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Research Paper

The effect of external wind speed and direction onsampling point concentrations, air change rate andemissions from a naturally ventilated dairy building

Chayan K. Saha a,c,*, Christian Ammon a, Werner Berg a, Christiane Loebsin b,Merike Fiedler a, Reiner Brunsch a, Kristina von Bobrutzki a

a Leibniz Institute for Agricultural Engineering Potsdam-Bornim (ATB), Department of Engineering for Livestock Management,

Max-Eyth-Allee 100, 14469 Potsdam, Germanyb State Institute for Agriculture and Fishery MV, Institute of Animal Production, Wilhelm-Stahl-Allee 2, 18196 Dummerstorf, GermanycDepartment of Farm Power and Machinery, Bangladesh Agricultural University, 2202 Mymensingh, Bangladesh

a r t i c l e i n f o

Article history:

Received 28 June 2012

Received in revised form

23 November 2012

Accepted 3 December 2012

Published online 1 February 2013

* Corresponding author. Leibniz Institute forManagement, Max-Eyth-Allee 100, 14469 Pot

E-mail addresses: csaha@atb-potsdam.de1537-5110/$ e see front matter ª 2012 IAgrEhttp://dx.doi.org/10.1016/j.biosystemseng.20

Natural ventilation (NV) of buildings refers to the exchange of indoor air with outdoor air

due to pressure differences caused by wind and/or buoyancy. Increased knowledge of the

factors that affect NV and emissions from naturally ventilated dairy (NVD) buildings may

lead to a better understanding of indoor air quality, an improvement of emission abate-

ment technologies and a refinement of emission models. The influence of external wind

speed and direction on point concentration, air change rate, ammonia (NH3) and methane

(CH4) emissions was evaluated in an NVD building located in northern Germany. The

measured data were classified according to four wind direction groups: 0�e10� (N), 85�e95�

(E), 175�e185� (S), and 265�e275� (W), with consideration for similar wind frequencies and

representation of each major side for further analyses and comparisons. The results

showed that wind speed and wind direction had significant influence on air change per

hour (ACH) (P < 0.05) both individually and when interacting. In contrast, only wind speed

and interactions of external wind speed and direction significantly affected NH3 and CH4

emissions (P < 0.05). The surrounding obstacles, other climate parameters (temperature

and relative humidity) and other emission sources should be taken into account when

interpreting the effects of wind direction on ACH and emissions. Empirical models for ACH,

NH3 and CH4 emissions were developed. Intensive experiments in the lab (e.g. scale model

in boundary layer wind tunnel) and long-term measurement including all seasons at full

scale are required to establish a good empirical model.

ª 2012 IAgrE. Published by Elsevier Ltd. All rights reserved.

1. Introduction temperature, relative humidity and air speed) in buildings and

Natural ventilation (NV) is an energy-saving passive ventila-

tion method for regulating indoor air parameters (such as air

Agricultural Engineeringsdam, Germany., cksahabau@yahoo.com. Published by Elsevier Lt12.12.002

for removing contaminated air from an indoor space to create

a comfortable environment with acceptable indoor air quality

(Chen, 2009). At the same time, NV in livestock buildings

Potsdam-Bornim (ATB), Department of Engineering for Livestock

(C.K. Saha).d. All rights reserved.

Nomenclature

a model constant

b model constant

C gas concentration, g m�3

CF correction factor

c model constant

d model constant

E gas emission rate, g h�1 or g LU�1 h�1

LU livestock unit, 500 kg

m average mass of a cow, kg cow�1

N total number of cows

P CO2 excretion rate per cow, g cow�1 h�1

p days after insemination, d

Q ventilation rate, m3 h�1

q required energy or heat production, W

RH relative humidity, %

T temperature, �Cu wind speed, m s�1

Y milk yield, kg d�1

a model constant

b model constant

3 residual of estimate

m intercept or constant

q wind direction, �

Subscript

a ammonia

cor corrected

i indoor/group

j hour of the day

m methane

o outdoor

t total

b i o s y s t em s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8268

transports pollutants (i.e., gas, odour and dust) (Saha, Zhang,

Kai, & Bjerg, 2010; Zhang et al., 2005) that adversely affect

animals, workers, neighbours and the environment (Bull &

Sutton, 1998; Campagna et al., 2004). Therefore, a balance

between regulating indoor air parameters and controlling

emissions in NV livestock buildings is needed.

