A multivariate study for characterizing particulate matter (PM(10), PM(2.5), and PM(1)) in Seoul metropolitan subway stations, Korea

The composition of the bacterial communities collected from the PM10 samples inside the Seoul subway and railway station

Health implications of indoor air quality (IAQ) have drawn more attention since the COVID epidemic. There are many different kinds of studies done on how IAQ affects people's well-being. There hasn't been much research that looks at the microbiological composition of the aerosol in subway transit systems. In this work, for the first time, we examined the aerosol bacterial abundance, diversity, and composition in the microbiome of the Seoul subway and train stations using DNA isolated from the PM10 samples from each station (three subway and two KTX stations). The average PM10 mass concentration collected on the respective platform was 41.862 µg/m3, with the highest average value of 45.95 µg/m3 and the lowest of 39.25 µg/m3. The bacterial microbiomes mainly constituted bacterial species of soil and environmental origin (e.g., Acinetobacter, Brevundimonas, Lysinibacillus, Clostridiodes) with fewer from human sources (Flaviflexus, Staphylococcus). This study highlights the relationship between microbiome diversity and PM10 mass concentration contributed by outdoor air and commuters in South Korea's subway and train stations. This study gives insights into the microbiome diversity, the source, and the susceptibility of public transports in disease spreading.

Indoor air quality of urban public transportation stations in China: Based on air quality evaluation indexes

Indoor air quality (IAQ) of urban public transportation stations (UPTS) has adverse health impacts on the station employees and commuters. However, there is a lack of comprehensively evaluations of IAQ in waiting rooms of UPTS. Therefore, it is crucial to select appropriate air quality indexes (including fuzzy synthetic index (B), comprehensive index (P), I1 index, and indoor air quality index (IAQI)) to assess air quality and potential health risks. Our study is a subsample of the CPPEHS 2019 study, which included 224 UPTS in 126 cities of China. We found that P index showed an excellent air quality rate of 95.96% in the railway stations and 83.19% in the inter-city bus stations. The P index was correlated with UPTS usage years, useable area, and per passenger useable area. Furthermore, waiting rooms in UPTS with good (OR = 1.9187, 95% CI: 1.1204, 3.2859) and bad (OR = 2.0854, 95% CI: 1.2182, 3.5698) air quality evaluated by P index had a higher risk of rhinitis compared to those with excellent air quality. Similarly, UPTS with good (OR = 2.2202, 95% CI: 1.3427, 3.6711), bad (OR = 1.7897, 95% CI: 1.0807, 2.9637), and serious (OR = 1.7478, 95% CI: 1.0098, 3.0250) air quality evaluated by P index were associated with a higher risk of pharyngitis. These findings suggested that P index is the optimal index for assessing air quality in UPTS, while IAQI may exaggerate indoor air pollution and the B index may underestimate it. Overall, this study aims to identify and evaluate the more suitable air quality index (P) in nationwide UPTS, providing valuable insights for control of IAQ and guiding the air quality management and standards.

A review on characteristics and mitigation strategies of indoor air quality in underground subway stations

Due to the rapidly increasing ridership and the relatively enclosed underground space, the indoor air quality (IAQ) in underground subway stations (USSs) has attracted more public attention. The air pollutants in USSs, such as particulate matter (PM), CO₂ and volatile organic compounds (VOCs), are hazardous to the health of passengers and staves. Firstly, this paper presents a systematic review on the characteristics and sources of air pollutants in USSs. According to the review work, the concentrations of PM, CO₂, VOCs, bacteria and fungi in USSs are 1.1-13.2 times higher than the permissible concentration limits specified by WHO, ASHRAE and US EPA. The PM and VOCs are mainly derived from the internal and outdoor sources. CO₂ concentrations are highly correlated with the passenger density and the ventilation rate while the exposure levels of bacteria and fungi depend on the thermal conditions and the settled dust. Then, the online monitoring, fault detection and prediction methods of IAQ are summarized and the advantages and disadvantages of these methods are also discussed. In addition, the available control strategies for improving IAQ in USSs are reviewed, and these strategies are classified and compared from different viewpoints. Lastly, challenges of the IAQ management in the context of the COVID-19 epidemic and several suggestions for underground stations' IAQ management in the future are put forward. This paper is expected to provide a comprehensive guidance for further research and design of the effective prevention measures on air pollutants in USSs so as to achieve more sustainable and healthy underground environment.

Evaluation of Health Economic Loss Due to Particulate Matter Pollution in the Seoul Subway, South Korea

Evaluating an illness's economic impact is critical for developing and executing appropriate policies. South Korea has mandatory national health insurance in the form of NHIS that provides propitious conditions for assessing the national financial burden of illnesses. The purpose of our study is to provide a comprehensive assessment of the economic impact of PM_2.5 exposure in the subway and a comparative analysis of cause-specific mortality outcomes based on the prevalent health-risk assessment of the health effect endpoints (chronic obstructive pulmonary disease (COPD), asthma, and ischemic heart disease (IHD)). We used the National Health Insurance database to calculate the healthcare services provided to health-effect endpoints, with at least one primary diagnosis in 2019. Direct costs associated with health aid or medicine, treatment, and indirect costs (calculated based on the productivity loss in health effect endpoint patients, transportation, and caregivers, including morbidity and mortality costs) were both considered. The total cost for the exposed population for these endpoints was estimated to be USD 437 million per year. Medical costs were the largest component (22.08%), followed by loss of productivity and premature death (15.93%) and other costs such as transport and caregiver costs (11.46%). The total incurred costs (per 1000 persons) were accounted to be USD 0.1771 million, USD 0.42 million, and USD 0.8678 million for COPD, Asthma, and IHD, respectively. Given that the economic burden will rise as the prevalence of these diseases rises, it is vital to adopt effective preventative and management methods strategies aimed at the appropriate population.

