NIST Special Publication 1158
 

Greenhouse Gas Emissions and Dispersion 

1. Optimum Placement of Gas Inlets on a Building 

Rooftop for the Measurement of Greenhouse Gas 

Concentration 

Kuldeep Prasad 

Anthony Bova 

James R. Whetstone 

Elena Novakovskaia 

http://dx.doi.org/10.6028/NIST.SP.1158 
 

 

 

 

 

 

 

NIST Special Publication 1158 

Greenhouse Gas Emissions and Dispersion 

1. Optimum Placement of Gas Inlets on a Building 

Rooftop for the Measurement of Greenhouse Gas 

Concentration 

Kuldeep Prasad 

Fire Research Division 

Engineering Laboratory 

Anthony Bova 

Excet, Inc. 

James R. Whetstone 

Special Programs Office 

Laboratory Programs 

Elena Novakovskaia 

Earth Networks, Inc. 

http://dx.doi.org/10.6028/NIST.SP.1158 

April 2013 

U.S. Department of Commerce 

Rebecca Blank, Acting Secretary 

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
 

 

Certain commercial entities, equipment, or materials may be identified in this 

document in order to describe an experimental procedure or concept adequately. 

Such identification is not intended to imply recommendation or endorsement by the 

National Institute of Standards and Technology, nor is it intended to imply that the 

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Special Publication 1158 

Natl. Inst. Stand. Technol. Spec. Publ. 1158, 26 pages (April 2013) 

http://dx.doi.org/10.6028/NIST.SP.1158 

CODEN: NSPUE2 
1

Greenhouse Gas Emissions and Dispersion

1. Optimum placement of gas inlets on a building rooftop for the

measurement of greenhouse gas concentrations

Kuldeep Prasad, Anthony Bova, James R. Whetstone, and Elena Novakovskaia

Abstract

Inverse atmospheric dispersion models are used to provide measurement-based, or “topdown”, estimates of greenhouse gas (GHG) emissions for comparison with input-based, or

“bottom-up”, estimates. To minimize uncertainty, inverse estimates require accurate measurements of GHG concentrations and meteorological data, and are improved when networks of

sensor sites are used in concert. To closely approximate free stream mixing ratio values, it is

widely-used practice to mount measurement equipment on isolated open-lattice towers to reduce

the potential influences of a boundary layer formed by the supporting structure. However, this

is often not possible when GHG concentrations are measured in urban environments, where

open locations are unavailable or the use of such towers would be prohibitively expensive. In

these environments, networks of rooftop-mounted sensors are more likely to be cost-effective

and simpler to implement. Unfortunately, the flat-topped buildings that are typical of urban

settings generate wind recirculation zones and turbulence that may interfere with rooftop trace

gas mixing ratio measurements. In this study, large eddy simulations (LES) of wind flow over

a large office building were performed to estimate the effect and potential error introduced by

performing measurements on building rooftops as compared with free stream or tower-based

measurements. Time dependent concentrations of carbon dioxide were computed at a number

of locations and heights above the roof of a tall building and compared with the original input signal. Simulation results are used to develop guidelines for optimum placement of sensors

on rooftop for accurate measurement of GHG mixing ratio that are necessary for atmospheric

inversion models.

INTRODUCTION

Inverse methods for greenhouse gas (GHG) emission involve using atmospheric measurements to

identify source location and emission flux [1]-[12]. Inversion techniques and top-down methods [2]

are a means to independently verify emissions inventory data obtained from bottom-up estimates [3].

While inaccuracies in bottom-up estimates stem from input parameters [13] [14], top-down methods

are subject to both error in measurements [8] and to uncertainty resulting from inverse dispersion

estimates [1], [4], [5]. Depending on the spatial and temporal scales of the inverse problem, small

uncertainties and loss of information in measurements may translate to large uncertainties in source

estimates [6] [16]. Measurements of GHG concentrations and meteorological data used in inverse

atmospheric dispersion models should therefore be as accurate and detailed as possible [9], [10].

GHG mixing ratio measurements for inversion analysis are usually obtained from sensors

mounted on isolated, open-lattice towers [10] (usually cell phone and communication towers at

a height of 50m to 100m above the ground) to reduce or eliminate the effect of the boundary layer

formed by the structure on observations. Inversion studies [1], [4], [5] indicate that source prediction capability improves significantly in the immediate vicinity of the tower location. Installation

of more measurement towers for obtaining GHG measurements and reducing the distance between

measurement location and the sources is a way of reducing uncertainty. However, finding suitably isolated tower locations in an urban environment can be difficult and expensive. Since urban

locations usually have tall buildings, there is a growing interest in mounting sensors on building

rooftops to measure GHG concentrations, provide high resolution GHG mixing ratio data at several

locations, and improve the accuracy of inversion analysis [3]. GHG measurements from building

1
 
rooftops (coupled with a footprint analysis) can also be used to design a network of observing points.

Note that installation cost [11] associated with rooftop measurements is generally far less than that

of installation on towers, and monetary losses can be correspondingly higher if the selected tower

location ultimately proves to be unsuitable for inversion analysis.

If rooftop mounted sampling points are to be used for inversion analysis or as test locations for

observing network design, then the measurements must be representative of GHG concentration in

the atmospheric boundary layer. GHG measurements on top of buildings must consider the influence of the boundary layer contributed by the building itself as measurements can be contaminated

by the presence of gases trapped by recirculation zones caused by the building as well as turbulence

generated from nearby buildings. In this report, we estimate the error introduced by performing

GHG measurements on building rooftops as compared with tower-based measurements, and provide guidance to optimize placement of sampling points on buildings. To accurately measure GHG

mixing ratio in the free stream of the atmospheric boundary layer, two factors are critical,

• the resolution of the measurement instrument and

• location of the point where the gas sample is obtained.

Developments in measurement technology [18] and the commercial development and use of Cavity

Ring-Down Spectrometers (CRDS) have made it possible to make CO2 measurements resolved to

approximately 50 ppb and with a temporal frequency of approximate 1 measurement per second. In

spite of the high sensor resolution, measurements may not accurately reflect the GHG concentration

in the boundary layer of the immediate vicinity where the sample is take because the sensors are not

placed at appropriate locations or are not exposed to the environment that needs to be measured.

The procedure to install sensors on a building is straight-forward and familiar for technicians who

install communications antennas or other building mounted fixtures. The location of sensors on the

rooftop is generally driven by practical aspects such as existing wiring, accessibility, aesthetics, as

well as considerations of rooftop turbulence and recirculation zones. Figure 1 shows sensor inlets

installed on the rooftops of the Earth Networks Headquarters building (Germantown, MD) 1 as

well as pole mounted meteorological instruments. There has not been an extensive scientific study

of ideal locations for installation of GHG sensors.

