RESUMEN

Fetzara Lake, considered one of the most important wetlands in northeastern Algeria, was designated a Ramsar site in 2002. The waters in its watershed are affected by salinity, which influences their suitability for irrigation. To identify the factors influencing the quality of these surface waters, geochemical and statistical analyses were carried out on the basis of the results of chemical analyses of 51 samples collected, during two monitoring campaigns, from all the tributaries in the watershed. The findings show the dominance of three hydrochemical facies over the two campaigns: Na-Cl facies (55.17% and 22.73%) characterizes the waters water from Fetzara Lake outlet (drainage channel and wadi Meboudja), in relation to the influx of saliferous elements due to water evaporation in the lake. Ca-Mg-Cl (27.59% and 40.91%) and Ca-Mg-HCO3 (13.79%. and 13.79%) facies characterize the waters of the remaining tributaries, reflecting the dissolution of carbonate formations and the alteration of the Edough metamorphic basement. Multivariate statistical analysis, using principal component analysis (PCA), shows three water types: highly mineralized (EC > 3000 µS/cm), moderately mineralized (1000 < EC < 3000 µS/cm), and weakly mineralized (EC < 1000 µS/cm). Evaporation and silicate weathering are the main mechanisms controlling water mineralization according to the different bivariate plots. Furthermore, cation exchange indices (CAI-I and CAI-II) reveal that these reactions involve the adsorption of Na+ and K+ onto clay minerals, as well as the simultaneous release of Ca2+ and Mg2+ ions. Finally, the various quality indices (SAR, %Na, RSC and KR) revealed that the water in 36% of tributaries is unsuitable for irrigation. These findings will provide important information on surface water quality in the study area, particularly for irrigation purposes, and will contribute to the thoughtful and sustainable management of this resource.

Asunto(s)

RESUMEN

Basin water pollution caused by livestock, poultry and fish breeding is still a serious problem for remote villages, however, reliable regional breeding management programming have the potentials to improve pollution status. This paper focuses on the optimal model design and water quality analysis of the livestock, poultry and fish breeding system for Wenchang City, China. Methods of multi-objective programming (MOP), interval parameter programming (IPP), fuzzy-stochastic parameter programming (FSPP), and chance constrained programming (CCP) were incorporated into the developed model to tackle multi uncertainties described by interval values, probability distributions, fuzzy membership function. Based on the estimation of local breeding potential and current situation of surface water section, a multi-objective mixed fuzzy-stochastic nonlinear programming optimization model is presented with one-dimensional water quality model. In order to evaluate the environmental carrying capacity of livestock, poultry and fishery manure, predict its development trend and investigate the implementation effect of different emission reduction policies, this paper designs quantization system of the urban water environmental carrying capacity for the model. The results indicated that the water environment pollutant absorption capacity and carrying capacity of Wenchang city have approached the limit especially the towns in the northeast of City which limited the overall development space of the City. The modeling results are valuable for supporting the adjustment of the existing livestock, poultry and fish breeding schemes within a complicated system benefit and surface water quality situation under uncertainty.

RESUMEN

Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water.

RESUMEN

Italy is the leading rice producer in Europe and the second in the Mediterranean basin (after Egypt), with most of the production concentrated in a large paddy area between the Lombardy and Piedmont regions (northern Italy). In this area, irrigation of rice was traditionally carried out by wet seeding and continuous flooding; in the last fifteen years, this technique has been gradually replaced by dry seeding followed by a delayed flooding (DFL) or by an alternation of flooding and dry periods (FTI), which are economically more advantageous. This study presents the results of an extensive monitoring campaign designed and carried out in 2021 in a representative paddy district of the Lomellina area (Pavia, northern Italy) to assess the impact of the actual rice cropping strategies on surface water and groundwater quality, with particular attention to two widely used herbicides (MCPA and clomazone) and to nutrient losses (e.g., N, P, K). Results show that MCPA and clomazone concentrations detected in surface water and groundwater are always below the RAC (Regulatory Acceptable Concentration) values. As to nutrients, they do not show significant trends along the season in surface water and groundwater: this may be due to the fact that nutrient sources are many. Concerning the concentrations, nitrates may pose a problem for the area, especially for groundwater. However, further studies would be needed to understand to which extent rice cropping can be considered the major source of contamination for water resources.

