Sensitivity of ADOM dry deposition velocities to input parameters: A comparison with measurements for SO2 and NO2 over three land use types

Abstract

Dry deposition velocity measurements of SO₂ and NO₂ over a deciduous forest, a carrot field and a snow surface are compared with estimates obtained from the dry deposition module in the regional Eulerian Acid Deposition and Oxidant Model (ADOM). The comparison with measurements taken in the fall and winter shows large model overestimates, sometimes as large as a factor of 5. The NO₂ estimates are particularly poor and support existing evidence that models that employ the constant flux assumption for NO₂ are inadequate. The canopy and the snow surface resistances are the largest contributors to the total resistances for SO₂ and NO₂, except for situations in which some of the snow turns into liquid water, when the aerodynamic resistance becomes important.

Increasing the magnitudes, taken from measurements, of the ADOM original values for the stomatal, cuticle, ground and snow resistances and decreasing the NO₂ mesophyll resistance and the Leaf Area Index (LAI) yield improved model results, particularly for SO₂, reducing the error by almost a factor of 5 at times. The new estimates compare favourably with those from a model that includes Wesely's canopy resistance parametrization. Over snow the NO₂ estimates are improved by as much as a factor of 6. Observed deposition velocities for SO₂ vary from 0 to 0.65 cm s^?2 over a deciduous forest, 0 to 0.60 cm s^?2 over a carrot field and are generally less than 0.05 cm s^?2 over snow.     

An experimental study of sulfur and NOx fluxes over grassland

Three independent sulfur sensors were used in a study of sulfur eddy fluxes to a field of wheat stubble and mixed grasses, conducted in Southern Ohio in September, 1979. Two of these sensors were modified commercial instruments; one operated with a prefilter to measure gaseous sulfur compounds and the other with a denuder system to provide submicron particulate sulfur data. The third sensor was a prototype system, used to measure total sulfur fluxes. The data obtained indicated that the deposition velocity for gaseous sulfur almost always exceeded that for particulate sulfur; average surface conductances were about 1.0 cm s^–1 for gaseous sulfur in the daytime and about 0.4 cm s^–1 for particulate sulfur. The data indicate that nighttime values were probably much lower. The total sulfur sensor provided support for these conclusions. The boundary-layer quantity ln(z ₀ /z _H )was found to be 2.75 ± 0.55, in close agreement with expectations and thus providing some assurance that the site was adequate for eddy flux studies. However, fluxes derived using a prototype NO_x sensor were widely scattered, partially as a consequence of sensor noise but also possibly because of the effects of nearby sources of natural nitrogen compounds.Formerly with Argonne National Laboratory.     

The prediction of dust erosion by wind: An interactive model

We have devised a partial differential equation for the prediction of dust concentration in a thin layer near the ground. In this equation, erosion (detachment), transport, deposition and source are parameterised in terms of known quantities. The interaction between a wind prediction model in the boundary layer and this equation affects the evolution of the dust concentration at the top of the surface layer. Numerical integrations are carried out for various values of source strength, ambient wind and particle size. Comparison with available data shows that the results appear very reasonable and that the model should be subjected to further development and testing.Notation (x, y, z, t) space co-ordinates and time (cm,t) - u, v components of horizontal wind speed (cm s^–1) - u _g, v_g components of the geostrophic wind (cm s^–1) - V=(u²+v²)^1/2 (cm s^–1) - (û v)= 1/(h – k) _k ^h(u, v)dz(cm s^–1) - V _* friction velocity (cm s^–1) - z ₀ roughness length (cm) - k ₁ von Karman constant =0.4 - V _d deposition velocity (cm s^–1) - V _g gravitational settling velocity (cm s^–1) - h height of inversion (cm) - k height of surface layer (cm) - potential temperature (°K) - _gr potential temperature at ground (°K) - _K potential temperature at top of surface layer (°K) - P pressure (mb) - P ₀ sfc pressure (mb) - C _p/C_v - (t)= /z lapse rate of potential temperature (°K cm^–1) - A(z) variation of wind with height in transition layer - B(z) variation of wind with height in transition layer - Cd drag coefficient - C _HO transfer coefficient for sensible heat - C dust concentration (g m^–3) - C _K dust concentration at top of surface layer (g m^–3) - D(z) variation with height of dust concentration - u, v, w turbulent fluctuations of the three velocity components (cm s^–1) - A ₁ constant coefficient of proportionality for heat flux =0.2 - Ri Richardson number - g gravitational acceleration =980 cm s^–2 - Re Reynolds number = - D _s thickness of laminar sub-layer (cm) - v molecular kinematic viscosity of air - coefficient of proportionality in source term - dummy variable - t time step (sec) - n time index in numerical equations On sabbatical leave at University of Aberdeen, Department of Engineering, September 1989–February 1990.     

