A Comprehensive Model Test of the HONO Sources Constrained to

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Environmental Processes

A Comprehensive Model Test of the HONO Sources Constrained to Field Measurements at Rural North China Plain Yuhan Liu, Keding Lu, Xin Li, Huabin Dong, Zhaofeng Tan, Haichao Wang, Qi Zou, Yusheng Wu, Limin Zeng, Min Hu, kyung-Eun Min, Simonas Kecorius, Alfred Wiedensohler, and Yuanhang Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.8b06367 • Publication Date (Web): 27 Feb 2019 Downloaded from http://pubs.acs.org on March 2, 2019

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Environmental Science & Technology

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A Comprehensive Model Test of the HONO Sources Constrained to Field Measurements at Rural North China Plain

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Yuhan Liu1, Keding Lu1*, Xin Li1*, Huabin Dong1, Zhaofeng Tan1,a, Haichao Wang1,

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Qi Zou1, Yusheng Wu1,b, Limin Zeng1, Min Hu1, Kyung-Eun Min2,c, Simonas

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Kecorius3, Alfred Wiedensohler3, Yuanhang Zhang1*

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1. State Key Joint Laboratory of Environment Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China

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2. Chemical Sciences Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO 80305, USA

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3. Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany

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a. now at: Institute of Energy and Climate Research, IEK-8: Troposphere, Forschungszentrum Juelich GmbH, 52425 Juelich, Germany

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b. now at: Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, 00014 Helsinki, Finland

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c. now at: School of Earth Science and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea

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ABSTRACT

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As nitrous acid (HONO) photolysis is an important source of hydroxyl radical (OH),

22

apportionment of the ambient HONO sources is necessary to better understand

23

atmospheric oxidation. Based on the data HONO-related species and various

24

parameters measured during the one – month campaign at Wangdu (a rural site in North

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China plain) in summer 2014, a box model was adopted with input of current literature 1 ACS Paragon Plus Environment


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parameterizations for various HONO sources (nitrogen dioxide heterogeneous

27

conversion, photo-enhanced conversion, photolysis of adsorbed nitric acid and

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particulate nitrate, acid displacement and soil emission) to reveal the relative

29

importance of each source at the rural site. The simulation results reproduced the

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observed HONO production rates during noontime in general but with large uncertainty

31

from both the production and destruction terms. NO2 photoenhanced conversion and

32

photolysis of particulate nitrate were found to be the two major mechanisms with large

33

potential of HONO formation but the associated uncertainty may reduce their

34

importance to be nearly negligible. Soil nitrite was found to be an important HONO

35

source during fertilization periods, accounted for (80 ± 6)% of simulation HONO

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during noontime. For some episodes of the biomass burning, only the NO2

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heterogeneous conversion to HONO was promoted significantly. In summary, the study

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of the HONO budget is still far from closed, which would require a significant effort

39

on both the accurate measurement of HONO and the determination of related kinetic

40

parameters for its production pathways.

41

INTRODUCTION

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In areas with high urban NOx or soil direct emissions, nitrous acid is thought to make

43

a significant contribution to hydroxyl radical formation, not only in the early morning

44

but also throughout the day1-3. Although HONO plays an important role in tropospheric

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chemistry, HONO sources are still unclear especially during daytime, e.g., atmospheric

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HONO concentrations measured were much larger than those predicted from known 2 ACS Paragon Plus Environment

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gas phase chemistry4-8. Even with considering the current known HONO formation

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mechanisms (e.g. direct vehicular emissions, gas-phase reactions and heterogeneous

49

reactions) in models, the HONO concentrations predicted generally couldn’t explain

50

those observed9-11. Accurate quantification of HONO sources, especially those have

51

been proposed recently, is therefore an important current research topic in the field of

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atmospheric chemistry.

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Reaction of nitrogen dioxide (NO2) with adsorbed water on the ground surface and

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on aerosol surfaces has been considered to be a major HONO source at night12.

