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Article

Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P

by
Shenghao Xu
and
Xinxiang Yang
*
National & Local Joint Engineering Research Center of Harbor Oil & Gas Storage and Transportation Technology, Zhejiang Provincial Key Laboratory of Petrochemical Pollution Control, School of Petrochemical Engineering & Environment, Zhejiang Ocean University, Zhoushan 316022, China
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(5), 517; https://doi.org/10.3390/atmos16050517
Submission received: 26 March 2025 / Revised: 25 April 2025 / Accepted: 27 April 2025 / Published: 28 April 2025
(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))
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Abstract

:
The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO2 concentration at key highway toll stations in Zhejiang Province, China. By selecting and comparing typical cases, the analysis reveals differentiated characteristics in how various factor combinations influence NO2 concentration. The main findings are as follows: Under COVID-19 lockdown measures, prolonged vehicle waiting times and a shift towards family car usage among the public have led to a significant increase in NO2 concentration at highway toll stations. Promoting the Electronic Toll Collection (ETC) system and encouraging public transportation are of great importance. The synergistic effects of COVID-19 lockdown policies and urban land area, resulting in the reduction in the number of family cars and the excellent air circulation conditions in large cities, have contributed to the decrease in NO2 concentration at highway toll stations. Increasing urban green spaces and promoting clean energy vehicles are crucial for advancing urban sustainable development. The combined analysis of the region area and the number of family cars indicates that a higher proportion of large vehicles contributes to improving transportation efficiency, but also results in elevated NO2 concentration at highway toll stations due to diesel emissions. Optimizing the transportation structure and reducing reliance on large vehicles are of significant importance.

1. Introduction

Air pollution has emerged as the fourth leading global risk factor for human health. According to data from the World Health Organization (WHO), approximately 6.5 million people die prematurely each year due to air pollution [1]. Among the various components of air pollution, they can be broadly categorized into greenhouse gases and non-greenhouse gases. In recent years, the surge in greenhouse gas emissions and the resulting climate change, along with their numerous adverse effects, have garnered widespread global attention [2]. Nitrogen oxides (NOx), as indirect greenhouse gases, not only serve as common precursors to PM2.5 and O3 but also react with other atmospheric chemical components to form direct greenhouse gases, thereby contributing to global warming. The latest Global Burden of Disease study [3] highlighted that incorporating NO2 as a new air pollution factor opened new dimensions in air pollution research. Inhalation of NO2 not only increases the risk of respiratory diseases, posing a threat to human health [4], but may also elevate the incidence of cardiovascular and cerebrovascular diseases [5]. Therefore, given the potential health and environmental impacts of NO2 emissions, enhancing its monitoring efforts is of paramount importance.
NO2 is a product formed when NOx undergo chemical reactions with oxygen in the atmosphere, with its emissions primarily originating from traffic activities [6]. This conclusion has been validated through numerous scholarly studies. For instance, Paraschiv et al. [7] conducted an in-depth investigation of the temporal distribution of NO2 in two urban areas of Romania using data from fixed monitoring stations. The results indicated that traffic-related emission sources contributed more significantly to urban air pollution. Obara et al. [8] conducted NO2 monitoring and found that the average concentration of NO2 was higher in winter and near roadways, decreasing gradually with increasing distance from the emission sources. Stuart et al. [9] performed environmental NO2 level measurements near an elementary school over a one-week period and discovered that monitoring stations located near the city center recorded the highest values. Huynh et al. [10] presented NO2 levels at five research sites in Ho Chi Minh City at different times of the day, with results showing that all test locations near busy urban roads exhibited higher average concentrations of NO2.
Highway toll stations, serving as transportation hubs, are generally located at the edge of administrative regions, acting as fortresses for urban access and exit passages and bearing the intensive traffic activities brought about by the movement of a large number of people and vehicles. These facilities are not only significant hotspots for NO2 emissions but also represent critical sites for public health risk prevention and control. Li et al. [11] conducted a study on a highway toll station in Tianjin, China, and found that workers involved in vehicle inspections and campus security personnel were at high risk of exposure to particle-bound heavy metals, which also posed health impacts to these workers. Zhao et al. [12] conducted an in-depth analysis of the health status of highway toll station workers by employing personal particulate matter sampling techniques and urinary biomonitoring methods. They found that long-term exposure to polycyclic aromatic hydrocarbons emitted from traffic sources significantly increases oxidative stress burden in the human body. Based on the above findings, highway toll stations are designated as key research subjects in this study.
As highlighted by numerous studies, factors affecting NO2 concentration extend beyond traffic activities to include urban spatial layout characteristics, the impact of holidays, and the effects of the COVID-19 pandemic. Gurung et al. [13] pointed out that the terrain and land use patterns of Asian cities differed significantly from those of Western cities, and these differences might have affected transportation-related NO2 emissions. Poplawski et al. [14] employed a land use regression model to analyze the effects of urban spatial layout on transportation NO2 emissions. Jin et al. [15] found that in major metropolitan areas such as the United States, Europe, Japan, and Russia, ground-based measurements and remote sensing data indicated a significant weekend effect on NO2 emissions. The results showed a reduction of 50% or more in NO2 levels on weekends compared to weekdays. Tan et al. [16] examined the characteristics of NO2 emissions in Taiwan and identified a noticeable holiday effect. Filonchyk et al. [17] studied Eastern China and found that NO2 emissions significantly decreased during the COVID-19 pandemic due to the implementation of lockdown measures. Tobías et al. [18] revealed that in Barcelona, vehicular travel restrictions during the pandemic resulted in a reduction in NO2 concentration.
With an increasing understanding of the complexity of the driving factors behind NO2 concentration, single factors can no longer sufficiently explain the reasons for the fluctuations in NO2 levels at highway toll stations. Consequently, an increasing number of studies are adopting configurational analysis methods to explore how combinations of various factors jointly influence outcomes. The complementarity and synergy among different condition variables are emphasized in configurational research, enabling the identification of innovative pathways generated by the combined effects of multiple factors, thereby providing a unique perspective for understanding complex mechanisms [19].
Against this backdrop, fsQCA adeptly combines qualitative data with fuzzy set theory to explore complex causal relationships. This method has increasingly garnered widespread attention and application within the academic community due to its ability to flexibly accommodate diverse types of data and its adoption of a holistic perspective in causal analysis (i.e., focusing on the combined effects of causes rather than the independent effects of individual causes) [20,21].
Despite these advancements, research on NO2 concentration still encounters several issues, including a relatively simplistic analysis of the causes behind changes in NO2 levels. Therefore, the fsQCA method is employed in this study to analyze the comprehensive impact of different configurations on NO2 concentration at key highway toll stations, considering factors such as region, year, and vehicle types. The innovative contribution of this study lies in the application of the advanced fsQCA method to conduct an in-depth examination of the factors influencing NO2 concentration levels. Simultaneously, the study integrates satellite data with real-time vehicular traffic data from the preceding hour, providing a more comprehensive perspective.