HNO3 deposition to a deciduous forest

The deposition velocity (V _d) of nitric acid vapor over a fully leafed deciduous forest was estimated using flux/gradient theory. HNO₃ deposition velocities ranged from 2.2 to 6.0cm/s with a mean V _don the order of 4.0cms^-1. Estimates of V _dfrom a detailed canopy turbulence model gave deposition velocities of similar magnitude. The model was used to investigate the sensitivity of V _dto the leaf boundary-layer resistance and leaf area index (LAI). Although modeled deposition velocities were found to be sensitive to the parameterization of the leaf boundary-layer resistance, they were less sensitive to the LAI. Modeled V _d's were found to peak at LAI = 7.     

An empirical expression for aerodynamic resistance in the unstable boundary layer

In unstable conditions, the set of equations defining the aerodynamic resistance to sensible heat transfer, r _a , cannot be solved analytically. An iterative technique must be used to obtain r _a exactly, but this is cumbersome and time consuming. In this paper, a new, empirical equation is presented relating the ratio, Q, of the aerodynamic resistances in neutral and unstable conditions, to the bulk Richardson number, Ri _B . The equation takes the form Q = a + b(–Ri) ^c , where a, b and c are empirical functions of (z – d)/z _om . This model is shown to predict r _awith a mean absolute error of 0.06 s m^–1 over the ranges -15 < Ri _B < 0 and 10 < (z – d)/z _om < 2300. Statistical comparison with other equations that have been proposed for r _a in unstable conditions indicates the superior precision of the model presented here.     

Convective heat transfer measurements of plants in a wind tunnel

Heat transfer was studied between intact leaves of various sizes and shapes in vivo under free and forced air conditions. Use of a wind tunnel and a microwave transmitter to heat the leaves facilitated measurements of convective, along with radiative and evaporative, heat losses from plant leaves. Knowledge of input energy, analysis of cooling curves, and established formulae, respectively, formed the basis of the steady-state, unsteady-state, and analytical methods for the determination of heat transfer coefficients.Typical values of steady-state free convection coefficients for Peperomia obtusifolia varied from 1.5 × 10^–4 to 1.9 × 10^–4 cal cm^–2 s^–1 C^–1 as the temperature difference was increased from 5.9 to 9.6°C, whereas the forced convection coefficient was found to be 4.2 × 10^–4 cal cm^–2 s^–1 C^–1 at 122 cm s^–1 wind velocity. For egg-plant, this value was about 9 × 10^–4 cal cm^–2 s^–1 C^–1 at 488 cm s^–1 wind velocity. Convection coefficients as determined under steady-state conditions are compared with those of the unsteady-state and with analytical values for a single leaf and leaves of three different plants. In general, experimental values were found to be higher than the analytical ones.     

Sulfur dioxide plume dispersion and subsequent absorption by coniferous forests: Field measurements and model calculations

