NO 3 oxidation of isoprene: model analyses of aircraft observations during RONOCO
campaign.
Fabio Biancofiore (1,2), Piero Di Carlo (1,3), Eleonora Aruffo (1), Cesare Dari Salisburgo (1), Marcella
Busilacchio (1), Franco Giammaria (3), Mat Evans (4), James Lee (4), Sarah Moller (4), Claire Reeves (5), Grant
Forster(5), Stephane Bauguitte (6), Carl Parcival (7), Michael Le Breton (7), Jennifer Muller (7), Bin Ouyang (8),
Oliver Kennedy (8), Rod Jones (8)
(1)Centro di Eccellenza CETEMPS Universita' degli Studi di L'Aquila, via Vetoio 67010 Coppito, L'Aquila, Italy
(2)Dipartimento di Chimica, Ingegneria Chimica e Materiali, Universita’ degli studi di L’Aquila, Via Vetoio, 67010
Coppito, L’Aquila, Italy. email: [email protected]
(3)Dipartimento di Fisica, Universita' degli Studi di L'Aquila, via Vetoio 67010 Coppito L'Aquila, Italy
(4) Department of Chemistry, University of York, York YO10 5DD, UK
(5) School of Environmental Sciences, University of East Anglia, Norwich, UK
(6)FAAM, Cranfileld University, UK
(7) School of Earth, Atmospheric and Environmental Sciences, Manchester University, UK
(8) Department of Chemistry, University of Cambridge, UK
ABSTRACT
Isoprene represents the main component of the total
biogenic volatile organic compound emissions. Its
oxidation may have a significant impact on the ozone
production. Observations of total peroxy nitrates and
total alkyl nitrates carried out during summer 2010
above the United Kingdom (RONOCO campaign) are
analyzed using a detailed chemical mechanism
(MCM).
In order to investigate the oxidation of isoprene by
NO 3 sensitivity tests involving the yield of these
reactions and the recycling of the alkyl nitrates derived
from isoprene are carried out.
The impact of isoprene on production of alkyl nitrates
and peroxy nitrates is discussed.
1.
INTRODUCTION
Nitrogen oxides (NO x = NO 2 + NO) are key factors in
the chemistry of troposphere controlling the ozone
production. NO y that include alkyil nitrates (ΣANs),
peroxy nitrates (ΣPNs), nitric acid (HNO 3 ), HONO,
NO 3 , and N 2 O 5 , can act as source or a sink of NO x .
During the night the NO 3 radical acts as primary
oxidant and it is involved in the production of ΣANs
and ΣPNs.
Study the nighttime chemistry of NO x and NO y in the
troposphere is a challenge, due to difficulties in their
measurements. On the other hand NO x and NO y
allow a better understanding of the mechanisms which
regulate the rates of production and loss of NO 3
radical.
Isoprene (2-methyl-1,3-butadiene) is the most
abundant non-methane Volatile Organic Compound
(VOC), his global emissions are estimate to be between
440 and 660 Tg/yr [1]. The large emissions and the
high reactivity with the atmospheric oxidants make the
isoprene a major factor in the control of production of
ozone, of secondary aerosols and of the NO y budget.
In this work the Dynamically Simple Model of
Atmospheric Chemical Complexity (DSMACC) [2],
developed at the University of Leeds, is used to model
ΣPNs and ΣANs using data collected during the
RONOCO (ROle of Nighttime chemistry in controlling
the Oxidising Capacity of the atmOsphere) aircraft
campaign carried out in the United Kingdome in July
2010 and January 2011.
2.
MODEL SIMULATIONS
The DSMACC is a tropospheric chemistry box model
[2] [3] designed to study the composition of the
troposphere and can be used to calculate the expected
concentrations of atmospheric species. The model uses
the Kinetic Pre-Processor (KPP) [4], which is a
software tool that assists the computer simulation of
chemical kinetic systems. In this work the chemical
scheme used is the Master Chemical Mechanism
(MCM) v3.2 [5] [6] [7] [8] (http://mcm.leeds.ac.uk),
which is a near-explicit chemical mechanism
describing the degradation of 142 primary emitted
Volatile Organic Compounds (VOCs), including
isoprene and other biogenic VOCs in detail. The model
was used to simulate the nocturnal degradation of the
VOCs observed during the flight 537, for this purpose
the nocturnal trends of ΣPNs and ΣANs are analyzed.
The time period ranging from 3:25 to 24:15 and was
divided in 34 time steps of 1.5 minutes each; during
this part of the flight was flown over a limited and
homogenous area of sea.
a
b
Figure 1. panel a time series of measured (plus), and modeled ΣPNs, scheme 1 (triangles), mean with the base loss rate
value, scheme 2 (squares) mean with the high ΣPNs loss rate. The y-axes positioned to the left refers to the ΣPNs
simulated using the scheme 1; the y-axes positioned to the right refers to the ΣPNs measured and simulated using the
scheme 2 . Panel b as for panel a but for ΣANs.
Each step is characterized by several initial parameters
based on observed data like: temperature, latitude,
longitude, day of the year, atmospheric pressure, and
the concentrations of some species: carbon monoxide,
(CO), nitrogen dioxide (NO 2 ), water vapor (H 2 O),
ozone ( O 3 ) and some VOCs.
A first-order loss rate was included: in the first
simulation this rate was set to 1 x 10-5 s-1, this can be
considered as the base value; in the second simulation
was set to 5 x 10-5 s-1 for the ΣPNs and to base value
for other species.
Figure 1a shows the nighttime concentrations of ΣPNs,
calculated by the model, the first simulation is labeled
scheme 1 and the second scheme 2, and measured by
the Thermal Dissociation–Laser Inducted Fluorescence
(TD-LIF). The y-axes positioned to the left refers to the
concentrations simulated by the model using the
scheme 1 and is increased 5 times compared to the
y-axes to the right, which refers to the concentrations
measured by TD-LIF and simulated by the model using
the scheme 2. In the first simulation the ΣPNs are
overestimated. Whereas in the second simulation the
ΣPNs have a good agreement with the measured data.
Figure 2. process controlling the production and loss
of ΣPNs. The names of the compounds are specified in
the MCM (http://mcm.leeds.ac.uk).
Figure 1b shows the nighttime concentrations of ΣANs
calculated by the model, the first simulation is labeled
scheme 1 and the second scheme 2, and measured by
TD-LIF. The ΣANs concentrations are underestimated
in both simulations.
Figure 2 shows the Rate Of Production Analysis
(ROPA) of ΣPNs carried out for the first simulation.
The production is due only to the reaction between an
acyl peroxy radical and NO 2 ; the loss is due to the
thermal decomposition and the loss of the species
composing ΣPNs.
Figure 3 shows the ROPA of ΣANs carried out for the
first simulation. The production is dominated by the
reaction between the isoprene and NO 3 all the other
reactions are negligible compared to this. The loss is
due primary to the loss of alkyl nitrates which derive
from isoprene.
4.
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Figure 3. process controlling the production and loss
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the MCM (http://mcm.leeds.ac.uk).
3.
CONCLUSION
In this work data observed during the RONOCO
campaign were analyzed using a detailed box model
and comparing the results with the observations. The
model shows the primary role of isoprene in the
nighttime chemistry, especially in ΣANs production.
The ΣPNs concentration is overestimated by the model
when the basic loss rate was used, while it agrees with
the measures using the increased value. The ΣANs
concentration was underestimated by the model in both
simulations. Further analysis are planned to understand
the reason of the model underestimation of ΣANs.
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