Delft University of Technology
FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING
Department Maritime and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
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Specialization: Transport Engineering and Logistics
Report number: 2015.TEL.7922.
Title:
Air emission modeling during bulk
handling and storage
Author:
A. Nuur
Title (in Dutch) Lucht emissie tijdens bulk handelingen en opslag
Assignment: Literature Confidential: no
Initiator (university): Dr.ir. D. Schott
Delft University of Technology
FACULTY OF MECHANICAL, MARITIME AND MATERIALS ENGINEERING
Department of Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Student: A. Nuur Assignment type: Literature
Supervisor (TUD): Dr. ir. D.L. Schott Creditpoints (EC): 10
Supervisor (Company) None Specialization: TEL
Report number: 2015.TEL.7922. Confidential: No
until 2, 17, 2015
Subject: Air emission modeling during bulk handling and storage
Subject: Air emissions during bulk handling and storage
An ongoing topic for the bulk handling industry is the amount of material that is emitted to the air. From a health point of view the interest is mainly on PM10 (particulate matter <10μm). From a business economics point of view the total amount of material loss is of interest as shown by Oscar Maan (reportnr 2007.TEL.7165).
Our Dutch government asks the companies to use the so-called IPO Luchtkwaliteitstoets to estimate the expected material emissions. This is mainly based on (local) Dutch research mostly carried out before 2000. We are not the only country that has to deal with limiting air emissions, after all we need to act accordingly to the decisions of the European Union.
An open question to the air emissions models is: how well are the models validated? Or in other words, to what extent can a set of single particles represent the behaviour of a particle system (in interaction with equipment).
Your assignment is to investigate and make an overview of the literature about determining and estimating air emissions during bulk handling operations. That comprises amongst others the following:
What has been published internationally in this field of interest?
Describe how the models work and which parameters are taken into account.
Classify and make an overview of emission models and measurements.
Critically evaluate the found literature.
It is expected that you conclude with a recommendation for further research based on the results of this study.
The report should comply with the guidelines of the section. Details can be found on blackboard.
The professor,
3
Summary
Bulk handling industries want to figure how much material they emit into the air. This is a trending topic. Sharper regulations by governments is limiting these industries, governments use several air quality indexes to expect the material emissions by these companies. Companies want to know how much they pollute comply with these air quality indexes.
In chapter two the different materials are described for the emission models. Here the different particulate matters PM2.5 and PM10 are defined and their characteristics. In chapter three the different models such as the dispersion model, POEM-PM model and atmospheric tracer technique are described, evaluated and their results are also discussed. In chapter four a method of determining the emission of a bulk terminal and its dispersion is introduced by using Grimm cameras and the CALPUFF dispersion model.
It was concluded that Atmospheric tracer technique only follows a certain path, for the truck operations the atmospheric tracer technique can be a used, but not for the other operations like loading and unloading.
The POEM-PM model was capable to reproduce some measurements, but the validation of the main pollutant estimates weren’t available. Further analyses for the validation is needed to use this model.
The dispersion model CALPUFF used with source monitors such as Grimm cameras can measure the live emission from accurately any source. With meteorological data form CALMET it can give accurate dispersion models. CALPUFF is the mainly used reliable model by the Environmental Protection Agency.
4
Contents
Summary ... 3
1. Introduction ... 5
2. Material description for emission models ... 6
2.1 Particulate matter ... 6
2.2 Material characteristics ... 9
2.2.1 Coal ... 9
2.2.2 Iron ore ... 12
2.3 Health effects ... 12
3. Air emission models ... 14
3.1 The Dispersion model ... 14
3.1.1 Puff vs Plume models ... 14
3.2 CALPUFF ... 15
3.2.1 Validation ... 16
3.2.3 Results and evaluation ... 17
3.3 POEM-PM ... 20
3.3.1 The methodology ... 20
3.3.2 Model Description ... 21
3.3.3 Model evaluation ... 24
3.4 Atmospheric tracer technique ... 26
3.4.1 The methodology ... 26
3.4.2 Model Description ... 27
3.4.3 Model evaluation ... 28
3.4.4 Model results ... 28
3.5 Summary ... 29
4. Emission rates during bulk handling and storage ... 30
4.1 Processes during bulk handling and storage ... 30
4.1.2 Methodology of estimating the emission rates of these processes ... 30
4.1.2 Estimation emissions of traffic on dusty roads ... 32
4.1.3 Validation with emission monitors ... 33
4.2 Numerical values bulk terminal ... 34
5. Conclusions ... 35
5
1. Introduction
Air emission is a delicate subject nowadays. An ongoing topic in the bulk handling industry is how much material is emitted into the air. Governments use several air quality indexes to predict the material emissions. Industries are limited in what they emit and pollute into the environment due to the rules and regulations by the governmental institutions1.
Goal of this research is which emission models are available in literature to estimate the emission of pollutants into the atmosphere and how well can these be applied for bulk handling and storage2.
In chapter two, the material for the emission models is discussed. Here the different particulate matters are defined and their characteristics. In chapter three the different models such as the dispersion mode, POEM-PM model and atmospheric tracer technique are described, evaluated and their results are also discussed if applicable. In chapter four a method of determining the emission of a bulk terminal and its dispersion is introduced. In the final section a conclusion and recommendation is.
1 http:\\www3.epa.gov
6
2. Material description for emission models
In this chapter the definition of particulate matter is given for the emission models. Here the different particulate matters and their characteristics are defined.
