Aerosol particles

Table of Contents

9.1. Sources and sinks of atmospheric aerosols

9.1.1. Sources of aerosol particles

9.1.2. Sink processes

9.2 Physical and chemical characteristics of aerosols

9.2.1. Concentrations:

9.2.2. Size distribution:

9.2.3. Chemical composition

9.2.4. Water solubility

9.2.5. Atmospheric lifetime

9.3. Effects of atmospheric aerosols

9.3.1. Direct effects: direct radiative forcing due the scattering radiation.

9.3.2. Indirect effects: indirect radiative forcing through cloud formation effects

Aerosol is a system of solid or liquid particles suspended by a mixture of gases. The term aerosols covers a wide spectrum of small particles, like sea salt particles, mineral dust, pollen, drops of sulphuric acid and many others. Aerosols have a great impact on several atmospheric phenomena, Earth’s climate and on biosphere. During their atmospheric residence, different size solid and liquid particles influence the radiation and energy budget of the Earth’s, the hydrological cycle, atmospheric circulation and the abundance of trace gases. Aerosol particles can be characterized by their concentration, size distribution, structure and chemical composition, which are highly variable both temporally and spatially.

9.1. Sources and sinks of atmospheric aerosols

After the emission of aerosol particles, they undergo various physical and chemical processes. During these processes, size, composition and structure of particles can be changed. Finally, they can be removed from the atmosphere to the surfaces by dry or wet deposition processes (Figure 9.1).

Figure 9.1: Atmospheric cycles of aerosol particles

9.1.1. Sources of aerosol particles

Aerosols originate from a wide variety of natural and anthropogenic sources. Various aerosol particles are generated through a combination of physical, chemical and biological processes. Based on the formation processes, different source types can be distinguished. Primary particles emitted directly to the atmosphere as liquids or solids, through a wide range of processes (Bulk-to-Particle Conversion, BPC). Some particles formed by nucleation^[18] and condensation^[19] of precursor gases (Gas-To-Particle Conversation, GPC), and others from the reactions of dissolved substance in cloud droplets.
During Bulk-to-Particle Conversion, many different aerosol particles are generated from solid or liquid base materials. Oceans and dry continental regions are the two main natural sources of atmospheric aerosol. A large amount of water droplets and sea salts are released to the atmosphere through sea spray and air bubbles at the surface of the seas (Figure 9.2).

Figure 9.2: Droplet formation during the bursting of small bubble

As a water droplet evaporates, the salt is left suspended into the atmosphere forming a maritime aerosol particle (e.g. sodium-chloride (NaCl), magnesium sulphate (MgSO₄)). Other major source of primary particles is the windblown mineral dust from dry continental area, like deserts and semi arid regions. Further natural sources are volcanic ash, clay particles from soil erosion and biological materials (plant debris, pollen etc.). Additionally, different particles are formed by anthropogenic activities, like biomass burning, combustion of fossil fuel or industrial activities (Table 9.1).
Aerosol particles originated during Gas-To-Particle Conversion (e.g., sulphates, secondary organics) are not directly emitted, but are formed in the atmosphere from gaseous precursors (Table 9.2). Two basic processes can cause the formation of these secondary particles: an existing particle may grow through material condensing from gas phase, or new particle may forms through homogeneous nucleation.
Some other particles can form or transform by cloud droplets. When a cloud condensation nucleus^[20] as an aerosol particle dissolves in the water, and then reacts with other substances, it can build new aerosol substance and form a new aerosol particle when the water evaporates.
Table 9.1: Primary particle emissions (Tg / year) Source: IPCC (2001)

Sources	Range
Carbonaceous aerosols	66 – 217
Organic Matter:
Biomass burning	45 – 80
Fossil fuel	10 – 30
Biogenic	0 – 90
Black carbon:
Biomass burning	5 – 9
Fossil fuel	6 – 8
Aircraft	0.006
Industrial dust	40 – 130
Sea salt	1000 – 6000
Mineral (soil) dust	1000 – 3000

Table 9.2: Annual source strength for present day emissions of aerosol precursors (Tg N, S or C /year). Source: IPCC (2001)

Sources	Range
NOx (Tg N y–1)	28 – 58
Fossil fuel	21.0
Aircraft	0.4 – 0.9
Biomass burning	2 – 12
Agricultural soil	0 – 4
Natural soil	3 – 8
Lightning	2 – 12
NH3 (Tg N y–1)	40 – 70
Domestic animals	10 – 30
Agriculture	6 – 18
Human	1.3 – 6.9
Biomass burning	3 – 8
Fossil fuel and industry	0.1 – 0.5
Natural soils	1 – 10
Wild animals	0 – 1
Oceans	3 – 16
SO2 (Tg S y–1)	67 – 130
Fossil fuel and industry	60 – 100
Aircraft	0.03 – 1.0
Biomass burning	1 – 6
Volcanoes	6 – 20
DMS and H2S (Tg S y–1)	12 – 42
Oceans	13 – 36
Land biota and soils	0.4 – 5.6
Volatile organic emissions (Tg C y–1)	100 – 560
Anthropogenic	60 – 160
Terpenes	40 – 400

