# Box Model Approach for Indoor Air Quality (IAQ) Management in a Subway Station Environment

^{‡}; Rajib Pokhrel

^{‡}; Heekwan Lee

^{*}; Shin-Do Kim

^{1)}

1)Department of Environmental Engineering, University of Seoul, Seoul, Korea

^{‡}Both authors have equal contribution

Correspondence to:

^{*}Tel: +82-32-835-4689, E-mail: airgroup@incheon.ac.kr

## Abstract

Air quality in a subway tunnel has been crucial in most of the subway environments where IAQ could be affected by many factors such as the number of passengers, the amount and types of ventilation, train operation factors and other facilities. A modeling approach has been introduced to manage the general IAQ in a subway station. Field surveys and CO_{2} measurements were initially conducted to analyze and understand the relationship between indoor and outdoor air quality while considering internal pollution sources, such as passengers and subway trains, etc. The measurement data were then employed for the model development with other statistical information. For the model development, the algorithm of simple continuity was set up and applied to model the subway IAQ concerned, while considering the major air transport through staircases and tunnels. Monitored CO_{2} concentration on the concourse and platform were correlated with modeling results where the correlation values for the concourse and platform were R^{2}=0.96 and R^{2}=0.75, respectively. It implies that the box modeling approach introduced in this study would be beneficial to predict and control the indoor air quality in subway environments.

## Keywords:

Carbon Dioxide, Field Measurements, Indoor Air Pollution, Subway Station, Ventilation## 1. INTRODUCTION

Urbanization, which has rapidly been expanding recently throughout the world, leads to a significant increase in the urban population as well as other related demands. For instance, residents in the Seoul metropolitan area account for approximately a quarter of Korea’s entire population. This urbanization trend demands that more land space is planned for high efficiency use. High- rise buildings are also commonly designed and used in modern society to ease land requirements. Along with this demand for land, mass public transportation is another essential aspect that makes a city function well. In reality, however, the increasing need for transportation is not easily satisfied due to solid urban structures and space limitation.

Efforts have been made to utilize underground spaces in urban areas in order to ease land and transport demands. The advent of the subway system has played a great role in public transportation in urban areas. This utilization of the subway system also has various advantages such as easing traffic jams and reducing the environmental impact to ground level urban air quality. In fact, it implies that citizens in urban areas have increased exposure to subway air pollutants while waiting at stations or being transported. However, the IAQ in subway systems is neither well examined nor understood. The indoor air quality of subway stations, underground terminals and underground shopping centers have especially generated more interest due to health implication for passengers, pedestrians and workers etc. It is a well-known fact that people in modern society spend most of their time in an indoor environment; however, the fact that most of the time spent commuting to their places of work and homes is also spent “indoors” i.e. in subways, has been less appreciated and emphasizes the importance of the management of indoor air pollutants as well as the ventilation system operation. For this, researchers and engineers in the different part of the world work differently. Liu *et al*. (2013) designed the ventilation control system for the subway station based on the indoor and outdoor PM10 concentration data. For the efficient ventilation with less piston impact in the subway tunnel by TIW, tunnel design (geometric parameters) is one of the key parameter (Moreno *et al*., 2014).

In this study of IAQ in subway stations, an IAQ model was developed for a subway station. Initially, the characteristics of IAQ in subway stations were surveyed by means of onsite field measurements and the results were then introduced to the IAQ model (box model) development for the further analysis. Finally, correlation analysis was carried out for the validation of the predicted data with the in-situ measurement data. In this study. CO_{2} is considered as an indoor air pollutant as it is known as an indicator of indoor air pollutants.

## 2. IAQ CONSIDERATION IN A SUBWAY STATION

In Seoul, there are 263 underground subway stations currently being used. The number of stations is also increasing gradually with newly planned subway routes. According to a long-term plan for Seoul, subway journeys will account for 49% of public transportation by 2011.

Fig. 1 demonstrates a typical subway station in the Seoul area. Passengers normally have access to the concourse from ground level via stairs. As shown, the concourse is usually located on the first floor below ground level. The platform including train tracks is built on the second floor below ground level. This design indicates that the IAQ in the concourse is influenced more by the external ambient air than by the IAQ in the platform.

Fig. 2 shows a simplified schematic diagram representing the movement of air and pollutants in a subway station. As denoted, the air from outside is being supplied into the concourse and the platform simultaneously by means of mechanical ventilation and natural ventilation, mainly via the stairs. In the case of natural ventilation, it is normally thermal buoyancy driven or train-induced wind.