2.1 Particulate matter
Pollutants that are floating in the air, which are a mixture of solid particles and liquid droplets and are hardly traceable by the eye, are mostly a risk for people’s health. This sort of air pollution is called particulate matter (PM), it comes in various sizes, shapes and can be a composition of different materials and chemicals. The smaller the particle, the easier it can be inhaled and this can have a negative side effect on the health3.
In fig.1 the size distribution of airborne particles PM types are shown, also the most common types for those PM levels are described. Fine particles are mainly made by combustion or supersaturated (chemical solution as result of cooling from a higher temperature which increases the concentration of a solution beyond saturation) conditions, but on the other hand coarse particles mostly come from natural causes.
3 http:\\www3.epa.gov
7 pm (µm) Fig.1. Airborne particulate size chart (Jisaac, 2010)
Super coarse particles up to 100 micrometers in diameter are called PM100. These do not cause health risks but are inhalable4.
Mostly they are:
- insect debris, room dust, soot aggregates, coarse sand, gravel, and sea spray The cause:
- nature
Coarse particles are between 2.5 and 10 micrometers in diameter, these particles are called PM10, which means particulate matter up to 10 micrometers in size. These particles cause less severe health effects, because they mostly get stuck or wedged in the narrow passages of the lungs and do not travel further into the lungs5.
Mostly they are:
- Smoke, dirt and dust from factories, farming, and roads, - mold, spores, and pollen6.
4 http://www.engineeringtoolbox.com/particle-sizes-d_934.html 5 http://www.engineeringtoolbox.com/particle-sizes-d_934.html 6 http://www.engineeringtoolbox.com/particle-sizes-d_934.html
8 The cause:
- Crushing and grinding rocks and soil then blown by wind - nature
PM10 particles can stay in the air for minutes or hours and can a reach a distance as little as 27.4 meters up to 1.6 kilometers7.
Fine particles which are smaller than 2.5 micrometers in diameter are called PM2.5, which means particulate matter up to 2.5 micrometers in size. These cause severe health problems because PM2.5 particles can easily pass through the smaller airways and can so get deeper into the lungs.
Mostly they are:
- toxic organic compounds - heavy metals
The cause:
- driving automobiles
- burning plants (brush fires and forest fires or yard waste) - smelting (purifying) and processing metals
PM2.5 particles can stay in the air for days or even weeks and can a reach a distance of many hundreds of kilometers8.
The United States of environmental protection agency (EPA) classifies two types of PM, PM2.5 and PM10. In fig.2 the size distribution of airborne particles PM types are shown, also description of types they mainly are for those PM levels is also given. The particulate matter size distribution follows a bimodal distribution (Fig.2) (McKendry et al,2005a).
Fig.2. Particulate matter distribution (McKendry et al,2005a).
7 https://webcms.pima.gov/ 8 https://webcms.pima.gov/
9
2.2 Material characteristics
In this section the characteristics of coal and iron ore are described, the reason here for is that these two are mostly common materials during bulk handling and storage.
2.2.1 Coal
Coal is classified in two ranks; high rank and low rank coals9. This rank is based on the carbon content and the moisture content. The higher the carbon content the lower the moisture rate, thus the higher the coal’s ranking fig.3. This describes that the coal type gives more energy, and is cleaner to use compared to a low ranking coal type. There are four types of coal these are as follows: anthracite, bituminous coal, subbituminous coal, and lignite10.
Lignite coal
Lignite coal is mostly used for generating electric power. This type of coal is brownish/black of colour, has a high moisture content up to 45%, consists of around 25% to 35% carbon with a heating value less than 5 kw/kg. This coal type has the lowest energy content of the four types.
Subbituminous coal
Subbituminous coal is also mainly used for generating electric power and heating. It has a dull black colour and a moisture rate of 20 to 30 %. The carbon content of subbituminous coal is 35% to 45% and has a heating value of approximately 5 - 6.8 kW/kg.
Bituminous coal
Bituminous coal also called soft coal, is a dense black coal and is the most common found over the world. It has a moisture content less than 20 % and a Bituminous coal is also used for generating electric power and heating. Bituminous coal has heating values ranging approximately from 6.8 - 9 kW/kg.
Anthracite coal
Anthracite coal also called hard coal is a hard black coal. It has a low sulphur rate and has the highest carbon content which is around 86% to 97%. Its moisture content is generally less than 15%. This is what makes Anthracite the highest ranking of coal. This coal type is mostly used for domestic
purposes. Anthracite has a heating value of approximately 9 kW/kg.
Coal can come in all kind of different kind of forms. These are granular, pulverized, dust, powder and raw forms. The density of the different coal types varies and is as follows11:
Anthracite Coal: 800 - 929 (kg/m3)
Bituminous Coal: 673 - 913 (kg/m3)
Lignite Coal: 641 - 865 (kg/m3)
The density of subbituminous coal lies between the density values of lignite and bituminous coal.
9 http://www.worldcoal.org/ 10 http://www.utahmining.org/
10 Fig.3. Coal ranking and types of coal (http://www.worldcoal.org/)
Fig.4 shows the particle size distribution of coal samples collected from different processing plants in Illinois and West Virginia from the paper by (Mohanty et al, 2013). Bituminous coal was mined from the Illinois sample, with a moisture content of 18% - 22% and ash content of 11% on a dry basis. The second sample mined from West Virginia is coke coal with a moisture content of 24.81%. Here can be seen that the Illinois coal has an approximate top size of 2 mm and a d80 size of 0.83 mm. The West Virginia metallurgical coal has a top size of 0.5 mm and d80 size of 0.22 mm. When compared the West Virginia coal is finer compare to the coal from Illinois. This particle size distribution does not contain any PM2.5 and PM10 sized particles. These are respectively 0% in the cumulative passing axis.