9.1.2. Sink processes

Aerosols can be removed from the atmosphere by different ways in the function of their size and disposition. Two main types of removing processes of aerosol particles are wet and dry deposition (see e.g. Sportisse, 2007; Petroff et al., 2008). In an annual global mean, about 80–90% of aerosol particles are removed from the atmosphere by in-cloud and below-cloud scavenging (wet deposition). Remaining part of particles is removed by different ways of dry deposition.
Wet deposition processes (the main sink of atmospheric aerosol particles):
Rain-out and washout: a part of cloud droplets form precipitation which reaches Earth’s surface removing aerosols from cloud and from the column of air below the cloud.
Cloud deposition: deposition form of aerosols in high elevation ecosystems due to interception of cloud droplets by vegetation.
Dry deposition processes (less important on a global scale):

• Turbulent diffusion: for larger particles (with a diameter larger than 1 µm) eddy diffusivity becomes important.
• Gravitational settling (sedimentation): larger particles are influenced more by gravity and fall back to the surface. This process becomes increasingly important for particle sizes above 1 µm.
• Impaction: if a particle cannot follow the flow streamline around an obstacle (e.g. a larger particle), small particle can hit this obstacle (Figure 9.3).
• Interception: if an object is not directly in the path of particle moving in the gas stream (as in case of impaction), but particle approaches the edge of the obstacles, it may collected by the obstacle (Figure 9.4).
• Brownian diffusion: randomly moving smaller particles bump each other (thermal coagulation^[21]) or to a larger obstacles (Figure 9.5). This process dominates for particle sizes below 0.2 µm. Brownian diffusion coefficients increase as particle diameter decreases. Additionally, in a very thin (about 1 mm) layer over the surface, the Brownian diffusion becomes more important for larger particles too.

Figure 9.3: Impaction of an aerosol particle on an obstacle (for example on a larger water droplet)

Figure 9.4: Particle interception by an obstacle (for example by a larger water droplet)

Figure 9.5: Brownian diffusion of a small aerosol particle

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^[18] Nucleation: generally defined as creation of molecular embryos or clusters prior to formation of a new phase during the transformation of vapor → liquid → solid. Nucleation can occur within the original phase (homogeneous nucleation), or on another phase, e.g. on a small particles (heterogeneous nucleation).

^[19] Condensation: gas to liquid phase change.

^[20] Cloud condensation nuclei (CCN): hygroscopic aerosol particles that can serve as nuclei of atmospheric cloud droplets, that is, particles on which water vapour condenses.

^[21] Particle coagulation: a process, in which small particles collide with each other and coalesce completely to form a larger particle.

Dispersion of air pollutants

There are many accidents and natural events, where harmful and toxic chemical species can be emitted into the atmosphere (e.g., accidental release at nuclear power plant (NPP), volcano’s eruptions, forest fires). These air pollutants can travel hundreds and thousands kilometres from their release points across the globe depending on their chemical (chemical composition) and physical (e.g., solubility in water, size distribution for aerosol particles) properties, and they affect the human health and result in a long-term effect on our environment. Moreover, such incidents could have huge economical impact. For example the eruption of Eyjafjallajökull in Iceland over a period of six days in April, 2010 caused enormous disruption to air travel in most part of Europe because of the closure of airspace. Estimated lost of airlines was about US$1.7 billion.
It is important to note that model simulations must have a high degree of accuracy and must be achieved faster than real time to be of use them in an effective decision support. Therefore, accurate and fast simulation of dispersion of toxic chemical substances or radionuclides in the atmosphere is one of the most important and challenging tasks in atmospheric sciences. Chernobyl disaster and an increased demand from the society have stimulated the development of accidental release programs and complex decision making softwares (e.g., RODOS). Underestimating the maximum concentrations of air pollutants may have serious health consequences, and conversely, applying remediation measures in regions where significant dosage will not be received would waste valuable resources and may have significant social implications if evacuation or other interventions is required. This demand and extreme pressure from the society and business bodies can be illustrated by the following statement by Giovanni Bisignani, chief executive of IATA, during the Eyjafjallajökull incidents: “Airspace was being closed based on theoretical models, not on facts. Test flights by our members showed that the models were wrong.”
Dispersion of air pollutants in the troposphere is mainly governed by advection (wind) field, however, other processes like turbulent diffusion (turbulence) or radioactive decay, chemical reaction and deposition of air pollutants play important role in the spatiotemporal evolution of dispersion pattern. Development of models requires complex thinking and interaction of researchers from different fields. For simulating the dispersion of air pollutants, various modelling approaches have been developed. The main aim of this chapter is to provide a comprehensive review of air pollution modelling. The chapter is structured as follows. Section 2 provides an overview of air pollution modelling. Following 3 sections describe Gaussian, Lagrangian and Eulerian dispersion models with their advantages and drawbacks. Finally, section 6 discusses computational fluid dynamics models for environmental modelling.

