نشریه سنجش از دور و GIS ایران

شماره جاری

بر اساس شماره‌های نشریه

بر اساس نویسندگان

بر اساس موضوعات

نمایه نویسندگان

نمایه کلیدواژه ها

درباره نشریه

اهداف و چشم انداز

اعضای هیات تحریریه

اصول اخلاقی انتشار مقاله

بانک ها و نمایه نامه ها

پیوندهای مفید

پرسش‌های متداول

فرایند پذیرش مقالات

اخبار و اعلانات

یک مدل ترکیبی رگرسیون کاربری اراضی فازی برای ارزیابی کمی آثار سلامت محیطی ناشی از سناریوهای ترافیکی، نمونة مطالعاتی: شهر اصفهان

نوع مقاله : علمی - پژوهشی

نویسندگان

1 استادیار گروه نقشه‌برداری، دانشکدة عمران و حمل‌ونقل، دانشگاه اصفهان

2 دانشیار گروه سیستم‌های اطلاعات مکانی، دانشکدة نقشه‌برداری، دانشگاه صنعتی خواجه نصیرالدین طوسی

چکیده

در این مقاله، روش جدیدی برای مدل‌سازی کمی آثار سلامت محیط ناشی از سناریوهای ترافیکی پیشنهاد شده است. برای این منظور، دو مدل مبتنی بر سیستم‌های استنتاج فازی سلسله‌مراتبی عرضه شده است. در توسعة مدل مورد استفاده برای ارزیابی تأثیر سیستم حمل و نقل در غلظت ذرات معلق، از داده‌های مربوط به یک مدل پراکنش استفاده شده است. مدل رگرسیون کاربری اراضی فازی1 حاصل افزون بر مزایایی همچون امکان مدل‌سازی تغییرات غلظت آلاینده با رزولوشن بالا، حجم پردازش مناسب و لحاظ‌کردن برهمکنش میان انتشار و فرایندهای آب‌وهوایی، امکان استفاده از داده‌های توصیفی و غیرقطعی را نیز دارد. برای توسعة مدل مورد استفاده در ارزیابی تأثیر غلظت ذرات معلق ناشی از ترافیک در سلامت، از یک متریک‌ حاصل از مطالعات اپیدمیولوژیک استفاده شده است. مدل پیشنهادی قابلیت‌های متریک مذکور را، با ایجاد امکان مدل‌سازی عدم قطعیت ارتباط میان پارامترها و عدم قطعیت مقادیر پارامترها، بهبود داده است. برای افزایش کارآیی هر دو مدل، از ساختار سلسله‌مراتبی مبتنی‌بر مسئله و تولید و تنظیم هم‌زمان توابع عضویت و مجموعة قواعد ازطریق یادگیری استفاده شده است. برای بررسی کارآیی روش پیشنهادی، تأثیر سه سناریوی ترافیکی در سلامت محیطی در شهر اصفهان ارزیابی شده است. از میان سناریوهای یادشده، طرح مناطق کم‌انتشار و طرح زوج و فرد، به‌ترتیب، بیشترین و کمترین مزایای مرتبط با سلامت محیطی را دارند. نتایج حاصل نشان می‌دهد روش پیشنهادی برای ارزیابی سلامت محیطی، در مقایسه با روش‌های مشابه، دقت و کارآیی مطلوبی دارد.

کلیدواژه‌ها

عنوان مقاله [English]

A Hybrid Dispersion\ Fuzzy Landuse Regression Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning in Isfahan, Iran

نویسندگان [English]

  • B Tashayo 1
  • A Alimohammadi 2

1 Dep. of Surveying Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan

2 Center of Excellence in Geospatial Information Technology, Faculty of Geodesy and Geomatics Engineering, K.N. Toosi University of Technology

چکیده [English]