11 Fig.4. Particle size distribution of coal samples used in this study (Mohanty et al, 2013)
Fig.4 did not give any information about PM2.5 an PM10 particles. Fig.5 shows information of the desired particle sizes which are important for the emission modeling. Particle size distribution of a falcon feeder handling ground coal sample is given. Here can be seen that the cumulative passing percentage is around 30% for PM2.5 and PM10 combined. This is a significant large amount.
12
2.2.2 Iron ore
Iron ore are usually found in these forms; magnetite (Fe3O4), hematite (Fe2O3), goethite (FeO(OH), limonite (FeO(OH).n(H2O)) or siderite (FeCO3). The prominent iron ores are hematite with 70% iron content and magnetite with 72% iron content. Iron ore has a density of 2595 kgm-3.
Hematite
Hematite is a mineral that consists of iron oxide. Its colour variations ranges from steel silver to reddish brown. Hematite is considered pure when it at least contains 69.9% iron. When hematite carries an iron content greater than 62%, it is called DSO (direct shipping ore). This means that they can directly be fed into iron making furnaces.
Magnetite
Magnetite is also a mineral consisting of iron oxide, it is mostly black of colour black and highly
magnetic compared to hematite which is nonmagnetic. Magnetite has an iron content of 72.4%, which is higher than hematite, but due to impurities this will result in lower grade, making it costlier to produce concentrate for steel smelters.
Fig.6. Particle size distribution of ‘as received’ iron ore concentrate (P. Mbele, 2012)
In fig.6 the particle size distribution of iron ore concentrate from the Sishen mine ‘as received’ is given. These results were conducted with a laser diffraction size analyzer. Here can be seen that the cumulative passing percentage is around 20% for PM2.5 and PM10 combined.
2.3 Health effects
PM10 and PM2.5 can cause respiratory health problems. PM2.5 consists mainly of toxic materials (cancer causing organic compounds and heavy metals), it can travel deeper into the lungs. It has a broader reach and can stay longer in the air compared to PM10 particles. Therefore, particles with a diameter with 2.5 micrometer or less are more hazardous and can cause severe health risks. Most common health effects due to emitted particular matter into atmosphere are:
- Coughing, wheezing, shortness of breath - Aggravated asthma
- Lung damage (including decreased lung function and lifelong respiratory disease) - Premature death in individuals with existing heart or lung diseases
13 To describe the risks of air pollution for the health, the EPA uses an (AQI) air quality index12. The higher the AQI-level, the greater the risk of air pollution. An AQI level of 100 is considered to be moderate and less than 100 is considered satisfactory. The higher the value, the greater the air pollution, thus greater health risks. These are described in Table 1. The Meaning of the different colors are described in Table 2. The AQI-index also describes which groups are sensitive to which level of AQI-index.
Table 1
Air quality index (https://www.airnow.gov/)
Table 2
Colors Air quality index (https://www.airnow.gov/))
12 https://www.airnow.gov/)
14
3. Air emission models
In this chapter three air emission models are discussed. These were the only papers found in
literature which use calculation models to estimate emissions. The dispersion model, POEM-PM model and the atmospheric tracer technique. The different models are also evaluated and their results are also discussed if they are available.
3.1 The Dispersion model
The dispersion model is a simulation based on Gaussian and/or Lagrangian mathematical models which calculates the emission and disperse of air pollutants. The most common is the Gaussian-plume model (Perry et al, 1989). Plume models move in a straight downwind direction. A more advanced dispersion models is the puff model; this model uses a Lagrangian trajectory which emits puffs in small units (Lagzi et al, 2013)
A computer program which uses puff models solves the mathematical equations and algorithms. This model specifically the CALLPUFF dispersion model is used by the EPA to predict or estimate the air pollutants in the downwind direction emitted from various sources.
3.1.1 Puff vs Plume models
The Gaussian plume model is derived by steady-state conditions. Therefore, the Gaussian plume model does not depend on time, but it does represent a collection of time averages. It is as follows:
where c is a concentration at a given position, Q is the source term, x is the downwind, y is the crosswind and z is the vertical direction and u is the wind speed at the h height of the release. The σy, σz deviations describe the crosswind and vertical mixing of the pollutant.
Puff models in contrary are an intersection between Gaussian and Lagrangian mathematical models, is derived by non-steady conditions and has less limitations. These have the following advantages over the plume model, they can be modelled for larger transport distances, they can take into account temporal and spatial wind changes and have a more valid approach due the fact that plume models use straight-line steady state assumptions which are not always valid in most cases13.
