Evaluating Students’ Dose of Ambient PM_2.5 While Active Home-School Commuting with Spatiotemporally Dense Observations from Mobile Monitoring Fleets

Abstract

Understanding the dose of ambient PM_2.5 inhaled by middle school students during active commuting between home and school is essential for optimizing their travel routes and reducing associated health risks. However, accurately modeling this remains challenging due to the difficulty of measuring ambient PM_2.5 concentrations along commuting routes at a population scale. In this study, we overcome this limitation by employing spatiotemporally dense observations of on-road ambient PM_2.5 concentrations collected through a massive mobile monitoring fleet consisting of around 200 continuously operating taxis installed with air quality monitoring instruments. Leveraging these rich on-road PM_2.5 observations combined with a GIS-terrain-based PM_2.5 dosage modeling approach, we (1) assess middle school students’ PM_2.5 dosages during morning (7:00 am–8:00 am) home–school walking commuting along the shortest-distance route; (2) examine the feasibility of identifying an alternative route for each student that minimizes PM_2.5 dosages during commuting; (3) investigate the trade-off between the relative reduction in PM_2.5 dosage and the relative increase in route length when opting for the alternative lowest-dosage route; and (4) examine whether exposure inequalities exist among students of different family socioeconomic statuses (SES) during their home–school commutes. The results show that (1) 18.8–57.6% of the students can reduce the dose of PM_2.5 by walking along an alternative lowest-dose route; (2) an alternative lowest-dose route could be found by walking along a parallel, less-polluted local road or walking on the less-trafficked side of the street; (3) seeking an alternative lowest-dose route offers a favorable trade-off between effectiveness and cost; and (4) exposure inequities do exist in a portion of students’ walking commutes and those students from higher-SES are more likely to experience higher exposure risks. The findings in our study could offer valuable insights into commuter exposure and inspire future research.

1. Introduction

Over the past decade, China’s air pollution problem has been significantly improved under the vigorous efforts of the Chinese government [1,2]. However, fine particulate matter (PM_2.5) remains the primary air pollutant in major cities across western China [3]. Extensive research has demonstrated that both long-term chronic and short-term acute exposure to PM_2.5 can result in a wide range of adverse health effects [4,5,6]. Across various research populations, children and adolescents are more vulnerable to the harmful effects of air pollution than adults, as their bodies are still in the developmental stage [7,8]. Therefore, the PM_2.5 exposures in the daily lives of such groups should receive more attention.

Recent studies on children and adolescents’ exposure to air pollution in various micro-environments show that while commuting accounts for only a small portion of daily life, the exposure during this time contributes disproportionately to their total daily exposure [8,9,10,11]. This highlights the importance of gaining a deeper understanding of their exposure to PM_2.5 specifically during commuting. As governments increasingly promote active transportation, such as walking and cycling, emphasizing their considerable benefits for public health and the environment [12], it becomes even more critical to examine the exposure risks associated with children and adolescents’ daily active commuting between home and school. Research in this area can assist the government in promoting active transportation while also maximizing the health and environmental benefits brought by such initiatives.

Recent studies have begun to examine children and adolescents’ exposure to air pollution during various modes of active commuting (e.g., walk, bike, inline skates, and scooter) between home and school [8,13,14]. Walking and cycling are the most common modes of active commuting among such groups. Gilliland et al. investigated PM_2.5 exposure levels among 36 students over 101 routes from home to school. Their findings revealed that students who walked to school experienced lower average PM_2.5 concentrations than those who commuted by car or school bus [13]. Gao et al. measured six schoolchildren’s exposure to PM and black carbon (BC) during daily school commutes, revealing that walking posed the lowest exposure levels, while e-bike rides led to higher PM₁ and BC exposure due to proximity to vehicle emissions [15]. These two studies both employed personal monitoring, where students carried portable devices, offering a direct method to measure individual exposure across various commute modes. However, a notable limitation of this approach is the small sample size, constrained by the substantial costs and time required for such fieldwork, making it challenging to scale for larger studies. To facilitate large-scale population exposure assessment, modeling methods were introduced and have since been extensively applied in related research. Wolfe et al. used the AERMOD dispersion model to estimate NO_X concentrations in Detroit, analyzing NO_X exposure during home–school commutes under different modes (walking, automobile, and school bus) in two neighborhoods with contrasting air quality environments—one with heavy diesel and high traffic and the other with low diesel and low traffic [16]. Air dispersion models offer a feasible approach to simulating ambient air pollutant concentrations when assessing schoolchildren’s exposure. However, implementing this method demands extensive parameters and input data, which can be challenging to obtain. On the other hand, the spatial resolution of dispersion models is also a significant concern [17]. More recently, Ma et al. used a cost-effective and approachable land use regression (LUR) method to generate the distribution map of annual mean nitrogen dioxide (NO₂) concentrations for Auckland, assessing students’ dosage of NO₂ (using a terrain-based modeling approach) during their home–school walking commutes along (1) the shortest-distance route and (2) an alternative route with the lowest dosage [18]. Boniardi et al. demonstrated that LUR models can predict black carbon (BC) exposure during home-to-school commutes, though the model tended to underestimate absolute concentrations by an average of 29% [19]. Most previous LUR models are built primarily on stationary sites, with many of these sites located away from roads or sidewalks. Consequently, while these models can effectively estimate air pollutant concentrations in residential areas, directly using these estimations to assess exposure during commuting may introduce some bias. Therefore, the existing studies based on either personal monitoring or modeling approaches have certain limitations when it comes to large-scale estimation of students’ exposure to air pollution during their home–school active commuting.

