Application of Transportation Analysis Models for Evaluating Korean Air Quality Policy 1

Transcription

1 Application of Transportation Analysis Models for Evaluating Korean Air Quality Policy 1 Jin Guk Kim, 2 Choong Heon Yang, Chun Joo Yoon 1 Researcher, Korea nstitute of Civil Engineering and Building Technology, Korea 2 Senior Researcher, Korea nstitute of Civil Engineering and Building Technology, Korea (* Corresponding Author) Professor, University of Science & Technology, Korea Researcher, Korea nstitute of Civil Engineering and Building Technology, Korea 1 jingukkim@kict.re.kr, 2 chyang@kict.re.kr, cjyoon@kict.re.kr ABSTRACT This study evaluates air quality improvements based on transportation analysis models. Potential air quality reduction alternatives can be compared by analyzing changes in local emissions and the corresponding predicted impacts on public health. The results for evaluation came from the run of two main models such as emission and air dispersion. Finally, a health impact analysis was compared to the comprehensive air quality index suggested by the Korean Ministry of Environment. This study demonstrates the validity of the results using a case study examining alternative traffic operational strategies in Korea. The most significant reason for applying such an evaluation in the transportation sector is the estimation of the total amount of pollutant emission by means of different level of analysis using traffic simulation to enable the development of an inter-linking analysis system for dispersion models. Eventually, those models can be applicable to transportation alternatives to identify the effects of different traffic environments. Keywords: Air quality evaluation, air dispersion model, public health, air quality index, emission 1. NTRODUCTON Rapid motorization and urbanization have caused an increase in transportation activities along with a sharp increase in pollutants emitted from vehicular traffic. Thus, there have been many attempts to estimate total mobile pollutant emissions caused by transportation activities (Barth et al., 2001; Scora and Barth, 2006; Boriboonsomsin and Barth, 2008; Park and Rakha, 2006; Ahn et al., 1999; Cohen et al., 2005; Yang and Regan, 2007).Estimating the concentration of air dispersion due to atmospheric conditions as well as total emission of mobile pollutants attributable to transportation is recognized as a critical matter in terms of public health, urban planning and land use issues. Costabile and Allegrini (2008) analyzed the functional relationship linking air quality and air pollution from vehicular traffic. Major findings show that the integrated system enables users to link traffic air pollution measurements via diverse modeling modules to automate transport-related air pollution evaluations. Lee et al (2009) estimated mobile pollutant emissions on the nterstate-710 (-710) freeway connecting the Port of Long Beach to downtown Los Angeles in Los Angeles County, California and linked the findings to public health issues through air dispersion analysis. n South Korea, many research efforts have been performed involving air quality analysis (Cho, 2012; Koo, 2005, 2008; Yang, 201). However, there are few case studies dealing with transportation models, vehicle emissions and air dispersion models, and public health impact analysis as a major tool in South Korea. This study performs air quality evaluations based on transportation analysis models. Potential air quality reduction alternatives can be compared by analyzing changes in local emissions and the corresponding predicted impacts on public health. The results for evaluation came from the run of two main models. They include emission models, air dispersion models, and a health impact analysis based on the comprehensive air quality index suggested by the Ministry of Environment in South Korea. The primary feature of this study is to help support public agencies decision-making related to air quality improvement policies. We present in-depth details in the following sections. 2. FUNDAMENTAL EVALUATON FRAMEWORK We used a fundamental evaluation framework to consider health impacts by analyzing traffic-related mobile air pollution and its harmful effects and report on the potential risks of current and future air pollution. This consists of four steps as depicted in Figure 1. 9