A Forest SO₂ Absorption Model (ForSAM) was developed to simulate (1) SO₂ plume dispersion from an emission source, (2) subsequent SO₂ absorption by coniferous forests growing downwind from the source. There are three modules: (1) a buoyancy module, (2) a dispersion module, and (3) a foliar absorption module. These modules were used to calculate hourly abovecanopy SO₂ concentrations and in-canopy deposition velocities, as well as daily amounts of SO₂ absorbed by the forest canopy for downwind distances to 42 km. Model performance testing was done with meteorological data (including ambient SO₂ concentrations) collected at various locations downwind from a coal-burning power generator at Grand Lake in central New Brunswick, Canada. Annual SO₂ emissions from this facility amounted to about 30,000 tonnes. Calculated SO₂ concentrations were similar to those obtained in the field. Calculated SO₂ deposition velocities generally agreed with published values.Notation c air parcel cooling parameter (non-dimensional) - E foliar absorption quotient (non-dimensional) - f areal fraction of foliage free from water (non-dimensional) - f _w SO₂ content of air parcel - h height of the surface layer (m) - H height of the convective mixing layer (m) - H _stack stack height (m) - k time level - k _drag coefficient of drag on the air parcel (non-dimensional) - K _z eddy viscosity coefficient for SO₂ (m²·s^–1) - L Monin-Obukhov length scale (m) - L _A single-sided leaf area index (LAI) - n degree-of-sky cloudiness (non-dimensional) - N number of parcels released with every puff (non-dimensional) - PAR photosynthetically active radiation (W m^–2) - Q emission rate (kg s^–2) - r _b diffusive boundary-layer resistance (s m^–1) - r _c canopy resistance (s m^–1) - r _cuticle cuticular resistance (s m^–1) - r _m mesophyllic resistance (s m^–1) - r _s stomatal resistance (s m^–1) - r _exit smokestack exit radius (m) - R normally distributed random variable with mean of zero and variance of t (s) - u _* frictional velocity scale, (m s^–1) - v lateral wind vector (m s^–1) - v _d SO₂ dry deposition velocity (m s^–1) - VCD water vapour deficit (mb) - z _can mean tree height (m) - Z zenith position of the sun (deg) - environmental lapse rate (°C m^–1) - dry adiabatic lapse rate (0.00986°C m^–1) - von Kármán's constant (0.04) - _B vertical velocities initiated by buoyancy (m s^–1) - canopy extinction coefficient (non-dimensional) - ()a denotes ambient conditions - ()can denotes conditions at the top of the forest canopy - ()h denotes conditions at the top of the surface layer - ()H denotes conditions at the top of the mixed layer - ()s denotes conditions at the canopy surface - ()p denotes conditions of the air parcels     

Rate constants for the reactions of the NO3 radical with HCOOH/HCOO− and CH3COOH/CH3COO− in aqueous solution between 278 and 328 K

The kinetics of the aqueous phase reactions of NO₃ radicals with HCOOH/HCOO^– and CH₃COOH/CH₃COO^– have been investigated using a laser photolysis/long-path laser absorption technique. NO₃ was produced via excimer laser photolysis of peroxodisulfate anions (S₂O ₈ ^2– ) at 351 nm followed by the reactions of sulfate radicals (SO ₄ ^– ) with excess nitrate. The time-resolved detection of NO₃ was achieved by long-path laser absorption at 632.8 nm. For the reactions of NO₃ with formic acid (1) and formate (2) rate coefficients ofk ₁=(3.3±1.0)×10⁵ l mol^–1 s^–1 andk ₂=(5.0±0.4)×10⁷ l mol^–1 s^–1 were found atT=298 K andI=0.19 mol/l. The following Arrhenius expressions were derived:k ₁(T)=(3.4±0.3)×10¹⁰ exp[–(3400±600)/T] l mol^–1 s^–1 andk ₂(T)=(8.2±0.8)×10¹⁰ exp[–(2200±700)/T] l mol^–1 s^–1. The rate coefficients for the reactions of NO₃ with acetic acid (3) and acetate (4) atT=298 K andI=0.19 mol/l were determined as:k ₃=(1.3±0.3)×10⁴ l mol^–1 s^–1 andk ₄=(2.3±0.4)×10⁶ l mol^–1 s^–1. The temperature dependences for these reactions are described by:k ₃(T)=(4.9±0.5)×10⁹ exp[–(3800±700)/T] l mol^–1 s^–1 andk ₄(T)=(1.0±0.2)×10¹² exp[–(3800±1200)/T] l mol^–1 s^–1. The differences in reactivity of the anions HCOO^– and CH₃COO^– compared to their corresponding acids HCOOH and CH₃COOH are explained by the higher reactivity of NO₃ in charge transfer processes compared to H atom abstraction. From a comparison of NO₃ reactions with various droplets constituents it is concluded that the reaction of NO₃ with HCOO^– may present a dominant loss reaction of NO₃ in atmospheric droplets.     

A numerical modeling study of the marine boundary layer over the Gulf Stream during cold air advection

