Indoor air quality in subway microenvironments: Pollutant characteristics, adverse health impacts, and population inequity.

Rapidly increasing urbanization in recent decades has elevated the subway as the primary public transportation mode in metropolitan areas. Indoor air quality (IAQ) inside subways is an important factor that influences the health of commuters and subway workers. This review discusses the subway IAQ in different cities worldwide by comparing the sources and abundance of particulate matter (PM2.5 and PM10) in these environments. Factors that affect PM concentration and chemical composition were found to be associated with the subway internal structure, train frequency, passenger volume, and geographical location. Special attention was paid to air pollutants, such as transition metals, volatile/semi-volatile organic compounds (VOCs and SVOCs), and bioaerosols, due to their potential roles in indoor chemistry and causing adverse health impacts. In addition, given that the IAQ of subway systems is a public health issue worldwide, we calculated the Gini coefficient of urban subway exposure via meta-analysis. A value of 0.56 showed a significant inequity among different cities. Developed regions with higher per capita income tend to have higher exposure. By reviewing the current advances and challenges in subway IAQ with a focus on indoor chemistry and health impacts, future research is proposed toward a sustainable urban transportation systems.

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.

The influence of existing piles on station settlement during the construction of a tunnel undercrossing under existing stations.

In the construction of tunnels under existing stations, it is necessary to control their settlement. When there is a pile foundation in the existing station, the pile cutting has a significant impact on the settlement of the existing station. To determine the influence of existing piles on the settlement of subway stations, a reasonable pile-cutting time is proposed. Based on the Chengdu Metro Line 9 underpassing the existing Line 1 hatchery station, the settlement law of the tunnel underpassing the existing Line 1 station is analysed via a numerical simulation. Furthermore, the deformation and stress characteristics of the existing piles, pipe roofs, and tunnel linings and the supporting effect on the existing station are discussed. It is concluded that the cutting of existing piles causes a change in the tunnel bearing system, thus resulting in a certain deformation of the station. The influence of different pile cutting times on the settlement of the existing station is then analysed, and it is clarified that the tunnel support stiffness is significantly enhanced after the construction of the secondary lining. At this time, the settlement of the existing pile station is significantly reduced. Finally, through a field investigation, the effect of surface grouting, pipe shed, and multilayer lining on the settlement control of the existing station while the existing pile foundation exists is determined. This research can provide a reference for the settlement control and foundation underpinning of existing stations at ultra-small distances in underground excavation tunnels.

Ferruginous components of particulate matters in subway environments, α-Fe2O3 or Fe3O4, exacerbates allergies.

The respiratory effects of particulate matter (PM) in subway station platforms or tunnels have attracted considerable research attention. However, no studies have characterized the effects of subway PM on allergic immune responses. In this study, iron oxide (α-Fe2O3 and Fe3O4) particles-the main components of subway PM-were intratracheally administered to BALB/c mice where ovalbumin (OVA) induced allergic pulmonary inflammation. Iron oxide particles enhanced OVA-induced eosinophil recruitment around the bronchi and mucus production from airway epithelium. The concentrations of type 2 cytokines, namely, interleukin (IL)-5 and IL-13, in bronchial alveolar lavage fluids were increased by iron oxide particles. Iron oxide particles also increased the number of type 2 innate lymphoid cells and CD86+ cells in the lung. Moreover, phagocytosis of particles in lung cells was confirmed by Raman spectroscopy. In a subsequent in vitro study, bone marrow-derived antigen-presenting cells (APCs) isolated from NC/Nga mice were exposed to iron oxide particles and OVA. They were also exposed to outdoor ambient PM: Vehicle Exhaust Particulates (VEP) and Urban Aerosols (UA) as references. Iron oxide particles promoted the release of lactate dehydrogenase, C-X-C motif chemokine ligand 1 and IL-1α from APCs, which tended to be stronger than those of VEP. These results suggest that iron oxide particles enhance antigen presentation in the lungs, promoting allergic immune response in mice; iron oxide particles-induced death and inflammatory response of APCs can contribute to allergy exacerbation. Although iron oxide particles do not contain various compounds like VEP, iron oxide alone may have sufficient influence.

