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Article

Modeling Analysis of Nocturnal Nitrate Formation Pathways during Co-Occurrence of Ozone and PM2.5 Pollution in North China Plain

by
Wei Dai
1,*,
Keqiang Cheng
1,
Xiangpeng Huang
2 and
Mingjie Xie
1
1
Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
2
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(10), 1220; https://doi.org/10.3390/atmos15101220
Submission received: 15 August 2024 / Revised: 10 September 2024 / Accepted: 12 October 2024 / Published: 13 October 2024
(This article belongs to the Section Air Quality)
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Abstract

:
The rapid formation of secondary nitrate (NO3−) contributes significantly to the nocturnal increase of PM2.5 and has been shown to be a critical factor for aerosol pollution in the North China Plain (NCP) region in summer. To explore the nocturnal NO3− formation pathways and the influence of ozone (O3) on NO3− production, the WRF-CMAQ model was utilized to simulate O3 and PM2.5 co-pollution events in the NCP region. The simulation results demonstrated that heterogeneous hydrolysis of dinitrogen pentoxide (N2O5) accounts for 60% to 67% of NO3− production at night (22:00 to 05:00) and is the main source of nocturnal NO3−. O3 enhances the formation of NO3 radicals, thereby further promoting nocturnal N2O5 production. In the evening (20:00 to 21:00), O3 sustains the formation of hydroxyl (OH) radicals, resulting in the reaction between OH radicals and nitrogen dioxide (NO2), which accounts for 48% to 64% of NO3− formation. Our results suggest that effective control of O3 pollution in NCP can also reduce NO3− formation at night.

1. Introduction

Fine particulate matter (PM2.5) in the air influences human health and causes climate change by altering the radiation balance [1,2]. The components of PM2.5 include nitrate (NO3⁻), ammonium (NH4⁺), sulfate (SO42⁻), and organic aerosols (OA), which originate from both primary emissions (e.g., anthropogenic activities, wildfires, and dust) and secondary formation. Due to the implemented emission reduction policies, the concentration of PM2.5 in eastern China has decreased significantly [3]. However, compared with other secondary components of PM2.5, the concentration of NO3− declined more slowly [4,5,6]. Previous studies have indicated that NO3− is gradually becoming a crucial component of PM2.5, especially during severe haze events in the North China Plain (NCP) region [7,8,9].
The NO3⁻ is formed by the gas-to-particle partitioning of nitric acid (HNO3), a process that depends on temperature, relative humidity, and ammonia (NH3) [10,11,12]. As an essential precursor of NO3⁻, the formation processes of HNO3 are complicated. Previous studies have shown that there are three main pathways for the formation of HNO3: (1) the oxidation reaction of hydroxyl (OH) radicals and nitrogen dioxide (NO2), (2) the heterogeneous hydrolysis reaction of dinitrogen pentoxide (N2O5) at the aerosol surface under the condition of high relative humidity and (3) serial reactions of nitrate (NO3) radicals with oxygenated volatile organic compounds (OVOCs) [13,14,15,16]. Previous studies have suggested that the reaction of OH radicals and NO2 (OH + NO2) dominates the production of HNO3 during the daytime and accounts for more than 90% of the total production [13,17,18]. During the night, the heterogeneous hydrolysis of N2O5 (HET N2O5) becomes the main production process of HNO3, accounting for 44% to 97%, replacing the “OH + NO2” pathway [19,20,21]. This is because the OH and NO3 radicals dominate the atmospheric oxidation capacity during the day and night, respectively, and drive the chemical reactions in the troposphere. The production of the OH radical depends on photolysis. However, the NO3 radical is mainly formed by the reaction of NO2 with ozone (O3) and removed by photolysis and reaction with NO during the daytime [22,23,24,25,26].
Since 2013, the Chinese government has implemented strict emission reduction policies in order to improve air quality. As a result, the concentration of PM2.5 has continued to decline in recent years, while the O3 concentration has reversed. O3 is not only harmful to human health and plants but is also an important oxidant in the troposphere [27]. From 2013 to 2019, the mean daily maximum 8-h average (MDA8) of O3 in summer in the NCP region illustrated an increasing trend of 3.3 ppb per year [3,28]. The emission reduction policies were unable to completely prevent the occurrence of PM2.5 pollution, owing to complex meteorological conditions and the formation of secondary PM2.5 [29,30,31]. Observation and simulation studies have suggested that the nocturnal formation of NO3− dominates the chemical process of PM2.5, accounting for about 30% of its composition during haze events [19,32,33]. In addition, previous studies have shown that high concentrations of O3 enhanced atmospheric oxidation capacity, accelerating the generation of other secondary pollutants during the warm season [34,35,36]. Wang et al. have indicated that with the increase of MDA8 O3 during summer (June–July) in NCP, there is a corresponding rise in the proportion of NO3⁻ [37].
During the summer, the process of NO3− formation induced by O3 is more complex, and there is comparatively less research on this topic. Previous research has predominantly focused on individual PM2.5 pollution events during the cold season. Although the average PM2.5 concentration is lower in summer than in winter, there is still insufficient research on the mechanisms that lead to the rapid increase of NO3− during summer nights. In this study, we investigated the nocturnal formation processes of NO3− during combined pollution events with O3 and PM2.5 in the NCP region. The WRF-CMAQ model was used to simulate the O3-PM2.5 combined pollution process in summer in the North China Plain (NCP) region, and the process analysis (PA) tool was used to diagnose the series reactions rate for the formation of HNO3, N2O5 and NO3 radicals. The purpose of this study is to quantify the chemical pathways of NO3− formation during O3 and PM2.5 co-pollution events and to investigate the effects of O3 on NO3− formation.

