Assessing health risks from bioaccessible PM2.5-bound toxic metals in Nanchang metro: Implications for metro workers and emissions control.

High concentrations of PM2.5 with enriched levels of metallic constituents could significantly affect the health and comfort of metro employees. To avoid overestimating the exposure risks, we investigated the bioaccessibility of toxic metals (TMs) bound in PM2.5 from the Nanchang metro using Gamble's solution method, and qualitatively analyzed the impact of valence state and various sources on the bioaccessibility of TMs bound to PM2.5. The results showed that the bioaccessibility of the studied TMs ranged from 2.1% to 88.1%, with As, Ba, Co and Pb being the most bioaccessible and V, Fe and Cr being the less bioaccessible. The bioaccessibility of TMs in our subway PM2.5 samples varied based on their valence and species, showing higher valence states associated with increased bioaccessibility. Vehicle traffic, secondary aerosols and wheel/rail sources were found to be significantly and positively associated with the bioaccessibility of several TMs, implying a severe potential risk from these three sources. Although both non-carcinogenic and carcinogenic risks associated with total TMs were found to be high, only As and Cr(VI) posed a considerable carcinogenic risk to metro workers based on the bioaccessible fractions and were therefore priority pollutants. In addition, potential carcinogenic risk was found to be more severe in platform than that in ticket counter. The results indicate that considerable efforts are required to control and manage PM2.5 and the associated TMs in the Nanchang subway, particularly from traffic, wheel/rail and secondary sources, to protect the health of metro staff and the public.

Interplay of xenobiotic-degrading and antibiotic-resistant microorganisms among the microbiome found in the air, handrail, and floor of the subway station.

Investigating the quality of the subway environment, especially regarding antibiotic resistance genes (ARGs) and xenobiotics, conveys ecological and health impacts. In this study, compositions and relations of microorganisms harboring ARGs and xenobiotic degradation and metabolism genes (XDGs) in the Sukhumvit subway station (MRT-SKV) in Bangkok was assessed by analyzing the taxonomic and genetic diversity of the microbiome in the air and on the surfaces of floor and handrail. The major bacteria in the MRT-SKV (including Moraxella, which was abundant in the bioaerosol and handrail samples, and Staphylococcus, which was abundant in the bioaerosol samples) were found to contain both ARGs and XDGs. The co-abundance correlation network revealed notable relationships among bacteria harboring antibiotic resistance genes (ARGs) and xenobiotic degradation genes (XDGs). Significant associations were observed between ARGs linked to glycopeptide and fluoroquinolone resistance and genes associated with benzoate, styrene, and atrazine degradation pathways, as well as between ARGs related to cephamycin, cephalosporin, and MLS resistance and XDGs associated with the cytochrome P450-dependent drug metabolism pathway. These correlations suggested that selective pressure exerted by certain xenobiotics and antibiotics can simultaneously affect both ARGs and XDGs in the environment and should favor correlations and co-survival among ARG- and XDG-containing bacteria in the environments. The correlations may occur via shared mechanisms of resistance to both xenobiotics and antibiotics. Finally, different correlation pairs were seen in different niches (air, handrail, floor) of the subway environment or different geolocations. Thus, the relationship between ARG and XDG pairs most likely depends on the unique characteristics of the niches and on the prominent types of xenobiotics and antibiotics in the subway environment. The results indicated that interactions and connections between microbial communities can impact how they function. These microorganisms can have profound effects on accumulation of xenobiotics and ARGs in the MRT-SKV.

Metal and oxidative potential exposure through particle inhalation and oxidative stress biomarkers: a 2-week pilot prospective study among Parisian subway workers.