The major factors affecting the subway IAQ are air temperature, humidity, ventilation by train-induced wind and mechanical systems and air pollutants. In this study, CO_{2} which has been used as the main indicator of indoor air quality was subjected. In indoor space, the CO_{2} concentration is normally increased by the occupants’ breathing with the subsequent effect that IAQ reduces as CO_{2} concentration increases. Indoor CO_{2} has also been used as an alternative method to estimate ventilation flow. Cheng and Yan (2011) carried out a comparison study on CO and CO_{2} levels in underground and ground level station in the Taipei rapid transit mass system where CO_{2} concentration was 574-1051 ppm where CO_{2} concentration in the Seoul subway station was approximately 500-800 ppm (Song *et al*., 2008).

The IAQ in the concourse is affected by the movement of air around the platform as well as the air from outside. In the case of the platform, the IAQ is affected by the air from outside and within the concourse. In addition, train-induced wind (TIW) also has a remarkable impact on the subway IAQ, where TIW is defined as, of the transport ventilation caused by a moving train. TIW has a dual role, which is to export the air pollutants in a platform toward the subway tunnel and to import the air pollutants in a subway tunnel onto platforms, as depicted in Fig. 3. More reading on this issue is provided by the reference (Seoul Metro, 2008).

## 3. MODELING APPROACH FOR SUBWAY IAQ

As demonstrated in Fig. 2, the underground space in a subway station is divided by two compartments, *i.e*. the concourse and the platform. These two compartments have different IAQ factors as discussed previously. In this study, the modeling approach which considers the impact of continuity in a single cell space is introduced to those two compartments, as depicted in Fig. 4. The confined space in Fig. 5 has several IAQ factors related to the contaminant sources and the control approaches. Table 1 lists those IAQ factors reported in previous studies of subway IAQ (Cheng and Yan, 2011).

Two-way approaches are made in this study to consider the generation and transport of air pollutants to the concourse and the platform. When considering the IAQ in the concourse, Fig. 5 demonstrates the schematic diagram of air exchange between the ambient and the concourse environment. The passengers and pedestrians are the major pollutant sources, while ventilation by the mechanical system and thermal/train-induced winds are the major factors for pollutant transportation. Variables are listed and defined in Table 1. Box model introduced in this study is based on the mass balance approach as in Fig. 4. Numerically, the model is derived as in Eq. (1) where the parameters are listed in the Table 1. Eq. (2) is the derived from Eq. (1) and it is used to predict the contaminant concentration in the concourse (Cheng and Yan, 2011).

$$$\begin{array}{l}\left\{\left[\left({V}_{29}+{V}_{30}\right)\times {C}_{1}\right]+\left[\left({V}_{27}+{V}_{28}\times {C}_{1}\right)\right]\right.\\ +\left[\left({V}_{1}+{V}_{2}+{V}_{3}+{V}_{4}+{V}_{5}+{V}_{6}+{V}_{7}+{V}_{8}\right)\times {C}_{3}\right]\\ +\left.\left[\left({P}_{1}+{P}_{2}\right)\times {K}_{1}\right]\right\}-\left[{C}_{2}\times \left({V}_{31}+{V}_{32}+{V}_{25}\right.\right.\\ +{V}_{26}+{V}_{9}+{V}_{10}+{V}_{11}+{V}_{12}+{V}_{13}+{V}_{14}+{V}_{15}\\ +\left.\left.{V}_{16}\right)\right]=0\end{array}$$$ | (1) |

$$$\begin{array}{l}{C}_{2}=\frac{\left\{\left[\left({V}_{29}+{V}_{30}\right)\times {C}_{1}\right]+\left[\left({V}_{27}+{V}_{28}\right)\times {C}_{1}\right]+\left[\left({V}_{1}+{V}_{2}+{V}_{3}+{V}_{4}+{V}_{5}+{V}_{6}+{V}_{7}+{V}_{8}\right)\times {C}_{3}\right]+\left[\left({P}_{1}+{P}_{2}\right)\times {K}_{1}\right]\right\}}{\left({V}_{31}+{V}_{32}+{V}_{25}+{V}_{26}+{V}_{9}+{V}_{10}+{V}_{11}+{V}_{12}+{V}_{13}+{V}_{14}+{V}_{15}+{V}_{16}\right)}\end{array}$$$ | (2) |

$$$\begin{array}{l}\left\{\right[({V}_{17}+{V}_{19})\times {C}_{1}]-{L}_{1}\{\left[\right({V}_{21}-{V}_{24})\times {C}_{4}]+\left[\right({V}_{9}+{V}_{10}+{V}_{11}+{V}_{12}+{V}_{13}+{V}_{14}+{V}_{15}+{V}_{16})\times {C}_{2}]\}\\ +{L}_{2}\left[\right({V}_{23}-{V}_{22})\times {C}_{4}]+({P}_{1}\times {K}_{1})+(T\times {K}_{2})\}-{C}_{3}[({V}_{18}+{V}_{20})+{L}_{2}({V}_{1}+{V}_{2}+{V}_{3}+{V}_{4}+{V}_{5}+{V}_{6}+{V}_{7}+{V}_{8})]\\ +\left\{\right\{n\times {C}_{4}\times \left[\frac{\left({V}_{18}+{V}_{20}-{V}_{17}-{V}_{19}\right)}{{V}_{18}+{V}_{20}}\right]\}\times [({V}_{18}+{V}_{20})+{\mathrm{L}}_{2}({V}_{1}+{V}_{2}+{V}_{3}+{V}_{4}+{V}_{5}+{V}_{6}+{V}_{7}+{V}_{8})\left]\right\}=0\end{array}$$$ | (3) |