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.

Suicide on the Toronto Transit Commission subway system in Canada (1998-2021): a time-series analysis.

Background: The Toronto Transit Commission (TTC) operates the public transit system in Toronto, Canada. From 1954 to 1980, there were 430 suicide deaths/attempts on the TTC subway system. In 2011, TTC implemented Crisis Link, a suicide helpline to connect subway passengers with counsellors. Upstream factors such as media reporting about suicide incidents may also influence suicidal behaviour. Our objectives were to investigate how Crisis Link and media reports about TTC suicide incidents influenced suicide rates. Methods: Suicide data were obtained from the TTC and Coroner, with Crisis Link data provided by Distress Centres of Greater Toronto (1998-2021). Media articles were identified through a database search of Toronto media publications. Interrupted time-series analysis investigated the association between Crisis Link calls, media articles, and quarterly suicide rates on the subway system. Findings: There were 302 suicides on TTC's subway system from 1998 to 2021. The introduction of Crisis Link was associated with a large but non-significant decrease in TTC-related suicide rate in the same quarter (IRR = 0.64, 95% CI = 0.36-1.12). Each subsequent post-Crisis-Link quarter experienced an average 2% increase in suicide rate (IRR = 1.02, 95% CI = 1.004-1.04). Furthermore, for each TTC-related media article in the previous quarter, the suicide rate on the TTC increased by 2% (IRR = 1.02, 95% CI = 1.004-1.04). Interpretation: The Crisis Link helpline was associated with a large but non-significant short-term decrease in suicide rates. However, this outcome was not sustained; this may, in part, be attributable to media reporting which was associated with increased suicides. This should inform suicide prevention policies in Canada and worldwide. Funding: No funding.

Unraveling mechanisms and failures of stray current corrosion in urban Subway adjacent pipeline networks through statistical insights and 3D simulation.

The escalating expansion of urban subway systems in recent years has accentuated the issue of stray current corrosion within pipeline networks, emerging as a critical concern for urban safety. This paper delves into the intricate interplay between these phenomena, employing data-driven statistical analyses to elucidate the coupling characteristics between subway lines and the occurrence of failures in adjacent buried pipelines. An advanced three-dimensional finite element model was developed for stray current corrosion in pipelines, seamlessly integrating empirical data and physics-based modeling, which is to uncover the spatial nuances and multifaceted impacts on subway pipeline corrosion from both macro and micro perspectives under varying influencing factors. The study unveils a pronounced geographical and functional affinity between urban subway networks and metallic pipeline networks. The coupling attributes between subway systems and pipelines, such as distance, angle, and pipeline-specific characteristics including material and age, assume pivotal roles. The results further emphasize the hierarchical order of influence, with stray current intensity holding the greatest sway, followed by the distance between subway and pipelines, the angle between them, and soil resistivity. This paper offers a comprehensive investigation of the interrelationships and influential factors between subway systems and adjacent pipelines. It contributes to the mitigation and management of stray current corrosion in pipelines induced by nearby rail transit, thereby enhancing the resilience of both subway and pipeline networks within urban areas.

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.

Model validation and Robust State Feedback control for nonlinear subway traffic networks.

This paper develops a nonlinear Discrete-Event (DE) model for Intersecting Lines (ILs) of a Subway Network (SN). A class of Monte Carlo (MC) methods based on repeated random values is employed to validate the model. Model validation is performed by generating random values for delay rates and external disturbances. After model validation, a Robust State Feedback (RSF) controller is designed to compensate the delays caused by disturbances. Simulations are performed based on the characteristics and actual data of lines 2 and 4 of the Tehran subway to demonstrate the effectiveness of the proposed approach. Simulation results show the accuracy and proper performance of the proposed model. In addition, the closed-loop responses demonstrate the effectiveness of the proposed RSF controller for centralized and decentralized control configurations.