Puff models hold the assumption that the concentration pattern can be defined with a Gaussian distribution; however, the centerline of a plume is not a straight downwind direction, but a Lagrangian trajectory (figure 7). For this reason, puff models compared to plume models can take into account temporal and spatial wind changes. They split the released mass of pollutants into small units, “puffs”, and then calculate the trajectories of all the puffs in a Lagrangian method, but keep a Gaussian concentration pattern inside each puff. The final concentration field is given as a superposition of all the puffs’ concentration distributions:
c is the ground level concentration in (g/m³)
is the pollutant mass in the puff from source (g) N is the number of puffs
(xk, yk, zk) is the position of the kth puff in (m)
13 http://www.weblakes.com/
15 σxk is the x-directional wind standard deviation of the Normal distribution (m)
σyk is the y-directional wind standard deviation of the Normal distribution (m) σzk is the z-directional wind standard deviation of the Normal distribution (m)
Taking a time-dependent emission rate in Q multiplied with the release frequency of puffs, the temporal variability of the source can be represented in the model (Lagzi et al, 2013).
Fig.7. Puff vs. plume (Lagzi et al, Atmospheric Chemistry, 2013)
3.2 CALPUFF
Main used dispersion model by the EPA is the CALPUFF model. The CALPUFF model consists of three components: CALPUFF, CALMET AND CALPOT. CALPUFF is a dispersion model see (Puff models), CALMET is a meteorological model that develops hourly wind and temperature fields in a 3-D grid domain, it also includes 2-D fields like mixing heights, surface characteristics and dispersion
properties. Finally CALPOST is used to process these files. In figure 4 the system module of CALPUFF model is shown (Scire et al, 2000).
16 Fig.8. Steps CALLPUFF model (Scire et al, A user guide for the CALPUFF dispersion model, 2000).
The emission inventory (not shown in figure 8) is also an input to the CALLPUFF. The emission inventory is the data collected from different source terms that are relevant for the desired air quality testing. These can be guidelines that can be found in on the EPA website or actual emission rate measurements from the desired source term.
3.2.1 Validation
The results of the CALLPUFF model are compared to an actual air concentration measurement to validate it. In the paper by (Y. Song et al, 2006), this CALLPUFF model is used for the estimation of the dispersion of pollutants in Beijing. In table 3 the air quality concentration PM10 measurements in urban Beijing from November 1999 to March 2000 is shown.
Table 3
The 24-h PM10 concentrations, in mgm-3, averaged over seven stations in urban Beijing from November 1999 to March 2000 (Y. Song et al, 2006).
For the CALMET a 50 x 50 grid is used with a 2km horizontal-resolution. Ten vertical layers were located 3000m and 20m. The United States Geological Survey (UGS) global 30- data was used. A maximum elevation of 200m above sea level was used, because the city eastern and northern sides border the Yan Mountain. Most densely populated part of the city is located Within the Northern China plain, with elevations of 1000m see fig. 9. Data was collected from 20 surfaces stations, within the
17 modeling plain there were 13 and sounding profile data (measured twice daily, at 0700 LST and 1100 LST) were used in the CALMET.
For the Emission inventory a PM10 emission survey was used that was done by the Beijing
Environment Protection Bureau (BJEPB) from 2000 to 2001. For the determination of the stationary emissions, the emission consumption an emission factor was used provided by the Ministry of Science and Technology. Motor vehicle emissions were estimated according to the EPA-42.
Fig.9. Terrain surrounding Beijing. The domain inside the dashed lines is the modeling domain. The dots and the rectangle indicate surface stations and the sounding site, respectively. (Y. Song et al, 2006)
3.2.3 Results and evaluation
The observed measurements of the four different regions compared to the CALPUFF model matched well shown in figure 10.
18 Fig.10. Observed and modeled PM10 concentrations (mgm-3) in Qianmen (QM; a), Nongzhanguan (NZG; b), Gucheng (GC; c), and Chegongzhuang (CGZ; d) from 1 January 2000 to 29 February 2000 (Y. Song et al, 2006).
These results are evaluated with performance measures, Willmott (1982) and Seigneur et al. (2000) uses statistical performance measures that have been applied (Barna and Gimson, 2002; Zhang et al. 2004). The performance of the CALPUFF is evaluated using statistics calculated from the observed measurements compared to the modelled measurements PM10 time series in the four different regions. All calculations are paired in space and time.
The statistics that are used to measure the performance are the following; the fractional gross error (FGE), fractional bias (FB), geometric mean bias (MG), correlation coefficient (R), Willmott’s index of agreement (D) and fraction of predictions within a factor of two of observations (FAC2). For a perfect model the score would be 0.0 for FGE and FB, 1.0 for MG, R and D 100% and 100% for FAC2. Acceptable models approach these values. The evaluation of these are shown in table 4.
19 Table 4
Statistical measures of model performance for the four observational Sites: Qianmen (QM), Nongzhanguan (NZG), Gucheng(GC), and Chegongzhuang (CGZ) (Y.Song et al, 2006).
Here we can see that the values of the model are acceptable to a degree compared to the monitored observations. At the CGZ district the values are not so in line with the observations. In
Chegongzhuang significant high ratios of calcium an aluminum has been found since July 1999 (He et al, 2001). This indicates that there was a considerable wind dust from buildings. The PM10
measurements included probably some of these wind dusts but were not specifically estimated in the model, it was possibly an important factor in the emission inventory as reflected by the
underestimates of the modeling results in the beginning of February. The algorithm has a few problems with low wind speeds, this can be seen on of 5 February (fig. 10(d)). The peaks are
underestimated in that period (figs.10(a-d)). The measured wind speeds and temperatures are shown in fig.11.
Fig.11. Passages of cold fronts, denoted by ellipses, from 1 January 2000 to 29 February 2000. The temperatures were measured twice daily at 0800 and 2000 LST (Y. Song et al, 2006).
