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A MODEL FOR THE DISPERSION OF FROM A ROAD NETWORK Jari Harkonen, Esko Valkonen, Jaakko Kukkonen, Erkki Rantakrans, Kimmo Lahtinen, Ari Karppinen and Liisa Jalkanen

OCT 131998 OSTI

llmatieteen laitos Meteorologiska institutet Finnish Meteorological Institute

Helsinki 1996 DISCLAIMER

Portions of this document may be illegible electronic image products. Images are produced from the best available original document. ILMANSUO JELUN JULKAISUJA

LUFTVARDS PUBLIKATIONER

PUBLICATIONS ON AIR QUALITY

No. 23 504.054 504.064.2 519.24

A MODEL FOR THE DISPERSION OF POLLUTION FROM A ROAD NETWORK

Jari Harkonen, Esko Valkonen, Jaakko Kukkonen, Erkki Rantakrans, Kimmo Lahtinen, Ari Karppinen and Liisa Jalkanen

Ilmatieteen laitos

Meteorologiska institutet

Finnish Meteorological Institute

Helsinki 1996 ISBN 951-697-449-X ISSN 0782-6095

Yliopistopaino Helsinki 1996 Series title, number and report code of publication Published by Publications on Air Quality No. 23 FMI-AQ-23 Finnish Meteorological Institute ______P.O.Box 503 Date FiN-00101 Helsinki 13 j^e 1996 Finland

Authors Name of project Jari Harkonen, Esko Valkonen, Jaakko Development of a road dispersion model Kukkonen, Erkki Rantakrans, Kimmo Lahtinen, Commissioned by Ari Karppinen and Liisa Jalkanen Ministry of Environment Finnish National Road Administration Title A model for the dispersion of pollution from a road network

Abstract

We present a mathematical model for predicting the dispersion of pollution from a road network, for use in a regulatory context. The model includes an emission model, a treatment of the meteorological and background concentration time series, a dispersion model, statistical analysis of the computed time series of concentrations and a Windows-based user interface. The dispersion model is based on a partly analytical solution of the Gaussian diffusion equation for a finite line source. It allows for any wind direction with respect to the road.

The dispersion parameters are modelled in a form which facilitates the use of the meteorological pre­ processor. The chemical transformation is modelled by using a modified form of the discrete parcel method, developed in this study. The chemistry model contains the basic reactions of nitrogen oxides, oxygen and ozone. We have also presented an operational model for evaluating the meteorological and background concentration data for the model applications. The model does not take into account the influence of buildings and inhomogeneous terrain on the dispersion processes.

The validity of the mathematical solution presented has been tested against a more detailed numerical model. The overall differences are reasonable, and the solution can be used with confidence in an operational model. The program has been implemented on a personal computer and on a main-frame computer, and in the later case also executed on a Cray C94 supercomputer.

The validation of the model against experimental data is reported elsewhere. Testing of the model near a major road (Turunvayla, Finland, 1994) showed that the overall agreement of the measured and predicted values for NOx and NO2 concentrations was fairly good.

Publishing unit Air Quality Research Classification (UDC) Keywords 504.054; 504.064.2; 519.24 , dispersion, model, road, line source,

ISSN and series title 0782-6095 Publications on Air Quality Language ISBN English 951-697-449-X Sold by Pages Price Finnish Meteorological Institute, Library 34 P.O. Box 503 Note FIN-00101 Helsinki Julkalsija Julkaisun saija, numero Ja raporttikoodi limafieteen laRos nmansuojelunjulkaisujaNo. 23 FMI-AQ-23 fiVWYYa Vuorikatu 24 ______PL 503 Julkaisuaika 00101 HELSINKI 13.6.1996 TeMjafl) Projektin nimi Jari Harkonen, Esko Valkonen, Jaakko Maantieliikenteen paastojen leviamismalli Kukkonen, Erkki Rantakrans, Kimmo Lahtinen, Toimeksiantaja Ari Karppinen and Liisa Jalkanen Ymparistoministerio Tielaitos Nimeke Maantieliikenteen paastojen leviamismalli

Tirvistelma Julkaisussa esitetaan matemaattinen malli tieverkoston aiheuttamien ilman epapuhtauksien leviamiselle. Malli on keMtetty liikenne-ja kaupunkisuunnittelun seka ymparistoviranomaisten kayttoa varten. Malli arvioi liikenteen paastot, meteorologisen aineiston ja taustapitoisuuksien aikasaqan, epapuhtauksien leviamisen ja kemiallisen muutunnan, seka analysoi tilastollisesti lasketun pitoisuusaikasajjan. Mallissa on helppokayttoinen Windows-kayttaj aliittyma. Tuloksia esitetaan mm. graafisesti tilastollisina tunnuslukuina, joita voidaan verrata ilmanlaadun ohjearvoihin.

Leviamismalli pemstuu Gaussin leviamisyhtalon osittain analyyttiseen ratkaisuun aarellisen viivalahteen tapauksessa. Leviamisparametrit lasketaan kayttaen meteorologisen aineiston kasittelymallilla, joka pemstuu ilmakehan rajakerroksen similariteettiteoriaan. Malli arvioi typenoksidien kemiallisen muuntuman menetelmalla, jossa tarkastellaan reaktioita kutakin havaintopistetta vastaavassa erillisessa ilmapaketissa. Kemialliset reaktiot sisaltavat typenoksidien ja otsonin perusyhtalot Malli ei ota yksityiskohtaisesd huomioon rakennusten ja maastomnuotojen vaikutusta leviamiseen.

Esitetyn matemaattisen difhtusioyhtalon ratkaisun tarkkuutta on analysoitu vertaamalla tuloksia yksityiskohtaisemman numeerisen ratkaisun tuloksiin. Tulokset osoittavat, etta mallin matemaattinen ratkaisu on riittavan tarkka operatiivisiin sovelluksiin. Mallista on kaytettavissa mikrotietokoneelle sovitettu versio ja supertietokoneella (Cray C94) ratkaistava versio.

Mallin todentaminen kokeellisen aineiston pemsteella kuvataan muissa julkaisuissa. Mallin testaaminen Espoossa Tumnvaylan laheisyydessa (1994) osoitti, etta ennustetut NOx ja N02 -pitoisuudet vastasivat keskimaarin hyvin mitattuja arvoja.