Particulate air pollution in the Copenhagen metro part 1: Mass concentrations and ventilation

The Copenhagen Metro comprises four lines, the M1, M2, M3 and M4, with 25 subterranean stations and an additional 14 stations above ground, serving ca. 80 million passengers annually. In this study we measure fine particulate matter (PM_2.5) and carbon dioxide (CO₂) concentrations in stations and in trains across the entire system. In partially underground lines, high PM_2.5 concentrations with an average of 109 μg m^-3 are found in below-ground stations. The observed correlation between PM_2.5 concentration and distance between a station and a tunnel exit is attributed to ventilation via the piston effect. The piston effect via tunnel draught relief shafts was therefore found to be relatively limited. Filter samples of particulate matter are analysed using particle-induced X-ray emission and show an iron content of 88.6 % by mass which is quite different from above-ground particulate matter and consistent with particle production by train wheels, rails and brakes. The average concentration measured at the stations of a recently opened (2019) fully underground M3 closed loop line is 168 μg m^-3, further demonstrating that while piston effect-driven ventilation is effective in close proximity to tunnel openings, it is relatively limited via tunnel draught relief shafts. Measurements onboard trains show even higher PM_2.5 concentrations and the patterns in CO₂ concentrations suggest carriage ventilation by tunnel air. Ventilation via doors during platform stops caused a drop in observed PM (and CO₂) at stations, but the system is surprisingly polluted despite its recent construction. CO₂ mixing ratios ranged from ambient to around 600 ppm. Measures should be taken to control PM levels using a combination of source control and increased clean air supply of the Copenhagen and other similar metro systems.

On the airborne transmission of SARS-CoV-2 and relationship with indoor conditions at a hospital

The limited knowledge about the mechanism of SARS-CoV-2 transmission is a current challenge on a global scale. Among possible transmission routes, air transfer of the virus is thought to be prominent. To investigate this further, measurements were conducted at Razi hospital in Ahvaz, Iran, which was selected to treat COVID-19 severe cases in the Khuzestan province. Passive and active sampling methods were employed and compared with regard to their efficiency for collection of airborne SARS-COV-2 virus particles. Fifty one indoor air samples were collected in two areas, with distances of less than or equal to 1 m (patient room) and more than 3 m away (hallway and nurse station) from patient beds. A simulation method was used to obtain the virus load released by a regularly breathing or coughing individual including a range of microdroplet emissions. Using real-time reverse transcription polymerase chain reaction (RT-PCR), 11.76% (N = 6) of all indoor air samples (N = 51) collected in the COVID-19 ward tested positive for SARS-CoV-2 virus, including 4 cases in patient rooms and 2 cases in the hallway. Also, 5 of the 6 positive cases were confirmed using active sampling methods with only 1 based on passive sampling. The results support airborne transmission of SARS-CoV-2 bioaerosols in indoor air. Multivariate analysis showed that among 15 parameters studied, the highest correlations with PCR results were obtained for temperature, relative humidity, PM levels, and presence of an air cleaner.

Design of a Spark Big Data Framework for PM2.5 Air Pollution Forecasting

In recent years, with rapid economic development, air pollution has become extremely serious, causing many negative effects on health, environment and medical costs. PM_2.5 is one of the main components of air pollution. Therefore, it is necessary to know the PM_2.5 air quality in advance for health. Many studies on air quality are based on the government’s official air quality monitoring stations, which cannot be widely deployed due to high cost constraints. Furthermore, the update frequency of government monitoring stations is once an hour, and it is hard to capture short-term PM_2.5 concentration peaks with little warning. Nevertheless, dealing with short-term data with many stations, the volume of data is huge and is calculated, analyzed and predicted in a complex way. This alleviates the high computational requirements of the original predictor, thus making Spark suitable for the considered problem. This study proposes a PM_2.5 instant prediction architecture based on the Spark big data framework to handle the huge data from the LASS community. The Spark big data framework proposed in this study is divided into three modules. It collects real time PM_2.5 data and performs ensemble learning through three machine learning algorithms (Linear Regression, Random Forest, Gradient Boosting Decision Tree) to predict the PM_2.5 concentration value in the next 30 to 180 min with accompanying visualization graph. The experimental results show that our proposed Spark big data ensemble prediction model in next 30-min prediction has the best performance (R² up to 0.96), and the ensemble model has better performance than any single machine learning model. Taiwan has been suffering from a situation of relatively poor air pollution quality for a long time. Air pollutant monitoring data from LASS community can provide a wide broader monitoring, however the data is large and difficult to integrate or analyze. The proposed Spark big data framework system can provide short-term PM_2.5 forecasts and help the decision-maker to take proper action immediately.

