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An Investigation on the use of air quality models in ship emission forecasts

Year 2024, Volume: 7 Issue: 1, 15 - 30, 25.03.2024

Gizem Kodak

https://doi.org/10.51513/jitsa.1425614

Abstract

Today's world trade operates on the basis of maritime transportation. Combating the environmental impacts of maritime transportation has become a global concern. MARPOL Annex VI contains rules for the prevention of air pollution from ships. Air pollution represents a more abstract pollution than other annexes of MARPOL. In this context, the first and most critical step is to measure air pollution. Nowadays it is possible to calculate ship emissions with individual ship activity data instead of the traditional, fuel-based approach. One of the most ideal data sources for this calculation is AIS data. The critical question to be answered in the literature is how to obtain the highest resolution output using AIS data. Here air quality modelling gains strategic importance. In this study, air quality models used in ship emission calculations were examined with the PRISMA method and the most commonly used Eulerian and Lagrangian models were discussed. For this purpose, studies using AIS data in ship emission calculations were separated through the Scopus database and air quality modelling studies were filtered. Thus, the profile of air quality models used in ship-borne air pollution studies in the literature was obtained and a methodological reference source was created for future studies.

Keywords

Air quality modelling Automatic Identification System ship emissions

References

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Hava kalitesi modellerinin gemi emisyon tahminlerinde kullanılması üzerine bir araştırma

Year 2024, Volume: 7 Issue: 1, 15 - 30, 25.03.2024

Gizem Kodak

https://doi.org/10.51513/jitsa.1425614

Abstract

Günümüz dünya ticareti denizyolu temelinde işlemektedir. Deniz taşımacılığının çevresel etkileriyle mücadele ise bugün küresel bir endişe haline gelmiştir. Denizlerin Gemilerden Kirlenmesini Önleme Uluslararası Sözleşmesi (MARPOL) Ek VI, gemilerden kaynaklanan hava kirliliğinin önlenmesine ilişkin kuralları içermektedir. Hava kirliliği MARPOL’ün diğer eklerine göre daha soyut bir kirliliği ifade etmektedir. Bu bağlamda ilk ve en kritik adım hava kirliliğinin ölçülmesidir. Günümüzde geleneksel, yakıt bazlı yaklaşım yerine gemi emisyonlarını bireysel gemi faaliyet verileriyle hesaplamak mümkündür. Bu hesaplama için en ideal veri kaynaklarından biri AIS verisidir. Literatürde cevaplanması gereken kritik soru, AIS verileri kullanılarak en yüksek çözünürlüklü çıktının nasıl elde edileceğidir. Burada hava kalitesi modellemesi stratejik önem kazanmaktadır. Bu çalışmada, gemi emisyon hesaplamasında kullanılan hava kalitesi modelleri PRISMA metodu ile incelenmiş ve en yaygın kullanılan Eulerian ve Lagrangian modeller tartışılmıştır. Bu amaçla gemi emisyon hesaplamasında AIS verilerini kullanan çalışmalar Scopus veri tabanı üzerinden ayrıştırılmış ve hava kalitesi modelleme çalışmaları filtrelenmiştir. Böylece literatürde gemi kaynaklı hava kirliliği çalışmalarında kullanılan hava kalitesi modellerinin profili elde edilmiş ve gelecek çalışmalar için metodolojik bir referans kaynağı oluşturulmuştur.