2. Methods

2.1. Model Configuration

The Community Multiscale Air Quality (CMAQ, version 5.3.3) model was applied to investigate the formation of nocturnal NO3− during O3 and PM2.5 co-pollution episodes in the NCP region [38]. We configure the CMAQ model with two nested domains, as depicted in Figure 1. The parent domain (D01) covers most of eastern China with a horizontal resolution of 27 km. The nested domain (D02) focuses on the NCP region (marked by the blue dashed square in Figure 1) with a horizontal resolution of 9 km. The CMAQ model utilized the State-wide Air Pollution Research Center Version 07 (SAPRC07tic) photochemical mechanism and the seventh-generation aerosol (AERO7i) module [39]. The Weather Research and Forecasting (WRF) version 4.2.3 provided essential meteorological field for the CMAQ model, with initial and boundary conditions from the European Center for Medium-Range Weather Forecasts (ECMWF) producing ERA5 reanalysis data, which has a spatial resolution of 0.25° × 0.25° [40]. The detailed information and physical configurations of the WRF model are consistent with Chen et al. [41]. Anthropogenic emissions were obtained from the Multi-resolution Emission Inventory for China (MEIC, version 1.4) and the MIX for surrounding areas (http://meicmodel.org/, last access: 20 June 2024), which was developed by Tsinghua University [42]. The biogenic emissions were generated by using the Model of Emissions of Gas and Aerosols from Nature (MEGAN, Version 2.1, Guenther, Karl [43]). The real-time biomass burning emissions were calculated from the Global Fire Emission Database Version 4 (GFED4), including small fires (GFED4s) [44].
The process analysis (PA) tool in the CMAQ model was used to diagnose the integrated process rate (IPR) and integrated reaction rate (IRR) for each species [45]. In this study, the IRR analysis tool was employed to explore the complicated gas-phase chemical reaction pathways of the HNO3 and N2O5 [32,46,47]. The details of HNO3, N2O5, and NO3 radical chemical production pathways are listed in Table 1. In order to analyze easily, these chemical reaction pathways are grouped into “OH + NO2”, “HET N2O5”, “NO3 + VOC”, “Others”, “NO2 + NO3” and “O3 + NO2”, according to their contributions [46,48].

2.2. Observation Data

The meteorological surface observations, including 2 m temperature (T2), 2 m relative humidity (RH2), and 10 m wind speed (WS10) with temporal resolution of 3 h at 10 stations (marked with red dots in Figure 1), were obtained from the website of https://www.ncei.noaa.gov/maps/hourly/ (last access: 10 May 2024). The data on hourly PM2.5 and O3 concentration were downloaded from the China National Environmental Monitoring Center (CNEMC, http://106.37.208.233:20035, last access: 10 May 2024). According to the Chinese National Ambient Air Quality Standard (NAAQS), the concentrations of MDA8 O3 (daily mean PM2.5) exceed the Grade I and II air quality standards when concentrations are higher than 100 μg m−3 (35 μg m−3) and 160 μg m−3 (75 μg m−3), respectively. In this study, we define the combined O3 and PM2.5 pollution process as a period of at least 5 consecutive days in which the MDA8 O3 concentration exceeds 100 μg m−3 and daily mean PM2.5 concentration simultaneously over 35 μg m−3. Following this definition, we perform simulations of five co-pollution episodes of O3 and PM2.5 in the NCP region: Episode 1 spans from 16 to 22 May 2017; Episode 2 from 11 to 2 June 2017; Episode 3 from 25 June to 7 July 2017; Episode 4 from 30 May to 8 June 2018; Episode 5 from 11 to 23 June 2018.

