Environmental Monitoring and Assessment最新文献

Ensuring the safety of water supplies is critical for urban areas requires rapid response when water quality anomalies are detected in the pipeline network. Prompt action is essential to prevent widespread contamination, protect public health, and mitigate potential social unrest. The particle swarm optimization (PSO) algorithm has faced challenges for contamination source identification (CSI) in water distribution systems (WDS), primarily due to its susceptibility to locally optimal solutions. Addressing this issue is critical to quickly and accurately identify contamination sources. Therefore, this research integrates the Metropolis criterion from the simulated annealing (SA) algorithm into a SA-PSO algorithm, to overcome the limitations of PSO. This study conducts contamination localization experiments using SA-PSO, with the publicly available NET-3 pipeline network as the case to generate sudden contamination events. By collecting pollutant concentration data from predefined monitoring points over time through simulation, a simulation-optimization inverse location model is constructed to fit the pollutant concentrations at each monitoring point. The results of the case study show that SA-PSO outperforms PSO in both speed and accuracy in solving the CSI problem, and the findings provide an efficient and effective contamination localization tool for urban water supply management.

Integrating native ornamental plants with substrate amended with lignocellulosic biomass and biochar in vertical sub-surface flow constructed wetlands offers a novel and effective approach to wastewater treatment. This study evaluates the potential of mesocosm constructed wetland systems using native ornamental plants (Canna indica, Lilium wallichianum, and Tagetes erecta) grown in substrates amended with lignocellulosic biomass and biochar. The influent and effluent were analyzed for pH, total dissolved solids (TDS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), phosphorus (PO₄-P), and nitrogen forms, i.e., ammonia (NH₄-N) and nitrate (NO₃-N) for 5 weeks. Investigated mesocosms showed an average removal efficiency of 49.21% for BOD, 53.76% for COD, 40.64% for NH₄-N, 41.76% for NO₃-N, and 21.53% for PO₄-P. Canna indica demonstrated the highest removal efficiencies, achieving 58.19% for BOD and 64.49% for COD, followed by Lilium wallichianum with 56.12% for BOD and 62% for COD, while Tagetes erecta showed lower efficiencies of 49.63% for BOD and 52.24% for COD. The result shows that the designed mesocosms are a promising nature-based alternative to the technologically complex and expensive conventional technologies, with numerous additional ecological benefits. This study also indicates that the locally available organic materials are effective substrate components for constructed wetlands and after their use in wetlands; these digested organic materials may further be used as an effective source of nutrient-rich fertilizers or soil amendments in agriculture.

With the increase in pollution and improper waste disposal, aquatic ecosystems are experiencing escalating degradation leading to various detrimental effects, including eutrophication and adverse impacts on the health of the population reliant on these water resources. Consequently, microalgae have demonstrated efficacy in nutrient removal, minimal environmental disruption, and superior cost-effectiveness in comparison to traditional treatment methods. Thus, this study aimed to investigate wastewater treatment in an aerobic batch system, using two strains of non-axenic mixotrophic chlorophytes, Chlorella sp. and Desmodesmus sp., across distinct light regimes: continuous light exposure for 24 h, a photoperiod of 12 h light and 12 h darkness, and complete absence of light for 24 h. The Desmodesmus sp. strain exhibited superior efficiency in the proposed biological treatment, yielding more favorable nutrient removal results across all conditions, except for total nitrogen removal under the 24-h continuous light condition in which Chlorella sp. removed 0.199 ± 0.02% by biomass. In other parameters, Desmodesmus sp., remediated by biomass 0.408 ± 0.013% of inorganic phosphorus in 24 h light, 0.372 ± 0.011% of COD and 0.416 ± 0.004% of carbohydrate in 24 h dark. While Chlorella sp. removed 0.221 ± 0.01% of inorganic phosphorus in 24 h light, 0.164 ± 0.02% of COD in 24 h light and 0.214 ± 0.002% of carbohydrates in 24 h dark. Nevertheless, both strains displayed potential as viable alternatives for wastewater biological treatment, indicating that nutrient removal is achievable across all tested light conditions, albeit with variations in efficiency depending on the specific nutrient type.