Asunto(s)

RESUMEN

Identifying the driving forces of surface water quality variations is crucial for urban environmental management, especially in densely populated regions. Statistic mapping is an approach that allows researchers to directly explore the response of surface water quality to potential drivers. Conventionally, these methods encounter a mixture of issues, including nonlinear relationships and information on multiple time-scale, caused by disparities in the influencing frequencies and degrees of driving factors. In this research, a nonlinear direct-mapping approach was developed to quantitatively analyze the driving force of surface water quality under multiple time scales. This approach separated the fluctuation and trend information from water quality data and then established a direct-mapping relationship, thereby achieving the visible multilayer structure containing both linear and nonlinear information from the time scale. Typical water pollutants including total nitrogen (TN) and total phosphorus (TP) in the Pearl River Delta (PRD), were used to verify the methodology and compare its ability to analyze driving forces with traditional statistic approaches. The results demonstrated that this approach could establish a visual multilayer mapping structure with strong interpretability, which effectively captured the contained nonlinear information, thus improving the fitting degree by 12.43% compared with traditional methods. Moreover, it successfully identified the dominant driving forces of TN and TP in the PRD as human activities related to NO2 and PM and natural factors. Its application in the changing environment demonstrated a potentially increased risk of TP in the PRD under multiple scenarios. Overall, this approach could serve as a reliable reference for pollution early warning in the short term and for industrial structure adjustment planning in the long term.

Asunto(s)

RESUMEN

The impacts of landscape patterns on river water quality are commonly acknowledged, but understanding the complex processes by which landscape patterns affect water quality is still limited, especially in densely populated urban areas. Exploring the mechanisms through which landscape characteristics influence water quality changes in urbanized rivers will benefit regional water resource protection and landscape-scale resource development and utilization. Utilizing daily water quality monitoring data from rivers in the urbanized area of the Pearl River Delta in 2020, our research employed canonical analysis and partial least squares structural equation modeling (PLS-SEM) to explore the processes and mechanisms of the influence of urbanized river landscape patterns on surface water quality. The results indicated that total nitrogen (TN) was the critical indicator limiting the water quality of rivers in the Pearl River Delta. The landscape composition and configuration indexes exhibited non-linear variations with scale, and the landscape fragmentation was higher closer to the river. Landscape patterns had the most significant influence on water quality under the characteristic scale of a 5.50 km circular buffer zone, and landscape composition dominated the change of water quality of urbanized rivers, among which 30.64% of the percentage patch area of construction (C_PLAND) contributed 46.40% to the explanation rate of water quality change, which was the key landscape index affecting water quality. Moreover, landscape patterns had a higher interpretive rate of 39.29% on water quality in the wet season compared to 36.62% in the dry season. Landscape composition had an indirect negative impact on water quality, with a value of 0.47, by affecting the processes of runoff and nutrient migration driven by human activities, while landscape configuration had an indirect negative impact on water quality, with a value of 0.11. Our research quantified the impacts of landscape patterns driven by human activities on surface water quality and proposed management measures to optimize the allocation of landscape resources in riparian zones of urbanized rivers. The results provide a scientific basis for water quality management and protection in urbanized rivers.

Asunto(s)

RESUMEN

Pesticides are critical for protecting agricultural crops, but the off-site transport of these materials via spray drift and runoff poses risks to surface waters and aquatic life. California's Central Coast region is a major agricultural hub in the United States characterized by year-round production and intensive use of pesticides and other chemical inputs. As a result, the quality of many waterbodies in the region has been degraded. A recent regulatory program enacted by the Central Coast Regional Water Quality Control Board set new pesticide limits for waterways and imposed enhanced enforcement mechanisms to help ensure that water quality targets are met by specific dates. This regulatory program, however, does not mandate specific changes to pest management programs. In this study, we evaluate the economic, environmental, and pest management impacts of adopting two alternative pest management programs with reduced risks to surface water: 1) replacing currently used insecticide active ingredients (AIs) that pose the greatest risk to surface water with lower-risk alternatives and 2) converting conventional arthropod pest management programs to organic ones. We utilize pesticide use and toxicity data from California's Department of Pesticide Regulation to develop our baseline and two alternative scenarios. We focus on three crop groups (cole crops, lettuce and strawberry) due to their economic importance to the Central Coast and use of high-risk AIs. For Scenario 1, we estimate that implementing the alternative program in the years 2017-2019 would have reduced annual net returns on average by $90.26 - $190.54/ha, depending on the crop. Increased material costs accounted for the greatest share of this effect (71.9%-95.6%). In contrast, Scenario 2 would have reduced annual net returns on average by $5,628.12 - $18,708.28/ha during the study period, with yield loss accounting for the greatest share (92.8-97.9%). Both alternative programs would have reduced the associated toxic units by at least 98.1% compared to the baseline scenario. Our analysis provides important guidance for policymakers and agricultural producers looking to achieve environmental protection goals while minimizing economic impacts.

