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Article

Characterization and Atmospheric Drivers of Nocturnal Ozone Enhancement in Putian City, China

by
Chunsheng Fang
1,2,3,
Xiaowei Zhou
1,
Yuxuan Cai
1 and
Ju Wang
1,2,3,*
1
College of New Energy and Environment, Jilin University, Changchun 130012, China
2
Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University, Changchun 130012, China
3
Jilin Province Key Laboratory of Water Resources and Environment, Jilin University, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(1), 45; https://doi.org/10.3390/atmos16010045
Submission received: 26 November 2024 / Revised: 18 December 2024 / Accepted: 2 January 2025 / Published: 3 January 2025
(This article belongs to the Section Air Quality)
Browse Figures
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Abstract

The increasingly severe nocturnal ozone enhancement (NOE) event pollution is widely concerning. Therefore, based on the observed hourly O3 concentrations from 2015 to 2023, this study analyzes the characteristics of NOE events over Putian City. The analysis results show that the frequency of NOE events over Putian City is high, at about 127 days annually, with a high frequency in April and a low frequency in July and August. Most NOE events corresponded to a nocturnal O3 peak concentration (NOP) of <120 μg/m3. Moreover, they mainly occurred between 1:00–3:00 and 7:00. The physicochemical processes over Putian City in April, October, and November 2020 were simulated using the Weather Research and Forecasting (WRF, version 4.3.3) model coupled with the Community Multiscale Air Quality (CMAQ, version 5.4) model. The results suggest that O3 transport, especially horizontal transport from the eastern sea and Zhejiang Province and vertical transport from the upper atmosphere, could be the major cause of NOE events over Putian City. Furthermore, the nocturnal movement of the pollution zone, along with the aggregation of O3 due to weakened dry deposition and the influence of a stable boundary layer obstructed by mountain terrain, significantly influenced the overall O3 concentration. Thus, NOE events over Putian City stem from the interaction among these physicochemical processes. The study results emphasize the importance of O3 control in Putian City and suggest the implementation of strict joint regional control measures for to improve air quality.