The main influencing factors of water environment and the spatiotemporal differences of eutrophication were identified in the Dianchi Lake, the results indicated a comparatively poor and fluctuating the water environmental condition in the Caohai compared to the Waihai, and differences in the correlation between water environment indicators were observed in Caohai and Waihai. The absolute contribution rates of the inner sources to water temperature, pH, electrical conductance, total nitrogen, chlorophyll a and algal density were the largest in Caohai, while offshore sources are pH, electrical conductance, permanganate index, total phosphorus and chlorophyll a in Waihai. The eutrophication level is relatively high near the Xiyuan Suidao section, and the comprehensive trophic level indexes are 61.14, 64.45 and 64.45 in spring, summer and autumn, respectively, which all reach the state of moderate eutrophication; the comprehensive trophic level index is 55.72 in winter, which reaches the state of light eutrophication. The algal density near the Xiyuan Suidao and Luojiaying sections exhibited high levels, reaching a state of moderate algal bloom in summer. Spatial autocorrelation analyses highlighted significant positive and negative spatial autocorrelation for comprehensive Trophic Level Index and algal density, respectively, in Dianchi Lake. The High-High aggregation of the comprehensive Trophic Level Index and algal density was mainly concentrated in the Caohai, while the Low-Low aggregation of the comprehensive Trophic Level Index was primarily observed in the Waihai. Consequently, the risk of eutrophication and algal bloom outbreak in Caohai surpassed that in Waihai. Therefore, it is imperative to propose appropriate treatment measures based on the varying eutrophication level and algal bloom outbreak during different time periods and in distinct regions of the aquatic ecological environment in Dianchi Lake.

Fish diversity and water quality are vital indicators of the health of freshwater ecosystems, however these systems are increasingly threatened by environmental changes and anthropogenic activities. This investigation aimed to assess the fish diversity and evaluate the physicochemical parameters of water at Ghazi Swabi near Tarbela Dam, River Indus, Khyber Pakhtunkhwa, Pakistan, between March and June 2020. Monthly water samples were collected and analyzed for various physicochemical parameters such as temperature, dissolved oxygen (DO), total dissolved solids (TDS), pH, turbidity, salinity, and concentrations of heavy metals including lead (Pb), copper (Cu), cadmium (Cd), and mercury (Hg). Using various nets and traps the fish specimens were collected with the help of local fishermen. Morphometric and meristic analyses were conducted to identify the species, while systematic keys were used to remove misidentifications. A total of 110 fish specimens were collected, representing 7 species across 6 genera, 3 families, and 3 orders. The family Cyprinidae was the most abundant, with species including Puntius conchonius, Puntius waageni, Crossocheilus diplocheilus, Barilius modestus, and Aspidoparia morar. The Belonidae family was represented by Xenentodon cancila, and the Ambassidae family by Chanda nama. Physicochemical analysis showed that the water quality remained within permissible limits for aquatic life, however higher levels of certain heavy metals were detected, which may pose long-term risks. The study concluded that the fish diversity at Ghazi Swabi near Tarbela Dam reflects a moderately healthy ecosystem, the presence of heavy metals in the water raises concerns about future ecological health. Regular monitoring and mitigation efforts are recommended to preserve biodiversity and maintain water quality. The study suggests the need for sustainable management practices to protect freshwater ecosystems from anthropogenic impacts.

In recent years, pollutants released into the environment from various sources threaten environmental health. Rapid industrialization and the constantly increasing needs of people facilitate the release of more hazardous wastes into the ecosystem. The presence of pollutants in water resources causes a wide range of adverse effects. In this study, calcium phosphate nanomaterial (Ca₃(PO₄)₂ NM) was synthesized from biological waste eggshells for the cadmium removal in synthetic domestic wastewater, and a treatment method was developed using these NMs. The Ca₃(PO₄)₂ NMs were produced by using a biowaste which provides the synthesis procedure greener approach. The biogenic NMs were used to remove toxic cadmium ions from wastewater samples. Cytotoxicity and genotoxicity studies of the synthesized NMs were also carried out, and their possible effects on the health of living organisms and the ecology were examined. In the developed method, the parameters affecting the removal of cadmium from wastewater samples were optimized and the removal efficiency was calculated by determining cadmium in a flame atomic absorption spectrophotometer system (FAAS). Synthetic domestic wastewater samples were utilized for evaluating the applicability of the developed treatment strategy. In addition, the adsorption capacity of the material for Cd²⁺ ion was calculated and the values obtained were modeled by using Langmuir adsorption isotherm (LAI). The calculated LAI parameters were within the appropriate limits, which proved that the developed NM can be used as an effective material for cadmium removal. Moreover, a new, rapid, and feasible synthesis strategy for the synthesis of Ca₃(PO₄)₂ NM was presented in the literature.