3. Results and Discussion

3.1. Model Evaluation

In this section, the performance of the model is validated using observed meteorological and chemical variables of the surface layer averaged over observation sites in the NCP region. Figure 2 shows the model simulation results compared with 3-hourly observed meteorological parameters. Overall, the WRF model performs well and can reproduce the variations in T2, RH2, and WS10 during the air pollution episodes. The simulation results of T2 and RH2 exhibit a good agreement with observations; correlation coefficient (R) values range from 0.94 to 0.97 and 0.91 to 0.96, respectively. The R values of WS10 (0.69 to 0.83) were lower in the different episodes than in T2 and RH2, with 38.47% to 53.76% overestimation compared to the meteorological observation. This tendency of overestimation in WS10 has been widely reproduced in previous studies, which can be attributed to the unresolved topographic features in the surface drag parameterization and the coarse resolution [49,50].
Figure 3 shows the time series of observed and simulated major air pollutants over the NCP region for the five episodes. The simulated temporal variation of O3 illustrates good agreement with the observed data, with R values of 0.88 to 0.93 for pollution episodes. Compared to the hourly observed O3 concentrations, the CMAQ model shows a slight underestimation, with the NMB of −3.08% to −37.66%. For PM2.5, the R and NMB are 0.51 to 0.73 and −13.61% to −29.55%, respectively. The negative bias in PM2.5 simulation results is attributed to the uncertainty of anthropogenic emissions and the deviation between the simulated meteorological field and reality [51,52,53]. Despite the underestimation of O3 and PM2.5, the simulated results reasonably reproduce the temporal and spatial variations of the pollution episodes.

3.2. Diurnal Variation of PM2.5 Components

Figure 4 illustrates the average hourly concentrations of PM2.5 components and O3 for five pollution episodes in the NCP region. The diurnal variations of O3 and PM2.5 are completely opposite, with O3 concentrations peaking in the afternoon (15:00–17:00) and reaching their lowest levels at midnight (3:00), while PM2.5 shows the opposite trend. The concentrations of NO3− and NH4+ in PM2.5 exhibit similar temporal variations, with the lowest values being reached in the later afternoon (17:00) and elevating to the highest values before sunrise (5:00). Primary aerosols (including black carbon, dust and primary organic aerosol) also show similar variations, reaching a maximum concentration value in the early morning (5:00–6:00) and decline continued until the afternoon (16:00). Previous research has indicated that the uplift of planetary boundary layer height (PBLH) during the daytime creates favorable meteorological conditions for the diffusion of pollutants [54,55,56]. Meanwhile, the thermal decomposition of NO3− at high temperatures also inhibits its accumulation during the daytime [6]. In contrast to NO3−, NH4+, and primary aerosol diurnal cycle, SO42− and secondary organic aerosol (SOA) do not show significant diurnal variations. SO42− and SOA do not rapidly decrease as same as other PM2.5 components (i.e., NO3− and NH4+) during daytime, suggesting that the concentrations of SO42− and SOA are generated under strong atmospheric oxidation [57].
The average concentrations of NO3−, NH4+, SO42− and SOA are 2.9, 3.3, 7.0, and 11.6 μg m−3 during the pollution episodes, accounting for 7.1%, 7.8%, 17.0% and 28.3% of PM2.5 concentrations, respectively. Compared to daytime, nighttime PM2.5 pollution is more severe, with concentrations increasing from 31.8 to 50.9 μg m−3. Concentrations of NO3−, NH4+ and primary PM2.5 (PRI) increased significantly between 21:00 and 6:00, which account for 18%, 9%, and 65% of the increase in PM2.5, respectively. Previous studies have indicated that favorable meteorological conditions (higher relative humidity, lower PBLH, and lower wind speed in the near-surface layer) are the basic environmental conditions for the uplift of PM2.5 concentration [58,59,60]. However, the formation of secondary PM2.5 components, especially secondary nitrate aerosols, also plays a significant role and cannot be neglected [61,62].

