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 NO2 concentrations over Europe between 2004 and 2010. We isolate long-term (timescales greater than one year) variability in the daily NO2 observations from the Ozone Monitoring Instrument (OMI) using a spectral analysis. In 2010, we find substantial reductions in NO2 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 NOx emission controls. Our results demonstrate that realistic concentration pathways of NO2 do not follow simple linear trends, but reflect a compilation of environmental policy and economic activity.
Abstract. Biomass burning is an important and uncertain source of aerosols and NOx (NO + NO2) to the atmosphere. Satellite observations of tropospheric NO2 are essential for characterizing this emissions source, but inaccuracies in the retrieval of NO2 tropospheric columns due to the radiative effects of aerosols, especially light-absorbing carbonaceous aerosols, are not well understood. It has been shown that the O2–O2 effective cloud fraction and pressure retrieval is sensitive to aerosol optical and physical properties, including aerosol optical depth (AOD). Aerosols implicitly influence the tropospheric air mass factor (AMF) calculations used in the NO2 retrieval through the effective cloud parameters used in the independent pixel approximation. In this work, we explicitly account for the effects of biomass burning aerosols in the Ozone Monitoring Instrument (OMI) tropospheric NO2 AMF calculation for cloud-free scenes. We do so by including collocated aerosol extinction vertical profile observations from the CALIOP instrument, and aerosol optical depth (AOD) and single scattering albedo (SSA) retrieved by the OMI near-UV aerosol algorithm (OMAERUV) in the DISAMAR radiative transfer model. Tropospheric AMFs calculated with DISAMAR were benchmarked against AMFs reported in the Dutch OMI NO2 (DOMINO) retrieval; the mean and standard deviation of the difference was 0.6 ± 8 %. Averaged over three successive South American biomass burning seasons (2006–2008), the spatial correlation in the 500 nm AOD retrieved by OMI and the 532 nm AOD retrieved by CALIOP was 0.6, and 68 % of the daily OMAERUV AOD observations were within 30 % of the CALIOP observations. Overall, tropospheric AMFs calculated with observed aerosol parameters were on average 10 % higher than AMFs calculated with effective cloud parameters. For effective cloud radiance fractions less than 30 %, or effective cloud pressures greater than 800 hPa, the difference between tropospheric AMFs based on implicit and explicit aerosol parameters is on average 6 and 3 %, respectively, which was the case for the majority of the pixels considered in our study; 70 % had cloud radiance fraction below 30 %, and 50 % had effective cloud pressure greater than 800 hPa. Pixels with effective cloud radiance fraction greater than 30 % or effective cloud pressure less than 800 hPa corresponded with stronger shielding in the implicit aerosol correction approach because the assumption of an opaque effective cloud underestimates the altitude-resolved AMF; tropospheric AMFs were on average 30–50 % larger when aerosol parameters were included, and for individual pixels tropospheric AMFs can differ by more than a factor of 2. The observation-based approach to correcting tropospheric AMF calculations for aerosol effects presented in this paper depicts a promising strategy for a globally consistent aerosol correction scheme for clear-sky pixels.
Abstract. Observations of multiwavelength Mie-Raman lidar taken during the SHADOW field campaign are used to analyze a smoke-dust episode over West Africa on 24-27 December 2015. For the case considered, the dust layer extended from the ground up to approximately 2000 m while the elevated smoke layer occurred in the 2500-4000 m range. The profiles of lidar measured backscattering, extinction coefficients, and depolarization ratios are compared with the vertical distribution of aerosol parameters provided by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). The MERRA-2 model simulated the correct location of the near-surface dust and elevated smoke layers. The values of modeled and observed aerosol extinction coefficients at both 355 and 532 nm are also rather close. In particular, for the episode reported, the mean value of difference between the measured and modeled extinction coefficients at 355 nm is 0.01 km −1 with SD of 0.042 km −1 . The model predicts significant concentration of dust particles inside the elevated smoke layer, which is supported by an increased depolarization ratio of 15 % observed in the center of this layer. The modeled at 355 nm the lidar ratio of 65 sr in the near-surface dust layer is close to the observed value (70 ± 10) sr. At 532 nm, however, the simulated lidar ratio (about 40 sr) is lower than measurements (55 ± 8 sr). The results presented demonstrate that the lidar and model data are complimentary and the synergy of observations and models is a key to improve the aerosols characterization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.