This paper demonstrates the development of a moving point source (MPS) model for simulating the atmospheric dispersion of pollutants emitted from ships under movement. The new model is integrated into the chemistry transport model EPISODE–CityChem v1.3. In the new model, ship parameters, especially speed and direction, are included to simulate the instantaneous ship positions and then the emission dispersion at different simulation time. The model was first applied to shipping emission dispersion modeling under simplified conditions, and the instantaneous and hourly averaged emission concentrations predicted by the MPS model and the commonly used line source (LS) and fixed point source (FPS) models were compared. The instantaneous calculations were quite different due to the different ways to treat the moving emission sources by different models. However, for the hourly averaged concentrations, the differences became smaller, especially for a large number of ships. The new model was applied to a real configuration from the seas around Singapore that included hundreds of ships, and their dispersion was simulated over a period of a few hours. The simulated results were compared to measured values at different locations, and it was found that reasonable emission concentrations were predicted by the moving point source model.
Maritime transport plays an important role in the global transportation for passengers and goods. Compared to other transportation modes, such as road and air transport, maritime transport is considered as the most energy efficient and environment-friendly mode
To evaluate the contributions of ship emissions on air quality in coastal areas, atmospheric dispersion modeling of the pollutants, such as NO
Furthermore, a Lagrangian or Eulerian chemistry transport model (CTM) that solves the advection–diffusion equation and the atmospheric chemistry to predict the transport and chemical reactions of emission species received increasing attention
In pollutant dispersion modeling, it is necessary to include an appropriate assumption or model for treating the emission sources. For a typical setup of shipping emission dispersion simulation, the definition for an emission source usually depends on the ship status, namely that the ship is at berth (hoteling) or on cruise (maneuvering and cruising). Usually, the ship at hoteling phase is treated as a fixed point emission source
From the above literature review, it is evident that either a (or multiple) fixed point(s) source model or a line source model is commonly used for ships under movement in the air pollution dispersion modeling. However, neither of these assumptions is realistic as the ship position is changing when it is moving. The ship movement is not explicitly included in current air pollution dispersion modeling and leads to a research gap. In this paper, a moving point source (MPS) model that can update the ship positions at different times based on the ship speed and direction and then simulate the emission release from the moving ships in the dispersion modeling was hence developed. The new developed MPS model was integrated into the 3D Eulerian chemistry transport model EPISODE–CityChem
The MPS model developed in this paper was integrated into the chemistry transport model, EPISODE–CityChem
EPISODE–CityChem simulates the transport, chemical reactions and deposition of pollutant species in both a 3D Eulerian grid and a ground-level sub-grid
Illustration of the moving point source model. The ship variables in the figures are explained in Table
Setup of the moving point source model.
Note that in each simulation hour, all five variables are assumed constant and only need to be updated hourly.
In the sub-grid receptor model, the pollutants generated by emission sources (either a point or a line source) are calculated by using simple Gaussian models. The LS model used in the EPISODE–CityChem package is a steady-state integrated Gaussian plume model (HIWAY-2) with a simplified street canyon model (SSCM), which affects the concentrations on the receptor points close to the line source (usually within an influence distance of 500 m). The emitted mass from line sources is integrated into the 3D Eulerian model in each simulation time step. For the point source, a Gaussian segmented plume model, SEGPLU
As found from the literature review, LS and FPS models are the common approaches to simulate the emissions generated by the moving ships; however, they are not realistic as they cannot update the instantaneous ship positions. The MPS model is hence developed to fill the gap in the pollutant dispersion modeling.
In the MPS model, five new parameters are defined to determine the ship movement route, as presented in Fig.
Based on these five variables, the ship position is estimated and updated in each simulation time step (
Virtual point for plume release in each time step (
The purpose of this study is to evaluate the new developed MPS model, and two simulations are conducted in the Singapore area in this paper. One is a simplified dispersion simulation that only includes moving ships with simplified input conditions, and the results by using the MPS model are compared with the LS and FPS models. Another is a real case study that simulates all the ships around the city of Singapore using the new model and compares them with the concentrations of emission species measured in different stations.