2 Figure1: Fundamental evaluation framework and its corresponding data flow High concentrations of particulate matter (PM) and nitrogen oxides (NO X) are known to have a potentially fatal impact not only on the elderly and the young, but also on adults with pulmonary diseases. Therefore, this can generate useful information for those at risk or their caretakers to prepare in advance. 2.1 Step 1 Transportation Model Transportation model can be largely specified into three types: macroscopic, Mesoscopic, and microscopic model. Macroscopic model is either transportation planning model (e.g., conventional four step demand model) or macroscopic traffic model considering traffic flow theory. The main features of this model can deal with large scaled transportation network, and the performance measures obtained from the macroscopic model are typically VKT (vehicle kilometer traveled), VHT (vehicle hour traveled), and travel speed. On the other hand, microscopic traffic model considers an individual vehicle movements and interactions between vehicles, and evaluates various traffic management strategies such as TS (intelligent transportation systems). Furthermore, this model can generate second-by-second individual vehicle trajectory (i.e., speed profiles). n terms of the size of transportation network, the microscopic model may fail to cover large scaled transportation network because of huge computation burdens and the detailed data (i.e., geometry and traffic data including includes origin-destination tables) for running the model. Mesoscopic model can take the advantage from macro and micro model. This addresses large transportation network and generate detailed level of traffic performance measures such as average link speeds including VKT and VHT. 2.2 Step 2 - Emission Model n this step, the mobile source emissions are estimated using the input data from the previous step: one using a traffic demand model with an average velocitybased emission estimation model, and the other using traffic simulation and emission models. The amount of exhaust gas of each vehicle can be calculated based on standard technical specifications and second-by-second dynamics of a vehicle such as location, speed, deceleration / acceleration, gradient, and others. n particular, vehicle types and ages are one of critical information to generate vehicle emissions factors such as gram per kilometer-vehicle type. Emission models can be specified into two types based on the output generated from the previous step. f average speed and VKT are only generated from a traffic model, then macroscopic emission model is applied. On the other hand, second-bysecond vehicle trajectory (e.g., speed profile of each vehicle) can be obtained, and then microscopic emission model can be used. 2. Step Air Dispersion Model Air dispersion modeling is the mathematical simulation that shows how air pollutants disperse with respect to both the geographic and topographical characteristics of the analysis area and the ambient atmosphere. Dispersion models are used to estimate concentration of air pollutants emitted from various emission sources such as traffic, industrial plants, and the like. Typical pollutants that can be evaluated by dispersion models are sulfur dioxide (SO 2), nitrogen dioxide (NO X), and particulate matter (PM 10 or PM 2.5). Recently, ozone (O ) concentrations have been evaluated using the dispersion models as well. Since the 1950s, US-EPA has played a key role in their extensive efforts on developing sophisticated dispersion models. Their studies have greatly contributed to the application of dispersion modeling throughout the world. As a matter of fact, many countries have been using the U.S models with slight change or in modified and complementary forms. The dispersion models primarily used in South Korea are derived from the US-EPA s models. n general, dispersion models can be classified into four different types depending on their estimation approaches; Gaussian, numerical model, statistical or empirical and physical models (Karl, 1999). Among these, the Gaussian model is the most commonly used in air quality analysis research. n this study, two different types of dispersion models are chosen and briefly summarized as below. 40

3 CALNE (CAlifornia LNE Source Model) was initially developed by the California Department of Transportation (Caltrans). This model can be used to estimate the concentrations of non-reactive pollutants from vehicular traffic. This steady-state Gaussian model can be applied to determine air pollution concentrations at receptor locations downwind of at-grade, fill, bridge, and cut section roadways located in relatively uncomplicated terrain (Daly et al., 2007). This model is mainly used to estimate pollutant concentrations within a 500-meter range from the roadside by using input data on emission intensity and, atmospheric conditions. n addition, it can estimate the concentrations of inert pollutants such as CO or PM. n summary, the CALNE model is