Asunto(s)

RESUMEN

A comprehensive understanding of water quality is essential for assessing the complex relationship between surface water and sources of pollution. Primarily, surface water pollution is linked to human and animal waste discharges. This study aimed to investigate the physico-chemical characteristics of drinking water under both dry and wet conditions, assess the extent of bacterial contamination in samples collected from various locations in District Shangla, and evaluate potential health risks associated with consuming contaminated water within local communities. For this purpose, 120 groundwater and surface water samples were randomly collected from various sources such as storage tanks, user sites, streams, ponds and rivers in the study area. The results revealed that in Bisham, lakes had the highest fecal coliform levels among seven tested sources, followed by protected wells, reservoirs, downstream sources, springs, rivers, and ditches; while in Alpuri, nearly 80% of samples from five sources contained fecal coliform bacteria. Similarly, it was observed that the turbidity level, total dissolved solids, electrical conductivity, biological oxygen demand, and dissolved oxygen in the surface drinking water sources of Bisham were significantly higher than those in the surface drinking water sources of Alpuri. Furthermore, the results showed that in the Alpuri region, 14% of the population suffers from dysentery, 27% from diarrhea, 22% from cholera, 13% from hepatitis A, and 16% and 8% from typhoid and kidney problems, respectively, while in the Bisham area, 24% of residents are affected by diarrhea, 17% by cholera and typhoid, 15% by hepatitis A, 14% by dysentery, and 13% by kidney problems. These findings underscore the urgent need for improved water quality management practices and public health interventions to mitigate the risks associated with contaminated drinking water. It is recommended to implement regular water quality monitoring programs, enhance sanitation infrastructure, and raise awareness among local communities about the importance of safe drinking water practices to safeguard public health.

Asunto(s)

RESUMEN

River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.

Asunto(s)

RESUMEN

Understanding the dynamic change in abundance of both fecal and opportunistic waterborne pathogens in urban surface water under different abiotic and biotic factors helps the prediction of microbiological water quality and protection of public health during recreational activities, such as swimming. However, a comprehensive understanding of the interaction among various factors on pathogen behavior in surface water is missing. In this study, the effect of salinity, light, and temperature and the presence of indigenous microbiota, on the decay/persistence of Escherichia coli and Pseudomonas aeruginosa in Rhine River water were tested during 7 days of incubation with varying salinity (0.4, 5.4, 9.4, and 15.4 ppt), with light under a light/dark regime (light/dark) and without light (dark), temperature (3, 12, and 20 °C), and presence/absence of indigenous microbiota. The results demonstrated that light, indigenous microbiota, and temperature significantly impacted the decay of E. coli. Moreover, a significant (p<0.01) four-factor interactive impact of these four environmental conditions on E. coli decay was observed. However, for P. aeruginosa, temperature and indigenous microbiota were two determinate factors on the decay or growth. A significant three-factor interactive impact between indigenous microbiota, temperature, and salinity (p<0.01); indigenous microbiota, light, and temperature (p<0.01); and light, temperature, and salinity (p<0.05) on the decay of P. aeruginosa was found. Due to these interactive effects, caution should be taken when predicting decay/persistence of E. coli and P. aeruginosa in surface water based on a single environmental condition. In addition, the different response of E. coli and P. aeruginosa to the environmental conditions highlights that E. coli monitoring alone underestimates health risks of surface water by non-fecal opportunistic pathogens, such as P. aeruginosa. KEY POINTS: Abiotic and biotic factors interactively affect decay of E. coli and P. aeruginosa E.coli and P.aeruginosa behave significantly different under the given conditions Only E. coli as an indicator underestimates the microbiological water quality.

Asunto(s)

RESUMEN

The contamination detection technology helps in water quality management and protection in surface water. It is important to detect sudden contamination events timely from dynamic variations due to various interference factors in online water quality monitoring data. In this study, a framework named "Prediction - Detection - Judgment" is proposed with a method framework of "Time series increment - Hierarchical clustering - Bayes' theorem model". Time to detection is used as an evaluation index of contamination detection methods, along with the probability of detection and false alarm rate. The proposed method is tested with available public data and further applied in a monitoring site of a river. Results showed that the method could detect the contamination events with a 100% probability of detection, a 17% false alarm rate and a time to detection close to 4 monitoring intervals. The proposed index time to detection evaluates the timeliness of the method, and timely detection ensures that contamination events can be responded to and dealt with in time. The site application also demonstrates the feasibility and practicability of the framework proposed in this study and its potential for extensive implementation.