1. Introduction

Recently, with the continuous advancement of industrialization and urbanization, the photochemical pollution on the east coast of China has significantly worsened [1]. Consequently, O3 pollution has received increasing attention [2]. O3 is a typical secondary pollutant. Near-surface O3 is mainly generated from the anthropogenic emissions of volatile organic compounds (VOCs), nitrogen oxides (NOx), and carbon monoxide (CO) under ultraviolet solar radiation through various complex photochemical reactions [3], and it adversely affects human health, crop growth, and the ecological environment.
Since the promulgation of the Ambient Air Quality Standards (GB3095-2012) in 2012 [4], O3 monitoring has been conducted in 338 prefecture-level cities in China, which are administrative divisions encompassing both urban and surrounding rural areas. According to the Ambient Air Quality Standards (GB3095-2012), the acceptable limits for O3 in outdoor air are a maximum daily 8-h average (MDA8) concentration of 160 μg/m3, applicable to general residential, commercial, and mixed-use areas under the Class II standard. However, O3 pollution has become increasingly severe in most of the monitored cities, with many exceeding these limits.
Generally, the diurnal variation of O3 is characterized by an unimodal distribution, which peaks during 12:00–17:00 and then decreases during the night [5,6]. This is because O3 is mainly produced during the daytime when sunlight drives the redox reactions of oxygen and nitrogen oxides. On the other hand, at night, in the absence of sunlight, the O3 concentrations decrease. However, nocturnal O3 concentrations can increase under certain conditions, especially late at night when there is little to no wind, stable atmospheric conditions, and cleaner air. The ozone concentrations near the ground surface at night can increase due to chemical reactions in the atmosphere. Nocturnal ozone enhancement (NOE) denotes a nocturnal phenomenon where ground-level O3 concentration unexpectedly increases during the night, contrary to the typical pattern of nocturnal ozone depletion due to reduced photochemical activity. NOE events have been globally observed, including in the United States of America, the United Kingdom, Portugal, Sweden, India, Bolivia, and China [7,8,9,10,11,12,13], indicating that NOE events are globally prevalent. Salmond et al. observed NOE events in the Lower Fraser Valley, Canada, and defined them as nocturnal ozone concentration increases of at least 10.71 μg/m3 lasting for at least 15 min, followed by decreases of at least 10.71 μg/m3 [14]. Eliasson et al. classified the increase in nocturnal O3 concentration of ≥10 μg/m3 as NOE events [10].
NOE events primarily stem from typhoons, regional transport of O3-rich air masses, vertical mixing due to low-altitude jets, and the intrusion of ozone from the stratosphere into the troposphere, which can increase ground-level ozone [15,16,17,18,19]. The above mechanisms often interact and influence the NOE dynamics. For example, Alvim-Ferraz et al. observed that sea–land breezes contribute to nocturnal O3 maxima through the horizontal advection of O3 in Porto, Portugal [20]. Wu et al. identified low-level jets, convective storms, and daytime O3 levels as critical factors influencing NOE events in the Pearl River Delta, highlighting the importance of vertical transport [17]. Through the stationary observations of Shaoguan, a city located in the Guangdong Province, China, He et al. showed that the nighttime O3 concentrations are affected by the vertical transport of tropospheric O3 arising from subtropical highs and nighttime valley winds [21]. The effect of Typhoon Nida on nighttime O3 concentrations has also been analyzed in Guangzhou [22].
The study of nocturnal ozone characteristics is closely related to monitoring and model development. With the advancement of methodology, the study of NOE events has evolved from the initial observation of only the increase in nocturnal ozone concentration to the gradual use of vertical observational evidence for explaining the causes of NOE events and analyzing the process simulation using air quality models. Currently, NOE events can be studied using various methods. For instance, An et al. comparatively investigated the nocturnal ozone concentrations and NOE events in Dongying using automatic monitoring devices such as the Thermo Fisher Scientific models 43i, 42i, and 49i for monitoring SO2, NO-NO2-NOx, and O3, respectively [23]. Debreka et al. utilized ozone and NO-NO2-NOx analyzers for continuous monitoring in Kolkata, India, and determined the magnitude, frequency, and timing of nocturnal ozone maxima [24]. Pavan et al. comprehensively analyzed the atmospheric conditions in Portugal using the Weather Research and Forecasting (WRF) model to assess the mechanisms causing NOE events [25]. Similarly, Peng et al. used The Air Pollution Model (TAPM) to simulate the temporal and spatial characteristics of nighttime ozone in Kaohsiung, Taiwan [26]. The above studies employed statistical methods such as correlation analysis, regression modeling, and agreement indices to compare observed and modeled data, ensuring the robustness of the results. These diverse approaches highlight advancements in the understanding of NOE events, demonstrating the importance of both observational and modeling techniques in addressing this environmental issue.
In China, NOE events have been mainly reported in densely populated areas like the Pearl River, Yangtze, and Beijing–Tianjin–Hebei. He et al. determined that the average annual frequency of NOE events was 41 ± 10% between 2014 and 2019, affecting approximately 140 days annually, with an increase of 46% across 814 Chinese stations. This frequency surpasses that in Europe and the United States of America, necessitating extensive research on NOE events in China [27]. Further studies are required to elucidate the specific mechanisms, frequency, and locations of NOE events, and to develop preventive measures. Such research will enhance the understanding of atmospheric physicochemical processes and provide guidance for effective environmental protection strategies.
The O3 concentration in the coastal area of the Fujian Province is higher than that in the inland region, with the central coastal area exhibiting particularly elevated levels [28]. The research on NOE events in the Fujian Province is limited, and few studies have utilized numerical models to investigate the underlying mechanisms. Consequently, this study aims to address these gaps and provide insights into the region’s NOE dynamics. Putian City is a typical coastal city in the east-central part of the Fujian Province. Using observed pollutant concentration data, this study characterizes NOE events in Putian City from 2015 to 2023. The WRF model (WRF4.3.3) and the Community Multiscale Air Quality (CMAQ v5.4) model are employed to explore the mechanisms behind three typical NOE events in Putian City. Moreover, the process analysis module in CMAQ is utilized to provide detailed insights into these mechanisms.
Therefore, this study specifically focuses on analyzing the characteristics and driving mechanisms of NOE events in Putian City. By combining observational data and numerical simulations, this study aims to advance the understanding of the physicochemical processes driving NOE events and to offer actionable insights for managing ozone pollution in coastal regions.