As shown in Fig.
Configuration of the simulation domain in Singapore used in the simplified simulation. Ships to China are indicated by blue circles, and ships to Europe are indicated by red crosses; lines denote ship routes.
To simplify the simulation, a constant meteorological condition taken from two weather stations (as shown in Fig.
Meteorological inputs applied in MCWIND pre-processing utility for the simplified simulation.
The 2D plot of ground-level diagnostic wind field calculated by MCWIND for the simplified dispersion modeling.
In the simplified simulation, a total number of 44 ships with different types and sizes are included. The ships are separated into two groups, where one group (22 ships) is assumed to move towards China and another is heading to Europe. The ship data, such as ship position, speed, direction and gross tonnage, are collected from online ship resources (such as VesselFinder). In order to better illustrate the feature of the new MPS model and also compare its results with those simulated by the LS and FPS models, only ships on the China–Europe route (west–east direction) are kept as the initial conditions by removing all other ships (such as those are at berthed or moving in a north–south direction), as shown in Fig.
Setups of shipping emission dispersion modeling.
The ships are then divided into different categories (such as liquid bulk ships, dry bulk carriers, containers and cargo) based on those defined in the MEET (Methodologies for estimating air pollutant emissions from transport) methodology by
Shipping emission dispersion modeling with MEET method
The MPS model was applied to a real case study in this paper as well. The hourly averaged emission values for several hours (11:00 to 16:00 on 23 April 2020) in Singapore were simulated by using the MPS model, and the results at different observation stations were compared to the measured data. The model setups (such as the grid size) and numerical methods (such as MEET method for emission rate calculation) are the same as those used in the simplified simulation, except for those (such as the meteorology and background concentrations) introduced in this section. The configurations and setups of the simulation are shown in Fig.
Configuration of the simulation domain used in the real case simulation.
The first difference in this real case study compared to the simplified simulation is that all the ships around the city of Singapore are included in the simulation, the ships are only updated each hour and their rates are estimated by using MEET method (Eq.
In this section, the results for two studies are presented. The first one (Sect.
The new MPS model was first tested by simulating only one ship, which moves from the east side to the west side. In this preliminary simulation, the ship movement parameters are constant, and all other conditions such as wind speed and direction are the same as mentioned in Table
Instantaneous NO
In comparison, the LS model gives quite different results, as shown in Fig.
In addition, the simulated emission profiles by using a FPS model are illustrated in Fig.
After comparing the three emission source models for only one ship simulation, the three models were applied to a simplified study (cases 1–3) with 44 ships involved, in order to further evaluate the performance of different models for predicting the effects of moving ships on air quality in coastal cities. Both of the instantaneous and average results are presented in this section to fully compare the different emission models. The meteorological conditions and simulation setups are the same as presented in Tables
In the case study for more moving ships, the simulation was first conducted by using the MPS model (case 1), and the instantaneous ground-level NO
Instantaneous NO
Figure
Vertical NO
In addition, the time history of NO
Time history of NO
Ship movements during
The ship initial positions and movements during
In the simplified case study, the simulation was then conducted by treating each moving ship as a line source (case 2). The instantaneous NO
Instantaneous NO
The NO
In addition, the simulation was conducted by using the FPS model as well, assuming that the ships are staying at the middle points of the ship routes in each hour. The ground-level NO
Instantaneous NO
The simulation results in previous sections are the instantaneous NO
Hourly averaged NO
The hourly averaged 2D plots in Fig.
The hourly averaged NO
Hourly averaged NO
After comparing with the LS and FPS models in a simplified study, the new developed MPS model was applied to the real case by predicting the emission results generated by all ships (including those under cruise and at berth) around the Singapore area during a couple of hours. The predicted hourly averaged NO
Comparison of hourly averaged emission concentrations between simulation and measurement at different locations.