2. Data and Methods

2.1. Study Area

Figure 1a presents data imagery captured by the Sentinel-5P satellite, clearly highlighting the selected study area within Zhejiang Province. As China’s first demonstration zone for common prosperity, Zhejiang achieves remarkable economic milestones. Currently, the province boasts a total GDP of 115.47 billion USD, a per capita GDP of 17,491 USD, and a permanent population of 66.27 million [22]. In terms of both economic scale and population size, Zhejiang becomes one of the most representative provinces in the Yangtze River Delta region [23]. Five significant highway toll stations in Zhejiang Province (Figure 1b, S1–S5) are primarily selected as target locations for further investigation of the related phenomenon in this study.

2.2. Data Sources

The TROPOMI sensor onboard the Sentinel-5P satellite was utilized in this study to collect NO2 concentration data from five highway toll stations in Zhejiang Province during all statutory holidays and the seven days before and after each holiday, spanning from January 2019 to June 2023. TROPOMI is a passive optical sensor capable of measuring the total column amounts of trace gases within the ultraviolet to shortwave infrared spectral range [24]. As a nadir-viewing push-broom instrument, it has a wide observational coverage of up to 2600 km and provides daily global-scale NO2 column concentration data with an original spatial resolution of 7.5 km × 3.5 km, which has been recently improved to 5.5 km × 3.5 km [25]. Its products include Level 1 (L1) and Level 2 (L2) data, each available in two versions: near-real-time and offline [26]. The L2 offline products are primarily employed by this study, offering higher data quality and greater applicability, albeit with more stringent processing requirements [27].
The NO2 concentration data were downloaded from the Copernicus Data Access Center, with the original format being NC format [28]. By utilizing MATLAB (R2023b) and Python (version 3.11) programming software, codes were written to convert the NC format into the TIF format [29]. The converted data encompass four key components, namely longitude, latitude, NO2 mixing ratio column concentration data, and quality assurance values. Further processing and modification were conducted on the converted data in this paper, ultimately obtaining the distribution map of NO2 concentrations over Zhejiang Province.
To reflect the overall characteristics of NO2, descriptive statistical analysis was conducted on regional variables in this paper. The NO2 concentrations during holiday periods and the seven days before and after each holiday were aggregated for each region annually, and key statistical indicators, including mean, standard deviation, minimum, and maximum values, were calculated for each region. Based on statistical data, the validity and representativeness of the data were further evaluated, thereby providing data support for the reliability of the research findings (see Table 1 for details).
The geographic information software systems were implemented in this research, fully leveraging their robust backend capabilities to perform detailed classifications of vehicle types. These systems, with their exceptional data collection, processing, and analysis capabilities, can not only track and monitor vehicle flow on roads in real-time but also employ sophisticated techniques, such as classification based on vehicle wheelbases or type, to accurately obtain data for various vehicle types. These mapping information software systems possess all-weather, comprehensive real-time monitoring capabilities, enabling the timely detection and effective management of various traffic anomalies, thereby ensuring the smooth and safe passage of road traffic. The statutory holidays and their corresponding dates from January 2019 to June 2023 were compiled in this study (as shown in Table 2), serving as a temporal reference for the subsequent analysis.
In this study, vehicles were categorized into different intervals based on their wheelbases: 2.4–2.6 m, 2.6–2.7 m, 2.7–2.8 m, and 3.6 m and above. Daily data corresponding to each vehicle type were obtained through the backend program of the map. For classification purposes, vehicles with a wheelbase of 2.8 m or less were classified as family cars, while those with a wheelbase greater than 2.8 m were classified as trucks. The statistical daily data were then summarized by year, holiday, and location.