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Heterogeneous redox reactions of NO2 with organic compounds proceed at

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considerable rates13, 14, and these rates are further enhanced under irradiated conditions,

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which might be competitive with the heterogeneous hydrolysis of NO2 on humid

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surfaces. Recent studies have proposed that nitric acid (HNO3) adsorbed on surfaces

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(plants or artificial surfaces) or particulate nitrate produce HONO in the presence of

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sunlight15-17, and this photolysis is proposed to explain some of the unexpected high

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HONO concentrations at low NOX sites15, 17.

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On the basis of earlier studies, Lee et al. (2015)18 added a homogeneous reaction

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between NO2 and HO2H2O and the photolysis of nitrophenols and adsorbed nitric acid

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into the Master Chemical Mechanism (complete detail of the kinetic and photochemical

65

data

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http://mcm.leed.ac.uk/MCM/) to model HONO mixing ratios in London, but the

used

in

the

mechanism

are

available

at

the

MCM

website

3 ACS Paragon Plus Environment


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simulated data were still lower than the observations during daytime. Nevertheless, it

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was found that simulation could be improved by changing the uptake coefficient of

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NO2. A recent publication by Gall et al. (2016)19 focused on HONO from soil emissions

70

and found that there still exists an unknown HONO source (approximately 70%), even

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after soil emissions had been included.

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We recently performed a one-month long campaign conducted between 8 June and

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5 July 2014 in Wangdu county, a rural site in the North China Plain. HONO

74

concentrations and HONO-related parameters (hydroxyl radical, nitrogen dioxide,

75

nitric oxide concentrations and photolysis rates, etc.) were measured. One of aims of

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this study was to understand the contribution of the different HONO production

77

mechanisms during daytime.

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METHODS

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Instrumentation. The observation site was located in Wangdu County (38.66 °N,

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115.2 °E) in Hebei Province, the center of the Beijing-Tianjin-Hebei region and the

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most polluted area on the North China Plain. The campaign provided an ideal

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opportunity to study daytime nitrous acid (HONO) because it featured a variety of

83

instruments for HONO related parameters. HONO was measured by instruments with

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two different methods: one was Long Path Absorption Photometer (LOPAP)20, and the

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other was Cavity Enhanced Absorption Spectroscopyr (CEAS)21. There were two sets

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of LOPAP instruments deployed, one was from Peking University22 and the other was

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from Forschungszentrum Juelich23. In order to obtain measurements closer to the true 4 ACS Paragon Plus Environment

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HONO concentrations, HONO concentrations in this study were average of two sets of

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LOPAPs, and the CEAS observations were within the uncertainty of the combined

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concentrations of the two LOPAPs. It should be clarified that CEAS technique had

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higher selectivity compared to LOPAP due to the use of the specific absorption spectra

92

of HONO. Nevertheless, the CEAS technique cannot distinguish the ambient HONO

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and the HONO produced on its cavity wall. Moreover, the change of mirror reflectivity

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due to the pollution of mirrors is a problem to lower than measurement accuracy and

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the daytime HONO concentrations can drop down to its detection limit (Figure S2). For

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the above reasons, HONO concentrations measured by CEAS cannot be recognized as

97

the absolute values but is a valuable independent dataset since its measurement

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principle are distinct from LOPAP; for example can avoid the interference from

99

ambient species (HNO4, organic nitrite, etc) which can convert to nitrite compounds.

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We compared two sets of LOPAPs and one set of CEAS through regression analysis

101

weighted by the uncertainty in each measurement (Figure S1). Some significant

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differences were found among these instruments which cannot be explained by

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different ambient conditions. Thus, we estimated the daytime HONO measurement

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uncertainty to be 40%.

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fluorescence (LIF)24, and NOx (NO, NO2) was measured by a commercial instrument

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(Thermo – 42i). Nitric acid (HNO3), nitrate (NO3-), gas phase hydrochloric acid (HCl),

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ammonia (NH3) were measured by Gas and Aerosols Collector25; aerosols size

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distributions and surface area were measured by a TROPOS – type dual mobility

Hydroxyl radicals (OH) were measured by Laser-induced



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particle size spectrometer26; and solar radiation (jNO2 and jHONO) was measured by

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spectrophotometers. In this study, we also included observed data of boundary layer

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height that was obtained with a ceilometer (instrument information is listed in Table

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S1). A detailed description of campaign instruments can be found in Tan et al. (2017)24.