More recently, large-scale taxi fleets equipped with air quality monitoring systems have been deployed across major cities in China [17,20,21]. These fleets enable the collection of high-resolution spatiotemporal air quality data across the majority of urban road networks, offering valuable new opportunities for studying air pollution exposure during students’ active home–school commuting at the population scale. This study addresses the limitations of previous research [22,23] by utilizing on-road PM_2.5 measurements collected through a massive mobile monitoring network consisting of around 200 continuously operating taxis, combined with a GIS-terrain-based PM_2.5 dosage modeling approach. The aims of our study are to (1) assess students’ PM_2.5 dosages during morning (7:00 am–8:00 am) home–school active commuting (e.g., walking) along the shortest-distance route; (2) examine the feasibility of identifying a route for each student that minimizes PM_2.5 dosages during commuting, as an alternative to the shortest-distance route; (3) investigate the trade-off between the relative reduction in PM_2.5 dosage and the relative increase in route length when opting for the alternative lowest-dosage route; and (4) examine whether exposure inequalities exist among students of different family socioeconomic statuses (SES) during their walking home–school commutes. To the best of our knowledge, this is the first study to model students’ PM_2.5 doses during active home–school commuting at a population scale using such a large-scale on-road mobile monitoring PM_2.5 data, rather than relying on simulated PM_2.5 concentrations derived from models based on spatially sparse fixed monitoring stations. Thus, the findings in our study could offer valuable insights into commuter exposure and inspire future research.

2. Materials and Methods

2.1. Study Area

The study area for this research is Xi’an City, one of China’s national central megacities with a population of approximately 13.17 million, and we focus on the core area (approximately 400 km²) within the Third Ring Road of Xi’an (Figure 1). Xi’an occasionally experiences severe air pollution, with PM_2.5 being the primary air pollutant. The study area is characterized by a dense population, complex urban configuration, and numerous potential air pollution sources [24,25]. Such an environment is well-suited for studying PM_2.5 dose/exposure during active commuting among the student population.

2.2. Schools and School Districts

To evaluate students’ dose of ambient PM_2.5 while walking home–school commuting, we selected three highly representative middle schools located in different districts in Xi’an. They are Xi’an 3rd middle school, 38th middle school, and 58th middle school. Our study focused exclusively on middle schools, as the majority of their students live within their corresponding school district boundaries (easier to simulate the home–school commute routes) and are more likely than primary school students to adopt active transportation.

Figure 2 illustrates the geographical locations and surrounding environments of the three selected middle schools. Figure 3 shows the locations of the schools, school catchments, and corresponding residential communities in each school catchment.

Table 1. General information of schools involved in the study.

No.	School Name	Location	No. of Residential Communities Within the School Catchment
1	3rd middle school	Beilin District	139
2	38th middle school	Xincheng District	59
3	58th middle school	Weiyang District	16

depicts general information of these schools. The 3rd middle school is located in the city center, within a high-density residential district of the school catchment area, which comprises 139 corresponding residential communities. The 38th middle school is located in the Xincheng district, within a medium- to high-density residential district of the school catchment area, which comprises 59 corresponding residential communities. Meanwhile, the 58th middle school is located in Weiyang district, on the northern outskirts of the city. Compared with the areas surrounding the previous two schools, it is part of a later-developed urban district with a relatively low population density. The school catchment area comprises only 16 corresponding residential communities.

