Environmental Monitoring and Assessment最新文献

Current methods for defining traffic low emission control zones (TLEZ) often face limitations that hinder their widespread implementation and effectiveness. This study addresses these challenges by employing a comprehensive approach to analyze PM_2.5 concentration levels within TLEZ. This study utilizes PM_2.5 data collected by taxi fleets, integrating static road network features and dynamic time series features to gain a detailed understanding of pollution distribution patterns across different urban areas. To capture these complex distribution patterns of PM_2.5, a sophisticated deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Attention Mechanism (AM) is deployed. This model adeptly identifies spatial and temporal variations in PM_2.5 concentrations, allowing for a more accurate and responsive analysis of pollution levels. A multi-objective optimization model is developed to minimize the overall impact on residents' daily lives, which considers both environmental and social factors in the delineation of TLEZ. The optimization model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is a robust evolutionary algorithm that facilitates the identification of Pareto-optimal solutions. These solutions can help define the optimal boundaries for Low, Ultra-Low, and Zero Emission Zones. By establishing a framework for assessing and optimizing these zones, this study provides valuable insights and actionable guidance for policymakers and urban planners.

The use of stone columns as a ground improvement technique in landfill sites has gained significant attention due to their potential to enhance stability, reduce settlement, and facilitate efficient drainage within the clay barrier layer. This study examines the performance of stone columns placed beneath a clay barrier in a controlled landfill environment. A laboratory-based physical experiment was conducted using two lysimeters filled with organic waste to simulate landfill conditions, with continuous monitoring over 5 months. This research specifically aims to evaluate the effectiveness of stone columns in controlling settlement, optimizing drainage, and preserving liner integrity under waste accumulation and structural loading. The experimental setup involved the installation of 2.5 cm diameter stone columns at a spacing of 7.5 cm (3D), with a length-to-diameter ratio (L/D) of 8 key parameters—including moisture content, temperature, liner pressure, total dissolved solids (TDS), pH, and settlement—were systematically recorded using embedded sensors. Additionally, a structural load was applied to assess its influence on the soil mass beneath the barrier. The findings indicate that stone columns significantly enhance landfill stability by improving drainage efficiency, maintaining consistent pressure, regulating temperature variations, reducing settlement, minimizing TDS accumulation, and stabilizing pH levels. These results underscore the viability of stone columns as an effective long-term solution for improving the performance and containment efficiency of landfill liners.

Soil Organic Carbon (SOC), a key component of the global carbon cycle, remains poorly understood with respect to its linkage to ecological succession. The study aimed to unravel SOC dynamics during ecological succession in a tropical deciduous forest in the foothills of the northwest Himalaya (NWH), India. Ecological parameters derived from satellite remote sensing in conjunction with field sampled SOC was used to predict soil organic carbon density (SOCD) employing four techniques viz., multiple linear regression (MLR), random forest (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost). Cross-validation with hundred replications was employed to evaluate the performance of different models. Significant variability in SOCD was observed across the study area varying from 2.7 t/ha to 65.7 t/ha. The RF model with RMSE of 12.17, R² of 0.81 and mean bias of 0.16 performed best among all the models. Vegetation parameters emerged as primary predictors, with SOC accumulation increasing alongside vegetation succession—from 24.7 t/ha in pioneer stages to 35.9 t/ha in climax community. The mature forests with dense, tall canopies and substantial biomass contribute significantly to soil carbon storage. For the majority of the tree community types, the uncertainty in predicted SOCD remained below 3 t/ha except for the post-climax community (6–9 t/ha) due to high SOCD and moisture. Study stresses on the roles of successional stages in carbon sequestration in tropical deciduous forests, underscore the importance of the protection of these communities to safeguard SOC stocks effectively.