Julkaisijayksikko Umadeteen laitos, Ilmanlaadun tutkimus Luokitus (UDK) Asiasanat 504.054; 504.064.2; 519.24 Umanlaatu, leviaminen, malli, maantie, viivalahde, liikenne

ISSN Ja avainnimeke 0782-6095 Umansuojelun julkaisuja Kieli ISBN England 951-697-449-X Myynti Sivumaara Hinta Ilmatieteen laitos, Khjasto 34 PL 503 Lisalietoja 00101 Helsinki Acknowledgements

We wish to thank Ms. Sari Korhonen and Ms. Mervi Karhula (Finnish National Road Administration, FNRA) and Ms. Taya Lahtinen (Ministry of Environment in Finland) for useful discussions in developing the model. The emission model presented has been developed at the FNRA. Our thanks are also due to Mr. Jukka Ristikartano (FNRA) and Mr. Kari Makela (Technical Research Centre of Finland) for their valuable help on the analysis of emissions. The model development has taken advantage of earlier work on road dispersion models by Mr. Pentti Vaajama (Finnish Meteorological Institute, retired).

We thank Dr. Veli-Matti Kerminen and Mr. Ukka Valkama for their useful comments on the manuscript. We would also like to thank Mr. Robin King for linguistic assistance with the manuscript.

Financial support from the Ministry of Environment in Finland, the Finnish Road Administration and the Academy of Finland is gratefully acknowledged.

Contents

Abstracts

Acknowledgements ...... 5

Nomenclature ...... 8

1. Introduction ...... 9

2. The mathematical model ...... 12 2.1 Dispersion equations for a finite line source ...... 12 2.2 Dispersion parameters...... 14 2.3 Traffic-originated turbulence ...... 15 2.4 Chemical transformation ...... 16 2.4.1 The chemical reactions ...... 16 2.4.2 The discrete parcel method ...... 17

3. The numerical solution ...... 19 3.1 The numerical solution near the source ...... 19 3.2 The validity of the numerical solution ...... 21

4. The integrated model ...... 24

5. The meteorological and background concentration data ...... 25 5.1 The meteorological data ...... 25 5.2 The background concentrations...... 26 5.2.1 The regional variation of background concentrations...... 26 5.2.2 The temporal variation of background concentrations ...... 27

6. Conclusions ...... 28

7. References...... 29

Appendix A. Vertical wind speed profile ...... 32

Appendix B. The emission model ...... 33 Nomenclature

C concentration (pg m"3 ) Hs height of the line source relative to a reference level (m) Ho plume height relative to the height of the line source (m) H effective source height (=H$ + Ho) (m) h Planck constant (J s) i turbulence intensity (s) Kp chemical reaction rate constant (1/ppb s) Kr chemical reaction rate constant (1/s) k von Karman constant (-) L Monin-Obukhov length (m) P half-length of the line source (m) Q source strength per unit length (jxg s'1 m"1) Or solar radiation intensity (W/m2) T temperature (K) t time(s) u average wind speed (m s-1) Ue effective wind speed (m s"1) u, friction velocity (m s_1) u0 correction factor due to traffic-originated turbulence (m s-1) x,y,z coordinates in the line source coordinate system (m) x i> Yi» zi coordinates in the wind coordinate system (m) X,Y,Z geographical coordinates (m) zi mixing height (m) zo roughness length (m) X« effective distance from a line source

Greek a angle between the road and the X-axis (deg) 'I'm stability function for momentum flux (-) lateral dispersion parameter (m)

Subscripts a ambient i initial value t atmospheric turbulence 0 origo; traffic-originated turbulence v lateral direction w vertical direction 9

1. Introduction

In most European cities traffic is the most important source of air pollution, and pollution from major roads is also significant in suburban and rural areas. Vehicular dispersion models are therefore essential computational tools in up-to-date municipal and urban plannin g. These include models for evaluating dispersion in a street canyon and from road dispersion models.

Most frequently, due to their simplicity and direct applicability for estimates on a local scale, various versions of the Gaussian line source model have been used for dispersion evaluations from a road. Such models include HIWAY-2 (Petersen, 1980), CALINE-4 (Benson, 1984 and 1992), GM (Chock, 1978), GFLSM (Luhar and Patil, 1989) and OMG (Kono and Ito, 1990). The ROADWAY (Eskridge and Catalano, 1987) and MGO (Berlyand et aL, 1990) models are based on a K-theory approach. An obvious advantage of the K-theory models is that they can readily include the interaction of diffusion processes and chemical transformation.

Table 1 presents a summary of some features of line source dispersion models. In cases where several model versions exist, we refer to the latest published model version.

In the presence of buildings and obstacles, the use of Lagrangian models combined with surface wind field simulation has been suggested (for instance, Janicke et aL, 1994). Eerens et aL (1993) have presented a simple parametrized model (CAR International) for analysing concentrations alongside roads, including street canyons. The model is based on empirical data in the presence of various obstacle configurations. However, the discussion of street canyon dispersion models is outside the scope of this paper.

In the HIWAY-2 and CALINE-4 (California line source model) models the concentrations predicted by a Gaussian line source equation for an arbitrary wind direction are solved by a numerical procedure. This procedure divides the road into a series of elements, from which incremental concentrations are then computed and summed up. Both models allow for a finite mixing height in the computations.

The textbook of Seinfeld (1986) presents an analytic solution of the Gaussian equation for an infin ite line source, derived by assuming power law expressions for the wind profile and the vertical eddy diffusivity. Csanady (1972) presented an analytic solution of the Gaussian equation for a finite line source, for the special case when the wind is perpendicular to the road. In the GFLSM (General Finite Line Source Model) model this solution has been extended to allow for any wind direction with respect to the road. The analytic solution in the GFLSM model was originally derived from Gaussian formulae similar to, for instance, the HIWAY-2 and CALINE-4 models, except that the mixing height was assumed to be infinite. The analytical solution is computationally much more economic than the above- mentioned numerical solutions.

This report addresses the development of an operational model for predicting pollutant concentrations from a road network. The model hasbeen named CAR-FMI, Contaminants in the Air from a Road, by the Finnish Meteorological Institute. This report presents the 10

complete structure of the model; a brief overview has been presented previously by Harkonen et al. (1995). We address the validity of the applied mathematical solution of the diffusion equation for a finite line source. We also present an operational method for evaluating the meteorological and background concentration data in model applications.

The testing and validation of the model against experimental data is outside the scope of this report; the reader is referred to Walden et al. (1995) and Harkonen et al. (1996).

The basic diffusion equations of the model are based on the Gaussian finite line source model by Luhar and Patil (1989). In this model the solution of the concentration field from the model equations is analytical (except for the numerical evaluation of the error functions), which makes it well suited for desktop computer applications. However, we have modelled the dispersion parameters somewhat differently from the GFLSM model, which applies parametrization based on the Pasquill classes. In the CAR-EMI-model, dispersion parameters are evaluated using stability data produced by a meteorological pre­ processing model, developed at the Finnish Meteorological Institute (Karppinen et al, 1996a and b).