2.3. On-Road PM_2.5 Mobile Monitoring

This study uses on-road PM_2.5 concentration data collected by the Xi’an Atmospheric Mobile Monitoring Platform. The platform employs more than 200 operational taxis as mobile sampling vehicles. Each taxi is fitted with an integrated onboard air quality monitoring system installed within the rooftop lightbox, enabling real-time monitoring of the outdoor ambient PM_2.5 concentrations on the roads they traverse. The monitor employed to collect air quality data was the Novafitness-SDS019 (unit cost approximately USD 6000). To enhance data quality and reliability, four identical sensors were installed in each monitor, and the final PM_2.5 concentrations were obtained by integrating their outputs through a designed algorithm. The monitoring platform was managed by a professional maintenance company, ensuring continuous and stable operation. In addition, the monitor on each taxi was periodically calibrated against regular fixed-site stations, which further guaranteed the accuracy and consistency of the measurements. The sampling interval is once every three seconds for each monitor.

In our study, mobile PM_2.5 data collected over a two-week period from 2 August to 15 August 2021 were used. The three-times median absolute deviation (MAD) method was used as the threshold to remove outliers in the raw observations. Each PM_2.5 observation was assigned to its corresponding road segment based on the recorded GPS coordinates using the Hidden Markov Model (HMM)-based map matching algorithm. To ensure the quality of the data used in this study, only road segments with at least 14 repeated visits within the same week were retained in the subsequent data aggregation. To assess students’ PM_2.5 dosages during morning (7:00 am–8:00 am) home–school active commuting, the PM_2.5 concentration for each road segment was calculated as the mean value observed between 07:00 and 08:00 AM (it corresponds to the morning peak commuting time for students) during the two-week period from 2 August to 15 August 2021. Figure 4 illustrates the taxi-based monitoring equipment and the spatial distribution map of temporally aggregated PM_2.5 concentrations for the hours of 7:00 to 8:00 AM over the period from 2 August to 15 August 2021. The platform’s data coverage encompasses over 95% of motor vehicle roads.

2.4. Determine the Active Commuting Road Network

In this study, an active commuting road network was developed using a road network layer obtained from the OpenStreetMap (OSM) website. The shapefile for the study area contains over 34,000 line features, which are classified into 20 distinct categories, including motorways, primary roads, cycle ways, footways, etc. To focus solely on active transportation, features such as motorways, motorway links, and train tracks were excluded from the network. Conversely, local paths and footways in free-entry parks and open spaces were incorporated, as students are typically permitted to traverse these areas. The road network was refined and adapted for analysis using ArcGIS 10.5. The refinement process involved two key steps: (1) Each road feature was segmented at intersections to create multiple smaller line segments, and (2) any segment exceeding 20 m in length was further subdivided into 20 m sub-segments (considering model spatial resolution, GPS positioning errors in mobile monitoring, and computational efficiency). This approach facilitated a more precise and detailed representation of the active transportation network, supporting subsequent modeling of students’ PM_2.5 dosages while active home–school commuting.

2.5. Modeling PM_2.5 Dosages While Walking

A terrain-based dose modeling approach, as introduced in previous studies, was employed to estimate PM_2.5 dosages during students’ home–school walking commutes [18,22]. It accounts for variable walking velocities and ventilation rates influenced by the topography of the road network. The modeling workflow is briefly illustrated below while traversing one road segment (this applied calculation method also accounts for students’ unique respiratory characteristics compared to adults).

Step 1: Calculate slope-related walking velocity (v, km/h):

$$v=0.8\times 6e^{[-3.5\times abs (S+0.05)]}$$

(1)

where S is the directional-related slope of the road segment (expressed in +/−%).

Step 2: Calculate energy expenditure (E, VO₂ in ml/s) of the walking student:

$$E=5.4v^2+0.37 M+0.0054 MSv+0.011S^{2}v-18.9$$

(2)

where M is the body mass. For simplicity, an average body mass of 48 kg for primary school students in grades 7 to 9 was employed in the calculation.

Step 3: Calculate the ventilation rate V_e (unit: L/min) of the walking student.

$$\ln {\displaystyle \frac{V_e}{M}}=4.4329+1.0864\ln {\displaystyle \frac{E}{M}}-0.2829\ln A+0.0513X+\epsilon$$

(3)

where A is the student’s age, and an average age of 13 was employed in the calculation. Since the sex (X) of each sample was unknown, the mean ventilation rate was calculated by averaging the results from a male and a female student.

Step 4: Calculate the dosage (D_s) inhaled along the road segment:

$$D_s={\displaystyle \frac{L}{v}}\times Con{c}_{{\mathrm{PM}}_{2.5}}\times V_e$$

(4)

where L is the road segment length. $Con{c}_{{\mathrm{PM}}_{2.5}}$ is the PM_2.5 concentration at the road segment.

The total dose for the entire route is obtained by summing the PM_2.5 dosages from all road segments along the commute route.

