Characteristics of indoor air quality in underground metro stations: A critical review

Passi, Amit; Nagendra, S.M. Shiva; Maiya, M.P.

doi:10.1016/j.buildenv.2021.107907

Cited by 58 publications

(26 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These ratios indicate that inside metro areas, the total amount of PM comprised of > 70% particles of the fine size range (< 2.5µm). There are various sources of fine particles inside metro stations such as from tunnels (wear of rails, wheels, catenaries, and pantographs; service of metro lines; sparking of electric wires; wear of brake pads), resuspension due to the movement of trains and passengers, train piston force, and infiltration of outdoor fine particles suspended in the air [8]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Several factors that influence particle concentrations in subway metro include the age of metro, design of tunnel and station, passenger density, frequency of trains, depth of station, wheel or rail track material, train speed, air-conditioning /ventilation system, frequency of station cleaning, etc. [8]. Due to the various operations of the metro, exposure to several PM2.5 metals such as Fe, Mn, Mo, Cu, Ni, Cr, Ca, Na, Zn, and Pb was observed in metro stations [9], [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Indoor and Outdoor Exposure to Airborne Particles in Subway Metro Stations and Their Associated Health Risks

Passi

Nagendra

Maiya

2021

WIT Transactions on Ecology and the Environment

Self Cite

View full text Add to dashboard Cite

This investigative study examines the exposure of metro commuters to inhalable airborne particulate matter (PM10, PM2.5, and PM1) in and around the subway premises, and their associated health risk due to regular exposure to high levels of PM. PM monitoring was carried out in five metro stations (four underground and one elevated) in Chennai, India. PM measurements were taken for 3 days a week (two weekdays and one weekend) in each metro station. The adverse impacts on health and the risk of disease were assessed using AIRQ+, a tool developed by World Health Organization (WHO). Exposure to PM was found to be up to twofold higher than the WHO stipulated safe limits. Exposure to PM2.5 was noted to be dominant (59%-70% of total PM mass). PM accumulation was found to be high in stations having a large number of commuters and those located in the busiest areas of the city. Infiltration of outdoor road emissions was found to have a significant influence on the deterioration of subway air quality. Underground metro stations were found to have the least dispersion and high accumulation of air pollutants whereas elevated metro stations were noted to have high dispersion and least accumulation. Health risk assessment indicated the non-carcinogenic risk to passengers (Hazard quotient = 1.18 and Cancer risk < 10 -4 ), who were exposed to PM regularly for a period of 1 year. Continuous, long-term exposure up to 8 years will lead to the development of carcinogenic risk (Hazard quotient > 10 and Cancer risk > 10 -4 ). Further, the health impact evaluation indicated high incidence per 100,000 population; asthma (816.92), unhealthy days (424.7), and lost workdays (416.57) due to acute exposure and ischemic heart disease (60.11), stroke (50), acute lower respiratory infections (30.52), and chronic obstructive pulmonary diseases (21.17) due to chronic exposure.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Indoor and Outdoor Exposure to Airborne Particles in Subway Metro Stations and Their Associated Health Risks

Passi

Nagendra

Maiya

2021

WIT Transactions on Ecology and the Environment

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies indicated an explicit link between material concentration and spectral signal strength [54]. Thus, the relation between spectral intensity and element content can be assumed with Equation (6). lnI_C = M…”

Section: Neural Networkmentioning

confidence: 99%

“…In this context, the monitoring of air quality in the living areas has aroused great attention. For instance, Gupta et al researched the environment in office spaces [5], and Passi et al investigated the characteristics of indoor air quality in the underground [6]. Furthermore, Zhang et al studied the air quality in the bedroom and proposed that the indoor CO 2 concentration influences sleep quality of residents [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

et al. 2022

View full text Add to dashboard Cite

The air quality of the living area influences human health to a certain extent. Therefore, it is particularly important to detect the quality of indoor air. However, traditional detection methods mainly depend on chemical analysis, which has long been criticized for its high time cost. In this research, a rapid air detection method for the indoor environment using laser-induced breakdown spectroscopy (LIBS) and machine learning was proposed. Four common scenes were simulated, including burning carbon, burning incense, spraying perfume and hot shower which often led to indoor air quality changes. Two steps of spectral measurements and algorithm analysis were used in the experiment. Moreover, the proposed method was found to be effective in distinguishing different kinds of aerosols and presenting sensitivity to the air compositions. In this paper, the signal was isolated by the forest, so the singular values were filtered out. Meanwhile, the spectra of different scenarios were analyzed via the principal component analysis (PCA), and the air environment was classified by K-Nearest Neighbor (KNN) algorithm with an accuracy of 99.2%. Moreover, based on the establishment of a high-precision quantitative detection model, a back propagation (BP) neural network was introduced to improve the robustness and accuracy of indoor environment. The results show that by taking this method, the dynamic prediction of elements concentration can be realized, and its recognition accuracy is 96.5%.

show abstract

“…In recent years, studies on air pollution in subway stations have gradually increased. Initially, scholars started with the chemical composition of pollutants and focused on the measurement of different pollutant concentrations, including gaseous pollutants, particulate pollutants, and microorganisms [ 6 , 7 ]. Then, the effects of various types of equipment in subway stations on the distribution of pollutants were also included in the study.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station

Liu

2022

IJERPH

View full text Add to dashboard Cite

With the increase in subway travelers, the air quality of underground enclosed spaces at subway stations has attracted much more attention. The study of pollutants exposure assessment, especially fine particulate matter, is important in both pollutant control and metro station design. In this paper, combining pedestrian flow analysis (PFA) and computational fluid dynamics (CFD) simulations, a novel surrogate-assisted particulate matter exposure assessment method is proposed, in which PFA is used to analyze the spatial-temporal movement characteristics of pedestrians to simultaneously consider the location and value of the pedestrian particulate generation source and their exposure streamline to particulate matter; the CFD model is used to analyze the airflow field and particulate matter concentration field in detail. To comprehensively consider the differences in the spatial concentration distribution of particulate matter caused by the time-varying characteristics of the airflow organization state in subway stations, surrogate models reflecting the nonlinear relationship between simulated and measured data are trained to perform accurate pedestrian exposure calculations. The actual measurement data proves the validity of the simulation and calculation methods, and the difference between the calculated and experimental values of the exposure is only about 5%.

show abstract

Characteristics of indoor air quality in underground metro stations: A critical review

Cited by 58 publications

References 85 publications

Assessment of Indoor and Outdoor Exposure to Airborne Particles in Subway Metro Stations and Their Associated Health Risks

Assessment of Indoor and Outdoor Exposure to Airborne Particles in Subway Metro Stations and Their Associated Health Risks

Analysis and Dynamic Monitoring of Indoor Air Quality Based on Laser-Induced Breakdown Spectroscopy and Machine Learning

Surrogate-Assisted Fine Particulate Matter Exposure Assessment in an Underground Subway Station

Contact Info

Product

Resources

About