Emissions from ocean-going vessels-Rewriting

Emissions from ocean-going vessels-Rewriting 150 150 Affordable Capstone Projects Written from Scratch

Emissions from ocean-going vessels present a significant health risk to populations surrounding ports, and damage the environment. Emissions from vessels using heavy fuel oil include large amounts of sulphur dioxide (SO2), nitrogen oxides (NOx) and particulate matter (PM). To assess the risk of these emissions, a complete methodology has been developed, based on the Australian Environmental Health Risk Assessment Framework. The methodology includes a detailed inventory of in-port and at-sea emissions using an activity-based approach applying downwash and near-field areas from first principles equations as well as the air-shed areas from CALPUFF dispersion modelling results for Port of Brisbane in the calendar year 2013. The final risk values are validated against national and European guidelines. Various health impact assessments as well as carcinogenic and ecological effects are discussed in depth. This study offers 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 suggests that, given the expected development of many Australian ports in the near future, the need for continual monitoring of shipping emissions is an important and necessary area of research.

Keywords: Shipping, Risk Assessment Framework, Health Impact Assessment, Risk Values

It is widely agreed that emissions from shipping exhausts are a significant source of air pollution (Cooper, 2003). The most prominent and worrying emissions from diesel combustion are sulphur oxides (SOx), nitrogen oxides (NOx) and particulate matter (PM). It is predicted that by 2020, based on current rates, worldwide NOx SOx emissions will nearly double and emissions from shipping will increase by two thirds (Eyring, 2010). Global implementation of the amendments to the MARPOL Annex VI regulations is an attempt to reduce shipping emissions (Clarkson, 2015) (Federation, 2013), but as these emissions tend to be dispersed they are not easily traced to their sources.

In-port emissions account for a relatively small proportion of the total emissions from shipping, but have significant health impacts on nearby populations (Clarkson, 2015) (Cooper, 2003), and have been linked to cardiopulmonary and cancer-related health problems. Winebrake et al. (Winebrake, 2009), for example, estimated that SOx emissions from shipping during 2012 were implicated in approximately 87,000 deaths worldwide. While studies of ship emissions in Australia have estimated total emissions within the wider Australian coastal waters (Goldsworthy, 2015), and within a specific time frame for a particular port (Goldsworthy, 2013), no work has considered the distribution of emissions to quantify their risk to the local population.

The level of risk can be described either qualitatively (i.e. by putting risks into categories such as ‘high’, ‘medium’ or ‘low’) (Figure 2) or quantitatively (with a numerical estimate). Practical guidance on how to manage risks is the approach taken in AS/NZS ISO 31000:2009 (Standards Australia, 2009).

Current risk assessment methods do not enable accurate quantitative estimates of risk for low levels of exposure to environmental hazards. Numerical estimates of risk can be presented, but caution must be exercised in assigning strict meaning to the numbers. Complexity of the exposure conditions, variability in the environmental agents and exposed populations, and any inherent limitations in toxicological data may limit the accuracy of numerical risk estimates.

While a degree of quantification may be possible for some components, such as data collection and exposure assessment, it is important that all uncertainties are reflected in the risk assessment outcomes.

This study adopts a quantitative assessment to calculate risks numerically, which does not rely so heavily on judgement. It is more reliable while taking into account more complexity than is possible with a qualitative approach (Department of the Environment, 2016). The quantitative approach, in this study, deals with the calculations of final risk values from the near-field and far-field concentrations. It includes the Gaussian plumes and outcomes from CALPUFF dispersion modelling in terms of the outcomes from the health impact assessment, short-term and long-term guideline validation assessment, ecological effects and assessment of carcinogenic risks from the diesel particulate.

There are three tiers to quantitative risk assessment. The tiered approach allows the problem under consideration to be assessed at an appropriate level of complexity. The degree of health protection achieved is equal at each tier. As the amount of data and assessment detail increases and the conceptual understanding of site conditions (i.e. the conceptual site model) is refined, the level of uncertainty decreases. In turn, the amount of caution, which must be substituted for knowledge in the risk assessment process may be, reduced.