Asunto(s)

RESUMEN

Regulation of per- and polyfluorinated substances (PFAS) in surface water is a work-in-progress with relatively few criteria promulgated in the United States and internationally. Surface water quality criteria (SWQC) or screening values derived for perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) by Australia, Canada, the European Union (EU), and four US states (Florida, Michigan, Minnesota, and Wisconsin), and the San Francisco Bay Regional Water Quality Control Board (SFB RWQCB; California) were compared. Across these eight jurisdictions, promulgated numeric criteria for the same compound and receptor span over five orders of magnitude as a result of different approaches and data interpretations. Human health criteria for PFOS range from 0.0047 to 600 ng/L depending on route of exposure (e.g., fish consumption or drinking water) and are lower than most ecological criteria for protection of aquatic and wildlife receptors. Data gaps and uncertainty in chronic toxicity and bioaccumulation of PFOS and PFOA, as well as the use of conservative assumptions regarding intake and exposure, have resulted in some criteria falling at or below ambient background concentrations and current analytical detection limits (around 1 ng/L for commercial laboratories). Some jurisdictions (e.g., Australia, Canada) have deemed uncertainty in quantifying water-fish bioaccumulation too great and set fish tissue action levels in lieu of water criteria. Current dynamics associated with the emerging and evolving science of PFAS toxicity, exposure, and environmental fate (i.e., data gaps and uncertainty), as well as the continuous release of scientific updates, pose a challenge to setting regulatory limits. Integr Environ Assess Manag 2024;20:36-58. © 2023 AECOM Technical Services, Inc and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Asunto(s)

RESUMEN

The exponential growth of human population and anthropogenic activities have led to the increase of global surface water contamination especially in river, lakes and ocean. Safe and clean surface water sources are crucial to human health and well-being, aquatic ecosystem, environment and economy. Thus, water monitoring is vital to ensure minimal and controllable contamination in the water sources. The conventional surface water monitoring method involves collecting samples on site and then testing them in the laboratory, which is time-consuming and not able to provide real-time water quality data. In addition, it involves many manpower and resources, costly and lack of integration. These make surface water quality monitoring more challenging. The incorporation of Internet of Things (IoT) and smart technology has contributed to the improvement of monitoring system. There are different approaches in the development and implementation of online surface water quality monitoring system to provide real-time data collection with lower operating cost. This paper reviews the sensors and system developed for the online surface water quality monitoring system in the previous studies. The calibration and validation of the sensors, and challenges in the design and development of online surface water quality monitoring system are also discussed.

Asunto(s)

RESUMEN

Agricultural production, urbanization, and other anthropogenic activities, the major causes of surface water pollution in China, have dramatically altered hydrological processes and nutrient cycles. Identifying and quantifying the key factors affecting water quality are essential for the better prevention and management of water pollution. However, due to the limitations of traditional statistical analysis methods, it is difficult to evaluate the spatial changes and interactions of influencing factors on water quality. In addition, research on a national scale is difficult, as it involves large-scale and long-term water quality monitoring work. In this study, we collected and collated the monthly average concentrations of four water quality parameters, dissolved oxygen, ammonia nitrogen, chemical oxygen demand, and total phosphorous, based on data from 1547 water quality monitoring stations in China. The combined pollution level of the water quality was assessed using the water quality index. Based on the water quality characteristics, water quality monitoring sites in the dry and wet seasons were grouped using k-means clustering. Eleven environmental factors were evaluated using geodetector software, including six human factors and five natural factors. The results showed that there are high-risk areas for water quality pollution in the eastern and southeastern coastal regions of China in both the dry and wet seasons and that surface water pollution in China is highly spatial heterogenous in both the dry and wet seasons. Among the anthropogenic factors, urban land area is the main factor of water quality pollution in the dry season, and the explanation rate of spatial heterogeneity of integrated water quality pollution index is 20.3%. The number of poultry farms and the area of farmland explained 12.4% and 12.1% of the integrated water quality pollution index in the wet season. The nonlinear relationship between these three anthropogenic and natural factors and their interaction exacerbated water quality pollution. Based on this analysis, we identified the key factors affecting surface water quality in China during the dry and wet seasons, evaluated the achievements of the water environmental protection policies in China in recent years, and proposed future management measures for the effective prevention and control of water quality pollution in high-risk areas.