2. Data and Methods

2.1. Study Area

Figure 1 shows that Putian City is situated in the eastern part of the Fujian Province, China, between 24°59′ N and 25°45′ N latitude and 118°23′ E and 119°10′ E longitude. The Putian City terrain slopes from northwest to southeast, with mountains in the west and north, alluvial and marine plains in the center and east, and peninsulas and hilly terraces along the southeastern coast. Owing to its unique location, this varied geography contributes to the complexity of O3 pollution in Putian City. Putian City has a typical subtropical maritime monsoon climate. In the summer and fall, the city experiences subtropical high pressure, leading to high temperatures, low humidity, weak winds, and intense sunlight, all of which lead to high ozone concentrations. According to the 2020 Environmental Quality Report of Putian City and the report on solar radiation in Fujian Province, the city experienced an annual mean NO2 concentration of 16 μg/m3 and an ozone day-specific percentile concentration of 140 μg/m3 in 2020. Additionally, the annual solar radiation ranged from 4780 to 5400 MJ/m2. These air quality and solar radiation parameters highlight the region’s relevance for investigating the physicochemical processes driving ozone formation. Consequently, this climatic backdrop provides a dynamic environment for studying ozone formation and dispersion, making Putian City an ideal location for atmospheric modeling research.

2.2. Sources of Observation Data

The observation data were obtained from four state-controlled automatic atmospheric environment monitoring stations and one meteorological monitoring station in Putian City (Table 1). They were used to analyze the characteristics of NOE events in Putian City from 2015 to 2023 and perform model validation. The air quality data were obtained from the China Environmental Monitoring Center (http://www.cnemc.cn/ (accessed on 4 January 2024)). The State National Environmental Monitoring Center regularly monitors these data, providing hourly observations of major pollutants such as O3, in μg/m3. Meteorological data, including temperature, wind speed, barometric pressure, humidity, and precipitation, were obtained through hourly and daily observations from the near-surface urban meteorological stations through the China Meteorological Data Network (http://data.cma.cn (accessed on 4 January 2024)). The data were subjected to stringent quality checks and were utilized to evaluate the WRF model simulation. The planetary boundary layer height (PBLH) data were derived from the ECMWF (https://cds.climate.copernicus.eu/ (accessed on 25 March 2024)) reanalysis dataset with an hourly temporal resolution. This dataset provides detailed hourly atmospheric information, enabling fine temporal analysis of weather patterns and climatic conditions. The horizontal resolution of the high-resolution data from the ERA5 dataset is about 31 km (0.28125°).

2.3. Criteria for NOE Events

This study defines an NOE event as the nighttime (20:00–07:00) hourly O3 concentration increase of >10 μg/m3 within an hour, followed by a decrease of <10 μg/m3 in the subsequent hour. If the O3 concentration increases by >10 μg/m3 multiple times during the same night, the increase is considered to be part of a single NOE event for that day. Nocturnal ozone peak (NOP) denotes the highest ozone concentration observed during an NOE event. The time intervals in this study, as well as those in related research, are based on local time (LT). The characteristics of the NOE events in Putian City were analyzed based on data from four environmental monitoring stations.