Tier I considers some data but perhaps not much, and some guideline values. The assessment simply notes if the risk falls above or below the guideline. Because of the cost and complexity of contemporary environmental health risk assessment, circumstances may suggest a tiered approach to formulating a site- or issue specific assessments. The simplest approach (Tier 1) would be an initial screening-type evaluation of risks using conservative default exposure parameter estimates and comparison with published health-based guidelines. Tier II involves more modelling, more data and a deeper understanding of what is going on. It evaluates the risks involved. It works in terms of calculations and considers parameters and data sets. Tier III is much more complex, and studies at this level may take years. They can involve personal monitors, as when people observe their individual exposure to a hazard under study (EnHealth, 2012). They can involve an infinite amount of detail and can be probabilistic, like Monte Carlo simulations. Tier three assessments are rare, partly because any risk assessment tends to move gradually from tier 1; but also because if tier 1 indicates a risk is acceptable, there is no point moving to tier II. Tier 2 and Tier 3 processes would involve collecting additional exposure data and a more detailed analysis of dose–response data, possibly including calculation of target tissue doses or translating animal doses into human-equivalent dose estimates. The tiered approach in risk assessment is common in many jurisdictions, although the number of tiers and their precise usage may differ. Figure 3 is a schematic depiction of some of the elements that might comprise Tier 1 to Tier 3

A tier two assessment is applied in this study, assuming that concentrations and ship stacks are port-wide, and their final calculations are validated with available guidelines.

The identification of concentrations and their risk to the population around Port of Brisbane were carried out with regard to existing sensitive receptors. In formulating the scope of the problem, the chemicals to focus on and their sources, we decided to follow the pathway that connects the sources and receptors in a risk scenario: inhalation. A complete source–pathway–receptor chain is shown in Figure 4.

CALPUFF is an advanced dispersion modelling for long-range transport (source-receptor distances of 50 to several hundred kilometres) of emissions from point, volume, area, and line sources. CALPUFF produces files of hourly concentrations of ambient concentrations for each modelled species, wet deposition fluxes, dry deposition fluxes, and for visibility applications, extinction coefficients.

The aim of the present section is to discuss the state of the art of the study of the downwash phenomena of emission pollutants. Accounting for the downwash of pollutant dispersion is of interest because it can contribute to the prevention of dangerous situations by determining in advance what configuration of buildings, stacks, and effluents could cause a high concentration of harmful effluents in a particular area. Recent and less recent studies concerning both building and stack downwash are presented. The presented models in this study are well established and implemented in regulatory air pollutions codes, while other ones are more sophisticated and still under development.

Downwash calculations are derived from first principle equations suggested by Briggs (Briggs, 1974) on an empirical downward shift of the virtual plume origin. This downwash correction is still used by most regulatory plume rise models. It is recommended that 1-hour averages are used when possible (or 24 hours of PM) to assess risk. In this case, interest is in the maximum concentration of the emissions under consideration that might affect someone on a ship. Building a downwash algorithm requires a cross-sectional area to be chosen and its dimensions combined with the velocity assumption: that the mass emission rate from the chimney equals the mass emission rate in downwash carry; this works on the principal of the conservation of mass. All that remains is to choose the downwash area and downwash velocity, to which to apply the algorithm.

The stack outlet area should be perpendicular to the direction of travel. If it is within a certain distance of the chimney (and if the ship is within the wake region), we assume that a certain percentage of concentrations is dragged into the wake. This gives the rules for choosing the area and velocity; the calculations may then be made. In theory, if there is a very high chimney (typically more than 2.5 times the height of surrounding buildings in the port area), concentrations will not be dragged down at all; or if the plume rises very quickly, then nothing will be dragged down. A plume may rise quickly if it has higher velocity than a low wind, or if there is high momentum associated with temperature. A reasonable assumption is that there is an area equal to the cavity area and that the plume area equals the cavity area. This leads to assuming a 100% downwash option, which has been considered in this study. The dimension of the cavity is called the cross stream width: it is roughly the width of the building and the height of the cavity. The height of the cavity is taken usually around a factor of three: three × two, or three × the building height. The specific empirical values depend on the shape of the building, which in this study is the area that the crew occupy on the ship.

Two types of downwash can be considered: the cavity (or recirculating flow region), which downwashes off the ship into the ocean and a stack tip downwash. A cavity module calculates the fraction of plume mass captured by and recirculated within the near wake. This is not considered in this study because this is an area in which no crew could have been noticed. The stack tip downwash, however, which occurs when the velocity of the stack is divided by the free stream velocity average (Briggs, 1974), has been considered in this study (Table 1).

