Found 9989 publications. Showing page 56 of 400:
2008
2016
2013
2005
Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects
Using low-cost air quality sensors (LCS) in citizen science projects opens many possibilities. LCS can provide an opportunity for the citizens to collect and contribute with their own air quality data. However, low data quality is often an issue when using LCS and with it a risk of unrealistic expectations of a higher degree of empowerment than what is possible. If the data quality and intended use of the data is not harmonized, conclusions may be drawn on the wrong basis and data can be rendered unusable. Ensuring high data quality is demanding in terms of labor and resources. The expertise, sensor performance assessment, post-processing, as well as the general workload required will depend strongly on the purpose and intended use of the air quality data. It is therefore a balancing act to ensure that the data quality is high enough for the specific purpose, while minimizing the validation effort. The aim of this perspective paper is to increase awareness of data quality issues and provide strategies to minimizing labor intensity and expenses while maintaining adequate QA/QC for robust applications of LCS in citizen science projects. We believe that air quality measurements performed by citizens can be better utilized with increased awareness about data quality and measurement requirements, in combination with improved metadata collection. Well-documented metadata can not only increase the value and usefulness for the actors collecting the data, but it also the foundation for assessment of potential integration of the data collected by citizens in a broader perspective.
2021
2005
System design NBV. NILU OR
System design for «nasjonalt beregningsverktøy» describing data flow and infrastructure requirements.
2015
2023
2002
2002
1999
Synthetic musks in ambient and indoor air. Handbook of environmental chemistry. Vol. 3, Anthropogenic compounds, pt. X
2004
2002
2005
2001
2005
2022
The main goal of this feasibility study was to evaluate the potential of adding value to the Sentinel 5P TROPOMI methane product over Norway and the Arctic through the synergistic use of relevant observations from other Sentinel satellites and machine learning. We assessed the data availability of ESA operational and research-based WFMD XCH4 products over the Northern hemisphere, the Nordic countries and the Arctic/Northern latitudes. ESA’s XCH4 data have poor coverage over Norway. Seeing the two datasets as complementary, seems to be the most reasonable approach for utilization them. Furthermore, we investigated potential synergies between satellite products from different platforms. A random forest (RF) machine learning algorithm was implemented. It shows the importance of daytime land surface temperature (LST) as predictor variable for CH4. Our results indicate that the RF-model has a very good capability of filling small gaps in the data.
NILU
2022
2022