MinJeong Kim

Sensor Validation for Monitoring Indoor Air Quality in a Subway Station

Indoor air quality (IAQ) is an important factor, which can influence the health and comfort of passengers in subway stations. Various types of hazardous pollutants, such as particulate matters, remain accumulated in the subway space due to overcrowding and inadequate ventilation system. Subway stations are extremely crowded during rush hours and indoor air of the subway stations could be strongly affected, which in turn, affects passengers’ respiratory system. In this study, several key air pollutants data were collected every minute by the air sampler and tele-monitoring system (TMS) to effectively monitor and control IAQ in subway stations. The quality of the online measurement could decide the failure and success in environmental process assessment. Therefore, prompt detection of the occurrence of sensor faults and identification of those locations are of primary importance for efficient monitoring and control of IAQ. In this paper, Principal components analysis based approach is used to detect, identify and reconstruct the sensor faults in monitoring IAQ. Four types of sensor failures, namely, bias, drifting, complete failure and precision degradation are tested for monitoring IAQ. Several test results of a real subway TMS showed that the developed sensor validation technique can work well for the four kinds of sensor faults.

Statistical Evaluation of Indoor Air Quality Changes after Installation of the PSD System in Seoul’s Metro

The purpose of this study was to evaluate changes in the concentration of air pollutants in the indoor environments, which could be caused by seasonal changes or changes in operating conditions of subway metro stations. In fact, there are many different types of pollution that can cause contamination in subway stations, and changes in operating conditions can also lead to changes in the indoor air quality (IAQ). Therefore, in order to establish a proper management of IAQ, it would be necessary to evaluate the changes in IAQ according to the changes in conditions. To do this, the present study used a multivariate analysis of variance (MANOVA). The results of testing the hypothesis proved that two groups, divided by the condition of a platform screen door (PSD) system, could differ statistically. Furthermore, those multidimensional differences were caused by installation of a PSD system. When applied to a real-time tele-monitoring system, MANOVA could clearly identify the daily and weekly variations of IAQ in the subway station, as well as the PSD system’s condition. Accordingly, this method could be useful for developing a multivariate system to statistically evaluate the experimental IAQ results in order to optimise operating conditions in a subway metro station to improve IAQ, and to minimise adverse health effects on passengers by exposure to harmful substances.

Evaluation of passenger health risk assessment of sustainable indoor air quality monitoring in metro systems based on a non-Gaussian dynamic sensor validation method

Sensor faults in metro systems provide incorrect information to indoor air quality (IAQ) ventilation systems, resulting in the miss-operation of ventilation systems and adverse effects on passenger health. In this study, a new sensor validation method is proposed to (1) detect, identify and repair sensor faults and (2) evaluate the influence of sensor reliability on passenger health risk. To address the dynamic non-Gaussianity problem of IAQ data, dynamic independent component analysis (DICA) is used. To detect and identify sensor faults, the DICA-based squared prediction error and sensor validity index are used, respectively. To restore the faults to normal measurements, a DICA-based iterative reconstruction algorithm is proposed. The comprehensive indoor air-quality index (CIAI) that evaluates the influence of the current IAQ on passenger health is then compared using the faulty and reconstructed IAQ data sets. Experimental results from a metro station showed that the DICA-based method can produce an improved IAQ level in the metro station and reduce passenger health risk since it more accurately validates sensor faults than do conventional methods.

Periodic Local Multi-way Analysis and Monitoring of Indoor Air Quality in a Subway System Considering the Weekly Effect

Indoor air quality (IAQ) is one of the major concerns to people who spend most of the time in an indoor environment, as many hazardous pollutants are emitted from the buildings or underground spaces, or enter from outside sources through the various vents and other entry/exit points. In particular, the IAQ in subway stations have periodic variations in indoor air pollutants, depending on the numbers of passengers and trains, as well as the previous IAQ. Global and weekly models (divided into weekday and weekend model) have been developed and compared with one another using parallel factor analysis to consider the periodic characteristics, as IAQ shows changeable aspects within a day or the week, owing to differences in the passengers’ travel patterns. The IAQ monitoring results of the weekly model showed that the proposed monitoring method could detect abnormal IAQ conditions in a timely manner when compared to the global model. The proposed monitoring method could identify not only the most active time periods but also the main contaminant sources resulting in the abnormality of the IAQ, based on fault detection.