Ideally, building-mounted gas sensor inlets should be outside recirculation zones or boundary

layers that might skew a measurement relative to ambient GHG mixing ratios. Although simple

formulas exist to estimate dimensions of recirculation zones [19], they provide only rough guidance

and do not account for the effect of the turbulent wake around a building or over the rooftop.

To date, research focused on flow around buildings, and particularly rooftop recirculation, has

been directed toward establishing the heights of rooftop stacks, to ensure that building effluents

are injected above the re-circulation zones, and the location of intake vents around a building

avoid internal recirculation of polluted air (see, e.g., [20]). In response to the growing need for

meteorological data in urban areas, the World Meteorological Organization (WMO) and the U.S.

Environmental Protection Agency have published guidelines [21], [22] for site selection and meteorological instrument exposure. However, densely developed sites (urban areas) make it difficult

to conform to the standard WMO guidelines due to obstruction of airflow and radiation exchange

by buildings and trees as well as anthropogenic activities. Oke [23] has provided further guidance

1Certain commercial equipment, instruments, or materials are identified in this report in order to adequately

specify the materials used and the experimental procedure. Such identification does not imply recommendation of

endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment

identified are necessarily the best available for the purpose.

2
 
Figure 1: Rooftop sensor inlets for measuring greenhouse gases (centered in the photograph) and polemounted meteorological instruments [17]. Sensor inlets are located at approximately 3 m above the rooftop.

for choosing a location for an urban station and has considered the effect of variables such as temperature, humidity, wind speed and direction, precipitation, radiation and evaporation. McAlpine

and Ruby [26] have used computational fluid dynamics (CFD) to determine the location of sensors on a power plant building for air quality modeling. Because of the effect of local topography,

vegetation and built environment, it was determined that the top of the power plant building was

an appropriate location. However, recent studies [24], [25] have indicated that GHG measurement

through sensors located on building rooftops can be affected by local emissions of GHG, such as

those due to ventilation from buildings and respiration from building occupants. This report adds

to the existing literature on this subject by considering the effect of ventilation from rooftop outlets

as well as respiration from building occupants.

We describe the results of a CFD study to evaluate the accuracy of GHG measurements made

at various locations on the rooftop of a rectangular test building with a penthouse. Large eddy

simulations (LES) were performed to estimate the effects of building-induced turbulence and recirculation zones on measurement locations at various points above the test building’s rooftop.

Simulation results were qualitatively compared with available data. The building was subjected to

a synthetic signal in which the concentration of a GHG gas varies as a series of triangular spikes

with decreasing duration. Measurements made at several rooftop and penthouse locations (and at

various heights) were compared with the original signal to estimate error. Statistical measures were

used to identify GHG measurement locations with minimal to no influence of building boundary

layer. Spike durations that are captured accurately at these sampling locations are compared with

those from freestream (tower) measurements. Simulations were performed to examine the effect of

wind from different directions (including those that introduce asymmetry) and wind speed. The

effect of exhaust from building vents and respiration of building occupants was considered. Based

on the results of these simulations, we provide general guidelines for the placement of gas inlets or

sensors on tall buildings in urban environments.

Problem Description and Model Formulation

Figure 2 is a schematic representation of wind flow over a flat-roofed building that results in a

building boundary layer and vortical formations known as recirculation zones over the rooftop, as

well as the windward and leeward sides of the building. As illustrated, the wind impinging on a

building face will split below and above a stagnation point located slightly above the center of the

3
 

2
face. Wind below the stagnation zone forms an upwind vortex (as shown in Figure 2). Due to the

edge effect at the top of the building, wind traveling over the rooftop will form a recirculation zone

that, depending on the “fetch” (i.e., along wind length) of the rooftop, will cover a portion of the

rooftop or, as illustrated in figure 2, may extend to the leeward side by several building widths.

Figure 2: Illustration of flow field over a flat-roofed building. Wind with a typical atmospheric profile

(UH) from left to right results in a stagnation zone as well as recirculation zones that spans the rooftop and

leeward side of the building [19].

Gases or pollutants vented on the rooftop may be trapped in such recirculation zones for

an extended time, resulting in lower dilution of GHG than would otherwise occur. The same

recirculation pattern that traps vented pollutants may also prevent upwind air (greenhouse gases

in the atmospheric boundary layer), from entering the zone near the rooftop at mixing ratio values

representative of the incoming atmosphere. As a result, mixing ratios of GHG in the free air

stream may be different from that in the recirculation zone. Accordingly, it is important to place

rooftop-mounted gas inlets high enough (outside the building boundary layer) so that they will

be exposed to target gases carried by the free air stream. Placing tall meteorological towers on

building rooftops can be expensive in terms of time and cost. Depending on their height, they may

violate building codes or aesthetic sensibilities. Therefore, it is desirable to find the locations for

rooftop gas inlets that guarantee exposure to air that is least affected by the building boundary

layer and recirculation zones generated by wind flow over a building.

Simple formulas are available to estimate the average height and length of a recirculation zone

over the rooftop of a building ([19]). However, these rough estimates do not capture the dynamics

of the wind field over and around a building, especially in cases where the rooftop is not a single, flat

plane, or when rooftop structures, such as large cooling units or penthouses, are present. To provide

general guidelines for the placement of rooftop sensors, we performed a suite of CFD simulations

to understand the flowfield around a large office building, and these calculations are described in

the following sections.

4
 
3 Fire Dynamics Simulator

3.1 General Description

NIST’s Fire Dynamics Simulator (FDS Version 5.5) is an open source CFD code containing a hydrodynamic solver suitable for low-speed flow (Mach <0.3), including a thermal radiation transport

models. In the hydrodynamic model, a modified form of the Navier-Stokes equations are solved by

a predictor-corrector scheme that is second-order accurate in time and space. Mesh cells in FDS

simulations are rectilinear, and a staggered grid scheme is employed in which vector quantities,

such as velocity, are assigned to cell-faces, while scalars, such as temperature, are assigned to cell

centers [27]. Below we describe basic equations relevant to the GHG dispersion simulations. A

detailed mathematical description of the various models in FDS is available [28].

3.2 Equation of state

In FDS, gas pressure, p, is decomposed into a “background”, or static pressure, p(z,t), and a

perturbation, or hydrodynamic pressure , p ̃(x, y, z,t). Note that z is the vertical coordinate, thus

allowing for stratification of the atmosphere. The equation of state is:

 Zα ρRT

p = ρTR ≡ (1)

α Wα W

where R is the universal gas constant, T is temperature (K), ρ is density (kg/m3), Zα is the mixture

fraction of species α, and Wα is its molecular weight (the overbar denotes a mean). Note that the

equation uses the background pressure only. Atmospheric motion, and fluid motion in general,

is driven by the pressure perturbation, p ̃ [28]. For flows typical of wind and fire simulations

(O(10m/s)), the perturbation pressure is less than 1 % of the background pressure.