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Model. A zero dimensional box model based on RACM2 (Regional Atmospheric

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Chemistry Mechanism version 2)27 was used in this study to explore the HONO budget.

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The mechanism in this model contains 17 stable inorganic compounds, 4 inorganic

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intermediates, 55 stable organic compounds and 43 organic intermediates. The model

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was constrained by observed values (Table S2), and the simulated interval was 5

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minutes. The model was allowed to spin-up for 2 days to allow the intermediates to

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reach steady states. Considering the atmospheric lifetime of HONO (τPSS, Figure S3),

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we only simulated daytime HONO within the box model for τPSS less than 30 minutes.

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The impact of transport for HONO formation can be minimized between 11:00-14:00

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(τPSS was less than 15 mins, Figure S3a). The initial source of HONO in the model is

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only the homogeneous reaction between OH and NO (Figure S4), but in order to discuss

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the HONO budget in field observations, we added other HONO formation mechanisms

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into the model to calculate HONO. The added mechanisms in this study are summarized

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in Table 1.

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Table 1 Parameterized HONO source mechanisms included in the box model

Mechanism

HONO formation reactions

parameterization

Lower

Upper


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NO2+aerosol

NO2 + aerosol  0.5 HONO

γNO2 = 8 × 10-6

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2 × 10-7

29

1 × 10-5

30

NO2+ground

NO2 + ground  HONO

γNO2 = 8 × 10-6

28

2 × 10-7

29

1 × 10-5

30

NO2+aerosol+hv

NO2 + aerosol +hv HONO

4 × 10-6

14

1 × 10-3

31

6 × 10-5

31

jNO2

γNO2 = 1 × 10 -3 × jNO ,noon 2

31

jNO2,noon = 0.005 s-1 NO2+ground+hv

NO2 + ground +hv HONO

jNO2

γNO2 = 6 × 10 -5 × jNO ,noon

1.7 × 10-5

13

2

31

jNO2,noon = 0.005 s-1 HNO3HONO

HNO3 + hv HONO

jHNO3= 1.2 × 10-5 s-1

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1 × 10-5 s-1

1.4 × 10-5 s-1

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vHNO3= 0.02 m/s NO3-HONO

NO3- +hv  HONO

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jNO3- =1.3 × 10-4 s-1

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6.2 × 10-6 s-1

5 × 10-4 s-1

33

Vehicle

HONO/NOX = 0.18% 34

0.18%34

1.7%35

Emission NO2+soot

NO2 +soot  0.61 HONO

ABET = 122 m2/g 36, γBET=5 × 10-7

NaNO2(s) +HCl(g) NaCl(s) +HONO(g)

Acid displacement

4 × 10-7

36

Displacement efficience= 20%

36

6 × 10-7

---

---

---

---

37

NaNO2(s)+HNO3(g)NaNO3(s)+HONO(g) Soil emission

[HONO]*=50 ppbv, Z=300m

38

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RESULTS AND DISCUSSION

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Overview of HONO and related parameters. The time series of the concentrations

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of HONO and related species as well as the key parameters during the one-month

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campaign were illustrated in Figure S5, and the average diurnal variations of HONO,

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j(HONO), NO, NO2, OH, NO3- and particle surface (PS) concentrations were shown in 7 ACS Paragon Plus Environment

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Figure 1. HONO concentrations showed a diel profile that peaks (1.56 ppbv) during the

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night and had a minimum (0.38 ppbv) at noon. The maximum concentrations of NO

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and NO2 in the diel average were 3.23 ppbv and 23.42 ppbv, respectively, and the

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maximum concentration of OH in the diel average reached 9106 cm-3 in this campaign.

