Environmental Monitoring and Assessment最新文献

Prolonged consumption of foods containing toxic metals can elevate the risk of noncommunicable diseases, including chronic kidney disease of uncertain etiology (CKDu). Despite the increasing number of CKDu cases in Maradankulama and Mahakanadrawa Grama Niladhari Divisions (GN) in Sri Lanka, no prior studies have examined the accumulation of heavy metal(loid)s and their potential association with CKDu prevalence. Furthermore, there is an absence of comprehensive analyses using chemometric techniques such as PCA and hierarchical studies regarding CKDu and heavy metal contamination in Sri Lanka. This study aims to provide initial insights into the accumulation and potential pathways of toxic metals in staple foods within local diets and their subsequent presence in the agricultural environment of examined GNs. Cr, Cd, As, and Ni concentrations in analyzed foods were within permissible limits (MPLs), whereas Pb levels exceeded MPLs in rice (Oryza sativa), gotukola (Centella asiatica), lime (Citrus crenatifolia), and inland fish (Etroplus suratensis). High target hazard quotient (THQt) values in polished rice suggest possible health risks with prolonged intake. Hierarchical analysis suggested a common source of Pb accumulation. PCA and hierarchical clustering revealed the intricate connection between As and Cd, with their concurrent clustering in samples suggesting a potential common origin. This indicates that while individual concentrations comply with acceptable standards, the potential synergistic effects of Cd and As accumulation might pose elevated health risks. Further, the gut tissues of inland fish exhibited pronounced metal concentrations and significant (p < 0.05) positive correlations with toxic metals in the tank sediments suggesting a diet-based bioaccumulation pathway through sediments.

An increase in erratic industrial activity has been a major contributing factor to the significant contamination in environmental matrices. This study aimed to ascertain the level of heavy metal contamination in soils in major industrial estates of Tamil Nadu as well as the threats to human health and the environment that concomitant with it. This led to the use of the total reflection X-ray fluorescence (TXRF) spectroscopy technique to analyse 31 soil samples. Thus, the mean heavy metal concentrations were found to be in the ascending order: As < Ni < Pb < V < Cu < Zn < Cl < Mn < Sr < Cr < Al < Fe. Pb, Cr, Cu, As, and Zn have been identified as predominant contaminants in the study area using the conventional pollution indices such as geo-accumulation index (I_geo), contamination factor (CF), and enrichment factor (EF). The magnetic susceptibility measurements indicates that the percentage of frequency dependence of magnetic susceptibility of the studied soil samples varied from 0.88 to 11.15 and that represents the presence of admixture of superparamagnetic (SP) particles in the soil samples. From the results of multivariate statistical analyses, the sources of identified heavy metals were classified as anthropogenic sources (Cr, Cu, Pb, As, and Zn), natural sources (V, Mn, Fe, Ni, and Sr ), and intermediate sources (Al and Cl).

Land is a finite resource that must be managed wisely to ensure its sustainability. Consequently, land evaluation has become essential. Identifying and utilizing productive capacity of land efficiently and profitably is crucial; otherwise, resource degradation can severely impact natural ecosystems and food production. Over the years, various methodologies have been employed to assess land resources, and one such method is the Land Capability Classification (LCC). LCC is a widely used and fundamental approach to land-use planning, traditionally assessing land based on its intrinsic qualities and climate. This study aims to highlight new approaches and developments in land evaluation techniques. It provides a brief overview of recent technological and scientific advancements integrated into land evaluation. The findings suggest that LCC alone is inadequate for precise land assessment, emphasizing the need to integrate new technologies. Remote Sensing (RS) and Geographic Information System (GIS) technologies are becoming increasingly significant. Integrating various software, decision-making systems, and mathematical models can also enhance the accuracy of land assessment results. The continuous advancement of GIS and remote sensing technologies is paving the way for new tools to facilitate natural resource mapping, appraisal, surveillance, and management. Utilizing these technologies for future projections will be highly beneficial in accurately assessing the long-term impacts of current land management practices.