Asunto(s)

RESUMEN

The dyke system plays a vital role in cultivating rice intensively in the Vietnamese Mekong Delta, which protects rice paddy fields from annual floods. This study aimed to examine whether the full-dyke system (FD, which restricts water exchange for a long time) can cause degradation of surface water quality and reduction in benthic invertebrate biodiversity. The surface water quality and benthic invertebrate community were compared between the FD and semi-dyke systems (SD, which permits water exchange during flooding season) using a large number of samples collected seasonally in 2019. The results showed that the surface water quality within the FD system had significantly higher concentrations of TSS, COD, BOD5, N-NO3-, N-TKN, P-PO43-, and TP than compared to the SD system (p < 0.05), indicating greater pollution levels. The benthic invertebrate community was less diverse in the FD system than in the SD system. Only 17 species (belonging to 4 families) were detected in the FD system, and 30 species (belonging to 5 families) were detected in the SD system. The benthic invertebrate community structure changes and biodiversity loss were associated with degraded water quality. The P-PO43- and TP parameters were negatively correlated with the number of species, density, and biomass in the FD system and with the Shannon-Wiener (H') index in the SD system. In conclusion, the FD system has been degrading water quality and causing biodiversity loss.

Asunto(s)

RESUMEN

The continuous availability of spatial and temporal distributed data from satellite sensors provides more accurate and timely information regarding surface water quality parameters. Remote sensing data has the potential to serve as an alternative to traditional on-site measurements, which can be resource-intensive due to the time and labor involved. This present study aims in exploring the possibility and comparison of hyperspectral and multispectral imageries (PRISMA) for accurate prediction of surface water quality parameters. Muthupet estuary, situated on the south side of the Cauvery River delta on the Bay of Bengal, is selected as the study area. The remote sensing data is acquired from the PRISMA hyperspectral satellite and the Sentinel-2 multispectral instrument (MSI) satellite. The in situ sampling from the study area is performed, and the testing procedures are carried out for analyzing different water quality parameters. The correlations between the water sample results and the reflectance values of satellites are analyzed to generate appropriate algorithmic models. The study utilized data from both the PRISMA and Sentinel satellites to develop models for assessing water quality parameters such as total dissolved solids, chlorophyll, pH, and chlorides. The developed models demonstrated strong correlations with R2 values above 0.80 in the validation phase. PRISMA-based models for pH and chlorophyll displayed higher accuracy levels than Sentinel-based models with R2 > 0.90.

Asunto(s)

RESUMEN

This paper evaluates diatom biomass as a biosorbent for removing Cr+6, Cd2+, and PO43- ions from water. The diatom was characterized by X-ray Diffraction (XRD), Fourier Transform Infrared (FTIR), and Scanning Electron Microscopy (SEM-EDS) for its crystallinity, functional groups, and morphology. A batch sorption study was conducted to evaluate the parameters influencing Cr+6, Cd2+, and PO43- ions adsorption, and the mechanisms were explored. The FTIR spectra revealed Si-O, O-H, N-H, and C-O as the main functional groups present on the surface of the adsorbent. The SEM showed a rough and irregular-shaped morphology, while the EDS indicated that the diatom biomass is an aluminosilicate material. The rate-limiting steps for Cr+6 and Cd2+ were pseudo-first order, and pseudo-second order sorption favored PO43- based on their R2 values. Moreover, the dominant adsorption model that best described the equilibrium data was the Freundlich isotherm. The maximum adsorption capacities obtained for Cr+6 was 5.66 (mg/g), and Cd2+ was 5.27 (mg/g) at 313 K while PO43- was 19.13 (mg/g) at 298 K. The thermodynamic data revealed that the reaction was endothermic for Cd2+ and exothermic for Cr+6 and PO43-, respectively. Diatom biomass was observed to be a promising bio-sorbent for removing Cr6+, Cd2+ and PO42- from wastewater.