2.4. WRF-CMAQ Model Configuration

The spatial and temporal distributions of the O3 concentration were simulated using the WRF-CMAQ model (versions WRF v4.3.3 and CMAQ v5.4). WRF-CMAQ is a third-generation air quality model that considers various physicochemical processes in the atmospheric environment and considers the atmosphere as a whole. Moreover, it is able to accurately simulate and analyze the characteristics of the spatial and temporal distributions of atmospheric pollutants and the chemical change processes. Consequently, it has been widely employed [29,30] in ozone studies [31,32,33,34]. In addition to the core features of the WRF-CMAQ model, the Process Analysis (PA) module in CMAQ was utilized. The PA module isolates and quantifies the contributions of various physicochemical processes to the changes in the predicted pollutant concentrations. By examining these processes, the PA module provides a detailed understanding of how different atmospheric processes impact ozone levels and other pollutants. This study, employed the PA module in CMAQ to analyze the effects of various physicochemical processes on the O3 concentrations and to comprehensively analyze the O3 changes, including vertical advection (ZADV), vertical diffusion (VDIF), horizontal advection (HADV), horizontal diffusion (HDIF), dry deposition (DDEP), cloud processes and aqueous chemistry (CLDS), and chemistry production or consumption (CHEM). This enabled a comprehensive analysis of the factors driving NOE events in Putian City. Simulations were performed for the months of April, October, and November 2020.
Before the simulation, the nested domain was configured using the Inventory Space Allocation Tool (ISAT v2.0) [35]. Allocations were based on population, GDP, and land utilized raster data from the Center for Resource and Environmental Sciences and Data of the Chinese Academy of Sciences (https://www.resdc.cn/ (accessed on 4 January 2024)). The WRF model simulations use a Lambert projective coordinate system with a central latitude and longitude of 25.7° N and 116.8° E, employing a three-layer nesting. Figure 1 presents the three-layer nested structure of the study area. The outermost, middle, and inner domains cover the southeastern coast with a grid resolution of 18 km × 18 km, Fujian Province within the simulation area with a grid resolution of 6 km × 6 km, and Putian City with a resolution of 2 km × 2 km, respectively. The initial and boundary conditions of the meteorological inputs in the WRF model were derived from the final analysis data (FNL) of the National Centers for Environmental Prediction (NCEP) with a temporal resolution of 6 h and spatial resolution of 1.0° × 1.0°.
The gridded emission inventory required for the CMAQ model was based on the 2020 China Multiscale Emission Inventory [36,37] (MEIC v1.4, 25 km × 25 km) from the MEIC team at Tsinghua University (http://meicmodel.org.cn (accessed on 16 January 2024)), covering mainland China and mainly including the following five sectors: power, industry, civil, transportation, and agriculture emission data. The pollutants covered were SO2, NOx, CO, NMVOC, NH3, PM2.5, BC, OC, and CO2, and the allocation was based on population, GDP data, and road land use raster data, which were obtained from the Center for Resource and Environmental Science and Data of the Chinese Academy of Sciences (https://www.resdc.cn/ (accessed on 4 January 2024)). The MEIC emission inventory was assigned using MEIAT-CMAQ (the Modular Emission Inventory Allocation Tools for the Community Multiscale Air Quality Model) [38].

2.5. Evaluation Indicators

To verify the accuracy of the simulation results, the WRF simulations were validated using the mean fractional bias (MFB), mean fractional error (MFE), normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R). During the WRF simulations, all model performance criteria were considered satisfied when NMB ≤ ±30%, NME ≤ 50%, and R ≥ 0.40 [39,40]. For the CMAQ simulations, based on the reference criteria proposed by Boylan et al. [41], the model simulation was excellent when MFB ≤ ±30% and MFE ≤ ±50% and the model simulation was acceptable when MFB ≤ ±60% and MFE ≤ ±75%. The specific formulae for the five indicators are as follows:
R = ∑ i = 1 N P i − P ¯ O i − O ¯ ∑ i = 1 N P i − P ¯ 2   ∑ i = 1 N O i − O ¯ 2
N M B = ∑ i = 1 N P i − O i ∑ i = 1 N O i
N M E = ∑ i = 1 N P i − O i ∑ i = 1 N O i
M F B = 1 N ∑ i = 1 N P i − O i ( O i + P i ) / 2
M F E = 1 N ∑ i = 1 N P i − O i ( O i + P i ) / 2
where N is the number of samples, P i is the simulation result at moment i , O i is the observation at moment i , P ¯ is the mean value of the simulation result, and O ¯ is the mean value of the observation.