20
3.3 POEM-PM
The POEM-PM model (Pollutant Emission Model for gas and Particulate Matter) (Carnevale et al, 2005) in frame with the GAMES (Gas Aerosol Modelling Evaluation System, estimates emissions at local and mesoscale level. It uses a combined top-down and bottom-up method. The model is validated by using simulations over a period of time and compared to the observed emission rates.
3.3.1 The methodology
POEM-PM uses an integrated top-down and bottom-up approach to produce actual and alternative emission fields. It sorts emission data according to CORINAIR-SNAP (European environment agency, 1996) and includes diffuse point source emission data. To estimate emission fields disaggregating a large space time scale inventory, processing surrogate variables, highly correlated with emission and the defined by means of national and local statistical sources, and GIS and land use information the top-down approach is used. Surrogate variables for the temporal modulation of the emission activities are described in table 5, the main indicators are temperature, working time, fuel use, traffic counts, road statistics, degree-days and production cycle.
Table 5
Surrogate variables for space disaggregation and time modulation (Carnevale et al, 2005)
The method splits the national CORINAIR inventory data, with CH4, NH3, NOx, N2O, CO, CO2, NMVOC, TSP and SOx, yearly contributions of more than 250 activity categories at NUTS3 level (province).
The procedure of the disaggregation is divided into four steps:
- Spatial disaggregation into municipal areas and a grid domain; - Hourly modulation;
- Splitting of total NMVOC into components; - Chemical and granulometric splitting of PM.
21 Fig.12. Top-down approach steps overview and allowed model outputs (Carnevale et al, 2005). In fig.12 the POEM-PM’s main steps, allowable model outputs are presented. The future scenarios can be built up using the bottom-up approach depending on the selected activities, along inventorying the activities of the pollutants and applying the emission factors.
3.3.2 Model Description
The model’s algorithm is described in this part to treat the diffuse emissions. With the top-down or bottom-up approach the emissions due to each CORINAIR category can be calculated.
Fig.13. Schematic representation of the input and output data for POEM-PM model (Carnevale et al, 2005).
POEM-PM is made up by two segments fig.13 Pre-Poem provides space disaggregation, time
modulation, NMVOC splitting coefficients by using national and local statistical sources, GIS and land use data. The second segment computes the hourly emissions spatially resolved on a municipality level or gridded domain. Photochemical transport models are used and the NMVOC are split into SAROAD (US Environmental Protection Agency, 1991a) classification of compounds and lumped into emission classes.
22 First step is the spatial disaggregation matrix estimation. These rows represent the province
municipalities, the columns represent the different pollutant activities featuring the SNAP (distribution coefficient). The matrix is as followed:
where sdmc,a is the spatial disaggregation value, sc,a is the proxy variable related with the activity a, and rc,a is the distribution coefficient; the three coefficients state to the municipality c and to pollutant activity a. Spatial and temporal disaggregation of the pollutant activity’s temporal cycle need to be made uniform by means of normalization, thus the space-time normalization coefficient is defined as:
where 𝛾sa is the spatial normalization factor and 𝛾ta is the temporal normalization factor. The emission of pollutant i, for municipality c, due to activity a, for time period t, is given by
𝑒𝑖,𝑐,𝑎,𝑡= 𝐸𝑖
𝑠𝑑𝑚𝑐,𝑎𝛾𝑡𝑎
Σ𝑎=1 𝛾𝑠𝑎 𝛾𝑡𝑎
𝑎𝑡𝑡 𝑎𝑏𝑙𝑖,𝑐,𝑎𝑡𝑡,𝑎
where ei,c,a,t is the amount of the emitted pollutant at municipal level, Ei is the yearly provincial emission amount, sdmc,a is the spatial disaggregation value, gta is the temporal normalization factor, abli,c,a is the reduction pollutant coefficient, referring to municipality c, due to pollutant activity a, Tt,a is the time coefficient for the period t, and att is the number of the pollutant activities taken into account for each category.
The total emission into the air ascribed to category j, is acquired by summing the contribution of all related activities is:
The municipality emission splitting on grid domains through GIS data is ass followed:
where gi,k,j,t is the emission in the grid cell k, supc,k is the percentage of municipality c, which pertains to cell k, and ck are the municipalities belonging to cell k.
Splitting the total amount of NMVOC into SAROAD classification compounds lumped into emission classes used by the photochemical transport model. The single chemical species emission of NMVOC municipal is written as,
where specv,a represents the percentage value of the species v for pollutant activity a.
To characterize the chemical composition and defining the emitted particles diameter splitting the PM emissions is needed. The contribution to each size chemical classes can be written as:
where ePM,c,a,t is the PM municipal emissions chimv,a and granh,a are respectively the percentage value of particles of species v and h size class diameter for pollutant activity a.