appropriate to predict concentrations of inert pollutant emitted from highways and has outstanding user friendliness. Protection Agency Regulatory Model mprovement Committee), a collaborative working group of scientists from the AMS and US-EPA, in order to remedy the complexity of models that simulate simple and complex terrains (Zou, 2009). This is a steady-state model designed for short-range (up to 50km) dispersion of emissions from various sources. The maximum concentration estimation can be made for each pollutant receptors in time scales of 1,, 8 and 24-hour as well as a year. n addition, specific weather information at the moment of analysis is essential for air dispersion models. t is noted that required data may vary slightly depending upon the selection of models, but the start and end point coordinates of the roadway link, altitude, and link length are commonly required for most dispersion models; depending on the type of dispersion model, knowing the width, length, angle, area, altitude, and coordinates of roadway sections may also be required. AERMOD (American Meteorological Society/United States Environmental Protection Agency Regulatory Model mprovement Committee) is the most recent officially approved US-EPA model. This model was developed by the AERMC (American Meteorological Society/Environmental 2.4 Step 4 Public Health Analysis Based on the enforcement decree of the framework act on environmental policy in South Korea, all types of air pollutants are subject to environmental standards as shown in Table 1. Table 1: National environmental standard in South Korea Emission sources National environmental standards Annually Below 0.02ppm SO2 24- hour Below 0.05ppm 1-hour Below 0.15ppm CO 8- hour Below 9 ppm 1- hour Below 25ppm Annually Below 0.0ppm NO2 24- hour Below 0.06ppm 1-hour Below 0.10ppm PM2.5 PM2.5 O Annually Below hour Below 100 Annually Below hour Below hour Below 0.06ppm 1- hour Below 0.1ppm Pb Annually Below 0.5 Benzene Annually Below 5 The Ministry of Environment (ME) in South Korea developed a Comprehensive Air Quality ndex (CA) which is similar to the U.S. Air Quality ndex (AQ) developed by the US-EPA (Mintz, 2006). n other words, CA is a modified AQ to represent the Korean environment. n addition, the ME has developed a realtime air quality dissemination system (RADS) that offers air quality-related services to the general public. RADS includes valuable information on five emission sources (SO 2, CO, NO 2, PM 10, PM 2.5, and O ) summarized by time, date, and day of the week, and also provides indices using CA which is capable of accounting for the effects of different levels of air pollution on public health and the maximum permissible levels of pollutants with guidelines to facilitate people s understanding of current air quality as well as to prevent pollution-induced health damage. Scores are calculated 41

4 for each of the five specified air pollutants, and the highest one is used as the CA. Additional points need to be added to the CA when two or more air pollutants (of the five separately calculated index values) fall under the unhealthy for sensitive groups or a lower category ( The following equation shows how to estimate the index for pollutant p. - f one pollutant: the highest index score is the CA link LO Maximum index value of pollutant p within a Minimum index value of pollutant p within a link CA value corresponding to (maximum index value for the link) LO CA value corresponding to LO (minimum index value for the link) Where, p C p - f two pollutants: the pollutant with the highest index is identified as an impact pollutant and 50 points are added to its score - f three pollutants: the pollutant with the highest index is identified as an impact pollutant and 75 points are added to its score LO p CP LO ) LO ndex for pollutant p ( (1) Ambient concentration of pollutant p LO n general, CA ranges from 0 to 500. These numerical values are assigned to six levels where the air quality is worse for higher values of index, as specified in the following Table 2 and (ME, 2009, 2012). As a result, analysts can choose transportation analysis model according to the purpose of analysis, characteristics of alternative policy and so on. Once spatio-temporal emission concentrations are estimated from dispersion models, Table 2 can be used as a reference, and the corresponding harmful impacts for public health can be analyzed on the basis of the index presented in Table. Division Score Table 2: ndex estimation variables CA value A B C D E F Good Moderate Unhealthy for sensitive groups Unhealthy Very unhealthy Hazardous LO ndex Value Division LO LO LO LO LO LO SO2 (1hr) ppm NO2 (1hr) ppm CO (1hr) ppm O (1hr) ppm PM (24hr) g /m