Overview of air dispersion modelling

Overview of air dispersion modelling

10.2.1. The transport equation

In a selected V₁ volume of the fluid, mass conservation of the component described with c concentration can be expressed as:

,

(10.1)

Where is the wind vector, S_c is the source term and D_c is the diffusion coefficient. Equation (10.1) represents the change of the total mass of the material within volume V₁ as the sum of the advective flux through the borders of the volume, source terms inside the volume, and the diffusive flux. In dispersion models, wind field and other meteorological data is obtained from measurement or a numerical weather prognostic (NWP) model, thus the only unknown term in equation (10.1) is the c concentration field. We can transform equation (10.1) into a differential form using Gauss’ formula and generalizing the integrates to any V₁ volumes:

.

(10.2)

This is the dispersion equation that describes advection, source and molecular diffusion processes. Dry and wet deposition, chemical or radioactive decay is part of the S_c term, while gravitational settling can be added as an extra advection component. Turbulent diffusion, however, is not represented in equation (2).
Turbulence is usually taken into account with Reynold’s theory that splits the wind and concentration field into time-averaged and turbulent perturbation values:

	(10.3)
	(10.4)

From equations (10.2) and (10.3, 10.4) we can construct a dispersion equation that represents both time-averaged and perturbation components:

(10.5)

Time averaging equation (10.5) will eliminate all the components that contain a single perturbation term, as the Reynolds model is based on the assumption that turbulent perturbations’ time average is zero. However, not all perturbations will disappear as the covariance term’s time average is not necessarily 0:

(10.6)

We can here conclude that dispersion equation (10.5) for turbulent flows can be written in the same form as equation (10.2) with the addition of three eddy covariance terms. Writing the turbulent components explicitly and assuming an isotropic molecular diffusion, equation (10.6) can be rewritten in a form that is widely used in atmospheric dispersion modelling:

(10.7)

The right side of equation (10.7) describes advection, source terms, molecular diffusion and horizontal and vertical turbulent fluxes. In the atmosphere, turbulent mixing is magnitudes more efficient than molecular diffusion thus the third component of equation (10.6) can usually be neglected. However, in the laminar layer within 0.1 − 3 cm of the ground, turbulence is very weak, therefore molecular diffusion gets a large importance in the investigation of soil-atmosphere fluxes and deposition processes, often treated as a resistance term.
Equation (10.7) involves four new variables in the equation. There are two ways for the closure of the turbulent dispersion equation: either we construct new transport equations for the turbulent fluxes or we use parameterization to express the turbulent fluxes with time-averaged concentration and wind values. The former approach leads to the Reynolds Stress Models (RSM), while the latter is the widely used gradient transport theory, or K-theory. These are presented in details in Stull (1988), here we provide a brief outline of their results.
As an analogy of Fick’s law for molecular diffusion, gradient transport theory is based on the assumption that the x directional turbulent flux is proportional to the first component of the gradient of the concentration field:

,

(10.8)

where K_x is the x directional turbulent diffusion coefficient or eddy diffusivity. Using this approach equation (10.7) results in a form where turbulent fluxes are expressed as an additional diffusion term:

,

(10.9)

where K is a diagonal matrix of the K_x, K_y, K_z eddy diffusivities. Due to the different atmospheric turbulent processes in horizontal and vertical direction, K cannot be assumed to be isotropic. Furthermore, while D_c is a property of the chemical species, K is a property of the flow, thus it varies in both space and time. Assuming an incompressible fluid and isotropic horizontal turbulence and neglecting the molecular diffusion, the dispersion equation can be written in a form:

,

(10.10)

where is the horizontal divergence operator, K_h is the horizontal and K_z is the vertical eddy diffusivity. These two parameters need to be estimated at each grid point and timestep through various parameterizations.
Reynolds Stress Models (RSM) construct new transport equations for the turbulent fluxes based on the turbulent kinetic energy and dissipation values (Launder et al., 1975). This approach has larger computational cost than gradient transport models, however, its results proved to be more accurate in several microscale cases (Rossi and Iaccarino, 2009, Chen, 1996). While RSMs have been successfully applied in CFD-based microscale atmospheric models (Riddle et al., 2004), on meso- and macroscale, computationally more efficient eddy diffusivity approach is used (Draxler and Hess, 1998, Lagzi et al., 2009).
The dispersion equation (10.10) can be solved numerically with spatial discretization of variables on a grid, which is often referred to as the Eulerian approach. Under some assumptions, (10.10) can also be solved analytically and provides a Gaussian distribution that is widely used in Gaussian dispersion models. A stochastic solution also exists where instead of solving the partial differential equation (PDE), equation (10.10), the concentration field is given as a superposition of a large number of drifting particles (Lagrangian approach).