OBJECTIVE: In this pilot study on subway workers, we explored the relationships between particle exposure and oxidative stress biomarkers in exhaled breath condensate (EBC) and urine to identify the most relevant biomarkers for a large-scale study in this field. METHODS: We constructed a comprehensive occupational exposure assessment among subway workers in three distinct jobs over 10 working days, measuring daily concentrations of particulate matter (PM), their metal content and oxidative potential (OP). Individual pre- and post-shift EBC and urine samples were collected daily. Three oxidative stress biomarkers were measured in these matrices: malondialdehyde (MDA), 8-hydroxy-2'deoxyguanosine (8-OHdG) and 8-isoprostane. The association between each effect biomarker and exposure variables was estimated by multivariable multilevel mixed-effect models with and without lag times. RESULTS: The OP was positively associated with Fe and Mn, but not associated with any effect biomarkers. Concentration changes of effect biomarkers in EBC and urine were associated with transition metals in PM (Cu and Zn) and furthermore with specific metals in EBC (Ba, Co, Cr and Mn) and in urine (Ba, Cu, Co, Mo, Ni, Ti and Zn). The direction of these associations was both metal- and time-dependent. Associations between Cu or Zn and MDAEBC generally reached statistical significance after a delayed time of 12 or 24 h after exposure. Changes in metal concentrations in EBC and urine were associated with MDA and 8-OHdG concentrations the same day. CONCLUSION: Associations between MDA in both EBC and urine gave opposite response for subway particles containing Zn versus Cu. This diverting Zn and Cu pattern was also observed for 8-OHdG and urinary concentrations of these two metals. Overall, MDA and 8-OHdG responses were sensitive for same-day metal exposures in both matrices. We recommend MDA and 8-OHdG in large field studies to account for oxidative stress originating from metals in inhaled particulate matter.

In Situ Test and Numerical Analysis of the Subway-Induced Vibration Influence in Historical and Cultural Reserves.

Although the rapid expansion of urban rail transit offers convenience to citizens, the issue of subway vibration cannot be overlooked. This study investigates the spatial distribution characteristics of vibration in the Fayuan Temple historic and cultural reserve. It involves using a V001 magnetoelectric acceleration sensor capable of monitoring low amplitudes with a sensitivity of 0.298 V/(m/s2), a measuring range of up to 20 m/s2, and a frequency range span from 0.5 to 100 Hz for in situ testing, analyzing the law of vibration propagation in this area, evaluating the impact on buildings, and determining the vibration reduction scheme. The reserve is divided into three zones based on the vertical vibration level measured during the in situ test as follows: severely excessive, generally excessive, and non-excessive vibration. Furthermore, the research develops a dynamic coupling model of vehicle-track-tunnel-stratum-structure to verify the damping effect of the wire spring floating plate track and periodic pile row. It compares the characteristics of three vibration reduction schemes, namely, internal vibration reduction reconstruction, periodic pile row, and anti-vibration reinforcement or reconstruction of buildings, proposing a comprehensive solution. Considering the construction conditions, difficulty, cost, and other factors, a periodic pile row is recommended as the primary treatment measure. If necessary, anti-vibration reinforcement or reconstruction of buildings can serve as supplemental measures.

Where environmental microbiome meets its host: Subway and passenger microbiome relationships.

Subways are urban transport systems with high capacity. Every day around the world, there are more than 150 million subway passengers. Since 2013, thousands of microbiome samples from various subways worldwide have been sequenced. Skin bacteria and environmental organisms dominate the subway microbiomes. The literature has revealed common bacterial groups in subway systems; even so, it is possible to identify cities by their microbiome. Low frequency bacteria are responsible for specific bacterial fingerprints of each subway system. Furthermore, daily subway commuters leave their microbial clouds and interact with other passengers. Microbial exchange is quite fast; the hand microbiome changes within minutes, and after cleaning the handrails, the bacteria are re-established within minutes. To investigate new taxa and metabolic pathways of subway microbial communities, several high-quality metagenomic-assembled genomes (MAG) have been described. Subways are harsh environments unfavorable for microorganism growth. However, recent studies have observed a wide diversity of viable and metabolically active bacteria. Understanding which bacteria are living, dormant, or dead allows us to propose realistic ecological interactions. Questions regarding the relationship between humans and the subway microbiome, particularly the microbiome effects on personal and public health, remain unanswered. This review summarizes our knowledge of subway microbiomes and their relationship with passenger microbiomes.

Temporal, compositional, and functional differences in the microbiome of Bangkok subway air environment.

With the growing numbers of the urban population, an increasing number of commuters have relied on subway systems for rapid transportation in daily life. Analyzing the temporal distribution of air microbiomes in subway environments is crucial for the assessment and monitoring of air quality in the subway system, especially with regard to public health. This study employed culture-independent metabarcode sequencing to analyze bacterial diversity and variations in bacterial compositions associated with bioaerosols collected from a subway station in Bangkok over a four-month period. The bacteria obtained were found to consist primarily of Proteobacteria, Firmicutes, and Actinobacteria, with variations at the family, genus, and species levels among samples obtained in different months. The vast majority of these bacteria are most likely derived from outside environments and human body sources. Many of the bacteria found in Bangkok subway station were also identified as "core microorganisms" of subway environments around the world, as suggested by the MetaSUB Consortium. The diversity of bacterial communities was shown to be influenced by several air quality variables, especially ambient temperature and the quantity of particulate matters, which showed positive correlations with several bacterial species such as Acinetobacter lwoffii, Staphylococcus spp., and Moraxella osloensis. In addition, metabolic profiles inferred from metabarcode-derived bacterial diversity showed significant variations across different sampling times and sites and can be used as a starting point to further explore the functional roles of specific groups of bacteria in the subway environment. This study thus introduced the information required for surveillance of microbiological impacts and their contributions to the well-being of subway commuters in Bangkok.