Greenhouse emission pinch analysis (GEPA) for evaluation of emission reduction strategies

Wastewater treatment plants (WWTPs) are one of the important sources of greenhouse gas (GHG) emission. There is a need for systematical tool that can be used to analyze GHG emission from WWTPs, and to evaluate the associated reduction strategies. In this paper, a systematic analysis methodology, called the greenhouse emission pinch analysis (GEPA), is developed for this purpose. GEPA is graphical in nature, and can be used to analyze the on-site and off-site GHG emissions of the WWTP. Furthermore, three GHG reduction strategies, i.e., increased aeration capacity, external carbon source controller, and reuse of biogas, are evaluated for their environmental load and operational cost reduction using the GEPA. A case study is used to elucidate the proposed method. In this study, the third strategy which reuses biogas from anaerobic digestion shows the largest reduction of GHG emissions.

A real-time monitoring and assessment method for calculation of total amounts of indoor air pollutants emitted in subway stations.

Subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, in this study, a new cumulative calculation method for the estimation of total amounts of indoor air pollutants emitted inside the subway station is proposed by taking cumulative amounts of indoor air pollutants based on integration concept. Minimum concentration of individual air pollutants which naturally exist in indoor space is referred as base concentration of air pollutants and can be found from the data collected. After subtracting the value of base concentration from data point of each data set of indoor air pollutant, the primary quantity of emitted air pollutant is calculated. After integration is carried out with these values, adding the base concentration to the integration quantity gives the total amount of indoor air pollutant emitted. Moreover, the values of new index for cumulative indoor air quality obtained for 1 day are calculated using the values of cumulative air quality index (CAI). Cumulative comprehensive indoor air quality index (CCIAI) is also proposed to compare the values of cumulative concentrations of indoor air pollutants. From the results, it is clear that the cumulative assessment approach of indoor air quality (IAQ) is useful for monitoring the values of total amounts of indoor air pollutants emitted, in case of exposure to indoor air pollutants for a long time. Also, the values of CCIAI are influenced more by the values of concentration of NO2, which is released due to the use of air conditioners and combustion of the fuel. The results obtained in this study confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well. Implications: Nowadays, subway systems are considered as main public transportation facility in developed countries. Time spent by people in indoors, such as underground spaces, subway stations, and indoor buildings, has gradually increased in the recent past. Especially, operators or old persons who stay in the indoor environments more than 15 hr per day usually influenced a greater extent by indoor air pollutants. Hence, regulations on indoor air pollutants are needed to ensure good health of people. Therefore, this paper presents a new methodology for monitoring and assessing total amounts of indoor air pollutants emitted inside underground spaces and subway stations. A new methodology for the calculation of cumulative amounts of indoor air pollutants based on integration concept is proposed. The results suggest that the cumulative assessment approach of IAQ is useful for monitoring the values of total amounts of indoor air pollutants, if indoor air pollutants accumulated for a long time, especially NO2 pollutants. The results obtained here confirm that the proposed method can be applied to monitor total amounts of indoor air pollutants emitted, inside apartments and hospitals as well.