10.2.2. Turbulence parameterization

Turbulence is a key process in dispersion simulations: while downwind dispersion is usually dominated by advection, crosswind or even upwind turbulent mixing is magnitudes more efficient than molecular diffusion. Turbulence is treated on different scales: macroscale turbulence, with a scale larger than the numerical weather prediction (NWP) model’s grid resolution, is computed explicitly within the NWP thus it is taken into account in the advection term. Subgrid-scale turbulence causes a velocity and concentration fluctuation, often referred to as turbulent diffusion. We note that subgrid-scale velocity fluctuation also has an effect on the large scale flow through turbulent viscosity. This effect is treated with various turbulence parameterizations in the NWP models.
Atmospheric dispersion can be regarded as a sum of two main effects: the mechanical turbulence caused by wind shear, and the thermal turbulence caused by buoyancy. Their characteristics and dependence on measurable variables is very different (Table 10.1).
Mechanical turbulence estimates rely on 3D wind field measurement data to obtain wind shear values. Surface roughness also has an important role in generating mechanical turbulence through friction in the ground layer. Surface roughness is usually measured with the z₀ roughness length, a characteristic length of surface obstacles. Typical roughness lengths of different surfaces are shown in table 10.2. The models make the difference between complex terrain and surface roughness upon the scale of the problem: while the flow around large scale geometry covered with multiple grid points is computed explicitly in the model, sub-grid scale geometry is treated as roughness and parameterized through its effect on turbulence.
Table 10.1: Parameters that affect turbulence patterns in the planetary boundary layer (PBL)

Mechanical turbulence (wind shear)	Thermal turbulence (buoyancy)
3D PBL wind field	3D PBL (potential) temperature field
Surface roughness	Sun elevation
Complex terrain	Cloud cover
	Albedo
	Surface land use (sensible heat)
	Surface evapotranspiration (latent heat)

Table 10.2: Typical roughness length and albedo values of different surfaces

Surface	Roughness length	Albedo
Open water	1 mm	0.1
Fresh snow	5 mm	0.9
Flat terrain with low grass	5 cm	0.25
High crops	25 cm	0.2
Forest	1 m	0.15
Buildings	several meters	0.05

Thermal turbulence depends largely on the atmospheric stability and the surface’s radiation budget. Under stable conditions, thermal turbulence is low, and mixing is determined by the wind shear strength. On the other hand, in a convective boundary layer (CBL), thermal turbulence has a significant role in dispersion processes. Surface parameters, like albedo (Table 10.2) or potential evapotranspiration have a large importance in the estimation of sensible and latent heat fluxes that determine the thermal turbulence intensity.
The strength of the turbulence can be described using various measures. One of them is eddy diffusivities (m²s^-1) that represent a diffusion coefficient in the dispersion equation (10.10). Another approach is to estimate the u’, v’, w’ wind fluctuation components (equation 10.3), and calculate the turbulent kinetic energy (TKE, Jkg^-1) that describe the time-averaged energy of subgrid-scale turbulent eddies (Stull, 1988):

.

(10.11)

While eddy diffusivities and turbulent kinetic energy explicitly describe turbulence intensity, there are dispersion-oriented characteristics that try to estimate the mixing efficiency instead of describing the turbulence itself. Mixing efficiency is often treated as the deviation of a Gaussian plume, which is widely used in Gaussian and puff models. Lagrangian models use a stochastic random-walk simulation for mixing.
As both wind and temperature changes in space and time, atmospheric turbulence is a non-homogenous, non-stationary field. Furthermore, thermal turbulence is highly anisotropic, which means a difficult challenge for turbulence models especially in a convective boundary layer and leads to the separated treatment of horizontal and vertical turbulence in equation (R10.10). While horizontal dispersion is usually dominated by advection, vertical mixing of the planetary boundary layer (PBL) is caused by turbulence because of the large vertical wind shear and temperature gradients, together with the fairly low vertical wind speeds. This means that vertical turbulence is a key process in atmospheric dispersion simulations, and requires sophisticated methods to estimate its strength.
The vertical profile of vertical eddy diffusivity is presented in Figure 10.1 after Kumar and Sharan (2012). It can be seen that far from the ground, turbulence intensity decreases with height thus the h planetary boundary layer height can be defined as the elevation where turbulence becomes neglectable. On the other hand, near-ground turbulence intensity fast increases with height, and reaches its maximum value at the elevation approximately 30–40 % of the PBL height. Near-zero eddy diffusivity at the top of the PBL means that there is no vertical mixing upward, thus pollutants released from the surface will stay in the boundary layer. The PBL height and the advection speed together determine the volume in which the pollutant can dilute within a specified time, thus they have a very significant effect on concentration estimates.

