Environmental Monitoring and Assessment最新文献

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.

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.

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.

Water contamination from rapid urbanization, industrialization, and agricultural activities has emerged as a critical environmental challenge, leading to widespread waterborne diseases and millions of annual fatalities. Conventional water treatment methods such as coagulation, flocculation, and sedimentation exist; they are often hindered by high chemical and energy costs. The limitations of traditional water treatment approaches have necessitated the exploration of alternative technologies that can provide more efficient and cost-effective solutions for water purification. Nanotechnology-based water treatment methods, leveraging the unique physicochemical properties of nanoparticles, can potentially overcome the limitations of conventional water treatment techniques and provide enhanced pollutant removal efficiency. This review critically evaluates the latest advances in magnetic nanoadsorbent technologies for wastewater remediation, distinguishing itself from existing literature by integrating theoretical principles with practical application. The analysis reveals that nanoparticle-based treatment methods demonstrate superior wastewater remediation performance compared to conventional techniques. The unique properties of nanoparticles enable efficient removal of various contaminants, including heavy metals, organic compounds, and bacterial populations. These findings suggest that nanotechnology-based approaches represent a viable and sustainable solution for addressing current water treatment challenges, offering a promising direction for future water purification technologies.

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The transference of heavy metals from forage to feeding animals is a crucial animal welfare issue. In this article, the effects of using municipal solid waste compost in corn cultivation on animal’s health were assessed. The study was run at the Soil and Water Research Institute of Iran and followed an incomplete strip block design composed of six treatments and three repeats. The treatments were control, with no fertilizer or municipal solid waste compost, multiple combinations of N, P, and K chemical fertilizers, and municipal solid waste compost which were used either once or twice a year. Mature corn plants (Single Cross 704) from the lowest collar were sampled during the end of summer 2019. Corn samples were examined for cadmium, lead, nickel, and zinc concentration. Using the Oracle Crystalball software, health risks were estimated for sheep, dairy cows, beef, broiler, and laying poultry. The highest exposure to cadmium, nickel, and zinc was observed in sheep feeding corn cultivated in T5 treatment, but the highest exposure to lead was observed in sheep fed with corn cultivated in T2 treatment. Cd, Pb, Ni, and Zn exposure and related risk indices were lower than the reported reference values. It can be concluded that the quality of fodder corn grown in the treatments in this study was within the safe range in terms of heavy metal content and did not threaten the health of the investigated animals.

Soil is a crucial resource providing essential ecosystem services and is facing rapid degradation. To ensure its sustainability, conservation measures are essential, and establishing the tolerable soil loss limit (TSLL) is critical for assessing degradation risks, prioritizing actions, and evaluating their effectiveness. This study aimed to determine the TSLL for the Andit Tid watershed using a biophysical model that evaluates soil erosion resistance by integrating key soil parameters. Therefore, to capture the status of soil parameters 40 disturbed and undisturbed soil samples, including soil depth data, were collected, analyzed in a soil laboratory, and subjected to rigorous normality tests and trend analysis for reliability. The normalized data were processed through a unitless score converter matrix to obtain the soil aggregate score followed by the determination of TSLL through the soil aggregate group and soil depth matrix. Accordingly, the study establishes a critical TSLL of 10 t ha⁻1 yr⁻1 for the Andit Tid watershed. Specifically, the watershed experienced five TSLLs 7.50, 8.80, 10, 11.30, and 12.50 t ha⁻¹ yr⁻¹ covering an area of 4.10%, 23.00%, 10.70%, 45.40%, and 16.70% respectively. These findings provide a foundational benchmark for targeted soil conservation and sustainable land management by highlighting the role of targeted, watershed-specific TSLL in enhancing ecosystem resilience.