2.6. GIS-Based Road Network Analysis and Route Generation

To examine students’ dose of ambient PM_2.5 walking along different home–school route choices, we generated two routes for each home–school pair: (1) the shortest distance route, which is the preferred choice for most students, and (2) the lowest-dose route (with the least cumulative dosage), designed to minimize pollution inhalations and reduce exposure risk along the way. This analysis was performed using the road network analysis module in ArcGIS 10.5. It was set as follows: (1) Any vertex connectivity (an edge endpoint can connect to any vertex (including endpoints or intermediate vertices) of another edge in the network) was enabled; (2) turning and one-way restrictions were not imposed; (3) cost barrier functions were set to account for the scenario of waiting at traffic signals, to be more specific, we assumed a 60 s wait at a major crossing and 30 s at a minor crossing, both at a resting ventilation rate of 6 L•min⁻¹ to calculate the PM_2.5 dosages during the wait; (4) route length and dosage were set as impedances, respectively; and (5) they were also set as accumulation attributes. The results of the analysis were the origin, destination, total route length, and total PM_2.5 dosages for each home–school route under different impedance setting (the shortest distance route vs. the lowest-dose route). Considering that students may be unwilling to use the alternative lowest-dose route if it were excessively longer, a restriction was applied, permitting an additional distance of up to 500 m.

2.7. Exposure Justice During Commuting

This study also investigated exposure justice (EJ) during commuting to test the following hypotheses: (1) Students with higher SES are associated with lower PM_2.5 exposure risks during home–school commutes, and (2) students with lower SES are associated with higher PM_2.5 exposure risks during home–school commutes. In our study, middle school students’ SES was represented by average housing price in the residential community [26]. In brief, the average housing price for each residential community within the school districts of the selected schools was sourced from the online real estate website xa.lianjia.com. To ensure a fair comparison of students’ PM_2.5 dosage levels across different routes, the Unit Dosage Index (UDI), introduced in a previous study [18], was employed. This index represents the dosage of PM_2.5 a student inhaled per kilometer traveled, calculated by dividing the total PM_2.5 dosages by the length of the route. Numerous studies have shown that students tend to choose the shortest-distance route for their home–school commutes. Consequently, UDIs were calculated for each home–school pair, using the shortest-distance route between home and school. We test the proposed hypothesis by examining the regression relationship between the housing prices of students’ residential communities (a proxy for students’ SES) and the UDI of their home–school routes.

3. Results

3.1. Descriptive Statistics

This subsection presents descriptive statistics of the on-road PM_2.5 concentrations revealed by mobile monitoring data within the zones of the three schools. Figure 5 shows the geographic locations of the three middle schools and the surrounding road networks, while Figure 6 presents the boxplot of on-road PM_2.5 concentrations in and around the three middle school zone areas. These figures show that the PM_2.5 concentration levels along the roads within and around the three school districts are generally comparable and at a similar level. Among them, the 38th middle school district has the highest PM_2.5 concentrations, with an average of 27.46 µg/m³. The concentrations near the 3rd and 58th middle schools are close to this level but slightly lower overall, with average PM_2.5 concentrations of 25.07 µg/m³ and 25.71 µg/m³, respectively. The slight differences in PM_2.5 concentrations among these schools may be attributed to the fact that the 3rd middle school is located in a busy downtown area, where the morning rush hour sees heavy pedestrian and vehicular traffic. Additionally, many alleys nearby host bustling morning markets, which may contribute to the slightly elevated PM_2.5 levels along the roads.

According to the current World Health Organization (WHO) Air Quality Guidelines (AQG), the recommended standards for annual and 24 h mean PM_2.5 concentrations are 5 μg/m³ and 15 μg/m³, respectively [2]. In our study, the PM_2.5 concentration for each road segment was calculated as the mean value observed between 07:00 and 08:00 AM during the two-week period from August 2 to August 15, 2021. As such, these values are not directly comparable with the WHO AQG for PM_2.5 standards. Nevertheless, even when compared to the WHO AQG 24 h mean PM_2.5 standard—which is more analogous to our temporal scale—the PM_2.5 concentrations measured on roads within the catchment areas of the three schools remain substantially higher than the WHO AQG recommended standard (exceeding the standard by more than 10 μg/m³).

3.2. Road Network Analysis Results

This subsection presents the results of the road network analysis. Figure 7 shows the comparison of two types of walking routes from homes to schools for the three middle schools. If the lowest-dose route from home to school does not coincide with the shortest-distance route, we refer to the lowest-dose route as an alternative lowest-dose route (ALDR). We are particularly interested in the probability that students can find an alternative lowest-dose route.

Table 2. The proportion of ALDRs found for students in each school.