3.3. Nocturnal Formation Processes of Nitrate

The precursors (HNO3, N2O5, and NOx) and atmospheric oxidants (e.g., HOx radicals, O3, and NO3 radicals) are involved in the formation of NO3− [63,64]. The diurnal variation of the HNO3 production rate generally presents a bimodal pattern, the first peak, 2.36 ppb h−1, occurring at 12:00, and the second peak, 0.77 ppb h−1, at midnight (22:00–23:00) (Figure 5a). The average production rate of HNO3 is 1.7 ppb h−1 during daytime, which is slightly higher than the seasonal mean value of 1.55 ± 0.59 ppb h−1 in summer [46]. During the daytime (7:00 to 18:00), the “OH + NO2” pathway dominates the production rate of HNO3, accounting for 97%. This is due to the strong photochemical effect during the daytime, which leads to the generation of a large number of hydroxyl (OH) radicals. Fu et al. and Liu et al. indicated that the reaction of NO2 and OH is the predominant source of HNO3 production, accounting for 89.9% in winter in the NCP region [32,65]. Consistent with our simulation results, Wen et al. indicated that the “OH + NO2” pathway contributes between 94% and 96% to HNO3 formation during the summer, which is slightly higher than the contribution in winter [6]. After the sunset (19:00 to 6:00), the contribution of the “OH + NO2” pathway decreases to 40% for the total HNO3 production rate. During the nighttime (19:00 to 6:00), the contribution of the “HET N2O5” and “NO3 + VOC” pathways increase to 57% and 3%, respectively. The formation of OH radicals essentially depend on the photolysis of VOCs and O3 during daytime, while at night, the reaction between VOCs and O3 replaces the photochemical reactions and becomes an important source of OH radicals [66,67]. As shown in Figure 5a, O3 also maintains the formation of OH radicals immediately after sunset (20:00 to 21:00), thereby enhancing NO3− formation via the “OH + NO2” pathway, which accounts for 48% to 65% of total NO3− production during this period.
The N2O5 heterogeneous hydrolysis reactions (“HET N2O5” pathway) are the dominant source of HNO3, accounting for 60% to 67% of its production before sunrise (22:00 to 5:00). More favorable meteorological conditions at night facilitate the accumulation of NO3−. Similarly, previous studies have simulated that the “HET N2O5” pathway is more important than “OH + NO2” at nighttime, with a contribution of approximately 65% during summer and exhibiting a slightly higher contribution in winter, ranging from 83.6% to 97% [6,33,46,65]. Moreover, N2O5 is similar to HNO3, and the uptake of N2O5 plays a key role in the NO3 formation process. As shown in Figure 5b, the production rate of N2O5 increases at nighttime due to the reaction between NO2 and NO3 radicals. During the daytime, the NO3 radical is rapidly photolyzed and reacts with NO, preventing its accumulation. The highest concentrations of N2O5 and NO3 radicals occur after sunset (20:00 to 21:00). Observational studies have indicated that NO3 radicals dominate the nocturnal gas-aerosol chemical reactions [68,69,70,71].
The NO3 radical drives nocturnal NO3− formation by reacting with NO2 to produce N2O5. Previous studies have suggested that the reaction of O3 and NO2 is the essential source of the NO3 radical compared to other chemical pathways [69,72]. As depicted in Figure 6, the concentration of NO3 radical rises sharply after sunset (17:00), which coincides with a decrease in O3 concentration. The diurnal variation of NO3 production rate increases slightly after sunrise (5:00 to 10:00) and then decreases until 16:00, with two peaks occurring at 10:00 and 21:00. The simulation results illustrate that the NO3 radical production rate exhibits a bimodal pattern attributed to the alter concentrations of O3 and NO2 during the day. The production rate of NO3 radical is restricted by the concentration of NO2 and O3 during daytime (8:00 to 19:00) and nighttime (20:00 to 7:00), respectively. Ma et al. indicated that reduced O3 concentrations lead to decreased NO3− production (via N2O5 heterogeneous hydrolysis) in O3-limited areas [73]. Thus, O3 plays a predominant role in the formation of NO3 radicals at night, which subsequently drive the production of N2O5 and accelerate nocturnal NO3− formation.