$$$\begin{array}{l}{C}_{3}=\frac{\left\{\left[\left({V}_{17}+{V}_{19}\right)\times {C}_{1}\right]-{L}_{1}\left\{\left[\left({V}_{21}-{V}_{24}\right)\times {C}_{4}\right]+\left[\left({V}_{9}+{V}_{10}+{V}_{11}+{V}_{12}+{V}_{13}+{V}_{14}+{V}_{15}+{V}_{16}\right)\times {C}_{2}\right]\right\}+{L}_{2}\left[\left({V}_{23}-{V}_{22}\right)\times {C}_{4}\right]+\left({P}_{1}+{K}_{1}\right)+\left(T\times {K}_{2}\right)\right\}}{\left[\left({V}_{18}+{V}_{20}\right)+{L}_{2}\left({V}_{1}+{V}_{2}+{V}_{3}+{V}_{4}+{V}_{5}+{V}_{6}+{V}_{7}+{V}_{8}\right)\right]}\\ +\left\{n\times {C}_{4}\times \left[\frac{\left({V}_{18}+{V}_{20}-{V}_{17}-{V}_{19}\right)}{{V}_{18}+{V}_{20}}\right]\}\right.\end{array}$$$ | (4) |

Fig. 6 depicts the IAQ consideration in the subway platform. The IAQ factors in this case are similar to the concourse case as in Fig. 5 except for the train-induced wind demonstrated in Fig. 3. Eq. (3) gives the mass balance in the platform and the Eq. (4) is the solution of Eq. (3). Eq. (4) produces the contaminant concentration in the platform.

## 4. FIELD MEASUREMENT AND SURVEY

In order to study the characteristics of the IAQ in a subway station, a subway station located in an area of Seoul, Korea was selected, see Fig. 1. The air qualities in the ambient environment outside the station in the concourse, in the platform, and in the subway tunnel were monitored by continuously measuring systems. Along with the field measurements, subway statistics and other station information were also collected.

Train ventilation pressure is generated at the front of the train due to the piston effect of the train at the front side and the surface roughness of the train body with air inside the train. The ventilation amount due to the movement of the train inside the tunnel is calculated from the Eq. (5) to Eq. (7) which has already been published (Kim *et al*., 2004). The train-induced wind velocity was monitored continuously with interval of 1 s for 70 s which is effective time management for train induced ventilation. The data were monitored at the tunnel above 15 cm from the bottom of the tunnel and they were normalized by using the factors 1.47 and 1.59 for the arrival and departure of the train. The data were monitored for 300 s repetitions and the average train induced velocity was calculated using Eq. (10). Fago *et al*. (1991), Lin *et al*. (2008), Huang *et al*. (2012) and Pflitsch *et al*., 2012 described the wind monitoring procedure inside the tunnel, underground section of the subway station in the different part of the world by overcoming different limitations. Moreover Kim and Kim (2007) conducted an experimental study in laboratory scale (1:20) to monitor the TIW.

$$$\u2206P=\frac{{A}_{t}}{{A}_{T}}\frac{\gamma}{2g}{V}_{t}^{2}$$$ | (5) |

$$${\mathcal{Q}}_{t}={A}_{T}\sqrt{\frac{2g}{\gamma}}\sqrt{\u2206P}$$$ | (6) |

$$${\mathcal{Q}}_{\mathrm{t}}=\sqrt{{A}_{t}\times {A}_{T}{V}_{t}}$$$ | (7) |

- Where,
- A
_{t}=Cross section area of train [12.8m^{2}=3.98m× 3.2 m] - A
_{T}=Cross section area of tunnel [18.6m^{2}=5.15m ×3.6 m] - g=Acceleration of gravity [9.8 m/s
^{2}] - Q1=Train ventilation amount [m
^{3}/s] - V
_{t}=Train induced wind velocity [m/s] - V
_{T}=Average train induced wind velocity [m/s] - ΔP=Train ventilation pressure [mm H
_{2}O]

$$$S=\frac{1}{2}{at}^{2}$$$ | (8) |

$$${V}_{t}={a\times t}^{}$$$ | (9) |

$$${\overline{V}}_{T}=\sqrt{\frac{{A}_{t}}{{A}_{T}}}{\times V}_{t}$$$ | (10) |