Passengers’ Sensitivity and Adaptive Behaviors to Health Risks in the Subway Microenvironment: A Case Study in Nanjing, China

Abstract

Passenger behavior in subways has recently become a matter of great concern, with more attention being paid to the health risks of the subway microenvironment (sub-ME). This paper aimed to provide guidance for subway passengers on better adapting to the health risks presented by the sub-ME. A questionnaire-based survey was conducted in Nanjing, China, and descriptive analysis and a one-way analysis of variance were performed to understand the sensitivity levels of subway passengers and analyze their adaptive behaviors, based on their sensitivity to sub-ME health risks. The results showed that passengers over 66 years old and those who are frequently sick are more sensitive to the presented health risks. Additionally, passengers traveling for longer and those traveling in rush hours are more sensitive to sub-ME health risks. We also found that individual characteristics, knowledge structure, and information communication all influence passengers’ adaptive behaviors. It was ascertained that those with a positive attitude and those who had previously suffered from environmentally influenced diseases, as well as those who studied an environment-related subject, tended to demonstrate more adaptive behaviors. Moreover, passengers who are very familiar with the subway information communication channels and the related information adapted better to the health risks of the sub-ME. Our findings are beneficial for improving passengers’ adaptability to the health risks presented by the sub-ME and for promoting the sustainable operation of subway systems.

1. Introduction

The rail transit system plays an increasingly crucial role in public transportation. By the end of 2020, a total of 244 urban rail transit routes (7969.7 km) had been put into service in China, followed by Germany (3604.16 km), Russia (1840.5 km), and the USA (1688.91 km); in China, 78.8% of these routes (6280.8 km) were subways [1]. Owing to the relatively closed-off underground micro-environment and the high-density pedestrian traffic of the subway, some environment-related factors entering through various channels will change the environmental balance of the subway and impede its sustainable development [2]. As a result, in addition to providing travel convenience for its users, the subway also poses some threats to the health of passengers and staff.

The subway microenvironment (sub-ME) has always had a widespread and varied influence on passengers’ health. On the one hand, the various sensitivity levels of passengers, i.e., the likelihood of manifesting changes to their health influenced by the environment, are surprisingly diverse when encountering the same conditions of sub-ME. This might be closely related to passengers’ physical fitness, their behavioral patterns, and the degree of their exposure to the microenvironment, which makes them behave differently in response to adverse microenvironments [3]. On the other hand, as a result of differences in gender, knowledge reserves, and personal experience, passengers tend to embody different personal cognitive abilities regarding the sub-ME that reflect their diverse adaptabilities to the microenvironment, i.e., the ability to self-adjust, adapt, and recover when faced with environmental hazards [4].

In recent years, research on the subway microenvironment has primarily focused on the thermal environment from an objective standpoint, the influencing factors, optimization approaches, and thermal comfort evaluation [5,6]. However, there is a lack of research on subjective perceptions of the subway environment by the passengers. The growing awareness of the importance of sustainability in the construction industry has prompted new requirements for the sustainable operation of the subway. As the end-users of the subway, passengers’ sensitivity and adaptive behaviors to the subway environment are important considerations when promoting the sustainable operation of the subway system.

In this regard, this paper aims to explore the sensitivity of passengers exposed to the sub-ME and analyze their adaptive behaviors, based on their sensitivity to sub-ME health risks. Using the theory of risk perception, various hypotheses were proposed; a questionnaire was designed and conducted to clarify the feelings, perceptions, and adaptive behaviors of subway passengers, to analyze the factors influencing the sensitivity of passengers and their adaptive behaviors toward the attendant health risks. The findings of this paper offer some suggestions for passengers to ensure healthier subway travel and to provide a reference for the subsequent optimization of the subway network and sustainable operation management of the sub-ME.

The paper is organized as follows. After this introductory section, Section 2 presents a literature review. Section 3 explains the study’s theoretical background and hypotheses. Section 4 depicts the research process, while Section 5 and Section 6 present the research results and a discussion. Section 7 offers our research conclusions.

2. Literature Review

Transportation is an important part of everyday life. With different preferences and demands, people have many choices for modes of transport: walking, cycling, a private car, or public transit (e.g., taxi, subway, bus). Numerous studies have shown that personal travel modes are affected by a subjective sense of wellbeing. The main reason for this finding is that individuals may experience positive or negative feelings during the journey to their destination [7,8]. For example, car drivers may feel stressed and impatient when in a traffic jam [9]. Previous studies on travel modes and subjective wellbeing have mainly focused on travel satisfaction during commuting; most of the studies focused on the differences and influencing factors of travel satisfaction for different travel modes [10,11]. Generally, compared with car travel, slow-mode travel (e.g., walking and cycling) offers greater travel satisfaction, while travel on public transport (e.g., bus, subway) offers less satisfaction [12,13]. One of the factors that cannot be ignored is the environmental quality of the station. According to previous research, the station’s environmental quality significantly influences the overall travel satisfaction of public transport users [14,15,16]. As one of the main components of public transport operations, passengers are exposed to the station environment when they travel by public transport. Compared with other transit options, the subway is located underground, with enclosed spaces and heavy passenger flow, which will naturally lead to greater health impacts. For instance, air quality will affect the health of passengers, and the light levels or acoustic environment will have an impact on their comfort [17]. Furthermore, the diverse behaviors of passengers when faced with the same environmental conditions have attracted the interest of various researchers [18]. Therefore, we first review the published literature on the impact of the subway environment on passengers, then the relevant studies regarding their behavior during their journey. The gap between these two groups is highlighted as follows.

2.1. Impact of the Subway Environment on Its Associated Personnel

Since subway systems serve large numbers of people and the environment of the subway system is relatively closed off, there are various problems that seriously threaten the health of its staff and passengers. These issues comprise air pollution, noise, insufficient natural ventilation, a lack of natural light, densely crowded areas full of people with a high level of mobility, etc. [19,20,21]. In addition, smaller particles with a relatively large surface area are actually more harmful to the human body because they can penetrate deeper into parts of the lungs or become secondary pollutants by bonding with other atmospheric pollutants, such as nitrogen oxides. As such, many recent studies have analyzed the harm caused by the subway environment to passengers’ health. For instance, Kim et al. (2017) measured the concentrations of particulate matter (PM) of different types on an actual route taken by many subway users; they found that the concentrations of particles sized between 0.3 and 0.422 μm decreased as the tester moved deeper underground, while the concentrations of particles sized between 1 and 10 μm increased, suggesting that particles with a size of 0.01–0.422 μm are mainly inhaled by subway users from the outside air, whereas particles with a size of 1–10 μm are inhaled when passengers move deeper underground [5]. As is consistent with this finding, Xu et al. (2018) confirmed that short-term exposure to PM may interfere with the cardiac autonomic function of subway commuters, while its potential impact depends on the size of the PM and the gender of the commuters [22].