The natural ventilation system in dairy cow buildings

commonly used in mild climate regions has large side open-

ings and a roof opening (Samer et al., 2011). Hence, the

emission from a naturally ventilated dairy (NVD) building

directly depends on atmospheric influences under continu-

ously changing conditions (Ikeguchi, Zhang, Okushima, &

Bennetsen, 2003; Teitel, Liran, Tanny, & Barak, 2008). The

limitations to current knowledge related to the prediction of

the ventilation performance of an NVD building have been

focused for study in recent years (Norton, Grant, Fallon, & Sun,

2009; Van Buggenhout et al., 2009; Zhang et al., 2005).

External weather conditions, which affect ventilation

performance and emissions of NVD buildings, are highly

variable, i.e., these conditions change over time and seasons.

Few studies have been performed to elucidate the effects of

internal air temperature, air speed, and animal activity on air

change rate and emissions from an NVD building (Ngwabie,

Jeppsson, Gustafsson, & Nimmermark, 2011; Norton et al.,

2009). Limited knowledge on the effects of external wind

conditions on internal air change per hour (ACH) can be found

in the literature regarding NVD buildings. It is common to

consider a steady airflow based upon a single mean wind

speed and a predominant wind direction for the majority of

time through the opening of an NVD building. Transient

effects of external airflow, which can cause uncertainties in

the result, are normally neglected. For example, obstacles in

upwind of an NVD building and variations in wind direction

may cause considerable fluctuations in the air flow through

the openings and thus affect the air movement inside the

building.

Snell, Seipelt, and Van den Weghe (2003) found that wind

velocity is very important for ventilation, whereas other

climate factors only have a small, usually insignificant,

influence. External wind direction has been found to have

a significant influence on the internal airflow patterns and

ventilation rate in urban NV buildings (Horan & Finn, 2008; Ji,

Tan Kato, Bu, Takahashi, 2011; Jiang & Chen, 2002). Fiedler and

Mueller (2011) indicated that wind direction may affect

measured gas concentrations within naturally ventilated

livestock buildings. Becausewind is themost important factor

in an NVD building, a full understanding of the ventilation

process requires thorough insight into the relationship

between the external wind characteristics (wind speed and

direction) and the air change per hour of the building.

However, the effects of changes in external wind speed and

direction on emissions are often not quantified.

Norton et al. (2009) performed computational fluid

dynamics (CFD) simulations of NV buildings situated in open

terrain. They studied the ventilation effectiveness in livestock

buildings for wind directions ranging from 0�e90� with an

interval of 10� and found differences in ACH of up to 100%

depending on the wind direction. Shen, Zhang, and Bjerg

(2012) used response surface methodology to determine the

relationship of external wind speed and direction with

ventilation rate, but their study was based on the results from

a simulation using CFD and was limited to wind directions of

0�e90�. Note that all of the aforementioned CFD studies did

not consider the surroundings of the building. The effects of

wind speed and direction on ACH and emissions may depend

on site-specific conditions. In a study by Van Hooff and

Blocken (2010), the authors found that the simulated differ-

ences in ACH between wind directions can be up to 75%

without surrounding buildings and 152% with surrounding

buildings. The simulation was conducted for an isothermal

case in their study and was limited to influencing parameters

(e.g. wind direction and urban surroundings). Therefore, the

relationship of ACH with external wind speed and direction

found for one specific site may not be applicable for others.

Moreover, the effects of external wind speed and wind direc-

tion on gas emission together with ACH have not been

examined for an NVD building thus far.

Therefore, the objectives of this study were to investigate

the effects of wind speed and wind direction on sampling

point concentrations, ACH, and ammonia (NH3) and methane

b i o s y s t em s e ng i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8 269

(CH4) emissions, and to find out the relationship of ACH and

gas emissions from an NVD building for a specific site to wind

conditions and other climate factors.