Composition and exposure characteristics of PM2.5 on subway platforms and estimates of exposure reduction by protective masks

There is limited information on exposure to metallic constituents of fine particulate matter in subway stations. We characterized the concentrations and composition of airborne fine particulate pollution on six subway platforms in Nanjing, China in both summer and winter of 2019. A microenvironment exposure model was used to evaluate the concentrations of elements in fine particulate matter and the contribution of exposure duration (time spent in the subway station) to overall daily exposure of subway workers and commuters with and without the use of N95 respirators, surgical masks, and cotton masks. We found that airborne fine particulate pollution on station platforms was much higher than in an urban reference site of ambient air, and the same was true for metallic constituents of the particles, such as iron, copper, manganese, strontium, and vanadium. Subway workers were exposed to higher levels of these airborne metals than commuters. The average daily exposure concentration of fine particulate matter was 73.5 μg/m³ for subway workers and 61.8 μg/m³ for commuters, while the average daily exposure to iron was 15.5 μg/m³ for subway workers and 2.0 μg/m³ for commuters. Subway workers were exposed to iron, copper, manganese, and strontium/vanadium at levels approximately eight-fold, four-fold, three-fold, and two-fold greater than the exposure sustained by commuters, respectively. We calculated that wearing N95 respirators or surgical masks can reduce the exposure to these airborne metallic particles significantly for both subway workers and commuters. Overall, we estimate that personal exposure to airborne fine particulate matter on subway platforms can be reduced through the use of N95 respirators or properly fitting masks.

Air change rate effects on the airborne diseases spreading in Underground Metro wagons

The effect of the rate of change of fresh air inside passengers' wagons for Underground Metro on the spreading of airborne diseases like COVID-19 is investigated numerically. The study investigates two extreme scenarios for the location of the source of infection within the wagon with four different air change rates for each. The first scenario considers the source of infection at the closest point to the ventilation system while the other places the infection source at the farthest point from the wagon ventilation system. The effect of the wagon windows' status (i.e. closed or open) is also studied. It is found that under all conditions, open windows are always favored to decrease the infection spreading potential. A higher air change rate also decreases the infection spreading up to a certain value, beyond which the effect is not noticeable. The location of the infection source was found to greatly affect the infection spreading as well. The paper gives recommendations on the minimum air change rate to keep the infection spreading potentials to a minimum considering different times the passengers stay in the wagon.

Recent progress in research on PM2.5 in subways

Nowadays, PM2.5 concentrations greatly influence indoor air quality in subways and threaten passenger and staff health because PM2.5 not only contains heavy metal elements, but can also carry toxic and harmful substances due to its small size and large specific surface area. Exploring the physicochemical and distribution characteristics of PM2.5 in subways is necessary to limit its concentration and remove it. At present, there are numerous studies on PM2.5 in subways around the world, yet, there is no comprehensive and well-organized review available on this topic. This paper reviews the nearly twenty years of research and over 130 published studies on PM2.5 in subway stations, including aspects such as concentration levels and their influencing factors, physicochemical properties, sources, impacts on health, and mitigation measures. Although many determinants of station PM2.5 concentration have been reported in current studies, e.g., the season, outdoor environment, and station depth, their relative influence is uncertain. The sources of subway PM2.5 include those from the exterior (e.g., road traffic and fuel oil) and the interior (e.g., steel wheels and rails and metallic brake pads), but the proportion of these sources is also unknown. Control strategies of PM mainly include adequate ventilation and filtration, but these measures are often inefficient in removing PM2.5. The impacts of PM2.5 from subways on human health are still poorly understood. Further research should focus on long-term data collection, influencing factors, the mechanism of health impacts, and PM2.5 standards or regulations.

A Biplot-Based PCA Approach to Study the Relations between Indoor and Outdoor Air Pollutants Using Case Study Buildings

The 24 h and 14-day relationship between indoor and outdoor PM2.5, PM10, NO2, relative humidity, and temperature were assessed for an elementary school (site 1), a laboratory (site 2), and a residential unit (site 3) in Gainesville city, Florida. The primary aim of this study was to introduce a biplot-based PCA approach to visualize and validate the correlation among indoor and outdoor air quality data. The Spearman coefficients showed a stronger correlation among these target environmental measurements on site 1 and site 2, while it showed a weaker correlation on site 3. The biplot-based PCA regression performed higher dependency for site 1 and site 2 (p < 0.001) when compared to the correlation values and showed a lower dependency for site 3. The results displayed a mismatch between the biplot-based PCA and correlation analysis for site 3. The method utilized in this paper can be implemented in studies and analyzes high volumes of multiple building environmental measurements along with optimized visualization.

Concentration, composition, and exposure contributions of fine particulate matter on subway concourses in China

Concentrations of airborne metal-rich particles are typically higher on subway platforms and in subway tunnels than in ambient air. The subway concourse is an area of direct air exchange with both platforms and the outside environment, but few researchers have measured the concentrations and composition of fine particles on subway concourses. We characterized the concentrations and composition of fine particles on six subway concourses in Nanjing, China in both summer and winter. We used a respiration rate-adjusted microenvironment exposure model to estimate the contribution of a 6-h work period to daily mean exposure to fine particulate matter of subway workers and compared the estimate with those for general indoor and outdoor workers. We found that particle concentrations were typically higher on the station concourses than in ambient air. The most abundant elements composing the particles were Fe, S, Ca, Si, and K in both subway concourses and reference ambient air, but their contents varied greatly between indoor and outdoor air. The indoor/outdoor ratios of Fe, Cu, and Mn were highest, and subway workers were disproportionately exposed to these three metals. The mean daily exposure dose to Fe was 44.8 μg for subway workers, approximately five times the exposure dose of indoor and outdoor workers. Daily exposure doses of Cu, Mn, V, Sr, As, Co, Sn, and Cr were also higher for subway workers. The quality of indoor air at subway stations is therefore of occupational health concern and strategies should be formulated to reduce worker exposure.