2.3. Measurement of Variables in fsQCA

2.3.1. Outcome Variable

The outcome variable of this study focused on the NO2 concentration at key highway toll stations in Zhejiang Province. This metric specifically reflected the level of NO2 present in the air. As the core focus of the research, NO2 concentration was the key indicator that this paper aimed to thoroughly investigate the reasons for its fluctuations through fsQCA.

2.3.2. Conditional Variables

The analysis in this study is approached from four dimensions: region, holidays, year, and vehicle types. Five predictor variables were selected with the objective of thoroughly dissecting the factors influencing the variation in NO2 concentration at key highway toll stations in Zhejiang Province.

Region Area

The administrative regions at the municipal level where each highway toll station was located were selected by this research, using the total land area within these regions as a representative factor of regional characteristics. All relevant data were sourced from official government publication platforms at various administrative levels [30,31,32,33,34]. The aim was to deeply examine how the region area interacted with multiple other factors to jointly influence NO2 concentration.

Number of COVID-19 Infections

To capture the annual characteristics, the annual total of daily new COVID-19 infection cases in China was calculated. Daily COVID-19 infection case data were sourced from the official website of the WHO [35]. By collecting and analyzing daily data, the study aimed to explore the complex interactions between the number of COVID-19 infections and social, economic, and policy factors. This investigation sought to reveal how these factors synergistically influenced variations in NO2 concentration.

Average Temperature During Holidays

In terms of holiday factors, this paper selected the average temperature during each holiday as a conditional variable. All relevant data were obtained from authoritative meteorological websites [36]. Li et al. [37] conducted a study on the concentration of NO2 during the Spring Festival, a statutory holiday in China, and found that with the change in passenger traffic volume, there was a decreasing trend in NO2 concentration. Madhavi Latha et al. [38] conducted a study on air pollutants in the Reading area of the United Kingdom and discovered that the concentrations of air pollutants in this region were relatively low on Thanksgiving Day. By utilizing this meteorological indicator, an in-depth analysis of how holiday activities, tourism flows, and other related factors were associated with NO2 concentration was aimed to be conducted by the study, further elucidating their underlying mechanisms of influence.

Number of Holiday Days

Aside from the average temperature, the number of holiday days was also regarded as an important representative indicator, with relevant data sourced from government official documents [39]. The complex relationships between the number of holiday days and other socio-economic factors, as well as how socio-economic phenomena and individuals’ behavioral patterns during holidays collectively influence changes in NO2 concentration, were examined by this study.

The Number of Family Cars

With regard to vehicle types, the number of family cars was selected as a representative variable by this study. Through this indicator, the intrinsic connections between vehicle quantity and other related factors were revealed by the research, demonstrating how they interrelate to jointly impact NO2 concentration.
The traffic flow data were obtained from the open platform of a mapping software, leveraging its API interface to facilitate real-time traffic status queries. This process involved constructing HTTP requests with parameters such as city names and road names and sending these requests to the real-time traffic query endpoint of the Baidu Maps API. During implementation, the HTTP URL connection class provided by the Java standard library was utilized to handle the sending and receiving of requests. Subsequently, the JSON-formatted data returned by the API were further processed using online conversion tools to extract the necessary real-time traffic flow information. Such an operational procedure is designed to ensure the efficient acquisition of traffic flow data, thereby laying a data foundation for subsequent data screening processes.

2.3.3. Conditional Variable Data Processing

Regarding the aforementioned conditional variables, the key indicator data from 2019 to 2023 were systematically collected in this study, including the region area, number of COVID-19 infections, average temperature during holidays, number of holiday days, the number of family cars, and NO2 concentration. The detailed data are presented in Table 3. The data sources were primarily derived from authoritative statistical agencies, government public reports, and monitoring platforms of the Ministry of Ecology and Environment, ensuring the reliability, authority, and timeliness of the data.
To ensure the accuracy and applicability of the data, a rigorous data screening and integration process was adopted in this study, and only complete data entries containing all necessary variables were retained. Through this series of standardized procedures, a dataset containing 140 valid records was ultimately constructed. To characterize the overall features of the sample, descriptive statistical analysis was conducted for each variable, including key statistical measures such as mean, standard deviation, minimum, and maximum values. Based on these statistical data, the validity and representativeness of the data were further evaluated, thereby providing data support for the reliability of the research findings (see Table 4 for details).

2.4. Data Calibration

Based on the statistical analysis of the conditional variables mentioned above, an fsQCA dataset was constructed in this study, laying the groundwork for subsequent in-depth research into the factors influencing NO2 concentration changes at key highway toll stations in Zhejiang Province. The dataset underwent necessary calibration procedures, converting the raw data into fuzzy set membership scores to facilitate an appropriate data format for subsequent configurational analysis. Building on this, the 95th percentile, 50th percentile, and 5th percentile for each variable were calculated according to their distribution characteristics [40]. These statistical measures were then used to determine the thresholds for full membership, the crossover point, and full non-membership, as presented in Table 5. In order to prevent causal conditions from defaulting to a membership score of 0.5, this study uniformly applied a fine-tuning value of 0.001 to conditions where the calibrated membership score was lower than the complete membership score [41].

