Fant 9831 publikasjoner. Viser side 385 av 394:
1999
2008
American Geophysical Union (AGU)
2020
2015
Air pollution involves multiple health and economic challenges. Its accurate and low-cost monitoring is important for developing services dedicated to reduce the exposure of living beings to the pollution. Particulate matter (PM) measurement sensors belong to the key components that support operation of these systems. In this work, a modular, mobile Internet of Things sensor for PM measurements has been proposed. Due to a limited accuracy of the PM detector, the measurement data are refined using a two-stage procedure that involves elimination of the non-physical signal spikes followed by a non-linear correction of the responses using a multiplicative surrogate model. The correction layer is derived from the sparse and non-uniform calibration data, i.e., a combination of the measurements from the PM monitoring station and the sensor obtained in the same location over a specified (relatively short) interval. The device and the method have been both demonstrated based on the data obtained during three measurement campaigns. The proposed correction scheme improves the fidelity of PM measurements by around two orders of magnitude w.r.t. the responses for which the post-processing has not been considered. Performance of the proposed surrogate-assisted technique has been favorably compared against the benchmark approaches from the literature.
2022
In this report, we investigate the relative expanded uncertainty (REU) formula for comparing low-cost sensors (microsensors) and reference measurements. The purpose of the REU formula is to check if microsensor measurements follow the data quality objective (DQO) of the European Air Quality Directive 2008/50/EC to be considered equivalent to a reference instrument. The project aimed to obtain a good understanding of the REU formula for its proper use in current and future projects involving microsensors.
NILU
2020
2005
2012
2021
2014
2019
A short introduction to the GEOmon and EBAS databases at NILU, available for the CityZen project. NILU TR
2010
2013
2005
2020
2020
Urbanization presents numerous societal challenges and exacerbates environmental issues. It is crucial to comprehend the current state and future direction of cities to formulate strategies and actions that mitigate negative consequences while ensuring a prosperous future for citizens. This study presents a universally applicable method for selecting indicators to gauge urban environmental sustainability. It aims to aid in structuring thinking for understanding and implementing Sustainable Development Goals (SDGs) within urban settings, using Norway as a case study but with a clear potential for broader applications. To achieve this, a comprehensive literature survey was conducted to gain insight into how urban environmental sustainability is conceptualized and operationalized in Norway. This involved assessing the key environmental challenges, as well as the strategies and action plans associated with them. Standardized sustainable cities' indicators served as references, which were then tailored to the municipal level to address the identified environmental challenges specific to Norwegian cities. Furthermore, the study discussed the proposed indicators for tracking the progress and state of these specific environmental challenges. In doing so, it establishes a foundation for comprehending environmental issues and establishing connections between indicators and environmental strategies and action plans in the urban sustainability context. Importantly, the methodologies and indicators we have unveiled in this study are designed to be applicable to cities beyond Norway, offering a scalable and adaptable approach for evaluating environmental challenges internationally. This work proposes a novel approach for evaluating the status and trends of environmental challenges by employing targeted indicators. These indicators can be expanded to include social and economic dimensions, enabling decision-makers and stakeholders to prioritize actions towards urban sustainability.
Elsevier
2024
Citizen-operated low-cost air quality sensors (LCSs) have expanded air quality monitoring through community engagement. However, still challenges related to lack of semantic standards, data quality, and interoperability hinder their integration into official air quality assessments, management, and research. Here, we introduce FILTER, a geospatially scalable framework designed to unify, correct, and enhance the reliability of crowd-sourced PM2.5 data across various LCS networks. FILTER assesses data quality through five steps: range check, constant value detection, outlier detection, spatial correlation, and spatial similarity. Using official data, we modeled PM2.5 spatial correlation and similarity (Euclidean distance) as functions of geographic distance as benchmarks for evaluating whether LCS measurements are sufficiently correlated/consistent with neighbors. Our study suggests a −10 to 10 Median Absolute Deviation threshold for outlier flagging (360 h). We find higher PM2.5 spatial correlation in DJF compared to JJA across Europe while lower PM2.5 similarity in DJF compared to JJA. We observe seasonal variability in the maximum possible distance between sensors and reference stations for in-situ (remote) PM2.5 data correction, with optimal thresholds of ∼11.5 km (DJF), ∼12.7 km (MAM), ∼20 km (JJA), and ∼17 km (SON). The values implicitly reflect the spatial representativeness of stations. ±15 km relaxation for each season remains feasible when data loss is a concern. We demonstrate and validate FILTER's effectiveness using European-scale data originating from the two community-based monitoring networks, sensor.community and PurpleAir with QC-ed/corrected output including 37,085 locations and 521,115,762 hourly timestamps. Results facilitate uptake and adoption of crowd-sourced LCS data in regulatory applications.
Elsevier
2025
A satellite-based estimate of combustion aerosol cloud microphysical effects over the Arctic Ocean
Climate predictions for the rapidly changing Arctic are highly uncertain, largely due to a poor understanding of the processes driving cloud properties. In particular, cloud fraction (CF) and cloud phase (CP) have major impacts on energy budgets, but are poorly represented in most models, often because of uncertainties in aerosol–cloud interactions. Here, we use over 10 million satellite observations coupled with aerosol transport model simulations to quantify large-scale microphysical effects of aerosols on CF and CP over the Arctic Ocean during polar night, when direct and semi-direct aerosol effects are minimal. Combustion aerosols over sea ice are associated with very large (∼ 10Wm−2) differences in longwave cloud radiative effects at the sea ice surface. However, co-varying meteorological changes on factors such as CF likely explain the majority of this signal. For example, combustion aerosols explain at most 40% of the CF differences between the full dataset and the clean-condition subset, compared to between 57% and 91% of the differences that can be predicted by co-varying meteorology. After normalizing for meteorological regime, aerosol microphysical effects have small but significant impacts on CF, CP, and precipitation frequency on an Arctic-wide scale. These effects indicate that dominant aerosol–cloud microphysical mechanisms are related to the relative fraction of liquid-containing clouds, with implications for a warming Arctic.
2018
2014