23 The bottom-up methodology consists of source oriented inquiry to obtain all the information needed to define the emission behavior of a single source and this is used to directly compute the emissions. The emission of pollutant i, for municipality c, due to activity a, for time period t, is given by:
where EFi,a is the emission factor associated to activity as for the pollutant i. The space time normalization factor is set here to 1.For Biogenic emissions CORINAIR Guidebook (European Environment Agency (EEA), 2001) uses another equation:
where Ei,b is the hourly emission flux [mg m-2 h-1] and EFi,b is the emission factor [mg g-1 h-1], both defined for pollutant i and due to tree species b, Db is the foliar biomass density [gm-2], and ϒi is an unit less correction factor representing the effects of temperature and solar radiation changes on emissions. The model puts emission fields on the grid domain with GIS data (land use elevation) POEM-PM is has been built such a way that the estimated emissions are consistent with the chemical mechanism used in the chemical and transport model. Photochemical models using the lumped molecule approach as the chemical mechanism, there are two different phases fig.14:
- the initialization phase, here the model makes a set of emissions inputs providing the emission profile of the domain. This profile is evaluated with SAROAD class emission mass and is put in the chemical mechanism algorithm providing the lumping control file. With this file it can be determined how to convert model species into lumped model species, and also to get the kinetic and mechanic parameters for lump species.
- the running phase, here the NMCOC emissions are lumped, split into SARAOD classes, according chemical parameters estimated in the initialization phase.
Fig.14. Overview of relationships between data files used in the preparation of chemical mechanism for a photochemical modelling system (Carnevale et al, 2005).
24 Table 5
Emitted chemical species for SAPRC-97 and CBIV mechanisms (Carnevale et al, 2005).
3.3.3 Model evaluation
The model is evaluated by the GAMES modelling system, the CALMET model, POEM-PM and the photochemical transport model TCAM. The validation is performed in Northern Italy, the GAMES model has been deployed in 2 areas in fig.15:
- The mesoscale of the whole Lombardia Region on the left domain 240 X 232 km2, with a grid of 60 per 58 horizontal cells, with 4 km step size.
- The local scale of the Brescia metropolitan area on the right domain 44 X 44 km2, with a grid of 44 per 44 horizontal cells 1 km step size.
-
Fig.15. Northern Italy modelling domains (Carnevale et al, 2005).
POEM-PM, model estimated the emission fields for the selected domains. The emission compositions are given in table 6 for the mesoscale domain these have been retrieved by using the CORINAIR emission data and 1998 activity information.
25 Table 6
Total amount of pollutants emitted by each source in the Lombardia Region domain [tonnes/year] (Carnevale et al, 2005).
Emitted pollutants are mainly caused by road transport and followed by industrial processes as we can see in the table. When the NUTS3 yearly emissions are spatially distributed and temporally
modulated, and surrogate processing variables are coming from various sources: ENEL (National Electricity Board), MICA (Ministry of the
Industry), ISTAT (National Statistical Institute), ACI (National Automobile Association), Italian Highways Bureau, National Meteorological Service. The NMVOC and PM split profiles are collected from CORINAIR, US-EPA, literature and experimental operation. The results of disaggregation procedure for NOx are presented in fig.16.
Fig.16. Geographical distribution of NOx annual emissions from road traffic: the Northern Italy domain (left) and the Brescia urban domain (right) (Carnevale et al, 2005).
This does not show that the model is validated. So simulations were run for June 1-5, 1998. Data was already in hand (PIPAPO (Neftel et al., 2002), done with SATURN and LOOP sub-projects of
EUROTRAC-2). GAMES was deployed again on the mesoscale and local scale areas of Northern Italy. The results it gave in comparison with the monitored data, it revealed that the system was capable to reproduce the main spatial and temporal features of O3 (ozon) (Gabusi et al., 2002; Decanini and Volta, 2003) and PM distributions (Volta and Finzi, in press; Decanini et al., 2003). This can be seen in fig.17.
26 Fig.17. The observed and simulated ozone concentrations at Brescia–measurements site (June 2–4, 1998) (Carnevale et al, 2005).
3.4 Atmospheric tracer technique
The tracer technique can give accurate estimates of emission rates if the tracer release imitates the fugitive emissions. In the following paper by (Qui et al., 2008) the objectives were determining the spring wheat harvest PM10 emission factor in eastern Canada by using the tracer technique. The findings are evaluated by comparing it with the dispersion model to back calculate PM 10 emissions.
3.4.1 The methodology
The tracer gas (N2O) was released from a point source moving along with a combine, and the ratio of N2O to PM10 concentrations was then used to estimate PM10emission rate from the known release rate of N2O. N2O tracer released in the air that downwind concentrations is related to the
atmospheric dispersion conditions, this is as followed N20 = MQ(N2O)
where Q(N2O) is the N2 release rate (g/s), [N2O] µm-3 is the measured downwind concentrations above background the level of N2O and M is the atmospheric dispersion function. The ratio between N2O concentration to PM 10 concentration can be used to calculate the unknown PM 10 emission rate if the released tracer N2O and particles are released at the same time, from the same source and assuming that N2O spreads in the same way as the particles in the atmosphere, it is as followed
where PM10 is the measured downwind concentrations above background level of PM10. The dispersion model used in this paper is called the industrial source complex model, also
meteorological and unwind and downwind concentrations data were used to gather PM 10 emission rate from wheat harvest. To estimate the lateral and vertical plume dispersion model since Pasquill-Gilford diffusion curves, the solar radiation-delta-t (SRDT) method was used to determine the atmospheric stability classes ( Bowen et al., 1983). More details are described for the ISC3 in ( U.S EPA, 1995b). The 30 minute averaging data once you’ve been that ISC3 model for this study for the measurement-averaging period. PM emissions were created as an important point source.it is based on a similar algorithm by (Reed and Westma, 2005). Instead of predicting the concentration using the forward problem in this case the algorithm as the inverse problem to estimate the emission rate from concentration measurements. PM 10 concentration were calculated as the average of contributions of all point sources at the receptor.