3. Results and Discussion

3.1. Necessity Analysis

A necessity test was conducted on the calibration data in this study, with the results presented in Table 6. The necessity test is designed to identify whether any predictive variables exist as necessary conditions that exert a sustained and decisive influence on NO2 concentration at major highway toll stations in Zhejiang Province. The results show that the consistency indices of all conditions are no less than 0.5, indicating that individual variables have a certain degree of impact on NO2 concentration. However, none of the consistency indices reach 0.9, suggesting that no clear necessary condition is present that can independently determine changes in NO2 concentration.
Within the tested conditions, the region area exhibits a high consistency index (0.71) with NO2 concentration, indicating a strong association between these variables at key highway toll stations in Zhejiang Province. This suggests that the region area is likely a significant driving factor for changes in NO2 levels. Although the consistency index does not meet the stringent threshold of 0.9, its relatively high value emphasizes the significant role of the region area in influencing NO2 concentration.
The number of COVID-19 infections shows a relatively high consistency index (0.7), suggesting that the pandemic significantly impacts traffic flow at highway toll stations and, consequently, NO2 concentration. While the COVID-19 variable does not play a decisive standalone role, its influence as a result of interactions with other variables should not be overlooked.
NO2 concentration is jointly influenced by multiple factors, exhibiting differential effects under various conditions. Specifically, factors such as the region area, the number of COVID-19 infections, and the number of family cars play vital roles in affecting NO2 concentration. However, no single factor can comprehensively account for all variations in NO2 levels. This finding underscores the importance of configuration analysis, which, by considering the combined effects of multiple factors, allows for a more accurate determination of the potential influences on NO2 concentration.

3.2. Results of the fsQCA

A comparative analysis of the data is conducted by constructing a truth table in this study. The truth table analysis examines cases that share specific combinations of conditions and assesses whether these cases lead to the same outcome. Due to the small sample size, the case frequency threshold is set to 1, the consistency threshold to 0.8, and the PRI consistency threshold to 0.6. Finally, the intermediate solution is used as the primary basis for analysis, while the parsimonious solution serves as an auxiliary reference [42]. In interpreting the results, core and peripheral conditions are determined based on their co-occurrence in both the intermediate and parsimonious solutions: conditions appearing in both solutions are regarded as core conditions, whereas those appearing in only one are considered peripheral conditions. The research findings indicate a high degree of consistency between the intermediate and parsimonious solutions, both clearly identifying “region area,” “number of COVID-19 infections,” and “number of family cars” as three major core conditions. These core conditions constitute six different configurations, as detailed in Table 7.
An overall consistency of 0.84 is achieved, indicating that the identified configurations possess considerable robustness and consistency in explaining NO2 concentration. The overall coverage is 0.50, implying that approximately 50% of the NO2 concentration results are collectively explained by the four configurations. Figure 2 summarizes the cases matched to each configuration, with only those cases in which both combination membership and outcome membership exceed 0.5 being selected for display.
In the necessity tests, the consistency coefficients of the three core conditions do not exceed the threshold of 0.9, and thus, they fail to qualify as distinct necessary conditions for determining changes in NO2 concentration. In light of this, the control variable method is employed in the present study to conduct a detailed comparative analysis of the aforementioned configurations, aiming to comprehensively investigate the combined effects and influences of the three core conditions on NO2 concentration.

3.2.1. Combined Effects of COVID-19 Infections and Family Cars

The combined effects of the number of COVID-19 infections and family cars on NO2 concentration are delved into through a comprehensive analysis of C1 and C6 by this study. The results indicate that the coverage of C6 (0.32) is higher than that of C1 (0.273), suggesting that under the same conditions, the combination of a high number of COVID-19 infections and a high number of family cars has stronger explanatory power for NO2 concentration. To specifically illustrate this finding, this paper selects two of the most contrasting cases for analysis, namely the Qingming Festival in Jiaxing 2020 and New Year’s Day in Jiaxing 2021, with the analysis results presented in Figure 3.
It is clearly discernible from the figure that the combination of a high number of COVID-19 infections and a large number of family cars jointly leads to a significant increase in NO2 concentration. Research on factors related to COVID-19 reveals that the number of infections in 2022 surged several hundred times compared to 2020. Following the outbreak of the pandemic, lockdown policies were implemented nationwide, with all cities mandated to carry out compulsory nucleic acid testing and report infections in real-time. As transportation hubs, highway toll stations conducted multiple nucleic acid tests on passing individuals and vehicles and carried out disinfection procedures.
The Ministry of Transport of China [43] mandated that highway toll stations with heavy traffic volumes set up additional nucleic acid testing sites. According to a report from the Health Commission of Hangzhou Municipality [44], the Zhejiang Provincial Government established a closed-loop management system for key groups (such as truck drivers from severely affected neighboring cities and personnel assisting in makeshift hospitals) to strengthen epidemic prevention and control at toll stations. The Ministry of Transport of China [45] further required highway service areas and toll stations to enhance their epidemic prevention capabilities by deploying additional professional protective personnel to provide on-site guidance and ensure safety management and control. These measures resulted in increased waiting times and vehicle congestion at toll stations, indirectly leading to elevated NO2 concentrations in the surrounding areas. While these measures effectively controlled the spread of COVID-19, they also increased waiting times and vehicle gatherings at toll stations, indirectly raising NO2 concentrations in the vicinity of these stations.
As observed in Figure 3, an increasing number of COVID-19 infections led the public to avoid using public transportation and instead adopt family cars extensively for travel. This shift directly resulted in a sharp rise in the number of family cars. To thoroughly analyze the impact of this phenomenon on the environment surrounding highway toll stations, this study compares the number of trucks and family cars passing through highway toll stations under similar conditions, as shown in Figure 4. The data reveal that the number of family cars in both instances is ten times that of trucks, highlighting the significant contribution of family cars to the NO2 concentration levels around highway toll stations. Specifically, the surge in the number of family cars led to a substantial increase in automobile exhaust emissions, thereby causing a noticeable rise in NO2 concentration in the vicinity of highway toll stations.
This perspective has been extensively validated in numerous scholarly studies [46,47,48]. Huangfu et al. found that in urban areas with high traffic density, the levels of NOx (including NO and NO2) were typically elevated and often exhibited a negative correlation with ozone concentration during daytime hours. Paraschiv et al., by comparing data from 2014 and 2007, indicated that the NO2 concentration over Athens recorded by the Ozone Monitoring Instrument decreased by 43%  ±  28%, while ground-based traffic monitoring stations also showed approximately a 30% reduction during the same period. Shekarrizfard et al. further emphasized that policies aimed at changing travel behaviors, particularly those promoting the use of public transportation, were crucial for reducing traffic emissions and mitigating the negative impacts of traffic-related air pollution.
Based on the comprehensive discussions and analyses presented above, the combined effect of different years and vehicle types on the increase in NO2 concentration is further elucidated in this study by considering the practical significance of each variable. This finding indicates that, in specific regions, changes over time and variations in the number of family cars may intertwine under certain conditions, jointly influencing the variations in NO2 concentration.