A two-dimensional mesoscale model has been developed to simulate the air flow over the Gulf Stream area where typically large gradients in surface temperature exist in the winter. Numerical simulations show that the magnitude and the maximum height of the mesoscale circulation that develops downwind of the Gulf Stream depends on both the initial geostrophic wind and the large-scale moisture. As expected, a highly convective Planetary Boundary Layer (PBL) develops over this area and it was found that the Gulf Stream plays an important role in generating the strong upward heat fluxes causing a farther seaward penetration as cold air advection takes place. Numerical results agree well with the observed surface fluxes of momentum and heat and the mesoscale variation of vertical velocities obtained using Doppler Radars for a typical cold air outbreak. Precipitation pattern predicted by the numerical model is also in agreement with the observations during the Genesis of Atlantic Lows Experiment (GALE).List of Symbols u east-west velocity [m s^–1] - v north-south velocity [m s^–1] - vertical velocity in coordinate [m s^–1] - w vertical velocity inz coordinate [m s^–1] - gq potential temperature [K] - q moisture [kg kg^–1] - scaled pressure [J kg^–1 K^–1] - U _g the east-south component of geostrophic wind [m s^–1] - V _g the north-south component of geostrophic wind [m s^–1] - vertical coordinate following terrain - x east-west spatial coordinate [m] - y north-south spatial coordinate [m] - z vertical spatial coordinate [m] - t time coordinate [s] - g gravity [m² s^–1] - E terrain height [m] - H total height considered in the model [m] - q _s saturated moisture [kg kg^–1] - p pressure [mb] - p ₀₀ reference pressure [mb] - P precipitation [kg m^–2] - vertical lapse rate for potential temperature [K km^–1] - L latent heat of condensation [J kg^–1] - C _p specific heat at constant pressure [J kg^–1 K^–1] - R gas constant for dry air [J kg^–1 K^–1] - R _v gas constant for water vapor [J kg^–1 K^–1] - f Coriolis parameter (2 sin ) [s^–1] - angular velocity of the earth [s^–1] - latitude [^o] - K _H horizontal eddy exchange coefficient [m² s^–1] - t integration time interval [s] - x grid interval distance inx coordinate [m] - y grid interval distance iny coordinate [m] - adjustable coefficient inK _H - subgrid momentum flux [m² s^–2] - subgrid potential temperature flux [m K s^–1] - subgrid moisture flux [m kg kg^–1 s^–1] - u _* friction velocity [m s^–1] - _* subgrid flux temperature [K] - q _* subgrid flux moisture [kg kg^–1] - w _* subgrid convective velocity [m s^–1] - z ₀ surface roughness [m] - L Monin stability length [m] - _s surface potential temperature [K] - k von Karman's constant (0.4) - v air kinematic viscosity coefficient [m² s^–1] - K _M subgrid vertical eddy exchange coefficient for momentum [m² s^–1] - K subgrid vertical eddy exchange coefficient for heat [m² s^–1] - K _q subgrid vertical eddy exchange coefficient for moisture [m² s^–1] - z _i the height of PBL [m] - h _s the height of surface layer [m]     

Mixed-layer heat advection and entrainment during the sea breeze

A model is developed to simulate the potential temperature and the height of the mixed layer under advection conditions. It includes analytic expressions for the effects of mixed-layer conditions upwind of the interface between two different surfaces on the development of the mixed layer downwind from the interface. Model performance is evaluated against tethersonde data obtained on two summer days during sea breeze flow in Vancouver, Canada. It is found that the mixed-layer height and temperature over the ocean has a small but noticeable effect on the development of the mixed layer observed 10 km inland from the coast. For these two clear days, the subsidence velocity at the inversion base capping the mixed layer is estimated to be about 30 mm s^–1 from late morning to late afternoon. When the effects of subsidence are included in the model, the mixed-layer height is considerably underpredicted, while the prediction for the mean potential temperature in the mixed layer is considerably improved. Good predictions for both height and temperature can be obtained when values for the heat entrainment ratio,c, 0.44 and 0.68 for these two days respectively for the period from 1000 to 1300 LAT, were used. These values are estimated using an equation including the additional effects on heat entrainment due to the mechanical mixing caused by wind shear at the top of the mixed layer and surface friction. The contribution of wind shear to entrainment was equal to, or greater than, that from buoyant convection resulting from the surface heat flux. Strong wind shear occurred near the top of the mixed layer between the lower level inland flow and the return flow aloft in the sea breeze circulation.Symbols c entrainment parameter for sensible heat - c _p specific heat of air at constant pressure, 1010 J kg^–1 K^–1 - d ₁ the thickness of velocity shear at the mixed-layer top, m - Q _H surface sensible heat flux, W m^–2 - u _m mean mixed-layer wind speed, m s^–1 - u _* friction velocity at the surface, m s^–1 - w subsidence velocity, m s^–1 - W subsidence warming,^oC s^–1 - w _e entrainment velocity, m s^–1 - w _* convection velocity in the mixed layer, m s^–1 - x downwind horizontal distance from the water-land interface, m - y dummy variable forx, m - Z height above the surface, m - Z _i height of capping inversion, m - Z _m mixed-layer depth, i.e.,Z _i–Z_s, m - Z _s height of the surface layer, m - lapse rate of potential temperature aboveZ _i, K m^–1 - potential temperature step atZ _i, K - u _h velocity step change at the mixed-layer top - _m mean mixed-layer potential temperature, K     