3.3 Large Eddy Simulations

FDS is used primarily for large eddy simulations (LES), in which fluid motion and turbulent eddies

are resolved at scales greater than the mesh cell size, while the dissipative effects of turbulence at

subgrid scales are modeled [27]. Although it is capable of direct numerical simulation (DNS), in

which all spatial and temporal scales of motion are captured, this is impractical for simulations of

atmospheric dispersion as the smallest atmospheric motions have dimensions of O(10−3 m), and

would therefore require about 1015 cells in a simulation domain only 100 m on a side. The constant

Smagorinsky [28] model of turbulent dissipation was used in all of the simulations below. In this

model, the effects of turbulence at a subgrid scale are computed as a viscosity, μ, given by the

formula:

 1

2 u)

2 μ = ρ ̄(CsΔ)2 SijSij − 2

( · 2(2) 3

where Cs=0.2, Δ is the characteristic dimension of a grid cell (m), S ij is the rate of strain tensor

and u is the velocity vector. Tildes over the variables indicate that they are resolved values, i.e.,

values computed from the numerical grid which, as mentioned above, is coarser than that used a

DNS. Further details of the physical models and numerical methods used in FDS are available in

[28].

5
 
 

3.4 Species Transport

In the simulations below, “air” is the implicit background gas (MW=29), while transport of the

second species, CO2 (MW=44), is modeled by solving the equation:

∂ (ρZ) + · (ρZu) = · (ρD Z) (3) ∂t

where u is the velocity vector (m/s), Z is the mixture fraction of CO2, and D is mass diffusivity

(m/s2). Source terms are also included in this equation, but were omitted here as CO2 concentration

is a boundary condition and not the result of a source within the domain. The product of bulk

density and diffusivity, ρD, on the right hand side is calculated from the turbulent Schmidt number,

Sc, as:

ρD = μ (4) Sc

where the value of Sc is assumed to be 0.5 and the viscosity, μ, is calculated from the subgrid

turbulent kinetic energy, as discussed in the section above. The diffusivity of the implicit species

(air, in this case) is chosen so that the sum of all the diffusive fluxes is zero [28]. Although the

formulation described above and the simulations in the following sections describe the transport of

CO2 gases, the formulation can easily be extended to other GHG species of interest such as natural

gas.

4 Numerical Simulation of Wind Flow over Building Rooftops

4.1 General Configuration

We chose the NIST administrative building (Building 101) located at the NIST Gaithersburg campus as our test building for conducting the simulations. The primary reason for choosing this

building is that sensor inlets can be placed on the roof of Building 101 to evaluate its effectiveness

in generating accurate data for atmospheric dispersion models. In addition, the NIST Building 101

is also a large office building with a penthouse (similar to other tall urban buildings) and provides

an opportunity to evaluate the performance of sensors mounted on the rooftop as well as the penthouse. The dimensions of the building (shown schematically in Figure 3) are 68 m (L) x 15 m (W)

x 44 m (H). A penthouse with dimensions 28 m (L) x 10 m (W) x 6 m (H) is located near the

SE corner of the rooftop. We investigated several scenarios based on prevailing wind speed, wind

direction, role of building ventilation or respiration from building occupants, effects of surrounding

buildings and comparison with the isolated building case as part of our computational study.

For the single isolated building case (discussed in the next section), winds were either westerly,

northerly, or northwesterly (figure 3). Here we use wind directions in the conventional meteorological sense, i.e., westerly means “from the west.” The westerly and northerly wind cases are

considered to be worst-case scenarios because the mean wind was directly normal to either a narrow or wide face of the test building, analogous to the cases in [26]. The northwesterly wind is

considered since it introduces an asymmetry in the flowfield. In the westerly and northwesterly

cases, the domain (for the single isolated building case) spanned 192 m x 144 m x 150 m over

the east-west (x), north-south (y) and vertical (z) axes, respectively. In the northerly case, where

wind direction was perpendicular to the widest building face, the domain spanned 240 m x 240

m x 150 m, over the same respective axes, to ensure sufficient clearance between the crosswind

boundaries and the building. Grid cells in the domain were 2 m cubes. The domain was split into

eight separate meshes that were processed in parallel using message passing interface (mpi). The

6
 
 

Figure 3: Plan view schematic of the simulation domain. Wind directions for each of the simulations are

indicated by the arrows attached to the boundaries. Corners of the main test building are also labeled.

meshes abutted perpendicular to the direction of flow, except in the northwesterly wind case where

the westerly mesh configuration was employed.

Grid convergence studies were conducted with various levels of uniform grid spacing (discussed

in section 5.2.2). Based on these studies a uniform grid spacing of 2.0 m was selected. Ambient

temperature in all simulations was uniform at 25◦ C. Ambient pressure was 101325 Pa. Wind speeds

at the boundary were prescribed through a vertical profile typical of a neutrally stable atmosphere

according to the power law function:

0.143 z U(z) = U0 (5) z0

where the reference height, z0, was 10 m, a standard height above ground in meteorological studies,

and U0 was 2.5 m/s. The vertical profile roughly simulates the effect that surface drag would

have on wind that has traveled over a distance much longer than the length of the domain before

reaching the test building.

4.2 Introducing CO2 in the simulations

In this study, we restrict our analysis to understanding the effect of the building on mixing ratio

measurements made by sensors mounted on the roof, and comparison with tower measurements.

As an example of such measurements, a time series of CO2 concentrations from an Earth Networks

meteorological tower located in Middlesex, North Carolina (35.49N, 78.90 W, Elevation 461 ft,

sampling heights 50m and 95 m above ground level) is shown in figure 4 (top panel) for December

2011 [17]. Figure 4 (bottom panel) shows a portion of the data for December 13, 2011. Figure 4

(top panel) illustrates spikes of various durations both during day and night time, while the bottom

panel gives a sense of the duration of the spikes. Greenhouse gas mixing ratio spike duration ranges

from minutes to hours (note that the units on the horizontal axis is in days), and the data suggests

that spikes with the smallest duration were of the order of approximately 60-120 s. Spike duration

was consistent with data for other greenhouse gases measured at this tower, as well as those at

other tower locations (GHG25 tower located in Lewisburg, Pennsylvania 40.95 N, 76.88 W). The

goal of the current modeling effort is to find roof sensor locations that will give measurements of

CO2 concentration as close as possible to measurements that would be taken by instruments on an

7
 
open-lattice tower or other structure that, unlike a large building, does not have a great effect on

the flow field. Another objective for this numerical study is to identify the spike durations which

are not accurately resolved by roof mounted sensors, and compare the spike durations with those

observed in real tower measurements.

Figure 4: CO2 concentration (ppm) plotted as a function of time for GHG 15 station located in

North Carolina. Data obtained courtesy of Earth Networks. The bottom plot highlights time

between 12.6 and 12.8 days.