4. Conclusions

In this study, we investigate the nocturnal NO3− formation processes during the co-pollution episodes of O3 and PM2.5 in the North China Plain (NCP) region using the WRF-CMAQ model. The simulation results indicate that the NO3− concentration increased by 3 times, contributing to an 18% increase in PM2.5 concentration. The results of the IRR analysis illustrate that the reactions of OH radicals and NO2 dominate HNO3 production during daytime, accounting for 97%. However, unfavorable meteorological conditions during the daytime (high temperature and developed planetary boundary layer) constrained the accumulation of NO3− concentrations. Thus, NO3− concentrations frequently exhibit a rapid increase during the night. In the evening (20:00 to 21:00), the chemical reaction pathway of OH radicals and NO2 accounts for 48% to 64% of NO3− formation, as the reaction between O3 and VOCs sustains the formation of OH radicals even when photochemical reactions have ceased. During the midnight (22:00 to 5:00), N2O5 heterogeneous hydrolysis reaction is the predominant pathway for HNO3 production, with an average accounting for 64%. The formation of N2O5 at night relies on the NO3 radical, which is produced through the reaction between O3 and NO2. Therefore, the rapid formation of O3 during the daytime facilitates the formation of NO3 radicals at night, thereby accelerating the nocturnal formation of NO3− in summer. Our results suggest that implementing a strategy to control O3 pollution can also alleviate the rapid increase of NO3− at night.
There are some limitations in this work, such as the insufficient heterogeneous reaction processes at the surface of particulate matter in the model and uncertainties in NO3− precursors (e.g., NH3 and NOx) in anthropogenic emission inventory, which influence the model performance of NO3−. In addition, due to the lack of an effective method to diagnose the impact of O3 on nocturnal atmospheric oxidation capacity, it is impossible to quantitatively estimate the contribution of O3 and NOx to nocturnal NO3− formation.