This article develops and demonstrates a new quantitative modeling approach for environmental health impact assessment of traffic scenarios. For this purpose, two models based on hierarchical fuzzy inference system (HFIS) are developed. In order to develop HFIS for modeling the effect of transportation system on the PM2.5 concentrations, the data from an air dispersion model are utilized. There are several advantages to this approach such as modeling the spatial variation of PM2.5 with high resolution, suitable processing requirements, and consideration of interaction between emissions and meteorological processes. Moreover, the resulting fuzzy landuse regression (LUR) is capable of using accessible qualitative and uncertain data. In order to develop HFIS for modeling the impact of traffic-related PM2.5 on health, a metric derived from epidemiological studies is employed. The suggested model improved the metric capabilities by modeling the uncertainty of relationships among parameters and parameter value. Two solutions are used to improve the performance of both models. First, the topologies of HFISs are selected according to the problem. Second, used variables, membership functions and rule set is determined together through learning. We examine the capabilities of the proposed approach with assessing the impacts of three traffic scenarios to deal with air pollution in Isfahan, Iran and compare the accuracy of the results with representative models from existing literature. The models are first developed based on the current traffic conditions. Then; Low Emission-Zone and Odd/Even scenarios are examined. The examination shows that, they are the most and least effective scenarios in reducing air pollution and improving environmental health, respectively. The obtained results demonstrate that the proposed approach has desirable accuracy; beside that the model can provide better understanding of phenomena and investigating the impact of each of parameters for the planners. 

کلیدواژه‌ها [English]