Keywords

Hava Kalitesi Modelleme Otomatik Tanımlama Sistemi Gemi Emisyonları

References

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  • Byun, D., Schere, K.L. (2006), Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev., 59, 51-77.
  • Chen, D., Fu, X., Guo, X., Lang, J., Zhou, Y., Li, Y., Wang, W. (2020), The impact of ship emissions on nitrogen and sulfur deposition in China, Science of the Total Environment, 708, 134636.
  • Chen, D., Tian, X., Lang, J., Zhou, Y., Li, Y., Guo, X., Wang, W., Liu, B. (2019), The impact of ship emissions on PM2.5 and the deposition of nitrogen and sulfur in Yangtze River Delta, China, Science of the Total Environment, 649, 1609-1619.
  • Chen, D., Wang, X., Li, Y., Lang, J., Zhou, Y., Gıo, X., Zhao, Y. (2017), High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014, Science of the Total Environment, 609, 776-787.
  • Chen, D., Wang, X., Nelson, P., Li, Y., Zhao, N., Lang, J., Zhou, Y., Guo, X. (2017), Ship emission inventory and its impact on the PM2.5 air pollution in Qingdao Port, North China, Atmospheric Environment, 166, 351-361.
  • Chen, D., Zhang, Y., Lang, J., Zhau, Y., Li, Y., Guo, X., Wang, W., Liu, B. (2019), Evaluation of different control measures in 2014 to mitigate the impact of ship emissions on air quality in the Pearl River Delta, China, Atmospheric Environment, 216, 116911.
  • Chen, D., Zhao, N., Lang, J., Zhou, Y., Wang, X., Li, Y., Zhao, Y., Guo, X. (2018), Contribution of ship emissions to the concentration of PM2.5: A comprehensive study using AIS data and WRF/Chem model in Bohai Rim Region, China, Science of the Total Environment, 610-611, 1476-1486.
  • Chen, D., Zhao, Y., Nelson, P., Li, Y., Wang, X., Zhou, X., Lang, J., Guo, X. (2016), Estimating ship emissions based on AIS data for port of Tianjin, China, Atmospheric Environment, 145, 10-18.
  • Ekmekçioğlu, A., Kuzu, S.L. , Ünlügençoğlu, K., Çelebi, U.B. (2020) Assessment of shipping emission factors through monitoring and modelling studies, Science of the Total Environment, 743, 140742.
  • Eliassen, A. (1984). Aspects of Lagrangian Air Pollution Modelling. In: De Wispelaere, C. (eds) Air Pollution Modeling and Its Application III. Nato · Challenges of Modern Society, vol 5. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-2691-5_1
  • EPA, (2023). Support Center for Regulatory Atmospheric Modeling (SCRAM), Air Quality, Models, Retrieved from https://www.epa.gov/scram
  • EPA, (2023b). Air Quality Dispersion Modeling - Preferred and Recommended Models, Retrieved from https://www.epa.gov/scram/air-quality-dispersion-modeling-preferred-and-recommended-models#aermod
  • EPA, (2023c). Community Multiscale Air Quality Modeling System (CMAQ), WRF-CMAQ Model, Retrieved from https://www.epa.gov/cmaq/wrf-cmaq-model
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  • Foy, B. Smyth, A.M., Thompson, A.L., Gross, D.S., Olson, M.R., Sager, N., Schauer, JJ. (2012). Sources of nickel, vanadium and black carbon in aerosols in Milwaukee, Atmospheric Environment, 59, 294-301.
  • Grados, V.D., Mejias, J., Musina, L., Gutierrez, J.M. (2018). The influence of the waterjet propulsion system on the ships' energy consumption and emissions inventories, Science of the Total Environment, 631-632, 496 – 509.
  • Grell, G.A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., Eder, B. (2005). Fully coupled “online” chemistry within the WRF model. Atmos. Environ., 39, 6957-6975.
  • Gutierrez, J.M. and Grados, V.D. (2021). Calculating ships' real emissions of pollutants and greenhouse gases: Towards zero uncertainties, Science of the Total Environment, 750, 141471.
  • Gutiérrez, J.M., Velázquez, E.P., Sánchez, Y.A., Moreno, R.R., Cayetano, F.C., Grados, V.D. (2019). Comparative analysis between different methods for calculating on-board ship's emissions and energy consumption based on operational data, Science of the Total Environment, 650, 575-584.
  • He, L., Wang, J., Liu, Y., Zhang, Y., He, C., Yu, Q., Ma, W. (2021). Selection of onshore sites based on monitoring possibility evaluation of exhausts from individual ships for Yantian Port, China, Atmospheric Environment, 247, 118187.
  • He, S., Wu, X., Wang, J., Guo, J. (2022). A ship emission diffusion model based on translation calculation and its application on Huangpu River in Shanghai, Computers & Industrial Engineering, 172, 108569.
  • Herwehel, J.A. and Kang, D. (2006). Comparisons of the CMAQ and WRF/Chem Models for a 2006 Eastern U.S. Case Study Jerold A. Herwehe1 and Daiwen Kang, United States Environmental Protection Agency. Retrieved from https://www.cmascenter.org/conference/2008/slides/poster_pres/herwehe_comparisons_cmaq_cmas08.pdf