5 Table : Air quality reporting guidelines ndex Expression Description A Good Safe even for the air pollution-sensitive patient groups B Moderate t mildly affects patient groups in the case of a chronic exposure C D E F Unhealthy for sensitive groups Unhealthy Very unhealthy Hazardous Potentially harmful for patients and sensitive groups t may have harmful effects on patients and sensitive groups (e.g., children, weak, and elderly people) and cause even the healthy population to feel discomfort. t may have seriously harmful effects on patients and sensitive groups, in the case of acute exposure, and mildly harmful effects on even the healthy population t may necessitate emergency measures for patients and sensitive groups and may have harmful effects on the healthy population. CASE STUDY A case study was performed for a corridor of national highway No. 1 in the City of Suwon in South Korea. This 6-km long arterial roadway has eight signalized intersections with 11 links and high peak-hour traffic during commuting time. There is one underpass (link ) and four connection roads (link 5, 9, 10, and 11). Figure 2: PARAMCS networks of national highway No. 1 in Suwon, Korea.1 Simulating Micro Traffic Simulation and Estimating Vehicle Emission Model n this study, we employed the well-calibrated network and travel demand data from the previous study (Choi et al., 2010) where the time dependent OD (origindestination) table was estimated based on the path-based gradient OD estimation method together with the elaborate network calibration work. The observed turnbased traffic counts for four-hour morning peak (from 6 a.m. to 10 a.m.) were collected for every intersection s approach using video recordings and inductive loop detectors. Geometric structure such as number of lanes, lane width, and crossroad markings were prepared from both survey and satellite images. For more accurate 4

6 calibration travel speeds for each section between two consecutive intersections were measured using probe vehicles. Signal plans were also collected to set the traffic control in the traffic simulation. A PARAMCS model was employed with the CMEM (comprehensive modal emission model) plug-in (Barth et al., 2006) to calculate individual vehicle s emission. PARAMCS, a microscopic traffic simulation, is a stochastic simulation model, which relies on randomly generated numbers to trigger release of vehicles. PARAMCS users need to write their own source code in C++ or C in order to employ the APs and then make use of the built-in AP library to access the core models in PARAMCS. CMEM is mainly developed for vehicles in the U.S. n other words, the data used for developing CMEM was collected from vehicles including passenger cars and trucks registered in the U.S. t means that the estimated vehicle emissions may not represents Korean vehicle characteristics and fuel standards. To better estimate vehicle emissions using CMEM, it may require a calibration procedure of CMEM emissions Northbound Observed Travel Time Table 4: Validation of simulated travel time Simulated Travel Time factors, but we consider this issue as a future study. Table 4 briefly showed that validation of simulated travel results (Choi et al., 2010)..2 Estimating Air Dispersion Analysis The case study site constitutes a total of 18 roadway links in different directions. Because air dispersion models do not necessarily include directional information, the number of links is reduced to 11 by considering only one-way links within the site. n order to perform CALNE modeling, accurate data on the coordinates of the start and end points, altitude, and distance of these 11 links are required. The coordinates of start and end points can be generated from node shape files. That information can be used to calculate precise lengths. Altitudes can be derived from a digital map. Table 5 shows the specifications of the emission sources data for the CALNE model. Travel Time Difference Error (%) 6:00-7:00 a.m :00-8:00 a.m :00-9:00 a.m :00-10:00 a.m Southbound 6:00-7:00 a.m :00-8:00 a.m :00-9:00 a.m :00-10:00 a.m Table 5: Required data for the CALNE model Link number Altitude (m) Length (m) CO (g/s/km) NOx (g/s/km)