Aside from the perspective of damage to passengers’ health from the environment, scholars have also conducted research on the comfort of passengers in the subway, with a focus on the thermal environment. Tao et al. (2019) reported that the flow-field profile of subway compartments has a significant impact on passengers’ comfort levels [23]. Mortada et al. (2018) highlighted the fact that old and deep subway lines, particularly during the summer, normally suffer from overheating problems that are detrimental to passengers’ comfort and health [24]. Hasan et al. (2018) utilized computational fluid dynamics (CFD) software to develop a CFD station model for predicting static air temperature, velocity, relative humidity, and the predicted mean vote, aiming to bring the passengers’ thermal comfort in all modes of transport to acceptable levels [6]. After investigating passengers’ comfort over three seasons, Han et al. (2016) found that most respondents felt “neutral” about or “comfortable” with the environment in subway stations, even at very low temperatures [25].

However, previous studies mainly focused on the objective and direct impacts of the subway environment on passengers’ health, while scant attention has been paid to the subjective perceptions of the environment experienced by the passengers. How sensitive the passengers are to the environment of the subway is a leading dimension when reflecting on public perceptions of the sub-ME, which is crucial for promoting the sustainable development of rail transit operations. It is, therefore, necessary to investigate passengers’ sensitivity levels to factors associated with the subway environment, and the differences in sensitivity among various passengers.

2.2. Behaviors of Subway Passengers

Subway stations are important nodes for the gathering, dispersal, and transit of crowds. In terms of the behaviors demonstrated by subway passengers, numerous researchers have investigated the emergency evacuation behavior of passengers at subway stations. Hong et al. (2018) unified the principles of the ripple effect and the hypothesis of herd behavior to model the phenomenon of passenger self-evacuation behavior in an emergency situation at a subway station [26]. Wan et al. (2014) put forward a crowd evacuation simulation method for a bioterrorism scenario in the subway environment to better demonstrate individual behaviors in evacuation scenarios, such as competitivity, grouping, and herding [27].

Choice behavior is another topic of interest to scholars. Wang et al. (2018) analyzed subway passengers’ travel choice behavior in the context of subway network emergencies and revealed that network topology characteristics have shown an influence on passengers’ travel choices [28]. Xu et al. (2017) found that in-vehicle crowding plays a crucial role in passenger behavior when choosing a route [29]. Yang et al. (2018), however, put forward some important factors influencing passengers’ waiting-area choices and behaviors, including the distance to the waiting area, passenger density in terms of the visual field, and the size of the waiting area [30].

In addition, studies relevant to this topic were carried out regarding the unsafe behaviors demonstrated by passengers. Liang et al. (2017) concluded that the perceived severity of a subway fire significantly affected whether college students understood self-help escape information, according to the 24 model and the health belief model [31]. Furthermore, Wan et al. (2015) found, through a questionnaire-based survey, that the time a journey is taken, the number of stops experienced by passengers, transgressions, and sudden violations were significant predictors of incident involvement when exploring the classification and effects of passenger behavior and their relation to incident involvement [32].

To sum up, most of the previous research related to passengers’ behaviors has emphasized risk-related behaviors and behavioral choices during the trip. However, the environmental-adaptive behaviors of passengers have somehow been ignored. Considering the importance of passengers’ sensitivity levels and their adaptive behaviors, this paper strives to fill these gaps as a contribution to public health research and the improvement of subway operations.

3. Theoretical Background and Hypotheses

3.1. Theory of Risk Perception

The theory of risk perception describes an individual’s judgment and attitudes toward risks. The perception of risk refers to its assessment, including the possibility of risk occurrence, the level of risk outcome, and the judgment of risk control [33]. Gregory and Mendelsohn (1993) listed certain characteristics of risk, e.g., immediacy, likelihood, effects, etc., that are likely to influence an individual’s perception of a particular risk [34]. Furthermore, Slovic (2000) pointed out that risk perception mainly depends on the individual’s subjective knowledge and intuitive judgment [35]. Following this finding, Sitkin and Pablo (2016) reported that risk perception is influenced by the recognition of risk, the degree of risk control, and the degree of confidence in one’s own decision-making [36]. All in all, risk perception is subjective cognition, and its judgment is based on a variety of objective risk factors (e.g., the degree of risk, the conditions, the nature of the risk, etc.). Therefore, the theory of risk perception offers arguments to establish how individuals perceive risk and the factors of perception. The passenger is the main body of a sub-ME and the place where health risks exist. Hence, the health risks in a sub-ME cause changes in passengers’ health, motivate them to perceive those changes, and then encourage them to take different adaptive actions, accordingly. The theory of risk perception could be utilized to help identify sensitive passengers, clarify cognition and the feelings of passengers in the sub-ME, and analyze the differences among the adaptive behaviors of the passengers.

3.2. Hypotheses

According to the theory of risk perception, passengers with different characteristics vary in sensitivity to sub-ME health risks and behave differently in response to those health risks. This topic has increasingly attracted scientific interest at the interface of sub-ME research and studies on adaptive behavior. Therefore, we first reviewed the available literature on the effects of passengers’ sensitivity levels and then reviewed the relevant studies on what factors affect their adaptive behaviors.

3.2.1. Sensitivity

To study what factors influence passengers’ sensitivity to health risks in sub-ME, both the passengers’ characteristics and their degree of exposure to the sub-ME should be taken into consideration. The relevant factors fall into two categories, i.e., population structure and mode of travel [33,37].

On the one hand, since passengers have different physical qualities, they are subjected to the maximum impact of health risks differently when exposed to the sub-ME. For instance, Akha (2018) pointed out that passengers of different ages have different abilities to defend themselves against environmental hazards, and the immunity of young passengers is relatively stronger than that of the elderly [38]. It should be noted that due to defects in the knowledge structure and life experiences of the elderly and the younger generation regarding environmental sanitation, they are likely to deal with the environmental situations differently and are, thus, easily affected by the health risks inherent in a sub-ME [39]. The disparity of the sex ratio may also have an impact on the sensitivity of passengers. In terms of their physiological structure, men’s bone growth is generally more robust than women’s; their resistance ability is relatively strong and is less likely to be affected by the adverse microenvironment [40]. Besides this, Caserta et al. (2008) argued that the frequency of illness can reflect an individual’s general physical health [41]. Specifically, passengers with high rates of illness show low levels of physical health, and they are likely to be more sensitive to the microenvironment than those with low rates of illness. Accordingly, we propose the following hypotheses:

Hypothesis 1.1.

Age contributes to passengers’ sensitivity to health risks in the sub-ME.

Hypothesis 1.2.