  • Impact Assessment
  • Hierarchical Fuzzy Inference System
  • Transportation Planning
  • PM2.5
  • Environmental Health
مراجع
  1. Aggarwal, P. & Jain, S., 2015, Impact of Air Pollutants from Surface Transport Sources on Human Health: A Modeling and Epidemiological Approach, Environment International, 83, PP. 146–157.
  2. Alcala, R., Alcala-Fdez, J. & Herrera, F., 2007, A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and its Interaction with Rule Selection, IEEE Transactions on Fuzzy Systems, 15, PP. 616–635.
  3. Basagaña, X., Aguilera, I., Rivera, M., Agis, D., Foraster, M., Marrugat, J., Elosua, R. & Künzli, N., 2013, Measurement Error in Epidemiologic Studies of Air Pollution Based on Land-Use Regression Models, American Journal of Epidemiology, 178, PP. 1342–1346.
  4. Batterman, S., Chambliss, S. & Isakov, V., 2014, Spatial Resolution Requirements for Traffic-Related Air Pollutant Exposure Evaluations, Atmospheric Environment, 94, PP. 518–528.
  5. Brauer, M., Ainslie, B., Buzzelli, M., Henderson, S., Larson, T., Marshall, T., Nethery, E., Steyn, D. & Su, J., 2008, Models of Exposure for Use in Epidemiological Studies of Air Pollution Health Impacts, In: Air Pollution Modeling and Its Application XIX, Springer, PP. 589–604.
  6. Briggs, D.J., Sabel, C.E. & Lee, K., 2008, Uncertainty in Epidemiology and Health Risk and Impact Assessment, Environmental Geochemistry and Health, 31, PP. 189–203.
  7. Chart-asa, C. & Gibson, J.M., 2015, Health Impact Assessment of Traffic-Related Air Pollution at the Urban Project Scale: Influence of Variability and Uncertainty, Science of The Total Environment, 506, PP. 409–421.
  8. Dannenberg, A.L., Bhatia, R., Cole, B.L., Heaton, S.K., Feldman, J.D. & Rutt, C.D., 2008, Use of Health Impact Assessment in the US: 27 Case Studies, 1999–2007, American Journal of Preventive Medicine, 34, PP. 241–256.
  9. De Hoogh, K., Korek, M., Vienneau, D., Keuken, M., Kukkonen, J., Nieuwenhuijsen, M.J., Badaloni, C., Beelen, R., Bolignano, A., Cesaroni, G., Pradas, M.C., Cyrys, J., Douros, J., Eeftens, M., Forastiere, F., Forsberg, B., Fuks, K., Gehring, U., Gryparis, A., Gulliver, J. & Hansell, A.L., 2014, Comparing Land Use Regression and Dispersion Modelling to Assess Residential Exposure to Ambient Air Pollution for Epidemiological Studies, Environment International, 73, PP. 382–392.
  10. EPA, U., 2010, Transportation Conformity Guidance for Quantitative Hot-spot Analyses in PM2.5 and PM10 Nonattainment and Maintenance Areas, (No. EPA-420-B-10-040), United States Environmental Protection Agency, Research Triangle Park, NC.
  11. Hebert, K.A., Wendel, A.M., Kennedy, S.K. & Dannenberg, A.L., 2012, Health Impact Assessment: A Comparison of 45 Local, National, and International Guidelines, Environmental Impact Assessment Review, 34, PP. 74–82.
  12. HEI, 2010, Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure, and Health EffectsSpecial Reports.
  13. Herrera, F., 2008, Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects, Evolutionary Intelligence, 1, PP. 27–46.
  14. Herrera, F., Lozano, M. & Verdegay, J.L., 1998, Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis, Artificial Intelligence Review, 12, PP. 265–319.
  15. Hoek, G., Beelen, R., de Hoogh, K., Vienneau, D., Gulliver, J., Fischer, P. & Briggs, D., 2008, A Review of Land-Use Regression Models to Assess Spatial Variation of Outdoor Air Pollution, Atmospheric Environment, 42, PP. 7561–7578.
  16. Hoek, G., Krishnan, R.M., Beelen, R., Peters, A., Ostro, B., Brunekreef, B. & Kaufman, J.D., 2013, Long-Term Air Pollution Exposure and Cardio-Respiratory Mortality: A Review, Environmental Health, 12, P. 1.
  17. Isakov, V., Touma, J.S., Burke, J., Lobdell, D.T., Palma, T., Rosenbaum, A. & Özkaynak, H.A., 2009, Combining Regional- and Local-Scale Air Quality Models with Exposure Models for Use in Environmental Health Studies, Journal of the Air & Waste Management Association, 59, pp. 461–472.
  18. Jang, J.S.R., Sun, C.T. & Mizutani, E., 1997, Neuro-Fuzzy and Soft Computing; A Computational Approach to Learning and Machine Intelligence, Englewood Cliffs, NJ: Prentice-Hall.
  19. Jerrett, M., Arain, A., Kanaroglou, P., Beckerman, B., Potoglou, D., Sahsuvaroglu, T., Morrison, J. & Giovis, C., 2005, A Review and Evaluation of Intraurban Air Pollution Exposure Models, Journal of Exposure Science and Environmental Epidemiology, 15, PP. 185–204.
  20. JICA, 1997, The Study on an Integrated Master Plan for Air Pollution Control in the Greater Tehran Area in the Islamic Republic of Iran, Japan International Cooperation Agency.
  21. Johnson, M., Isakov, V., Touma, J., Mukerjee, S. & Özkaynak, H., 2010, Evaluation of Land-Use Regression Models Used to Predict Air Quality Concentrations in an Urban Area, Atmospheric Environment, 44, PP. 3660–3668.
  22. Karner, A.A., Eisinger, D.S. & Niemeier, D.A., 2010, Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data, Environmental Science & Technology, 44, PP. 5334–5344.
  23. Leung, W., Noble, B., Gunn, J. & Jaeger, J.A., 2015, A Review of Uncertainty Research in Impact Assessment, Environmental Impact Assessment Review, 50, PP. 116–123.
