Found 10000 publications. Showing page 195 of 400:
2013
The growing adoption of low-cost sensors (LCSs) has significantly enhanced environmental monitoring by enabling widespread, community-driven data collection, particularly in regions requiring dense monitoring, and in regions with limited or no reference instrumentation. Increased public awareness and demand for dense environmental monitoring have resulted in extensive air quality and meteorological datasets from diverse sources. However, the integration of such datasets into regulatory frameworks and large-scale environmental monitoring remains challenging due to persistent issues related to data quality, standardization, and interoperability. To address these challenges, the FILTER (Framework for Improving Low-cost Technology Effectiveness and Reliability) approach developed by Hassani et al. (2025) provides a suite of algorithms to harmonize, quality-check, flag, and perform in-situ corrections on crowdsourced PM2.5 LCS datasets. While FILTER was initially designed and validated for static PM2.5 sensors, it has since been extended to address data quality challenges associated with the dynamics of mobile and wearable sensing. Across both static and mobile LCS platforms, FILTER employs a unified processing pipeline that generates measurement-level quality flags based on multiple statistical tests, to quantify the reliability of each observation. Quality control (QC) includes statistical tests to: (a) assess physical measurement consistency (range validity test), (b) detect flatline behavior (constant value test), and (c) identify abnormal patterns (spatiotemporal outlier detection test) using both historical trends and spatial comparisons with neighboring LCSs. Beyond these mandatory QC steps, more advanced statistical procedures incorporate relative (spatial correlation test) and absolute (spatial similarity test) comparisons with nearby LCSs, higher-quality instruments, and reference monitoring stations. For mobile and wearable sensing, FILTER has been specifically adapted to support pairwise comparisons between mobile sensors and comparisons with higher-accuracy nodes, accounting for operation under dynamic environmental and operational conditions. In this context, statistical comparisons are evaluated during rendezvous events, that is, periods in which the mobile sensor and a higher-accuracy node provide temporally coincident measurements. The modified framework retains the core principles of transparency, scalability, and sensor independence, while introducing additional steps to address motion-related artifacts, intermittent time series, and location-specific uncertainties. FILTER is developed in the open-source R environment. Its modular and hierarchical design allows flexible adaptation of quality control and correction workflows based on data availability, the spatiotemporal characteristics of LCS networks, and application-specific requirements. By improving data reliability and usability, FILTER enables crowdsourced LCS datasets to serve as a reliable complement to official monitoring networks for air quality management, urban- and regional-scale modeling, and policymaking. References Hassani, A., Salamalikis, V., Schneider, P., Stebel, K., and Castell, N.: A scalable framework for harmonizing, standardization, and correcting crowd-sourced low-cost sensor PM2. 5 data across Europe, J. Environ. Manage., 380, 125100, 2025.
2026
Suspect screening helps detect chemicals in environmental samples without predefined target lists which can facilitate isolation of a larger number of substances. This study shows however that no single extraction method or analytical platform (LC-HRMS or GC-HRMS) can capture all relevant pollutants—at least half are missed. The technique works best for chemically similar families, where optimized methods can target specific classes or broader families of similar substances (e.g. PFAS). Effective grouping of similar substances is therefore essential. A broad coverage of substances can however be achieved if multiple sample extractions are performed and each extract is analysed on both LC- and GC-HRMS. Strategies where comparisons can be made over time or across locations will also help to isolate pollutant-related signals from the background. Such considerations must be integrated into programme design and budgeting for retrospective analysis. This will maximize likelihood of detection for the largest diversity of substances.
Norwegian Environment Agency, M-3038|2025
NILU
2026
Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.
2022
Improving model simulations of volcanic emission clouds and assessing model uncertainties. Geophysical Monograph Series
2016
The quality and relevance of nanosafety studies constitute major challenges to ensure their key role as a supporting tool in sustainable innovation, and subsequent competitive economic advantage. However, the number of apparently contradictory and inconclusive research results has increased in the past few years, indicating the need to introduce harmonized protocols and good practices in the nanosafety research community. Therefore, we aimed to evaluate if best-practice training and inter-laboratory comparison (ILC) of performance of the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium (MTS) assay for the cytotoxicity assessment of nanomaterials among 15 European laboratories can improve quality in nanosafety testing. We used two well-described model nanoparticles, 40-nm carboxylated polystyrene (PS-COOH) and 50-nm amino-modified polystyrene (PS-NH2). We followed a tiered approach using well-developed standard operating procedures (SOPs) and sharing the same cells, serum and nanoparticles. We started with determination of the cell growth rate (tier 1), followed by a method transfer phase, in which all laboratories performed the first ILC on the MTS assay (tier 2). Based on the outcome of tier 2 and a survey of laboratory practices, specific training was organized, and the MTS assay SOP was refined. This led to largely improved intra- and inter-laboratory reproducibility in tier 3. In addition, we confirmed that PS-COOH and PS-NH2 are suitable negative and positive control nanoparticles, respectively, to evaluate impact of nanomaterials on cell viability using the MTS assay. Overall, we have demonstrated that the tiered process followed here, with the use of SOPs and representative control nanomaterials, is necessary and makes it possible to achieve good inter-laboratory reproducibility, and therefore high-quality nanotoxicological data.
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In vivo Mammalian Alkaline Comet Assay: Method Adapted for Genotoxicity Assessment of Nanomaterials
The in vivo Comet assay measures the generation of DNA strand breaks under conditions in which the DNA will unwind and migrate to the anode in an electrophoresis assay, producing comet-like figures. Measurements are on single cells, which allows the sampling of a diversity of cells and tissues for DNA damaging effects. The Comet assay is the most common in vivo method for genotoxicity assessment of nanomaterials (NM). The Method outlined here includes a recommended step-by-step approach, consistent with OECD 489, taking into consideration the issues impacting assessment of NM, including choice of cells or systems, handling of NM test articles, dose determination, assay methods and data assessment. This method is designed to be used along with the accompanying “Common Considerations” paper, which discusses issues common to any genotoxicity assay using NM as a test article.
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