To estimate the error associated with roof measurements, the simulated sensors mounted on a

rooftop are subjected to a synthetic signal as part of a numerical study. As a proxy for the mixing

ratio signal shown in Figure 4, CO2 was injected over the entire inlet boundary as a series of spikes.

The maximum amplitude of each spike was 430 ppm (i.e., 40 ppm above background of 390 ppm),

but the duration of the spike events was varied as shown in Figure 5. Spike durations (600 s, 300

s, 150 s, 60 s and 30 s) chosen for this study were based on the CO2 measurement data shown in

Figure 4. Since the smallest duration of spikes observed in the tower measurement was 90 s, we

chose spike durations that cover a range beginning with 90 s and extending to 600 s. The synthetic

data that was imposed as a time varying boundary condition simulates the effect of upwind sources

whose emissions have been uniformly mixed in the domain of interest, as well as the atmospheric

transport that results in spikes at the sensor location. The spikes were introduced such that the

total volumetric flow rate did not change. A tower-mounted sensor would measure the imposed

spike profile without any distortion. The presence of the building boundary layer affects the sensor

measurements. The difference between the two is a measure of the error, or influence of the building

boundary layer on the measurements, discussed in detail in the remaining sections.

4.3 Evaluating the Influence of Building Boundary Layer on Measurements

Simulated CO2 concentrations were recorded at points 2 m and 10 m above the rooftop at the

four corners of the main test building and of the penthouse. These concentrations were compared

to the original signal shown in Figure 5, which is equivalent to comparing the measurements at

each location to an ideal case in which no building is present to obstruct the wind flow. Each

8
 
simulation ran for 2200 s of simulated time to allow the full sequence of spikes to pass through the

domain. Concentration data were output at approximately 0.25 s intervals. To quantify the effect

of the presence of the test building, the root mean squared error (RMSE) of the difference between

the reference signal and concentration measurements was calculated for each of the separate wind

directions. This is equivalent to finding the standard deviation of differences between the time

series of the ideal, free stream, mixing rations and the building roof measurements, and can be

thought of as an indicator of how measurements at a given instant differ between the two cases.

RMSE were calculated separately for each CO2 spike.

In addition, the least-squares correlation coefficient between each complete time-series of measurements and the original input signal was calculated. The coefficients indicate how closely the

data at the measurement points correspond temporally to the prescribed CO2 time series shown

in figure 5. In the tables below, all coefficients were rounded to two significant digits. RMSE and

correlation coefficients are considered in separate sections for each of the modeled wind directions.

Data from the rooftop of the main test building and the penthouse are given in each section. To

avoid clutter in the presentation, only a representative sample of windward and leeward measurements are given in the figures and tables shown in Section 5.

Figure 5: Prescribed CO2 profile at inlet of domain. Spike durations are labeled.

5 Results

5.1 Westerly Wind

In the westerly wind case, the wind direction is parallel to the longest axis of the building, giving

the greatest amount of rooftop fetch to affect the flow field. Figure 6 illustrates the mean velocity

field obtained from an FDS simulation, averaged over 600s. Results are shown as vectors in a

vertical slice through the long axis of the test building. Flow is from left to right in the image.

Measurement points for concentration measurements on the building rooftop (discussed in detail

in the following sections) are marked by green dots. Similarities to figure 2 are evident, including

a recirculation zone on the rooftop of the main building and the penthouse, a large recirculation

9
 
zone in the lee of the building, and a flow field at the windward face of the building that is split

between downward and upward directions.

Figure 6: Average velocity vectors in a vertical slice plane through the long axis of the building. Wind flow

is westerly (left to right). Vector color and length indicates velocity, with magnitudes that range from 0.1

m/s (blue) to 3.9 m/s (red). Green dots above the rooftop represent measurement locations.

CO2 concentrations near the beginning, middle and end of the 600 s spike, are shown in the left,

middle and right panels, respectively, of figure 7. Concentrations range from ambient (390 ppm,

blue) to 430 ppm (red). Flow is from left to right in the figure. In each panel, CO2 concentrations

above the rooftop of the building and penthouse, as well as in the lee (right side) of the building,

lag behind the concentration surrounding the building, as air in the recirculation zones mixes

slowly with the ambient air. The leeward side of the main building and penthouse are exposed to

turbulent flow generated over the rooftop. Note that concentrations near the windward edge of the

main building are relatively unaffected by the recirculation zone and boundary layer.

Time series of CO2 concentrations from selected sensors above the rooftop of the main test

building are shown in figure 8. As mentioned, measurements from only one windward (NW) and

one leeward (SE) corner of the main test building and penthouse are given as representative samples.

Results are shown for a height of 2 m and 10 m above the rooftop. Due to the symmetry of the

measurement points relative to the wind in this case, results at the remaining windward and leeward

points are very similar to those shown. In each plot, measured values (black lines) at the selected

rooftop locations are overlaid on the CO2 reference signal (green lines). Concentrations are read on

the left vertical axes of the plots. The differences between the measured and reference signal are

plotted as red lines with values that are read against the right vertical axes. The red line represents

the error associated with mounting the sensor inside the building boundary layer.

Windward (NW) measurements appear to closely match the prescribed signal perfectly, with

very little difference between points at 2 m and 10 m above that corner of the main building rooftop

(top-left and top-right panel in figure 8). The error (red line) for the windward sensors is very small

for all the imposed spikes. When the sensor is located at the windward edge of the building, the

height of the measurement has very little effect on the accuracy of the measurements. Results for

the leeward sensors (SE) are shown in the bottom-left panel (2 m) and bottom-right panel (10 m).

There is more noise in the measurements at both heights above the leeward (SE) corner, where

10
 
Figure 7: Vertical slices of CO2 concentrations through the centerline parallel to the longest building

axis in a westerly wind. Flow is from left to right in each panel. The penthouse rooftop is visible

as a gray rectangle slightly right of center. Concentrations range from the initial value of 390 ppm

(blue) to the peak value of 430 ppm (red). Green dots represent measurement locations. From left

to right, the panels illustrate concentrations near the beginning, peak and end, respectively, of the

600s CO2 spike.

differences of up to 30 ppm from the reference signal are apparent. The error curves (red lines)

for these sensors indicate that the error is smaller for the duration of the 600 s spike. As the

duration of the spike decreases, the adverse effects of the building boundary layer on mixing ratio

measurements become much more pronounced.

Figure 9 contains time series of measurements above the penthouse in a westerly wind. The

sensor mounted at 2 m above the windward (NW) corner of the penthouse shows more noise (error),

while the sensor at the 10 m windward (NW) point corresponds better to the reference signal. The

10 m point is located closer to the top of the boundary layer formed over the main building and

penthouse, while the 2 m point is inside the boundary layer. A rough outline of the boundary

layer can be seen in the color contours shown in figure 6. Referring again to figure 6, we see

that the sensors at the SE corner at both heights above the penthouse are within the boundary

layer and recirculation zone formed at the top of the main building and the penthouse. Accordingly,

measurements at these points appear to be equally noisy with errors of roughly the same magnitude

(bottom panel of figure 9).