7 Two emission pollutants, CO and NO X, were chosen for analysis. The measurement unit of the total emissions from each emission source is expressed in grams and the output is generated by time. Emissions as input data for the CALNE model are expressed as mass per unit time and unit distance (g/sec km). Emission for CALNE (g/sec km) Emission Amount (g) = Analysis time (sec) Link Distance(k m) (2) As shown in Table 5, the estimated NO X and CO emissions were greater in link 1, 2, 4, 6, 7, and 8 which are part of the main corridor with high traffic volume than those of link, 5, 9, 10, and 11 which are underpass and connection roads where traffic is diverted. To perform dispersion analysis, basic data are divided into four categories according to model options, a site for emission concentration estimation, emission sources, and atmospheric conditions. Except for emission sources, model options, concentration calculation points, and atmospheric conditions are all presented in Table 6. City landuse Table 6: Various parameters setting for the model Surface type Surface roughness : Z 0 (cm) Grass (60~70cm) 11.4 Wheat (60cm) 22 Corn (220cm) 74 Citrus orchard 198 Fir forest 28 Single family residential 108 Apartment residential 70 Office 175 Central Business District 21 Park 127 n CALNE, the number of spots within the study site for emission concentration estimation does not usually exceed 50. Emission concentrations at specific spots are analyzed according to atmospheric conditions; however, in order to analyze the results of spatial dispersion, the estimated concentration is usually the maximum concentration out of 6 site-measurements obtained by inputting 6 wind direction values at 10 intervals. n this study, maximum concentrations were calculated within a 5 km 5 km area under wind speed 1.9 m/s, stability class 4, and mixing heights of 1,000m as analysis conditions. Figure graphically presents the results under specific atmospheric conditions. NO2 and CO are dispersed along the roadways in a lengthy shape, and the point showing the maximum concentration where NO2 and CO was analyzed to show 146 ppb and ppm, respectively. (A) NOx (B) CO Figure : Air dispersion results from CALNE under the wind speed 1.9 m/s, stability class 4, and mixing heights of 1,000m conditions 45

8 . Public Health Analysis When running the CALNE model, the CA for each pollutant was calculated by applying the annual average NO 2 and CO concentrations in the City of Suwon in 2007 as base levels to the contribution rate of emission sources of pollution from traffic. Average NO 2 and CO concentrations were 4 and 700 ppm, respectively. The NO 2 maximum index under wind speeds of 1.9 m/s, stability class 4, and mixing heights of 1,000m conditions was found to be 210 (unhealthy), and the CO maximum index under the same conditions was found to be 120 (unhealthy for sensitive groups,). For the both pollutants, vehicle emissions generated from the highway corridor are potentially harmful for patients and sensitive groups (e.g., children, weak, and elderly people) of the residents living near the corridor. For NO 2 pollutant, the emissions emitted by vehicle activities may have harmful effects on patients and sensitive groups and cause even the healthy population to feel discomfort. Figure 4 illustrates the CA distribution of NO 2 and CO from CALNE model. (A) NO 2 (B) CO Figure 4: Spatial distribution of the CA value under speeds of 1.9 m/s, stability class 4, and mixing heights of 1,000m. 4. CONCLUSONS Because of the increasing public interest in environmental issues, there is a need for an air quality analysis having public agencies accountable for the public good in sustainable transportation. Therefore, we performed air quality analysis which enables urban planners and engineers to easily perform that accounts for the current and future atmospheric conditions of areas of interest based on transportation analysis models. The main feature of this study is crucial from the perspective that the public agencies generally want to know air quality improvements under diverse transportation alternatives. This analysis has a capability of including almost all possible functions that are demanded by the public agencies. n order to demonstrate the usability and performance of this, a case study was performed. Results showed that public health associated with the air dispersion of the study site is unhealthy condition for NO X concentration and unhealthy for sensitive group for CO concentration. As a result, there are several important reasons for applying such an evaluation in the transportation sector. The most significant reason is the estimation of the total amount of pollutant emission by means of different level of analysis using traffic simulation to enable the development of an inter-linking analysis system for dispersion models. Eventually, those models can be applicable to transportation alternatives of the effects of traffic environments. Therefore, we expect that it will bring about targeted results necessary for designing effective air quality policies at various stages of urban or transportation planning for