环境科学与生态学最新文献

Urban areas are experiencing rapid transformations, driven by population growth, economic development, and policy changes. Understanding and monitoring these dynamic changes is crucial for sustainable urban planning and management. This study leverages machine learning and Google Earth Engine to investigate urban dynamics and its interactions with biophysical conditions in the Kaduna River Basin (KRB), Nigeria. This study utilized a dataset of 192 points, initially extracted from Google Earth Engine, to analyze urban transitions between 1987 and 2020, incorporating biophysical and environmental variables such as population density, precipitation, and surface temperature. The dataset was processed in R using the CARET package, with missing data imputed via K-nearest neighbors (KNN), categorical variables transformed using One-Hot Encoding, and numerical variables rescaled for consistency. A tenfold cross-validation approach was used to train and validate machine learning models, including random forest, support vector machine, KNN, and multivariate adaptive regression splines, ensuring optimal model performance. Model evaluation metrics such as overall accuracy, kappa, F1 score, and area under the curve confirmed the reliability of the models in identifying the biophysical factors influencing urban changes. The findings revealed overall accuracy of 0.80, 0.73, 0.71, and 0.72 and kappa statistics of 0.60, 0.46, 0.42, and 0.45 for random forest (RF), multivariate adaptive regression splines, support vector machine, and KNN, respectively, with RF emerging as the most accurate model (80%) for predicting urban change patterns in KRB. Land cover changes reveal rapid urban expansion (154.81%), declining water bodies (− 95.79%), and vegetation growth (174%). Machine learning models identify population density and water stress index as key urban change drivers, with climate factors like temperature and precipitation playing crucial roles. The results of this study offer valuable insights into the processes driving urban transformation and present a robust methodology for monitoring and predicting future urban development. This study aids in the creation of strategies for sustainable urban growth and the mitigation of adverse environmental impacts.

Syngas are produced from wastewater through anaerobic reactions and thermochemical processes, using a catalyst that modifies the gas composition, reduces methane production, and achieves partial COD reduction. The current research is attempting to treat the tannery wastewater via an anaerobic process configured with 0.25, 0.3, and 0.35 volume units of granular activated carbon (GAC) with 10 nm size to minimize the concentration of chemical oxygen demand (COD) and improve the biological methane yield. During this anaerobic process, the up-flow anaerobic sludge blanket (UASB) reactor supports to enhance the bio-methane production and handle the high rate of organic load. Influences of GAC units and operating time (days) on COD and biological methane yield of an anaerobic system for tannery wastewater treatment are studied and measured in their value. The output results of COD and biological methane yield are compared, and it was spotted that the tannery water process with 0.3 volume units of GAC owns 87% of COD removal with biological methane yield of 66.3 mL/day (7.2 mL/g COD removed) and end of 30th day found 1939 mL.

Most studies evaluating chronic ambient exposure to nitrogen dioxide (NO₂) have used averages as the exclusive exposure metric. However, this approach may lead to an underestimation of potential health effects. The objective of this study is to evaluate the association between ambient exposure to NO₂ assessed by various metrics, and lung function in a cohort of healthy male youth. A cross-sectional analysis of 5,462 subjects was conducted using multivariate linear regression. Exposure to NO₂ was assessed by spatial interpolation using Empirical Bayesian Kriging (EBK). Five different exposure metrics were evaluated over two years, including average concentration, the number and intensity of exceedances of the 24-h NO₂ World Health Organization air quality guideline (AQG), and the number and intensity of 1-h NO₂ peaks. Lung function indices, including percent predicted forced expiratory volume in one second (FEV₁), forced vital capacity (FVC), forced expiratory flow between 25% and 75% of vital capacity (FEF_25-75), and FEV₁/FVC ratio, were assessed. The intensity of the 24-h AQG exceedances was associated with the largest reductions in FEV₁ (-0.82%, 95% CI: -1.61%, -0.03%) and FVC (-1.03%, 95% CI: -1.86%, -0.20%), while FEF_25-75 showed a significant decline only with the 1-h peak intensity metric (-2.78%, 95% CI: -5.02%, -0.54%). The study results support integrating diverse exposure metrics as part of NO₂ chronic exposure assessment, as these metrics may capture a wider range of potential health effects that could be underestimated or overlooked when relying only on average concentrations.

Prosopis juliflora, one of the most invasive trees, adversely affects the ecosystem and native plant communities in arid lands. This disrupts biodiversity and depletes water resources, posing significant ecological and economic challenges. Several attempts have been made to control this species in the United Arab Emirates (UAE) deserts but with little success. This study identifies and maps environmental variables influencing P. juliflora habitats using machine learning (ML); employs maximum entropy (MaxEnt) and statistical techniques to estimate its presence in Sharjah, UAE, home to one of its most intense populations; and conducts validation and sensitivity analysis. Eleven environmental variables representing geological, geomorphological, hydrological, eco-indicators, and climatological factors were selected to map the spread of the associated P. juliflora hazard. Variables were selected using collinearity and variance inflation factor (VIF) to eliminate bias, and ML techniques assigned weights based on overall accuracy (OA) and the Kappa coefficient before model implementation. Finally, a statistical comparison with MaxEnt was conducted to map P. juliflora habitats, classifying suitability as very high, high, low, and very low while estimating model accuracy. The results indicated that MaxEnt achieved a higher area under the curve (AUC 0.98) and more logical outcomes than statistical models (AUC 0.85) due to its superior handling of collinearity, complex environmental interactions, and capability of minimizing overfitting. The main findings show that the variable weights for MaxEnt and statistical models are primarily influenced by precipitation (27.0% and 18.18%), groundwater depth (14.9% and 26.8%), and total dissolved solids (TDS) (20.9% and 26.22%), respectively, indicating a shift in habitat distribution towards the eastern regions of the study area. Habitat mapping of P. juliflora is essential for local stakeholders and policymakers in decision-making regarding species conservation, sustainable land use, and climate adaptation. The findings conclude that ML offers a viable approach for habitat modeling of invasive species in similar arid regions worldwide.