As shown in the table, no stack tip downwash occurred during the time of this study because of the high stack outlet velocity and low reference wind speed. This means the people on the ship were not affected by either kind of downwash.

There is, however, another way that crew may be affected. Depending on the rate of plume rise, the cavity may extend or intercept the plume. A portion of that plume will then be down washed into the cavity area and envelop the depth of the ship so it covers the cavity of the ship, not just the stack tip. This will be deflected around the ship, but at some point a separation occurs and instead of following the contour of the ship, the flow separates and an eddy develops that may affect people aboard. As ships are designed to have velocities high enough to counter this, the enhanced turbulence effect is ignored in this study, which assumes the stacks are above the wake region. The effect is part of the screening assessment level, but it is a conservative, high estimation, and its application to ships is very different than to buildings because of their different geometrical shapes. The nature of a ship is that it is streamlined to flow through water, and the average wind speed in the cavity by definition is less in the downwash zone, resulting in a softer effect. The eddy, the down-wash cavity, will result in a lower concentration effect that may be better investigated in the near-field and air-shed area scenarios (Sections 3.2 and 3.3), because in the case of the eddy it is not the concentrated plume that matters but mixture of the plume and the entire wake; in this scenario, this is a small number that can be ignored.

The Gaussian plume model is considered as a valuable tool in predictions of the atmospheric transport of concentrations in the risk assessments. The dispersion of airborne concentrations by wind is a very complex phenomenon. In the real world, the irregular nature of the land surface complicates these problems immensely. There is, therefore, no general complete formula expressing the physical relationship between ambient concentrations of air pollution and the causative meteorological factors and processes. The Gaussian plume model is the most widely used model for air pollution dispersion.

Therefore, the distribution of near-field plume conforms to a Gaussian distribution. The expression is then a function of the height of the stack and buoyancy and emission velocity, which describes the shape of the plume in the nearest port areas. This study is a screening-level assessment, helping to find out about concentrations, to find their risk values and assess them, and to analyse how different variables may influence outcomes. This is done by selecting particular locations and heights at which to intersect the pure plume and provide the calculations for the concentrations under study, and to analyse their effect on people living in the port. These concentrations may dilute in a certain amount of air.

The four input variables for a Gaussian distribution are emission rate, emission velocity, emission temperature and wind speed. Other inputs such as surface roughness are ignored as this study does not consider emissions being spread over the countryside; it is looking at relative changes, such as how variance that affects the final risk values. For a Gaussian distribution there is no highly turbulent atmosphere, and neutral and stable atmospheric conditions are assumed in a generalised model.

Plume rise equations (Briggs, 1974) under neutral conditions are a function of the buoyancy of the plume and the exit velocity (Table 2). This includes both a simple plume that is stable, and an unstable plume that is meandering. The stable plume continues to be the same size and retains high concentration until it reaches instability; it then mixes vertically. Meandering plumes mix horizontally. Where the spread in the vertical direction and the horizontal direction of the test plume smoke is neutral, there is no change in height or temperature. Different stabilities, then, explain variable particulars. For example, D neutral means that it is adiabatic, which in atmospheric terms means there is no energy exchange between the various heights.

In near-field assessments, emissions estimates take into account the mass of emissions as well as the time and duration of their release (Isakov, 2004). Applying onsite turbulent velocities and winds as inputs returns adequate estimated measurements of concentrations (Isakov, 2004). This study collected data on emission patterns created in the vicinity of the source, and the results suggest that traditional dispersion models, commonly used for regulatory applications, generally underestimate the lower ranges of pollutant concentrations but overestimate concentrations in the near field (Venkatram, 2004). Studies also indicate that the PRIME algorithm, used to calculate dispersion in the wake cavity, neglects upwind meandering and overestimates pollutant concentrations in the near field (Isakov, 2004). Some studies (Barclay, 2013) offer an algorithm available in CALPUFF, a new regulatory dispersion model, that may more correctly calculate concentrations by accounting for upwind meandering near a source; however, CALPUFF cannot provide reliable near-field concentration estimates from sources responsible for urban emission if plume spread is measured using estimates of turbulent velocity close to a source (Carotenuto, 2018). This study, designed to develop refined modelling approaches for near-field regulatory applications, used a case study approach, and the evaluation of its result, showed a good agreement with the literature.

