Steady-state approximation for interpreting NO

The nitrate radical (NO

However, with the influence induced by complicated atmospheric conditions
and emission, steady state in ambient air mass will not always be the
case (as illustrated in Sect. S1 and Fig. S1 in the Supplement). These situations are
prevalent in nocturnal boundary layer (Phillips et al., 2016; Stutz et al., 2004; Wang et al., 2017a, c) and therefore increase the
difficulty of applying steady state directly to NO

Due to a faster approach to equilibrium than steady state, the application of

In this study, we formulate a half artificial dataset with expected
properties based on field campaigns. Specifically, most of the species contained
in the dataset are observed values while only NO

The framework of steady-state approximation for the NO

The steady-state model is reformed from a zero-dimensional box model to produce
NO

Two half-artificial datasets are derived from PKU2017 and TZ2018 field
campaigns (see Sect. S2) based on the steady-state model for analysis in the
following sections. The simulated NO

Rather than using observation data directly, a half-artificial dataset can
provide a larger amount of valid data for steady-state analysis with a known

The rates of NO

Evolution of the NO

In this case, the original equilibrium is imperfectly realized (a perfect
realization of the original equilibrium condition is that

Sensitivity plot of kNO

Even if the

To further elucidate the impact of

In order to further explore the impacting factors on the steady-state fit
method,

It can be noticed from Eq. (5) that the variability of kNO

Relationship between

In addition to the large variation in kNO

Numerical simulations for determining conditions available for the
steady-state approximation method in a parallel-axis plot. Each line simply
represents a simulation associated with different parameters in different
vertical axes. The first five axes from the left represent initial variables
used for constraining the simulations. The last two axes
represent the time required for achieving steady state and the

While a few studies have examined the validity of steady state under certain
conditions via numerical modeling when interpreting the ambient data
(Brown et al., 2009, 2003), a clear range well suited to
steady-state analysis of NO

Here almost 20 000 simulations are displayed in the parallel plot of Fig. 4, where each line connects five constraint parameters to the calculated steady-state time and

In this study, we found that the parameterized

The datasets used in this study are available from the corresponding author upon request (wanghch27@mail.sysu.edu.cn; k.lu@pku.edu.cn).

The supplement related to this article is available online at:

KL and HW designed the study. XC and HW analyzed the data and wrote the paper with input from KL.

The contact author has declared that neither they nor their co-authors have any competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Thanks for the data contributed by field campaign team.

This research has been supported by the National Natural Science Foundation of China (grant nos. 21976006 and 42175111), the Beijing Municipal Natural Science Foundation (grant no. JQ19031), the State Key Joint Laboratory of Environmental Simulation and Pollution Control (grant no. 21K02ESPCP), the National Key Basic Research Program For Youth (grant nos. DQGG0103-01 and 2019YFC0214800), and the National State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex (grant no. CX2020080578).

This paper was edited by Qiang Zhang and reviewed by two anonymous referees.