Found 9976 publications. Showing page 143 of 400:
Evoluzione della contaminazione da composti organici semivolatili (SVOC) in campioni di neve fresca. NILU F
2004
Abstract Low-cost air quality sensors (LCS) are increasingly used to complement traditional air quality monitoring yet concerns about their accuracy and fitness-for-purpose persist. This scoping review investigates topics, methods, and technologies in the application of LCS networks in recent years that are gaining momentum, focusing on LCS networks (LCSN) operation, drone-based and mobile monitoring, data fusion/assimilation, and community engagement. We identify several key challenges remaining. A major limitation is the absence of unified performance metrics and cross-validation methods to compare different LCSN calibration and imputation techniques and meta-analyses. LCSN still face challenges in effectively sharing and interpreting data due to a lack of common protocols and standardized definitions, which can hinder collaboration and data integration across different systems. In mobile monitoring, LCS siting, orientation, and platform speed are challenges to data consistency of different LCS types and limit the transferability of static calibration models to mobile settings. For drone-based monitoring, rotor downwash, LCS placement, flight pattern, and environmental variability complicate accurate measurements. In integrating LCS data with air quality models or data assimilation, realistic uncertainty quantification, ideally at the individual measurement level, remains a major obstacle. Finally, citizen science initiatives often encounter motivational, technological, economic, societal, and regulatory barriers that hinder their scalability and long-term impact.
2025
2025
2003
2003
Human adaptation to climate change is the outcome of long-term decisions continuously made and revised by local communities. Adaptation choices can be represented by economic investment models in which the often large upfront cost of adaptation is offset by the future benefits of avoiding losses due to future natural hazards. In this context, we investigate the role that expectations of future natural hazards have on adaptation in the Colorado River basin of the USA. We apply an innovative approach that quantifies the impacts of changes in concurrent climate extremes, with a focus on flooding events. By including the expectation of future natural hazards in adaptation models, we examine how public policies can focus on this component to support local community adaptation efforts. Findings indicate that considering the concurrent distribution of several variables makes quantification and prediction of extremes easier, more realistic, and consequently improves our capability to model human systems adaptation. Hazard expectation is a leading force in adaptation. Even without assuming increases in exposure, the Colorado River basin is expected to face harsh increases in damage from flooding events unless local communities are able to incorporate climate change and expected increases in extremes in their adaptation planning and decision making.
2022
2023
2012
2006
A review committee was established to examine the Phase II ExSIRA Part A project proposal. The committee consisted of 3 experts in the field. Each expert carefully reviewed background material (including Phase I reports) and the Part A project description; based on this work, each expert wrote an individual review. These individual reviews were used to create a consensus review for the committee in which this report is based upon.
The consensus review states that the project description as proposed is feasible and that the project team is competent to perform these investigations. However, one major comment from the committee is the consistent lack of detail regarding the study design and specific methods to be utilized. The committee recommends that this detail be documented at some point before experiments are conducted. The committee also identified a total of 11 specific comments and recommendations throughout the 4 tasks for the project ¿ many of these comments should be addressed before the project is funded.
2010
2016
Marine mammals are considered sentinel species and may act as indicators of ocean health. Plastic residues are widely distributed in the oceans and are recognised as hazardous contaminants, and once ingested can cause several adverse effects on wildlife. This study aimed to identify and characterise plastic ingestion in the Guiana dolphins (Sotalia guianensis) from the Southwestern Tropical Atlantic by evaluating the stomach contents of stranded individuals through KOH digestion and identification of subsample of particles by LDIR Chemical Imaging System. Most of the individuals were contaminated, and the most common polymers identified were PU, PET and EVA. Microplastics were more prevalent than larger plastic particles (meso- and macroplastics). Smaller particles were detected during the rainy seasons. Moreover, there was a positive correlation between the stomach content mass and the number of microplastics, suggesting contamination through trophic transfer.
Elsevier
2023
2023
Lifestyle diseases significantly contribute to the global health burden, with lifestyle factors playing a crucial role in the development of depression. The COVID-19 pandemic has intensified many determinants of depression. This study aimed to identify lifestyle and demographic factors associated with depression symptoms among Indians during the pandemic, focusing on a sample from Kolkata, India. An online public survey was conducted, gathering data from 1,834 participants (with 1,767 retained post-cleaning) over three months via social media and email. The survey consisted of 44 questions and was distributed anonymously to ensure privacy. Data were analyzed using statistical methods and machine learning, with principal component analysis (PCA) and analysis of variance (ANOVA) employed for feature selection. K-means clustering divided the pre-processed dataset into five clusters, and a support vector machine (SVM) with a linear kernel achieved 96% accuracy in a multi-class classification problem. The Local Interpretable Model-agnostic Explanations (LIME) algorithm provided local explanations for the SVM model predictions. Additionally, an OWL (web ontology language) ontology facilitated the semantic representation and reasoning of the survey data. The study highlighted a pipeline for collecting, analyzing, and representing data from online public surveys during the pandemic. The identified factors were correlated with depressive symptoms, illustrating the significant influence of lifestyle and demographic variables on mental health. The online survey method proved advantageous for data collection, visualization, and cost-effectiveness while maintaining anonymity and reducing bias. Challenges included reaching the target population, addressing language barriers, ensuring digital literacy, and mitigating dishonest responses and sampling errors. In conclusion, lifestyle and demographic factors significantly impact depression during the COVID-19 pandemic. The study’s methodology offers valuable insights into addressing mental health challenges through scalable online surveys, aiding in the understanding and mitigation of depression risk factors.
2024