Abstract
Nitrogen oxides (NO x =NO+NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations in NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements and surface EPA Air Quality System (AQS) observations as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; GOME-2A: Global Ozone Monitoring Experiment - 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument in July 2011 during the DISCOVER-AQ campaign over the Baltimore-Washington region. The model simulations at 36 and 4 km resolutions are in reasonably good agreement with the regional mean temporospatial NO2 observations in the daytime. However, we find significant overestimations (underestimations) of model-simulated NO2 (O3) surface concentrations during night-time, which can be mitigated by enhancing nocturnal vertical mixing in the model. Another discrepancy is that Pandora-measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. The higher-resolution 4 km simulations tend to show larger biases compared to the observations due largely to the larger spatial variations in NO x emissions in the model when the model spatial resolution is increased from 36 to 4 km. OMI, GOME-2A, and the high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than corresponding 36 and 4 km model simulations, likely reflecting the spatial distribution bias of NO x emissions in the National Emissions Inventory (NEI) 2011.
Highlights
Nitrogen oxides (NOx = NO + NO2) are among the most important trace gases in the atmosphere due to their crucial role in the formation of ozone (O3) and secondary aerosols and their role in the chemical transformation of other atmospheric species, such as carbon monoxide (CO) and volatile organic compounds (VOCs) (Cheng et al, 2017, 2018; Fisher et al, 2016; Li et al, 2019; Liu et al, 2012; Ng et al, 2017; Peng et al, 2016; Zhang and Wang, 2016)
We evaluate the performances of the 36 km and nested 4 km Weather Research and Forecasting (WRF) simulations using temperature, potential temperature, relative humidity (RH), and wind measurements from the P-3B spirals (Fig. 1) and precipitation data from the NCEP (National Centers for Environmental Prediction) Stage IV precipitation dataset
We find evident decreases in NO2 tropospheric VCDs (TVCDs) from GOME2A to OMI in Fig. 10a, which is consistent with Pandora, Regional chEmistry and trAnsport Model (REAM) results, and previous studies that showed decreasing NO2 TVCDs from SCIAMACHY to OMI due to photochemical losses in summer (Boersma et al, 2008, 2009)
Summary
Boersma et al (2008) compared NO2 tropospheric VCDs (TVCDs) retrieved from OMI (the Ozone Monitoring Instrument) and SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartography) in August 2006 around the world They found that the diurnal patterns of different types of NOx emissions could strongly affect the NO2 TVCD variations between OMI and SCIAMACHY and that intense afternoon fire activity resulted in an increase in NO2 TVCDs from 10:00 to 13:30 LT (local time) over tropical biomass burning regions. Satellite OMI and GOME-2A (Global Ozone Monitoring Experiment – 2A) instruments provided NO2 TVCD measurements over the campaign region at 13:30 and 09:30 LT, respectively These concurrent measurements of NO2 VCDs, surface NO2, and vertically resolved distributions of NO2 during the DISCOVER-AQ 2011 campaign, provide a comprehensive dataset to evaluate NO2 diurnal and spatial variabilities and processes affecting NO2 concentrations.
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