Gender contributes to passengers’ sensitivity to health risks in the sub-ME.

Hypothesis 1.3.

The frequency of illness contributes to passengers’ sensitivity to health risks in sub-ME.

On the other hand, the subway, as a means of public transportation, is wide-ranging and uninterrupted; it is necessary to clarify the traveling purpose of the passengers in the subway, and then explore the traveling patterns of the passenger. A passenger’s mode of travel is an important influencing factor for sensitivity analysis. The subway serves a specific purpose in cities; except for the network’s employees, a high proportion of its passengers take the subway for the purpose of traveling within the city. Their retention time is short and behavior patterns are quite different from those in other public places [42]. Furthermore, Frank et al. (2019) argued that the degree of exposure to sub-ME has an influence on passengers’ sensitivity to health risks in the sub-ME [43]. Specifically, the more frequent the subway ride and the more exposure there is to the sub-ME, the greater the likelihood of health changes, especially when passengers commute in rush hours, at which time they would be more sensitive to health risks due to more serious air pollution levels in the sub-ME [44,45]. There are disparities in the reasons for traveling, the time of day at which a journey is taken, the travel duration, and the frequency of travel in the subway. Hence, the following hypotheses can be proposed:

Hypothesis 2.1.

The purpose of the journey contributes to passengers’ sensitivity to health risks in the sub-ME.

Hypothesis 2.2.

Traveling frequency contributes to passengers’ sensitivity to health risks in the sub-ME.

Hypothesis 2.3.

Traveling duration contributes to passengers’ sensitivity to health risks in the sub-ME.

Hypothesis 2.4.

The time of day at which a journey is taken contributes to passengers’ sensitivity to health risks in the sub-ME.

3.2.2. Adaptability

Different passengers have different capabilities to adapt to health risks in the sub-ME. Behavioral adaptability to health risks in the sub-ME could be motivated by three aspects: individual characteristics, knowledge structure, and information communication.

Different individuals have different individual characteristics (e.g., gender, age, income, local area, state of health, attitudes toward life, etc.). These characteristics not only lead to differences in the cognition of events among individuals but also influence the formation of their ability to adapt to risks [46]. Hence, disparities of adaptability to health risks exist among passengers with different characteristics. The University of Portland in the United States once conducted a survey on climate change and found that males think more macroscopically than females, while females generally think more subtly, with a higher awareness of risk prevention than males. With increasing age, there is a requisite increase in an individual’s knowledge reserve and life experience; therefore, age may influence his/her perception of risks. Knowledgeable and experienced individuals tend to perceive more health risks in the sub-ME and become more adapted to them. According to the theory of Keynes’s absolute income, disposable personal income is the only explanatory variable that determines the consumption level. Besides, since one’s consumption level and lifestyle are influenced by one’s disposable income, individual income is also considered as a factor with an impact on subway passengers’ modes of travel and their protective behaviors regarding their environment [46]. Hence, one’s income is a factor in one’s ability to adapt to health risks in the sub-ME. An individual’s residential area and local environment also play a significant role in the formation of environmental consciousness. At the same time, this consciousness has a subtle influence on behavior, and those with a strong awareness of environmental protection tend to perceive more environmental changes and take more effective protective measures with regard to environmental risk [47]. Both positive and negative emotions impact the risk of cognition; positive individuals may reduce their risk losses, while groups who are relatively conservative or negative may adopt conservative or negative adaptive measures, accordingly [48]. Meanwhile, health status will influence one’s attention to health risks in the sub-ME [49]. Individuals who have previously had environment-related illnesses are relatively better at preventing and coping with sub-ME health risks [50]. For example, individuals with respiratory diseases are more concerned about air pollution, considering the fact that it may aggravate their symptoms. The following research hypotheses are, thus, formulated for the present study:

Hypothesis 3.1.

Gender contributes to passengers’ adaptive behaviors.

Hypothesis 3.2.

Age contributes to passengers’ adaptive behaviors.

Hypothesis 3.3.

Income contributes to passengers’ adaptive behaviors.

Hypothesis 3.4.

Living area contributes to passengers’ adaptive behaviors.

Hypothesis 3.5.

Life attitudes contribute to passengers’ adaptive behaviors.

Hypothesis 3.6.

Illness frequency contributes to passengers’ adaptive behaviors.

Hypothesis 3.7.

Environmental illness experience contributes to passengers’ adaptive behaviors.

Differences in the composition of the knowledge structure stimulate passengers’ different responses and behaviors when facing adverse health risks in a sub-ME. Furthermore, knowledge can be categorized into theoretical knowledge and behavioral knowledge, both of which may influence one’s ability to perceive and adapt to risks [51,52]. If an individual shows a great deficiency in either their theoretical knowledge or behavioral knowledge, his or her ability will be adversely influenced to a great extent. The formation of theoretical knowledge largely depends on one’s educational background, the field of study, and occupation. Individuals with a higher level of education tend to adapt to risks more appropriately. Occupation and field of study have a great impact on one’s knowledge and experience; accordingly, professional groups are expected to be more adaptive to health risks in the sub-ME since they have more access to environmental information. Regarding behavioral knowledge, this mainly comes from life experience and relies on access to information [53]. Therefore, people engaged in environment-related occupations tend to acquire more environmental protection information; they may have a stronger awareness of environmental health and pay more attention to adapting to health risks than the general public [54]. Therefore, we propose the following hypotheses:

Hypothesis 4.1.

Education background contributes to passengers’ adaptive behaviors.

Hypothesis 4.2.

Occupation contributes to passengers’ adaptive behaviors.

Hypothesis 4.3.

Field of study contributes to passengers’ adaptive behaviors.

Hypothesis 4.4.

Environmental health awareness contributes to passengers’ adaptive behaviors.

Along similar lines, whether the outside world can provide real-time and accurate risk information and whether individuals can effectively receive information and then take the corresponding protective actions in time is another important factor of one’s adaptability to health risks in the sub-ME. The level of ability to receive information depends on both the degree of information communication and public engagement [55,56]. Information communication is an interactive process involving exchanging opinions among individuals, groups, and institutions [57]. The preference for communication with others has impacts on the effectiveness of both information communication and public participation to a certain extent; thus influencing one’s adaptability. Its improvement is mainly determined by two factors: the public emergency mechanism and the information communication channel [58]. If individuals are familiar with the public emergency mechanism and information communication channels and are interested in the relevant environmental information, they will make timely and effective responses and then take a series of adaptive measures to protect their own health. The following research hypotheses are, therefore, stated as follows:

Hypothesis 5.1.

A preference for communication contributes to passengers’ adaptive behaviors.

Hypothesis 5.2.

Knowledge of feedback channels contributes to passengers’ adaptive behaviors.

Hypothesis 5.3.

