Development of a magnetic hybrid filter to reduce PM10 in a subway platform

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.

Study on the Ventilation Method to Maintain the PM10 Concentration in a Subway Cabin below 35 μg/m3

The city of Seoul will limit the maximum particulate matter (PM10) concentration to ≤35 μg/m3 (from 2024). Herein, a numerical parametric study was conducted on the PM removal efficiency of the heating, ventilation, and air conditioning (HVAC) filters installed in the ceiling of subway cabins. The PM10 concentration distribution was explored according to the flow rate and flow rate ratio of the air introduced into the cabin. Under the current ventilation conditions of the subway train HVAC system, the PM10 concentration was highest in the cabin central area where exhaust outlets are located and decreased toward both ends of the cabin. The indoor airflow was improved and the PM10 concentration was reduced by increasing the flow rate of the supplied air at both ends of the cabin while decreasing it in the central area. It was found that the strengthened PM10 concentration criterion of Seoul can be met by increasing the ventilation flow rate to 700 CMH (currently, 500 CMH) and the filter efficiency to 85% (currently, 70%) while maintaining the current flow rate ratio. These results are expected to be used as important reference data for reducing the PM concentration in subway cabins and thereby improving indoor air quality.

Deep Learning-Based Indoor Air Quality Forecasting Framework for Indoor Subway Station Platforms

Particulate matter (PM) of sizes less than 10 µm (PM10) and 2.5 µm (PM2.5) found in the environment is a major health concern. As PM is more prevalent in an enclosed environment, such as a subway station, this can have a negative impact on the health of commuters and staff. Therefore, it is essential to continuously monitor PM on underground subway platforms and control it using a subway ventilation control system. In order to operate the ventilation system in a predictive way, a credible prediction model for indoor air quality (IAQ) is proposed. While the existing deterministic methods require extensive calculations and domain knowledge, deep learning-based approaches showed good performance in recent studies. In this study, we develop an effective hybrid deep learning framework to forecast future PM10 and PM2.5 on a subway platform using past air quality data. This hybrid framework is an integration of several deep learning frameworks, namely, convolution neural network (CNN), long short-term memory (LSTM), and deep neural network (DNN), and is called hybrid CNN-LSTM-DNN; it has the characteristics to capture temporal patterns and informative characteristics from the indoor and outdoor air quality parameters compared with the standalone deep learning models. The effectiveness of the proposed PM10 and PM2.5 forecasting framework is demonstrated using comparisons with the different existing deep learning models.

Field Measurement and Evaluation of Effective Ventilation and Particulate Matter Discharge Efficiency of Air Shafts in Subway Tunnels

The ventilation performance of air shafts is important to the air quality of subway tunnels, but there is no unified evaluation index of ventilation performance. In this paper, the air shafts at different locations in subway tunnels were taken as research objects, and the wind speed as well as the particulate matter concentration of each air shaft was tested. The effective ventilation volume and PM2.5 discharge efficiency of the air shafts were defined to evaluate the ventilation performance. It was found that on average, during the subway train service, the station air shaft on the train-arriving side can discharge 2050 m3 of dirty air in the tunnels and inhale 218 m3 of fresh air from the outside environment, while the station air shaft on the train-leaving side can absorb 2430 m3 of fresh air but can hardly effectively discharge dirty air; meanwhile, the middle air shaft can not only effectively exhaust 1519 m3 of dirty air but can also absorb 7572 m3 of fresh air. In addition, the middle air shaft has better ventilation performance if its inner opening is set on the top rather than on the side of the tunnel. The PM2.5 discharge efficiency of the station air shaft on the train-arriving side is 52.0~62.8%, higher than that of the middle air shaft of which the value is 26.8~40.7%. This research can provide guidance for ventilation performance evaluation of subway air shafts and provide a reference for subway tunnel air shaft location design.

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.

Advanced Design of Fiber-Based Particulate Filters: Materials, Morphology, and Construction of Fibrous Assembly

With increasing air pollution and sporadic outbreaks of epidemics, there is ramping attention on the filtration devices. The main constituents of airborne pollutants are particulate matters of solid particles, liquid aerosol, bioaerosol/bio-droplets, and gas/vapor. With the growing demand for high-performance filters, novel materials and functionalities are being developed applying advanced technologies. In this paper, recent developments of fiber-based particulate filters are reviewed, with a focus on the important performance parameters and material properties. Trends in technology and research activities are briefly reviewed, and the evaluative measures of filtration performance are reported. Recent studies on the advanced filter materials are reviewed in the aspect of polymers and the fabrication process of fibrous assembly. The characterization method including 3D modeling and simulation is also briefly introduced. Multifunctional filters such as antimicrobial filter and gas and particulate filters are briefly introduced, and efforts for developing environmentally sustainable filters are noted.

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.

Current Status, Challenges and Resilient Response to Air Pollution in Urban Subway

Subway air pollution mainly refers to inhalable particulate matter (PM) pollution, organic pollution, and microbial pollution. Based on the investigation and calculation of the existing researches, this paper summarizes the sources of air pollutants, chemical compositions, and driving factors of PM variations in subway. It evaluates the toxicity and health risks of pollutants. In this paper, the problems and challenges during the deployment of air pollution governance are discussed. Results show that the global PM compliance rate of subway is about 30%. Subway air pollution is endogenous, which means that pollutants mainly come from mechanical wear and building materials erosions. Particles are mainly metal particles, black carbon, and floating dust. The health risks of some chemical elements in the subway have reached critical levels. The variations of PM concentrations show spatial-temporal characteristics, which are mainly controlled by train age, brakes types, and environmental control systems. The authors then analyze the dynamics of interactions among government, companies and public during the air pollution governance by adding the following questions: (a) who pays the bill; (b) how to evaluate the cost-effectiveness of policies; (c) how the public moves from risk perception to actions; (d) how to develop clean air technology better so as to ultimately incentivize stakeholders and to facilitate the implementation of subway clean air programme in a resilient mode.