Sea surface winds derived from cloud motion vectors over the tropical Atlantic

The relationship between satellite-derived low-level cloud motion, surface wind and geostrophic wind vectors is examined using GATE data. In the trades, surface wind speeds can be derived from cloud motion vectors by the linear relation: V = 0.62 V _s + 1.9 m s^–1 with a mean scatter of ±1.3 m s^–1. The correlation coefficient between surface and satellite wind speed is 0.25. Considering baroclinicity, i.e., the influence of the thermal wind, the correlation coefficient does not increase, because of the uncertainty of the thermal wind vectors. The ratios of surface to geostrophic wind speed and surface to satellite wind speed are 0.7 and 0.8, respectively, with a statistical uncertainty of ±0.3. Calculations of the ratio of surface to geostrophic wind speed on the basis of the resistance law yield V/V _g = 0.8 ± 0.2, in agreement with experimental results. The mean angle difference between the surface and the satellite wind vectors amounts to - 18 °, taking into account baroclinicity. This value is in good agreement with the mean ageostrophic angle - 25 °.     

Evaporation from the reservoir of the High Aswan Dam, Egypt: A new comparison of relevant methods with limited data

Summary Previous estimates of average annual evaporation from the lake formed by the High Dam at Aswan, Egypt, fall in the range from 4.65 mm d^–1 to 7.95 mm d^–1. The difference between these limits, more than 7 billion m³ yr^–1 at the highest storage level, is nearly one-eighth the share by treaty of Egypt, and more than one-third of the share of the Sudan. It is also more than the estimated increase of the annual water need for Egypt between 1990 and 2000. This state of affairs renders proper management of the river flow for the sake of Egypt and the Sudan quite difficult. This paper compares the relevant methods of estimating evaporation from the limited data available.These methods are:water-balance, energy budget, bulk aerodynamic (Dalton),combination (Penman) andComplementary Relations Lake Evaporation (CRLE) model (Morton). The new estimates have a much narrower range, from 5.70 mm d^–1 to 7.05 mm d^–1, or only a bit more than 4% of the annual Nile flow below the High Aswan Dam. The average of these annual estimates of evaporation, after excluding the bulk aerodynamic method because of its severe limitations, is 6.0 ± 0.3 mm d^–1 or 20% less than the 7.5 mm d^–1 adopted by the irrigation authorities in Egypt and the Sudan. This difference corresponds to 3 billion m³ yr^–1 at the highest storage level or more than 5% of the annual outflow from the reservoir. Even when the higher estimates from the bulk aerodynamic method and from the Penman method with its usual wind function are included, the new average is still 15% less than the figure of 7.5 mm d^–1. The monthly distribution of the annual evaporation varies more widely with the method applied. Similar comparative studies in future, aiming at obtaining improved estimates of evaporation, require all the data relevant to all the methods to be collected properly for a common period of several years at relatively stable lake level.With 1 Figure     

Atmospheric Dry Deposition to Marble and Red Stone

This paper presents dry deposition flux and deposition velocity of atmospheric particles on white marble and red stone at Dayalbagh, a suburban site of semi arid region, which is 10 km away from the industrial sector of the Agra city where due to agricultural practices vegetation predominates. The wind speed at Agra is mostly in the range of 1–2 m s^–1. The atmospheric calm conditions at Agra in summer, monsoon, and winter seasons are 47%, 35%, and 76%, respectively. Industrial areas of the city are away from Dayalbagh and are located in the NE, E, SE, and SW sectors. The main industrial activities, which are in operation in Agra city and its outskirts, are foundry and forging industry. The other industrial activities in Agra are rubber processing, lime oxidation and pulverization, chemicals, engineering and brick refractory kilns. Dry deposition samples were collected on dry days on white marble and red stone (0.224 m × 0.224 m × 0.02 m) using surface washing method. Both slabs were fixed to an iron stand (1.5 m height) at an angle of about 80 from the horizontal and exposed for 24 h on the roof of the faculty building. The order of deposition flux on white marble is NH₄⁺ > Mg²⁺ > Ca²⁺ > Na⁺ > Cl^– > K⁺ > NO₃^– > SO₄^2– > F^– and that on red stone is NH₄⁺ > Mg²⁺ > Ca²⁺ > SO₄^2– > Na⁺ > NO₃^– > K⁺ > F^– > Cl^–. Average dry deposition flux of major ions varies from 3.4 to 128.5 M m^–2 d^–1. The sum of major cations on white marble and red stone are 516.4 and 450.4 eq m^–2 d^–1, respectively while sum of major anions are 425.3 and 400.4 eq m^–2 d^–1 on white marble and red stone, respectively. Higher deposition of all ions was observed when wind blows from NE as most of the Agra Iron foundries and Ferozabad glass industries lie in this direction. The mean values of dry deposition velocity of ions vary between 0.22 cm s^–1 to 1.49 cm s^–1. Deposition velocity for all ions is higher on white marble than red stone inspite of rougher surface of red stone as compared to white marble. This could be due to the chemical nature of white marble, which is made of dolomite and hence adds significant amount of ions by dissolution during washing. Seasonally the deposition velocity was highest in winter.     