Investigation of a river-tunnel effect on PM2.5 concentrations in New York City subway stations.

It is well-documented that subway stations exhibit high fine particulate matter (PM2.5) concentrations. Little is known about the potential of river-tunnels to increase PM2.5 concentrations in subways. We hypothesized a "river-tunnel" effect exists: Stations adjacent to poorly ventilated tunnels that travel beneath rivers exhibit higher PM2.5 concentrations than more distant stations. Accordingly, the PM2.5 concentrations were monitored at stations adjacent to and two- and three-stations distant from the river-tunnel. Multivariate linear regression analysis was conducted to disentangle how proximity to a river-tunnel and other factors (e.g., depth) influence concentrations. Stations adjacent to a river-tunnel had 80-130% higher PM2.5 concentrations than more distant stations. Moreover, distance from a river-tunnel was the strongest PM2.5-influencing factor This distance effect was not observed at underground stations adjacent to a river-bridge. The "river-tunnel" effect explains some of the inter-station variability in subway PM2.5 concentrations. These results support the need for improving ventilation systems in subways.

Image-Processing-Based Subway Tunnel Crack Detection System.

With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to not only prolong the service life of the tunnel but also reduce the incidence of accidents. In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology. This method fully considers the characteristics of tunnel images and the combination of these characteristics with deep learning, while a deep convolutional network (Single-Shot MultiBox Detector (SSD)) is proposed based on deep learning for object detection in complex images. The experimental results show that the test set accuracy and training set accuracy of the support vector machine (SVM) in the classification comparison test are up to 88% and 87.8%, respectively; while the test accuracy of Alexnet's deep convolutional neural network-based classification and identification is up to 96.7%, and the training set accuracy is up to 97.5%. It can be seen that this deep convolutional network recognition algorithm based on deep learning and image processing is better and more suitable for the detection of cracks in subway tunnels.

Automatic Detection and Association Analysis of Multiple Surface Defects on Shield Subway Tunnels.

The surface defects on a shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise operation safety. To effectively detect multiple surface defects, this study uses a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting an inspection of the shield tunnel on a metro line section, various surface defects are identified with the TIT, including water leakage defects, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, association rules between different defects are calculated using an improved Apriori algorithm. The results show that: (i) there are significant differences in different association rules for various surface defects on the shield tunnel; (ii) the average confidence of the association rule "dislocation & spalling → water leakage" is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; (iii) the weakest rule appears at "water leakage → spalling", with an average confidence of 13%. The association analysis can be used for predicting the critical defects influencing structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for a shield subway tunnel.

Improved YOLOv7-Based Algorithm for Detecting Foreign Objects on the Roof of a Subway Vehicle.

Subway vehicle roofs must be inspected when entering and exiting the depot to ensure safe subway vehicle operations. This paper presents an improved method for detecting foreign objects on subway vehicle roofs based on the YOLOv7 algorithm. First, we capture images of foreign objects using a line-scan camera at the depot entrance and exit, creating a dataset of foreign roof objects. Subsequently, we address the shortcomings of the YOLOv7 algorithm by introducing the Ghost module, an improved weighted bidirectional feature pyramid network (WBiFPN), and the Wise intersection over union (WIoU) bounding-box regression loss function. These enhancements are incorporated to build the subway vehicle roof foreign object detection model based on the improved YOLOv7, which we refer to as YOLOv7-GBW. The experimental results demonstrate the practicality and usability of the proposed method. The analysis of the experimental results indicates that the YOLOv7-GBW algorithm achieves a detection accuracy of 90.29% at a speed of 54.3 frames per second (fps) with a parameter count of 15.51 million. The improved YOLOv7 model outperforms mainstream detection algorithms in terms of detection accuracy, speed, and parameter count. This finding confirms that the proposed method meets the requirements for detecting foreign objects on subway vehicle roofs.

Evaluation of Smart Sensors for Subway Electric Motor Escalators through AHP-Gaussian Method.

