Assessing Anthropogenic Impacts on Chemical and Biochemical Oxygen Demand in Different Spatial Scales with Bayesian Networks

Sediment-associated microplastics in Chilika lake, India: Highlighting their prevalence, polymer types, possible sources, and ecological risks

The primary objective of this research was to assess microplastics (MPs) in the sediments of Chilika lake. MPs were extracted from 22 sediment samples using the density separation method combined with vacuum pump filtration. A stereo-zoom microscope and Raman spectroscopy were employed to identify the sediment-associated MPs. The total MPs collected from all 22 sites was 440 ± 3.53 particles kg-1 wet sediments, with sizes ranging between 50 and 500 μm. In terms of morphology, fibers and fragments emerged as the dominant MP types, with counts of 210 ± 1.66 and 175 ± 1.76 particles kg-1 wet sediments, respectively. Raman spectroscopy verified the presence of various MP polymers in the sediments, predominantly HDPE (37 %), followed by PS (20 %), PET (18 %), PA (11 %), PP (7 %), and PC (7 %). A notable color variation was observed in MPs; black being the most prevalent (38.8 %), succeeded by blue (19.5 %), green (11.8 %), white (11.5 %), red (10.6 %), and transparent (7.5 %). ANOVA results indicated significant (p > 0.05) variations in MP abundance across the 22 sampling locations. However, principal component analysis (PCA) and multiple regression analysis indicated that water quality parameters did not significantly influence MP abundance, yet it was found that MP retention was higher in fine-grained sediments like clay and silt. The leading sources of MPs in Chilika lake were found to be aquafarming, trailed by river and sewage discharges, fishing activities, antifouling coatings and tourism. Additionally, the pollution load index (PLI) was employed to gauge the ecological risks, categorizing the lake under risk category 1, which implies a minimal level of MPs pollution. This research aims to serve as an early warning system for MPs pollution in productive brackish water habitats globally, including Chilika lake, guiding policymakers towards appropriate management strategies and preventive measures.

Response surface methodology optimization of the effect of pH, contact time, and microbial concentration on chemical oxygen removal potential of vegetable oil industrial effluents

The vegetable oil refinery industry generates highly polluted effluents during oil production, necessitating proper treatment before discharge to prevent environmental hazards. Treating such wastewater has become a major environmental concern in developing countries. Chemical oxygen demand (COD) is a key parameter in assessing the wastewater's organic pollutant load. High COD levels can lead to reduced dissolved oxygen in water bodies, negatively affecting aquatic life. Various technologies have been employed to treat oily wastewater, but microbial degradation has gained attention due to its potential to remove organic pollutants efficiently. This study aims to optimize the biodegradation treatment process for vegetable oil industrial effluent using response surface methodology (RSM). The wastewater's physicochemical properties were characterized to achieve this, and COD removal was analyzed. Furthermore, RSM was used to investigate the combined effects of pH, contact duration, and microbial concentration on COD removal efficiency. The result showed that the microbial strain used recorded a maximum COD removal of 92%. Furthermore, a quadratic model was developed to predict COD removal based on the experimental variables. From the analysis of variance (ANOVA) analysis, the model was found to be significant at p < 0.0004 and accurately predicted COD removal rates within the experimental region, with an R2 value of 90.99% and adjusted R2 value of 82.89%. Contour plots and statistical analysis revealed the importance of contact duration and microbial concentration on COD removal. PRACTITIONER POINTS: Response surface methodology (RSM) optimization achieved a significant chemical oxygen demand (COD) removal efficiency of 92% in vegetable oil industrial effluents. The study's success in optimizing COD removal using RSM highlights the potential for efficient and environmentally friendly wastewater treatment. Practitioners can benefit from the identified factors (pH, contact time, and microbial concentration) to enhance the operation of treatment systems. The developed predictive model offers a practical tool for plant operators and engineers to tailor wastewater treatment processes. This research underscores the importance of sustainable practices in wastewater treatment, emphasizing the role of microbial degradation in addressing organic pollutant loads.