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Particulate nitrate reached peak concentrations of approximately 14.35 μg /m3 at

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approximately 10:00, and the maximum particle surface concentrations of 2392 μm2/

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cm3 occurred at approximately 08:00 in the diel average (Figure 1).

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Figure 1 Averaged diurnal variability of HONO, j(HONO), NO, NO2, OH, NO3-and particle surface concentrations (PS) As heterogeneous NO2 conversion to HONO was found to be a major HONO

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source in previous studies, the ratios of HONO to NOX can reflect HONO

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formation if any differences in transmission and height are accounted for. The

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relative change of HONO as a result of its photolysis is also reflected in the NOx

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levels resulting in a nearly constant HONO/NOx ratio, and the ratio of HONO to NOX 8 ACS Paragon Plus Environment

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occurs at characteristic values in different regions39. A high correlation (R2=0.83) is

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obtained for the 24 h average values with a linear regression slope of 0.02 after

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adding the Wangdu campaign into the plot of measured campaign average

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HONO/NOX ratio as Elshorbany et al. (2012)39 proposed (Figure 2a). The ratio of

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HONO to NOX in the Wangdu campaign was similar to the DOMINO, BERLIOZ,

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HOxCOMP, PRIDE-PRD2006, Yufa, Kathmandu39 and Heshan campaigns which

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showed a slightly different regression slope of 0.05 (R2=0.96, Figure 2a), in part the

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high NO2/NOX ratio (NO2/NOX=0.92), especially during the night when the influence

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of direct NO emissions is small. It is inferred that simulation HONO concentrations

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with HONO/NOx = 0.05 can’t reflect observed HONO variety in the daytime(Figure

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2b). Simulation results only considering reaction of NO with OH are much lower than

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observed HONO (Figure S4). However, there remains an additional average HONO

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source of (1.32±0.35) ppbv/h (Figure S6), at photostationary state (PSS) in the

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daytime, assuming the instantaneous HONO concentration change due to chemistry is

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zero (equation 1).

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d[HONO] = kNO + OH[NO][OH] + P(HONO)unknown dt

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- jHONO[HONO] - kOH + HONO[OH][HONO] d[HONO] dt

(1)

≈ 0, kOH+HONO was 610-12 cm3/s and kNO+OH was 9.810-12 cm3/s at 298k

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and 1010 hpa40. [NO], [OH], [HONO] and jHONO (HONO photolysis rate) were the

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concentrations of NO, OH radicals, HONO and the photolysis rate constant of HONO 9 ACS Paragon Plus Environment


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measured during the campaign, respectively. P(HONO)unknown was missing HONO

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source in this study. The missing HONO source in Figure S6 exhibited a modest

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maximum at midday during peak photolysis rates, it still need mechanism simulation

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to explain daytime HONO.

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Figure 2 relations between HONO and NOX summarized over different campaigns.

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(a) correlation between 24 h HONO and NOX mixing ratios observed in different

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sites. Red diamonds represent the results of this study; black diamonds represent

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other site results. (b) average diurnal variability of HONO concentrations, red dots

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represent observed data, blue dots represent simulation results using HONO/NOX

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= 0.05.

179 180

Model results. HONO and HONO/NOX ratio observed are unexplained by

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conventional sources (homogeneous reaction between NO and OH) in the Wangdu

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campaign, and as discussed above, appears to be related to light intensity (Figure S6).

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Therefore, we discuss several published HONO formation mechanisms related to NO2

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and light intensity. Gas phase reaction between NO2 and HO2･H2O proposed by Li et

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al. (2014)5 can be neglected when a low HONO yield is applied41. Photolysis of

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ortho-nitrophenol was suggested as HONO source in previous studies42, 43, but it 10 ACS Paragon Plus Environment

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wasn’t an important HONO source in this study, because the maximum observed

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ortho-nitrophenol concentrations (assuming equal to NOz – HNO3 - PAN) were much

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lower than that in previous study 42 and a detailed explaination can be seen in the SI.