Asunto(s)

RESUMEN

Identifying potential sources of pollution in tributaries and determining their contribution rates are critical to the treatment of water pollution in main streams. In this paper, we conducted a multivariate statistical analysis on the water quality data of 12 parameters for 3 years (2018-2020) at six sampling sites in the Laixi River to qualitatively identify potential pollution sources and quantitatively calculate the contribution rates to reveal the tributaries' pollution status. Spatio-temporal cluster analysis (CA) divided 12 months into two parts, corresponding to the lightly polluted season (LPS) and highly polluted season (HPS), and six sampling sites were divided into two regions, corresponding to the lightly polluted region (LPR) and highly polluted region (HPR). Principal component analysis (PCA) was used to determine the potential sources of contamination, identifying four and three potential factors in the LPS and HPS, respectively. The absolute principal component score-multiple linear regression (APCS-MLR) receptor model quantitatively analyzed the contribution rates of identified pollution sources, and the importance of the different pollution sources in LPS can be ranked as domestic sewage and industrial wastewater and breeding pollution (33.80%) > soil weathering (29.02%) > agricultural activities (20.95%) > natural influence (13.03%). HPS can be classified as agricultural cultivation (41.23%), domestic sewage and industrial wastewater and animal waste (33.19%), and natural variations (21.43%). Four potential sources were identified in LPR ranked as rural domestic sewage (31.01%) > agricultural pollution (26.82%) > industrial effluents and free-range livestock and poultry pollution (25.13%) > natural influence (14.82%). Three identified latent pollution sources in HPR were municipal sewage and industrial effluents (37.96%) > agricultural nonpoint sources and livestock and poultry wastewater (33.55%) > natural sources (25.23%). Using multivariate statistical tools to identify and quantify potential pollution sources, managers may be able to enhance water quality in tributary watersheds and develop future management plans.

Asunto(s)

RESUMEN

Monitoring of water quality is one of the world's main intentions for countries. Classification techniques based on support vector machines (SVMs) and artificial neural network (ANN) has been widely used in several applications of water research. Water quality assessment with high accuracy and efficiency with innovational approaches permitted us to acquire additional knowledge and information to obtain an intelligent monitoring system. In this paper, we present the use of principal component analysis (PCA) combined with SVM and ANN with decision templates combination data fusion method. PCA was used for features selection from original database. The multi-layer perceptron network (MLP) and the one-against-all strategy for SVM method have been widely used. Decision templates are applied to increase the accuracy of the water quality classification. The specific classification approach was employed to assess the water quality of the Tilesdit dam in Algeria as a study area, defined with a dataset of eight physicochemical parameters collected in the period 2009-2018, such as temperature, pH, electrical conductivity, and turbidity. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected dataset corresponding to the accuracy and running time of training and test phases, and robustness to noise, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without feature selection of the input data. From the results, we found that the integration of SVM and ANN with PCA yields accuracy up than 98%. The combination by decision templates of two classifiers SVM and ANN with PCA yields an accuracy of 99.24% using k-fold cross-validation. The combination data fusion enhanced expressively the results of the proposed monitoring framework that had proven a considerable ability in surface water quality assessment.

Asunto(s)

RESUMEN

The COVID-19 era has profoundly affected everyday human life, the environment, and freshwater ecosystems worldwide. Despite the numerous influences, a strict COVID-19 lockdown might improve the surface water quality and thus provide an unprecedented opportunity to restore the degraded freshwater resource. Therefore, we intend to investigate the spatiotemporal water quality, sources, and preliminary health risks of heavy metal(loid)s in the Karatoya River basin (KRB), a tropical urban river in Bangladesh. Seventy water samples were collected from 35 stations in KRB in 2019 and 2022 during the dry season. The results showed that the concentrations of Ni, Cu, Zn, Pb, Cd, and Cr were significantly reduced by 89.3-99.7 % during the post-lockdown period (p < 0.05). However, pH, Fe, Mn, and As concentrations increased due to the rise of urban waste and the usage of disinfectants during the post-lockdown phase. In the post-lockdown phase, the heavy metal pollution index, heavy metal evaluation index, and Nemerow's pollution index values lessened by 8.58 %, 42.86 %, and 22.86 %, respectively. Besides, the irrigation water quality indices also improved by 59 %-62 %. The total hazard index values increased by 24 % (children) and 22 % (adults) due to the rise in Mn and As concentrations during the lockdown. In comparison, total carcinogenic risk values were reduced by 54 % (children) and 53 % (adults) in the post-lockdown. We found no significant changes in river flow, rainfall, or land cover near the river from the pre to post-lockdown phase. The results of semivariogram models have demonstrated that most attributes have weak spatial dependence, indicating restricted industrial and agricultural effluents during the lockdown, significantly improving river water quality. Our study confirms that the lockdown provides a unique opportunity for the remarkable improvement of degraded freshwater resources. Long-term management policies and regular monitoring should reduce river pollution and clean surface water.

Asunto(s)