No.	School Name	No. of Home–School Pairs	No. of ALDRs	Proportion
1	3rd middle school	139	46	33.1%
2	38th middle school	59	34	57.6%
3	58th middle school	16	3	18.8%
Sum		214	83	38.8%

shows the proportion of ALDRs found for students in each school. The proportion of students who are able to find such a route within the three school districts are (1) for 3rd middle school, 33.1% (46/139) of students; (2) for 38th middle school, 57.6% (34/59) of students; and (3) for 58th middle school, 18.8% (3/16) of students can find an alternative lowest-dose route.

This difference may be attributed to the following three reasons. First, road network configuration is crucial for identifying alternative lowest-dose routes. A dense road network not only enhances the flexibility of travel route choices but also significantly increases the likelihood of selecting a path with minimal PM_2.5 doses from multiple alternatives. Especially in urban environments with uneven distributions of PM_2.5 concentrations, a highly connected road network provides more feasible options to avoid pollution hotspots and bypass high-risk areas, thereby contributing to the goal of minimizing PM_2.5 doses. From Figure 7, it is evident that the 3rd and 38th school zones contain more houses, and accordingly, their road networks are more developed and denser, providing more route options. Second, differences in walking distance also play a crucial role in identifying alternative lowest-dose routes. In general, the longer the travel distance, the more route combinations are available, which increases the likelihood of finding a path with lower PM_2.5 doses. In contrast, for short-distance trips, the limited spatial scope restricts route options and detour flexibility, making it more difficult to identify alternative paths with significantly lower PM_2.5 doses. The average route lengths for the 3rd, 38th, and 58th middle schools are 958.95 m, 2685.21 m, and 1947.21 m, respectively. Third, the spatial distribution differences of PM_2.5 concentrations are also crucial for identifying alternative lowest-dose routes. When there is significant spatial heterogeneity in PM_2.5 levels across the road network, it becomes more feasible to identify routes that intentionally avoid high-exposure areas and instead pass through cleaner segments. Greater variation in PM_2.5 concentrations across space provides stronger contrasts between different route options, thereby enabling path selection algorithms to optimize not just for distance or time, but also for minimal PM_2.5 doses. Conversely, in environments where PM_2.5 concentrations are spatially uniform, the effectiveness of rerouting to reduce PM_2.5 doses becomes limited, as all available paths offer similar PM_2.5 concentration levels.

Table 3. Statistical results for the students of the three schools from the 214 home–school (residential community–school) pairs.

Route Types	Statistics	Length (m)	Dosage (μg)
all shortest-distance routes	max	3942.94	24.58
	min	3.44	0.02
	mean	1508.77	8.28
	std	972.11	5.85
all lowest-dose routes (some may coincide with the shortest-distance route)	max	3951.17	23.41
	min	3.44	0.02
	mean	1524.55	8.14
	std	971.03	5.79
alternative lowest-dose routes (those differ from their shortest-distance routes)	max	3951.17	23.41
	min	442.15	1.59
	mean	1941.65	10.37
	std	900.42	5.82

shows the statistical results for the students of the three schools from the 214 home–school (residential community–school) pairs. Notably, the minimum distance among all alternative lowest-dose routes is 442.15 m, which is far greater than the minimum shortest-distance route length of 3.44 m shown in the table. This suggests that forming an alternative lowest-dose route requires a certain travel distance because when the home–school distance is too short, there are almost no additional alternative paths available. As a result, it is difficult to identify a route with a lower exposure dose, since the shortest-distance route is already the one with the lowest dose.

From the perspective of road network structure, when the home–school distance is extremely short, adding an alternative path often requires a significant deviation from the direct route, leading to a substantial increase in travel distance. Such detours cannot outperform the shortest route in the trade-off between dose and distance. In contrast, once the distance exceeds a certain threshold (e.g., around 442 m in this study), the number of road branches and intersections increases considerably, offering multiple directional choices. This enables the possibility of finding routes with similar travel lengths but lower PM_2.5 doses. Therefore, when interpreting the distribution characteristics of alternative lowest-dose routes, it is important to consider not only the spatial distribution of PM_2.5 concentrations but also the density and structure of the road network. This helps explain why in some cases no better alternative lowest-dose route exists and clarifies the distance ranges within which the potential for exposure optimization is greatest.

Following this question and line of reasoning, we analyzed the spatial characteristics of all alternative lowest-dose routes and their corresponding shortest-distance routes, in order to discuss how alternative lowest-dose routes are formed and to explore the strategies students could use in real life to identify such lowest-dose routes. Cases in Figure 8 demonstrate how, compared with the shortest-distance routes (red), the alternative lowest-dose routes (purple) are formed. An alternative lowest-dose route could be found by walking along a parallel, less-polluted local road or walking on the less trafficked side of the street. Figure 8d demonstrates how walking on the less trafficked side of the street can significantly reduce PM_2.5 dosages at the cost of a few more meters walking (cross the road).