3.2.2. Combined Effects of Region Area and COVID-19 Infections

Through a comprehensive comparative analysis of C2 and C5, the combined effect of the region area and the number of COVID-19 infections on NO2 concentration is delved into in this study. The research results indicate that the coverage of C5 reaches 0.322, which is higher than that of C2 at 0.246. This clearly demonstrates that, under the same conditions, the combination of a larger region area and a higher number of COVID-19 infections has a more significant explanatory power for NO2 concentration. To further illustrate this finding, two highly contrasting cases are selected for in-depth analysis in this study, namely Jiaxing during the Qingming Festival in 2020 and Hangzhou during the Qingming Festival in 2022. The specific analysis results are presented in Figure 5.
Analysis of the data suggests that the combination of a larger region area and a higher number of COVID-19 infections unexpectedly results in a decreasing trend in NO2 concentration. This phenomenon can primarily be attributed to the fact that regions with larger land areas typically exhibit superior ventilation conditions. Although these areas often have significant population sizes, their relatively lower population density allows air pollutants to be effectively diluted and dispersed. This contributes to the observed decrease in NO2 concentration at specific locations (e.g., highway toll stations).
This finding is consistent with the results of previous studies [49,50]. For example, Lumet et al. demonstrated that high population density in urban environments hinders proper ventilation, leading to the localized accumulation of air pollutants. Similarly, Juan et al. found that good ventilation in urban areas was crucial for introducing cleaner airflow from rural regions into cities, thus mitigating the negative impacts of pollutants. However, larger cities with extensive land areas and large populations are generally more severely affected by the pandemic due to the sheer number of individuals.
During the peak of the COVID-19 pandemic, strict pandemic control measures were implemented by governments, effectively restricting population movement and significantly reducing vehicle traffic. This initiative played a critical role in lowering NO2 concentration around highway toll stations, aligning with findings from experts in related fields [51,52]. Li et al. pointed out that to curb the spread of COVID-19, governments worldwide implemented varying levels of social distancing measures, including stringent restrictions on population mobility, particularly in urban areas. As a result of the pandemic, cities across the globe experienced a substantial decrease in traffic volumes, with reductions exceeding 50% in some cases. Furthermore, De Santis et al. revealed that the global public health emergency caused by COVID-19 prompted many countries to adopt various containment measures, including travel restrictions, curfews, and quarantines, which greatly reduced traffic flow.
Building on the above discussion, the combined effects of these factors on the reduction of NO2 concentration across different years and regions are further delved into in this study. Under the condition of relatively stable vehicle types, the interplay between temporal changes and region area differences may produce outcomes regarding NO2 concentration at highway toll stations.