Knowledge of the emergency mechanism contributes to passengers’ adaptive behaviors.

Hypothesis 5.4.

Attention to relevant information contributes to passengers’ adaptive behaviors.

Based on the above hypotheses, a theoretical model was established in our empirical study, as shown in Figure 1.

4. Research Process

4.1. Questionnaire Design

In order to test our hypotheses, we adopted a survey approach to understand the feelings, perceptions, and adaptive behaviors of subway passengers, as well as the influencing factors of sensitivity and adaptive behaviors toward health risks. The questionnaire consisted of demographic data (population structure, individual characteristics), mode of travel, knowledge structure, and information communication. Passengers’ perception problems and the degree of satisfaction with the sub-ME were also examined in the survey. Furthermore, the sensitivity and adaptive behaviors of passengers were also measured in the survey.

Sensitivity (i.e., drowsiness, dizziness, shortness of breath, throat discomfort, nasal discomfort, tinnitus, skin itching, and pain, etc.) may occur when taking the subway. The sensitivity based on the frequency of uncomfortable symptoms was measured on a five-point scale (where 1 = seldom, 2 = occasionally, 3 = sometimes, 4 = often, 5 = usually).

Adaptive behavior scores were given according to a four-point scale, with 1 = definitely/very willing, 2 = often/more willing, 3 = sometimes/does not matter, 4 = seldom/unwilling. Nine adaptive behaviors were designed to measure the passengers’ adaptability to health risks in the sub-ME, such as wearing masks and earplugs, avoiding peak hours, changing travel modes, etc. In this research, the total value of the adaptive behaviors (R-value of between 9 and 36), was performed in order to evaluate passengers’ adaptability to sub-ME health risks. The larger the R-value is, the more positive the relevant adaptive behavior, and the better the adaptability.

4.2. Data Collection

The Nanjing subway has opened 10 lines, and the subway system has a total of 174 stations with a line length of 378 km, covering 11 municipal districts in Nanjing (see Figure 2). Hence, Nanjing has a complete and complex subway network (see Figure 3). Our survey was conducted from July 2017 to January 2018. Then, this study calculated the questionnaire sample size, based on the study by Charan and Biswas (2013) [59]. In Equation (1), $Z_{1-2/\alpha }$ represents the standard normal variate (at 5%, a type 1 error (p < 0.05). $Z_{1-2/\alpha }$ is 1.96 and at 1%, a type 1 error (p < 0.01) $Z_{1-2/\alpha }$ is 2.58). The p-values are considered significant below 0.01; hence, 2.58 is utilized in the formula. P represents the expected proportion of the population, based on previous studies or pilot studies. E represents the absolute error or precision; it is 0.05 in this study. According to Equation (1), this study calculated a sample size with a precision/absolute error of 5% and a type 1 error of 5%, and the minimum sample size is 340.

$$N=\frac{Z_{1-2/\alpha }{}^2\times \left[P\times \left(1-P\right)\right]}{E^2}$$

(1)

The survey was administered to a total of 360 participants who took the subway in Nanjing; 351 of their responses were valid, resulting in an effective response rate of 97.5%. All valid surveys were administered by trained data collectors through face-to-face communication. After administration, the questionnaire results were converted into an electronic version.

4.3. Statistical Analysis

The responses of the participants were checked thoroughly and coded for the purpose of statistical analysis. The SPSS 22.0 software was utilized to conduct descriptive statistics, an independent sample Student’s t-test and analysis of variance (ANOVA); samples with missing data were excluded. To examine the reliability of the empirical data, consistency analysis has been confirmed, utilizing the Cronbach’s alpha method. The validity of the study was also confirmed in this research. The ANOVA method is a one-way method to analyze the differences in the passengers’ levels of sensitivity and adaptability.

5. Results

5.1. Descriptive Statistics

According to

Table 1. Individual characteristics of the respondents.

Variable	Classification	Number	Percentage
Gender	Male	162	46.15%
	Female	189	53.85%
Age	≤18 years old	50	14.25%
	19–40 years old	150	42.74%
	41–65 years old	120	34.19%
	≥66 years old	31	8.83%
Income	≤2000	19	5.41%
	2000~4000	72	20.51%
	4000~8000	133	37.89%
	>8000	127	36.18%
Living area	Urban area	262	74.64%
	Rural area	46	13.11%
	Urban–rural junction	43	12.25%
Life attitudes	Positive	185	52.71%
	Conservative	131	37.32%
	Avoidant	35	9.97%
Illness frequency	Usually	11	3.13%
	Often	48	13.68%
	Sometimes	62	17.66%
	Occasionally	179	51.00%
	Seldom	51	14.53%
Environmental illness experience	Yes	104	29.63%
	No	247	70.37%

, the proportion of male/female passengers is approximately the same (with the former being 46.15% and the latter being 53.85%). Among these respondents, 14.25% are 18 years old and younger, and are mostly students; 42.74% are between 19 and 40 years old; 34.19% are between 40 and 65 years old, and are mostly office workers; and respondents of above 65 years old account for 8.83%. According to the 2018 report of salary levels (Nanjing city), the average salary is CNY 4620. Since 25.92% of the survey respondents’ monthly incomes are less than CNY 4000, they have not yet reached the average income level of the city, and 25.36% of the people have lived in rural areas or at the border of the urban and rural areas for the longest time reported in the past two decades. Meanwhile, in response to the question on life attitudes, 52.71% of the passengers showed an obviously optimistic character, and only 9.97% of passengers chose to avoid difficulties. Moreover, 14.53% of the respondents rarely got sick, while 29.63% of them had been sick themselves or had family members who had been sick due to environmental pollution.

Regarding the factor of occupation (see

Table 2. Knowledge structure of respondents.

Variable	Classification	Number	Percentage
Education background	Junior high school and below	20	5.70%
	Middle high school	30	8.55%
	Junior college	46	13.11%
	Undergraduate	186	52.99%
	Postgraduate and above	69	19.66%
Occupation	Student	87	24.79%
	Government/Public institution	83	23.65%
	Enterprise/Company	130	37.04%
	Self–employment and other	51	14.53%
Major	Environment	36	10.26%
	Medicine	35	9.97%
	Civil Engineering	39	11.11%
	Other	241	68.66%
Environmental health awareness	Very high	36	10.26%
	High	133	37.88%
	Moderate	118	33.62%
	Low	50	14.25%
	Very low	14	3.99%

), most of the respondents were company employees and students, accounting for 37.04% and 24.79%, respectively. The number of professionals in jobs related to the environment, medicine, and civil engineering reached 31.34%. In addition, a bachelor’s degree or above had the highest proportion of 72.65%. As shown in

Table 3. Travelling mode of respondents.