We have also incorporated a chemical transformation submodel, which applies a modified form of the discrete parcel method, used in the CALINE-4 model We suggest a revised version of the discrete parcel method, in which the reaction volume is defined separately for each receptor location (instead of the source location). The model includes the basic reactions of nitrogen oxides, oxygen and ozone, but the influence of the hydrocarbons and other compounds affecting the transformation process are neglected. 11

Table 1. Some features of models for atmospheric dispersion from a road. The models can be broadly classified as Gaussian numerical and analytic, or K-theory.

Model Dispersion Plume Chemical Dispersion References parameters rise Transform. of particles

HIWAY-2 traffic no no no Petersen, 1980 Gaussian induced Rao et al., 1980 numerical and ambient turbulence

CALINE-4 traffic no discrete yes Benson, 1984 Gaussian induced parcel Benson, 1992 numerical and ambient method turbulence

OMG eddy yes no no Kono and Ito, Gaussian diffusion 1990 numerical coefficients, volume source

GM traffic yes no no Chock, 1978 Gaussian induced Luhar and Patil, analytic and ambient 1989 turbulence Rao. et al., 1980

GFLSM traffic yes no yes Luhar and Patil, Gaussian induced 1989 analytic and ambient turbulence

CAR-FMI traffic no discrete no Harkonen et al., Gaussian induced parcel 1995 and 1996, analytic and ambient method this paper turbulence

ROADWAY eddy no interactive no Eskridge and K-theory diffusion with Catalano, 1987 coefficients diffusion Eskridge and Rao, 1986

MGO eddy no interactive no Berlyand et al., K-theory diffusion with 1990 coefficients diffusion 12

2. The mathematical model

2.1 Dispersion equations for a finite line source

The coordinate systems employed are illustrated in Figure 1. The line source and wind coordinates are needed for deriving the dispersion equations, and the geographical coordinates for presenting the final results. The vertical coordinate is the same for all the coordinate systems (z = zi = Z).

Figure 1. The geographical coordinate system (X, Y), the line source coordinate system (x,y) and the wind coordinate system (x,, y,), where the x% axis is parallel to the wind velocity (u). The finite line source extends from -p to +p and R(X,Y) is an arbitrary receptor point.

The transformation equations from the line source coordinate system (x,y) to the wind coordinate system (xi.yO are x, =xsin0 —ycosd y, =xcos 9 +ysin@ , (1) where the angle 6 is as defined in Figure 1.

The transformation equations from the geographical coordinate system (X, Y) to the line source coordinate system are x = (Xq — X) since + (y —Ifo j cosct y = (Xo-X)cosa -(Y-Y q )since, (2) 13

where Xo and Yo are the geographical coordinates of the centre of the line source and the angle a has been defined in Figure 1.

If the line source is assumed to be perpendicular to the wind, the diffusion equation can be written in the wind coordinate system (for instance, Csanady, 1972) as

f (z-H)2) Q 1 2*zl2 J Q = exp + exp 2na o* u l 2*212 J (3) +P J exp fa' -nr dyi' 2 o>l 2 ~P where C, is the concentration computed in the wind coordinate system (the subscript 1 refers to this system), Q is the source strength per unit length,

The effective source height is equal to the sum of the height of the line source H$ (from a reference level) and the plume height Ho, measured from the height of the line source. This nomenclature allows for the description of roads which are at an elevated level compared with their surroundings.

Equation (3) follows directly from the Gaussian plume equation, when integrated over the finite line source in the lateral direction. We assume a total reflection of contaminants from the ground, but ignore reflection at the mixing height level.

Equation (3) can be generalised to allow for any wind direction with respect to the line source. Rewriting equation (3) in the line source coordinate system yields (Luhar and Path, 1989)

,2 Q [z~H) C- exp + exp 2-J2K a7 usmd 2# (4) sin6{p-y) -xcosO ^sin 9 (p + y) + x cos 9 erf + eif V2 crv V2 o\ where erf is the error function. The relation erf (-x) = - erf (x) is used for negative values of the argument

The wind speed perpendicular to the road, u sin9, is replaced by u sin# + u0, where u0 is the wind speed correction due to the turbulence induced by the traffic (Chock, 1978). A constant numerical value, uq = 0.2 m/s, is applied in the computations. Clearly, this replacement also removes the numerical singularity for the wind direction parallel with the line source (0 = 0). We have neglected the plume rise, as it has only a minor influence compared with the influence of the traffic-induced turbulence (Rao et al., 1979). 14

Equation (4) constitutes the basic diffusion equation of the model. Close to the source, however, this solution is mathematically only approximate. We have therefore tested its accuracy; the results are presented later in this report.

2.2 Dispersion parameters

The dispersion parameters are modelled as a function of the Monin-Obukhov length, the friction velocity and the mixing height (instead of the discrete Pasquill classes). These quantities are computed by the meteorological pre-processing model.

Assuming that the processes generating the atmospheric turbulence and the traffic- originated turbulence are independent, the lateral and vertical dispersion parameters (

0"y(-%) = °"yt(x) &yo 2 2 2' O a{(x) = aLzt(x) + aLzo where the subscripts t and 0 refer to atmospheric turbulence and traffic-originated turbulence, respectively. The atmospheric turbulence is evaluated at the effective distance from the line source (x«) and at the effective source height (H).

The dispersion parameters for atmospheric turbulence can be written in terms of the turbulence intensities as (Gryning et al, 1987)

/ = i+ 2 7V

(6)

i+ 2 7V z J where iy and iz are the lateral and vertical turbulence intensities, fy and fz are functions of the dimensionless travel time t / Tl, TLy and T^are the Lagrangian time scales, and av and cw are the standard deviations of the turbulent velocity fluctuations in the lateral and vertical directions. The travel time t is defined as x«/ u(z).

We have applied the vertical wind speed profiles by van Ulden and Holtslag (1985) (see Appendix A); u(z) = (u. / k) f (zo, z, L), where u. is the friction velocity, k is the von Karman constant, zq is the roughness length and L is the Monin-Obukhov length. The turbulence intensities can be rewritten as 15

(7 a) y w(z) M* /(zo »z,L)

(7 b)

The turbulent wind velocity fluctuations are modelled according to Gryning et aL (1987). In the lateral direction,

forL< 0 (8 a)

for L > 0 (8 b) and in the vertical direction

forL<0 (8 c)

where z, is the mixing height. The values of the Lagrangian time scales are also obtained from Gryning et al. (1987).