  • Hu, X.M. (2015). Boundary layer (atmospheric) and air pollution, Air Pollution Meteorology, Reference Module in Earth Systems and Environmental Sciences Encyclopedia of Atmospheric Sciences (Second Edition), 2015, 227-236.
  • Huang, H., Zhou, C., Huang, L., Xia, C., Wen, Y., Li, J., Lu, Z. (2022). Inland ship emission inventory and its impact on air quality over the middle Yangtze River, China, Science of the Total Environment, 843, 156770.
  • Huang, Z., Zhong, Z., Sha, Q., Xu, Y., Zhang, Z., Wu, L., Wang, Y., Zhang, L., Cui, X., Tang, M., Shi, B., Zheng, C., Li, Z., Hu, M., Bi, L., Zheng, J., Yan, M. (2021). An updated model-ready emission inventory for Guangdong Province by incorporating big data and mapping onto multiple chemical mechanisms, Science of the Total Environment, 769, 144535.
  • Huyen, T.T., Oanh, N.T.K., Huy, L.N., Winijkul, E., Chi, N.N.H. (2022). Impact of lowering fuel sulfur content on atmospheric emissions from shipping activities in a World Heritage Bay in Vietnam, Environmental Technology & Innovation, 27, 102507.
  • Johansonn, L., Jalkanen, J.P., Kukkonen, J. (2017). Global assessment of shipping emissions in 2015 on a high spatial and temporal resolution, Atmospheric Environment, 167, 403-415.
  • Kodak, G. (2021). The Role of International Maritime Traffic on PM10 Pollutant in the Strait of Istanbul (Bosphorus), International Journal of Environment and Geoinformatics (IJEGEO), 9(3), 036-047. Doi: 10.30897/ijegeo.977393
  • Kodak, G. (2022). Reflections on using automatic identification system (ais) data in ship emission studies in the academic literature, Environmental Engineering and Management Journal, 21(9), 1461-1470, doi: 10.30638/eemj.2022.129
  • Kodak, G. (2023). Denizcilik Perspektifinden Python Program Dilinin Temelleri, Nobel Yayıncılık, ISBN: 978-625-398-244-7, Mart, 2023.
  • Li, C., Yuan, Z., Ou, J., Fan, X. Ye, S., Xiao, T., Shi, Y., Huang, Z., Ng, S.K.W., Zhong, Z., Zheng, J. (2016). An AIS-based high-resolution ship emission inventory and its uncertainty in Pearl River Delta region, China, Science of the Total Environment, 573, 1-10.
  • Liu, H., Meng, Z.H., Shang, Y., Lv, Z.F., Jin, X.X., Fu, M.L., He, K.B. (2018), Shipping emission forecasts and cost-benefit analysis of China ports and key regions’ control, Environmental Pollution, 236, 49-59.
  • Mao, J., Zhang, Y., Yu, F., Chen, J., Sun, J., Wang, S., Zou, Z., Zhou, J., Yu, Q., Ma, W., Chen, L. (2020). Simulating the impacts of ship emissions on coastal air quality: Importance of a high-resolution emission inventory relative to cruise and land-based observations, Science of the Total Environment, 728, 138454.
  • Mercio, E., Dinoi, A., Contini, D. (2019). Development of an integrated modelling-measurement system for near-real-time estimates of harbour activity impact to atmospheric pollution in coastal cities, Transportation Research Part D, 73, 108-119.
  • Mölders, N., Gende, S., Pirhalla, M. (2013). Assessment of cruise–ship activity influences on emissions, air quality, and visibility in Glacier Bay National Park, Atmospheric Pollution Research, 4, 435-445.
  • NOAA (n.d.). WRF-Chem Version 4.4 User’s Guide, accessed 06.06.2023, Retrieved from https://ruc.noaa.gov/wrf/wrf-chem/Users_guide.pdf
  • Nusa K., Kodak, G. (2023). Comparison of Road and Maritime Transportations in Emissions Perspective. International Journal of Environment and Geoinformatics (IJEGEO), 10(2): 048-060. Doi: 10.30897/ijegeo.1254161
  • Ring, A.M. Canty, T.P., Anderson, D.C., Vinciguerra, T.P., He, H., Goldberg, D.L., Ehrman, S.H., Dickerson, R.R., Salawitch, R.J. (2018). Evaluating commercial marine emissions and their role in air quality policy using observations and the CMAQ model, Atmospheric Environment, 173, 96 – 107.
  • Russo, M.A., Leitão, J., Gama, C., Ferreira, J., Monteiro, A. (2018). Shipping emissions over Europe: A state-of-the-art and comparative analysis, Atmospheric Environment, 177, 187-194.
  • Seinfield, J., Pandis, S. (2006). Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, A Willey Interscience publication, Wiley, 2nd edn., 2006.
  • Smit, R., Van, T.C., Suara, K., Brown, R.J. (2022). Comparing an energy-based ship emissions model with AIS and on-board emissions testing, Atmospheric Environment: X, 16, 100192.
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There are 55 citations in total.

Details

Primary Language English
Subjects Marine Transportation
Journal Section Articles
Authors

Gizem Kodak 0000-0002-1845-7901

Early Pub Date March 22, 2024
Publication Date March 25, 2024
Submission Date January 25, 2024
Acceptance Date February 16, 2024
Published in Issue Year 2024 Volume: 7 Issue: 1

Cite

APA Kodak, G. (2024). An Investigation on the use of air quality models in ship emission forecasts. Akıllı Ulaşım Sistemleri Ve Uygulamaları Dergisi, 7(1), 15-30. https://doi.org/10.51513/jitsa.1425614