Like other parts of the world, the source-specific generation of MSW in higher education institutes (HEIs) of Pakistan contain ≥ 70% compostables being managed through CMSWMS, a resource exhaustive environmental degradation approach. CMSWMS in the University of the Punjab, Lahore- 54590, a mother HEI in Pakistan, includes MSW containing over 2/3rd compostables (with ≥ 85% moisture contents) carrying poor collection efficiency and disposal at distantly located open dumps with linear economy. Here, DMSWMS effectiveness compared to CMSWMS was based on composting compostables at source-level to observe variations in the composition and characteristics of the MSW components, collection efficiency, cost, and combustibles’ earlier separation-driven impacts on its drying duration and calorific value for refuse-derived fuel (RDF), and assessing LCA-based acidification potential of carriage and disposal. Compared to CMSWMS, composting-based DMSWMS significantly improved characteristics of the compostables, combustibles, and recyclables. The DMSWMS resulted ≥ 97% collection efficiency, 89% reduction in collection and disposal cost, significant reduction in acidification potential through ≥ 94% GHGEs reduction, and rendered upfront availability of combustibles as RDF with 86% greater GCV. Cumulatively, all the DMSWMS-driven improvisations led the selected HEI in achieving 100% weighing score of MSWM component of the UI Green Metric World University Campus Ranking system. However, for prompting composting-based sustainable DMSWMS at broader scale in a city, multiple studies are required with stakeholders on board from union councils (smallest administrative units analogic to counties), which would streamline DMSWMS integration into MSW sanitation policy framework of municipalities.

Pakistan, located in an arid region characterized by low rainfall and high temperatures, faces significant vulnerability to climate change. The country’s diverse meteorological conditions pose significant challenges for effective climate modeling. This study focuses on analyzing long-term meteorological time series data (1981–2020) from various regions across Pakistan to examine regional climate variability and detect emerging weather trends. Seventeen climate indices were calculated to assess weather patterns, followed by trend analysis utilizing both parametric and non-parametric methods. The parametric approach employed ordinary least squares (OLS) regression, while the non-parametric methods included the Mann–Kendall (MK) test and Sen’s Slope (SS) estimator. Over the 40-year period, the analysis revealed significant trends, such as increases in hot days, cold nights, warm nights, and extreme precipitation events. These findings emphasize the distinct and complex regional impacts of climate change in Pakistan. By identifying these trends through robust statistical techniques like OLS, MK, and SS, the study provides critical evidence of climate shifts, emphasizing the urgent need for tailored, region-specific strategies to strengthen resilience against the adverse effects of climate change.

Soil salinity represents a critical environmental challenge that undermines agricultural productivity and accelerates soil degradation in arid and semi‐arid regions. In addition, it is a critical issue in Jordan, particularly in the Jordan Valley (JV) region, which is regarded as “the food basket of Jordan” as the region is experiencing a gradual increase in salinity. This study aims to assess soil salinity in the Northern Jordan Valley (NJV) using a comprehensive approach that encompasses (1) geochemical and mineralogical analysis of salt composition, (2) spectral characterization via reflectance spectroscopy, (3) GIS-based spatial mapping of salt distribution, and (4) evaluating the extent and origin of salinity. In this region, soils are classified as non-saline, slightly saline, or strongly saline. Citrus, the predominant crop and one that is highly sensitive to salinity, is grown in soils with elevated levels of calcite and quartz. The rise in soil salinity is attributed to several factors, including the inherent salinity of irrigation water, the types of crops cultivated, the absence of advanced irrigation technologies, mismanagement of fertilizers, and local climatic conditions. Consequently, the outcomes of this study are pivotal for devising effective strategies to mitigate soil salinity and promote sustainable agricultural practices.

Rapid identification of contaminant source information is critical for solving sudden groundwater contamination events. This paper constructs a combined EnKF-SPSO algorithm based on the ensemble Kalman filter (EnKF) and survival particle swarm optimization (SPSO) algorithms to groundwater contamination source identification, which includes determining the location of the source, initial concentration, and emission time. The proposed hybrid architecture improves upon conventional single-algorithm approaches by decoupling the identification process into two stages. First, the EnKF searches for the contaminant source’s location, thereby reducing the search space. Next, the SPSO estimates the initial concentration and emission time within the reduced domain. This two-stage process effectively mitigates the curse of dimensionality often encountered in standalone optimization methods. We set up two solute transport scenarios with different numbers of contaminant sources to examine the effectiveness of the algorithm and compare it with the EnKF, particle swarm optimization (PSO), and SPSO algorithms. The results show that the EnKF-SPSO algorithm can identify the contaminant characteristics more accurately without falling into a local optimum, and the average relative error is less than 1%. In addition, the EnKF-SPSO algorithm, for cases with measurement errors, is highly reliable. The combined algorithm can provide technical support for groundwater contamination remediations, risk assessments, and liability determinations.