Variable	Classification	Number	Percentage
Travel purpose	Commute and business affairs	168	47.86%
	Life and entertainment	99	28.21%
	Unfixed	84	23.93%
Travel frequency (times/week)	≤5	52	14.81%
	5–10	131	37.32%
	10–15	140	39.89%
	15–20	13	3.70%
	≥20	15	4.27%
Travel time(minutes)	≤10	34	9.69%
	10–20	103	29.34%
	20–30	97	27.64%
	≥30	117	33.33%
Travel duration	6 a.m.–9 a.m./5 p.m.–8 p.m.	103	29.34%
	9 a.m.–5 p.m.	79	22.51%
	After 8 p.m.	21	5.98%
	Unfixed	148	42.17%

, most passengers chose the subway as their mode of travel for daily commuting or city activities. The average number of times that the respondents took the subway every week was between 10 and 15 times. Ninety percent of the passengers spent more than 10 min each visit when traveling on the subway. Passengers took the subway mainly from 6 a.m. to 9 a.m. and from 5 p.m. to 8 p.m., which times are regarded as peak hours, accounting for 29.34% and 27.92% of responses in the valid questionnaires, respectively, the main passengers being either students or office workers.

Table 4. Information communication of respondents.

Variable	Classification	Number	Percentage
Preference for communication	Very high	44	12.54%
	High	105	29.91%
	Moderate	112	31.91%
	Low	56	15.95%
	Very low	34	9.69%
Knowledge of feedback channels	Very high	25	7.12%
	High	102	29.06%
	Moderate	137	39.03%
	Low	68	19.37%
	Very low	19	5.41%
Knowledge of the emergency mechanism	Very high	25	7.12%
	High	129	36.75%
	Moderate	111	31.62%
	Low	65	18.52%
	Very low	21	5.98%
Attention to relevant information	Very high	16	4.56%
	High	88	25.07%
	Moderate	134	38.18%
	Low	74	21.08%
	Very low	39	11.11%

shows the information communicated by the respondents; it is clear that the majority of the respondents (31.91%) had a moderate preference for communication, while nearly 30% of the respondents (29.06%) had a high level of knowledge of feedback channels. Additionally, 36.75% of respondents were highly familiar with the emergency mechanism, and 38.18% of respondents paid moderate attention to the relevant information.

The satisfaction evaluation of the sub-ME (Figure 4) shows that 29.63% of passengers were satisfied with the current sub-ME, while up to 69.8% rated it as moderately or less satisfactory, of which, 32.48% of passengers were dissatisfied with the sub-ME. From Figure 5, we can see that the microenvironment problem causing the most passenger dissatisfaction mainly included noise, ventilation, air quality, and noise in the carriage, summer temperatures, and the humidity of the platform. Specifically, more than half of the passengers claimed that they were not satisfied with the noise of the subway carriage and over 40% of passengers expressed dissatisfaction with the noise on subway platforms. Passengers’ dissatisfaction with ventilation and the air quality in the carriages (36.47% and 32.76%, respectively) was significantly higher than that on the platform (11.40% and 12.25%, respectively). In terms of temperature, passengers who were satisfied with the summer temperature of the carriage (37.61%) were significantly more numerous than in the winter (8.56%). On the contrary, passengers who were satisfied with the temperature of the platform in the winter (19.94%) were slightly more numerous than in the summer (11.4%). Meanwhile, in winter, the number of passengers who were satisfied with the temperature of the platform was more than twice those satisfied with that of the carriage, while in the summer, the number of passengers satisfied with the temperature of the carriage was more than three times that satisfied with that on the platform. In addition, there is not much difference in the number of passengers who were not satisfied with the humidity of the carriage and platform. Passengers’ dissatisfaction with the platform lighting (10.26%) was also slightly higher than that in the carriage (5.13%).

5.2. The Results of Sensitivity Levels

Table 5. Results of the sensitivity analysis.

			Mean ± SD	Levene’s Test (Sig)	p-Value	f-Value	t-Value	LSD Test/Dunnett’s Test
Population structure	Age	≤18	2.16 ± 0.650	0.021	<0.001 *	6.726	/	4 > 1 4 > 2 4 > 3
		19–40	2.26 ± 0.772
		41–65	2.38 ± 0.789
		≥66	2.87 ± 0.670
	Gender	Male	2.21 ± 0.799	0.907	0.004 *	/	−2.933	2 > 1
		Female	2.45 ± 0.732
	Illness frequency	Usually	3.27 ± 0.786	0.220	<0.001 *	5.783	/	1 > 2 > 3 1 > 4.5
		Often	2.52 ± 0.922
		Sometimes	2.23 ± 0.798
		Occasionally	2.26 ± 0.698
		Seldom	2.37 ± 0.692
Travelling mode	Traveling purpose	Business; Commuting	2.48 ± 0.674	0.021	0.001 *	7.407	/	1 > 2
		Daily life; Entertainment	2.11 ± 0.785
		Unfixed	2.32 ± 0.876
	Traveling frequency	Less than 5times	2.13 ± 0.687	0.457	<0.001 *	9.992	/	5 > 1.2.3 4 > 1.2 3 > 2.1
		5–10 times	2.16 ± 0.802
		10–15 times	2.45 ± 0.661
		15–20 times	2.77 ± 0.927
		More than 20 times	3.20 ± 0.775
	Traveling duration	Less than 10 min	1.82 ± 0.673	0.886	<0.001 *	20.516	/	4 > 3.2.1 3 > 2.1
		10–20 min	2.08 ± 0.776
		20–30 min	2.36 ± 0.680
		More than 30 min	2.70 ± 0.698
	Traveling time	6 a.m.–9 a.m. 5 p.m.–8 .m.	2.51 ± 0.625	<0.001	0.035 *	2.894	/	1 > 2
		9 a.m.–5 p.m.	2.19 ± 0.765
		After 8 p.m.	2.24 ± 1.221
		Unfixed	2.32 ± 0.774

Note: The LSD (least significant difference) test was conducted for a post-event test of the homogeneity of variance, and Dunnett’s test was conducted for a post-event test of the heterogeneity of variance. *: Difference is significant at the p < 0.05 level (2-tailed).

reveals that age, gender, and illness frequency had a significant impact on passengers’ levels of sensitivity (p < 0.05)—which supports the hypotheses H1.1, H1.2, and H1.3. It was established that differences in age composition, gender ratio, and health status were statistically significant. In the subway, passengers over 66 years old (2.87 ± 0.670) showed the highest frequency of discomfort. Women (2.45 ± 0.732) were more sensitive to sub-ME health risks than men (2.21 ± 0.799). Compared with those who were seldom sick (2.37 ± 0.692), the passengers who were frequently sick (3.27 ± 0.786) were more sensitive to the influence of the microenvironment and, therefore, have higher levels of sensitivity.