The concentration of PM_2.5 (particulate matter with a diameter < 2.5 µm) on Sumatra Island has increased, mainly because of forest and peatland fires, transportation, and industry. Biomass burning releases partially burned carbon into the atmosphere, resulting in a smoky haze containing PM_2.5. Air quality has deteriorated quickly, and PM_2.5 has become a major health hazard in Indonesia. Studies on long-term exposure to PM_2.5 have indicated its associations with both morbidity and mortality. Here, we measured long-term (2000–2014) exposure to PM_2.5 on the basis of satellite-derived aerosol optical depth measurements (1 × 1 km²) used to predict ground-level PM_2.5 concentrations. Additionally, population data on Sumatra Island residents from the fourth wave of the Indonesian Family Life Survey (IFLS) were obtained. We investigated the association between long-term PM_2.5 exposure and mortality with a retrospective cohort study design. A total of 2409 subjects aged ≥ 40 years participated in the IFLS-3 beginning in November 2000, and we examined mortality outcomes until the IFLS-5 in September 2014. We used Cox regression models to calculate hazard ratios (HRs) of mortality associated with PM_2.5 exposure. According to the adjusted model, the mortality HRs per 10 µg/m³ increase in PM_2.5 concentration were 1.10 (95% CI 1.03, 1.17) for all natural causes, 1.17 (95% CI 1.05, 1.25) for cardiovascular causes, and 1.19 (95% CI 1.04, 1.36) for respiratory causes. Long-term exposure to PM_2.5 was associated with all-natural, cardiovascular, and respiratory mortality on Sumatra Island, where PM_2.5 levels exceed the WHO and US-EPA air quality standards.

Ballast water is essential for ship operations, but can also transport harmful organisms between ports, threatening local environments. The Ballast Water Management (BWM) Convention, established by the International Maritime Organization (IMO), requires ships to implement ballast water management measures to address this issue. In this study, ballast water samples were collected from ships entering Shahid Rajaee Port in Iran before and after the Ballast Water Management Convention came into force in 2017. The sampling was conducted in coordination with Iranian authorities, following IMO guidelines. Vibrio cholerae, Escherichia coli, and Enterococci were identified in 97 ships, and physical parameters such as temperature, salinity, and pH were measured in 15 ships. To prevent sample contamination, rigorous protocols were followed, including using sterile equipment, appropriate storage, and immediate transfer to the lab. The results showed that before the BWM Convention, V. cholerae was found in 6 out of 14 ships (42%), with ballast water retention times of 2–58 days. After the convention, V. cholerae was found in only 2 out of 83 ships (2.4%), with ballast water retention of 2–3 days. This indicates a significant reduction in the risk of pathogen transfer through ballast water. Further analysis after the Convention showed that V. cholerae was initially detected in 4 out of 15 ships, but only 1 ship (6.6%) had levels above the standard. E. coli and Enterococci were also detected in multiple ships, but their levels were below the standard. The study investigated the relationship between the bacterial levels and the physical parameters. While some correlations were found between E. coli/Enterococci and parameters like pH, temperature, salinity, and water retention time, no significant impact of physical parameters on V. cholerae levels was observed. T-tests revealed significant relationships between the bacterial levels and the physical parameters, as well as the ballast water retention time. Ships were found to have followed the ballast water exchange guidelines of exchanging at least 200 or 50 nautical miles from shore and at depths over 200 m, as mapped out for the Persian Gulf region. The results indicate that the implementation of the BWM Convention has been effective in reducing the risk of pathogen transfer through ballast water. However, the study emphasizes the importance of ongoing monitoring and enforcement to ensure continued compliance with the convention’s requirements.

The spatiotemporal dynamics of forest cover are essential for understanding the patterns and processes of forest change over time and space. This research focused on the spatiotemporal trends and drivers of forest cover change in the Metekel Zone of Northwest Ethiopia. Landsat 5, Landsat 7, and Landsat 8 imagery, covering the period from 1986 to 2019, were used for land use/cover classification. Land use/cover classification was performed using random forest (RF) and support vector machine (SVM) algorithms in the Google Earth Engine (GEE) platform, with training samples obtained through visual image interpretation. Spectral indices, such as the normalized difference vegetation index, soil-adjusted vegetation index, leaf area index, and normalized difference water index, were analyzed to examine forest cover dynamics over time. In addition, key informant interviews (KIIs) and focus group discussions (FGDs) were conducted. Findings revealed that forest cover decreased significantly from 51.37% in 1986 to 17.20% in 2019, driven largely by human activities such as agricultural expansion, increased demand for firewood, and urban expansion. Findings from spectral indices further corroborated the finding that forest cover in the study region (mainly in the southwestern part) substantially decreased from 1986 to 2019. Concerning forest depletion, the lack of local community awareness has become a key challenge. This problem is attributed to communities prioritizing immediate needs such as fuel and land for agriculture over long-term forest conservation. To combat ongoing deforestation, the Metekel Zone Administration, in collaboration with the land administration office and other stakeholders, revisited and strengthened existing forest policies and control systems. It is also suggested that community awareness, chiefly among youth, should be enhanced through the strategic expansion of formal and nonformal educational initiatives, which empower the youth as agents of change and promote the dissemination of knowledge throughout the community.