Transfer learning driven sequential forecasting and ventilation control of PM2.5 associated health risk levels in underground public facilities

Particulate matter with aerodynamic diameter less than 2.5 µm (PM_2.5) has become a major public concern in closed indoor environments, such as subway stations. Forecasting platform PM_2.5 concentrations is significant in developing early warning systems, and regulating ventilation systems to ensure commuter health. However, the performance of existing forecasting approaches relies on a considerable amount of historical sensor data, which is usually not available in practical situations due to hostile monitoring environments or newly installed equipment. Transfer learning (TL) provides a solution to the scant data problem, as it leverages the knowledge learned from well-measured subway stations to facilitate predictions on others. This paper presents a TL-based residual neural network framework for sequential forecast of health risk levels traced by subway platform PM_2.5 levels. Experiments are conducted to investigate the potential of the proposed methodology under different data availability scenarios. The TL-framework outperforms the RNN structures with a determination coefficient (R²) improvement of 42.84%, and in comparison, to stand-alone models the prediction errors (RMSE) are reduced up to 40%. Additionally, the forecasted data by TL-framework under limited data scenario allowed the ventilation system to maintain IAQ at healthy levels, and reduced PM_2.5 concentrations by 29.21% as compared to stand-alone network.

A Systematic Review of Air Quality Sensors, Guidelines, and Measurement Studies for Indoor Air Quality Management

The existence of indoor air pollutants—such as ozone, carbon monoxide, carbon dioxide, sulfur dioxide, nitrogen dioxide, particulate matter, and total volatile organic compounds—is evidently a critical issue for human health. Over the past decade, various international agencies have continually refined and updated the quantitative air quality guidelines and standards in order to meet the requirements for indoor air quality management. This paper first provides a systematic review of the existing air quality guidelines and standards implemented by different agencies, which include the Ambient Air Quality Standards (NAAQS); the World Health Organization (WHO); the Occupational Safety and Health Administration (OSHA); the American Conference of Governmental Industrial Hygienists (ACGIH); the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE); the National Institute for Occupational Safety and Health (NIOSH); and the California ambient air quality standards (CAAQS). It then adds to this by providing a state-of-art review of the existing low-cost air quality sensor (LCAQS) technologies, and analyzes the corresponding specifications, such as the typical detection range, measurement tolerance or repeatability, data resolution, response time, supply current, and market price. Finally, it briefly reviews a sequence (array) of field measurement studies, which focuses on the technical measurement characteristics and their data analysis approaches.

Public Health Threat Assessment of Vehicular Load Index-Induced Urban Air Pollution Indices Near Traffic Intersections In Central India

OBJECTIVES

To assess traffic vehicular load, levels of various air pollutants, their correlation at selected traffic intersections of Bhopal city and to suggest suitable public health measures.

METHODS

A transverse study was conducted by convenience sampling with equated distribution among vehicular load-based large (Group1:G1: 10 TI), medium (Group2:G2: 5 TI), and small (Group3:G3: 5 TI) traffic-intersections (TI) through a systematic stratified random selection of study sites to assess traffic vehicle load index (VLI).

RESULTS

VLI,G1 (cumulative mean: 16.31; day-time (DT): 19.03, DT range 11.68-51.49; night-time (NT): 13.59, NT range 11.7-18.0), VLI,G2 (cumulative mean: 0.965; DT:0.971, DT range 08.56-11.67; NT: 0.960, NT range 07.54-11.39), and VLI,G3 (cumulative mean: 06.17; DT:06.08, DT range 04.12-06.86; NT: 06.27, NT range 03.74-07.53). There is a significant intergroup difference of the mean (G1 vs G2: p=0.03); (G1 vs G3: p=0.002); (G2 vs G3: p=0.003). The range of VLI is found to be wide within G1 (DT; 11.68-51.49; NT 11.7-18.00) as compared to narrow range in G2 (DT; 8.56-11.67; NT7.54-11.39) and G3 (DT; 4.12-6.86; NT 3.74-7.53).

CONCLUSION

High air pollution noted at TIs and associated exposure to unprotected commuters pose public-health risks. It has long-term health consequences requiring focused multidisciplinary preventive interventions.

A review of multivariate analysis: is there a relationship between airborne particulate matter and meteorological variables?