Bayesian inference of physicochemical quality elements of tropical lagoon Nokoué (Benin)

In view of the very strong degradation of aquatic ecosystems, it is urgent to set up monitoring systems that are best able to report on the effects of the stresses they undergo. This is particularly true in developing countries, where specific and relevant quality standards and funding for monitoring programs are lacking. The objective of this study was to make a relevant and objective choice of physicochemical parameters informative of the main stressors occurring on African lakes and to identify their alteration thresholds. Based on statistical analyses of the relationship between several driving forces and the physicochemical parameters of the Nokoué lagoon, relevant physicochemical parameters were selected for its monitoring. An innovative method based on Bayesian statistical modeling was used. Eleven physicochemical parameters were selected for their response to at least one stressor and their threshold quality standards also established: Total Phosphorus (<4.5mg/L), Orthophosphates (<0.2mg/L), Nitrates (<0.5 mg/L), TKN (<1.85 mg/L), Dry Organic Matter (<5 mg/L), Dissolved Oxygen (>4 mg/L), BOD (<11.6 mg/L), Salinity (7.6 ‰), Water Temperature (<28.7 °C), pH (>6.2), and Transparency (>0.9 m). According to the System for the Evaluation of Coastal Water Quality, these thresholds correspond to "good to medium" suitability classes, except for total phosphorus. One of the original features of this study is the use of the bounds of the credibility interval of the fixed-effect coefficients as local weathering standards for the characterization of the physicochemical status of this anthropized African ecosystem.

Decision Making Model for Municipal Wastewater Conventional Secondary Treatment with Bayesian Networks

Technical, economic, regulatory, environmental, and social and political interests make the process of selecting an appropriate wastewater treatment technology complex. Although this problem has already been addressed from the dimensioning approach, our proposal in this research, a model of decision making for conventional secondary treatment of municipal wastewater through continuous-discrete, non-parametric Bayesian networks was developed. The most suitable network was structured in unit processes, independent of each other. Validation, with data in a mostly Mexican context, provided a positive predictive power of 83.5%, an excellent kappa (0.77 > 0.75), and the criterion line was surpassed with the location of the model in a receiver operating characteristic (ROC) graph, so the model can be implemented in this region. The final configuration of the Bayesian network allows the methodology to be easily extended to other types of treatments, wastewater, and to other regions.

Small Water Body Detection and Water Quality Variations with Changing Human Activity Intensity in Wuhan

Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is important to properly identify and analyze them. In remote sensing technology, water body identification based on band analysis, image classification, and water indices are often designed for large, homogenous water bodies. Traditional water indices are often less accurate for small water bodies, which often contain submerged or floating plants or easily confused with hill shade. Water quality inversion commonly depends on establishing the relationship between the concentration of water constituents and the observed spectral reflectance. However, individual variation in water quality in small water bodies is enormous and often far beyond the range of existing water quality inversion models. In this study, we propose a method for small water body identification and water quality estimation and test its applicability in Wuhan. The kappa coefficient of small water body identification is over 0.95, and the coefficient of determination of the water quality inversion model is over 0.9. Our results show that the method proposed in this study can be employed to accurately monitor the dynamics of small water bodies. Due to the outbreak of the COVID-19 pandemic, the intensity of human activities decreased. As a response, significant changes in the water quality of small water bodies were observed. The results also suggest that the water quality of small water bodies under different production modes (intensive/casual) respond differently in spatial and temporal dimensions to the decrease in human activities. These results illustrate that effective remote sensing monitoring of small water bodies can provide valuable information on water quality.

An Empirical Study on the Ecological Economy of the Huai River in China

The Huai River is an important flood control and discharge river in the middle and east of China, and the development of ecological economy with regional advantages is significant for the protection and improvement of the resources and environment of the basin. On the basis of defining the connotation of an ecological economic system, this study constructed an index system, and it applied the methods of data envelopment analysis (DEA) and exploratory spatial data analysis (ESDA) to study the ecological economy of the Huai River. This study concluded that (1) the efficiency in most areas was efficient, but inefficient in a few areas; (2) the causes of inefficiency were unreasonable production scale and unqualified production technology, which led to redundant input of resources, insufficient output of days with good air quality, and excessive output of particulate matter with less than or equal to 2.5 microns in diameter (PM2.5); and (3) the efficiency was different in different regions, so it was necessary to respectively formulate and implement strategies for protection and development of resources and environment. The research results can be used as an important reference for formulating ecological economic policies around the world.