2.3 Traffic-originated turbulence

We adopt the simple semi-empirical treatment used in the HIWAY-2 model (Petersen, 1980), based mainly on the General Motors experiments (Cadle et ah, 1976). The following equations describe the traffic-originated turbulence in the lateral and vertical directions

a = 3.57 - 0.53 Uc zo (9)

The smallest allowed values for these dispersion parameters in the vertical and lateral directions are 1.5 m and 3.0 m, respectively. Uc is the aerodynamic drag caused by the traffic and is determined empirically, 16

Uc = 1.85 £/0'164 cos 2 6 , (10) where U is the wind speed at a height of 2 m and 6 is the angle between the road and the wind direction. The constants in equations (9) and (10) have been evaluated based on data in moderate and high traffic speed conditions.

2.4 Chemical transformation

The main problems in modelling the dispersion of chemically reactive plumes are: • the inclusion of an appropriate set of chemical reactions and • the modelling of the physical mixing of compounds within the reaction box (or between several reaction boxes) and with the background air. The model includes the basic reactions of nitrogen oxides, oxygen and ozone, but the influence of the hydrocarbons and other compounds affecting the transformation process is neglected. We have introduced a revised version of the discrete parcel method, in which the reaction volume is defined separately for each receptor location.

2.4.1 The chemical reactions

The model includes the following basic reactions of nitrogen oxides, oxygen and ozone

N02 + hv Kr >NO + 0 (11 a) o+o2+m-*o3+m (lib) no+o3-&-^no2+o2 , (11c) where the molecule M absorbs the quantum in the immediate reaction between an oxygen molecule and atomic oxygen; Kp(l / ppb s) and Kr(1 / s) are reaction rate constants.

The reaction rates Kp and Kr are functions of temperature and the solar radiation intensity (Seinfeld, 1986, Hertel and Berkowicz, 1989)

16.133 KF = (12 ) T

KR = 0.8 x 10"3 exp (-—) + 7.4 x 10QR , (13)

where T is the temperature (K) and Qr is the solar radiation intensity (W / m2). The model obtains both the ambient temperature and the solar radiation intensity from the meteorological hourly database. 17

2.4.2 The discrete parcel method

The chemical transformation equations are combined with the diffusion model based on a modified version of the discrete parcel method (DPM); the original version has been presented by Benson (1984). This method considers air parcels in whichthe emissions and background air are assumed to be instantaneously uniformly mixed. The chemical reactions in each parcel are assumed to proceed independently of the dispersion process.

This method therefore does not explicitly allow for the interaction of the chemical reactions and the physical mixing processes, nor the influence of the pollutant concentration profiles within the plume on the chemical transformation. The modelling of these processes would require the application of a more complex numerical solution. For instance, Kerminen and Wexler (1996) present a model for the nucleation of sulphuric acid in urban plumes. Each cross-section of the plume is divided into a large number of cells that expand as the plume grows. The transport of pollutants between different cells and the chemical reactions within each cell are then treated separately in the numerical model.

We suggest here a revised version of the discrete parcel method, in which the reaction volume is defined separately for each receptor location (instead of the source location). This method is named the receptor-oriented DPM (R-DPM); we call the original method the source-oriented DPM (S-DPM).

In the original S-DPM the size of the reaction volume is determined by the height of the source and the wind velocity. Benson (1984) assumes a rectangular volume with a constant height of 3.5 m and a differential width of u dt, where u is the wind velocity and t is the time. The effluents are assumed to be instantaneously mixed into the reaction box, and the chemical reactions are then allowed to proceed during the travel time, ie., the time of transport from the source to the receptor.

The initial mixing zone concentration can therefore be written in the S-DPM as

XiQ = Cia + (14) u2H0 ’ where Q is the initial mixing zone concentration of species i, Q, is the ambient concentration, % is the mass fraction of the species i in the emissions, Q is the emission strength per unit length of the source (kg/(m s)) and 2Ho is the initial source height. The mass fraction % is needed, for instance, to take into account the fraction of NO2 in the total NO, emissions.

The relevant length scales of the reaction volume in the R-DPM are determined by the lateral and vertical dispersion parameters Cy and az. We define the horizontal length-scale as 2 (p sin 0 + k Oy), where p is the half-length of the road, k is a constant, while the vertical length-scale is defined as H + kaz. The effluents are then assumed to be instantaneously mixed into the reaction box, and the chemical reactions are assumed to proceed during the travel time. 18

In both versions of the model, the time available for the reactions is a function of the transport distance. For longer transport distances, more background air is available for the reactions. This is duly allowed for in the R-DPM (but not in the S-DPM), as the reaction volume is a function of the transport distance. This is the main physicaldifference between the two models. However, the R-DPM does not explicitly define the size of the reaction volume; the constant k should therefore be evaluated from experimental data.

The initial concentration in the reaction volume in the R-DPM is equal to the source mass flux (2 & Q p) divided by the volume flux perpendicular to the line source, 2 (H+k c^) (p sin 9 + k Gy) (u sin 6 + u0), i.e.,

XiQp Q (0 — Qa + (15) (H + k

We have selected k = 3, based on numerical comparisons of the model against a more complex numerical solution (Berkowicz et al., 1996).

Alternatively, the initial reaction box concentration can be computed directly by adding the concentration from equation (4), evaluated at the receptor point coordinates, to the background concentration, Q(t) = Qa + C. It can be shown numerically that these two methods produce very similar results.

The model evaluates the initial concentrations of the compounds, denoted by [NO];, [NOa]; and [03];, from equation (15). The initial concentrations are then allowed to change, according to the chemical reaction equations.

The mathematical equations corresponding to the set of chemical reactions (lla-c) can be solved analytically. The resulting equation for the concentration of ozone after time t is (Benson, 1984)

2 A (exp (t s) -1) cxm S(l-exp(ts)) + s(l + exp(rs)) (16)

where terms A, B, C and s are given by

A = KF[03]i[N0]i-KR[N02]i (17)

R = -(tfF([^] + [M?] ). + £*) (18)

C-KrC~Kr (19)

/ 9 xl/2 r = fB2-4AC) (20) 19

In equation (18) we have corrected the printing error in the sign of the term B in Benson (1984).

The equilibrium solution of concentration (16) for large times t is

C =z£t£) . (21 ) xm 2 C

Again, we have corrected the printing error in the denominator in Benson (1984).