3.2.3. Combined Effects of Region Area and Family Cars

A comprehensive comparative analysis of C3 and C4 is conducted in this study to deeply explore the interplay between the region area and the number of family cars on NO2 concentration. To further illustrate this finding, two highly contrasting cases are selected for in-depth analysis: the situation in Jiaxing and Hangzhou on New Year’s Day in 2020. The detailed analysis results are presented in Figure 6.
From the chart, it can be observed that the combination of a high region area and a low number of family cars unexpectedly leads to an increase in NO2 concentration. Firstly, in regions with larger geographical areas, the land use scope tends to be broader, and the transportation network structure becomes more complex, reflecting a close mutual reinforcement between land use and the transportation network. Wang et al. [53] pointed out that changes in the land use system can influence passenger and freight demand, thereby affecting the overall operation of the transportation system. Meanwhile, the continuous improvement of transportation infrastructure enhances spatial accessibility, which, in turn, further promotes shifts in land use patterns. Li et al. [54] highlighted that factors such as land use significantly reshape the spatial structure and development patterns of cities. These transformations have sparked deeper reflection and ongoing discussions about the future development trends of urban transportation systems. Furthermore, the complexity of transportation networks results in diversified characteristics of traffic and modes of transportation.
The increasing diversification of transportation modes has set higher performance requirements for vehicles, emphasizing not only greater capacity but also economic efficiency. Against this backdrop, the proportion of large vehicles such as semi-trailers and heavy trucks has been steadily rising in major cities. Their frequency of appearance has also significantly increased at highway toll stations. To enhance transportation efficiency, these large vehicles are often equipped with engines several times more powerful than those in ordinary household cars to store larger amounts of fuel, with diesel being their primary fuel choice. As a result, their NO2 emissions are significantly higher than those of regular family cars.
A finding consistent with prior studies by certain scholars [55,56,57], Huang et al. highlighted that in conventional diesel engines, higher combustion temperatures result in increased emissions of NOx (including NO and NO2). Although lowering the combustion temperature can reduce nitrogen oxide emissions, it may lead to incomplete combustion and increased soot formation. Feng et al. found that within diesel engine exhaust, NO2 typically constituted less than 15% of the total NOx. However, when oxidation catalysts were used, NO could be oxidized to NO2 at temperatures between 300 and 350 °C, increasing the proportion of NO2 to 50% of the total NOx. Heywood et al. observed that NO2 accounts for a significant proportion of NOx emissions in the exhaust of conventional diesel engines.
This study revealed that under the interaction of region area and vehicle type combinations, the NO2 concentration at highway toll stations exhibits relatively significant and unexpected results. In the same year, the relationship between NO2 concentration and the combination of region area and vehicle type shows an inverse trend.

3.3. Robustness Analysis

For the analysis process of fsQCA, a constant consistency threshold of 0.80 and a PRI threshold of 0.60 are adopted as initial settings in this study. To further verify the robustness and reliability of the analysis results, rigorous robustness testing is conducted. Specifically, the consistency threshold is adjusted from 0.80 to a more stringent 0.85, and this change does not affect the configuration results. Similarly, when the PRI threshold is raised from 0.60 to 0.65, the resulting outcomes form a subset of the original findings, demonstrating a high level of consistency. These findings indicate that even under stricter threshold settings, the core configuration structure remains stable, providing further evidence supporting the robustness and reliability of this study.

4. Conclusions and Implications

4.1. Conclusions

TROPOMI satellite remote sensing technology is utilized to obtain NO2 concentration data from major highway toll stations in five regions of Zhejiang Province: Hangzhou, Ningbo, Wenzhou, Jiaxing, and Quzhou. Combined with hourly traffic volume data, an advanced fsQCA method is employed for in-depth exploration. The main findings are as follows:
  • The combined impact of the number of COVID-19 infections and family cars is analyzed, revealing that the implementation of lockdown policies during the COVID-19 pandemic had a significant effect on highway toll stations. Strict inspection measures led to a substantial increase in vehicle waiting times at toll booths. Simultaneously, the pandemic induced changes in public travel preferences, with family cars becoming the primary mode of transportation. This shift resulted in a sharp increase in the number of family cars at highway toll stations. The combined effect of these two factors caused a significant rise in NO2 concentration at highway toll stations.
  • Under the combined effects of region area and number of COVID-19 infections, it was found that lockdown policies during the COVID-19 pandemic significantly restricted public travel, leading to a reduction in the number of family cars. In cities with larger land areas, due to their vast geographic scope and better air circulation conditions, air pollutants were found to disperse more easily. The synergy of these factors led to an overall reduction in NO2 concentration at highway toll stations.
  • Through an in-depth analysis of the interaction between the region area and the number of family cars, it was discovered that cities with larger land areas often possess more complex transportation networks. Diverse modes of transportation and a trend towards economical transport vehicles were identified in these cities. Notably, the proportion of semi-trailers, heavy trucks, and other large vehicles increases significantly in larger cities. While these vehicles improve transportation efficiency, their larger engine sizes allow for greater fuel storage, primarily powered by diesel. However, NO2 emissions released during diesel combustion are significantly higher compared to family cars. Despite the reduction in the number of family cars during the pandemic, emissions from large vehicles were relatively greater. The combined effect led to an increase in NO2 concentration at highway toll stations.
From an academic perspective, the findings of this study fill a gap in the research on NO2 emissions at highway toll stations. It addresses previous shortcomings related to excessively broad study areas, overly simplistic analyses of factors influencing NO2 concentration variations, and insufficient exploration of the correlation between vehicles and toll stations. This study provides a significant theoretical framework for managing NO2 emissions at highway toll stations worldwide. From a practical perspective, this paper proposes feasible countermeasures for the management of NO2 emissions at highway toll stations, such as enhancing emission standards for trucks, optimizing transport routes, and promoting the adoption of new energy vehicles. These recommendations offer scientific support for relevant management authorities. From an international perspective, as a representative city within China’s Yangtze River Delta region, the results of this research hold important reference value for the air quality management of highway toll stations in other countries. The study contributes positively to the advancement of international green transportation.