This paper proposes the use of the AHP-Gaussian method to support the selection of a smart sensor installation for an electric motor used in an escalator in a subway station. The AHP-Gaussian methodology utilizes the Analytic Hierarchy Process (AHP) framework and is highlighted for its ability to save the decision maker's cognitive effort in assigning weights to criteria. Seven criteria were defined for the sensor selection: temperature range, vibration range, weight, communication distance, maximum electric power, data traffic speed, and acquisition cost. Four smart sensors were considered as alternatives. The results of the analysis showed that the most appropriate sensor was the ABB Ability smart sensor, which scored the highest in the AHP-Gaussian analysis. In addition, this sensor could detect any abnormalities in the equipment's operation, enabling timely maintenance and preventing potential failures. The proposed AHP-Gaussian method proved to be an effective approach for selecting a smart sensor for an electric motor used in an escalator in a subway station. The selected sensor was reliable, accurate, and cost-effective, contributing to the safe and efficient operation of the equipment.

"Green transit-oriented development": Exploring the association between TOD and visible green space provision using street view data.

Environmental inequalities generated by transit-oriented development (TOD) are of planning and policy relevance in developing countries. Existing literature has pointed out that TOD has the effect of 'place making', which means the newly developed transit systems may be able to change the environment and amenities of a certain area. While previous studies have largely focused on environment hazards such as noise and pollution induced by transit systems, scant attention has been paid to visible green space provision at station areas. This study develops a new and systematic framework to assess potential disparities in quality and quantity aspects of visible green space provision around subway stations. We explore the effects of TOD on visible green space provision around subway stations using spatial regression models. The results show that there are disparities in visible green space provision around subway stations, but such disparities tend to fade with distance away from stations. We also find that population density, land use mix, intersection density and bus stop density are significantly associated with quantity and quality aspects of visible green space provision around subway stations.

Is the expansion of the subway network alone capable of improving local air quality? A study case in São Paulo, Brazil.

One of the policies adopted to reduce vehicular emissions is subway network expansion. This work fitted interrupted regression models to investigate the effects of the inauguration of subway stations on the mean, trend, and seasonality of the NO, NO2, NOx, and PM10 local concentrations. The regions investigated in the city of São Paulo (Brazil) were Pinheiros, Butantã, and St. Amaro. In Pinheiros, after the inauguration of the subway station, there were downward trends for all pollutants. However, these trends were not significantly different from the trends observed before. In Butantã, only regarding NO, there was a significant reduction and seasonal change after the subway station's inauguration. In St. Amaro, no trend in the PM10 concentration was noted. The absence of other transportation and land use policies in an integrative way to the subway network expansion may be responsible for the low air quality improvement. This study highlights that the expansion of the subway network must be integrated with other policies to improve local air quality.

Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic.

Completing the Pythagorean fuzzy preference relations (PFPRs) based on additive consistency may exceed the defined domain. Therefore, we develop a group decision-making (GDM) method with incomplete PFPRs. Firstly, sufficient conditions for the expressibility of estimated preference values in PFPRs based on additive consistency are presented. Next, the correction algorithm is developed to correct the inexpressible elements in incomplete PFPRs. Then, a GDM method based on incomplete PFPRs is proposed to determine the objective weights of decision-makers. Finally, an example of subway station safety management during COVID-19 is selected to illustrate the applicability of the developed GDM method. The results show that the developed GDM method effectively identifies the crucial risk factor in subway station safety management and has better performance in terms of computational time complexity than the multiplicative consistency method.

An investigation of the PM2.5 concentrations and cumulative inhaled dose during subway commutes in Changchun, China.

Air quality in subway systems is crucial as it affects the health of passengers and staff. Although most tests of PM2.5 concentrations in subway stations have taken place in public areas, PM2.5 is less understood in workplaces. Few studies have estimated the cumulative inhaled dose of passengers based on real-time changes in PM2.5 concentrations as they commute. To clarify the above issues, this study first measured PM2.5 concentrations in four subway stations in Changchun, China, where measuring points included five workrooms. Then, passengers' exposure to PM2.5 during the whole subway commute (20-30 min) was measured and segmented inhalation was calculated. The results showed that PM2.5 concentration in public places ranged from 50 to 180 µg/m3, and was strongly correlated with outdoors. While the PM2.5 average concentration in workplaces was 60 µg/m3, and it was less affected by outdoor PM2.5 concentration. Passenger's cumulative inhalations in single commuting were about 42 µg and 100 µg when the outdoor PM2.5 concentrations were 20-30 µg/m3 and 120-180 µg/m3, respectively. The PM2.5 inhalation in carriages accounted for the largest proportion of the entire commuting, about 25-40%, because of the longer exposure time and higher PM2.5 concentrations. It is recommended to improve the tightness of the carriage and filter the fresh air to improve the air quality inside. The average daily PM2.5 inhaled by staff was 513.53 µg, which was 5-12 times higher than that of passengers. Installing air purification devices in workplaces and reminding staff to take personal protection can positively protect their health.