Soil erosion is a global concern, and tons of fertile topsoil are lost worldwide. Topography significantly influences soil erosion patterns, shaping how soil loss varies across landscapes. In the Revised Universal Soil Loss Equation (RUSLE), the topographic factor (LS-factor) quantifies this impact, with Digital Elevation Models (DEMs) serving as key inputs for its derivation. The soil loss over Kerala, India, is estimated using different DEMs. The study also explored two methods for deriving the LS-factor, one based on flow accumulation and another based solely on the slope length. Among the approaches tested for LS factor estimation, the slope-based method proved more effective than one incorporating flow accumulation, as the study is for a region rather than a distinct hydrologic unit. Four freely available Digital Elevation Models, ALOS, ASTER, SRTM, and Cartosat-1 were selected for the study. The study showed that the general pattern of soil erosion can be captured by using any of these DEMs despite differences in individual elevation values. The mean potential soil loss estimated for the year 2020 was 215.91 t/ha/year, 205.70 t/ha/year, 203.99 t/ha/year, and 207.97 t/ha/year when using ASTER, ALOS, SRTM, and Cartosat-1, respectively. The ASTER DEM shows a slightly higher mean value but exhibited the least uncertainty, which was confirmed by bootstrap resampling uncertainty analysis. These findings emphasize the need for careful DEM selection based on terrain characteristics, enhancing the accuracy of soil erosion assessments and informing more effective land management strategies.

Malaria remains a significant global health concern which continues to pose a life-threatening risk globally. The disease, transmitted by Anopheles mosquitoes acting as vectors, requires favorable environments for effective transmission. These environments are influenced by factors such as meteorological conditions and vegetation cover; a number of which have been examined in this study and incorporated into modeling the observed malaria incidence. This method provides a solution for common data inconsistencies encountered in healthcare and epidemiological research, while also offering predictions on incidence rates, thereby enabling more informed decision-making processes. A multivariate statistical modelling approach using the Vector Autoregressive (VAR) model has been employed, enabling dynamic analysis of all relevant parameters simultaneously. The environmental information obtained from satellite and reanalysis datasets, along with the recorded malaria cases in Dhalai district, Tripura, India, were evaluated for causality, refined, and subsequently utilized in the modelling process. The model’s reliability was assessed by comparing its short-term forecast with actual data using a number of accuracy metrics, revealing a mean absolute percentage error of 1.16% and a correlation coefficient of 0.721 between the testing and forecasted malaria incidence data. These observations highlight the model’s effectiveness in accurately capturing the variations in malaria incidence and its predictive capability. Notably, this model has yet to be widely utilized, which presents a unique opportunity for further exploration in other regions. Such studies could significantly contribute to the development of more targeted and effective control measures.

Urban areas with intense industrial activity and heavy traffic are among those most affected by increasing pollution levels. These areas experience a rise in air pollution, containing a complex mix of pollutants including particulate matter and potentially toxic elements. Trees located along urban and rural roadsides are used as environmentally sustainable tools for tracking and reducing air pollution impacts. In this study, the aim was to determine the variation of nickel (Ni) concentrations in the species Nerium oleander L., Salix babylonica L., Magnolia grandiflora L., Prunus laurocerasus L., Cercis siliquastrum L., Robinia pseudoacacia L., Aesculus hippocastanum L., Platanus orientalis L., and Acer negundo L. based on plant organs and traffic density. In this study, plant materials collected from the city center of Trabzon/Türkiye were used. The results indicate significant variations in Ni accumulation among species under different traffic densities based on average values. Differences in element concentrations have been observed both among the studied species and within the organs of the same species. Generally, the lowest Ni concentrations were observed in N. oleander (766.2 ppb), S. babylonica (935.7 ppb), and M. grandiflora (632.9 ppb), while the highest concentrations were recorded in R. pseudoacacia (3217.9 ppb) and A. negundo (3111.9 ppb). Therefore, R. pseudoacacia and A. negundo are considered suitable as bioindicator for Ni metal. These findings underscore the potential of plants to monitor heavy metal pollution from traffic and suggest that these species should be considered in environmental protection efforts.