Airborne solar spectroscopic measurements of nitrogen dioxide column density beneath the boundary layer

2005 ◽  
Author(s):  
A. Y. S. Cheng ◽  
M. H. Chan
Keyword(s):  
Boundary Layer ◽  
Nitrogen Dioxide ◽  
Column Density ◽  
Spectroscopic Measurements

scholarly journals Mercury oxidation from bromine chemistry in the free troposphere over the southeastern US

2016 ◽  
Vol 16 (6) ◽  
pp. 3743-3760 ◽  
Author(s):  
Sean Coburn ◽  
Barbara Dix ◽  
Eric Edgerton ◽  
Christopher D. Holmes ◽  
Douglas Kinnison ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Column Density ◽  
Atmospheric Mercury ◽  
Water Soluble ◽  
Reference Spectrum ◽  
Free Troposphere ◽  
Mercury Oxidation ◽  
Oxidation Rates ◽  
Southeastern Us

Abstract. The elevated deposition of atmospheric mercury over the southeastern United States is currently not well understood. Here we measure partial columns and vertical profiles of bromine monoxide (BrO) radicals, a key component of mercury oxidation chemistry, to better understand the processes and altitudes at which mercury is being oxidized in the atmosphere. We use data from a ground-based MAX-DOAS instrument located at a coastal site ∼  1 km from the Gulf of Mexico in Gulf Breeze, FL, where we had previously detected tropospheric BrO (Coburn et al., 2011). Our profile retrieval assimilates information about stratospheric BrO from the WACCM chemical transport model (CTM), and uses only measurements at moderately low solar zenith angles (SZAs) to estimate the BrO slant column density contained in the reference spectrum (SCDRef). The approach has 2.6 degrees of freedom, and avoids spectroscopic complications that arise at high SZA; knowledge about SCDRef further helps to maximize sensitivity in the free troposphere (FT). A cloud-free case study day with low aerosol load (9 April 2010) provided optimal conditions for distinguishing marine boundary layer (MBL: 0–1 km) and free-tropospheric (FT: 1–15 km) BrO from the ground. The average daytime tropospheric BrO vertical column density (VCD) of ∼  2.3  ×  1013 molec cm−2 (SZA  <  70°) is consistent with our earlier reports on other days. The vertical profile locates essentially all tropospheric BrO above 4 km, and shows no evidence for BrO inside the MBL (detection limit  <  0.5 pptv). BrO increases to  ∼  3.5 pptv at 10–15 km altitude, consistent with recent aircraft observations. Our case study day is consistent with recent aircraft studies, in that the oxidation of gaseous elemental mercury (GEM) by bromine radicals to form gaseous oxidized mercury (GOM) is the dominant pathway for GEM oxidation throughout the troposphere above Gulf Breeze. The column integral oxidation rates are about 3.6  × 105 molec cm−2 s−1 for bromine, while the contribution from ozone (O3) is 0.8  ×  105 molec cm−2 s−1. Chlorine-induced oxidation is estimated to add  <  5 % to these mercury oxidation rates. The GOM formation rate is sensitive to recently proposed atmospheric scavenging reactions of the HgBr adduct by nitrogen dioxide (NO2), and to a lesser extent also HO2 radicals. Using a 3-D CTM, we find that surface GOM variations are also typical of other days, and are mainly derived from the FT. Bromine chemistry is active in the FT over Gulf Breeze, where it forms water-soluble GOM that is subsequently available for wet scavenging by thunderstorms or transport to the boundary layer.

scholarly journals High-resolution measurements from the airborne Atmospheric Nitrogen Dioxide Imager (ANDI)

2015 ◽  
Vol 8 (11) ◽  
pp. 4735-4754 ◽  
Author(s):  
J. P. Lawrence ◽  
J. S. Anand ◽  
J. D. Vande Hey ◽  
J. White ◽  
R. R. Leigh ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Road Traffic ◽  
City Centre ◽  
Background Concentration ◽  
Atmospheric Nitrogen ◽  
Vertical Column ◽  
East Midlands ◽  
Test Flight ◽  
The City

