Open Access Repository

Development of the models to accurately assess the environmental impacts of shipping emissions


Downloads per month over past year

Jahangiri, S ORCID: 0000-0001-5139-8213 2019 , 'Development of the models to accurately assess the environmental impacts of shipping emissions', PhD thesis, University of Tasmania.

PDF (Whole thesis (published material removed))
Jahangiri_whole...pdf | Download (30MB)

| Preview
[img] PDF (Whole thesis)
Jahangiri_whole...pdf | Document not available for request/download
Full text restricted


Maritime transportation is amongst the most popular modes of transport due to its cost effectiveness & efficiency, overall safety and relatively environmentally friendly operations. This trend is expected to increase with the number of global fleet of vessels forecasted to continue increasing. However, exhaust emissions from ships are one of the major sources of air pollutants. Unlike land-based transportation, which is strictly governed by stringent environmental rules and regulations, the shipping environmental regulators are still continuously amending their legislation with regards to emissions.
While it is evident that shipping emissions are of concern globally, the global effects tend to be more dispersed and it is difficult to be attributed to the original sources. Continued implementation of the amendments to the MARPOL Convention Annex VI regulations is an attempt to reduce emissions on a global scale. In-port emissions account for a relatively small proportion of the total emissions due to shipping, yet they have some of the most significant health impacts on the surrounding population. It is commonly known that these emissions are linked to cardiopulmonary and cancer related health problems, with an estimated number of deaths due to sulphur oxides (SO\(_x\)) emissions from shipping alone during 2012 of approximately 87,000 worldwide. Regulated pollutants including SO\(_x\), nitrogen oxides (NO\(_x\)), particulate matter (PM) and the hundreds of other constituents of exhaust emissions generated by the combustion of fuels depend on the quality of the fuel and the characteristics of combustion.
To examine these risk and the potential benefits of control measures, ship exhaust emissions need to be quantified. However, precise measurement and collection of emission data is challenging due to factors such as diversity of engine types and configurations, various operation modes, ship mobility, etc. Several approaches to estimate shipping emissions have been developed. One such solution is online computer-based monitoring of shipping pollution, which utilizes measurement system as part of the fixed ship equipment. Online monitoring mechanisms provide data over an extended period, but it is costly in time, assets, and well-trained human resources. On top, the measurement results are with low precision and sometimes unreliable. Therefore, there is a need to consider more on-board measurements that will enhance the accuracy of emission prediction models.
Following the above, the objective of this thesis is to contribute to green and sustainable shipping operations by developing empirical-based models to better predict and assess the environmental impacts of shipping emissions. Although the methodologies developed in this PhD research have general applicability, the focus of experimental work was in Australian Ports due to their accessibility.
For the experimental work we collaborated with fellow researchers from Queensland University of Technology - Australia (QUT) and Maine Maritime Academy - USA (MMA) to develop in-vessel emission measurement systems. The measurements were taken in October and November of 2015 on two large cargo ships at the Ports of Brisbane, Gladstone, and Newcastle. The first on-board measurement was performed on CSL vessel I (to ensure confidentially the identity of ship is suppressed) from 26th to 31st of October 2015 when the vessel was en route from Port of Brisbane to Port of Gladstone. The second measurement was conducted on CSL vessel II from 3rd to 6th of November 2015 on a voyage from Gladstone to Sydney. All measurements were carried out on both the main and auxiliary engines of both ships for three ship operating conditions: at berth, while manoeuvring, and while cruising. Data from on-board measurements and laboratory analysis was used to develop a model for emission factor estimation for ships operating in different conditions.
Because the utilization of inbuilt measurements proves to be difficult, time-consuming and resource-demanding as well as restricted by limitations, it is difficult to convince ship-owners to purchase and install recommended measurement devices. Therefore, emission inventories are utilized, which are mathematical models to estimate emissions discharged into the atmosphere. This research presents a comprehensive study to identify vessel-specific inventory families predicting the primary emissions from ocean-going vessels when at berth, while maneuvering and while cruising.
The effectiveness of emission factors applied in current inventories, however, needs to be evaluated, because of their general over- or under-estimations. There is also a need to develop the models to predict the emissions considering different environmental and operational factors more preciously. Therefore, the on-board measurement data acquired in this study were utilized to develop new sets of emission factor equations for emission inventory considering different main engine types for at-sea and in-port operations. To this end, non-linear regression analysis was used to develop the new models and the results were statistically compared with the conventional models applied for emission inventories in shipping operations. Our results showed a better prediction of the developed emission quantity than current inventories for different engine types during in-port and at-sea activities, with the sum of primary emissions coming closest to the actual sea emission calculations and to the smallest standard values. This study also created a generalized rational algorithm to rank inventory families based on the precision of their predictions for a given operational mode of a specific vessel. The implications of this study, together with the developed algorithm to rank inventory families, were applied to offer a novel future policy for cost-effective and reliable emission estimation caused by shipping operations.
The emissions from vessels utilizing heavy fuel oil include large amounts of NOx, SOx and PM, presenting significant health risk to people living near ports. Atmospheric dispersion modelling can be used to predict the ground level concentrations of gaseous pollutants and similarly the deposition of PM. While several dispersion models such as AERMOD, AUSPLUME and ISCST3 exist, selecting an appropriate model is important to match the size and complexity of the domain. Some simple ones like Gaussian-plume models require less computational time and resources to run, requiring only simplified meteorological and geographic datasets where they approximate plume behavior mathematically incorporating a simple description of the dispersion process. This may result in inaccurate results in obtaining the final concentrations. To overcome the shortcomings of steady-state Gaussian-plume models, we applied a more advanced atmospheric dispersion model (CALPUFF) to assess ground-level concentrations. To the best of our knowledge, this is the first comprehensive report describing the concentration, distribution and sources of shipping emissions within Australian ports. Another point to add is that the health of residents living near ports is most likely affected by different shipping activities. Therefore, our study also helped provide guidance on the minimum distances between emission sources and urbanized areas (homes, schools, and businesses) needed to safeguard human health, through health impact risk assessment.
Lastly, to develop a baseline measurement of the current state of risk from shipping emissions, we developed a complete methodology, based on the Australian Environmental Health Risk Assessment Framework to assess the human health risk from shipping emissions, applying Downwash algorithm and Near-field modelling as well as the Air-shed areas from CALPUFF dispersion modelling results. We discussed carcinogenic and ecological impact assessments in depth. The final risk results are validated against National and European guidelines. The results showed no stack tip downwash happening as of the high stack outlet velocity and at a low reference wind speed. The results suggested that the dispersion models commonly used for regulatory applications generally underestimate the lower ranges of pollutant concentrations and overestimate high concentrations in the near field. This study also offered a significant contribution to developing a baseline measurement of the current state of risk from emissions of the ocean-going vessels visiting the port, and suggested that, given the expected development of many Australian ports in the near future, the need for continual monitoring of shipping emissions is an essential and necessary area of research.