decision-making support. ACKNOWLEDGEMENT This research was supported by a grant from the High-Tech Urban Development Program funded by the Ministry of Land, nfrastructure, and Transport of Korea. REFERENCES [1] Ahn, K., Trani, A., Rakha, H., and Van Aerde, M., Microscopic fuel consumption and emission models, Proceeding of the 78th Annual Meeting of Transportation Research Board, Washington DC, [2] Barth, M., Malcolm, C., Young love, T., and Hill, N., Recent validation efforts for a comprehensive modal emissions model, Transp. Res. Rec1750, pp.1-2, [] Barth, M., Young love, T., and Scora, G., Development of a heavy-duty diesel modal emissions and fuel consumption model, California PATH Research Report (UCB-TS-PRR ), University of California, Berkeley, CA, [4] Boriboonsomsin, K. and Barth, M., mpacts of freeway high-occupancy vehicle lane configuration 46

9 on vehicle emissions, Transp. Res. Part D 1, pp , [5] Cho, J.S., Koo, Y. S., and Choi, D. R., A study of wind-blown dust emissions estimation in Asia region using CMAQ dust module, Proceeding of the Meeting of KOSAE(CD-ROM), Seoul, Korea, [6] Choi, K. C., R, Jayakrishnan, Kim, H. M., Yang,. C., and Lee, J. W., Dynamic origin destination estimation using dynamic traffic simulation model in an urban arterial corridor,transp. Res. Rec 21, pp.1-141, [7] Cohen, J., Cook, R., Bailey, C.R., and Carr, E., Relationship between motor vehicle emissions of hazardous pollutants, roadway proximity, and ambient concentrations in Portland, Oregon. Environ, Modeling & Software 20, pp.7-12, [8] Costabile, F., and, Allegrini. A new approach to link transport emissions and air quality: An intelligent transport system based on the control of traffic air pollution, Environ. Modeling & Software, 2(), pp , [9] Daly, A. and Zannetti, P., Air pollution modeling an overview; chapter 2 of ambient air pollution, ASST and the Enviro Comp nstitute, CA, [10] Mintz, D., Guideline for the reporting of daily air quality-the air quality index (AQ), EPA-454/B , U.S. EPA, [11] Karl, B. S. and Partha, R. D., Atmospheric dispersion modeling compliance guide (1st Edition ed.), McGraw-Hill Professional, USA, [12] Koo, Y. S., Yoon, W. J., Kwon, H. Y., Yang, J. M., Choi, J. H., and Yoon, H. Y., Development of a PM10 forecasting system of the day before, Proceeding of the 46th Meeting of KOSAE, Seoul, Korea, [1] Koo, Y. S., Yoon, H. Y., Yoon, M. J., Choi, D. R., and Ko G. J., Comparison of MM5 with WRF in the Seoul metropolitan area, Proceeding of the 46th Meeting of KOSAE, Seoul, Korea, [14] Lee, G., You, S., Ritchie, S. G., Saphores, J. D., Sangkapichai, M., and Jayakrishnan, R., Environmental impacts of a major freight corridor: A study of -710 in California, Transp. Res. Rec. 212, pp , [15] Ministry of Environment, Guidelines for the utilization of Environmental mpact Forecasts Model, Korea, [16] Ministry of Environment, Air Pollution Emissions Statistics, Korea, [17] Park, S. and Rakha, H., Energy and environmental impacts of roadway grades, Transp. Res. Rec. 1987,pp , [18] Scora, G. and Barth, M., Comprehensive modal emission model (CMEM), Version.01 User s Guide, University of California, Riverside, [19] Yang, C.H. and Regan, A., Evaluation of general truck management strategies via integrated simulation studies (Case study: truck lane restriction on -710 in Southern California) Proceeding of the 86th Annual Meeting of Transportation Research Board, Washington DC, [20] Yang, C. H., Koo, Y. S., Kim,. S., and Sung, J. G., Studies on the methodology of a hybrid model for emission dispersion analysis, J. of Korean Society of Trans. 1, pp.69-79, 201. [21] Zou, B., Wilson, J. G., Zhan, F. B., and Zeng, Y., Spatially differentiated and source-specific population exposure to ambient urban air pollution, Atmos. Environ. 4, pp , AUTHOR PROFLE Jin Guk Kim received his master s degree in Transportation Engineering at the Kwandong University in Korea. Currently, he is a researcher at the Korea nstitute of Civil Engineering and Building Technology. His research interest covers sustainable transportation, snowremoval and intelligent transportation systems. Choong Heon Yang have completed his PhD degree in the University of California at rvine in the U.S. Currently, he is working as a senior researcher at the Korea nstitute of Civil Engineering and Building Technology as well as a professor at the University of Science & Technology. Chunjoo Yoon received his master s degree in geoinformatics engineering at the nha University in Korea. Currently, he is a researcher at the Korea nstitute of Civil Engineering and Building Technology as well as a Ph.D. student in Transportation Engineering at the University of Seoul. 47