Figure 10.1: Profiles of the normalized vertical eddy diffusivity for different normalized planetary boundary layer heights under (a) unstable conditions and (b) stable conditions from Ref. (Kumar and Sharan, 2012), where h is the planetary boundary layer height and L is the Monin–Obukhov-length, respectively.

Table 10.3: Typical PBL height values

	Night	Day
Spring	150 m	1700 m
Summer	150 m	1900 m
Autumn	150 m	1200 m
Winter	100 m	500 m

PBL height has a strong diurnal and annual variability: it extends over 2000 m on convective summer days, however, it can shrink to a few 10 meters on clear nights (Table 10.3). A key process is the collapse of the mixed layer approximately one hour before sunset, when heat fluxes from the surface are stopped and thermal turbulence is ceased (Figure 10.2). The night time stable boundary layer keeps the surface-based pollutants close to the ground, while the residual layer is detached from the surface until approximately one hour after sunrise.

Figure 10.2: A typical daily cycle of the planetary boundary layer in fair weather (after Stull, 1988).

10.2.2.1. Stability classes

Atmospheric turbulence is determined by wind shear and thermal stratification. In the early years of dispersion modelling, it was an obvious solution to define categories based on easily measurable wind and radiation characteristics like albedo, cloud cover and sun elevation. The most popular stability classification is Pasquill’s method that defines six categories: from the very unstable A to the very stable F class (Table 10.4). Pasquill took into account wind speed, sun elevation and cloud cover data to determine the stability class that provided pre-defined mixing efficiency values. Despite the fact that this classification can handle only six discrete values of turbulence intensity, Pasquill’s method was used for scientific and regulatory purposes for many decades (Turner, 1997, Sriram et al., 2006).
Table 10.4: Pasquill’s stability classes from the very unstable (A) to the very stable (F) atmosphere. Note: neutral (D) class has to be used for overcast conditions and within one hour after sunrise / before sunset. [Source: http://ready.arl.noaa.gov/READYpgclass.php]

	Daytime insolation			Night time
Surface wind speed	Strong	Moderate	Slight	Thin overcast or max. 3 octas low cloud	Min. 4 octas low cloud
< 2 m/s	A	A – B	B	E	F
2 – 3 m/s	A – B	B	C	E	F
3 – 5 m/s	B	B – C	C	D	E
5 – 6 m/s	C	C – D	D	D	D
> 6 m/s	C	D	D	D	D

10.2.2.2. Richardson number

The difference between thermal and mechanical turbulence can be regarded as a separation of the subgrid-scale turbulent energy to potential (buoyant) and kinetic energy. Their ratio is described with the gradient Richardson number:

,

(10.12)

where is density and g is the gravitational acceleration. The numerator of equation (10.12) describes buoyancy, while the denominator is the wind shear term. It can be observed that the buoyancy can take both positive and negative values, representing both the unstable stratification that generates turbulence, and the stable stratification that destroys turbulence. The wind shear term, however, can take only positive values as it is always a generator of turbulence. The buoyancy term is the square of the Brunt-Vaisala-frequency, and is often written with more practical potential temperature gradients instead of density (Stull, 1988).
Richardson number gives a first-guess estimate on atmospheric stability and turbulence intensity. In the Ri < 0 case, unstable stratification develops thermal turbulence, which describes a convective boundary layer (CBL). Zero Richardson number means a neutral stratification, thus only mechanical turbulence is present. If the Richardson number is positive, but less than a critical Ri_c value (0 < Ri < Ri_c), mechanical turbulence overrides stable stratification that leads to a mechanic turbulence dominated boundary layer. For Richardson numbers larger than Ri_c, atmospheric stability destroys mechanical turbulence and results in a stable boundary layer (SBL). Estimates for the critical Richardson number are in a range 0.15-0.5, but it was also showed that taking qualitative difference between turbulence patterns based on a pre-defined critical value can lead to unrealistic results. However, in many cases, when surface characteristics and fluxes are not available to perform more sophisticated simulations, the Richardson number is used to estimate the turbulence intensity and/or PBL height.

10.2.2.3. Monin–Obukhov-theory

Monin and Obukhov developed a theory in 1954 for atmospheric turbulence that fully described vertical profiles of turbulence intensities. Their basic idea was to combine the vertical turbulent momentum and heat fluxes into a single number with dimension of length, the so called Monin–Obukhov-length

,

(10.13)

where T’, u’, w’ are the temperature, horizontal and vertical wind turbulent fluctuations, T is the temperature and is the von Karman constant, usually taken equal to 0.4 (Stull, 1988). Equation (10.12) is valid for dry air, however, for moist air, virtual temperature or virtual potential temperature is used (Stull, 1988). Hardly measurable turbulent fluxes can be estimated with the definition of two parameters: the u^* friction velocity and the q turbulent heat flux. Using these new parameters, the Monin–Obukhov-length can be expressed as a function of measurable variables (Foken, 2006)