Author Contributions

Conceptualization, W.D. and M.X.; methodology, W.D.; software, W.D. and K.C.; validation, W.D., M.X. and X.H.; formal analysis, W.D.; investigation, W.D. and K.C.; resources, M.X.; data curation, M.X.; writing—original draft preparation, W.D.; writing—review and editing, W.D. and X.H.; visualization, W.D.; supervision, M.X.; project administration, M.X.; funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (42177211).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the simulation data also forms part of an ongoing study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The WRF-CMAQ simulation domains, with red and blue dots, denote the locations of meteorological and environmental observation sites. The blue dashed rectangle marked North China Plain.
Figure 1. The WRF-CMAQ simulation domains, with red and blue dots, denote the locations of meteorological and environmental observation sites. The blue dashed rectangle marked North China Plain.
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Figure 2. Time series of 3 hourly observations (black dashed line) and hourly simulation (red solid line), 2 m temperature (T2), 2 m relative humidity (RH2), and 10 m wind speed (WS10) during the five air pollution episodes. The statistical metric correlation coefficient (R) and normalized mean bias (NMB) are shown.
Figure 2. Time series of 3 hourly observations (black dashed line) and hourly simulation (red solid line), 2 m temperature (T2), 2 m relative humidity (RH2), and 10 m wind speed (WS10) during the five air pollution episodes. The statistical metric correlation coefficient (R) and normalized mean bias (NMB) are shown.
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Figure 3. Time series of hourly observation (black dashed line) and simulation (red solid line) O3 (ppb) and PM2.5 (μg m−3) concentration during the five air pollution episodes. The statistical metric correlation coefficient (R) and normalized mean bias (NMB) are shown. The values of 100 μg m−3 (51 ppb) and 35 μg m−3 were marked with blue dashed lines, respectively.
Figure 3. Time series of hourly observation (black dashed line) and simulation (red solid line) O3 (ppb) and PM2.5 (μg m−3) concentration during the five air pollution episodes. The statistical metric correlation coefficient (R) and normalized mean bias (NMB) are shown. The values of 100 μg m−3 (51 ppb) and 35 μg m−3 were marked with blue dashed lines, respectively.
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Figure 4. Average diurnal variations in concentrations of major PM2.5 composition, O3, and PM2.5 during pollution episodes. The black carbon (BC), dust, and primary organic aerosol (POA) are represented as primary aerosol components (PRI).
Figure 4. Average diurnal variations in concentrations of major PM2.5 composition, O3, and PM2.5 during pollution episodes. The black carbon (BC), dust, and primary organic aerosol (POA) are represented as primary aerosol components (PRI).
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Figure 5. Average diurnal variations of (a) HNO3 and (b) N2O5 production rates by different pathways, and associated with total HNO3 production rates (HNO3prod), total N2O5 production rates (N2O5prod), HNO3, N2O5, and NO3 radical concentrations during pollution episodes. “OH + NO2”, “HET N2O5”, “NO3 + VOC”, “Others” and “NO2 + NO3” represented different chemical reaction pathways described in Table 1 and Section 2.1.
Figure 5. Average diurnal variations of (a) HNO3 and (b) N2O5 production rates by different pathways, and associated with total HNO3 production rates (HNO3prod), total N2O5 production rates (N2O5prod), HNO3, N2O5, and NO3 radical concentrations during pollution episodes. “OH + NO2”, “HET N2O5”, “NO3 + VOC”, “Others” and “NO2 + NO3” represented different chemical reaction pathways described in Table 1 and Section 2.1.
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Figure 6. Average diurnal variations of NO3 radical production rates from the “O3 + NO2” pathway, the concentration of NO3 radicals (blue solid line), HOx radicals (blue dashed line), O3 (red solid line) and NO2 (red dashed line). “O3 + NO2” represented chemical reaction pathway is described in Table 1 and Section 2.1.
Figure 6. Average diurnal variations of NO3 radical production rates from the “O3 + NO2” pathway, the concentration of NO3 radicals (blue solid line), HOx radicals (blue dashed line), O3 (red solid line) and NO2 (red dashed line). “O3 + NO2” represented chemical reaction pathway is described in Table 1 and Section 2.1.
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Table 1. Reactions of HNO3 and N2O5 involved in CMAQ v5.3.3 (SAPRC07tic).
Table 1. Reactions of HNO3 and N2O5 involved in CMAQ v5.3.3 (SAPRC07tic).
IDNamePathwayDescriptions
1OH_NO2OH + NO2OH + NO2 → HNO3
2N2O5_H2OHET N2O5N2O5 + H2O → 2 × HNO3
3HET_N2O5HET N2O5N2O5 → HNO3
4NO3_VOCNO3 + VOCVOCs + NO3 → HNO3
5HET_NO2OthersNO2 → 0.5 × HNO3
6HET_NO3OthersNO3 → HNO3
7FromHydroOthersAMTNO3J → HNO3; AISOPNNJ → 2.0 × HNO3
8NO3_HO2OthersNO3 + HO2 → 0.2 × HNO3
9NO2_NO3NO2 + NO3NO2 + NO3 → N2O5
10O3_NO2O3 + NO2O3 + NO2 → NO3
Notes: “OH + NO2” represents oxidation reaction of OH radical and NO2, “HET N2O5” represents N2O5 heterogeneous hydrolysis reaction, “NO3 + VOC” represents series reactions of NO3 radical with VOCs, “Others” represents the other production reactions of HNO3 in CMAQ model, “NO2 + NO3” represents reaction of NO2 and NO3 to form N2O5 and “O3 + NO2” represents reaction of O3 and NO2 to form NO3 radical. The species of “AMTNO3J” and “AISOPNNJ” are secondary organic aerosols (SOA) from monoterpene nitrates and isoprene dinitrates, respectively.
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Dai, W.; Cheng, K.; Huang, X.; Xie, M. Modeling Analysis of Nocturnal Nitrate Formation Pathways during Co-Occurrence of Ozone and PM2.5 Pollution in North China Plain. Atmosphere 2024, 15, 1220. https://doi.org/10.3390/atmos15101220

AMA Style

Dai W, Cheng K, Huang X, Xie M. Modeling Analysis of Nocturnal Nitrate Formation Pathways during Co-Occurrence of Ozone and PM2.5 Pollution in North China Plain. Atmosphere. 2024; 15(10):1220. https://doi.org/10.3390/atmos15101220

Chicago/Turabian Style

Dai, Wei, Keqiang Cheng, Xiangpeng Huang, and Mingjie Xie. 2024. "Modeling Analysis of Nocturnal Nitrate Formation Pathways during Co-Occurrence of Ozone and PM2.5 Pollution in North China Plain" Atmosphere 15, no. 10: 1220. https://doi.org/10.3390/atmos15101220

APA Style

Dai, W., Cheng, K., Huang, X., & Xie, M. (2024). Modeling Analysis of Nocturnal Nitrate Formation Pathways during Co-Occurrence of Ozone and PM2.5 Pollution in North China Plain. Atmosphere, 15(10), 1220. https://doi.org/10.3390/atmos15101220

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