Economical control of indoor air quality in underground metro station using an iterative dynamic programming-based ventilation system

A set-point of ventilation control system plays an important role for efficient ventilation inside metro stations, since it affects level of indoor air pollutants and ventilation energy consumption concurrently. In this study, to maintain indoor air quality (IAQ) at a comfortable range with a lower ventilation energy consumption, the optimal set-points of the ventilation control system were determined. The concentration of air pollutants inside the station shows a periodic diurnal variation in accordance with the number of passengers and subway frequency. To consider the diurnal variation of IAQ, an iterative dynamic programming (IDP) that searches for a piecewise control policy by separating whole system duration into several stages was applied. The optimal set-points of the ventilation control system in underground D-subway station, Korea were determined at every 3, 2, and 1 h, respectively. Then, according to the set-point changes, the ventilation controller was adjusted to an appropriate ventilation fan speed, correlating to the amount of outdoor PM10 that flows into the station. The results showed that the ventilation control system with the IDP-based optimal set-points has a better economical ventilation performance than manual ventilation system, with a 4.6% decrease in energy consumption, maintaining a comfortable IAQ level inside the station.

Finding the optimal set points of a thermal and ventilation control system under changing outdoor weather conditions

The aim of this study was to propose a new multi-objective optimization (MOO) of a ventilation controller which finds the optimal set points for simultaneously improving the indoor air quality (IAQ) and increasing the energy efficiency in buildings under changes in outdoor air quality and climate change. The outdoor weather information, such as ambient temperature, humidity, solar radiation and wind speed, was applied and treated as external disturbances in the building system. Two control strategies including proportional-integral (PI) control and multivariate model predictive control (MPC) were implemented and compared while controlling the indoor air temperature and CO2 concentration in the targeted building system. A control performance assessment (CPA) technique was proposed and implemented for monitoring the MPC controller performance. With the goal of determining the optimal set points for the MPC controller, the multi-objective genetic algorithm was developed to enhance the energy consumption as well as to keep the IAQ within an acceptable range. The results indicate that the performance of the MPC controller with the optimized set points is superior to that of the PI controller in indoor building systems. More specifically, the MPC controller with the optimal set points for the indoor temperature and CO2 control could reduce energy consumption by 5.22% and CO2 content by 13.39% in comparison to the PI controller. In addition, the MPC controller equipped with MOO could be useful for building climate control depending on the variation of the outdoor air pollutants.

Determination of key sensor locations for non-point pollutant sources management in sewer network

As the importance of watershed management has emerged for water systems, non-point pollutant sources have been blamed as the main problem of water pollution. To control non-point pollutant sources, it is necessary to monitor sewers connected to the watershed and to analyze their effects on the sewer network. As the cost to monitor a sewer network depends on the number of sensors installed, the monitoring stations should be decided with proper guide of the installation location rule. In present paper, a new method to select the proper sensor location is proposed by combining monitoring information with data mining techniques. To estimate the amount of pollutants by wash-off and to find the sensor locations in a sewer network, three scenarios are considered based on rainfall intensity, influent concentrations and flow rate. The optimal locations of the sensor were selected based on the proposed method to facilitate the management of non-point pollutant source in sewer network. The presented approach can be extended to a complex sewer network system to design a minimum number of sensors and optimum locations for the sensors.

Sustainable Monitoring of Indoor Air Pollutants in an Underground Subway Environment Using Self-Validating Soft Sensors

The purpose of this study is to develop a self-validating soft sensor to improve the prediction performance of indoor air quality soft sensors in an underground subway station. The reconstruction-based self-validation method was proposed and implemented in order to: (1) determine the optimal number of principal components when building a principal component analysis training model, (2) enhance the diagnosis accuracy when identifying faulty sensors and (3) reconstruct faulty measurements in a straightforward manner. Two soft sensors based on partial least squares and recursive partial least squares models were developed and their prediction performance was compared in the cases of using faulty sensor measurements and using reconstructed sensor values. Two types of sensor faults including a bias fault and a drifting fault were evaluated using the proposed method. The monitoring results show that the developed sensor self-validation strategy has a powerful ability to correctly detect, identify and reconstruct the sensor faults in the subway system. In addition, the proposed self-validation soft sensing technique could achieve sustainable monitoring of indoor air pollutants in the underground subway environment, because the reconstructed values can be used to replace the measured data when sensor faults have been detected by the detection indices.