2. Materials and methods

2.1. Building and site description

The experimental site is located in Dummerstorf,

Mecklenburg-Vorpommern, northeast Germany (217 km

north-west Berlin, 54�10000N, 12�1306000E, altitude 43 m). The

measurements were carried out in a naturally ventilated dairy

building with a solid concrete floor. The dairy building is

96.15 m long and 34.2 m wide (Fig. 1a). The height of the sheet

metal roof varies from 4.2m at the sides to 10.73m at the gable

peak. The internal room volume of the building is 25,499 m3

(70 m3 per animal), and the building was designed to accom-

modate 364 dairy cows in loose housing with freestalls. There

were 338 cows in the NVD building during the measurement

period. The manure handling system is equipped with

a winch-drawn dung channel scraper. The floor was cleaned

with the scraper at 10 min intervals. The slurry was dumped

into a partly covered pit outside the east of the building and

the pit was emptied daily between 12.00 and 16.00 depending

on the fullness of pit. The dairy building is naturally ventilated

by a draught introduced into the building through long

sidewalls (protected by nets) with adjustable curtains (poly-

ethylene film, 1 mm), which were open fully during the

experiment from June to September. The building has an open

ridge slot (0.5 m) and space boards (11.5 cm width and 2.2 cm

Fig. 1 e (a) External and (b) internal views of the

investigated building.

thickness of wood board having solid core and spaced by

2.5 cm) in the gablewall of thewestern side of the building and

sheet metal wall of the eastern side; one gate (metal urethane

core with thermal break; 4 m � 4.4 m) in each gable wall; and

4 doors with adjustable curtains (where two doors are

3.2 m � 3 m, and two doors are 3.2 m � 4 m) in each gable wall

(Fig. 1). The gates and the doors were open during the exper-

iment. Three additional ceiling fans (Powerfoil� X2.0, Big Ass

Fans HQ, Lexington, KY, USA) were used to enhance the

uniformity of air distribution inside the building during the

summer season. The fans were mounted on the ceiling along

the building centre line and had a diameter of 7.34 m with

a maximum discharge of 546,000 m3 h�1 (Fig. 1b). The airfoils

of the fans are 5.63 m above the feeding alley. The fans were

regulated by the wall-mounted controller depending on the

outside weather conditions.

Around the investigated NVD building there are a milking

parlour, another dairy building, and a forage storage building

on the northern side, open fields on the southern and western

sides,manure storage tanks on the eastern, south-eastern and

north-western sides and a young stock house and workshop

on the north-eastern side (Fig. 2). The height and width of the

milking parlour house are 7.5 m and 63 m respectively. The

heights of manure storage tanks from the same floor level of

the NVD building are between 4.6 m and 4.9 m, and the

diameters are between 27.5 m and 29.24 m. The complex

surroundings of the NVD building indicate that wind blowing

to the investigated building may not be smooth except from

the southern andwestern sides.Wind conditions and external

emission sources (e.g. the manure tank and the other dairy

building) might affect point concentrations outside and inside

the building.

2.2. Experimental considerations and samplinglocations

Measurements were conducted from 16 June to 28 September

in 2010. Both the external weather conditions and internal

Fig. 2 e Farmstead layout: (1) milking parlour, (2) another

dairy building, (3) open field, (4) manure tanks, (5) young

stock housing, (6) workshop, (7) administration, and (8)

forage storage buildings. The symbol + indicates location

of the weather station.

b i o s y s t em s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8270

building climate were taken into account when designing the

experiment and choosing sample locations.

2.3. Measurement

2.3.1. Gas concentrationConcentrations of carbon dioxide (CO2), ammonia (NH3),

methane (CH4), and nitrous oxide (N2O) were continuously

measured inside the building at eight uniformly distributed

points and outside the building at four points (Fig. 3). The

experimental setup for concentration measurements was

similar to that described by Samer et al. (2011) and Samer et al.