To better illustrate the quantitative differences between the measurements and reference signal,

the RMSE for the various locations as well as for the duration of each spike are given in Table 1.

Table 1 also shows the correlation coefficient for each sensor location. RMSE (ppm) at the windward

(NW) corner of the main test building are very low and not significantly different at the 2 m and

10 m points. Although the RMSE values are low, they tend to increase as spike duration decreases.

The correlation coefficients, rounded to two significant digits, indicate perfect agreement for these

sensors. At the leeward (SE) corners of the main building, RMSE are approximately an order of

magnitude larger than in the windward corner (NW). Note that the RMSE increase as the spike

duration decreases, indicating poorer resolution of the original CO2 signal over smaller time steps.

Despite having higher RMSE than the windward points, the correlation coefficients are still high.

The reason for this is apparent in the bottom panels of figure 8, which indicate that, despite large

variability, the general patterns of rise and fall in the measurements correspond reasonably well

to those in the reference signal. The RMSE and correlation coefficients of the penthouse locations

(Table 1) indicate that sensors should not be mounted on the penthouse. RMSE for the penthouse

11
 
Figure 8: CO2 concentrations at two heights above a windward (NW) and leeward (SE) corner of

the main building in a westerly wind. Measured concentrations (black lines) are overlaid on the

prescribed profile (green lines). Concentrations are read on the left y-axes. The difference between

the two cases (error) is read against the right y-axes (red lines).

12
 
Figure 9: CO2 concentrations at two heights above a windward (NW) and leeward (SE) corner of

the penthouse (PH) in a westerly wind. Measured concentrations (black curve) are overlaid on the

prescribed profile (green curve). Concentrations are read on the left y-axes. The difference between

the two cases (error) is read against the right y-axes (red curve).

13
 
sensors are always higher than the corresponding sensors on the main building roof. The point 10

m above the windward corner of the penthouse gives RMSE values up to ten times lower than those

at the 2 m point. Note that, despite this improvement, RMSE at 10 m above the penthouse are

still higher than those of the windward points above the corners of the main building. Correlation

coefficients and RMSE at the SE corner of the penthouse are similar to those above the SE corner

of the main building. Unlike the windward points, the 10 m point gives RMSE that show little to

no improvement over the 2 m point. Points above the leeward corner of the penthouse give higher

RMSE than the windward locations.

Table 1: Westerly wind: RMSE (ppm) and correlation coefficients for various sensor locations.

Times indicate spike duration.

RMSE of CO2 spikes

Corner Location 600 s 300 s 150 s 60 s 30 s Correlation

NW main 2 m 0.1 0.3 0.3 0.8 1.4 1.00

NW main 10 m 0.1 0.2 0.3 0.7 1.5 1.00

SE main 2 m 1.9 2.7 6.8 7.8 15.0 0.95

SE main 10 m 1.2 2.8 6.9 8.6 12.3 0.94

NW PH 2 m 1.6 3.0 6.2 7.0 13.6 0.96

NW PH 10 m 0.4 0.3 0.8 3.2 1.8 1.00

SE PH 2 m 1.5 2.6 8.0 8.0 12.7 0.94

SE PH 10 m 1.4 3.2 4.6 8.1 9.5 0.95

5.1.1 Effects of Surrounding Buildings

In the previous section, we have considered the effect of an isolated buildings’ boundary layer and

recirculation zone (RZ) on rooftop mounted sensors. Results indicate that rooftop mounted sensors

on the leeward sides of the building are affected by the presence of the buildings’ boundary layer

and RZ, while those on the windward side are relatively unaffected. The measurements made at the

windward sensor will be influenced by the presence of surrounding buildings, or topography. This

has not been included in the analysis. In this section, we consider the effect of turbulence generated

by buildings surrounding the test building on sensors mounted on top of the test building.

In order to simulate the surrounding buildings, the domain described in section 3.1 was extended

in all directions, for a total domain size of 440 m x 560 m x 150 m along the x-, y- and z-axis,

respectively. The NIST administrative building is surrounded by eight large buildings. Eight blocks,

each 90 m x 40 m x 10 m, were added to the domain in locations corresponding to the location of

these buildings on the NIST campus (figure 10). Note that the height of the surrounding buildings

(10 m) is significantly smaller than that of the test building (44-50 m). Each surrounding building

was modeled as a block, and details of the surrounding buildings such as rooftop structures, were

not included. Cubic grid cells in the extended portions of the domain were 10 m on an edge,

while the inner domain retained its original dimensions and resolution (2 m). A westerly wind was

prescribed with the same speed and profile as in the single building case described in section 3.1.

Time series of CO2 concentrations from selected sensors above the rooftop of the main test

building are shown in figure 11. These plots can be directly compared with those shown in figure

8 for the isolated building case, since the sensor locations are identical. Including the surrounding

buildings in the FDS simulation introduced more noise in the measurements. The windward sensor

(NW) on the main building resulted in a noisy signal due to the surrounding buildings. This was not

14
 
Figure 10: Extended domain showing a schematic representation of the buildings surrounding NIST

Administrative Building (Building 101).

observed when the building was isolated. Measurements at the leeward side (SE) for the isolated

building case and the case with surrounding buildings contain roughly the same amount of error

(bottom panels of figures 11 and 8). Tables 2 shows the RMSE values and correlation coefficients

for the case with surrounding buildings and this table can be compared with table 1 for the isolated

building case.

As in the case above, error was evaluated by comparison with a signal (reference signal) measured by the sensors at the height of the rooftop sensors, but assuming that the building itself

was not present. For the isolated building case, this signal remains more or less unaltered before

reaching the windward edge of the main building. On the other hand, the presence of surrounding

buildings can significantly alter the signal that is reaching the sensors mounted on top of the test

building. The presence of surrounding buildings will equally affect sensors mounted on the test

building or on a tower. The goal of this study is to predict the differences in measurements that

would occur if building rooftop sensors were used instead of tower mounted sensors. In other words,

the aim is to find whether measurements at a point above a building rooftop could give the same

results as instruments at the same location if no building, or if only an open-lattice tower, was

present. The surrounding buildings will have the same effect on an instrument mounted on a tower

or on top of another building. While it is possible to evaluate a specific case, as above, given the

myriad possibilities for arrangements and heights of surrounding buildings, it is extremely difficult

to make statements about such cases in general. Therefore, our focus hereon is comparing the

measurements made on a tower (assumed to correspond to the prescribed inlet signal) to those

made above a building that is isolated, or is at least relatively unaffected by surrounding buildings.

5.2 Northerly Wind

In a northerly wind case, flow is normal to the building face with the greatest width, resulting in

a shorter rooftop fetch than in the westerly case, but generates a large recirculation zone in the

leeward side of the test building. Since wind speed and direction may change many times during

the course of a day and the direction is not known apriori, these simulations help to understand

15
 
Figure 11: CO2 concentrations in a westerly wind at two heights above a windward (NW) and

leeward (SE) corner of the main building. Results include the effect of surrounding buildings.