A laser has been measure the N2O concentrations at a sampling rate of 10 Hz. It is connected to the towers. Also particle counter is used and these were also located in the towers. A GPS was used for monitoring the location of the combine during the wheat harvest.
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3.4.2 Model Description
In fig.18 the experimental setup is shown, the combine routes and locations of the N2O and PM10 samplers and the meteorological measurement towers. To simulate PM 10 generations and emissions a point source release was designed. PM10 and N2O samplers were both on the downwind side of the combine route along the dominant wind direction. The PM10 and N2O concentrations were measured from two locations, an east Tower or west tower depending on the dominant wind direction and the combine harvesting location. The N2O release rate was set to 10-30 L min-1. This depends on the distance combine route to the concentration measurement tower. The release rate was manually controlled and average variation of the flow rate was less than ±20%.
Fig.18. Schematic layout of the release experiment during spring wheat harvest (from 13:00 to 15:20, 4th September 2007). The TDL inlet, TEOM and particle counter were co-located at the west tower. The wheat was first harvested far away from the tower, then the combine moved close to the measurement tower. The circles indicate the tower positions and the square the trailer location, hosting the N2O TDL, the sonic anemometer amplifier and the data acquisition system. Geo-referencing is in UTM (m) (Qui et al., 2008).
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3.4.3 Model evaluation
The data that was collected from the tracer method were used to compare it with the ISC3 dispersion model. Only relevant values and desirable conditions were used. In table 7 we can see the values of the emission rates of N2O are estimated by the dispersion model and measured by the tracer
technique. The average error is 31% with a worst case scenario error of 87%. From the tracer release experiment the estimated emission rate has an uncertainty not larger than factor 2, this is almost as same as the uncertainty level when using a dispersion model. The tracer experiments does increase the error in the emission rates, most uncertainty comes from the dispersion model.
Table 7
N2O emission rate (Q, g s-1) measured from the tracer release experiments and estimated using the ISC3 dispersion model ) (Qui et al., 2008).
3.4.4 Model results
In table 8 we can see that results of the of the estimated tracer technique release experiments. We found an average PM 10 emission rate of 0.58±0.12 g/s for wheat harvest, with a consistent emission rates for the whole field and the overall uncertainty was 20.7%. This experiment had an overall error of ±23%.
Table 8
PM10 emission rate (Q(PM10), g s-1) and emission factor (EF, kg ha-1) estimated from tracer release experiments (Qui et al., 2008).
Table 9 shows the results of the PM 10 emission rates using the ISC3 dispersion model. Here the average PM10 emission rate is 0.57±0.12 g/s, with an uncertainty of ±57.9%. The average PM 10 missionary is comparable to the tracer technique but the uncertainty in the version model however is higher compared to the tracer technique. The value of the estimated maximum emission rates is six times higher than the estimated minimum value. This is due to the uncertainty of factor 2 of the Gaussian dispersion model.
29 Table 9
The estimated average PM10 emission rates (Q(PM10), g s-1) using upper and lower limits background concentrations of N2O and PM10 from tracer release experiments (Qui et al., 2008).
3.5 Summary
CALPUFF dispersion model is a non-steady state Langrangian Gaussian puff model which uses emission inventory, CALMET meteorological model and CALPOST to process the files. It can simulate very accurately depending on how well the inputs are entered especially the emission inventory. It is also the main used dispersion model by the EPA.
Poem-pm estimates emissions at local and mesoscale level, it uses an integrated top-down and bottom up produce emission fields. It also uses an emission inventory called CORINAIR and the CALMET meteorological model. In the paper the validation process was not complete. Due to the fact that the validation of the main pollutants were not discussed.
Atmospheric tracer technique uses tracer gas N2O released from a point source moving along a certain path. It uses a ratio of N2O to PM10 concentrations to estimate the known release rate of N2O. When N2O is released in the air, that downwind concentrations is related to the atmospheric dispersion conditions. It uses a meteorological data instead of the CALMET model which is used in the other models. The atmospheric tracer model only moves in a certain path. This limits the usage of this model. Further the difference between the actual emission and the modeled is on average 31% and that is quiet large.
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4. Emission rates during bulk handling and storage
In the paper by (Martin et al, 2006) particles emission from bulk terminals from Spanish harbors have been estimated. The dispersion model and AP-42 by EPA is used. in this chapter, the emission estimations of various processes of handling and transportation are discussed.
4.1 Processes during bulk handling and storage
Different processes were described in this paper:
- loading of ship from dock with crane, trucks unloading over dock and a wheel dozer piling and moving material,
- unloading from ship to a hopper with a crane and hopper to truck, - wheel dozer loading material on trucks,
- traffic on dusty ground.
The estimation of these emissions are described in the next paragraphs.
4.1.2 Methodology of estimating the emission rates of these
processes
Estimation of the three processes of handling the materials the (EPA) AP-42 formulations is used. These are described by the following equation:
Ex = MTH*EFx*(1-ER/100)
Where:
Ex: Emission of contaminant x, kg;
MTH: Annual total material handled, tons; EFx.: Emission factor of contaminant x, kg/tons; ER: Overall emission reduction efficiency, %.