4.2. Implications

In November 2021, a report was released by China’s Ministry of Transport, indicating that by the end of the year, the number of first-class highway toll stations in the country had increased from 300 to 308, representing a growth rate of 2.7%. This growth trend was found to clearly reflect the continued expansion of toll station infrastructure in China’s highways [58]. Against this backdrop, the need to strengthen the monitoring and control of NO2 emissions at these toll station locations was highlighted as particularly crucial.
Based on the aforementioned analysis and discussion, the following three implications are derived from this study:
  • The promotion of ETC system adoption is critically important. Increasing the penetration of ETC devices and raising the proportion of ETC lanes can effectively reduce vehicle retention times at manual toll lanes, thereby lowering exhaust emissions. In addition, it is necessary to optimize the layout of public transportation networks and improve their service quality to enhance their appeal. This can encourage the public to prioritize green travel options. These measures will help further alleviate traffic congestion and mitigate environmental pollution issues.
  • To further improve air quality, cities should focus on establishing an efficient air quality monitoring system, promoting the concept of a simple, moderate, green, and low-carbon lifestyle, and popularizing clean energy vehicles, thereby reducing the impact of tailpipe emissions on air quality.
  • It is significant that the transportation structure be optimized to reduce reliance on large vehicles, transportation routes be refined, logistics efficiency be improved, and empty or inefficient vehicle trips be minimized. Furthermore, it is essential to enhance environmental standards for large vehicles. More stringent diesel vehicle emissions standards should be formulated and implemented, along with regular inspections of large vehicles to ensure compliance with environmental requirements.

4.3. Limitations and Future Work

Although satellite remote sensing technology is continuously evolving towards higher precision in monitoring atmospheric NO2 concentration, some limitations are inevitable. The effectiveness of TROPOMI satellite data is largely constrained by cloud cover conditions, which leads to discontinuity in the observational data in the time dimension. Additionally, due to privacy protection regulations, COVID-19 infection case data can only be obtained in the form of daily aggregated summaries through the WHO. The inability to access refined data specific to Zhejiang Province introduces errors and uncertainties into the research.
In view of these limitations, future work will focus on developing methods to effectively integrate multi-source data from ground-based monitoring stations, unmanned aerial vehicles, and other platforms. The aim is to compensate for the data gaps caused by high cloud cover. Meanwhile, efforts will be made to coordinate with relevant departments to obtain more refined COVID-19 infection data for Zhejiang Province and other regions. These advancements are expected to significantly enhance the accuracy and reliability of future work.