3. Results and Discussion

3.1. General Characteristics of the NOE

The hourly ozone concentration variations were compared for typical NOE and non-NOE periods from the four monitoring stations. Figure 2a,b exhibit the hourly ozone concentrations during NOE and non-NOE periods, respectively. Figure 2a depicts a distinct diurnal pattern with two noticeable peaks. The first peak occurs in the afternoon, which is consistent with the typical photochemical ozone production, while the second smaller peak appears at night, indicating an unusual accumulation of ozone. In contrast, Figure 2b displays a standard diurnal pattern with a single afternoon peak followed by a steady decline during the night, reflecting normal nocturnal ozone depletion.
As presented in Figure 3, the number of NOE events observed from 2015 to 2023 fluctuated across the stations. During 2015–2019, the number of NOE events was relatively high, with a peak in 2017. After 2017, the number of NOE events noticeably decreased, particularly between 2017 and 2021. The frequency of NOE events in Putian City after 2019 was similar to that observed by Li et al. for Qingdao, Yantai, and Weihai, which are coastal cities in the Shandong province [15].
As shown in Figure 4a, most NOE events occurred when the daily mean ozone concentration fell between 60–80 μg/m3 and 80–100 μg/m3; these ranges account for a significant proportion of NOE events occurrences across all stations. Figure 4b depicts that the majority of NOE events corresponded to NOP values < 120 μg/m3, accounting for nearly 80% of NOE events. In contrast, NOE events with NOP values > 160 μg/m3 were rare, comprising only about 5% of the total occurrences. This illustrates that while the daily mean ozone concentrations during NOE events occasionally exceeded 160 μg/m3, they predominantly remained <120 μg/m3.
Figure 5a indicates that the frequency of NOE events in Putian City was relatively high in April but relatively low in July and August, which differs from the monthly variations in NOE event frequency observed in other studies [42]. To investigate the reasons for this trend in Putian City, the observational data from 2015 to 2023 were analyzed. As exhibited in Figure 5b, the monthly mean ozone concentration in Putian City formed an M-shaped pattern. Moreover, the ozone exceedance mainly occurred in April, September, and October. Despite high solar radiation, long sunshine hours, and high temperatures in July and August, the increased rainfall and relative humidity during these months weakened the ozone-generating photochemical reactions, resulting in low ozone concentrations. This observation is consistent with the results of a previous study [43]. Figure 5a,b illustrate that nighttime ozone concentrations were higher during the ozone pollution season but lower during the non-ozone pollution season. This pattern suggests a strong association between NOE events and periods of ozone pollution. Hence, when the daytime ozone concentrations are high, NOE events are more likely to occur near the surface layer at night. These NOE events contribute to higher ozone concentrations the following day, as reported by He et al. [27].
As shown in Figure 6, NOE events in Putian City had high frequency during 1:00–3:00 and 7:00 and had low frequency during 20:00–22:00. This pattern primarily stemmed from the inability of ozone to be generated by photochemical reactions at night and the decrease in concentration due to chemical titration and dry deposition of NOx (mainly NO). Anthropogenic NOx emissions were relatively high at the beginning of the night, leading to significant chemical titration reactions that reduced the ozone concentrations. Consequently, the increase in the ozone concentration was less likely between 20:00 and 22:00. As the night progressed, the chemical depletion of ozone decreased, and the titration effect weakened. When vertical or horizontal transport of high ozone air masses occurred, the localized ozone concentrations easily increased. At sunrise, the photochemical reactions gradually intensified, further promoting ozone production. This resulted in a higher frequency of NOE events at 7:00 a.m., coinciding with the increased sunlight.

3.2. Model Analysis

3.2.1. WRF-CMAQ Simulation Validation Assessment

To evaluate the model’s simulation performance, simulations were performed for April, October, and November 2020, as these months exhibited typical NOE events in Putian City. The simulation results of these three months were verified and analyzed, and temperature at 2 m (T2) and wind speed at 10 m (WS10) were employed as meteorological factor indicators. Table S1 and Figures S1 and S2 illustrate the comparison between the simulation results of the WRF model and the observations. Overall, the correlations between the simulated and observed T2 and WS10 were both higher than 0.4, indicating that the correlation meets the model performance criteria. Furthermore, the R-value of WS10 was lower than that of T2. This could stem from the special characteristics of the southeast hilly terrain where Putian City is located, which makes the performance of wind speed less than that of temperature. However, the simulation results of this study were comparable to those of the WRF simulation of other related studies in China [44]. Therefore, they were used as the input data for the subsequent CMAQ simulation.
To ensure the stability of the model performance, the O3 concentration data output from the simulation using the CMAQ model was compared with the observation data from the monitoring stations, and the same time period as that simulated by the WRF model was selected. The O3 hourly monitoring data provided by the national air quality monitoring stations located in the built-up area of Putian City were employed for statistical comparison with the CMAQ model (Figure S3). Table S2 statistically compares the O3 concentration simulation results and monitoring values. The comparison showed that the simulation effect of pollutants was basically within the acceptable range [39,40,41], and the MFB and MFE of all months satisfied −60% ≤ MFB ≤ 60% and MFE ≤ 75%. Figure S3 exhibits the peak changes in pollutants simulated using the CMAQ model. The trends of the observed and simulated values were basically identical, and the overall simulated values for each site were low. This could be due to the errors between the emissions afforded by the simulation, and observation as well as the differences between the temporal- and spatial-allocated factors in the simulation and observation, among other reasons. Although the CMAQ model slightly underestimated the monitoring data, the data passed the test of the validation index and could simulate the O3 concentration variation in the study area. Additionally, the results can be used as the next step of the analysis.