Figure
Emission concentrations during the entire simulation period predicted by the MPS and LS models. Note that in panels
The simulation results by using the LS model are also presented in Fig.
In this paper, a MPS model was developed to simulate the emission generation and transport from the moving ships in pollutant dispersion simulations. For the dispersion modeling, the common assumption is to use a LS or a FPS model to treat the emissions generated by the moving ships. Both models cannot update the ship movements within a certain time period (usually an hour), which results in an unrealistic emission distribution. In the MPS model, the ship movement parameters, including speed and direction, are used to update the ship positions and then to estimate the emission dispersion at different simulation times. The new developed model was integrated into the city-scale chemistry transport model, EPISODE–CityChem, and then was evaluated by simulating the atmospheric dispersion of emission species emitted by the ships in the Singapore area.
The computational results by using the MPS model were first compared to those obtained from a LS model and a FPS model in simplified simulations. Under the simplified conditions with a limited number of ships, the results indicated that the new developed MPS model can simulate the ship movement and hence predicts more realistic instantaneous concentration profiles for the emission species (such as NO
In addition, a real case study was conducted as well to further evaluate the MPS model by simulating all ships around the Singapore area. Compared to the measured data, the MPS model was found to reasonably predict the emission concentrations at different observation stations located in Singapore, although gaps still exist due to the different setups and configurations between simulations and measurements. The LS model was compared in the study as well. The predicted emission concentrations by the MPS and LS models are quite different at the locations close to the ships, while these differences become smaller at the locations far away from the ships as the emission is diluted and deposited. Compared to the measured data, the MPS and LS models perform similarly, while a slightly better NO
To further evaluate the MPS model, the time step is investigated. In the reference case (case 1), the calculated time step is 15.8 s, and in this parameter study, the time step is adjusted to two different values of 10 s (case S1) and 30 s (case S2) as shown in Table
The instantaneous NO
In EPISODE–CityChem, parallel simulations of emission dispersion are conducted, with one Eulerian main grid (where
Setups of pollutant dispersion modeling for parameter study.
Instantaneous NO
As the wind field (speed and direction) estimated by EPISODE–CityChem is spatially different (as shown in Fig.
Another parameter study was conducted by changing the horizontal (case S3 and S4) and vertical (case S5) grid size. In this section, the dispersion of ship emissions was first simulated by using two different main grid resolutions, where the horizontal grid sizes (
The simulated NO
Instantaneous NO
A similar study was conducted by changing the vertical grid size (
In this study, the ship exit gas velocity was assumed to be 20 m s
Setups of emission sources for the MPS model in the parameter study.
Instantaneous NO
In addition, the ship building height was assumed to be 5 m below the chimney height, and the building width was set as 20 and 5 m for the large size and the small size ships respectively (case 1), as shown in Table
In this study, different species such as NO
Predicted emission concentrations for different species when using the MPS model.
The source code of the MPS model is available at
The following datasets are available upon request from the authors:
input and output data of EPISODE–CityChem simulations for simplified cases with 1 ship in Singapore ( input and output data of EPISODE–CityChem simulations for simplified cases with 44 ships in Singapore ( input and output data of EPISODE–CityChem simulations for the real case study in Singapore (
KP developed the model code, performed the simulations, and processed and evaluated the results. EM discussed the model development and provided suggestions for the model evaluations. KP prepared the manuscript with the contributions from all co-authors.
The authors declare that they have no conflict of interest.
Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This study is funded by the National Research Foundation, Prime Minister's Office, Singapore, under its Campus for Research Excellence and Technological Enterprise (CREATE) program. The authors appreciate the support for EPISODE installation from Matthias Karl from the Helmholtz-Zentrum Geesthacht, Department of Chemistry Transport Modelling, Geesthacht, Germany. Kang Pan also thanks Yichen Zong and Arkadiusz Chadzynski for their suggestions and help on obtaining input data for simulations.
This research has been supported by the National Research Foundation Singapore.
This paper was edited by Tim Butler and reviewed by two anonymous referees.