Guidelines from Australia’s National Environmental Management Plan – NEMP: the most relevant locally (Department of the Environment, 2016) and from the World Health Organisation (most applicable on a global scale) (WHO regional office for Europe, 2000) were applied to validate the results of this study against them (Tables 17, 18).

Following the recognition of WHO that protecting the environment benefits human health, this study focused on the ecological effects of SOx and NOx. A number of other atmospheric contaminants are known to have ecological effects including PM, but effective approaches to measuring them have not yet been developed.

Since the publication of Air Quality Guidelines for Europe in 1987 (WHO regional office for Europe, 1987), emissions from SO2 have fallen in many areas and it is no longer viewed as the direct danger it once was. However, it has a significant effect on plant life, with very low concentrations impacting on growth and yield and making plants more susceptible to other types of environmental stress (Bare, 2008). These new WHO data confirm the annual guideline value of 30 μg/m3 as an annual mean concentration; the measurement of SO2 in this study is much lower. However, this is an annual average; it is recommended that in winter the concentration should be lower, as its effect on winter crops is particularly severe. It has also been noted by WHO that an average daily guideline is not particularly useful as the cumulative effect is a more significant impact on plants; a guideline of 20 μg/m3 is now recommended (Mcleod, 1995). The level of SO2 in this study is lower than the critical range, and attention is mainly on the direct effects of exposure between one hour and one year long.

While there is an established guideline for acceptable levels of SO2, defining critical levels for NO, NO2 and NH3 is not so simple: these are the dominant forms of nitrogen deposition in many parts of the world according to WHO, but they have several important effects that are not adequately considered by the critical loads for nitrogen or acidity, which have been based on the physiological and ecological effects on plants (Camargo, 2005), not biochemical changes. While the current survey considers that, ecologically, both stimulation and reduction in growth are negative responses to pollutants, there is a need to understand more about long-term biochemical impacts on plants. Although a case can be made for the provision of critical levels for short-term exposure, there are insufficient data to establish what these should be. A value of about 75 μg/m3 for NOx as a 24-hour mean has been suggested by WHO; the criteria in this study are much lower. Interactive effects involving NO2 and SO2 or ozone have also been reported (Adaros, 1991, Cape, 1991, Caporn, 1994, Ito, 1984, Van de geijn, 1993), but a review of recent literature reveals that the lowest effective levels for NO2 are approximately equal to those for combination effects. Measuring critical levels for a full year may cover relatively long-term effects. This annual level for NOx is 30 μg/m3; the criteria in this study are much lower.

Particulate matter is of great concern because it is carcinogenic and disrupts endocrine activity (Davis, 1993). This study has calculated the risk of exposure through inhalation using a probabilistic risk assessment equation. For adults, the calculated risk of cancer suggests that risk via inhalation is 10e-6; it is the same for children. As the individual risk via inhalation individually (2.34e-04) is more than the recommended risk, the risk of developing cancer from inhaling particles is not negligible (Jiang, 2014, Wang, 2011). In the present study, the sequence of cancer risk was industrial sites > busy traffic sites > sensitive sites > residential sites.

Although inhalation accounts for a small percentage of the total intake of concentrations, the potential for additive or synergistic effects between fine particles and toxic organic compounds can increase the risk of developing lung cancer (Ma, 2014). Long-term studies are needed to better understand the sources of airborne fine particular matter, and may lead to more targeted and effective regulations to improve the quality of air in Australian ports (Ma, 2014).

To the best of our knowledge, this is the first comprehensive report describing the concentration, distribution, sources, and health risk assessments of primary emissions in Australian ports. There are a number of areas in which further research could be conducted. One is that since shipping exhaust emissions are the major source of pollution in ports, there is a need for further analysis using finer fractions of atmospheric aerosols. Source apportionments as well as dispersion modelling with higher number of samples can be performed to provide more information about emission sources and distribution. In addition, there is a need to conduct further research to characterise better those parameters that contribute most significantly to the risk estimates.

Further study may reduce the uncertainties in current assessments of the effect of exposure to air emissions, to better direct efforts to prevent exposure and to address the limitations identified in this risk assessment (McKenzie, 2012). Further work may model short- and longer-term exposures and collect relevant data by area, residence, and personal exposure, with emphasis on peak short-term emissions. There is also a need to examine the toxicity of hydrocarbons, such as alkanes, and the health effects of their mixtures and other air pollutants associated with primary emissions.

 


 

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