Coastal CCN measurements at Mace Head with enhanced concentrations in strong winds

Surface measurements of cloud condensation nuclei (CCN) number concentration (cm⁻³) are presented for unmodified marine air and for polluted air at Mace Head, for the years 1994 and 1995. The CCN number concentration active at 0.5% supersaturation is found to be approximately log-normal for marine and polluted air at the site. Values of geometric mean, median and arithmetic mean of CCN number concentration (cm⁻³) for marine air are in the range 124–135, 140–150 and 130–157 for the two years of data. Analysis of CCN number concentration for high wind speed, U, up to 20 m s⁻¹ show enhanced CCN production for U in excess of about 10–12 m s⁻¹. Approximately 7% increase in CCN per 1 m s⁻¹ increase in wind speed is found, up to 17 m s⁻¹. A relationship of the form log₁₀CCN=a+bU is obtained for the periods March 1994 and January, February 1995 for marine air yielding values a of 1.70; 1.90 and b of 0.035 for both periods.     

Kinetics of the Gas-Phase Reactions of OH Radicals, NO3 Radicals and O3 with Three C7-Carbonyls Formed From The Atmospheric Reactions of Myrcene, Ocimene and Terpinolene

Rate constants for the gas-phase reactions of OH radicals, NO₃ radicals and O₃ with the C₇-carbonyl compounds 4-methylenehex-5-enal [CH₂=CHC(=CH₂)CH₂CH₂CHO], (3Z)- and (3E)-4-methylhexa-3,5-dienal [CH₂=CHC(CH₃)=CHCH₂CHO] and 4-methylcyclohex-3-en-1-one, which are products of the atmospheric degradations of myrcene, Z- and E-ocimene and terpinolene, respectively, have been measured at 296 ± 2 K and atmospheric pressure of air using relative rate methods. The rate constants obtained (in cm³ molecule^–1 s^–1 units) were: for 4-methylenehex-5-enal, (1.55 ± 0.15) × 10^–10, (4.75 ± 0.35) × 10^–13 and (1.46 ± 0.12) × 10^–17 for the OH radical, NO₃ radical and O₃ reactions, respectively; for (3Z)-4-methylhexa-3,5-dienal: (1.61 ± 0.35) × 10^–10, (2.17 ± 0.30) × 10^–12, and (4.13 ± 0.81) × 10^–17 for the OH radical, NO₃ radical and O₃ reactions, respectively; for (3E)-4-methylhexa-3,5-dienal: (2.52 ± 0.65) × 10^–10, (1.75 ± 0.27) × 10^–12, and (5.36 ± 0.28) × 10^–17 for the OH radical, NO₃ radical and O₃ reactions, respectively; and for 4-methylcyclohex-3-en-1-one: (1.10 ± 0.19) × 10^–10, (1.81 ± 0.35) × 10^–12, and (6.98 ± 0.40) × 10^–17 for the OH radical, NO₃ radical and O₃ reactions, respectively. These carbonyl compounds are all reactive in the troposphere, with daytime reaction with the OH radical and nighttime reaction with the NO₃ radical being predicted to dominate as loss processes and with estimated lifetimes of about an hour or less.     