Measured concentrations (black lines) are overlaid on the prescribed profile (green lines). Concentrations are read on the left y-axes. The difference between the two cases (error) is read against

the right y-axes (red lines).

Table 2: RMSE (ppm) and correlation coefficients for Westerly wind case including the effects of

surrounding buildings.

RMSE of CO2 spikes

Corner Location 600 s 300 s 150 s 60 s 30 s Correlation

NW main 2 m 1.0 0.6 1.0 2.4 9.4 0.99

NW main 10 m 0.6 0.5 0.9 2.0 8.9 0.99

SE main 2 m 2.1 5.7 9.0 8.8 11.3 0.92

SE main 10 m 4.3 3.4 5.8 9.7 8.7 0.94

NW PH 2 m 6.3 8.3 11.6 8.6 11.2 0.73

NW PH 10 m 4.4 6.2 9.7 6.6 9.0 0.90

SE PH 2 m 5.1 5.3 8.2 11.2 12.3 0.87

SE PH 10 m 2.5 2.8 8.4 6.0 12.9 0.91

16
 
the effect of changing wind direction on rooftop mounted sensors by comparison with the westerly

wind case. Moreover, since the test building has a rectangular cross-section as seen in Figure 3

(width of the building in the westerly direction is much larger than in the northerly direction), the

flowfield and turbulence levels for the northerly wind case can differ significantly from the westerly

wind case.

Figure 12 illustrates the simulated mean velocity field, averaged over 600s, for the northerly

wind case. As in the westerly wind case (Figure 6), measurement points are marked by green

dots. The mean velocity field in figure 12 indicates a persistent recirculation zone at the lower

portion of the windward face (left face), as well as a large zone of recirculation and near-zero

wind speed in the leeward side (right side) of the building. The leeward (right) edges of the main

building and penthouse are shielded from the impinging flow by the rooftop boundary layer and

leeward recirculation zone. As in the westerly wind case, recirculation zones at the windward and

leeward sides of the building form regions in which CO2 concentration lags behind the ambient

concentration, though the effect is more pronounced due to the span of the building relative to the

wind direction.

Figure 12: Vertical slice of the velocity field in a northerly (left to right) flow. Vector color and length

indicates velocity, with magnitudes that range from 0 m/s (blue) to 4 m/s (red). Green dots above the

rooftop represent simulated measurement locations.

Time series of CO2 measurements above the corners of the main building for the northerly wind

case are shown in figure 13. The windward (NW) points again correspond very well to the ambient

concentration, with little apparent difference in accuracy between the 2 m and 10 m points. In

contrast, the point 2 m above the SE corner of the main building (figure 13, lower left panel), gives

noisy measurements with departures of up to about 40 ppm from the prescribed signal (i.e., from

the ambient concentration). This is due to the proximity of this point to the penthouse, which is

closest to that corner of the main building and produces additional turbulence and recirculation in

its lee. Note that the point 10 m above the same corner (lower right panel) more closely follows

the input concentration profile, as it is 4 m above the roof of the penthouse. However, its response

is significantly degraded relative to the Northwest corner at the same height.

Table 3 gives the RMSE and correlation coefficients of measurements for the northerly wind

case, in a format similar to the westerly wind case shown in Table 1. It is apparent that both

17
 
Figure 13: CO2 concentrations at two heights above a windward (NW) and leeward (SE) corner of

the main building in a northerly wind. Measured concentrations (black lines) are overlaid on the

prescribed profile (green lines). Concentrations are read on the left y-axes. The difference between

the two cases (error) is read against the right y-axes (red lines).

18
 
the 2 m and 10 m sensor positions at the windward (NW) location track nearly perfectly with

the original signal. In contrast to the westerly wind case, the point 10 m above the leeward (SE)

corner of the main building gives RMSE lower than the 2 m measurements by as much as a factor

of two, indicating that the 10 m location is less heavily influenced by the rooftop RZ and wake of

the nearby penthouse. Also unlike the westerly case, measurements at the 2 m leeward (SE) point

in a northerly wind have relatively high RMSE for all spike durations, not just the brief spikes. As

shown in figure 13 (lower left panel), the measurements at this location do not resolve the original

CO2 signal. This is reflected in the extremely low correlation coefficient for this location in table

3. RMSE values for sensors located on the penthouse are in general higher than those located on

the windward side of the main building, indicating that sensor installation on top of the penthouse

can lead to large measurement errors. If the height of the penthouse above the main building was

significantly greater, then sensors located on top of the penthouse could be outside the wake of the

main building. For such scenarios, it might make sense to install sensors on top of the penthouse.

Table 3: Northerly wind: RMSE (ppm) and Correlation coefficients for various sensor locations.

Times indicate spike duration.

RMSE of CO2 spikes

Corner Location 600 s 300 s 150 s 60 s 30 s Correlation

NW main 2 m 0.2 0.3 0.6 1.3 1.9 1.00

NW main 10 m 0.2 0.2 0.4 0.8 1.3 1.00

SE main 2 m 11.7 12.9 11.1 11.6 11.3 0.05

SE main 10 m 4.5 5.6 4.1 5.7 5.7 0.96

NW PH 2 m 0.5 0.5 0.6 0.9 1.7 1.00

NW PH 10 m 0.2 0.2 0.4 0.7 1.5 1.00

SE PH 2 m 10.1 10.2 9.0 10.6 13.5 0.64

SE PH 10 m 1.1 1.4 1.4 1.2 3.5 1.00

5.2.1 Effect of Mean Wind Speed

As a test for the effect of wind speed on the results, the northerly wind simulation was conducted

using twice the wind speed (5 m/s) of the previous case, though with the same vertical wind speed

profile. Correlation coefficients and RMSE at the corresponding locations were similar to those

from the lower wind speed case, in most cases differing by not more than a few tenths of a ppm. At

the higher wind speed, there were actually small improvements at both heights above the windward

(NW) corner of the main building for the narrower spikes. For example, the point 2 m above the

NW corner gave a RMSE of 0.8 ppm for the 60 s spike, as opposed to 1.3 ppm in a 2.5 m/s wind.

The sole exceptional change occurred in the correlation coefficient at the SE corner 2 m above the

main building, which increased from 0.05 to 0.26, as the wind speed increases. However, the RMSE

at this point remained very similar at both wind speeds. These results suggest that the effect of

wind direction is more important than the effect of wind speed at a given measurement location.

5.2.2 Grid Resolution Studies

To test the effects of mesh resolution (grid cell size) on the results, the northerly wind case was

simulated using cubic grid cells of 2 m, 4 m and 6 m on a side in separate simulations. Correlations

between the original CO2 input signal and the measurements at corner locations above the main

19
 
building (see section 4.2 below) are given in table 4. The NW (windward) corners at both heights,

as well as the SE corner at 2 m above the main building, show little change in correlation as cell

size decreases. The same pattern was true for the SW and NE locations not shown here.