The final concentrations in the mixing volume are obtained as the sums of the initial concentrations and the change due to the chemical reactions

[0.], (22 )

(23)

(24) in which c%m refers to the general solution for the ozone concentration (16).

3. The numerical solution

The applied analytic solution of the diffusion equation (4) is not strictly valid near the source, as can be seen from its mathematical derivation. However, this solution is computationally much more economic than the alternative numerical solutions.

We have made a slight modification in the mathematical solution near the source; this modification has also been implemented in the computer program. The validity of the numerical solution has also been tested against numerical results by dividing the road into a series of elements, from which incremental concentrations are then computed and summed up.

3.1 The numerical solution near the source

Some receptor points, which are located in the immediate vicinity of the line source, can physically only be influenced by pollution from the upwind part of the source. Using equation (4) as such would imply the influence of pollution from the total length of the line source, which may not be physically realistic.

We have therefore modified the mathematical solution (4) in the computer program to allow for this "effect. The correction is only important for receptor points which are located near the line source. It can be shown that the original and modified solutions produce the same results far from the source. 20

The derivation of the diffusion equation (4) includes a rotation of coordinates, from the wind coordinate system to the line source coordinate system. Mathematically this implies the definition of a virtual line source, perpendicular to the wind direction. This concept is illustrated in Figure 2.

We define a virtual line source for each receptor point, to include the contribution from the part of the line source located upwind of the receptor. A line perpendicular to the wind velocity is drawn from the receptor point towards the line source. This line divides the line source into ‘upwind ’ and ‘downwind ’ parts. The upwind part of the source is then used for defining the virtual line source. For instance, the virtual line sources having their centres at O and O’ correspond to the receptor point Ri, and the receptor points R% and R3, respectively.

B

Figure 2. The geometry of the numerical solution near the source. The line source is AB, Ri are the receptor points and u is the wind velocity. The points O and O’ are the centres of the virtual line sources perpendicular to the wind direction.

The effective distance from the line source (used in the computation of the dispersion parameters) is defined as the distance of the receptor point from the line source, measured along the wind coordinate (x,). 21

3.2 The validity of the numerical solution

The CAR-FMI model computations were compared with CO concentrations obtained by the standard Gaussian UDM-FMI model (Karppinen et aL, 1996a), using the same dispersion parameters as the finite line source model. The line source of length 100 m was divided into 1000 point sources, having the same total emission rate as the original line source. The concentrations of carbon monoxide were computed at 650 receptor points, using a Cray X-MP supercomputer. The meteorological hourly time series for one year was used in the computations.

Various statistical error measures were computed, including the mean absolute error (MAE) and the root mean square error (RMSE)

«“-“E|vco|/co (25)

(26) where the subscripts p and o refer to concentrations predicted by the finite line source model (CAR-FMI) and the multiple point source model (UDM-FMI), respectively. N is the number of receptor points.

Figures 3 and 4 present the results of the comparison.

The deviations between the predictions of the two models were greatest near the source, with maximum values at theend points of the source. An examination of the results over a larger area shows that at greater distances from the source, MAE vanishes asymptotically. It can also be shown that thelargest errors occur for cases in which the wind speed is nearly parallel with the line source.

In summary, the overall differences are reasonable; for instance, the MAE is smaller than 5 - 10 % and RMSE is smaller than 25 % over the major part of the area considered. We conclude that the applied mathematical approximation, equation (4), can be used with confidence in models designed for operational use. 22

Figure 3. The mean absolute error (%) of the finite line source solution. 23

i i i | i i

Figure 4. The root mean square error (%) of the finite line source solution.

In summary, the overall differences are reasonable, for instance, the MAE is smaller than 5 - 10 % and RMSE is smaller than 25 % in the major part of the area considered. We conclude that the applied mathematicalapproximation, equation (4), can be used with confidence in models designed for operational use. 24

4. The integrated model

The overall structure of the model is shown in Figure 5. The model includes an emission model, a treatment of the meteorological and background concentration time series, a dispersion model, computation of the statistical concentration parameters and a graphical Windows-based user interface. The statistical analysis of the computed time series of concentrations facilitates a direct comparison of results with air quality guidelines. The emission model is described in Appendix B.

The relevant meteorological parameters for the model are evaluated using data produced by a meteorological pre-processing model. This model has been developed at our institute. For a detailed description of the model, the reader is referred to Karppinen et al (1996a and b). The model is based mainly on the energy budget method by van Ulden and Holtslag (1985).

CAR-FMI

Input module

Emission Meteorological model time series

Dispersion model

Gaussian equations Chemical Dispersion parameters transformation

Output module

Figure 5. The overall structure of the model.

The pre-processing model utilises the synoptic weather observations and meteorological soundings. The model estimates the hourly time series of the relevant atmospheric turbulence parameters (the Monin-Obukhov length scale, the friction velocity and the convective velocity scale) and the boundary layer height. The boundary layer height is analysed from atmospheric sounding observations. 25

The program execution time depends on the length of the meteorological time series, the number of line sources and receptor points, and the number of chemical compounds to be evaluated. The program has been implemented on a personal computer and on a main- frame computer. The main-frame version has been vectorized and optimized, and is executed on the Cray C94 supercomputer.

The main-frame version is needed, if the amount of input and receptor data is very large. Some applications require thousands of finite line sources and receptor points, and the meteorological hourly time series for several years. On the other hand, minor applications can easily be computed with the personal computer version. For instance, for hourly meteorological data for one year, including one line source, twenty receptor points, and three compounds (CO, NO, and N02), one program execution on a 486 PC with an arithmetic coprocessor takes less than five minutes.

The program has been structured in a strictly modular way to permit an easy extension of new features. In the personal computer version of the model, the user inputs the data and handles the results using a Windows-based user-friendly menu system. The results can be printed in graphical and tabulated form. An on-line help system is available in English and in Finnish.

5. The meteorological and background concentration data

The model has also been designed to be utilizable by non-expert users. However, the evaluation of the meteorological data and background concentrations (for NO, N02, 03 and CO) requires expertise. We have therefore produced ready-made databases for the relevant meteorological data and regional background concentrations. Both the spatial and temporal variations of meteorological conditions and background concentrations have been taken into account in collecting the databases.

5.1 The meteorological data

Climatological conditions vary substantially in different parts of Finland. Figure 6 shows the definition of the climatological zones, which is based on the classification by Solantie (1990). We have assumed that these zones represent with reasonably accuracy the long ­ term variations of the relevant dispersion parameters.