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The parameters selected in the simulation were based on the values in Table 1, the

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major sink of HONO in the box model (Figure S8) was photolysis (maximum loss rate

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was 1.75 ppbv/h), reaction with OH (kHONO+OH= 6 × 10-12 cm3/s, maximum loss rate

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was 0.09 ppbv/h)40, and deposition with low loss rate (deposition velocity: 0.152 cm/s

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19,

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HONO formation, we choose photolysis and HONO reaction with OH as two major

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sinks of HONO. Assuming the HONO production rate is equal to the loss rate at each

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moment of the daytime, the observed HONO production rate (PHONO) is equal to the

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loss rate of HONO photolysis and reaction with OH. It was demonstrated that daytime

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variety (6:00-18:00) of HONO production rate from simulation was able to reproduce

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the observed HONO production rate (Figure S9), including homogeneous reaction

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between NO and OH, NO2 heterogeneous conversion, photoenhanced NO2

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heterogeneous conversion, photolysis of adsorbed nitric acid and particulate

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nitrate(Figure 3a). It demonstrated that the simulation without HONO emissions from

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soil can reproduce the observed HONO data in general and that NO2 heterogeneous

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conversion on the ground mainly contributed to HONO production in the morning.

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With the increasing of light intensity, photolysis of particulate nitrate becomes the

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predominant source of HONO production. Taking into account the uncertainty of the

maximum loss rate was less than 0.007 ppbv/h). Because we focused on the daytime



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observed data and the uncertainty of reaction rate, the gas phase reaction of NO and

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OH on the contribution of HONO production rate during the daytime (6:00-18:00) was

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very limited (16% ± 9%), and the photolysis of particulate nitrate using current

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literature parameterizations for this process accounted for a large part (53% ± 25%) of

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the HONO production rate. Due to higher morning NO2 mixing ratios, heterogeneous

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reactions of NO2 to HONO affected the morning (6:00-8:00) HONO source the most

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(46% ± 2%), but were much less significant during noontime.

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The contributions from vehicular emissions and NO2 heterogeneous conversion

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on soot particles were small (less than 0.005 ppb/h), and were not included in the

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following analysis. The relative proportion of each HONO production mechanism

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varied throughout the whole observation period. Based on the rates in Table 1, the

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photolysis of particulate nitrate and photoenhanced NO2 conversion were the major

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HONO source, though there were large uncertainty (Figure 3a).

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The uncertainty of simulation depended on measurement uncertainty and values of

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parameterization, and simulation results varied greatly in daytime (dash lines in Figure

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3b) after adding the uncertainty of the model. Figure 3b analyzes the uncertainty of the

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simulation based on the measurement and parameterization uncertainty. The upper and

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lower dashed lines represent simulation results using maximum and minimum

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parameter values, respectively. The solid line represents the simulation results using

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the recommended parameter values, while the black line represents the HONO


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production rate based on the observed mixing ratios. Simulation results varied by up to

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a factor of 6 as a result of the different values of parameters. The sum of the

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parameterized HONO sources in this study can reproduce observations, but the

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contribution of the different mechanisms to HONO formation have significant

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uncertainty. The uncertainty of major HONO formation mechanism (Figure S10)

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showed that the maximum HONO production rate from photolysis of particulate nitrate

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reached 1.47 ppbv/h using faster photolysis frequency and 0.06ppbv/h using slower

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values, uncertainty of the HONO production rates for this reaction channel was up to

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1.4 ppbv/h. Maximum daily nitric acid production rate was 2.34 ppbv/h around 12:00,

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and then decreased to 0.63 ppbv/h around 18:00 (Figure S11). The accumulation rate

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of nitric acid and nitrate (d[HNO3+NO3-]/dt) deduced from the observed nitric acid and

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nitrate data is close to zero during daytime. There should be large amount of nitric acid

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removal between 11:00 and 17:00, and comparable to the missing HONO sources

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diagnosed herein. Photolysis of particulate nitrate or other pathway to convert nitric

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acid to HONO would be a possible way to link them.