3.2.2. Mechanisms for a Typical NOE Event in Putian City

NOE events were observed at multiple sites in Putian City on 29 April, 29 October, and 8 November 2020. Since the study area is concentrated within the Putian City region, the variations among monitoring sites are relatively small. Therefore, three representative NOE events were selected for in-depth analysis, and the CMAQ model was applied to explore the causal mechanisms behind these events. Additionally, to address concerns regarding the potential influence of COVID-19 restrictions on ozone formation, we analyzed ozone (O3) and nitrogen dioxide (NO2) concentrations in April 2017, 2020, and 2023. The results showed no significant differences, confirming the representativeness of the 2020 data for this study.
Figure 7 illustrates the results of the PA module for the surface O3 (the bottom layer of the model). Positive values indicate an increase in O3 in the grid box and vice versa.
As depicted in Figure 7a, the NOE event at the PMS site occurred between 0:00 and 3:00 on 29 April, with high nighttime O3 concentrations mainly due to HADV and ZADV. Moreover, the positive contribution of HADV increased during the first two hours of the NOE event, peaking at 23:00 on 28 April. This indicates that O3 flowed in from the periphery of Putian City, leading to an increase in ozone concentration at 0:00 on 29 April. Additionally, the prevailing northeasterly winds within the Putian land area and the southeasterly winds, transported ozone-rich air from northern Fujian, southern Zhejiang, and the oceans. Ozone diffusion was blocked by the mountainous terrain in the western part of Putian City, contributing to the nighttime ozone enhancement. Figure 8c presents that the oceanic wind speeds in Putian City were low after 23:00. According to [45], high wind speeds typically disperse atmospheric pollutants quickly, reducing ozone concentrations. However, herein, the low wind speeds in Putian City during the observed period resulted in a lack of effective dispersion mechanisms, leading to the accumulation of pollutants in the air, including ozone, which increased the ozone concentrations. Low wind speeds can also trigger temperature inversions, where the atmospheric temperature at a certain height above the ground is higher than at the ground level. This inversion creates a “stabilization layer” that prevents the increase in ozone and other pollutants, causing them to accumulate in the lower atmosphere near the ground, which further increases the ozone concentrations.
VDIF is the primary source of surface ozone, while CHEM, due to the combined effect of ozone production and destruction, is the primary means of ozone reduction. Figure 7b shows that the NOE event at the HSMS site occurred from 21:00 to 23:00 on 29 October. After sunset at approximately 17:00, the O3 concentration decreased, primarily due to horizontal export and chemical reactions depleting O3. Around 21:00, DDEP decreased to 25.38 μg/m3. Since DDEP reduces surface ozone, deposition losses decreased due to the weakening of its negative contribution, leading to an increase in the O3 concentration. Figure 9 shows that the negative contribution of HADV was larger below 2000 m, denoting a downward movement of air from higher altitudes to the surface, bringing ozone from the upper atmosphere and increasing the O3 concentration at the HSMS site after 20:00. In summary, near-surface ozone levels remained relatively high after sunset due to the reduced impact of DDEP and the continued input from VDIF.
The NOE event at XDG occurred between 20:00 and 23:00 on 8 November (Figure 7c). During this event, the positive contribution of the near-surface HADV increased. To explore the specific impact of horizontal advection, O3 transport was examined using the ISAM source resolution module in the CMAQ model. The Putian, Fuzhou, and Quanzhou cities were labeled in the second layer of the grid. Using ISAM, the model traced the initial conditions (ICON), boundary conditions (BCON), and other unlabeled areas (OTH). Transportation was defined as the contribution from the boundary conditions. The results showed that the boundary conditions and other regions contribute the most to ozone in Putian, accounting for 71.08%, while local emissions contributed only 21%. Moreover, daily external transport contributed up to 90% of O3 in Putian City, with it contributed a combined 39% in Fuzhou and Quanzhou cities. This indicates that regional transport significantly impacts the O3 levels in Putian. Nighttime winds transported high concentrations of ozone and its precursors from Fuzhou and Quanzhou cities to Putian City, increasing the local ozone concentrations. The decrease in the negative contribution of HADV resulted in increased atmospheric stability and weakened vertical motion, reducing ozone transport from the upper layers to the lower layers. Consequently, ozone accumulated near the ground and could not be quickly dispersed, causing its concentration to increase. Analysis of the atmospheric boundary layer height during the O3 pollution period on the night of November 8th showed that the boundary layer height started decreasing from 14:00 (940 m) and then gradually increased from 21:00 (727 m) on 8 November 2020 (Figure 10). The O3 concentration increased from 12:00 on 8 November 2020, while the boundary layer height decreased, indicating an inverse relationship. The O3 concentration peaked at 23:00 when the boundary layer height was relatively stable. This stable boundary layer height hindered atmospheric diffusion, leading to the accumulation of O3. In summary, the combined effects of vertical transport and meteorological conditions led to the NOE event at 21:00 on 8 November 2020.