N-nitrosamines such as N-nitrosodimethylamine (NDMA), N-nitrosodiethylamine (NDEA), N-nitrosopiperidine (NPIP), and N-nitrosopyrrolidine (NPYR) have been established as potent carcinogens that can induce diverse types of cancer. Several studies have extensively investigated the accurate quantification of total N-nitrosamines (TONO) and the intricate nature of the matrix in which they are detected. The potential for the formation of N-nitrosamines in post-combustion CO₂ capture (PCCC) and water treatment has raised concerns. This study outlines a unique method for the quantification of TONO in aqueous matrices using UV photolysis and the subsequent detection of NO by chemiluminescence. This method offers benefits such as operation in the continuous mode and handling of high sample flow rates to achieve a low limit of detection (LOD) and a low limit of quantification (LOQ). The observed LODs for the individual N-nitrosamines of NDMA, N-nitrosomorpholine (NMOR), N-nitrosodibutylamine (NDBA), and NPIP range between 0.06 and 0.2 µM at a sample flow rate of 0.25 mL/min, while the LOD range is reduced to between 0.02 and 0.06 µM at 0.75 mL/min. Linear responses for the NO produced from specific N-nitrosamines are observed between 0.5 and 10 µM. The developed method is resistant to interfering chemicals (i.e., nitrite, amines, and carbonyls) and exhibits high specificity.

Microplastic contamination in soil ecosystems is a major environmental concern in the world. The current study aims to explore the extent of microplastic pollution in unregulated village dumpsites in India, focusing on the movement of these pollutants from soil to aquatic environments. Soil samples from eight distinct sites (A to H) in six villages were analyzed for various properties, including pH, bulk density, porosity, water retention capacity, hydraulic conductivity, and particle size distribution. The attenuated total reflection–Fourier-transform infrared spectroscopy (ATR-FTIR) method was used to identify prevalent plastic types. The research classifies microplastics by their shape and color, identifying a wide range of particles such as sheets, fibers, foams, fragments, and films. The study also examines the presence and concentration of microplastics in both soil and sediment samples. It was found that PE and PP microplastics are significantly present across different size fractions. Sample A contains a variety of items in the 1–5 mm size range, mainly PE, while the 0.3–1 mm fraction is largely PP. Samples B to H are mostly composed of PE microplastics in different forms. Sample F is unique with a mix of PE, EPS, and a higher amount of red and blue foam particles in the 0.3–1 mm fraction. Microplastics were quantified using stereomicroscopy, revealing concentrations between 80 and 840 numbers per kilogram in soil and 20 to 60 numbers per kilogram in sediments. The findings emphasize the widespread nature of microplastic pollution across ecosystems and the importance of developing effective strategies for monitoring and mitigating their impact on environmental health and human well-being.

Monitoring wastewater is an effective strategy for supporting clinical surveillance for viral infections. Wastewater monitoring, also known as wastewater-based epidemiology (WBE), uses existing wastewater collection networks to obtain a composite sample of a population that can be used to predict disease dynamics in a specific area. Viruses such as dengue and chikungunya are primarily transmitted through the bites of infected Aedes mosquito species. The prevalence of the Aedes mosquito in tropical and subtropical regions makes these diseases a serious threat to public health. Employing wastewater surveillance, monitoring, and regulating the spread of diseases like dengue and chikungunya—notably caused by mosquitoes—has been recommended. However, understanding the dynamics of viral release and its persistence in wastewater is critical for monitoring purposes. Although methods for recovering RNA for some viruses from wastewater have been developed, the same approach does not work equally well for viruses such as dengue and chikungunya due to low levels of viral RNA and susceptibility to degradation. As a result, a tailored approach to recovering these viruses from wastewater is required. This review summarizes viral release from infected hosts, its dynamics, and approaches for dengue and chikungunya wastewater surveillance. The review also identifies existing knowledge gaps in viral persistence in wastewater and recovery.

Graphical Abstract

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM₁₀: 318.65 ± 45.80 µg/m³) and 2023 (PM₁₀: 383.70 ± 61.49 µg/m³), compared to 2008 (PM₁₀: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM₁₀ and urban indices (+ 0.63) and a negative correlation between PM₁₀ and vegetation indices (− 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.