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Reductions in nitrogen oxides over Europe driven by environmental policy and economic recession.

ABSTRACT: Fuel combustion is a significant source of numerous air pollutants, which reduce local air quality, and affect global tropospheric chemistry. Satellite observations of nitrogen dioxide, emitted by combustion processes, allow for robust monitoring of atmospheric concentrations at high spatial resolution on continental scales. Here we evaluate changes in tropospheric NO(2) concentrations over Europe between 2004 and 2010. We isolate long-term (timescales greater than one year) variability in the daily NO(2) observations from the Ozone Monitoring Instrument (OMI) using a spectral analysis. In 2010, we find substantial reductions in NO(2) concentrations of at least 20% throughout Europe. These reductions are as much the result of temporary reductions prompted by the 2008-2009 global economic recession, as of European NO(x) emission controls. Our results demonstrate that realistic concentration pathways of NO(2) do not follow simple linear trends, but reflect a compilation of environmental policy and economic activity.

SUBMITTER: Castellanos P

PROVIDER: S-EPMC3280598 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Reductions in nitrogen oxides over Europe driven by environmental policy and economic recession.

Castellanos Patricia P Boersma K Folkert KF

Scientific reports 20120216

Fuel combustion is a significant source of numerous air pollutants, which reduce local air quality, and affect global tropospheric chemistry. Satellite observations of nitrogen dioxide, emitted by combustion processes, allow for robust monitoring of atmospheric concentrations at high spatial resolution on continental scales. Here we evaluate changes in tropospheric NO(2) concentrations over Europe between 2004 and 2010. We isolate long-term (timescales greater than one year) variability in the d ...[more]

PMID: 22355777

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Project description:The Lisbon Strategy was adopted by the Heads of State and Government of the European Union (EU) in 2000. By moving science into a central position for the development of a European knowledge-based economy and society, its adoption at political level seems to have been a powerful catalyst for the increased involvement of scientists in science policy in the EU. Recognising the need for scientists to act collectively in order to contribute to shape the future of science policy in Europe, a pioneering group of European science organisations leaders and representatives, as well as other scientists, initiated a European, interdisciplinary, inclusive movement leading to the creation of the European Research Council (ERC) to support basic research of the highest quality. Having scientists' campaign for the funding of bottom-up research by the EU Framework Programmes exclusively on scientific grounds, and for an ERC, was a unique event in the recent history of European science policy. For the first time, the scientific community acted collectively and across disciplinary or national boundaries as a political actor for the sake of a better science policy for Europe. As is often the case when first-hand experience is gained through the creation of a new organization, novel forms of collaboration arise. The European biomedical community has recently proposed the creation of a strategic action plan for health research (the European Council of Health Research; EuCHR), provisionally translated at present into a Scientific Panel for Health (SPH) research in Horizon 2020, the EU's research-funding programme for the period 2014-2020. The creation of such Scientific Panel should be viewed as an important contribution by the biomedical community as a major political agreement has been reached on the need for a comprehensive and long-term scientific strategy to accelerate research and facilitate innovation at EU level. It is our belief that describing and analyzing the process leading to the creation of the ERC and SPH (2002-2014) should be widely shared with the research community in general, as this may contribute to the understanding of the evolving relations between scientists and science-policy making.

Project description:BACKGROUND:Accurate estimation of nitrogen dioxide (NO2) and nitrogen oxide (NOx) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acute health outcomes. For estimation over large regions like California, high spatial density field campaign measurements can be combined with more sparse routine monitoring network measurements to capture spatiotemporal variability of NO2 and NOx concentrations. However, monitors in spatially dense field sampling are often highly clustered and their uneven distribution creates a challenge for such combined use. Furthermore, heterogeneities due to seasonal patterns of meteorology and source mixtures between sub-regions (e.g. southern vs. northern California) need to be addressed. OBJECTIVES:In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO2 and NOx concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. METHODS:We used a comprehensive Kruskal-K-means method to cluster the measurement samples from multiple heterogeneous sources. Spatiotemporal cluster-based bootstrap aggregating (bagging) of the base mixed-effects models was then applied, leveraging the clusters to obtain balanced and less correlated training samples for less bias and improvement in generalization. Further, we used the machine learning technique of grid search to find the optimal interaction of temporal basis functions and the scale of spatial effects, which, together with spatiotemporal covariates, adequately captured spatiotemporal variability in NO2 and NOx at the state and local levels. RESULTS:We found an optimal combination of four temporal basis functions and 200?m scale spatial effects for the base mixed-effects models. With the cluster-based bagging of the base models, we obtained robust predictions with an ensemble cross validation R2 of 0.88 for both NO2 and NOx [RMSE (RMSEIQR): 3.62?ppb (0.28) and 9.63?ppb (0.37) respectively]. In independent tests of random sampling, our models achieved similarly strong performance (R2 of 0.87-0.90; RMSE of 3.97-9.69?ppb; RMSEIQR of 0.21-0.27), illustrating minimal over-fitting. CONCLUSIONS:Our approach has important implications for fusing data from highly clustered and heterogeneous measurement samples from multiple data sources to produce highly accurate concentration estimates of air pollutants such as NO2 and NOx at high resolution over a large region.

Dataset Information

Reductions in nitrogen oxides over Europe driven by environmental policy and economic recession.

Publications

Reductions in nitrogen oxides over Europe driven by environmental policy and economic recession.

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