This study explores the synthesis of a novel titanium oxide-cetyltrimethylammonium bromide (TO@CTAB) nanocomposite for the effective removal of malachite green (MG) and methyl orange (MO) dyes. The optimization of the nanocomposite's performance was carried out using response surface methodology (RSM). The adsorption characteristics were further evaluated through isotherm models, kinetic studies and thermodynamic analyses. The mesoporous nature of TO@CTAB was confirmed through BET analysis, revealing a pore diameter of 4.625 nm. The crystalline size of TO@CTAB is 54.78 nm, and its crystalline index is 70.84%. The optimal operating conditions were established based on the results obtained from the ANOVA. The Langmuir isotherm model demonstrates superior adsorption performance compared to the Freundlich isotherm model, with adsorption efficiencies of 317.46 mg/g for MO and 306.748 mg/g for MG. The pseudo-second-order model, with an R² value of 0.998 and 0.997 for MO and MG, respectively, provides a more accurate and reliable explanation of the adsorption process compared to the pseudo-first-order model. Furthermore, the high reusability and minimal deterioration of TO@CTAB were observed for up to 5 cycles. The analysis of the adsorption mechanism indicates that the adsorption of MG and MO occurs through H-bonding, electrostatic and π-π interactions. A comprehensive cost analysis of the process was conducted to evaluate the cost-effectiveness; total expenditure incurred during the process was determined to be within acceptable limits. TO@CTAB was assessed using real wastewater samples, demonstrating a decolourization efficiency of 82%. Additionally, it resulted in a reduction of COD, BOD, TSS and TDS.

The air quality index (AQI), based on criteria for air contaminants, is defined to provide a shared vision of air quality. As air pollution continues to rise in global cities due to urbanization and climate change, air pollution monitoring and forecasting models for effective air quality monitoring that gather and forecast information about air pollution concentration are essential in every city. Air quality predictions have evolved to be more helpful for management. Recently, better performance and ability have developed due to the involvement of machine learning (ML) and artificial intelligence (AI) in forecasting air quality in urban cities in India. This paper focuses on air pollution as a significant ecological problem that directly impacts human health and the distribution of an environmental system in urban areas. Hence, we have developed advanced models for daily AQI forecasting to understand the air effluence level in the upcoming days. In this research, six data-driven models have been developed and implemented for daily AQI forecasting in the study area; it is crucial for understanding the future air pollution levels to plan and control air pollution in the entire city. The developed model is applied to air quality datasets. A comparison of the performance of ML models tested here indicates that the XGBoost algorithm achieves the highest coefficient of determination (R²) and root-mean-square deviation (RMSE) value of 0.99 and lower values value of 4.65 than other models in the testing phase. The results of the artificial neural network (ANN) algorithm are slightly lower than the extreme gradient boosting (XGBoost model); the ANN model results are as R², mean squared error (MSE), and RMSE values of 0.99, 13.99, and 198.88, respectively. All the models were subjected to a ten-fold cross-validation model. However, the RF cross-validation model outperforms other models; the RF model result shows the R², RMSE, and MSE values of 0.99, 3.64, and 4.12, respectively. This study also employed two interpretable models, namely feature importance analysis and Shapley additive explanation (SHAP), to evaluate both the global and local methods in a manner that is independent of specific ML models. The feature importance shows that particle matter (PM) 2.5, PM10, carbon monoxide (CO), and nitrogen oxides (NO_x) were the most influential variables. The results determined that such novel DL and ML models may improve the accuracy of AQI forecasts and understanding of air pollution, particularly in metropolitan cities.