Among statistical tools for the study of atmospheric pollutants, trajectory regression analysis (TRA), cluster analysis (CA), and principal component analysis (PCA) can be highlighted. Therefore, this article presents a systematic review of such techniques based on (i) air mass influences on particulate matter (PM) and (ii) the study of the relationship between PM and meteorological variables. This article aims to review studies that use TRA and to review studies that adopt CA and/or PCA to identify the associations and relationship between meteorological variables and atmospheric pollutants. Papers published between 2006 and 2018 and indexed by five of the main scientific databases were considered (ScienceDirect, Web of Science, PubMed, SciELO, and Scopus databases). PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations supported this systematic review. From the resulting most relevant papers, eight studies analyzed the influence of air mass trajectories on PM using TRA and twenty-one studies searched for the relationship between meteorological variables and PM using CA and/or PCA. A combination of TRA and time series models was identified as the possibility of future works. Besides, studies that simultaneously combine the three techniques to identify both the influence of air masses on PM and its relationship with meteorological variables are a possibility of future papers, because it can lead to a better comprehension of such a phenomenon.

Implementation of IoT-Based Air Quality Monitoring System for Investigating Particulate Matter (PM10) in Subway Tunnels

Air quality monitoring for subway tunnels in South Korea is a topic of great interest because more than 8 million passengers per day use the subway, which has a concentration of particulate matter (PM₁₀) greater than that of above ground. In this paper, an Internet of Things (IoT)-based air quality monitoring system, consisting of an air quality measurement device called Smart-Air, an IoT gateway, and a cloud computing web server, is presented to monitor the concentration of PM₁₀ in subway tunnels. The goal of the system is to efficiently monitor air quality at any time and from anywhere by combining IoT and cloud computing technologies. This system was successfully implemented in Incheon’s subway tunnels to investigate levels of PM₁₀. The concentration of particulate matter was greatest between the morning and afternoon rush hours. In addition, the residence time of PM₁₀ increased as the depth of the monitoring location increased. During the experimentation period, the South Korean government implemented an air quality management system. An analysis was performed to follow up after implementation and assess how the change improved conditions. Based on the experiments, the system was efficient and effective at monitoring particulate matter for improving air quality in subway tunnels.

Environmental and Health Effects of Ventilation in Subway Stations: A Literature Review

Environmental health in subway stations, a typical type of urban underground space, is becoming increasingly important. Ventilation is the principal measure for optimizing the complex physical environment in a subway station. This paper narratively reviews the environmental and health effects of subway ventilation and discusses the relevant engineering, environmental, and medical aspects in combination. Ventilation exerts a notable dual effect on environmental health in a subway station. On the one hand, ventilation controls temperature, humidity, and indoor air quality to ensure human comfort and health. On the other hand, ventilation also carries the potential risks of spreading air pollutants or fire smoke through the complex wind environment as well as produces continuous noise. Assessment and management of health risks associated with subway ventilation is essential to attain a healthy subway environment. This, however, requires exposure, threshold data, and thereby necessitates more research into long-term effects, and toxicity as well as epidemiological studies. Additionally, more research is needed to further examine the design and maintenance of ventilation systems. An understanding of the pathogenic mechanisms and aerodynamic characteristics of various pollutants can help formulate ventilation strategies to reduce pollutant concentrations. Moreover, current comprehensive underground space development affords a possibility for creating flexible spaces that optimize ventilation efficiency, acoustic comfort, and space perception.

Multivariate statistical monitoring of subway indoor air quality using dynamic concurrent partial least squares

To maintain the health level of indoor air quality (IAQ) in subway stations, the data-driven multivariate statistical method concurrent partial least squares (CPLS) has been successfully applied for output-relevant and input-relevant sensor faults detection. To cope with the dynamic problem of IAQ data, the augmented matrices are applied to CPLS (DCPLS) to achieve the better performance. DCPLS method simultaneously decomposes the input and output data spaces into five subspaces for comprehensive monitoring: a joint input-output subspace, an output principal subspace, an output-residual subspace, an input-principal subspace, and an input-residual subspace. Results of using the underground IAQ data in a subway station demonstrate that the monitoring capability of DCPLS is superior than those of PLS and CPLS. More specifically, the fault detection rates of the bias of PM₁₀ and PM_2.5 using DCPLS can be improved by approximately 13% and 15%, respectively, in comparison with those of CPLS.

Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits

Indoor air pollution has been ranked among the top five environmental risks to public health. Indoor Air Quality (IAQ) is proven to have significant impacts on people’s comfort, health, and performance. Through a systematic literature review in the area of IAQ, two gaps have been identified by this study: short-term monitoring bias and IAQ data-monitoring solution challenges. The study addresses those gaps by proposing an Internet of Things (IoT) and Distributed Ledger Technologies (DLT)-based IAQ data-monitoring system. The developed data-monitoring solution allows for the possibility of low-cost, long-term, real-time, and summarized IAQ information benefiting all stakeholders contributing to define a rich context for Industry 4.0. The solution helps the penetration of Industrial Internet of Things (IIoT)-based monitoring strategies in the specific case of Occupational Safety Health (OSH). The study discussed the corresponding benefits OSH regulation, IAQ managerial, and transparency perspectives based on two case studies conducted in Spain.