Henry's law and the behavior of weak acids and bases in fog and cloud

Experimental data from two field experiments on ground based clouds were used to study the distribution of formic acid, acetic acid, ammonia and S(IV) species between liquid and gas phase. The ratio of the concentrations of these compounds between the phases during concurrent measurements was compared to ratios expected according to Henry's law (considering the pH influence). Large discrepancies of several orders of magnitude were seen. Three hypotheses have been investigated to explain the observed discrepancies: The existence of a microscale equilibrium which does not persist in a bulk sample, a thermodynamic shift of the equilibrium due to competing reactions, and nonequilibrium conditions due to mass transfer limitations. Approximate quantitative calculations show that none of these hypotheses is sufficient to explain all of the discrepancies, so a combination of different effects seems to be responsible for this observation. The same theoretical considerations also suggest that mass transfer limitation may be an important factor for highly soluble compounds. The data presented here indicates that it is not possible to simply extrapolate interstitial gas phase composition from measured bulk liquid phase concentrations of a fog or cloud.Notation [r _max] liquid phase molar uptake rate (mol l^–1 s^–1) - [A _g ] concentration ofA in gas phase (atm) - [A _l ] concentration ofA in liquid phase (mol l^–1) - [A _g , 0] concentration ofA in gas phase (atm) at time 0 - LWC liquid water content (g m^–3) - R universal gas constant (0.082 l atm mol^–1 K^–1 - D _g diffusivity (for all gases 0.1 cm² s^–1 was used) - K _H ^* effective Henry's law coefficient (mol l^–1 atm^–1) - t _f lifetime of fog droplet (s) - a droplet radius (cm) - accommodation coefficient - R factor of discrepancy - T temperature (K) - v mean molecular speed (cm s^–1) formic acid: 35 000 acetic acid: 31 000 ammonia: 58 000     

A three-resistance model of crop and forest evaporation

Summary The evaporation of deep crops such as forests is usually considered in terms of the two-resistance Penman-Monteith model, though this conflates two of the three resistances actually involved, i.e. the canopy resistancer _c between the transpiring leaves and the top of the canopy, and the resistancer _s due to the stomates of the leaves. A review of the literature on these and the aerodynamic resistancer _a (between the crop and the atmosphere) shows how distinctly different they are, and therefore how inappropriate it is to lump any two together.Once the soil has dried substantially,r _s depends approximately onM ^–2, whereM is the fractional available soil moisture.As regards grassed surfaces,r _a is 300/u s m^–1, whereu is the wind speed at 2 m.With 2 Figures     

Radiometrically determined skin temperature and scalar roughness to estimate surface heat flux. Part I: Parameterization of radiometric scalar roughness

A series of experiments carried out in a pasture field during a growing season, allowed a radiometric determination of the scalar roughness for sensible heatz _oh,r . The values ofz _oh,r are shown to vary over the range of 10^–1–10^–7m both diurnally and seasonally, and an existing theoretical model for the estimation of scalar roughness for sensible heat is found to be inappropriate for the precise estimation ofz _oh,r . To parameterizez _oh,r better, a multiple regression analysis was performed, with predictor candidates such as solar elevation, solar radiationR _s , leaf area index LAI, canopy height, the ratio of the solar radiation and the extraterrestrial radiationR _s /R _e , the ratio of the direct and the total solar radiationR _d /R _s , and the roughness Reynolds number among others. The best regression equation which usesR _s , LAI,R _s /R _e , andR _d /R _s is derived withr=0.75; with smaller numbers of predictors, values ofr tend to deteriorate gradually down tor=0.52 when only one predictor, LAI, was incorporated into the equation.     

Etude experimentale d'une relation entre le coefficient de frottement et le facteur de rafales en regime de vent faible et au-dessus d'une surface d'eau