Item Type: Thesis - PhD
Authors/Creators:Jahangiri, S
Keywords: On board Measuremnt, Emission Inventories, Dispersion Modeling, Risk Assessment, Environmental Engineering, Emission Measurements
DOI / ID Number: 10.25959/100.00031937
Copyright Information:

Copyright 2019 the author

Additional Information:

Parts of chapters 2, 3 and 4 were used in a paper published as: Jahangiri, S., Nikolova, N., Tenekedjiev, K., 2018. Empirical testing of inventories applying on-board measurements of exhaust emissions at port and at sea, Journal of sustainable development of transport and logistics, 3(2), 6-33. It is published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (

Chapter 3 appears to be, in part, the equivalent of a post-print version of an article published as: Jahangiri, S., Nikolova, N., Tenekedjiev, K., 2018. An improved emission inventory method for estimating engine exhaust emissions from ships, Sustainable environment research, 28(6), 374-381. It is published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license (

Chapter 3 appears to be, in part, the equivalent of a post-print version of an article published as: Jahangiri, S., Nikolova, N., Tenekedjiev, K., 2018. Application of a developed dispersion model to Port of Brisbane, American journal of environmental sciences, 14(4), 156-169 It is published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license (

Chapter 6 appears to be, in part, the equivalent of a post-print version of an article published as: Jahangiri, S., Nikolova, N., Tenekedjiev, K., 2019. Health risk assessment of engine exhaust emissions within Australian ports: a case study of Port of Brisbane, Environmental practice, 21(1), 20-35

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page