3.3. Trade-Off Between the Two Kinds of Routes

This subsection presents the analysis of the trade-off between walking along the lowest-dose route (ALDR) and the shortest-distance route. We know that using the lowest-dose route can reduce students’ PM_2.5 doses; however, it also increases their additional walking distance. For the 83 residential community–school pairs (out of 214 total) from the three middle schools where alternative lowest-dose routes were available from home to school, the trade-off between the routine shortest-distance route and the alternative lowest-dose route was analyzed. By conducting a comparative analysis between the percentage reduction in PM_2.5 doses and the percentage increase in walking distance when using the lowest-dose route instead of the shortest-distance route, we investigate the cost-effectiveness of the two different routing strategies.

Figure 9 shows the relative reduction in PM_2.5 dosages vs. relative increase in route length for the two kinds of routes in the three school districts. In the figure, the red line represents the 1:1 line. Points above the line indicate that a 1% increase in route length was associated with a more than 1% reduction in PM_2.5 dosages, whereas points below the line indicate that a 1% increase in route length was associated with a less than 1% reduction in PM_2.5 dosages. Averagely, students from the 3rd, 38th, and 58th middle schools walk 5.78%, 0.30%, and 1.02% longer distances but reduce their PM_2.5 doses by 7.03%, 1.23%, and 3.93%, respectively, when choosing the lowest-dose route instead of the shortest-distance route (

Table 4. Relative increase in walking distance and relative reduction in PM_2.5 dosages when choosing the ALDR instead of the shortest-distance route.

No.	School Name	Relative Increase in Route Length (%)	Relative Reduction in PM2.5 Dosage (%)
1	3rd Middle School	5.78	7.03
2	38th Middle School	0.30	1.23
3	58th Middle School	1.02	3.93
Average		3.36	4.54

). We can find that the relative reduction in PM_2.5 dosages along the lowest-dose route tends to be much larger than the relative increase in route length. This finding highlights the importance of students seeking such an alternative lowest-dose route, as it can lead to a significant reduction in PM_2.5 dose at the cost of only a slightly longer walking distance.

3.4. Exposure Inequities During Walking Commutes

This subsection presents the analysis of EJ during students’ walking commutes. Figure 10 shows the scatter plot of UDI vs. house prices in the three middle school areas. From the figure and the corresponding statistical results, it can be observed that there is a moderate positive correlation between UDI and housing prices for 38th and 58th middle schools, with correlation coefficients (R) of 0.37 and 0.34, respectively. For 3rd middle school, however, no evident correlation is observed, with an R value of only 0.05.

Overall, these results suggest heterogeneity in EJ across schools. For 3rd middle school, the near-zero correlation indicates that students’ exposure levels were largely independent of household housing prices. In contrast, for 38th and 58th middle schools, the moderate positive correlations imply that students from higher-priced residential communities were more likely to experience higher exposure risks during their home–school walking commutes. One possible explanation is that in the school districts of the 38th and 58th middle schools, high-priced housing may be located in central or traffic-dense neighborhoods, where pollution levels are elevated, thereby offsetting the expected protective effect of higher socioeconomic status.

In terms of the EJ issue, our findings partially contradict the traditional hypothesis of environmental injustice, as a large part of students from higher SES families tend to face higher exposure risks. This may be determined by the characteristics of residential distribution in Chinese cities. Unlike in Western countries, many high-priced residential communities in the central areas of major Chinese cities are often located near subway stations, bus stops, and densely populated commercial districts, which may result in higher PM_2.5 exposure risks compared to some lower-priced communities. This finding also highlights the context-specific nature of socioeconomic disparities in exposure, and suggests that housing price alone may not be a consistent predictor of air pollution exposure across different urban settings.

4. Discussion

This study employed a large-scale, taxi-based mobile monitoring fleet to obtain spatiotemporally dense on-road PM_2.5 measurements and applied a terrain-based dosage modeling approach to estimate middle school students’ inhaled doses of PM_2.5 during morning (07:00 am–08:00 am) home–school walking commutes. We evaluated both the shortest-distance routes and alternative lowest-dose routes, quantified the trade-off between reduced PM_2.5 dose and increased travel distance, and assessed exposure disparities across students from different SES groups. By integrating high-resolution mobile monitoring with GIS-based dosage modeling for commuter route optimization, our approach bridges the gap between small-scale personal monitoring studies and large-scale but less spatially resolved modeling approaches.

