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective

This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine learning (ML) models - artificial neural network (ANN), recurrent neural network (RNN), and adaptive fuzzy neural inference system (ANFIS) - for streamflow prediction under the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSPs), specifically SSP245 and SSP585. Four key performance indicators, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), guide the evaluation. These models employ monthly precipitation, maximum and minimum temperatures as inputs, and discharge as the output, spanning 1985-2014. The ANN model with a 3-10-1 architecture outperforms RNN and ANFIS, displaying lower MSE, RMSE, MAE, and higher R2 values for both training (MSE = 20417, RMSE = 142, MAE = 71, R2 = 0.94) and testing (MSE = 9348, RMSE = 96, MAE = 108, R2 = 0.92) datasets. Subsequently, the superior ANN model predicts streamflow up to 2100 using SSP245 and SSP585 scenarios. These results underscore the potential of ANN models for robust futuristic streamflow estimation, offering valuable insights for water resource management and planning.

Enhanced Escherichia coli removal from stormwater with bermudagrass-derived activated biochar filtration systems

Stormwater treatment and reuse can alleviate water pollution and scarcity while current sand filtration systems showed low treatment performance for stormwater. For enhancing E. coli removal in stormwater, this study applied the bermudagrass-derived activated biochars (BCs) in the BC-sand filtration systems for E. coli removal. Compared with the pristine BC (without activation), the FeCl3 and NaOH activations increased the BC carbon content from 68.02% to 71.60% and 81.22% while E. coli removal efficiency increased from 77.60% to 81.16% and 98.68%, respectively. In all BCs, the BC carbon content showed a highly positive correlation with E. coli removal efficiency. The FeCl3 and NaOH activations also led to the enhancement of roughness of BC surface for enhancing E. coli removal by straining (physical entrapment). The main mechanisms for E. coli removal by BC-amended sand column were found to be hydrophobic attraction and straining. Additionally, under 105-107 CFU/mL of E. coli, final E. coli concentration in NaOH activated BC (NaOH-BC) column was one order of magnitude lower than those in pristine BC and FeCl3 activated BC (Fe-BC) columns. The presence of humic acid remarkably lowered the E. coli removal efficiency from 77.60% to 45.38% in pristine BC-amended sand column while slightly lowering the E. coli removal efficiencies from 81.16% and 98.68% to 68.65% and 92.57% in Fe-BC and NaOH-BC-amended sand columns, respectively. Moreover, compared to pristine BC, the activated BCs (Fe-BC and NaOH-BC) also resulted in the lower antibiotics (tetracycline and sulfamethoxazole) concentrations in the effluents from the BC-amended sand columns. Therefore, for the first time, this study indicated NaOH-BC showed high potential for effective treatment of E. coli from stormwater by the BC-amended sand filtration system compared with pristine BC and Fe-BC.

Water quality variation and driving factors quantitatively evaluation of urban lakes during quick socioeconomic development

Rapid urbanisation has caused a significant impact on the ecological environment of urban lakes in the world. To maintain the harmonious development of urban progress and water quality, it is essential to evaluate water quality variation and explore the driving factors quantitatively. A comprehensive evaluation method with cluster analysis and Kriging interpolation was used to explore the spatiotemporal variation in a typical urban lake in China, Chaohu Lake, from 2011 to 2020. The correlation between water quality and socioeconomic factors was evaluated by Pearson correlation analysis. Results indicated that: total phosphorus (TP) and total nitrogen (TN) were the key pollution parameters of Chaohu Lake. The pollution situation was gradually improving, however, and the improvement in chemical oxygen demand (COD) is more evident due to anthropogenic control. The spatial heterogeneity of water quality in Chaohu Lake is remarkable, and the water quality is poor in the west but better in the east. Natural attributes of lakes and external load were the main reasons for the spatial heterogeneity. The western residential areas of Chaohu Lake Basin (CLB) are concentrated, and a large amount of industrial and domestic sewage exacerbates water pollution in the west of tributaries. In contrast, the implementation of water environmental governance policies in recent years has alleviated water pollution. From 2011 to 2020, water quality has improved by 23%-35% in the west and 7%-14% in the east. This study provided a framework for quantitatively assessing water quality variation and its driving forces in urban lakes.

An integrated geotechnical and geophysical investigation of landslide in Chira town, Ethiopia

Landslides pose a significant threat to infrastructure, property, and human lives in many regions worldwide, including Chira town in Ethiopia. This study presents an integrated geotechnical and geophysical investigation aimed at identifying the contributing factors to landslides in Chira town, Ethiopia, with a focus on a recent landslide event. The methodology employed a combination of geotechnical and geophysical techniques to comprehensively analyze the landslide problem. The geotechnical investigation involved a detailed analysis of the soil characteristics in the area, including the composition of fine-grained soil and the determination of cohesion and angle of internal friction through triaxial testing. The geophysical investigation utilized electrical resistivity tomography to assess the subsurface soil profile. The findings revealed the presence of a massive basaltic tertiary volcanic rock layer underlying a very low resistivity layer of sticky clay soil. Through this study, it was established that rainfall, soil type, land use, elevation, and proximity to streams, slopes, and aspects were the main factors contributing to the landslide, accounting for 22.03%, 18.89%, 15.75%, 15.46%, 10.87%, 9.7%, and 7.5% of the overall influence, respectively. Based on these findings, the study proposes a range of interventions to enhance resilience against landslides, including surface drainage, the implementation of appropriate land use management practices, and the introduction of vetiver vegetation. The integration of geotechnical and geophysical methodologies provided a comprehensive understanding of the landslide problem in Chira town. The proposed interventions aim to inform future land use planning, infrastructure development, and disaster risk reduction efforts in the region. By expanding our knowledge of the mechanisms driving landslides, this study offers valuable insights that can be utilized in similar regions facing comparable geotechnical and geophysical conditions.