Abstract
The abundance of NO2 in the boundary layer relates to air
quality and pollution source monitoring. Observing the spatiotemporal
distribution of NO2 above well-delimited (flue gas stacks, volcanoes,
ships) or more extended sources (cities) allows for applications such as
monitoring emission fluxes or studying the plume dynamic chemistry and its
transport. So far, most attempts to map the NO2 field from the ground
have been made with visible-light scanning grating spectrometers. Benefiting from a
high retrieval accuracy, they only achieve a relatively low spatiotemporal
resolution that hampers the detection of dynamic features.
We present a new type of passive remote sensing instrument aiming at the
measurement of the 2-D distributions of NO2 slant column densities
(SCDs) with a high spatiotemporal resolution. The measurement principle has
strong similarities with the popular filter-based SO2 camera as it
relies on spectral images taken at wavelengths where the molecule absorption
cross section is different. Contrary to the SO2 camera, the spectral
selection is performed by an acousto-optical tunable filter (AOTF) capable of
resolving the target molecule's spectral features.
The NO2 camera capabilities are demonstrated by imaging the
NO2 abundance in the plume of a coal-fired power plant. During this
experiment, the 2-D distribution of the NO2 SCD was retrieved with a
temporal resolution of 3 min and a spatial sampling of 50 cm (over a
250 × 250 m2 area). The detection limit was close to 5 × 1016 molecules cm−2, with a maximum detected SCD of 4 × 1017 molecules cm−2. Illustrating the added value of the NO2
camera measurements, the data reveal the dynamics of the NO to
NO2 conversion in the early plume with an unprecedent resolution:
from its release in the air, and for 100 m upwards, the observed NO2
plume concentration increased at a rate of 0.75–1.25 g s−1. In joint
campaigns with SO2 cameras, the NO2 camera could also help in
removing the bias introduced by the NO2 interference with the SO2 spectrum.
Citation
ID:
186456
Ref Key:
dekemper2016atmosphericthe