In usual aerodynamic bulk formulas, the drag coefficient C _d has been best estimated in the 5 to 16 m s^–1 range of mean wind velocity; a value of 1.3 × 10^–3 is often considered for operational use. However, in the 0 to 5 m s^–1 range of mean wind velocity, corresponding to meteorological conditions of very light wind, experimental results have not resulted in any convincing agreement between various authors (Hicks et al., 1974; Wu, 1969; Kondo and Fujinawa, 1972; Mitsuta, 1973; Brocks and Krugermeyer, 1970).In the present paper, the drag coefficient is experimentally determined in conditions of very light wind and limited fetch (about 250 m). Due to this limited fetch, we have to be cautious in the extrapolation of our results to other sites. Nevertheless, some of experimental results are worth describing, considering the paucity of data in light wind conditions.Mean value and standard deviation (respectively 1.84 × 10^–3 and 1.24 × 10^–3) are obtained from 70 runs of 10-min duration. Mean wind velocities observed at 2 m above water surface are found to lie between 1.2 and 3.6 m s^–1. Whereas this mean value is in fair agreement with C _{d 10} = 1.3 × 10^–3, usually given for the 5 to 16 m s^–1 range (Kraus, 1972), the above value for the standard deviation seems too large to be left without further analysis.A more exhaustive analysis of the 70 values obtained for C _d shows that it depends on a parameter characteristic of longitudinal fluctuations of the wind velocity. A similar idea was put forward earlier by Kraus (1972). Relations between the drag coefficient and wind fluctuations may be tentatively given by: % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4qamaaBa% aaleaacaWGKbGaaGOmaaqabaGccqGH9aqpdaqadaqaaiabgkHiTiaa% igdacaGGUaGaaGimaiaaiEdacqGHRaWkcaaIXaGaaGinaiaac6caca% aIZaGaaGinamaalaaabaGaeq4Wdm3aaSbaaSqaaiaadwhacaGGNaaa% beaaaOqaaaaaaiaawIcacaGLPaaaruqqYLwySbacfaGaa8hEaiaa-b% cacaaIXaGaaGimamaaCaaaleqabaGaeyOeI0IaaG4maaaakiaabcca% caqGGaGaaeiiaiaabccacaqGXaGaaeOlaiaabAdacaqGGaGaaeyBai% aabccacaqGZbWaaWbaaSqabeaacaqGTaGaaeymaaaakiabgsMiJkqa% dwhagaqeamaaBaaaleaacaaIYaaabeaakiabgsMiJkaaiodacaGGUa% GaaGOnaiaab2gacaqGGaGaae4CamaaCaaaleqabaGaaeylaiaabgda% aaaaaa!634E!\[C_{d2} = \left( { - 1.07 + 14.34\frac{{\sigma _{u'} }}{{}}} \right)x 10^{ - 3} {\text{ 1}}{\text{.6 m s}}^{{\text{ - 1}}} \leqslant \bar u_2 \leqslant 3.6{\text{m s}}^{{\text{ - 1}}} \] and % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaGaam4qamaaBa% aaleaacaWGKbGaaGOmaaqabaGccqGH9aqpdaqadaqaaiabgkHiTiaa% iodacaGGUaGaaGioaiaaiAdacqGHRaWkcaaIZaGaaiOlaiaaiodaca% aI2aGaam4raaGaayjkaiaawMcaaerbbjxAHXgaiuaacaWF4bGaa8hi% aiaaigdacaaIWaWaaWbaaSqabeaacqGHsislcaaIZaaaaOGaaeilaa% aa!4B42!\[C_{d2} = \left( { - 3.86 + 3.36G} \right)x 10^{ - 3} {\text{,}}\] where _u/\-u ₂ and G, respectively, represent the standard deviation of u normalized with \-u ₂ and the longitudinal gust factor quoted in Smith (1974).We have established a relationship between these fluctuation parameters and the stability as given by a bulk layer Richardson number (between 0 and 2 m). These relations are given by: % MathType!MTEF!2!1!+-% feaafiart1ev1aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn% hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr% 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq-Jc9% vqaqpepm0xbba9pwe9Q8fs0-yqaqpepae9pg0FirpepeKkFr0xfr-x% fr-xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaWaaSaaaeaacq% aHdpWCdaWgaaWcbaGaamyDaiaacEcaaeqaaaGcbaGabmyDayaaraWa% aSbaaSqaaiaaikdaaeqaaaaakiabg2da9iaaicdacaGGUaGaaGymai% aaikdacqGHRaWkcaaIZaGaaiOlaiaaiIdacaaI1aGaaeiiaiaabkfa% caqGPbWaaSbaaSqaaiaabcdacaqGTaGaaeOmaaqabaaaaa!4802!\[\frac{{\sigma _{u'} }}{{\bar u_2 }} = 0.12 + 3.85{\text{ Ri}}_{{\text{0 - 2}}} \] and G=1.35+14.56 Ri_0–2. The increase in gustiness with stability is in qualitative agreement with Goptarev (1957)'s experimental results.In spite of the high-level correlation between C _d and _u/\-u ₂(G) on the one hand and between _u/\-u ₂(G) and Ri_0–2on the other hand, we found a poor relationship between C _d and Ri_0–2. It is worth noting too that the trend observed here for C _d to increase with stability is in complete disagreement with the usual theoretical expectation for C _d to decrease with increasing layer stability above water.

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E.R.A. du C.N.R.S. n 259.