,

(10.14)

where c_p is the specific heat and is the density of the air. There are different methods for the determination of u^* and q using net radiation components and (potential) temperature gradient, which largely differ in the convective and in the stable boundary layer.
The Monin–Obukhov-length in (10.13) is a reference scale for boundary layer turbulence, thus any z height can be given with the dimensionless z/L parameter. The z/L is not only a new vertical coordinate, but also a stability parameter: at any given z height, turbulence intensity can be described with the z/L value (Figure 10.1).
Monin and Obukhov defined universal functions for neutral, stable and unstable cases that describe vertical profiles of wind and temperature as a function of the z/L height (Foken, 2006). The u^* friction velocity is often also computed as a function of z/L using an iterative solution (Cimorelli et al., 2005). The turbulent momentum and heat fluxes are given explicitly from the u^* and q values (as in equations 10.13-10.14), however, dispersion models require eddy diffusivity profiles as well. These are obtained through model-dependent parameterizations using the u^* value, the vertical wind and temperature profile provided by the Monin–Obukhov-theory and the universal functions of z/L that describe stability. The Monin–Obukhov-theory was validated against numerous measurement and advanced computational fluid dynamics datasets, and although some weaknesses were shown, it still means the most reliable basis for all fields of atmospheric modelling (Foken, 2006).

10.2.3. Chemical reactions and radioactive decay

Chemical transformation of air pollutants can be diversified and complex especially in the troposphere or stratosphere, where thermal as well as photochemical reactions can occur. Photochemical reactions can produce radicals, which have high affinity to react with other chemical compounds in the atmosphere. This driving force and rich variety of atmospheric components can really provide a complex network of chemical reactions. Formation of air pollutants can be described by reaction mechanisms. A reaction mechanism is a set of elementary reactions by which overall chemical change occurs. Variation of concentrations of air pollutants in the atmosphere can be constructed from the rate equations and can be calculated by a set of ordinary differential equations (ODEs)

,

(10.15)

where is the chemical rate constant for n^th reaction and is the order of reaction in respect for a given chemical species. Usually, for a thermal reaction the rate constant is a “real” constant, however, for a photochemical reaction the rate constant depends on light intensity, and this dependence can be expressed through the annual and diurnal variation of the solar zenith angle ()

,

(10.16)

where a and b are parameters depending on the given photolytic reaction.
Radioactive decay in environmental models can be interpreted as a first order chemical reaction

.

(10.17)

Solution of this equation (10.17) is an exponential function in time. Radioactive decay constant (k) is an inherent property of the radionuclides. Models can take into account consecutive decays with different decay constants, which is mathematically not a complicated task. Equations (10.16, 10.17) are added to the source term (S_c) of atmospheric transport equations (10.10). Models using concentration field (e.g., Eulerian model) can easily handle this mean field description of the concentrations described by ODEs.
However, if we consider dispersion of air pollutants as a dispersion of individual particles, using deposition, chemical and decay rate constants could be meaningless, because these quantities represent “bulk” properties of the system. Here we deal with individual particles, where macroscopic parameters can hardly be used. We can overcome this problem using probabilistic nature for first order reaction, radioactive decay and deposition. The probability that individual particles during a time step will transform, decay or deposit can be estimated by the following relation

,

(10.18)

where k is either the “macroscopic” radioactive decay constant (first order rate constant) or wet or dry deposition constant. Thus we can connect microscopic event to macroscopic properties and constants.

Gaussian dispersion models

. Gaussian dispersion models

10.3.1. Theory and limitations of Gaussian models

The turbulent diffusion equation (10.10) is a partial differential equation that can be solved with various numerical methods. Assuming a homogenous, steady-state flow and a steady-state point source, equation (10.10) can also be analytically integrated and results the well-known Gaussian plume distribution

,

(10.19)

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 σ_y, σ_z deviations describe the crosswind and vertical mixing of the pollutant, thus they are constructed from the K_h, K_z values of equation (10.10). Equation (10.19) describes a mixing process that results a Gaussian concentration distribution both in crosswind and in vertical direction, centered at the line downwind from the source (Figure 10.3). The last term of equation (10.19) expresses a total reflection from the ground, therefore this formula does not count with dry and wet deposition. Adding a third vertical component to the equation, total reflection from an inversion layer can also be computed. Gravitational settling and chemical or radioactive decay are neglected.
One can observe that the x downwind distance from the source does not appear in equation (10.19). It originates from the obvious assumption that advection is more dominant than diffusion, that, however, can cause large error in situations with low wind speeds where a three-dimensional diffusion dominates. Unfortunately, these situations proved to have been the most dangerous ones in real-life atmospheric dispersion problems as they were often connected to stably stratified atmosphere or low-level inversions (Sharan and Gopalakrishnan, 1997).