  24. Li, Y., Gibson, J.M., Jat, P., Puggioni, G., Hasan, M., West, J.J., Vizuete, W., Sexton, K. & Serre, M., 2010, Burden of Disease Attributed to Anthropogenic Air Pollution in the United Arab Emirates: Estimates Based on Observed Air Quality Data, Science of the Total Environment, 408, PP. 5784–5793.
  25. Lindström, J., Szpiro, A.A., Sampson, P.D., Oron, A.P., Richards, M., Larson, T.V. & Sheppard, L., 2014, A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates, Environmental and Ecological Statistics, 21, PP. 411–433.
  26. Marshall, J.D., Nethery, E. & Brauer, M., 2008, Within-Urban Variability in Ambient Air Pollution: Comparison of Estimation Methods, Atmospheric Environment, 42, PP. 1359–1369.
  27. Martenies, S.E., Wilkins, D. & Batterman, S.A., 2015, Health Impact Metrics for Air Pollution Management Strategies, Environment International, 85, PP. 84–95.
  28. Mesa-Frias, M., Chalabi, Z. & Foss, A.M., 2014, Quantifying Uncertainty in Health Impact Assessment: A Case-Study Example on Indoor Housing Ventilation, Environment International, 62, PP. 95–103.
  29. Mesa-Frias, M., Chalabi, Z., Vanni, T. & Foss, A.M., 2013, Uncertainty in Environmental Health Impact Assessment: Quantitative Methods and Perspectives, International Journal of Environmental Health Research, 23, PP. 16–30.
  30. Michanowicz, A., 2015, Hybrid Dispersion/Land Use Regression Modeling for Improving Air Pollutant Concentration Estimates, University of Pittsburgh
  31. Michanowicz, D.R., Shmool, J.L., Tunno, B.J., Tripathy, S., Gillooly, S., Kinnee, E. & Clougherty, J.E., 2016, A Hybrid Land Use Regression/AERMOD Model for Predicting Intra-Urban Variation in PM 2.5, Atmospheric Environment, 131, PP. 307–315.
  32. Mölter, A., Lindley, S., de Vocht, F., Simpson, A. & Agius, R., 2010, Modelling Air Pollution for Epidemiologic Research—Part I: A Novel Approach Combining Land Use Regression and Air Dispersion, Science of the Total Environment, 408, PP. 5862–5869.
  33. Ostro, B. & Chestnut, L., 1998, Assessing the Health Benefits of Reducing Particulate Matter Air Pollution in the United States, Environmental Research, 76, PP. 94–106.
  34. Rhodus, J., Fulk, F., Autrey, B., O’Shea, S. & Roth, A., 2013, A Review of Health Impact Assessments in the US: Current State-of-Science, Best Practices, and Areas for Improvement, Environmental Protection Agency: Cincinnati, OH, USA.
  35. Rowangould, G.M., 2015, A New Approach for Evaluating Regional Exposure to Particulate Matter Emissions from Motor Vehicles, Transportation Research Part D: Transport and Environment, 34, PP. 307–317.
  36. Shahbazi, H., Babaei, M., Afshin, H. & Hosseini, V., 2015, Emission Inventory of Tehran for 1392 - Mobile Sources (In Persian).
  37. Singleton-Baldrey, L., 2012, The Impacts of Health Impact Assessment: A Review of 54 Health Impact Assessments, 2007-2012, University of North Carolina at Chapel Hill, Chapel Hill.
  38. Stoeglehner, G., 2010, Enhancing SEA Effectiveness: Lessons Learnt from Austrian Experiences in Spatial Planning, Impact Assessment and Project Appraisal, 28, PP. 217–231.
  39. Su, J.G., Brauer, M., Ainslie, B., Steyn, D., Larson, T. & Buzzelli, M., 2008, An Innovative Land Use Regression Model Incorporating Meteorology for Exposure Analysis, Science of the Total Environment, 390, PP. 520–529.
  40. Tashayo, B. & Alimohammadi, A., 2016, Modeling Urban Air Pollution with Optimized Hierarchical Fuzzy Inference System, Environmental Science and Pollution Research, PP. 1–15.
  41. Tenailleau, Q.M., Mauny, F., Joly, D., François, S. & Bernard, N., 2015, Air Pollution in Moderately Polluted Urban Areas: How does the Definition of “Neighborhood” Impact Exposure Assessment?, Environmental Pollution, 206, PP. 437–448.
  42. Walker, W.E., Harremoës, P., Rotmans, J., van der Sluijs, J.P., van Asselt, M.B., Janssen, P. & Krayer von Krauss, M.P., 2003, Defining Uncertainty: A Conceptual Basis for Uncertainty Management in Model-Based Decision Support, Integrated Assessment, 4, PP. 5–17.
  43. White, L. & Noble, B.F., 2013, Strategic Environmental Assessment for Sustainability: A Review of a Decade of Academic Research, Environmental Impact Assessment Review, 42, PP. 60–66.
  44. Wilton, D., Szpiro, A., Gould, T. & Larson, T., 2010, Improving Spatial Concentration Estimates for Nitrogen Oxides Using a Hybrid Meteorological Dispersion/Land Use Regression Model in Los Angeles, CA and Seattle, WA, Science of the Total Environment, 408, PP. 1120–1130.
  45. Wilton, D.C., 2011, Modelling Nitrogen Oxides in Los Angeles Using a Hybrid Dispersion/Land Use Regression Model, AA (University of Washington).
  46. Yazdi, M.N., Delavarrafiee, M. & Arhami, M., 2015, Evaluating near Highway Air Pollutant Levels and Estimating Emission Factors: Case Study of Tehran, Iran Science of The Total Environment, 538, PP. 375–384.
  47. Zajaczkowski, J. & Verma, B., 2012, Selection and Impact of Different Topologies in Multi-Layered Hierarchical Fuzzy Systems, Applied Intelligence, 36, PP. 564-584.
  48. Zarrabi, A., Mohammadi, J. & Abdollahi, A., 2010, Evaluation of Mobile and Stationary Sources of Isfahan Air Pollution (In persian), Geography, 26, PP. 151–164.
نشریه سنجش از دور و GIS ایران
دوره 9، شماره 3 - شماره پیاپی 3
اردیبهشت 1396
صفحه 91-110
فایل ها
هم رسانی
ارجاع به این مقاله
آمار