Abstract. Nitrogen dioxide is both a primary pollutant with direct health effects and a key precursor of the secondary pollutant ozone. This paper reports on the development, characterisation and test flight of the Atmospheric Nitrogen Dioxide Imager (ANDI) remote sensing system. The ANDI system includes an imaging UV/Vis grating spectrometer able to capture scattered sunlight spectra for the determination of tropospheric nitrogen dioxide (NO2) concentrations by way of DOAS slant column density and vertical column density measurements. Results are shown for an ANDI test flight over Leicester City in the UK on a cloud-free winter day in February 2013. Retrieved NO2 columns gridded to a surface resolution of 80 m × 20 m revealed hotspots in a series of locations around Leicester City, including road junctions, the train station, major car parks, areas of heavy industry, a nearby airport (East Midlands) and a power station (Ratcliffe-on-Soar). In the city centre the dominant source of NO2 emissions was identified as road traffic, contributing to a background concentration as well as producing localised hotspots. Quantitative analysis revealed a significant urban increment over the city centre which increased throughout the flight.

scholarly journals Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterization, and trends

2018 ◽  
Author(s):  
Marina Zara ◽  
K. Folkert Boersma ◽  
Isabelle De Smedt ◽  
Andreas Richter ◽  
Enno Peters ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Random Noise ◽  
Uncertainty Budget ◽  
Optical Absorption Spectroscopy ◽  
Statistical Uncertainty ◽  
Climate Variables ◽  
Climate Data ◽  
Uncertainty Estimates ◽  
Climate Data Record

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone and aerosol Essential Climate Variables, and it is essential to quantify and characterize their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from 4 different research groups (BIRA-IASB, IUP, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in Differential Optical Absorption Spectroscopy (DOAS) fitting techniques, and by the desire to provide a fully traceable uncertainty budget for climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement, but with substantial differences in the reported uncertainties between groups and instruments. As a check of the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8 × 1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12 × 1015 molec. cm−2 ), but the closure between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite’s level-1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 %/yr related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase

Estimating surface nitrogen dioxide and ozone concentrations using satellite-based and numerical model-based data

2020 ◽  
Author(s):  
Minso Shin ◽  
Jungho Im
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Prolonged Exposure ◽  
Continuous Distribution ◽  
Ground Level ◽  
Air Pollutant ◽  
Concentration Data ◽  
Vertical Column ◽  
Model Based ◽  
Vertical Column Density

&lt;p&gt;Prolonged exposure to high concentrations of nitrogen dioxide (NO&lt;sub&gt;2&lt;/sub&gt;) and ozone (O&lt;sub&gt;3&lt;/sub&gt;) at ground level could be harmful to human health. In-situ air pollutant concentration data observed from ground monitoring stations are limited in providing spatially continuous information. Since there are only a few stations installed above the sea, it is difficult to monitor the concentrations of air pollutants over the sea. In this study, machine learning-based models were developed to estimate ground-level NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations using satellite-based remote sensing data and model-based meteorological and emission data over East Asia during 2015-2017, to overcome such limitations. NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; vertical column density products from the Aura Ozone Monitoring Instrument (OMI) were used as essential predictors to estimate NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations. Missing pixels of OMI products due to row anomalies were filled using a temporal convolution approach to generate the spatiotemporally continuous distribution of NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations. In order to estimate the air pollutant concentrations in both land and ocean, specific values were assigned to the ocean for land-only variables. Random forest (RF) was used to develop the estimation models for NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; concentrations. The RF-based models showed the results with R&lt;sup&gt;2&lt;/sup&gt; values of 0.72 and 0.75, and RMSEs of 6.24 ppb and 10.56 ppb for NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt;, respectively. The estimated results over the ocean were validated using coastal stations that are located within a 1 km distance from the coast. Compared to the model without land-only variables, the models using all variables had slightly better results. The satellite-based NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; vertical column density were identified as significant variables in both models. Besides, urban land cover ratio, wind-related variables such as wind vectors, and stacked maximum wind speed had relatively high variable importance. The spatial variation of NO&lt;sub&gt;2&lt;/sub&gt; and seasonal variation of O&lt;sub&gt;3&lt;/sub&gt; were well shown in the estimated spatiotemporal distribution.&lt;/p&gt;

scholarly journals Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

2018 ◽  
Vol 11 (7) ◽  
pp. 4033-4058 ◽  
Author(s):  
Marina Zara ◽  
K. Folkert Boersma ◽  
Isabelle De Smedt ◽  
Andreas Richter ◽  
Enno Peters ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Trend Detection ◽  
Optical Absorption Spectroscopy ◽  
Statistical Uncertainty ◽  
Random Component ◽  
Climate Variables ◽  
Total Uncertainty ◽  
Uncertainty Estimates ◽  
Calculation Step

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8  ×  1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12  ×  1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase  <  1 % year−1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to  <  3 % year−1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼  65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼  35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

Sign in / Sign up

Export Citation Format

Share Document