Figure 10.3: Schematic figure of a Gaussian plume. The H_e effective stack height and the crosswind and vertical deviation of the profile are the key parameters of the model. (Source: Wikipedia)

Table 10.5: Features and assumptions of most Gaussian dispersion models

Represented in most Gaussian models	Not represented in most Gaussian models
Advection	Wind shear
Horizontal turbulent diffusion	Change of wind over time
Vertical turbulent diffusion	Change of source parameters over time
Reflection from ground	Wet and dry deposition
Reflection from inversion layer	Gravitational settling
Elevated source	Chemical reactions
Buoyancy (effective stack height)	Radioactive decay
Multiple source points	Complex terrain
	3D diffusion (low-wind case)

Besides turbulence, the elevation of the source, often referred to as stack height is a key parameter of a Gaussian model, because ground concentrations are computed in an analytical way assuming the maximum concentration in the stack height (Figure 10.3). If buoyant pollutants are present, the horizontal advection starts from considerably higher than the stack’s top due to the buoyant rise of the released gas. It led to the definition of the effective stack height that is the stack height added to the buoyant plume rise. Both empirical and theoretical formulas exist to compute the plume rise using the temperature, specific heat capacity, release speed and flux of the material as input data, which can be successfully used even in extremely buoyant cases like pool fires. We note that in situations where buoyant pollutants and a low-level thermal inversion are present, more sophisticated simulations are required to estimate the penetration of the inversion layer by the plume.

10.3.2. History of development

The greatest advantage of Gaussian models is that they have an extremely fast, almost immediate response time. Their calculation is based only on solving a single formula (equation 10.11 or similar) for every receptor point, and the model’s computational cost mainly consists of meteorological data pre-processing and turbulence parameterization. Depending on the complexity of these submodules, the model’s runtime can be extremely reduced that enables its application in real-time GIS-based decision support software.
Gaussian dispersion models have become a uniquely efficient tool of air quality management for the past decades, especially in the early years when high performance computers had an unreachable price for environmental protection organizations and authorities. They have been successfully used for a wide range of studies of air quality in urban and industrial areas. However, industrial incidents like the ones in Seveso, Bhopal and Chernobyl showed out some critical weaknesses of Gaussian models, and strongly motivated the development of more advanced simulations to satisfy the scientific and public interest in the safety of atmospheric environment.
Although the toxic gas release in Seveso, Italy in 1976 happened during daytime in weakly unstable conditions, Gaussian models couldn’t perform well because of the strong horizontal wind shear and fast changing wind direction. As exact deposition maps became available, Cavallaro et al. (1982) managed to give a better computational result with a statistical method that estimated dispersion directions driven by measured wind vectors, which can be regarded as a Lagrangian approach. Eight years later, another serious accident happened in Bhopal, India. The high number of victims warned the world that release of toxic heavy gas in a situation where low-level night time inversion is present can cause catastrophic consequences. Because of the low wind speed, fast settling pollutant and strong temperature inversion connected with local scale terrain effects, it was impossible to obtain reliable results from Gaussian models, however, later simulations with advanced Lagrangian software showed a good agreement with measurements.
Inspired by the serious accidents and the more and more efficient computers, there were large efforts to develop Gaussian models in a way that they could provide more accurate air quality forecasts as well as to take into account some of the unrepresented physical processes (Table 10.5). It led to some respectable results like the more sophisticated treatment of vertical mixing in convective boundary layer or the parameterization of complex terrain effects. Due to the developments, today’s advanced Gaussian dispersion models like AERMOD, CTDM or ADMS still have a significant role in environmental modelling.
While the incidents in Seveso and Bhopal and other air pollution episodes were concentrated on a local scale, the Chernobyl accident in 1986 had serious consequences in several countries and the radioactive I-131 gas was measured globally (Pudykiewicz, 1988). It was clear that the steady-state assumption of Gaussian models couldn’t handle continental scale dispersion processes, however, existing Eulerian and Lagrangian models provided prescious information in the estimation of the impact of the accident (Pudykiewicz, 1988). The fast development of computers and NWP-s allowed researchers to create more and more efficient dispersion simulations using girded meteorological data. Eulerian and Lagrangian models are state-of-the-art tools of recent atmospheric dispersion simulations (Mészáros et al., 2010, Dacre et al., 2011).