4. Conclusions

This study explored the mechanisms driving nocturnal ozone enhancement (NOE) events in Putian City, Fujian Province, from 2015 to 2023, using both observational data and WRF-CMAQ simulations. The temporal analysis revealed a general decline in the frequency of NOE events after 2017, with a higher occurrence in spring, particularly April, and fewer events in the summer months. NOE events were influenced by a combination of local meteorological conditions, such as wind speed, boundary layer stability, and terrain effects, alongside regional ozone transport. For instance, vertical mixing and advection played a dominant role in spring, while stable boundary layers and weakened deposition processes were more critical in autumn. These findings provide a comprehensive understanding of the spatial and temporal characteristics of NOE events in coastal cities like Putian.
The results emphasize the necessity of tailored air quality management strategies, especially during the ozone pollution season when NOE events are more frequent. Moreover, the role of regional transport, particularly from neighboring cities such as Fuzhou and Quanzhou, highlights the importance of coordinated regional emission reduction measures. By addressing precursor emissions like NOx through joint control efforts, both daytime and nighttime ozone levels can be effectively reduced.
However, this study has certain limitations. The analysis focused on three representative NOE events in 2020, which might not fully reflect interannual variability in mechanisms. Additionally, uncertainties in emission inventories and model parameterizations could influence the simulation results. Future research should include a broader range of observational data, such as higher-resolution meteorological measurements, and investigate other influencing factors, such as sea-salt aerosols, low-level jets, and convective storms. Extending the findings to other coastal regions in Fujian Province could further enhance the understanding of NOE mechanisms and inform regional ozone pollution control strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/atmos16010045/s1. Figure S1: WRF simulated and monitored the WS10 time series at the PT station for April, October, and November 2020; Figure S2: WRF simulated and monitored temperature time series at the PT station for April, October, and November 2020; Figure S3: Time series of simulated and monitored O3 values in Putian City; Table S1: WRF simulation validation assessment; Table S2: CMAQ simulation validation assessment.

Author Contributions

Conceptualization, X.Z. and J.W.; Data curation, C.F. and X.Z.; Formal analysis, X.Z. and Y.C.; Investigation, X.Z.; Methodology, C.F., X.Z. and Y.C.; Supervision, C.F.; Visualization, C.F., X.Z. and Y.C.; Writing—original draft, X.Z.; Writing—review and editing, C.F. and J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Materials, further inquiries can be directed to the corresponding author.

Acknowledgments

The authors would like to thank the group members of Laboratory 537 and 142 of Jilin University for their help and guidance for this study. Additionally, the authors would like to thank the MEIC team from Tsinghua University for providing the Multiscale Emission Inventory of China (MEIC).