Construction of environmental vibration prediction model for subway transportation based on machine learning algorithm and database technology.

Vibrations generated in the metro transport environment are mainly caused by, vibrations generated by the interaction between the metro and the track during operation. and the change of vibration factors will affect the normal operation of the subway. However, it is difficult to have a model that can achieve the characteristics of high accuracy, fast computing speed and wide range of use in the traditional metro rail transportation environment prediction. Therefore, this research uses database theory and machine learning algorithms to predict the vibration of subway transportation environment. The experimental results show that the average difference between the whole prediction value and the real value is 1.4 dB, of which the maximum difference error value is 0.29%, the maximum error difference is 8.2%, and the approximate value is 6.2 dB, and the four averages predicted in 40 m are relatively small as 1.6 dB, and the average error value of prediction ability between 40 and 100 m is 1.72 dB, and the experimental prediction value and real value are in good agreement. The agreement between the experimental prediction and the real value is very good. Therefore, the model is able to predict the vibration model of the subway transportation environment with a high degree of agreement and accuracy.

Safety risk assessment of subway shield construction under-crossing a river using CFA and FER.

Numerous subway projects are planned by China's city governments, and more subways can hardly avoid under-crossing rivers. While often being located in complex natural and social environments, subway shield construction under-crossing a river (SSCUR) is more susceptible to safety accidents, causing substantial casualties, and monetary losses. Therefore, there is an urgent need to investigate safety risks during SSCUR. The paper identified the safety risks during SSCUR by using a literature review and experts' evaluation, proposed a new safety risk assessment model by integrating confirmatory factor analysis (CFA) and fuzzy evidence reasoning (FER), and then selected a project to validate the feasibility of the proposed model. Research results show that (a) a safety risk list of SSCUR was identified, including 5 first-level safety risks and 38 second-level safety risks; (b) the proposed safety risk assessment model can be used to assess the safety risk of SSCUR; (c) safety inspection, safety organization and duty, quicksand layer, and high-pressure phreatic water were the high-level risks, and the onsite total safety risk was at the medium level; (d) management-type safety risks, environment-type safety risks, and personnel-type safety risks have higher expected utility values, and manager-type safety risks were expected have higher risk-utility values when compared to worker-type safety risks. The research can enrich the theoretical knowledge of SSCUR safety risk assessment and provide references to safety managers for conducting scientific and effective safety management on the construction site when a subway crosses under a river.

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.

Single-cell transcriptomics reveals the pulmonary inflammation induced by inhalation of subway fine particles.

People generally take the subway and inevitably inhale the fine particles (PM2.5) on subway platforms. This study revealed whether and how subway PM2.5 causes lung inflammation. Herein, the pulmonary inflammatory response to subway PM2.5 was observed in mice, manifesting as the inflammatory cells infiltration and collagen deposition in tissue, inflammatory cytokine enhancement in bronchoalveolar lavage fluid and Toll-like receptors signal pathway activation in the lungs. Furthermore, single-cell RNA sequencing unearthed subway PM2.5-induced cell-specific responses in the lungs. Twenty immune subsets were identified by the molecular and functional properties. Specific cell populations of CD4+ T and γδ T cells were regarded as the predominant sources of pneumonitis induced by subway PM2.5. Moreover, we demonstrated that the lung inflammatory injury was significantly more attenuated in Rag1-/- mice lacking functional T cells and B cells than that in wild type mice. We proved the slight inflammation of lung tissue in Rag1-/- mice may be dependent on monocytes and neutrophils by activation of the intracellular molecular network. This is the first experimental study on subway PM2.5 causing pulmonary inflammatory damage. It will set an alarm for people who usually travel by subway and efficient measures to reduce PM2.5 should be developed in subway stations.

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.

Job Exposure Matrix, a Solution for Retrospective Assessment of Particle Exposure in a Subway Network and Their Long-Term Effects.