We have applied the data from selected meteorological stations in these zones: Helsinki- Vantaa (zone 1), Jyvaskyla (zone 2) and Rovaniemi (zone 3) airports. The relevant atmospheric turbulence parameters (the Monin-Obukhov length scale, the friction velocity and the convective velocity scale) and the boundary layer height have been evaluated using the meteorological pre-processing model The meteorological databases include hourly pre- processed meteorological parameters for one year, 1993. 26

iRovaniemit

Oulanka

Ahtari

Jyvaskyla

Luukki Helsinki

Figure 6. Finnish climatological zones of (based on Solantie, 1990). The meteorological stations are marked by a dot (•) and the monitoring stations for background concentrations by a triangle (A).

5.2 The background concentrations

The background concentration consists of regional and urban contributions. The evaluation of the urban background concentrations is a complex task. It depends, among other things, on the size and geometry of the city, the location and strength of major mobile and stationary sources and the average meteorological conditions. An example of the estimation of urban background concentrations, based on experimental results from an urban measurement network, is given in Harkonen et aL (1996). We focus in the following on the evaluation of the regional background concentrations.

5.2.1 The regional variation of background concentrations

Let us consider the selection of measurement stations for obtaining regionally representative background concentrations. We have selected one measurement station for each of the climatological zones shown in Figure 6.

Typical ozone concentration ranges at some urban and regional measuring stations in Finland are presented in Table 2. Both the annual and daily O3 -concentrations are the lowest in central Helsinki (the Toolo station) due to the photochemical consumption of O3 in the oxidation of NO. The values at the Uto station are highest, due to long-range transport from Central and Eastern Europe. 27

Table 2. Ozone concentrations (fig m'3) at selected urban and regional measuring stations in Finland in 1991-1993. The zone numbers refer to Figure 5. The values in the Helsinki metropolitan area and at the regional background stations have been taken from Aamio et al. (1995) and Leinonen and Juntto (1991,1992,1993), respectively.

Ozone concentration (jig m"3) 1991-1993 Station Annual average Highest daily average Helsinki Metropolitan Area (zone 1) T06I6, central 25-32 81- 90 Tikkurila, suburban 38-46 94-113 Luukki, rural 46-58 97 - 131 Regional background stations Uto, (zone 1) 71-75 114-138 Virolahti, (zone 2) 54-60 101-114 Ahtari, (zone 2) 54-65 101 - 124 Oulanka, (zone 3) 64-70 110-147

We consider the concentration values at Luukki as being best representative of the regional background in zone 1. Based on similar considerations, representative regional background stations have also been selected for the other zones: Ahtari (zone 2) and Oulanka (zone 3).

5.2.2 The temporal variation of background concentrations

The background concentrations also have a distinct temporal variation, including diurnal, weekly and monthly cycles. Furthermore, the nature of these variations varies from pollutant to pollutant. The application of temporally constant (for instance, annual average) background values is therefore not acceptable.

It would also be problematic to apply the measured hourly concentration time series directly. Measured concentrations may include high episodic concentrations, originating either from local or regional sources, or from long-range transport. Our objective is to obtain background concentrations which would be reasonably accurate for a period of several years.

We have therefore computed the diurnal hourly average concentrations for each month. This procedure averages the incidental anomalously high hourly or daily concentrations, but preserves the proper diurnal and monthly concentration cycles. Clearly, the weekly variation is also averaged out of the data. The procedure produces a matrix of 12 times 24 background concentration values, for each pollutant and each measurement station.

The above-mentioned method was used to produce the hourly time series of the background concentrations of O3 and NO*. The NO and NO2 concentrations were then computed by assuming a photostationary state, and using the NO* and O3 concentrations, 28

the intensity of solar radiation and the ambient temperature. The regional CO concentration was assumed to be negligible, compared with the corresponding urban and roadside concentrations.

6. Conclusions

An operational model has been developed for the dispersion of pollution from a road network. The model is based on a partly-analytic solution of the Gaussian diffusion equation for a finite line source. Dispersion parameters are evaluated using data produced by a meteorological pre-processing model The overall model also includes an emission model, treatment of the meteorological and background concentration time series, a statistical analysis of the computed time series of concentrations and a graphical Windows- based user interface.

The validity of the analytic solution presented has been tested against more complex numerical computations by dividing the road into a series of elements, from which incremental concentrations are then computed and summed. This comparison showed that the overall differences were reasonable, i.e., smaller than 5-10%, except for very near the end points of the finite line source. Our conclusion is that the applied mathematical approximation can be used with confidence in an operational model.

The chemical transformation is modelled by a modified version of the discrete parcel method (DPM), called the receptor-oriented discrete parcel method. In this model the amount of background air available for the chemical reactions increases correctly with increasing transport distance. This is a model improvement, compared with the original source-oriented DPM. However, neither version of the DPM explicitly allows for the interaction of the chemical reactions and the physical mixing processes, nor for the influence of the pollutant concentration profiles within the plume. The modelling of these processes would require the application of a more complex numerical solution.

A simple method has been presented for evaluating the meteorological and regional background concentration data operationally. The method is based on a climatological classification of the country into three zones. The meteorological data from one station in each zone is then analysed by a meteorological pre-processing model, and used as input data for the dispersion computations. Similarly, the background concentration data from one station in each zone is processed by a statistical technique, whichcomputes the diurnal hourly average concentrations for each month, based on the measured hourly values.

In 1994 an experimental measurement campaign was conducted for testing the model near a major road in a suburban environment (Turunvayla, Espoo, Finland). Walden et al. (1995) have discussed selected experimental results, and Harkonen et al. (1996) have compared the measured and predicted results. The comparison showed that the overall agreement of the measured and predicted values for NOx and N02 concentrations was fairly good. However, the internal variation of the data is substantial, due to the many disturbing factors in a suburban area. 29

A new measurement campaign has also been conducted for model validation purposes near a major road in a rural area (Elimaki, Finland, 1995). The analysis of the measured data from this campaign is in progress.

7. References

Aamio, P., Koskentalo, T. and Hamekoski, K., 1995. Air Quality in the Helsinki metropolitan area in 1994. YTV C 1995:7 (in Finnish).

Benson, ?., 1984. CALINE4 - a dispersion model for predicting air pollutant concentrations near roadways. FHWA/CA/TL-84/15. California Department of Transportation, Sacramento, CA.

Benson, P., 1992. A review of the development and application of the CALINE3 and 4 models. Atmos. Environ. 26B:3. p. 379-390.

Berkowicz, R., Valkonen, E., Kukkonen, J., Harkonen, J., 1996. Numerical comparisons of chemical transformation submodels in road dispersion models. Internal report. National Environmental Research Institute, Roskilde, 8 p.