(2012). Gas concentrations were measured using an infrared

photo-acoustic analyser (INNOVA 1312, Innova AirTech

Instruments, Ballerup, Denmark) coupled to a multipoint

sampler for 12 sampling points. The detection thresholds of

the gases were: 1.5 ppm for CO2, 0.4 ppm for CH4, 0.2 ppm for

NH3 and 0.03 ppm for N2O. The analyser had a repeatability of

1% and a range drift of 2.5% of the measured values according

to data sheets from the manufacturer. The monitor equipped

with filters at the inlet was placed inside a van parked outside

(north-east) the building. Air from the sampling locations was

drawn through 12 channels of the multiplexer (valve mani-

fold) to the analyser using 3 mm (inner diameter) polytetra-

fluoroethylene (PTFE) tubes. The sampling tubes connecting

the gas instruments were between 5 m and 50 m long. The

sampled air was drawn at a rate of 15 l min�1 by a diaphragm

vacuum pump (Model N022AN.18, KNF, Germany) connected

to the exhaust of the multiplexer. This pump ensured that air

being analysed at any time was fresh from the sampling

locations. Its location also ensured that sampled air was not

contaminated by the pumps before analysis.

The concentration measurements took place in a contin-

uous sequence. As a result, each sampling point was assessed

at intervals of approximately 12 min (w1 min per sampling

point). The gas sampling points (SP) in Fig. 3 were placed at

a height of 2.9 m. The external gas sampling point (SP4) was

placed 11m south of the building (36.7m from the eastern side

of the building) to sample the gases from fresh external air as

SP8SP10

THS5

SP9SP11

THS6

SP12

Fig. 3 e Plan view of the investigated building where, the blue ci

represent the temperatureehumidity sensors (THS). The light g

areas represent the freestalls. (For interpretation of the reference

web version of this article.)

a reference. The external gas sampling point SP7 was located

3 m north of the building. The sampling points SP1 and

SP12 were located outside the east and west gable walls

respectively.

2.3.2. Climate dataMeasurements of temperature and relative humidity were

carried out every minute using sensors (Comark Diligence EV

N2003, Comark Limited, Hertfordshire, UK; temperature

accuracy of �0.5 �C for �25 �C to þ50 �C, and RH accuracy of

�3% for �20 �C to þ60 �C) positioned at four locations inside

the building and at two locations outside the building (Fig. 3).

The temperatureehumidity sensors (THS) in Fig. 3 were

placed at the same height as SPs (i.e., 2.9 m). The temper-

atureehumidity loggers THS3 and THS4 were placed at the

same location and height as SP4 and SP7 respectively. The

ambient wind conditions weremeasured by a weather station

(DALOS 515c-M, F&C Forschungstechnik & Computersysteme

GmbH, Gulzow, Germany) located near the NVD building

(i.e.150 m east of the building). The height of the wind

measurement sensor of this station is 2.5 m. There were no

obstacles (trees or buildings etc) within a 50 m radius of the

weather station. Hence the data from the weather station

represent the local climate. The data for wind speed and

direction were recorded every hour. The ranges of wind speed

and direction measurements are 0.5 m s�1 to 40 m s�1 and

0�e360� respectively.

2.4. Air change rate and emission

In this study, the ventilation rate through the building was

determined by calculating the mass balance of CO2 flow.

Carbon dioxide, from animal respiration and manure, can be

used as a natural tracer gas. The CO2 balance and the CO2

excretion calculations are based on heat and CO2 excretion

models from several studies (Albright, 1990; CIGR, 2002;

Hellickson & Walker, 1983). Concerning the uncertainties in

measuring CO2 concentrations and ventilation rates, the CO2

production from manure was neglected in the mass balance

SP2

THS1

SP3

THS2SP6 SP1

SP5

THS3SP4

THS4

SP7

rcles indicate gas sampling points (SP), and the red squares

rey area represents the feeding table, and the dark grey

s to colour in this figure legend, the reader is referred to the

Fig. 4 e Wind rose of wind direction and speed measured

from 16 June to 28 September in 2010 at the experimental

site of Dummerstorf, Germany.

b i o s y s t em s e ng i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8 271

model. The relationship between the ventilation rate and the

gas production rate, assuming ideal mixing with the air inside

the building, is described by Eq. (1):

Q ¼ N$PCO2

Ci � C0; (1)

where PCO2represents the excretion rate of CO2 from one cow

(g cow�1 h�1), N is the number of cows housed inside the

building, Q is the ventilation rate calculated according to CO2-

balance (m3 h�1), and Ci and Co are the average concentrations

of the gas inside and outside the building, respectively (gm�3).