A review of traditional and advanced technologies for the removal of particulate matter in subway systems

The pollution status of particulate matter (PM) in a subway system and technological trends in their reduction were discussed in this study. The levels of PM_2.5 and PM₁₀ are generally found to be higher in the underground platforms and tunnels than those in the outdoor air. It has also been reported that the composition of fine dust in the subway consists of various substances including heavy metals (like Fe), carbonaceous matter, and solvent extractable organic matter (SEOM). It was confirmed that subway dust was created mainly by wearing on wheels, rails, and brakes. In addition, the concentration of PM in such environment was influenced not only by internal factors (eg, operating conditions of trains and ventilation systems, number of passengers, and the structure of subway stations) but also by outside factors (eg, ambient air concentration). Up to now, various techniques (ventilation fans, platform screen doors (PSDs), magnetic filters, small jet fans, artificial intelligent ventilation systems, hybrid filters, etc) have been studied to reduce PM in underground subway systems. In this study, we reviewed the air quality of major subway stations with the focus on PM and relevant technologies for its reduction.

Size-dependent characteristics of diurnal particle concentration variation in an underground subway tunnel

Understanding characteristics of diurnal particle concentration variation in an underground subway tunnel is important to reduce subway passengers' exposure to high levels of toxic particle pollution. In this study, real-time particle monitoring for eight consecutive days was done at a shelter located in the middle of a one-way underground subway tunnel in Seoul, Republic of Korea, during the summer of 2015. Particle mass concentration was measured using a dust monitor and particle number concentration using an optical particle counter. From the diurnal variations in PM₁₀, PM_2.5, and PM₁, concentrations of particles larger than 0.54 μm optical particle diameter were affected by train frequency whereas those of particles smaller than 0.54 μm optical particle diameter were not changed by train frequency. Number concentration of particles smaller than 1.15 μm optical particle diameter was dependent on outdoor ambient air particle concentration level, whereas that of particles larger than 1.15 μm optical particle diameter was independent of outdoor ambient air due to low ventilation system transmission efficiency of micrometer-sized particles. In addition, an equation was suggested to predict the diurnal particle concentration in an underground tunnel by considering emission, ventilation, and deposition effects.

Aerosol sources in subway environments

Millions of people use rail subway public transport around the world, despite the relatively high particulate matter (PM) concentrations in these underground environments, requiring the identification and quantification of the aerosol source contributions to improve the air quality. An extensive aerosol monitoring campaign was carried out in eleven subway stations in the Barcelona metro system, belonging to seven subway lines. PM_2.5 samples were collected during the metro operating hours and chemically analysed to determine major and trace elements, inorganic ions, and total carbon. The chemical compositions of subway components such as brake pads, rail tracks and pantographs were also determined. The mean PM_2.5 concentrations varied widely among stations, ranging from 26 µg m^-3 to 86 µg m^-3. Subway PM_2.5 was mainly constituted by Fe₂O₃ (30-66%), followed by carbonaceous matter (18-37%) for the old stations, while for new stations equipped with Platform Screen Doors (PSD) these percentages go down to 21-44% and 15-30%, respectively. Both the absolute concentrations and the relative abundance of key species differed for each subway station, although with common patterns within a given subway line. This is a result of the different emission chemical profiles in different subway lines (using diverse types of brakes and/or pantographs). The co-emission of different sources poses a problem for their separation by receptor models. Nevertheless, receptor modelling (Positive Matrix Factorization) was applied resulting in ten sources, five of them subway-specific: RailWheel, RailWheel+Brake, Brake_A, Brake_B, Pb. The sum of their contributions accounted for 43-91% of bulk PM_2.5 for the old stations and 21-52% for the stations with PSD. The decrease of the activity during the weekends resulted in a decrease (up to 56%) in the subway-specific sources contribution to the -already lower- bulk PM_2.5 concentrations compared to weekdays. The health-related elements are mainly apportioned (> 60%) by subway sources.

Evaluation of PM10, CO2, airborne bacteria, TVOCs, and formaldehyde in facilities for susceptible populations in South Korea

Poor indoor air quality can have adverse effects on human health, especially in susceptible populations; however, few studies have measured multiple pollutants in facilities for susceptible populations at a national scale in South Korea. Therefore, we measured the concentrations of indoor pollutants (fine particulate matter (PM₁₀), CO₂, airborne bacteria (AB), total volatile organic compounds (TVOCs), and formaldehyde) to determine their possible relation to other indoor environmental factors and characteristics of facilities with susceptible populations, such as hospitals, geriatric hospitals, elderly care facilities, and postnatal care centers throughout South Korea. Indoor pollutants were sampled at 82 indoor facilities, including 62 facilities for susceptible populations. Spearman's correlation, Kruskal-Wallis, and Mann-Whitney analyses were used to examine the relationship among and differences between pollutants at indoor facilities and indoor/outdoor differences in PM₁₀ concentration. There were significant correlations between indoor temperature and AB concentration (r = 0.37, p < 0.01), TVOCs, and formaldehyde (r = 0.264, p < 0.01). Indoor PM₁₀ concentrations were higher than outdoor concentrations at all facilities for susceptible populations (p < 0.01). CO₂ might be a good indicator for predicting indoor pollutants when categorized into two levels (≤750 ppm and >750 ppm). The hazard quotient of formaldehyde was higher than the acceptable level of 1 for children under the age of eight in postnatal care centers, indicative of unsafe levels. Therefore, more depth study for exposure characteristics of formaldehyde and indoor air quality (IAQ) in postnatal care facilities as a national scale is needed for finding the children exposure levels.