INTRODUCTION: Health effects after long-term exposure to subway particulate matter (PM) remain unknown due to the lack of individual PM exposure data. This study aimed to apply the job exposure matrix (JEM) approach to retrospectively assess occupational exposure to PM in the Parisian subway. METHODS: Job, the line and sector of the transport network, as well as calendar period were four JEM dimensions. For each combination of these dimensions, we generated statistical models to estimate the annual average PM10 concentration using data from an exhaustive inventory of the PM measurement campaigns conducted between 2004 and 2020 in the Parisian subway and historical data from the Parisian air pollution monitoring network. The resulting JEM and its exposure estimates were critically examined by experts using the uncertainty analysis framework. RESULTS: The resulting JEM allows for the assignment of the estimated annual PM10 concentration to three types of professionals working in the subway: locomotive operators, station agents, and security guards. The estimates' precision and validity depend on the amount and quality of PM10 measurement data used in the job-, line-, and sector-specific models. Models using large amounts of personal exposure measurement data produced rather robust exposure estimates compared to models with lacunary data (i.e., in security guards). The analysis of uncertainty around the exposure estimates allows for the identification of the sources of uncertainty and parameters to be addressed in the future in order to refine and/or improve the JEM. CONCLUSIONS: The JEM approach seems relevant for the retrospective exposure assessment of subway workers. When applied to available data on PM10, it allows for the estimation of this exposure in locomotive operators and station agents with an acceptable validity. Conversely, for security guards, the current estimates have insufficient validity to recommend their use in an epidemiological study. Therefore, the current JEM should be considered as a valid prototype, which shall be further improved using more robust measurements for some jobs. This JEM can also be further refined by considering additional exposure determinants.

Effectiveness of Tap Water in Reducing the Generation of Ultrafine Wear Particles from the Wheel-Rail Contact by Eliminating the Water Vapor Effect.

This study aimed to assess the impact of tap water application on reducing the generation of ultrafine particles from the wheel-rail contact using a twin-disk rig under dry and wet conditions, with train velocities of 45 and 80 km/h. A small amount of 0.3 L/min tap water was applied at the wheel-rail contact, and a diffusion dryer was used to eliminate water vapor. The Fast Mobility Particle Sizer measured the number concentration (NC) of nano-sized wear particles in the range of 6 to 560 nm. The tap water application method effectively reduced the NC of ultrafine and fine particles by 67-72% and 86-88%, respectively. Positive reduction rates were observed for all diameters at 45 km/h and for most diameters, except for approximately 70 nm and 80 nm, at 80 km/h. Even with a small amount of water, this approach successfully decreased nano-sized wear particle generation. However, the potential influence of mineral crystals in tap water on NC requires further investigation. Overall, this method shows promise for enhancing air quality and public health by mitigating nano-sized wear particle generation in subway systems.

The characteristics of particulate matter in different subway station environmental control systems.

Particulate matter (PM2.5, PM10) in urban subway stations can significantly impact passengers' health. The particle concentration in subway stations is influenced by many factors. However, few existing studies have explored the impact of environmental control systems in-depth, especially under different outdoor pollution conditions. To address this research gap, this study focused on measuring and comparing the characteristics of PM2.5 and PM10 at subway stations with three control systems (open, closed, and screen door) under varying pollution conditions in Beijing. Particle concentrations from platforms, carriages, and outdoors were monitored and analyzed using statistical methods. The results showed that the particle concentration in the closed system was generally 20-40 µg/m3 higher than that in the screen system at the platform, which might be attributed to the piston wind, as the air from the tunnel with a lot of dirt. The pollution in the carriage was more severe for the open system than that of the screen system. The PM2.5/PM10 ratio in the carriage was 91%, 90%, and 83.84% for the closed, open, and screen systems, respectively. This indicates that the screen door could reduce the particle concentration in the platform to 10%-50%. The particle concentration varied among subway stations with different environmental control systems, suggesting that the prevention and control strategies for particulate matter pollution should be different for stations with different systems.

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.

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.

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.