Found 10000 publications. Showing page 272 of 400:
2011
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
2013
2013
2006
Evaluating the effectiveness of a stove exchange programme on PM 2.5 emission reduction
Residential wood combustion (RWC) is one of the most important sources of particulate matter () in urban areas. As a consequence, different types of regulatory instruments are being implemented to reduce emissions. In this study, we evaluate both the potential and actual effect of a subsidy programme for stove exchange, which has been in place for over 20 years in Oslo (Norway). The subsidy programme provides economic support to the inhabitants for substituting old stoves for RWC with new and cleaner stoves as a measure to reduce emissions. Different approaches were selected to assess the potential effect of the Oslo subsidy programme. First, we evaluate the potential for reductions in emissions and pollution levels through the use of emission and dispersion modelling under different scenarios. We then assess the actual reductions associated with the stoves already replaced with the subsidy. We conclude the study by evaluating the time variation (2005 to 2018) in emissions, wood consumption and emission factors in Oslo in comparison with other municipalities with and without subsidy programmes in place. Results from emission and dispersion modelling show that the replacement of old wood stoves for new ones could have a significant effect on the reduction of emissions (up to 46%) and levels (up to 21%). Despite that, with near 8% of the total existing stoves in Oslo being exchanged with subsidy, the potential for reduction based on improved emission factors was estimated to be smaller by an order of magnitude. We find no evidence that municipalities with subsidy reduce emissions faster than those without subsidy. We therefore conclude that there is no evidence from our modelling results, supported by available observation data, that indicate that the emissions or concentrations in Oslo have been reduced as a result of the subsidy programme.
2020
Evaluating the Combined Effect of Land and Marine CDR
With the global annual mean temperature in 2024 exceeding 1.5°C above preindustrial levels, the world faces increasing risks from climate impacts. Achieving the long-term temperature goals of the Paris Agreement will require not only deep emission reductions but likely also large-scale deployment of carbon dioxide removal (CDR). However, major uncertainties remain regarding the Earth system’s response to CDR, its efficacy under overshoot conditions, and the potential of CDR to reverse warming beyond net-zero emissions.
Here, we use emission-driven simulations with activity-driven implementation of CDR in the Norwegian Earth System Model (NorESM2-LM) to assess the carbon sequestration efficacy and climate response of two CDR methods, Bioenergy with Carbon Capture and Storage (BECCS) and Ocean Alkalinity Enhancement (OAE), deployed individually and in combination. Our scenarios follow a high-overshoot trajectory (SSP5-3.4-OS) combined with ramped-up deployment of CDR. Additional CDR amounted to 5.2 million km² of bioenergy feedstock for BECCS in addition to the BECCS already present in the SSP5-3.4-OS and a CaO deployment rate of 2.7 Gt/year for OAE, derived from life cycle analysis. OAE is applied across the exclusive economic zones of Europe, the United States, and China. BECCS alone accounts for a 16 ppm reduction using 5.2 million km² of bioenergy crops, while OAE contributes 7 ppm reduction with a cumulative addition of 82.3 Gt of CaO, yielding a CDR effectiveness of 0.08 ppm per Gt of CaO. During the overshoot phase (2050–2060), the combined simulation shows a gross atmospheric CO₂ reduction of 2-4 ppm, increasing to a reduction of 23 ppm by 2100, indicating nearly additive contributions from the two methods.
Despite the substantial CO₂ drawdown and a net reduction of anthropogenic emissions by 5.4 GtCO₂/year by 2100 through additional CDR, the global temperature response remains modest and indistinguishable from internal variability. This highlights the importance of designing robust, scalable CDR portfolios along with ambitious emission cuts. Our results also call for better integration of CDR pathways into IAMs scenarios so that we can have them in ESMs to fully capture biogeophysical feedback and Earth system constraints in overshoot scenarios.
2025
2015
2015
2017
2024
2018
2002
Evaluating a forecast system for long-range atmospheric transport episodes of POPs. NILU PP
Background air measurements of persistent organic pollutants (POPs) within existing monitoring programs are typically conducted by use of active air samplers (AAS), but the high cost of AAS limits their spatial and temporal coverage. Sampling at many such sites furthermore occurs at fixed intervals (e.g. one day per week) without any a priori consideration of air mass transport (i.e., whether the air is likely to be polluted or not). While the current strategy is appropriate for the purpose of assessing long-term trends (years, decades), the fixed interval non-continuous sampling approach is at risk of missing out key long-range atmospheric transport (LRAT) episodes. The objectives of this study have been to (i) develop a forecast system using the Lagrangian transport model FLEXPART to predict long-range atmospheric transport episodes of POPs using PCB-28 as a model compound, (ii) to evaluate the capability of the forecast system to capture specific LRAT events at a background site in southern Norway (Birkenes) through targeted sampling (i.e. when LRAT events are predicted), (iii) to assess whether predicted LRAT events for PCB-28 coincide with elevated concentrations of additional PCBs and other POPs, and (iv) to identify source regions of POPs during individual episodes. The system has been initially evaluated by comparing targeted samples collected over 12 to 25 hours during individual LRAT episodes, with monitoring samples regularly collected over one day per week throughout 2011. The FLEXPART model was clearly successful in identifying LRAT episodes for both PCB-28 and other PCBs. The model fails to accurately reproduce the magnitude of PCB-28 concentrations during individual episodes, but this can be mainly attributed to uncertainties in the absolute emission rates of PCB-28 used to drive simulations. We conclude that forecasting of pollution episodes has the potential to add value to relevant monitoring efforts which are normally collecting active air samples at fixed intervals in a non-continuous manner. Observations targeted at strong pollution episodes (as in this study) or on transport from specific source regions with highly uncertain emissions (as could be done in a very similar forecasting framework) could significantly enhance our understanding of POP sources.
2013
2009
2003
2021
2022
The report evaluates current mapping methodology with respect to city- and NUTS3-levels mapping across Europe. It states that the current mapping can be used at the city and the NUTS3 levels, despite a mild smoothing effect at locations of the measurement stations. However, it suggests a post-processing correction based on the mapping residuals.
A potential new approach for the city ranking have been examined, namely the population-weighted concentration based on the mapping results. While the averaged measurement data from the background stations (as used in the current city ranking) provides a superior information for the whole city in general, the population-weighted concentration also well represents the whole city and gives a consistent information for all cities, including those without station measurements.
Next to this, alternative treatments of rural and urban stations has been evaluated. If the urban traffic areas should be better represented in the final maps, an increased map resolution is recommended.
Several possibilities of future development towards the European-wide city level mapping in a fine resolution have been suggested, namely exploitation of a high-resolution model output in the existing methodology, geostatistical downscaling of the existing spatial maps using fine-resolution proxy datasets and exploitation of existing low-cost sensor networks.
ETC/ATNI
2021
2016