Station-Level Effects of the COVID-19 Pandemic on Subway Ridership in the Seoul Metropolitan Area.

This paper investigates the station-level impacts of the coronavirus disease (COVID-19) pandemic on subway ridership in the Seoul Metropolitan Area. Spatial econometric models are constructed to examine the association between ridership reduction caused by the pandemic and station-level characteristics during the pandemic years 2020 and 2021. The results reveal unequal effects on station-level ridership, based on the pandemic waves, the demographics, and the economic features of pedestrian catchment areas. First, the subway system was severely disrupted by the pandemic, with significant decreases in ridership-by about 27% for each of the pandemic years-compared with the pre-pandemic year (2019). Second, the ridership reduction was sensitive to the three waves in 2020 and responded accordingly; however, it became less sensitive to the waves in 2021, indicating that subway usage was less responsive to pandemic waves during the second year of the pandemic. Third, pedestrian catchment areas with higher numbers of younger residents (in their 20s) and older residents (65 years and older), those with more businesses requiring face-to-face interactions with consumers, and stations located in the employment centers were hit the hardest in ridership reduction caused by the pandemic.

Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City.

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Modeling the factors that influence exposure to SARS-CoV-2 on a subway train carriage.

We propose the Transmission of Virus in Carriages (TVC) model, a computational model which simulates the potential exposure to SARS-CoV-2 for passengers traveling in a subway rail system train. This model considers exposure through three different routes: fomites via contact with contaminated surfaces; close-range exposure, which accounts for aerosol and droplet transmission within 2 m of the infectious source; and airborne exposure via small aerosols which does not rely on being within 2 m distance from the infectious source. Simulations are based on typical subway parameters and the aim of the study is to consider the relative effect of environmental and behavioral factors including prevalence of the virus in the population, number of people traveling, ventilation rate, and mask wearing as well as the effect of model assumptions such as emission rates. Results simulate generally low exposures in most of the scenarios considered, especially under low virus prevalence. Social distancing through reduced loading and high mask-wearing adherence is predicted to have a noticeable effect on reducing exposure through all routes. The highest predicted doses happen through close-range exposure, while the fomite route cannot be neglected; exposure through both routes relies on infrequent events involving relatively few individuals. Simulated exposure through the airborne route is more homogeneous across passengers, but is generally lower due to the typically short duration of the trips, mask wearing, and the high ventilation rate within the carriage. The infection risk resulting from exposure is challenging to estimate as it will be influenced by factors such as virus variant and vaccination rates.

Metagenomic characterization of bacterial community and antibiotic resistance genes found in the mass transit system in Seoul, South Korea.

Mass transit systems, including subways and buses, are useful environments for studying the urban microbiome, as the vast majority of populations in urban areas use public transportation. Microbial communities in urban environments include both human- and environment-associated bacteria that play roles in health and pathogen transmission. In this study, we used shotgun metagenomic sequencing to profile microbial communities sampled from various surfaces found in subway stations and bus stops within the Seoul mass transit system. The metagenomic approach and network analysis were used to investigate broad-spectrum antibiotic resistance genes (ARGs) and their co-occurrence patterns. We uncovered 598 bacterial species in 76 samples collected from various surfaces within the Seoul mass transit system. All samples were dominated by the potential human pathogen Salmonella enterica (40 %) and the human skin bacterium Cutibacterium acnes (19 %). Significantly abundant biomarkers detected in subway station samples were associated with bacteria typically found in the human oral cavity and respiratory tract, whereas biomarkers detected in bus stop samples were associated with bacteria commonly found in soil, water, and plants. Temperature and location had significant effects on microbial community structure and diversity. In total, 41 unique ARG subtypes were identified, associated with single-drug or multidrug resistance to clinically important and extensively used antibiotics, including aminoglycosides, carbapenem, glycopeptide, and sulfonamides. We revealed that Seoul subway stations and bus stops possess unique microbiomes containing potential human pathogens and ARGs. These findings provide insights for refining location-specific responses to reduce exposure to potentially causative agents of infectious diseases, improving public health.