Berlyand, M.E., Burenin, N.S., Genikhovich, E.L., Onikul, R.I., Panfilova, G.A. and Tsyro, 5. G., 1990. Experimental investigations of atmospheric pollution due to motor . Proceedings of the Soviet American symposium on mobile-source air pollution. Novgorod, p. 152.

Cadle, S., Chock, J., Heuss, J. & Monson, P., 1976. Results of the General Motors sulfate dispersion experiments, General Motors Research Publication, GMR-2107.

Chock, D.P, 1978. A simple Line-Source Model for Dispersion Near Roadways, Atmos. Environ.12. p.823 - 829.

Csanady, G. T. ,1972. Crosswind Shear Effects on Atmospheric Diffusion. Atmos. Environ. 6, p.221 - 232.

Eerens, H., C., Sliggers, C., J. and van den Hout, K., D., 1993. The Car Model: The Dutch method to determine city street air quality. Atmos. Environ. 27B, p.389 - 399.

Eskridge, R. and Rao, S., T., 1986. Turbulent Diffusion Behind Vehicles: Experimentally Determined Turbulence Mixing Parameters. Atmos. Environ. 20. p. 851- 860.

Eskridge, R. and Catalano, J., 1987. ROADWAY - A numerical model for predicting air pollutants near highways - user's guide. EPA-68-02-4106. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 125 p.

Finnish Road Administration, 1990. KEHAR 2.0, User handbook (in Finnish). T1BL 703603. Helsinki, 65 p. + appendices 30

Gryning, S.E., Holtslag, A.A.M., Irwin, J.S. and Sievertsen, B., 1987. Applied Dispersion Modelling Based on Meteorological Scaling Parameters. Atmos. Environ.21. p. 79 - 89.

Hertel, O. and Berkowicz, R., 1989. Modelling NO% concentrations in a street canyon. DMULuft-A131. National Environmental research Institute, Roskilde, 31 p.

Harkonen, J., Valkonen, E., Kukkonen, J., Rantakrans, E., Jalkanen, L. and Lahtinen, K, 1995. An operational dispersion model for predicting pollution from a road. International Journal of Environment and Pollution. Vol. 5. Nos. 4-6. 602 - 610.

Harkonen, J., Walden, J. and Kukkonen, J., 1996. Comparison of model predictions and measurements near a major road in an urban area. Proceedings of the 4th Workshop on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. 6-9 May 1996, Oostende, Belgium, 8 p.

Janicke L., Kost, W., D. and Rockle, R., 1994. Modelling of motor immisions in a street system by combination of Lagrange models and surface wind field simulation in complex city structures. Meteorol. Zeitschrift. N.F.3. p. 172 - 175.

Karppinen, A, Kukkonen, J., Nordlund, G., Rantakrans, E., and Valkama, I., 1996a. A dispersion modelling system for urban air pollution. Finnish Meteorological Institute. Publications on Air Quality. Helsinki, 30 p.

Karppinen, A, Jofire, S. and Vaajama, P., 1996b. Boundary layer parametrization for Finnish regulatory dispersion models. The Proceedings of the 4^ Workshop on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes. 6-9 May 1996, Oostende, Belgium, 8 p. (in print).

Kerminen V.-M. and Wexler, A, S., 1996. The occurrence of sulfuric acid-water nucleation in plumes: urban environment Tellus 48B. 65 - 82.

Kono, H. and Ito, S., 1990. A micro-scale dispersion model for motor vehicle exhaust gas in urban areas - OMG VOLUME-SOURCE model. Atmos. Environ. 24B:2. p. 243-251.

Leinonen, L. and Juntto, S., eds. 1991, 1992 and 1993. Air Quality Measurements, Finnish Meteorological Institute. Helsinki. .

Luhar, A. and Patil, R., 1989. A General Finite Line Source Model for Vehicular Pollution Dispersion. Atmos. Environ. 23, p. 555 - 562.

Petersen, W., 1980. User's guide for HIWAY2, a air pollution model. EPA- 600/8-80-018. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 69 p.

Rao, S., T., Sedefian, L. and Czapski, U., H., 1979. Characteristics of Turbulence and Dispersion of Pollutants Near Major Highways. J. Appl. Meteorol. 18. p. 283 - 293.

Rao, S., T. and Keenan, M., T., 1980. Suggestions for Improvement of the EPA-HTWAY Model. J. Air Pollution Control Association 30. p. 247 - 256. 31

Seinfeld, J.H., 1986. Atmospheric chemistry and of air pollution. John Wiley & Sons. New York, 738 p.

Solantie, R., 1990. The climate of Finland in relation to its , ecology and culture, Finnish Meteorological Institute Contributions. No 2. Helsinki, Finland. van UIden, AP. and Holtslag, AAM, 1985. Estimation of Atmospheric Boundary Layer Parameters for Difiusion Applications. Journal of Climate and Applied Meteorology. 24. p. 1196.-. 1207.

Walden, J., Harkonen, J., Pohjola, V., Kukkonen, J. and Kartastenpaa, R., 1995. Vertical concentration profiles in urban conditions - comparison of measurements and model predictions. In: Anttila, P. et aL (ed.), Proceedings of the 10th World Clean Air Congress. Espoo, Finland, May 28 - June 2, 1995. VoL 2. The Finnish Air Pollution Prevention Society, Helsinki, p. 268 (4 pages). 32

Appendix A. Vertical wind speed profile

According to surface layer similarity theory, the mean wind speed u(z) can be written as a function of height (for instance, van Ulden and Holtslag, 1985) as follows:

u* u(z) = —f(z0,z,L) , where (Al)

where u, is the friction velocity, k is the von Karman constant (= 0.4), z0 is the roughness length, L is the Monin-Obukhov length and is the influence function for momentum flux.

We assume that the third term in equation (Al) is negligible compared with the other two terms. The wind speed can then be written in the form

(A2)

where z’ is the height of a reference level.

We apply the parametrisation of the function '¥M presented by van Ulden and Holtslag (1985). In unstable atmospheric conditions (L < 0)

(A3) where

and A = 16. (A4)

In neutral and stable conditions (L > 0),

(A5) 33

Appendix B. The emission model

The overall model includes a simple submodel for predicting vehicular CO and NOx - emissions. This model is based on the national emission factors of the traffic planning system KEHAR 2.0, developed by the Finnish Road Administration (1990). The model input values are the following: the speed limit and type of the road, the average number of vehicles per day, the percentage of heavy vehicles, and the year for the computations.