The SE (downwind) corner at 10 m shows a significant change in correlation coefficient as cell

size decreases from 4 m to 2 m (table 4). However, this has less to do with the resolution of the flow

field than the fact that the height of the nearby, upwind penthouse (figure 3) changes according to

grid cell size. The height of the penthouse is 6 m, but its height in the simulations must conform

to the grid cells in which it is located. Thus, its height is 6 m when the grid cells are 2 m or 6 m on

a side, but is rounded down to 4 m when the grid cells are 4 m on a side. Because the remaining

locations show convergence, or little change, of correlation coefficients as grid cell size decreases, 2

m resolution was deemed sufficient to capture the primary effects of the building on the flow field.

Additional parameters, such as CPU time per simulation time step, are also given in table 4.

Table 4: Effects of mesh resolution

Grid cell size (m)

Parameters 2 m 4 m 6 m

NW 10 m correlation 1.000 1.000 0.999

NW 2 m correlation 1.000 1.000 0.998

SE 10 m correlation 0.367 0.784 0.817

SE 2 m correlation 0.492 0.539 0.691

CPU time / time step 0.39 0.05 0.02

Time step (s) 0.26 0.52 0.79

Number of time steps 8530 4200 2800

Total CPU time (s) 9427 1154 265

5.3 Northwesterly Wind

Simulations were performed to evaluate the effect of a Northwesterly wind on the test building and

to study the effects on simulated rooftop mounted sensors. Rooftop flow due to a northwesterly

wind results in a flow asymmetry around the test building relative to the wind direction. Table 5

gives the RMSE and correlation coefficients of measurements for the Northwesterly wind case, in a

format similar to the westerly wind case shown in Table 1.

Low RMSE values were observed for the windward (NW) sensor and higher values for the

leeward (SE) corner as in the westerly wind case. Since the sensors located on the NE corner and

the SW corner also behave as windward sensors, the RMSE for these sensors was similar to that

for the NW corner.

5.4 Role of Building Emissions

Exhaust vents located on building rooftops may emit CO2 in the form of combustion products

from boilers or air from a ventilation system. These gases may be trapped for extended periods

in recirculation zones around buildings, as in figure 2, and could affect the readings of rooftopmounted sensors. In addition, CO2 resulting from human respiration may be emitted from building

faces with open windows and could also be transported to rooftop locations. It is clear that

leeward measurement locations, in particular, will be sensitive to these additional emissions from the

building itself. However, our results suggest that a measurement location that extends horizontally

20
 
Table 5: Northwesterly wind: RMSE (ppm) and correlation coefficients for various sensor locations.
 

RMSE of CO2 spikes

Corner Location 600 s 300 s 150 s 60 s 30 s Correlation

NW main 2 m 0.1 0.1 0.3 0.8 1.9 1.00

NW main 10 m 0.1 0.1 0.3 0.7 1.4 1.00

SE main 2 m 10.6 10.8 11.8 11.5 11.5 0.49

SE main 10 m 11.1 11.0 10.8 11.4 10.4 0.37

NW PH 2 m 1.8 1.7 2.9 2.4 8.0 0.99

NW PH 10 m 0.7 1.0 0.8 1.2 1.5 1.00

SE PH 2 m 11.6 11.8 10.9 11.3 11.8 0.13

SE PH 10 m 6.2 5.7 5.6 6.8 4.4 0.92

outward from the windward edge of a building is less likely to be affected, especially by emissions

from the top of a building or building occupant respiration. Figure 14 illustrates this arrangement

of the sensors. While it could be argued that emissions from a windward building face might have

an effect on windward measurement locations, this seems unlikely for points that are located some

distance above, and perhaps outward from, the windward edge of the rooftop, where the emissions

would be swept below the measurement points.

Figure 14: Schematic diagram showing the proposed location / configuration of sensors for measurement of GHG on building rooftops.

To consider the potential for use of sampling points located away from the face of a building,

once can consider as an approximation, that building air ventilated through windows at a windward

building face is confined to the turbulent velocity boundary layer over that face and that the flow

is upward over roughly the upper half of the building. The depth, δ, of the boundary layer at

the building face may be estimated from a flat plate approximation, δ = 0.37xRe−1/5, where x is

the distance along the direction of flow [29]. Using the half-height of the NIST’s Administration

Building (22 m) and a flow speed of 5 m/s (Re=7.3x106), the approximate depth of the layer is

about 1 m when it reaches the rooftop edge. Therefore, measurement points that extend 2 m

outward horizontally from the rooftop edge, are likely to be well away from the emissions confined

to the boundary layer. This is likely to be a conservative estimate, as the upward flow over the

rooftop edge is pushed leeward before it attains much height above the rooftop edge.

21
 
6 Concluding Remarks

Large eddy simulations (LES) of wind flow over a large office building were performed to estimate

the influence of building’s boundary layer on rooftop GHG measurements, and to compare these

measurements with tower-based measurements (which closely approximates free stream measurements). Time dependent concentrations of carbon dioxide were computed at a number of locations

and heights above the rooftop of the building and compared with the original input signal. Simulation results are used to develop guidelines for optimum placement of sensors on rooftop for accurate

measurement of GHG mixing ratio that are necessary for atmospheric inversion models. Results

are also used to identify concentration spike durations that are accurately resolved by the sensors

and the spike durations are compared with those from the free stream measurements.

When concentration sensor inlets are mounted at rooftop locations, as in urban studies, simulation results indicate that they should be mounted on the windward side of the building where

they are less influenced by the rooftop recirculation zone and boundary layer. This is because

the building boundary layer and recirculation zone have far less effect on windward measurement

points. Results also indicate that there does not seem to be an advantage in placing windward

sensor inlets more than 2 m above the rooftop edge. On the leeward corner, placing sensors at

10 m above the rooftop gave smaller error as opposed to the 2 m sensor. In general, windward

measurement points, whether located at 2 m or 10 m above the main test building or penthouse

rooftop, are superior to the corresponding leeward measurements.

Sensors mounted on the windward side of the penthouse could be affected by the boundary

layer of the main building. Therefore, installation of sensors on a penthouse should be avoided

unless the height of the penthouse above the main building is large enough to avoid the main

building boundary layer. Windward and leeward measurements from points above the penthouse

were generally inferior to those made above the corners of the main test building.

For the particular case of NIST building 101, results suggest that an optimal configuration of

CO2 sensor inlets would be achieved by mounting them at 2 m above the four corners of the main

building, thus ensuring that at least one inlet will always be windward. Measurements from the

windward sensor(s) would then be used exclusively, or perhaps, weighted favorably, if the average

of concentrations at all inlets is used. This approach, however, could result in large data files and

would require post-processing of the sensor data before it can be used for analysis. The effect

of ventilation from the building or respiration from building occupants on measurements can be

further reduced by locating the sensor horizontally by a distance of 2 m beyond the windward edge

of the building (Figure 14).