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As the NO and OH radicals were directly observed with high accuracy, the best

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quantified HONO production channel was NO + OH. All the other HONO production

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channels were associated with large uncertainty due to the related kinetic parameters

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such as uptake coefficients and photolysis frequencies. The difference between

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summed HONO production and destruction rates were still showed a significant

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missing HONO source. Nevertheless, the uncertainty is as large as the diagnosed 13 ACS Paragon Plus Environment


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missing HONO source. Consequently, due to the large uncertainty of the HONO

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destruction rates caused by the accuracy of the HONO measurement and the large

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uncertainty of the HONO production rates caused by applied kinetic parameters,

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accurate quantitative closure experiment of the HONO budget was still difficulty to be

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achieved.

254 255 256 257 258

Figure 3 the daytime HONO budget analysis. (a) average HONO production rates from each mechanism of HONO formation and those for HONO destruction, error bars denote the associated uncertainties; (b) the observed and modeled HONO production rate and the uncertainties: dashed line for model and shadow for measurement.

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Model results with HONO emission from soil. Though simulation results were able

260

to reproduce the observed HONO production rate roughly, there still showed an

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underestimation of the simulation results for the late afternoon even using the upper

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limits of the parameterizations (Figure 3). In order to improve the accuracy of

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simulation results under the photostationary state approach, we only perform daily

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analysis of the simulation results between 11:00-14:00 to find out the potential

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mechanism of HONO formation. Simulation results can reproduce observed HONO

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in general (Figure 4 (a)), there are still several days on which the observed data were 14 ACS Paragon Plus Environment

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much larger than the simulation results. This indicated that there may have been

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another HONO mechanism that supported HONO production at midday on certain

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days. VandenBoer et al. (2015)37 noted that acid-displacement-driven HONO surface

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release was an important mid- to late-day mechanism. We have observed data of

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HNO3 and HCl in this study, so HONO emission from soil by acid displacement

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(Facid_displacement, ppbv/h) can be calculated by equation 2:

273

Facid_displacement =

274

(2)

275

[HNO3] and [HCl] represent concentrations of HNO3 (ppbv) and HCl (ppbv), which are

276

the observed results from GAC (Gas-Aerosol-Collector). Vdrepresents deposition

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velocity of HNO3 and HCl, taken as 1.75 cm/s. h represents the height of mixing layer

278

(m). η represents the displacement efficiency, setting as 20% in this study.

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VandenBoer et al. (2015)37 proposed a displacement efficiency ranging from 1% to 9%

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to 20%, and 20% efficiencies could reproduce 50% of the measured total HONO at

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noon. In this study, 20% acid displacement efficiencies could reproduce (18% ± 8%)

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of the observed HONO production rate at noon. Though differences between simulation

283

and observation were reduced after adding acid displacement, there still existed large

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discrepancy on several days (red narrows in Figure 4b) even using maximized

285

displacement efficiency of acid displacement.

[HNO3] + [HCl] h

vdη × 36



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286 287 288 289 290 291

Figure 4 daytime HONO source analysis. (a) noontime (11:00 - 14:00) HONO source analysis, (b) noontime (11:00 – 14:00) HONO source analysis adding acid displacement mechanism, (c) noontime (11:00 – 14:00) HONO source analysis adding acid displacement mechanism and emissions from soil nitrite for the day with large missing HONO sources, (d) daytime HONO source analysis on 28th June.

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As June was a season with intense agricultural activity, there were some farming

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activities around the site (e.g. harvesting on June 12, fertilization with nitrogen fertilizer

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on June 27, high NH3 concentrations on June 23) during the observation period. We

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found the days when farming activities occurred were days that existed large

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discrepancy between simulation and observation (Figure 4b). We selected these days

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(6/12, 6/23, 6/24, 6/27, 6/28, 6/30) on which appeared large discrepancies between the

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simulation and observation for further analysis. Previous studies38, 44-47 proposed that

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soil-based abiotic and biogenic processes can release HONO to ambient air. To test this

300

hypothesis, we added soil nitrite mechanism into the box model.