From Table 2, it was found that the purpose for travel, traveling frequency, traveling duration, and the time of day of their travel influenced passengers’ levels of sensitivity (p < 0.05); hypotheses H2.1, H2.2, H2.3, H2.4 were, therefore, tested. It is notable that these four factors regarding the mode of travel all have significant impacts on personal discomfort. The sensitivity levels of business groups (2.48 ± 0.674) were higher than those traveling for the purposes of daily life and entertainment (2.11 ± 0.785). Comparatively speaking, passengers with longer traveling frequency and traveling duration were more sensitive to health risks in the sub-ME. Besides this, significant differences in traveling time were detected (p < 0.05). Compared with those (2.19 ± 0.765) who traveled between 9 a.m. and 5 pm., passengers (2.51 ± 0.625) who traveled in rush hours (7 a.m.–9 a.m., 5 p.m.–8 p.m.) were more frequently prone to discomfort.

5.3. The Results of Adaptability Level

The results of differences in the adaptive behaviors are shown in

Table 6. Results of the adaptability analysis.

			Mean ± SD	Levene’s Test (Sig)	p-Value	f-Value	t-Value	LSD Test/Dunnett’s Test
Individual characteristics	Gender	Male	17.28 ± 4.096	0.240	0.667	/	0.431	-
		Female	17.46 ± 3.838
	Age	≤18	18.98 ± 4.138	0.873	0.004	4.586	/	1 > 2.3.4
		19–40	17.26 ± 3.808
		41–65	17.26 ± 3.988
		≥ 66	15.81 ± 3.516
	Income	≤ CNY 2000	18.95 ± 5.328	0.542	0.020	3.325	/	1 > 2.3 4 > 2.3
		CNY 2000–4000	16.76 ± 3.538
		CNY 4000–8000	16.89 ± 4.002
		> CNY 8000	17.99 ± 3.785
	Living area	Urban area	17.52 ± 4.053	0.807	0.527	0.642	/	-
		Rural area	16.93 ± 3.838
		Urban-rural junction	17.00 ± 3.457
	Life attitudes	Positive	17.90 ± 4.035	0.638	0.027	3.655	/	1 > 2.3
		Conservative	16.87 ± 3.659
		Avoidant	16.49 ± 4.280
	Illness frequency	Usually	20.27 ± 6.358	0.533	0.042	2.506	/	1 > 3.4.5
		Often	18.08 ± 4.047
		Sometimes	16.65 ± 4.208
		Occasionally	17.35 ± 3.662
		Seldom	17.08 ± 3.682
	Environmental illness experience	Yes	18.20 ± 4.352	0.079	0.011	/	2.559	1 > 2
		No	17.03 ± 3.729
Knowledge structure	Education background	Junior high school and below	17.75 ± 6.758	0.013	0.526	0.799	/	-
		Middle high school	16.17 ± 3.563
		Junior college	17.46 ± 3.174
		Undergraduate	17.51 ± 3.825
		Postgraduate and above	17.39 ± 3.885
	Occupation	Student	17.78 ± 4.241	0.434	0.225	1.462	/	-
		Government/Public institution	17.55 ± 3.842
		Enterprise/Company	17.38 ± 3.599
		Self-employment and others	16.37 ± 4.418
	Major	Environment	19.22 ± 5.038	0.287	0.020	3.316	/	1 > 3.4
		Medical science	17.69 ± 3.504
		Civil engineering	17.44 ± 4.285
		Others	17.05 ± 3.717
	Environmental health awareness	Very high	20.57 ± 6.711	0.009	<0.001	10.48	/	2 > 4.5 3 > 4.5
		High	19.10 ± 3.677
		Moderate	17.92 ± 3.705
		Low	16.47 ± 3.263
		Very low	15.31 ± 4.214
Information communication	Preference for communication	Very high	19.16 ± 3.362	0.345	<0.001	23.551	/	1.2 > 3 > 51.2 > 3 > 4
		High	19.19 ± 3.790
		Moderate	17.13 ± 3.321
		Low	15.18 ± 3.664
		Very low	13.91 ± 3.279
	Knowledge of feedback channels	Very high	18.84 ± 4.346	0.058	<0.001	6.663	/	1 > 4.52 > 4.52 > 3
		High	18.35 ± 3.606
		Moderate	17.43 ± 3.541
		Low	15.63 ± 4.055
		Very low	16.05 ± 5.307
	Knowledge of emergency mechanism	Very high	18.48 ± 3.595	0.869	0.017	3.073	/	1 > 4.5 2 > 4.5
		High	17.98 ± 4.159
		Moderate	17.28 ± 3.899
		Low	16.37 ± 3.638
		Very low	15.95 ± 3.471
	Attention to relevant information	Very high	14.56 ± 3.366	0.6	0.066	2.224	/	_
		High	17.64 ± 4.284
		Moderate	17.50 ± 3.964
		Low	17.53 ± 3.772
		Very low	17.23 ± 3.406

. Differences in gender and long-term residential area were not noted in this study (p > 0.05), whereas factors in terms of age, income, life attitudes, personal illness frequency, and environmental illness experience had significant influences on an individual’s adaptability to sub-ME health risks. Hypotheses H3.2, H3.3, H3.5, H3.6, H3.7 were therefore supported.

Overall, the total level of adaptive behaviors of the subway population was relatively poor. Compared with the group under 18 years old (18.98 ± 4.138), respondents aged 66 years old (15.81 ± 3.516) had lower adaptability to the sub-ME health risks. In addition, the group with an income below CNY 2000 (16.76 ± 3.538) had a better adaptability score than those with an income between CNY 2000 and 8000 (16.76 ± 3.538, 16.89 ± 4.002, respectively). Moreover, compared with individuals having conservative and avoidant life attitudes (16.87 ± 3.659 and 16.49 ± 4.280, respectively), the group with positive personalities (17.90 ± 4.035) obtained a higher score. It is clear that they were more willing to adopt a variety of adaptive behaviors to cope with the sub-ME health risks. Additionally, in contrast to those who had no experience of environmental disease (17.03 ± 3.729), passengers who had suffered environmental diseases before (18.20 ± 4.352) were more adaptable when addressing the health risks of the sub-ME.