10.3.3. Advanced Gaussian models

AERMOD is an open-source Gaussian air dispersion model developed by the US Environmental Protection Agency (EPA). It has a sophisticated turbulence parameterization based on the Monin–Obukhov-theory, and has built-in models to handle complex terrain and urban boundary layer (Cimorelli et al., 2005). It uses the Plume Rise Model Enhancements (PRIME) algorithm that gives a hard approximation of complicated turbulent processes like the downwash effect near the source. Although AERMOD uses the steady-state approximation for the flow and the source, it can be used within 10–100 km distance as a long-term statistical tool through its meteorological preprocessor, AERMET. AERMET assimilates detailed surface and meteorological data from both surface measurements and upper air soundings and enables the model to perform several model runs from which time averaged concentrations can be estimated through a certain period. AERMOD is a powerful tool to carry out impact studies of planned or existing industrial sites as well as to estimate the average load on environmental protection or agricultural areas. Besides AERMOD, EPA also developed the Complex Terrain Dispersion Model (CTDM), which can estimate concentration patterns in mountain regions without solving the complex mesoscale flow field.
In Europe, the British ADMS model has become the most popular for air quality simulations. It provides a wide range of parameterizations for complex effects like coastal circulations, complex terrain, deposition processes and radioactive decay. Like AERMOD, it can be used in statistical mode to obtain long-term average loads for impact studies. UK Met Office and Cambridge Environmental Research Consultants (CERC) also developed the ADMS-Urban module that aims to provide air quality forecasts for cities as an alternative of costly and time consuming computational fluid dynamics (CFD) modelling. The basic idea behind ADMS-Urban is to create the complex concentration field of a city as a superposition of plumes from different point, line and field sources based on emission estimates and a built-in chemistry model.
There are several other Gaussian models available like CALINE3 for highway air pollution, OCD for coastal areas, BLP and ISC for industrial sites or ALOHA for accidental and heavy gas releases. They are widely used by authorities, environmental protection organizations and industry for impact studies and health risk investigations. Their fast runtime allows users to make long-term statistical simulations (Leelőssy et al., 2011) or detailed sensitivity studies (Bubbico and Mazzarotta, 2008), and provide immediate first-guess information if an accidental release occurs. They are often coupled with GIS software to create an efficient decision support tool for risk management.

kakinada may 2016 monthly weather report

	Max	Avg	Min	Sum
Temperature
Max Temperature	44 °C	37 °C	27 °C
Mean Temperature	36 °C	33 °C	26 °C
Min Temperature	31 °C	28 °C	24 °C
Degree Days
Heating Degree Days (base 65)	0	0	0	0
Cooling Degree Days (base 65)	33	26	13	796
Growing Degree Days (base 50)	48	41	28	1260
Dew Point
Dew Point	29 °C	26 °C	20 °C
Precipitation
Precipitation	140.0 mm	6.4 mm	0.0 mm	205.40 mm
Snowdepth	-	-	-	-
Wind
Wind	28 km/h	4 km/h	0 km/h
Gust Wind	-	-	-
Sea Level Pressure
Sea Level Pressure	1012 hPa	1005 hPa	995 hPa

With best regards,
 “Join the race to make the world a better place”.(2016)

Dr. AMAR NATH GIRI

EHSQ , NFCL
M.Sc. -Environmental Science,Ph.D -Environmental Science law & DIPLOMA AS - P.G.D.E.P.L,CES, DCA,
EX IIM LUCKNOW FELLOW, EX RESEARCH SCIENTIST
IGIDR-MUMBAI 
9912511918
amarnathgiri@nagarjunagroup.com
http://www.nagarjunagroup.com
http://www.nagarjunafertilizers.com
http://www.gprofonline.com/members/Default.aspx

EHSQ BLOG : http://dramarnathgiri.blogspot.in/?view=magazine
http://dramarnathgiri.blogspot.in/2013/10/curriculum-vitae-of-dr-amar-nath-giri.html?q=BIO+DATA
http://dramarnathgiri.blogspot.in/2012/05/nagarjuna-management-services.html

Local Weather Report and Forecast For: Kakinada Dated :Jun 01, 2016

Local Weather Report and Forecast For: Kakinada    Dated :Jun 01, 2016

Past 24 Hours Weather Data Maximum Temp(oC) (Recorded on 31/05/16) 39.2 Departure from Normal(oC) 1 Minimum Temp (oC) (Recorded. on 01/06/16) 29.9 Departure from Normal(oC) 2 24 Hours Rainfall (mm) (Recorded from 0830 hrs IST of yesterday to 0830 hrs IST of today) NIL Todays Sunset (IST) 18:31 Tommorows Sunrise (IST) 05:27 Moonset (IST) 14:40 Moonrise (IST) 02:05

7 Day's Forecast Date Min Temp Max Temp Weather 01-Jun 30.0 39.0 Partly cloudy sky in the morning hours becoming generally cloudy sky towards evening or night with possibility of rain or thundershowers accompanied with squall 02-Jun 30.0 39.0 Partly cloudy sky in the morning hours becoming generally cloudy sky towards evening or night with possibility of rain or thundershowers accompanied with squall 03-Jun 29.0 38.0 Partly cloudy sky in the morning hours becoming generally cloudy sky towards evening or night with possibility of rain or thundershowers accompanied with squall 04-Jun 29.0 38.0 Partly cloudy sky with possibility of rain or Thunderstorm or Duststorm 05-Jun 29.0 37.0 Partly cloudy sky with possibility of rain or Thunderstorm or Duststorm 06-Jun 28.0 37.0 Partly cloudy sky with possibility of rain or Thunderstorm 07-Jun 28.0 37.0 Partly cloudy sky with possibility of rain or Thunderstorm