Generally, this strategy seems likely to be effective for a variety of building configurations. However, CFD modeling of the building targeted for use in trace gas observations should be performed

to identify optimal locations, especially for complicated asymmetric building structures. In circumstances where multiple inlets cannot be used, the best strategy, based on the results above, would

be to mount an inlet on the side or corner of a building closest to the prevailing wind direction.

Although mounting the inlet on a pole or tower at the greatest practical height above the building,

or a rooftop structure, may reduce the impact of the building’s boundary layer on the measurement

and reduce errors when the inlet is not windward, CFD simulations can aid in optimizing sensor

location points. Regardless of the rooftop location(s) of GHG sensor inlets, measurements should

be made on rooftops of the tallest buildings in a particular urban environment, as stated in the

EPA guidelines [22]. Furthermore, the building should be chosen so that it is relatively isolated so

as to avoid the effect of surrounding structures.

Simulation results indicate that it is possible to choose sensor location so as to accurately

capture short duration spikes (of the order of 30 s) with acceptable levels of fidelity relative to the

22
 
free stream concentration value. It should be noted that the duration of these spikes is smaller

than that observed in real tower measurement data (90 s).

6.1 Recommendations for Future Research

In future studies, the effect of building emissions should be explored through CFD simulations as

well as physical experiments. A relatively simple physical experiment could involve the comparison

of CO2 concentration measurements made during weekdays versus weekends, when the number of

occupants is presumably lower. In addition, measurements must be made near to or directly at

exhaust vents, and at a windward building face, and these results could be checked for correlation

with the target measurement points. Simulations can also be performed to estimate the effect of

CO2 exhaled by building occupants or through the building ventilation system.

Varying turbulence intensity at the inlet boundary, atmospheric stability and the presence of

thermals will affect rooftop measurements and should be simulated in more detail as part of a

future project, but the authors expect that these effects will be small compared to the effect of the

building boundary layer.

Highly resolved simulations that can capture the detailed construction of the building exterior

as well as rooftop structures can reveal the effects that these have in formation of the local boundary

layer of the building. In the simulations described in this study, the buildings were modeled as a

smooth-surfaced block, neglecting the detailed construction of the exterior.

7 Acknowledgements

The authors acknowledge the suggestions and support from Robert Marshall and Christopher Sloop

(Earth Networks) for providing the tower data. The authors also recognize support from the entire

INFLUX team, especially Prof. Ken Davis for his useful comments and suggestions.

23
 
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25
 
List of Figures
 

1 Rooftop sensor inlets for measuring greenhouse gases (centered in the photograph) and polemounted meteorological instruments [17]. Sensor inlets are located at approximately 3 m

above the rooftop. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Illustration of flow field over a flat-roofed building. Wind with a typical atmospheric profile

(UH) from left to right results in a stagnation zone as well as recirculation zones that spans

the rooftop and leeward side of the building [19]. . . . . . . . . . . . . . . . . . . . . . . 4

3 Plan view schematic of the simulation domain. Wind directions for each of the simulations

are indicated by the arrows attached to the boundaries. Corners of the main test building

are also labeled. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

4 CO2 concentration (ppm) plotted as a function of time for GHG 15 station located

in North Carolina. Data obtained courtesy of Earth Networks. The bottom plot

highlights time between 12.6 and 12.8 days. . . . . . . . . . . . . . . . . . . . . . . . 8

5 Prescribed CO2 profile at inlet of domain. Spike durations are labeled. . . . . . . . . 9

6 Average velocity vectors in a vertical slice plane through the long axis of the building. Wind

flow is westerly (left to right). Vector color and length indicates velocity, with magnitudes

that range from 0.1 m/s (blue) to 3.9 m/s (red). Green dots above the rooftop represent

measurement locations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

7 Vertical slices of CO2 concentrations through the centerline parallel to the longest

building axis in a westerly wind. Flow is from left to right in each panel. The penthouse rooftop is visible as a gray rectangle slightly right of center. Concentrations

range from the initial value of 390 ppm (blue) to the peak value of 430 ppm (red).

Green dots represent measurement locations. From left to right, the panels illustrate

concentrations near the beginning, peak and end, respectively, of the 600s CO2 spike. 11

8 CO2 concentrations at two heights above a windward (NW) and leeward (SE) corner

of the main building in a westerly wind. Measured concentrations (black lines) are

overlaid on the prescribed profile (green lines). Concentrations are read on the left

y-axes. The difference between the two cases (error) is read against the right y-axes

(red lines). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

9 CO2 concentrations at two heights above a windward (NW) and leeward (SE) corner

of the penthouse (PH) in a westerly wind. Measured concentrations (black curve)

are overlaid on the prescribed profile (green curve). Concentrations are read on the

left y-axes. The difference between the two cases (error) is read against the right

y-axes (red curve). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

10 Extended domain showing a schematic representation of the buildings surrounding

NIST Administrative Building (Building 101). . . . . . . . . . . . . . . . . . . . . . 15

11 CO2 concentrations in a westerly wind at two heights above a windward (NW) and

leeward (SE) corner of the main building. Results include the effect of surrounding

buildings. Measured concentrations (black lines) are overlaid on the prescribed profile (green lines). Concentrations are read on the left y-axes. The difference between

the two cases (error) is read against the right y-axes (red lines). . . . . . . . . . . . 16

12 Vertical slice of the velocity field in a northerly (left to right) flow. Vector color and length

indicates velocity, with magnitudes that range from 0 m/s (blue) to 4 m/s (red). Green dots

above the rooftop represent simulated measurement locations. . . . . . . . . . . . . . . . . 17

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13 CO2 concentrations at two heights above a windward (NW) and leeward (SE) corner
 

of the main building in a northerly wind. Measured concentrations (black lines) are
 

overlaid on the prescribed profile (green lines). Concentrations are read on the left
 

y-axes. The difference between the two cases (error) is read against the right y-axes
 

(red lines). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
 

14 Schematic diagram showing the proposed location / configuration of sensors for measurement of GHG on building rooftops. . . . . . . . . . . . . . . . . . . . . . . . . . 21
 

List of Tables

1 Westerly wind: RMSE (ppm) and correlation coefficients for various sensor locations.
 

Times indicate spike duration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
 

2 RMSE (ppm) and correlation coefficients for Westerly wind case including the effects
 

of surrounding buildings. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
 

3 Northerly wind: RMSE (ppm) and Correlation coefficients for various sensor locations. Times indicate spike duration. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
 

4 Effects of mesh resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
 

5 Northwesterly wind: RMSE (ppm) and correlation coefficients for various sensor
 

locations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
 

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