The present work develops a multidisciplinary approach for evaluating the air pollution and economic impact from ships operating in the port of Varna. The work collects and analyses automatic identification system (AIS) data of the arriving and queuing dry cargo ships in the seaport of Varna in identifying the statistical descriptors of the length of the ships, gross tonnage (GT), and ship engine power. The queueing theory (QT) is employed to analyse the ship operations in a single queue and is processed by three parallel terminals, satisfying the port regulations. The Gaussian dispersion model (GDM) is adopted to predict the pollution concentration from ships arriving at the seaport, queuing, approaching, waiting, processing at the berth, and departing. The gas emission is estimated as a function of the ship movement trajectory, and the time duration at any stage is defined by QT for the most critical surrounding areas, considering the wind speed, as well as horizontal and vertical dispersion as a function of the location of the ship, accounting for the effective emission height, weather conditions, and speed. To mitigate the potential impact on health, the gas emissions of oxides of nitrogen (NO_x), sulphur dioxide (SO₂), and air-borne particles (PM₁₀) generated by ships during the queuing and port operation are evaluated. Potential cleaning measures for any ship are implemented to satisfy the maximum allowable concentrations (MAC) in surrounding areas. The implemented ship pollution cleaning measures and overall ship and terminal operating costs are minimised to identify the most efficient berth operation. The developed approach is flexible and can be used for any particular conditions for ships operating in ports.

This review looks at the advancements in shipping-related air pollution prevention in the context of ship life cycle assessment and energy efficiency. It discusses which design option is best for implementing various strategies to lower greenhouse gas emissions. It covers logistics, digitization, environmental requirements, and the greenhouse gases produced. Among the issues for enhancing the propulsion system’s performance are air lubrication, ship hull optimization, and hull and propeller maintenance and cleaning. Alternative fuels, wind-assisted propulsion, and nuclear energy are given special attention. Energy-efficient design solutions, risk-based environmental ship design, and retrofitting older ships to improve energy efficiency are also covered. Several trends and recommendations for lowering shipping-related air pollution have been found in the review.

Natural gas is cheaper than fuel on an energy basis, making it an alternative ship fuel which leads to a reduced operating cost and clean gas environmental conditions. The current study analyses the retrofit of an ageing multi-purpose ship to use liquefied natural gas as a primary ship fuel in the context of a short-ship sea operation. The objective is to transform an existing commercial ageing ship propulsion system into a green energy propulsion one and to analyse the economic feasibility considering the high volatility and increased LNG price. Four scenarios were analysed based on the net present value representing Denying, Disinterested, Good and Acceptable financial cash outflow. It was concluded that in the present economic instability and price of LNG fuel and CO₂ taxes, the ship owner needs to rely on the long-term contract of buying LNG fuel to implement measures to reduce greenhouse gas emissions and keep good benefits in shipping.

This work develops a stochastic air quality dispersion to predict the pollution concentration originating from ships queuing in a seaport. The Gaussian dispersion model for five ships operating in the Black Sea queuing in the front of the port of Varna as sources of gas emission of NO_x, SO_x and PM₁₀ is used to define the air pollution concentration at receptors (crowded areas of the port and other reference points) and consequently the distance to the seaport queuing location. Uncertainties, which are inherent in the input data and mathematical model, are accounted for to estimate the propagating uncertainties of the emission concentration at the receptors accounting for the wind speed, horizontal and vertical dispersion parameters as a function of the geographical location of the emission sources (ships), effective emission height and weather conditions. The estimated uncertainties of the air quality prediction are of significant importance for the decision-making on the regulatory purposes, and the probability of exceeding the threshold limits needs to be quantified. The most expected value and the probability of exceeding the acceptable limits of pollution concentration are defined by employing the first-order reliability method. The target reliability level is defined as the failure cause and mode used for identifying the safety calibration factors that may be employed for defining the most suitable location of the ship queuing seaport. Several conclusions about the applicability of the developed stochastic model and its use for regulatory purposes are also provided.

The work studies extreme pollution events and their return period in the winter seaport of Varna, providing information for decision-makers, government agencies and civil society on how the intensity of shipping traffic may impact the air pollution in the vicinity of the port. Extreme value analysis employing the Weibull distribution is applied to investigate air pollution and the probability of higher concentrations of oxides of nitrogen ($N{O}_x$) generated by ships while queuing in the winter seaport. Potential cleaning of the air pollution generated by the anchored ships is introduced to meet the acceptable level of air pollution concentrations in coastal zones. The employed ship pollution cleaning and overall ship service costs are minimised to satisfy cleaner environmental conditions. The developed approach is adopted to analyse the air pollution of a port without a monitoring system to control and prevent pollution and with limited information on ship traffic and air pollution.

A formulation is presented for the assessment of the CO₂ generated by ships in operation and their evolution with time, conditional on the current legislation using Markov chains. Any potential deep repair or retrofitting of the ship propulsion system or enhancement of route operational characteristics during the service life are not accounted for. The Markov transition matrix is defined based on the ship operations and CO₂ history of A, B, C, D, and E carbon intensity indicator (CII) rates. The transition between different CII rate states in the survey data is used to estimate the probability of transition of the analysed ships between different CII grates. Distinct transition matrices employing the progressively tightened legislation of CII are employed and analysed. In addition, the transition matrices can be fed into risk-based models that take the CII rates as input for defining the most appropriate ship energy efficiency management plan.