6. Discussion

6.1. Analysis of Sensitivity

6.1.1. Population Structure

According to the results regarding sensitivity level, age had a significant impact on the sensitivity level of the passengers; the group over 66 years old showed the highest frequency of adverse reactions when taking the subway. One reason might be that as the body ages, physical function in the elderly gradually declines [60]. Since changes take place in skeletal muscle, there is a decrease in motor ability [61]. In addition, the elderly may encounter psychological issues with aging [48]. As a result, the elderly proved more susceptible to sub-ME health risks and paid considerably more attention to their physical and mental health. In other words, similar health risks may lead to more serious consequences compared with the younger groups. Another reason is that the old people tend to show a deficiency in their knowledge structure regarding environmental health—making them more sensitive to sub-ME health risks. In addition, this study found that female passengers’ frequency of experiencing discomfort was significantly higher than in male passengers. Male passengers generally had a stronger resistance than female passengers, resulting in less adverse sub-ME health risks. It was also found that in this study, an individual’s state of health had a remarkable influence on the frequency of his or her adverse symptoms. This finding is in line with the results of previous studies. For instance, Anna et al. (2019) reported that passengers with respiratory diseases such as wheezing are more susceptible to the microenvironment in the subway, considering that passengers who are not often ill usually have a stronger resistance to the health risks in sub-ME [62]. The sub-ME is relatively enclosed, with insufficient natural ventilation; in the daytime, lighting and ventilation are mainly through mechanical ventilation, such as air conditioning. When the sub-ME is poor, healthy people are certainly immune to adverse effects. On the contrary, due to the lack of oxygen and the air pollution, commuters with poor health conditions, especially in rush hours, will have insufficient blood sugar and energy levels—resulting in hypoglycemia, dizziness, irritability, and even cardiovascular symptoms [63]. Hence, people in poor health are often unable to defend themselves against health risks in a timely and effective manner.

6.1.2. Traveling Modes

The subway is an important city transport network and passengers generally spend a short time taking it. However, it should be noted that its microenvironment is rather complex and crowded, which easily causes the spread of disease. Particulate matter (PM) is a problem due to passengers’ exposure to numerous air pollutants associated with adverse effects on human health [64,65]. Meanwhile, the subway is a long-operating and uninterrupted public transportation system. Since passengers traveling for the purpose of commuting and business will generally travel in a fixed way, and their traveling time is concentrated in rush hours, the frequency of their discomfort reactions is the highest in this study. Furthermore, the results showed that traveling frequency and traveling duration influence an individual’s sensitivity level. The longer that passengers stay in the transport system and the more frequently they take the subway, the more exposure they experience to sub-ME and the higher their sensitivity level to sub-ME health risks. Li et al. (2015) found there were more particles in the subway compared with other transport systems; the more the passengers were exposed to the microenvironment, the greater the particles’ impacts on human health [66]. Similarly, it was detected that the frequency of adverse symptoms was the highest among passengers who took the bus more than 20 times a week and those who took the subway for more than 30 min each time. In addition, 6 a.m.–9 a.m. and 5 p.m.–8 p.m. were generally the daily rush hours when air quality was significantly worse than other periods, according to the results. Passengers who take the subway during this period are, therefore, relatively more prone to discomfort. This discomfort includes both physical and psychological manifestations that are caused by quarrels with other passengers or staff. It is suggested that passengers with poor health should avoid traveling in peak periods and might choose a more reasonable travel time instead, to reduce the possibility of discomfort.

To sum up, in this study, the proportions of people over 66 years old, women, and people who were often sick were 8.83%, 53.85%, and 3.13%, respectively. The above groups were identified as being more susceptible in this study, those who were more sensitive to the health risks of the sub-ME. It is suggested that these groups should pay greater attention to the possible harm of micro-environmental health risks and strengthen their resistance to health risks. In terms of commuting mode, it was found that passengers whose travel purpose was commuting or business, and those whose travel frequency was more than 20 times a week, whose travel time was more than 30 min each time, whose travel time was between 6 a.m. and 9 a.m. and between 5 p.m. and 8 p.m. accounted for 47.86%, 4.27%, 33.33%, and 29.34% of the total surveyed population, respectively. The listed groups were more sensitive to the influence of the sub-ME. These passengers ride on the subway and they are one of the stakeholders in the subway’s operation; hence, they are supposed to endeavor to avoid taking the subway in rush hours and to take protective measures, such as wearing masks and earplugs. Accordingly, the subway company, which bear the responsibility of subway operation management, should take some publicity measures to protect these sensitive groups and guide them regarding self-protection measures through radio broadcasting, so as to reduce the harm brought about by the micro-environmental health risks. In this regard, the operation of subway systems is expected to become more sustainable.

6.2. Analysis of Adaptability

6.2.1. Individual Characteristics

According to the results, the elderly had very high sensitivity levels due to the decline of their physical functions. Meanwhile, the lack of knowledge of micro-environmental prevention resulted in their poor adaptability. For this reason, the elderly fail to take the corresponding protective measures. Consistently, Giamalaki and Kolokotsa (2018) found that the elderly were the most vulnerable group, who were more susceptible to the influence of the external environment, and who need to strengthen their self-protection consciousness [67]. Through the publicity produced by the subway company, the elderly could actively take countermeasures, such as wearing masks and earplugs. On the other hand, as, currently, schools have attached more importance to microenvironment education, teenagers’ adaptability to conditions is better than that of other groups. As a means of transportation with relatively low travel costs, the subway is the main mode of travel for low-income families (with a monthly family income less than CNY 2000). As a result, low-income people showed relatively good adaptability to micro-environmental health risks. In addition, this paper established that the adaptability of positive passengers was better than that of the conservative and avoidant types. Some possible explanations could be found in other, relevant studies. Allyson et al. (2019) studied the relationship between personality and behavior and found that extroverts have a higher tendency to take positive action [68]. Xie et al. (2016) also found that negative people tend to magnify the risks and cannot face up to the risk positively, so as to take appropriate measures [69]. Additionally, people who are often sick tend to have low immunity and high sensitivity. They, therefore, usually pay more attention to changes in the subway environment. Then, they take action to protect their health and are more willing to participate in the improvement of the subway environment. In contrast, people who do not get sick frequently need to further increase their public participation awareness, taking more active, adaptive behaviors to improve the sub-ME. At the same time, an individual who has previously suffered from environmental diseases seems to have a stronger sense of prevention against an adverse environment and stronger adaptability regarding the health risks of a sub-ME.

6.2.2. Knowledge Structure

The results showed that groups with different fields of study varied in adaptability significantly. Passengers whose subject was related to the environment adapted to health risks more positively, compared with other professions. This is because environmental professionals are more familiar with environmental professional knowledge and the impacts of environmental health risks, and they were more willing to participate in activities to improve the sub-ME. In addition, individuals’ environmental health awareness reflected their attention to the environment and the protection of their health. People with stronger environmental health awareness tended to pay more attention to micro-environmental health when taking the subway, and their adaptive behaviors were relatively more effective.