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FHONO=-Vt([HONO]-[HONO]*)/100

(3)


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FHONO is the HONO flux rate ((ppbv ∙ m)/s), [HONO] represents observed HONO

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concentration (ppbv), [HONO]* represents the equilibrium gas-phase concentration

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over an aqueous solution of nitrous acid. Vt represents transport velocity, set as 1 cm/s38

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in this study. Su et al. (2011)38 calculated HONO production rate from soil emission at

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Xinken site (a rural site in China, 2004) assuming daily average [HONO]* = 15 ppb.

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The assumption of a homogeneously mixed layer height of 300m is used in this study,

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that is consistent with the adoption of Su et al. (2011)38 and in the range of values used

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in Stemmler et al. (2006)13.

310

Psoil=FHONO/BLH × 3600

311

(4)

312

Psoil represents HONO production from soil emissions (ppbv/h), BLH represents the

313

height of the boundary layer (m).

314

It could be inferred that a simulation that includes soil nitrite can generally match

315

the observed HONO (Figure 4c). As noted above, fertilization was carried out on the

316

farmland around 27 June, and the better agreement between the HONO observations

317

and the model that includes a soil source is consistent with soil emission producing

318

HONO at this site. With 28 June as an example, the peak HONO production rate was

319

4.46 ppbv/h at noon. Simulation results within all the HONO daytime sources discussed

320

above showed that the contributions from homogeneous reaction, NO2 heterogeneous

321

conversion and photolysis of particle nitrate accounted for only 13% of the total HONO 17 ACS Paragon Plus Environment


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production under this set of assumptions, while soil released accounted for 61% (Figure

323

4d). It further illustrated that peak HONO at noon on these days can be explained by

324

soil emissions caused by fertilization, and this process can last three days (from 28th

325

June to 30th June), which is consistent with the previous laboratory result that peak

326

HONO fluxes after 24 h was observed following addition of NH4+ to soil, and it lasted

327

72 -96 h48, 49. Ammonia concentrations increased significantly from the night of 23 June

328

to the day of 24 June, by as much as 413 ppbv (see in Figure S5); high ammonia

329

concentrations may be caused by fertilization nearby the sampling site. According to

330

the coupling of atmospheric HONO with soil nitrite proposed by Su et al. (2011)38 and

331

Meusel et al. (2018)50, ammonium ions become nitrite through nitrification processes.

332

High concentrations of nitrite in the soil may excess the equilibrium coefficient, and

333

gaseous HONO was released from soil on 24 June which is in good agreement with the

334

most recent publication about HONO emission from agricultural field close to the

335

sampling site of our study49.

336

The simulation results overestimated HONO on 12 June while adding the

337

mechanism of soil nitrite, and it can be inferred that the conditions of 12 June were not

338

the same as 27 or 28 June. According to the observation records, wheat harvesting

339

occurred between 12 June to 14 June. Removal of vegetation that had been covering

340

the soil surface may have been conducive to HONO release from the soil51, 52, but its

341

strength may be less than fertilization. It is worth noting that simulation results of

342

HONO from soil nitrite in this study was calculated based on a fixed gas-phase 18 ACS Paragon Plus Environment

Page 19 of 28


343

concentration over an aqueous solution of nitrous acid ([HONO]* = 15 ppbv), which

344

leads to the simulation results to be biased for individual simulation result (e.g. June

345

12). Similarly, HONO observed may be affected by the neighbor agricultural fields

346

with fertilization while the sampling field was not fertilized on the same day, using the

347

same parameters for simulating HONO from soil nitrite may easily lead to

348

overestimation or underestimation. However, we can still see the effect of fertilization

349

on the release of HONO from the soil if there was external intervention (e.g.

350

fertilization). The daytime HONO peak and the maximum OH concentrations appeared

351

on June 28th which was a day shortly after fertilize.

352

We also considered the influence of HONO formation by biomass burning as there

353

were intermittent biomass burning from 12 to 19 June24, 53, but it was not the dominant

354

factor compared to other light-induced HONO mechanism during the daytime (SI).