The emission factor (EF) for various drop operations kg/tons of material with a quality rating of A, is based on the empirical expression:
𝐸 = 0.0016𝑘(𝑢 2.2⁄ )
1.3
(𝑀 2⁄ )1.4 where:
E = emission factor, (kg/Tons)
k = particle size multiplier (dimensionless) U = mean wind speed, (m/s)
31 Table 10
Typical silt and moisture contents M of materials at various industries (Table Ap-42 13.2.4-1)
Table 11
The particle size multiplier in the equation, k, varies with aerodynamic particle size range (www.3epa.gov).
It is recommended that estimates from the equation of the emission factor be reduced 1 quality rating level (in letter) if the silt content used in a particular application falls outside the range given:
Table 12
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4.1.2 Estimation emissions of traffic on dusty roads
Estimates of the emission by traffic on dusty grounds the following EPA formula can be used. This is as followed:
Estimation of road dust emissions from unpaved road surfaces:
The yearly emission estimations can be found by means of the following general equation (USEPA, 2006):
Ex = VKT*EFx*(1-ER/100)
Where:
Ex: Emission of contaminant x, kg;
VKT: Annual total vehicle kilometers travelled, km; EFx.: Emission factor of contaminant x, kg/VKT; ER: Overall emission reduction efficiency, %.
The emission factor for unpaved road surfaces is founded on an empirical analysis of testing
information relating the emissions to vehicle characteristics taking considering the silt content of the road and mean mass of the vehicles riding on the road. The VKT represents the kilometers travelled by the vehicles run at the facility on unpaved roads.
VKT and EFx can be found using these equations.
Annual Total Vehicle Kilometers Travelled (VKT):
VKT = Average Daily Traffic x Length of Unpaved Roads x Operating Days/Year
The average daily traffic is the total amount of vehicle passing over the road sections per day.
Emission Factor (EF) for Emissions of TPM, PM10 and PM2.5 from Unpaved Road Surfaces: USEPA has developed an empirical equation for vehicles travelling on unpaved road surfaces at industrial sites (for more details refer to AP 42, Chapter 13: Miscellaneous Sources, Section 2.2, (USEPA, 2006)). The emission factor in metric units (i.e. kilograms/VKT) is calculated by the following equation:
EF = k (s/12)a (W/2.72)b (3) Where:
EF: Size-specific emission factor, kg/VKT; s: Surface material silt content, %; W: Mean vehicle weight, tons (metric); k, a, b: Numerical constants for calculation.
33 Table 13: Numerical constants used in the unpaved industrial road dust emission factor.
Constant PM2.5 PM10 TPM
k (kg/VKT) 0.042 0.423 1.381
A 0.9 0.9 0.7
B 0.45 0.45 0.45
The silt content (i.e. “s”) may be obtained by using the US EPA test method (Appendix C.1:
Procedures for sampling surface/Bulk dust loading, AP-42, US EPA, 2003). However, in the absence of site-specific value for the silt content, an appropriate mean value from Table AP-42 13.2.2-1 (US EPA, 2006) may be used as a default value
4.1.3 Validation with emission monitors
Environmental dust monitors called Grimm are used in the paper by (Martin et al, 2006). These can accurately measure real time emissions particulate matter levels by using 90-degree laser scattering. These are deployed on different locations to measure the emission levels. These are placed downwind to the source fig.19 to get the less errors.
Fig.19. Plot showing the deployment of the three GRIMM monitors (numbered dots) respect to source location (cross).
34 Furthermore, when it is known how much is emitted into the air, the dispersion model can model how these pollutants travel outside the bulk terminal. For the measurements of the fugitive dispersion of emitted pollutants, dispersion model can be used discussed in 3.1, with the weather forecasts. Emission estimations calculated with Ap-42 can also be validated by comparing them to the results of the Grimm monitors.
4.2 Numerical values bulk terminal
The Emo terminal in Rotterdam shared its emission of PM10 values from 2002-2013. In figure 20 can be seen what amount of dust a bulk terminal can emit. In this chart we can see the amount of tons PM10 with the amount of fine dust per manipulated ton. These are measured by devices similar to the Grimm camera.
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5. Conclusions
Goal of this research is which emission models are available in literature to estimate the emission of pollutants into the atmosphere and how well can these be applied for bulk handling and storage. This can be achieved by using the CALPUFF dispersion model.
To measure the air emission levels during bulk handling and storage certain models can be used. Atmospheric tracer technique only follows a certain path due to the crop harvesting. So for the truck operations the atmospheric tracer technique can be a candidate. But it cannot be used for the other operations.
The POEM-PM model was capable to reproduce some measurements, but the validation of the main pollutant estimates weren’t discussed in this paper. So comparable data was not sufficient wasn’t given except for O3. Further analyses for the validation is needed to tell if this model is suitable for bulk handling and storage.
The dispersion model uses the puff models to estimate how the pollutants disperse. Source monitors such as Grimm cameras measure the live emission from accurately any source. These measurements can then be put in the dispersion model called CALPUFF and these measurements can also be used for validating the emission estimates AP-42 by the EPA. With the meteorological model CALMET hourly wind and temperature fields in a 3-D grid domain are created, it also includes 2-D fields like mixing heights, surface characteristics and dispersion properties. Finally CALPOST is used to process these files. CALPUFF is the main used model by the EPA and is reliable depending on what source term measurements/ data is used.
Further research has to be done for the validation process of the emission estimates of the AP-42 by using the measurements of the Grimm monitors. In the paper by (Martin, et al) only the emission factors are calculated with the AP-42. The emissions of the different handling and storage processes have not been calculated and these have not been validated with the Grimm particle monitors.
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