Author Contributions

Conceptualization, X.Y.; Methodology, X.Y.; Validation, S.X.; Formal analysis, S.X.; Investigation, S.X.; Data curation, S.X.; Writing—original draft, S.X.; Writing—review & editing, X.Y.; Visualization, S.X.; Supervision, X.Y.; Project administration, X.Y.; Funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the Key R&D Program Project of Sichuan Province (2025YFHZ0335) and the Science Foundation of Zhejiang Ocean University (11025092222, JX6311020523).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Satellite data image of highway toll stations. (a) Satellite data strip imagery. (b) Various highway toll stations (S1–S5) in Zhejiang Province. S1––Xiasha highway toll station; S2––Ningbo east highway toll station; S3––Wenzhou east highway toll station; S4––Jiaxing east highway toll station; S5––Quzhou east highway toll station. Colored areas in (a) represent NO2 concentration stripes retrieved from satellite observations.
Figure 1. Satellite data image of highway toll stations. (a) Satellite data strip imagery. (b) Various highway toll stations (S1–S5) in Zhejiang Province. S1––Xiasha highway toll station; S2––Ningbo east highway toll station; S3––Wenzhou east highway toll station; S4––Jiaxing east highway toll station; S5––Quzhou east highway toll station. Colored areas in (a) represent NO2 concentration stripes retrieved from satellite observations.
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Figure 2. In the fsQCA case distribution chart, subplots a to f represent configurations C1 to C6, respectively. Among them, the axes of subplots (a–c,e,f) are identical to those of subplot d. Therefore, axis labels are only annotated in subplot (d), which simplifies the visualization and avoids redundant labeling. Circles indicate outcome memberships ≥0.9, whereas triangles indicate outcome memberships <0.9.
Figure 2. In the fsQCA case distribution chart, subplots a to f represent configurations C1 to C6, respectively. Among them, the axes of subplots (a–c,e,f) are identical to those of subplot d. Therefore, axis labels are only annotated in subplot (d), which simplifies the visualization and avoids redundant labeling. Circles indicate outcome memberships ≥0.9, whereas triangles indicate outcome memberships <0.9.
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Figure 3. Comparative charts of region area, NO2 concentration, number of family cars, and number of COVID-19 infections in Jiaxing during the 2020 Qingming Festival and 2021 New Year’s Day. (a) Region area comparison; (b) NO2 concentration comparison; (c) Number of COVID-19 infections comparison; (d) Number of family cars comparison.
Figure 3. Comparative charts of region area, NO2 concentration, number of family cars, and number of COVID-19 infections in Jiaxing during the 2020 Qingming Festival and 2021 New Year’s Day. (a) Region area comparison; (b) NO2 concentration comparison; (c) Number of COVID-19 infections comparison; (d) Number of family cars comparison.
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Figure 4. Comparison chart of the number of family cars and trucks during the Qingming Festival 2020 and New Year’s Day 2021 in Jiaxing.
Figure 4. Comparison chart of the number of family cars and trucks during the Qingming Festival 2020 and New Year’s Day 2021 in Jiaxing.
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Figure 5. Comparative charts of region area, NO2 concentration, number of family cars, and number of COVID-19 infections between Jiaxing (Qingming Festival 2020) and Hangzhou (Qingming Festival 2022). (a) Number of family cars; (b) NO2 concentration; (c) Region area; (d) Number of COVID-19 infections.
Figure 5. Comparative charts of region area, NO2 concentration, number of family cars, and number of COVID-19 infections between Jiaxing (Qingming Festival 2020) and Hangzhou (Qingming Festival 2022). (a) Number of family cars; (b) NO2 concentration; (c) Region area; (d) Number of COVID-19 infections.
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Figure 6. Comparative charts of the region area, NO2 concentration, number of family cars, and number of COVID-19 infections between Jiaxing and Hangzhou on New Year’s Day 2020. (a) Number of COVID-19 infections; (b) NO2 concentration; (c) Region area; (d) Number of family cars.
Figure 6. Comparative charts of the region area, NO2 concentration, number of family cars, and number of COVID-19 infections between Jiaxing and Hangzhou on New Year’s Day 2020. (a) Number of COVID-19 infections; (b) NO2 concentration; (c) Region area; (d) Number of family cars.
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Table 1. Statistical analysis of regional variables and data (unit: 10−4).
Table 1. Statistical analysis of regional variables and data (unit: 10−4).
AreaXiashaNingboWenzhouJiaxingQuzhou
Mean2.971.560.951.841.11
Standard deviation6.041.490.832.251.26
Minimum0.080.040.080.080
Maximum95.0215.618.5228.2311.81
Table 2. Holidays and specific dates, 2019–2023.
Table 2. Holidays and specific dates, 2019–2023.
HolidayYearDate
New Year’s Day20191–3 January
20201–3 January
20211–3 January
20221–3 January
2022–202330 December 2022–1 January 2023
Spring Festival20195–11 February
202025–31 January
202112–17 February
20221–7 February
202322–28 January
Qingming Festival20194–6 April
20204–6 April
20214–6 April
20223–5 April
20234–6 April
Labor Day20191–5 May
20201–5 May
20211–5 May
202230 April–4 May
20231–5 May
Dragon Boat Festival20196–8 June
202012–14 June
202112–14 June
20223–5 June
202321–23 June
National Day20191–7 October
20201–7 October
20211–7 October
20221–7 October
20231–7 October
Table 3. Overview of research variables and data.
Table 3. Overview of research variables and data.
Variable TypeVariablesData VolumeUnit
Condition
variable		Region area5Square kilometers
COVID-19 infections5Ten thousand people
Holiday days6Days
Average temperature6Celsius
Family cars9593Vehicles
Result variableNO22399ppb
Table 4. Statistical summary of research variables and data.
Table 4. Statistical summary of research variables and data.
Variable AbbreviationRegion AreaCOVID-19 InfectionsHoliday DaysAverage TemperatureFamily Cars
Mean10,182359851715,437
Standard deviation412245012812,196
Minimum42370361002
Maximum16,853992972671,307
Table 5. Threshold settings for data calibration.
Table 5. Threshold settings for data calibration.
VariablesFull MembershipCrossover PointFull Non-Membership
Region area16,85393654237
COVID-19 infections9929130
Holiday days743
Average temperature26206
Family cars33,61811,3052215
Table 6. The results of the necessity analysis for five conditional variables and their negated forms.
Table 6. The results of the necessity analysis for five conditional variables and their negated forms.
VariablesConsistencyCoverage
Region area0.710.60
~Region area0.630.59
COVID-19 infections0.700.56
~COVID-19 infections0.580.59
Holiday days0.520.51
~Holiday days0.640.53
Average temperature0.500.45
~Average temperature0.800.70
Family cars
~Family cars		0.60
0.68		0.54
0.60
Table 7. Configuration analysis results. ⊗ indicates that the relevant influencing factors do not play a role in this configuration. ● indicates that the relevant influencing factors play a role in this configuration.
Table 7. Configuration analysis results. ⊗ indicates that the relevant influencing factors do not play a role in this configuration. ● indicates that the relevant influencing factors play a role in this configuration.
ConditionsC1C2C3C4C5C6
Region area ⊗●⊗●
COVID-19 infections⊗⊗ ●●
Holiday days⊗⊗⊗⊗⊗⊗
Average temperature⊗⊗⊗⊗⊗⊗
Family cars⊗ ⊗● ●
Raw Coverage0.2740.2460.3030.2940.3230.321
Unique Coverage0.0050.0030.0120.0060.0110.010
Overall Consistency0.841
Overall Coverage0.485
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Xu, S.; Yang, X. Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P. Atmosphere 2025, 16, 517. https://doi.org/10.3390/atmos16050517

AMA Style

Xu S, Yang X. Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P. Atmosphere. 2025; 16(5):517. https://doi.org/10.3390/atmos16050517

Chicago/Turabian Style

Xu, Shenghao, and Xinxiang Yang. 2025. "Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P" Atmosphere 16, no. 5: 517. https://doi.org/10.3390/atmos16050517

APA Style

Xu, S., & Yang, X. (2025). Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P. Atmosphere, 16(5), 517. https://doi.org/10.3390/atmos16050517

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