Environmental Monitoring and Assessment最新文献

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.

Graphical Abstract

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.

The demand for strategic and environment-friendly swine waste (SW) management is critical in the northeastern states of India, which account for 46.7% of the country’s total swine population. This paper examines nutrient-rich compost production from SW with minimal negative environmental fallout, using cow dung microbiological inoculum and sawdust bulking agent for expeditious rotary drum composting. Aerobic biodegradation conducted in a rotary drum composter (RDC), raised the feedstock temperature to > 40 °C in just 24 h, which stimulated thermophilic decomposition. The thermophilic phase remained for 16 days in the cow dung-amended 10:1:1 (swine waste:cow dung:sawdust) trial (RDC1) versus 7 days for the sawdust-amended 10:1 (swine waste:sawdust) trial (RDC2). After 20 days, the RDC1 product exhibited superior nutritional characteristics, with a total nitrogen content of 2.52%, a significantly reduced coliform population, and an overall weight loss of 25%. These findings highlight that incorporating cow dung (10% w/w) into SW and bulking agents through RDC produces high-quality compost in just 20 days. Thus, the livestock industry benefits significantly from this laboratory-scale method of improved waste management by producing valuable bioproducts via RDC.

Industrial activities can release a variety of harmful substances, including organic and inorganic components, into the environment. Inadequate treatment and discharge of these pollutants into aquatic environments might have adverse effects. Cadmium (Cd) is a toxic element found in various environmental sources, both anthropogenic and geogenic, which can contaminate soils and groundwater crucial for providing healthy food and safe drinking water. This study aimed to develop a novel strategy by the help of nano-sized adsorbents to remove cadmium ions from wastewater through batch-type adsorption processes. CuFe₂O₄ nanoparticles having high magnetic properties were synthesized using a co-precipitation process for the efficient removal of analyte. Characterization of the nanomaterial was performed using field emission scanning electron microscopy (FE-SEM), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analysis. Method effective parameters were systematically optimized through univariate experiments to find proper conditions for the improvement of interaction between the adsorbent and cadmium ions. Removal efficiency (%RE) of Cd was assessed by using synthetic wastewater samples, and the accuracy/practicability of the recommended method proved highly efficient within the linear range of flame atomic absorption spectrophotometry (FAAS). In addition, the Langmuir isotherm model was applied to the experimental data, and the effective adsorption of cadmium from synthetic wastewater by the magnetic CuFe₂O₄ nanoparticles was proved.

This study gives empirical evidence on the drivers of land use change by conducting a qualitative assessment and then using time series data to quantify the relationship between land use land cover change and factors that cause the change. Vector autoregressive models with exogenous variables were used to analyze the time series data. The findings revealed demographic and environmental factors were the primary causes of land use and land cover change. Population growth was found among the key drivers for losses of the natural vegetation on the one hand and rehabilitation of bare lands and grazing lands on the other hand. Despite its pressure on the natural vegetation, the increase in population contributes to a productive labor force for improving land management through rehabilitating degraded grazing lands, implementing soil and water conservation measures, and planting trees on degraded lands. This implies that population growth can be an opportunity or a threat for sustainable natural resources management, depending on how the available labor force is used. Climatic factors like maximum temperature and precipitation were also important causes of change in land use land cover. The study has important contributions to improving land use practices through designing appropriate land resources management policies.

The sunflower crop is one of the most pro sources of vegetable oil globally. It is cultivated all around the world including Haryana, in India. However, its mapping is limited due to the requirement of huge computation power, large data storage capacity, small farm holdings, and information gap on appropriate algorithms and spectral band combinations. Thus, the current work has been done to identify an appropriate machine learning (ML) algorithm (after comparing random forest (RF) and support vector machine (SVM) reported as the best classifiers for land use and land cover) and best band combinations (among the six combinations (including Sentinel-Optical, Sentinel-SAR, and combined-Optical-SAR in single data and time series manner) for Sunflower crop mapping in Ambala and Kurukshetra districts of Haryana using Google Earth Engine (GEE) cloud platform. GEE cloud-computing system combined with RF and SVM provided Sunflower map with an accuracy ranging from 0.0% to 90% in various bands and classifiers combinations but was the highest for the RF with single date optical data. The SVM classifier tuned with parameters like kernel type, degree, gamma, and cost provided better overall accuracy for the classification of land use and land cover along with Sunflower ranging from 98.09% to 98.44% and Kappa coefficient ranging from 0.96 to 0.97 for optical data and combination of SAR and optical time series. The platform is efficient and applicable for a larger part of the country to map Sunflower and other crops with currently identified combinations of satellite data and methodology due to the availability of satellite images, advanced ML algorithms, and analytical modules on a single platform.