Our results highlight the potential of large-scale taxi-based mobile monitoring as a powerful tool for population-scale commuter exposure assessment. Unlike traditional portable monitoring—where participants carry personal air quality sensors—mobile monitoring can continuously collect high-resolution spatiotemporal data across the majority of an urban road network without the need for extensive recruitment or participant compliance. While personal monitoring remains the gold standard for measuring individual exposure [23], its high costs, logistical complexity, and limited sample sizes have constrained most studies to a handful of participants or routes [27]. In contrast, air quality data obtained from citywide mobile monitoring fleets enable systematic exposure analysis for population-scale commuters, making it feasible to investigate students’ commuting route optimization, exposure inequalities, and the impact of infrastructure or policy interventions at a population scale.

Mobile monitoring is particularly well-suited for commuter exposure studies because it measures air pollutant concentrations directly along or parallel the roadways where walking occurs. Previous studies often relied on modeling approaches—such as LUR or dispersion models—built and validated primarily on fixed regulatory station data [28]. However, such stations are sparse and typically located away from major roadways, limiting their ability to capture fine-scale spatial variations in air pollutant concentrations during commuting [8,17]. Near-road concentrations are significantly higher than urban background levels, and ignoring these gradients can lead to substantial underestimation of exposure during active commuting. By directly sampling near-road environments, mobile monitoring captures more actual exposure conditions experienced by commuters, including micro-scale variations associated with traffic volume, fleet composition, and roadway configuration.

In this study, we focused on middle school students, a group for whom walking is a common and practical mode of school commuting in many Chinese cities due to the school district policy. Compared to primary school students, middle school students are generally more independent, and parents are more comfortable allowing them to walk to school. We selected three representative middle schools, obtained the locations of residential communities within their catchment areas, and estimated PM_2.5 doses along the shortest-distance route, and we also tried to identify an alternative lowest-dose route for each home–school pair. Our trade-off analysis demonstrated that adopting the lowest-dose route could lead to substantial reductions in PM_2.5 dosages—sometimes exceeding 15%—at the cost of only a marginal (<5%) increase in walking distance. Such cost-effectiveness aligns with previous findings that for some students even small changes in route choice can significantly lower exposure risks without compromising travel convenience [29,30,31,32].

An alternative lowest-dose route can often be identified by making relatively small adjustments to the chosen path, such as walking along a parallel local road with lower pollution levels or selecting the less-trafficked side of the same street. These minor modifications can lead to substantial reductions in PM_2.5 dosages without significantly increasing travel distance. The effectiveness of such adjustments depends on the spatial variability of PM_2.5 concentrations, which is influenced by factors such as traffic density, building configurations, and the presence of vegetation barriers. In practice, this means that even in densely built urban environments where the shortest-distance route appears optimal in terms of time and distance, there may exist viable alternatives that achieve lower PM_2.5 doses by leveraging micro-scale spatial differences in air quality.

Our findings suggest that students and parents should be aware of the potential benefits of choosing alternative lowest-dose routes. Such routes may involve walking along a parallel, less-polluted local road, or choosing the less trafficked side of the same road during peak hours, when tidal traffic patterns can produce concentration differences of up to 5–40% between sidewalks [8]. In dense urban areas, even minor adjustments—such as crossing the street to walk on the side with lower traffic flow—can meaningfully reduce PM_2.5 doses, sometimes without appreciably increasing the total walking distance. This highlights the importance of disseminating route optimization strategies not only through academic publications but also via school-based health promotion programs and urban navigation apps [33,34], where real-time pollution information could guide students to safer routes.

In this study, we focused specifically on students’ morning commutes, comparing the shortest-distance routes with alternative lowest-dose routes in order to provide practical strategies for reducing PM_2.5 dosages. Morning routes are relatively straightforward, as students usually prioritize time efficiency and tend to select the shortest-distance paths to school. However, the situation after school can be more complex. Students may take different routes to accompany friends, run small errands, or attend extracurricular activities, which introduces additional variability into their travel behaviors and potential exposure levels. Future research could expand the scope to include both morning and afternoon commutes, aiming to optimize the full set of student travel routes. Such efforts would allow for a more comprehensive evaluation of exposure reduction strategies, better reflecting the real-world diversity of student mobility patterns and helping to design more effective interventions to safeguard students’ health.

Building on our findings, several practical recommendations can be made to help walking students reduce their PM_2.5 doses during daily home–school commutes. First, when feasible, students—particularly those living in central urban areas—should consider adopting an alternative lowest-dose route instead of the shortest-distance route, as the dense road network in city centers often provides multiple viable walking options with substantial differences in near-road pollution levels. Second, in cases where the shortest-distance route already follows quieter streets with limited traffic, such as in some peri-urban or residential neighborhoods, students may simply maintain their existing path, as the potential exposure reduction from rerouting would be minimal. Third, regardless of route length, students should avoid walking along major arterial roads during peak hours and instead use parallel, less-polluted local roads when available. Fourth, in high-traffic areas, walking on the side of the road with lighter traffic flow—taking into account tidal traffic patterns during rush hours—can meaningfully reduce PM_2.5 doses, even if it requires crossing the street and adding a small number of steps to the journey. Last, we also recommend that students wear masks on their way to school if they pass through roads of pollution hotspots, as an additional measure to reduce exposure to air pollution. These strategies require minimal additional travel time and effort yet can yield notable reductions in inhaled PM_2.5 dose, offering a cost-effective and accessible means of improving the health and safety of students’ daily commutes.