Modeling of subway indoor air quality using Gaussian process regression

Soft sensor modeling of indoor air quality (IAQ) in subway stations is essential for public health. Gaussian process regression (GPR), as an efficient nonlinear modeling method, can effectively interpret the complicated features of industrial data by using composite covariance functions derived from base kernels. In this work, an accurate GPR soft sensor with the sum of squared-exponential covariance function and periodic covariance function is proposed to capture the time varying and periodic characteristics in the subway IAQ data. The results demonstrate that the prediction performance of the proposed GPR model is superior to that of the traditional soft sensors consisting of partial least squares, back propagation artificial neural networks, and least squares support vector regression (LSSVR). More specifically, the values of root mean square error, mean absolute percentage error, and coefficient of determination are improved by 12.35%, 9.53%, and 40.05%, respectively, in comparison with LSSVR.

Factors affecting ambient endotoxin and particulate matter concentrations around air vents of subway stations in South Korea

Levels of airborne endotoxins and particulate matter less than 10 μm and 2.5 μm in diameter (PM) were measured in the air vents of subway stations in Seoul, South Korea, and factors affecting both pollutants were analyzed. The measurements were completed from March 2016 to February 2017 for eight air vents situated at the ground level around the subway stations. A total of 166 air samples were collected and analyzed using the kinetic limulus amebocyte lysate assay. Endotoxin levels ranged from not detected to 1.986 EU m^-3, with a mean of 0.227 EU m^-3. The results showed significantly different PM levels from the measurements reported by AIRKOREA as part of the comprehensive air quality index. This can be attributed to different sampling sites in the same area. Endotoxin levels tended to be higher in fall compared to summer. Airborne bacteria levels showed a pattern similar to the endotoxin levels, but no significant association was reported between them. The levels of endotoxins around air vents with a glass cover and streets that allowed smoking were significantly higher than those not containing a walled barrier and streets in which smoking was prohibited. Multivariate regression analysis showed that the factors affecting endotoxin levels comprised air vents with a glass cover (coefficient = 0.106, p = 0.014) and season (coefficient = 0.062, p < 0.0001). Therefore, installing barriers on the air vents and prohibiting smoking in streets to which the vents open may be effective ways to lessen exposure to airborne endotoxin levels around air vents.

Personal exposure measurements of school-children to fine particulate matter (PM2.5) in winter of 2013, Shanghai, China

Objective

The aim of this study was to perform an exposure assessment of PM_2.5 (particulate matter less than 2.5μm in aerodynamic diameter) among children and to explore the potential sources of exposure from both indoor and outdoor environments.

Methods

In terms of real-time exposure measurements of PM_2.5, we collected data from 57 children aged 8–12 years (9.64 ± 0.93 years) in two schools in Shanghai, China. Simultaneously, questionnaire surveys and time-activity diaries were used to estimate the environment at home and daily time-activity patterns in order to estimate the exposure dose of PM_2.5 in these children_. Principle component regression analysis was used to explore the influence of potential sources of PM_2.5 exposure.

Results

All the median personal exposure and microenvironment PM_2.5 concentrations greatly exceeded the daily 24-h PM_2.5 Ambient Air Quality Standards of China, the USA, and the World Health Organization (WHO). The median E_total (the sum of the PM_2.5 exposure levels in different microenvironment and fractional time) of all students was 3014.13 (μg.h)/m³. The concentration of time-weighted average (TWA) exposure of all students was 137.01 μg/m³. The median TWA exposure level during the on-campus period (135.81 μg/m³) was significantly higher than the off-campus period (115.50 μg/m³, P = 0.013 < 0.05). Besides ambient air pollution and meteorological conditions, storey height of the classroom and mode of transportation to school were significantly correlated with children’s daily PM_2.5 exposure.

Conclusions

Children in the two selected schools were exposed to high concentrations of PM_2.5 in winter of 2013 in Shanghai. Their personal PM_2.5 exposure was mainly associated with ambient air conditions, storey height of the classroom, and children’s transportation mode to school.

Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance.

Air quality inside subway metro indoor environment worldwide: A review

The air quality in the subway metro indoor microenvironment has been of particular public concern. With specific reference to the growing demand of green transportation and sustainable development, subway metro systems have been rapidly developed worldwide in last decades. The number of metro commuters has continuously increased over recent years in metropolitan cities. In some cities, metro system has become the primary public transportation mode. Although commuters typically spend only 30-40min in metros, the air pollutants emitted from various interior components of metro system as well as air pollutants carried by ventilation supply air are significant sources of harmful air pollutants that could lead to unhealthy human exposure. Commuters' exposure to various air pollutants in metro carriages may cause perceivable health risk as reported by many environmental health studies. This review summarizes significant findings in the literature on air quality inside metro indoor environment, including pollutant concentration levels, chemical species, related sources and health risk assessment. More than 160 relevant studies performed across over 20 countries were carefully reviewed. These comprised more than 2000 individual measurement trips. Particulate matters, aromatic hydrocarbons, carbonyls and airborne bacteria have been identified as the primary air pollutants inside metro system. On this basis, future work could focus on investigating the chronic health risks of exposure to various air pollutants other than PM, and/or further developing advanced air purification unit to improve metro in-station air quality.