Manika et al. (2017) believed that the knowledge structure affects not only self-cognition but also individual behaviors. People with a relatively good knowledge structure tend to have a more comprehensive understanding of micro-environmental health and the possible impacts of the health risks [70]. They are able to take the necessary preventive measures and make adequate traveling preparations. Therefore, how to improve the knowledge structure of passengers, especially knowledge related to micro-environmental health, is an important issue and deserves further study.

6.2.3. Information Communication

In this study, no statistically significant difference has been found regarding the degree of attention to relevant information. However, in terms of the degree of understanding of the emergency mechanism, information communication channels, and the preference for communication with others, the higher the degree of understanding or preference, the higher the score of the adaptive behavior. This may be because the information channels between the groups who are familiar with feedback channels or the emergency mechanism and the subway company are more efficient, and the information communication is easier, which makes them adapt better than other groups. People who like to communicate with others were more willing to offer feedback through communication when encountering risks and, therefore, were more adaptive to health risks. In the process of information communication, the ability to receive information mainly depends on the degree of information communication. For those passengers unfamiliar with communication channels or the emergency mechanism, improving their degree of information communication should be more emphasized. Firstly, the government, which plays the main supervisory role in subway operations management, should set up micro-environmental health management institutions, using an information database for effectively monitoring and managing the sub-ME. Secondly, it is necessary for the subway department to broaden the information communication channels and provide multiple platforms (i.e., platform feedback, internal mailboxes, a micro-blog, a public media account, etc.) to facilitate passengers’ feedback. Finally, the subway company is supposed to improve the micro-environmental management scheme and guide passengers in protecting their health in the sub-ME, according to micro-environmental problems and the feedback they receive from the passengers.

This study also found that public participation is another important factor in information communication. As passengers are the main body of the sub-ME, their traveling experiences and travel quality should be considered important. However, Yeung (2008) argued that the feelings of passengers seem to be ignored by the Mass Transit Railway Corporation, and it becomes difficult to guarantee public participation—causing the poor adaptability of some passengers [71]. Conversely, when passengers are more willing to participate in environmental information communication, they could adapt to the health risks in sub-ME. Hence, the subway needs to encourage passengers to provide feedback with regard to unsatisfactory situations and put forward suggestions for improving the subway’s environment management. Then, more emphasis should be placed on passengers’ feedback and a public participation mechanism established. If passengers’ suggestions are adopted, rewards could be provided accordingly. As a result, more passengers would tend to be willing to participate in improving sub-ME and their adaptability to health risks would be improved. Moreover, the subway needs to enrich the subway environment information channels and update the information on the sub-ME in a timely manner. Through cooperation between the government and the subway company, more effective health information in the subway could be available to the passengers, thus helping them better understand the health risks in the sub-ME. Once passengers had a comprehensive understanding of the sub-ME, they could enhance their adaptability and then take more positive measures to ensure the sustainability of the subway. The subway operation management stakeholders mainly comprise the government, the subway company, and passengers, and the recommendations for each stakeholder have been summarized below in

Table 7. Recommendations for stakeholders.

Number	Findings	Recommendations	Stakeholders
1	Passengers whose travel purpose was commuting or business and those whose travel frequency was more than 20 times a week, whose travel time was more than 30 min each time, whose travel time was between 6 a.m. and 9 a.m. and between 5 p.m. and 8 p.m., were groups sensitive to the influence of sub-ME.	(1) Avoid traveling in peak periods (2) Choose reasonable travel time (3) Wear masks and earplugs	Passengers
		(4) Guide passengers on self-protection through radio broadcasting	Subway company
2	Passengers with stronger environmental health awareness and related knowledge pay more attention to micro-environmental health when taking the subway, and their adaptive behaviors are relatively more effective.	(1) Educate passengers to strengthen environmental health awareness	Government
		(2) Distribute leaflets on environmental information	Subway company
3	Passengers with a higher degree of understanding of the emergency mechanism and information communication channels have more adaptive behaviors.	(1) Establish sub-ME health management institutions	Government
		(2) Broaden information communication channels and provide multiple platforms	Subway company
		(3) Give subway ride feedback and advice	Passengers

.

7. Conclusions

This paper aims to explore the sensitivity and adaptability of passengers in the sub-ME to micro-environmental health risks and to promote the sustainable development of rail transit operations. Through a questionnaire survey of passengers on Line X, this paper explored the composition and different travel modes of passengers, probed their perceptions of the current state of the sub-ME, and the differences among passengers’ adaptive behaviors. By analyzing the effects of different sensitivity factors on discomfort behaviors and the impacts of various adaptive factors on the adaptive behaviors of passengers, this paper clarified the specific performance of passengers’ sensitivity and adaptability to the health risks of the sub-ME.

Our main conclusions can be summarized as follows. (1) Population structure and the mode of travel influenced the sensitivity level: women’s frequency of discomfort was significantly higher than that of men; passengers over 66 years old, women and sickly people were more susceptible to discomfort; passengers whose travel was for commuting or business, whose traveling frequency was more than 20 times a week, where each journey took more than 30 min or who were traveling in rush hours, were more sensitive to the sub-ME health risks. (2) Individual characteristics, knowledge structure, and information communication influenced the passengers’ adaptive behaviors: the sensitivity level of the elderly was very high but their adaptability was poor; the adaptability of active passengers was better than those belonging to conservative and avoidant groups; negative passengers tended to magnify the risk and normally failed to take appropriate measures; passengers with a relatively good knowledge structure tended to have a more comprehensive understanding of the micro-environmental health risks; passengers with strong micro-environmental health awareness tended to pay more attention to the microenvironment health of individuals; people open to communicating were more willing to offer feedback when encountering risks. Given the passengers suffering health risks in the sub-ME, these findings make a contribution to identifying sensitive passengers and helping passengers to better adapt to micro-environmental health risks, based on their levels of sensitivity and adaptability, as well as providing a reference for more effective subway management. It is suggested that subway operation stakeholders, including the government, the subway company, and the passengers, could be involved in promoting subway management. Firstly, the subway company could utilize videos and radio broadcasts to educate passengers on self-protection and pay more attention to improving the sub-ME operational maintenance. In addition, the government could provide the passengers with more effective health information and supervise the subway operational maintenance to ensure an improved sub-ME. As for the passengers, they should strengthen their awareness of environmental health and improve their participation in operational maintenance. The sub-ME can be improved more sustainably if the passengers take more positive actions to enhance their adaptability and if the subway company adopts more sustainable measures. Despite these valuable findings, this case study was subject to some limitations since the data was only collected from Nanjing. Although the Nanjing subway provided a typical example for investigations into passengers’ sensitivity and adaptive behaviors to health risks, and we drew some conclusions with regularity, caution should be taken when generalizing our findings to other countries or regions with a different subway management system and development stage. In the future, in-depth analysis and comparative research on the subway microenvironment can be conducted based on the characteristics of different regions.