355

In order to explore the HONO sources, to enhance the measurement accuracy and

356

precision of HONO in the future field measurements is still of high importance. It is

357

also necessary to determine the kinetic parameters values of HONO production

358

pathways which could reduce the uncertainty of HONO budget analysis.

359

IMPLICATIONS ON THE OH CHEMISTRY

360

HONO is known to be an important initiation source of OH radicals, the maximum

361

production rate of OH radicals from HONO photolysis reached 1.68 ppb/h at noon

362

during the period between 12 June and 15 June (a pollution period), compared to the



Page 20 of 28

363

1.2 ppb/h OH production rate from ozone photolysis. It was illustrated that HONO

364

was an important primary source of near surface OH radicals, that was comparable to

365

the previous studies39, 54-58. HONO from NO2 conversion and photolysis of adsorbed

366

nitric acid or particulate nitrate greatly increased the production rate of OH radicals in

367

the daytime using values based on current literature parameterizations. The average

368

daytime production rate of OH radicals for HONO acid displacement from soils was

369

approximately 0.39 ppb/h. Unlike the pollution periods from 12 June to 15 June, a

370

very high concentration of OH radical was observed on 28 June. Correspondingly,

371

HONO concentration had a high value at noon on 28 June; this high concentration of

372

HONO mainly came from soil emissions, and the HONO from soil emissions

373

contributed approximately 2.6 ppb/h OH production at noon, a factor of three larger

374

than from ozone photolysis (0.9 ppb/h) (Figure 5). This contribution of HONO to OH

375

was also shown when the model was used to calculate OH radicals, and if only

376

homogeneous reaction was used to calculate HONO, there was a significant

377

underprediction of the OH radical concentration. It is worth noting that parameter

378

values used for HONO simulation had uncertainty, the effects of different HONO

379

formation mechanisms on OH production rate were also different, and an accurate

380

quantification of the HONO sources resulted in an accurate simulation of the OH

381

radical production rate.


Page 21 of 28


382 383 384 385 386

Figure 5 daytime primary OH production from HONO photolysis (black line) and O3 photolysis (red line) and OH concentrations (blue line). The estimated OH production from different HONO source terms are color coded as the figure legends. (a) averaged results for 12th – 15th June. (b) results for 28th June.

387

388

Though OH radicals measured and modeled in this study is locally generated, and

389

the surface sources for HONO described in this study affect OH radicals in the air

390

measured near the ground. The HONO production near ground is unlikely to affect

391

OH throughout the 1000 m column, since the mixing time is probably longer than the

392

HONO photolysis lifetime during daytime. The contribution of HONO from the

393

ground surface to the OH production in the vertical direction is gradually decreasing,

394

but as the height increases, the major HONO source may change. The observation on

395

the ground cannot accurately estimate the contribution to the vertical direction of the

396

OH radical. Therefore airborne or tower based measurements at a site such as

397

Wangdu would be an important next step to investigate the effect of HONO sources

398

on regional photochemistry.

399

ASSOCIATED CONTENT

400

Supporting Information 21 ACS Paragon Plus Environment


401

Additional materials and methods, two additional table (Table S1, Table S2), and

402

thirteen additional figures (Figures S1 – S13).

403

AUTHOR INFORMATION

404

Corresponding Authors

405

E-mail: [email protected]

406


407


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408 409

ACKNOWLEDGMENTS

410

This work was supported by the National Natural Science Foundation of China

411

(Grants 91544225, 21522701, 41421064, 41375124), the Strategic Priority Research

412

Program of the Chinese Academy of Sciences (Grant XDB05010500), the

413

Collaborative Innovation Center for Regional Environmental Quality, the EU-project

414

AMIS (Fate and Impact of Atmospheric Pollutants, PIRSES-GA-2011-295132). The

415

authors gratefully acknowledge the Wangdu science team, especially thanks A.

416

Wahner, R. Haeseler, A. Hofzumahaus, H. Fuchs, F. Holland, R. Franz, B. Bohn, and

417

S. Brown.

418

References

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