Health risk assessment and personal exposure to Volatile Organic Compounds (VOCs) in metro carriages - A case study in Shanghai, China

Air pollution in transportation cabins has recently become a public concern. However, few studies assessed the exposure levels of suspected air pollutants including Volatile Organic Compounds (VOCs). This paper studied the exposure levels of in-carriage VOCs (benzene, toluene, ethylbenzene, xylene, styrene, formaldehyde, acetaldehyde, acetone and acrolein) in Shanghai, China and estimated the health risk in different conditions. The results indicated that VOCs concentrations in metro carriages varied from different train models, due to the difference in carriage size and ventilation system. The concentrations of aromatic VOCs in old metro carriage were 1-2 times higher than the new ones, as better paintings were used in new trains. Poor air circulation and ventilation in the underground track was likely to be the cause of higher VOCs levels (~10%) than the above-ground track. Lower aromatic compounds levels and higher carbonyls levels were observed in metro carriages at suburban areas than those at urban areas, likely due to less aromatic emission sources and more carbonyls emission sources in suburban areas. Acetone and acrolein were found to increase from 7.71 to 26.28μg/m³ with number of commuters increasing from 40 to 200 in the carriages. According to the acceptable level proposed by the World Health Organization (1×10^-6-1×10^-5), the life carcinogenic risk of commuters by subway (8.5×10^-6-4.8×10^-5) was little above the acceptable level in Shanghai. Further application of our findings is possible to act as a reference in facilitating regulations for metro systems in other cities around world, so that in-carriage air quality might be improved.

Transient variation of aerosol size distribution in an underground subway station

As the number of people using rapid transit systems (subways) continues to rise in major cities worldwide, increasing attention has been given to the indoor air quality of underground stations. This study intended to observe the change of PM distribution by size in an underground station with PSDs installed located near the main road in downtown Seoul, as well as to examine causes for the changes. The results indicate that the PM suspended in the tunnel flowed into the platform area even in a subway station where the effect of train-induced wind is blocked by installed PSDs, as this flow occurred when the PSDs were opened. The results also indicate that coarse mode particles generated by mechanical friction in the tunnel, such as that between wheels and rail, also flowed into the platform area. The PM either settled or was re-suspended according to size and whether the ventilation in the platform area was in operation or if the platform floor had been washed. The ventilation system was more effective in removing PM of smaller sizes (fine particles) while the wash-out performed after train operations had stopped reduced the suspension of coarse mode particles the next morning. Despite installation of the completely sealed PSDs, inflow of coarse mode particles from the tunnel seems unavoidable, indicating the need for measures to decrease the PM generated there to lower subway user exposure since those particles cannot be reduced by mechanical ventilation alone. This research implicate that coarse PM containing heavy metals (generated from tunnel side) proliferated especially during rush hours, during which it is very important to control those PM in order to reduce subway user exposure to this hazardous PM.

Factors controlling air quality in different European subway systems

Sampling campaigns using the same equipment and methodology were conducted to assess and compare the air quality at three South European subway systems (Barcelona, Athens and Oporto), focusing on concentrations and chemical composition of PM2.5 on subway platforms, as well as PM2.5 concentrations inside trains. Experimental results showed that the mean PM2.5 concentrations widely varied among the European subway systems, and even among different platforms within the same underground system, which might be associated to distinct station and tunnel designs and ventilation systems. In all cases PM2.5 concentrations on the platforms were higher than those in the urban ambient air, evidencing that there is generation of PM2.5 associated with the subway systems operation. Subway PM2.5 consisted of elemental iron, total carbon, crustal matter, secondary inorganic compounds, insoluble sulphate, halite and trace elements. Of all metals, Fe was the most abundant, accounting for 29-43% of the total PM2.5 mass (41-61% if Fe2O3 is considered), indicating the existence of an Fe source in the subway system, which could have its origin in mechanical friction and wear processes between rails, wheels and brakes. The trace elements with the highest enrichment in the subway PM2.5 were Ba, Cu, Mn, Zn, Cr, Sb, Sr, Ni, Sn, Co, Zr and Mo. Similar PM2.5 diurnal trends were observed on platforms from different subway systems, with higher concentrations during subway operating hours than during the transport service interruption, and lower levels on weekends than on weekdays. PM2.5 concentrations depended largely on the operation and frequency of the trains and the ventilation system, and were lower inside the trains, when air conditioning system was operating properly, than on the platforms. However, the PM2.5 concentrations increased considerably when the train windows were open. The PM2.5 levels inside the trains decreased with the trains passage in aboveground sections.

A robust localized soft sensor for particulate matter modeling in Seoul metro systems

Developing accurate soft sensors to predict and monitor the indoor air quality (IAQ) of hazardous pollutants that accumulate in underground metro systems is of key importance. The just-in-time (JIT) learning technique possesses a local feature that can track the variations in the dynamic process more effectively, which is different from the traditional soft sensor modeling methods, such as partial least squares (PLS), which models the process in an offline and global way. In this study, a robust soft sensor that combined the JIT learning technique with a least squares support vector regression (LSSVR) method, named JIT-LSSVR, was derived in order to improve the prediction performance of a PM2.5 soft sensor in a subway station. Additionally, in order to eliminate the adverse effects caused by the outliers in the process variables, an outlier detection step was integrated into the JIT-LSSVR modeling procedure. The performance evaluation results demonstrated that the proposed robust JIT-LSSVR soft sensor has the capability to model nonlinear and dynamic subway systems. The root mean square error of the JIT-LSSVR soft sensor was improved by 55% in comparison with that of the LSSVR soft sensor.