From an environmental justice perspective, our analysis revealed that while for one school, students did not experience a significant exposure inequity issue, for the other two schools, students from higher-priced residential communities were more likely to experience elevated PM_2.5 doses during their home–school walking commutes. This pattern contrasts with the conventional EJ hypothesis, which often posits that lower SES populations bear disproportionate environmental burdens [35,36,37,38]. In the context of Chinese cities, this divergence may be explained by unique patterns of residential distribution. High-priced communities in many urban cores are frequently located in close proximity to major transportation hubs—such as subway stations and bus stops—as well as densely populated commercial districts. While these locations offer superior accessibility and amenities, they are also characterized by higher traffic density and more intense human activity, both of which contribute to elevated near-road PM_2.5 concentrations. In contrast, some lower-priced communities situated in peri-urban areas may be farther from major traffic corridors, thereby exposing residents to comparatively lower commuting-related pollution levels.

Notably, our findings in EJ partially align with observations from Auckland, New Zealand, where a recent study on assessing students’ dose of NO₂ during home–school walking commutes found a similarly complex SES–exposure relationship [18]. Specifically, students from lower-income families in suburban and citywide contexts were more likely to be exposed to higher NO₂ levels during walking commutes, which is consistent with the conventional EJ hypothesis. However, in central Auckland, students from higher-income families experienced higher NO₂ levels during walking commutes, likely because their residences—often high-rise apartments—were located close to major transport corridors and high-traffic areas. This parallel suggests that the reversal of the traditional EJ gradient in high-density urban cores may be a recurring phenomenon in cities where high-income housing is concentrated near mobility hubs and commercial activity centers. Such evidence from different geographical contexts reinforces the credibility of our results and underscores the need to interpret EJ patterns in light of local urban form, transportation infrastructure, and socio-spatial development histories. In Chinese megacities, the interplay between accessibility, housing markets, and traffic emissions appears to shape an atypical SES–exposure relationship, challenging assumptions derived from Western suburban-oriented urban models.

Although our analysis only focused on three representative middle schools in Xi’an, the proposed approach is highly transferable. By integrating high-resolution air quality mobile monitoring data with GIS-based route modeling, the methodology can be applied to any middle school within a city to evaluate and optimize walking routes for reduced PM_2.5 doses. This scalability makes it a valuable tool for municipal education and transport departments aiming to improve students’ environmental health. Furthermore, the approach could be adapted to other vulnerable groups—such as the elderly or patients with cardiovascular conditions—who may benefit from lower-exposure walking routes in their daily routines. As air pollution mobile monitoring networks expand in urban areas worldwide, this framework has the potential to become a standard component of healthy urban mobility planning.

5. Conclusions

This study evaluated students’ dose of ambient PM_2.5 while walking from home to school using on-road PM_2.5 observations from mobile monitoring and a GIS-terrain-based PM_2.5 dosage modeling approach. The PM_2.5 doses walking along the shortest-distance and an alternative lowest-dose routes were compared to provide strategic guidance for improving students’ healthy commuting. At this stage of our research, we have drawn the following preliminary conclusions:

(1)

A total of 18.8–57.6% of the students can reduce the dose of PM_2.5 by walking along an alternative lowest-dose route;

(2)

An alternative lowest-dose route could be found by walking along a parallel, less-polluted local road or walking on the less trafficked side of the street;

(3)

For students, seeking an alternative lowest-dose route offers a favorable trade-off between effectiveness and cost, as the relative reduction in PM_2.5 dosages tends to be much larger than the relative increase in route length;

(4)

Exposure inequities do exist in a portion of students’ walking commutes between home and school, but our findings in exposure inequities contradict the traditional hypothesis of environmental injustice.

These results highlight both the methodological and practical value of integrating high-resolution mobile monitoring into commuter exposure studies. The approach is scalable to other schools and population groups, and our findings suggest that simple behavioral adjustments—such as avoiding major arterial roads during peak hours or walking on the less trafficked side of the street—can meaningfully lower inhaled PM_2.5 doses. Incorporating such strategies into urban planning, school travel policies, and navigation tools can help protect vulnerable populations and promote healthier, more sustainable active commuting.