In the pursuit of advancing sustainable wastewater treatment solutions in the industry, this study investigates the effect of chemical and thermal modifications on adsorbent geopolymer (GP) structure. Optimal conditions of sulfuric acid treatment and calcination temperature were determined to enhance the adsorption of the direct red dye 28 (DR28). Functional groups (FTIR), mineralogical composition (DRX), morphology (SEM–EDS), and physical properties (BET/BJH) were employed to study the effect of attack with H₂SO₄ and calcination on GP characteristics. The modified GP exhibited a high specific area (190 m² g⁻¹). Adsorption tests indicated that the Elovich model satisfactorily describes the kinetics, while the Sips model represents the isotherms. The maximum adsorption capacity achieved was 107.5 mg g⁻¹. Remarkably, the adsorption capacity was doubled with GP regeneration, allowing reuse for three cycles. Furthermore, the selectivity profile uncovers a pronounced affinity hierarchy for dyes, with direct dyes manifesting a superior attraction, followed by acid, reactive, and disperse dye categories. Analyzing the efficiency of GPAT and comparing it with other GPs, it is evident that GPAT is an efficient and versatile adsorbent, featuring a simplified production process and requiring milder temperatures than those needed for activated carbon. Additionally, the cost–benefit analysis highlights geopolymers as a more economical and efficient alternative compared to conventional adsorbents.

Per- and polyfluoroalkyl substances (PFAS), a class of man-made chemicals, possess unique properties that have rendered them indispensable in various industries and consumer goods. However, their extensive use and persistence in the environment have raised concerns about their potential repercussions on human health and the ecosystem. This review provides insights into the sources, occurrence, transformation, impacts, fate, monitoring, and remediation strategies for PFAS. Once released into the environment, these chemicals undergo intricate transformation processes, such as degradation, bioaccumulation, and biomagnification, which result in their far-reaching distribution and persistence. Their chemical stability results in persistent pollution, with far-reaching ecological and human health implications. Remediation strategies for PFAS are still in their infancy, and researchers are exploring innovative and sustainable methods for treating contaminated environments. Promising technologies such as adsorption, biodegradation, and electrochemical oxidation have shown the potential to remove PFAS from contaminated sites, yet the search for more efficient and sustainable solutions continues. In conclusion, this review emphasizes the urgent need for continued research and innovation to address the global environmental challenge posed by PFAS. As we move forward, it is imperative to prioritize sustainable solutions that minimize the detrimental consequences of these substances on human health and the environment.

This study investigates the impact of crude oil contamination on the fungal community dynamics in the Evrona Nature Reserve, situated in Israel’s hyper-arid Arava Valley. The reserve experienced petroleum-hydrocarbon-spill pollution at two neighboring sites in 1975 and 2014. The initial contamination was left untreated, providing a unique opportunity to compare its effects to those of the second contamination event. In 2022, soil samples were collected from both contaminated areas and nearby clean (control) sites, 47 and 7 years after the spills. The taxonomic diversity of fungal community and functional guilds, as well as various properties of the soil, were analyzed. We focused on three functional groups within fungal communities: saprotrophs, symbiotrophs, and pathotrophs. The results revealed a significant decrease in number of fungal species in the contaminated samples over time. Consequently, prolonged effect of crude oil-contaminated soils can facilitate the development of a distinct fungal community, which has adapted to the conditions of oil contamination. This study aims to elucidate the dynamics of fungal communities in oil-contaminated soils, contributing to a better understanding of their behavior and adaptation in such environments.

In agricultural areas, soil physical quality controls critical properites for plant growth, such as aeration, soil water, and strength. This study investigated the impacts of different long-term land use types (LUTs) (natural pasture (control), kiwi fruit, cherry laurel, forage crops, soybean, and maize) on soil physical properties, such as structure stability index (SSI), bulk density (ρ_b), aeration capacity (AC), and Dexter’s index (S-index). The long-term LUTs significantly affected all the examined soil properties (p < 0.01 or p < 0.001). Other soil physical properties, except for plant available water content, macroporosity, clay content, and silt content, changed statistically depending on soil sampling depths. The S-index values (≥ 0.050) obtained for all LUTs indicate very good physical quality or structural quality in the study area, but the S-index values decreased with the effect of LUTs compared to the natural pasture (control) land use. Different LUTs, such as cherry laurel, soybean, and maize land uses, have caused different structural degradation due to tillage practices. While the natural pasture (control) land use type revealed the best results regarding primarily soil organic carbon (SOC) and SSI, these values were lower in LUTs, where soil tillage is the most common, such as cherry laurel, soybean, and maize land use types. The results regarding S-index reveal that the soils in the study area will continue to be degraded as the impacts of the current LUTs continue. Therefore, for these soils in the future, there is a need for sustainable soil management practices that will protect the physical or structural quality and increase soil organic matter content.