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Study area (yellow stars show atmospheric monitoring stations; the red star represents meteorological stations).
Figure 1. Study area (yellow stars show atmospheric monitoring stations; the red star represents meteorological stations).
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Figure 2. Hourly ozone concentration variation during (a) NOE periods and (b) non-NOE periods.
Figure 2. Hourly ozone concentration variation during (a) NOE periods and (b) non-NOE periods.
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Figure 3. Number of days with NOE at the four monitoring stations (Unit: days/year).
Figure 3. Number of days with NOE at the four monitoring stations (Unit: days/year).
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Figure 4. Relative frequency distribution of (a) daily mean ozone concentration and (b) NOP during NOE events.
Figure 4. Relative frequency distribution of (a) daily mean ozone concentration and (b) NOP during NOE events.
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Figure 5. (a) Monthly variation in the frequency of NOE events in Putian City, 2015–2023. (Unit: days/year); (b) Average inter-monthly ozone concentrations in Putian City, 2015–2023.
Figure 5. (a) Monthly variation in the frequency of NOE events in Putian City, 2015–2023. (Unit: days/year); (b) Average inter-monthly ozone concentrations in Putian City, 2015–2023.
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Figure 6. Occurrence time of NOE events in Putian City, 2015–2023.
Figure 6. Occurrence time of NOE events in Putian City, 2015–2023.
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Figure 7. Daily variation in the contribution (Unit: μg/m3) of processes to surface O3 (Unit: μg/m3) during different NOE events: (a) PMS NOE event on 29 April 2020; (b) HSMS NOE event on 29 October 2020; and (c) XDR NOE event on 8 November 2020. ZADV: Vertical advection, VDIF: Vertical diffusion, HADV: Horizontal advection, HDIF: Horizontal diffusion, DDEP: Dry deposition, CLDS: Cloud processes and aqueous chemistry, CHEM: Chemistry production or consumption. Red boxes represent time periods when NOE occurs.
Figure 7. Daily variation in the contribution (Unit: μg/m3) of processes to surface O3 (Unit: μg/m3) during different NOE events: (a) PMS NOE event on 29 April 2020; (b) HSMS NOE event on 29 October 2020; and (c) XDR NOE event on 8 November 2020. ZADV: Vertical advection, VDIF: Vertical diffusion, HADV: Horizontal advection, HDIF: Horizontal diffusion, DDEP: Dry deposition, CLDS: Cloud processes and aqueous chemistry, CHEM: Chemistry production or consumption. Red boxes represent time periods when NOE occurs.
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Figure 8. Spatial distribution of O3 concentration at (a) 23:00 on 28 April 2020, and (b) 00:00 on 29 April 2020, in Putian City, and (c) 10 m wind speed and direction at 00:00 on 29 April 2020, in Putian City.
Figure 8. Spatial distribution of O3 concentration at (a) 23:00 on 28 April 2020, and (b) 00:00 on 29 April 2020, in Putian City, and (c) 10 m wind speed and direction at 00:00 on 29 April 2020, in Putian City.
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Figure 9. Vertical budgets of O3 (Unit: μg/m3) at different heights (in m) for HSMS.
Figure 9. Vertical budgets of O3 (Unit: μg/m3) at different heights (in m) for HSMS.
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Figure 10. Atmospheric boundary layer height change in Putian City from 12:00, 8 November 2020, to 12:00, 9 November 2020.
Figure 10. Atmospheric boundary layer height change in Putian City from 12:00, 8 November 2020, to 12:00, 9 November 2020.
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Table 1. Details of the four individual air quality monitoring sites and one meteorological station.
Table 1. Details of the four individual air quality monitoring sites and one meteorological station.
NumberNameAbbr.Longitude (°E)Latitude (°N)
1Houcang RoadHR119.014125.4325
2Putian Monitoring StationPMS119.001525.4553
3Hanjiang Sixth Middle SchoolHSMS119.111925.4661
4Xiuyu District GovernmentXDG119.101325.3215
5PutianPT119.116425.2782
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Fang, C.; Zhou, X.; Cai, Y.; Wang, J. Characterization and Atmospheric Drivers of Nocturnal Ozone Enhancement in Putian City, China. Atmosphere 2025, 16, 45. https://doi.org/10.3390/atmos16010045

AMA Style

Fang C, Zhou X, Cai Y, Wang J. Characterization and Atmospheric Drivers of Nocturnal Ozone Enhancement in Putian City, China. Atmosphere. 2025; 16(1):45. https://doi.org/10.3390/atmos16010045

Chicago/Turabian Style

Fang, Chunsheng, Xiaowei Zhou, Yuxuan Cai, and Ju Wang. 2025. "Characterization and Atmospheric Drivers of Nocturnal Ozone Enhancement in Putian City, China" Atmosphere 16, no. 1: 45. https://doi.org/10.3390/atmos16010045

APA Style

Fang, C., Zhou, X., Cai, Y., & Wang, J. (2025). Characterization and Atmospheric Drivers of Nocturnal Ozone Enhancement in Putian City, China. Atmosphere, 16(1), 45. https://doi.org/10.3390/atmos16010045

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