The emission from the traffic stream on the road per unit length and time, Q (mg/m s), can be written as

Q = Dqnvh , (Bl) where D is a numerical factor allowing for the dimensions (= h/3600 s), q is the emission factor per unit length of the road (g/km) and n Vh is the total number of vehicles per hour (1/h).

The emission factor is modelled as

* = ("bv % + + "Ac) ' PS) where n is the fraction of light or heavy traffic and e is the emission coefficient for various types of vehicles (g/km). The subscripts 1 and h refer to light and heavy vehicles, and w and c to vehicles without and with a catalytic converter, respectively. For instance, the first term in equation (Bl) corresponds to the contribution of light vehicles without a catalytic converter.

The percentages of catalytic converters are evaluated by the model, based on annual national data of the car fleet. The model also estimates the future development of the percentages of catalytic converters.

The values of the emission factors are computed based on data from the traffic planning system KEHAR 2.0. The emission factors depend on the speed limit and type of the road, and the number of vehicles per hour. The roads have been divided into various classes according to the speed limit and type of the road. The speed limit classes are 120 km/h, 100 km/h 80 km/h, 70 km/h, 60 km/h and 50 km/h. The two highest speed limit classes are defined separately for motorways and other roads.

For each of these classes, there are eight different emission factors, depending on the number of vehicles per hour. This dependence allows for the increase of emissions with increasing traffic congestion. The values of all the emission factors are based on laboratory- scale measurements.

The total number of vehicles per hour is modelled as

(B3) 34

where G is a numerical factor allowing for the dimensions (= d/24 h), x^ is the coefficient for the monthly variation of traffic, x d is the coefficient for the weekly variation of traffic, x, is the coefficient for the diurnal variation of traffic and n Vd is the average number of vehicles per day (1/d).

When computing an hourly time series of emissions, the value of x^, depends on the month, the value of x^ depends on the day of the week, and the value of xt depends on the day of the week and the hour of the day. All the values of the coefficients are based on empirical data, measured on major roads.

The model takes into account the influence of traffic congestion on the emission factors; however, it does not explicitly allow for vehicle acceleration, deceleration or idling. The emission predictions therefore tend to be less accurate for severely congested traffic, during the rush hours and near busy intersections. Ilmansuojelun julkaisuja — LuftvSrds publikationer — Publications on air quality

1. Kukkonen, Jaakko, 1987. Modelling of discharges and atmospheric dis ­ persion of toxic gases. 73 p.

2. Walden, Jari, Lattila, Heikki, Hypponen, Mauri, Plathan, Pekka and Virta- nen, Timo, 1987. Intercalibration of sulphur dioxide monitors. 63 p.

3. Bremer, Pia, 1987. Meteorological aspects of wet deposition in Southern Finland. 46 p.

4. Kukkonen, Jaakko ja Savolainen, Anna Liisa, 1988. Vaarallisten aineiden leviamisen arviointi onnettomuustilanteissa. 112 s.

5. Lindfors, V. ja Joffre, S.M., 1989. liman kaasumaisten ja hiukkasmaisten epapuhtauksien kulkeutuminen, muutunta ja depositio Itameren alueella. 39 s.

6. Vesala, Timo, Kukkonen, Jaakko and Kulmala, Markku, 1989. A model for evaporation of freely falling droplets. 58 p.

7. Kulmala, Antti, Leinonen, Liisa ja Saynatkari, Tapani, 1990. Tausta- asemien ilmanlaatu Suomessa 1980-1986. 201 s.

8. Parviainen, Maija and Joffre, Sylvain M., 1991. Weekly variations of sulp­ hur dioxide and aerosol sulphate concentrations at Finnish background stations. 43 p.

9. Riikonen, Kari, Kukkonen, Jaakko, Nikmo, Juha ja Savolainen, Anna Lii­ sa, 1991. Helppokayttoinen laskentaohjelmisto vaarallisten aineiden onnettomuuksien seurauksien arvioimiseksi. 40 s.

10. Heikkila, Outi, 1991. liman epapuhtauksien vaikutusten arvioiminen infra- punavalokuvauksen avulla. Menetelmatutkimus mannyn neulasilla liike- nneymparistossa. - IR-photography for conifer forest damage assessment. Method study in traffic environment. 76 p.

11. Valkonen, Esko, 1993. Malli typen oksidien muutunnalle katukuilussa. 57 s.

12. Ilvessalo, Pekko, 1992. Ilmanlaatuindeksit ja niiden soveltaminen Suo- men kaupunkien ilmanlaadun seurantaan. 36 s.

13. Riikonen, Kari, Kukkonen, Jaakko, Nikmo, Juha and Savolainen, Anna Liisa, 1992. Laskentaohjelmisto kemikaalionnettomuuksien seurausten arvioimiseksi. 33 s. 14. Hongisto, Marke, 1993. A simulation model for the transport, transforma ­ tion and deposition of oxidized nitrogen compounds in Finland. Technical description of the model. 51 p.

15. Bremer, Pia, 1993. Assessment of two methods to predict SOa concentra ­

tions in the Helsinki area. 42 p.

16. Laine, Elina, Jokinen, Juhani ja Meinander, Outi, 1993. liman epapuhtauk- sien ilmeneminen Kaakkois-Suomen metsissa. 70 s.

17. Paatero, Jussi, Mattsson, Rolf and Hatakka Juha, 1994. Measurements of airborne radioactivity in Finland, 1983-1990, and applications to air quality studies. 106 p.

18. Pakkanen, T.A. et al., 1994. Nordic HNO3/NO3- and NH3/NH4+ gas/particle intercomparison in Helsinki, 11-22 May, 1992.125 p.

19. Kangas, Leena, Juntto, Sirkka, Laurila, Tuomas and Nordlund Goran, 1994. Comparison of EMEP model predictions and observations in Fin ­ land. 59 p.

20. Ruoho-Airola, Tuija ja Leinonen, Liisa, 1994. Ympariston yhdennetty seu- ranta. Mittaustuloksia 1991 - 1992, laskeuman yhteenveto 1987 -1992. 67

s.

21. Rantakrans, Erkki ja Savunen, Tarja, 1995. Hajuyhdisteiden leviamisen arviointi. 70 s.

22. Hypponen, M. and Walden, J. A., 1996. A system for vertical profile measu­ rements of sensible heat and chemical concentrations near the ground surface. 55 p.

23. Harkonen, Jari, Valkonen, Esko, Kukkonen, Jaakko, Rantakrans, Erkki, Lahtinen, Kimmo, Karppinen, Ari and Jalkanen, Liisa., 1996. A model for the dispersion of pollution from a road network. 34 p.