The ACH can then be calculated by dividing the ventilation

rate by the volume of the building.

However, the gas concentration inside the building is not

uniform and varies with time. Therefore, Eq. (1) is only an

approximate estimate for gas production in dairy buildings.

Within the CO2 balance, the CO2 excretion rate depends on

heat production. Hence, the CO2 excretion rate can be calcu-

lated as follows (CIGR, 2002):

qt ¼ 5:6ðmÞ0:75þ1:6� 10�5ðpÞ3þ22y (2)

CF ¼ 4� 10�5ð20� TiÞ3þ1 (3)

qcor ¼ qt$CF (4)

PCO2¼ 0:299$qcor (5)

where qt represents the total heat production (W) and qcor is

the corrected value of the total heat production (W). Several

additional parameters were considered: m represents the

average mass of the animals (kg cow�1), p is the number of

days after insemination (d), y designates the milk yield

(kg d�1), Ti represents the temperature inside the building (�C)

and CF is the temperature correction factor. In Eq. (5), PCO2is in

g h�1 cow�1 and can be substituted into Eq. (1). The average

weight and milk yield of the cows were 691 kg cow�1 and

35.6 kg d�1, respectively. The contribution of pregnancy days

of each cow to the total heat production was omitted (i.e., p

was taken to be 0 in the Eq. (2)) as like other studies (e.g.

Ngwabie et al., 2011), since it is difficult to get information on

the pregnancy days of each cow individually during the

measurement period.

The emission rate of a gas was calculated using the

following equation:

Et ¼ QðCi � CoÞ; (6)

where Et represents the emission rate of gas (g h�1), Q is the

ventilation rate calculated according to the CO2 balance

method (m3 h�1), and Ci and Co are the average concentrations

of the gas inside (i.e., average of 8 indoor sampling points) and

outside the building (i.e., average of 4 outdoor sampling

points), respectively (g m�3).

Theweight of the cows and the productionmay differ from

herd to herd. To make results comparable, the emission per

livestock unit (LU) is used inmodelling instead of emission per

cow. The LU is equivalent to 500 kg animal mass (Samer et al.,

2011). The emission rate per LU can thus be stated as:

E ¼ Et$LUN$m

; (7)

where E is gas emission rate per animal unit (g LU�1 h�1), Et is

the gas emission rate (g h�1), N is the total number of cows

housed inside building, and m is the average mass of a cow

accommodated in the building (kg cow�1).

2.5. Data analysis and statistical modelling

The data obtained from the measurements were used to

analyse the effects of external wind speed and wind direction

on sampling point concentration, ACH, and emissions. The

N2O concentration data were not considered for further

analysis since inside and outside concentrations of N2O were

at the same level and they were very low. During the

measurement period, wind came from all directions, although

higher frequencywas observed for winds from the south-west

and north-west (Fig. 4).

In the surroundings of the investigated building, there

were other buildings, a milking parlour, a storage tank, etc

(Fig. 2), which may have affected external wind speed and

ambient gas concentration and, consequently, sampling point

concentrations inside the building, ACH and emissions.

Therefore, to investigate wind direction effects for this

specific site, wind direction was classified into four groups

that had almost identical wind frequencies and were repre-

sentative of eachmajor direction. The groups were 0�e10� (N),

85�e95� (E), 175�e185� (S), and 265�e275� (W). The climate data

and gasmeasurement datawere filtered and selected from the

hourly average data according to the four groups of wind

directions for further analysis, comparison and modelling.

The statistical analysis was carried out using the SAS v.9.2

software package (SAS Institute, Cary, NC, USA). The classified

data according to wind direction was again divided randomly

into four data sets namely 1, 2, 3, and 4. Simultaneously, three

data setswere used formodel development and the remaining

data set was used for model validation. We assumed that

different sets of data may give different estimates for model

building. The inside temperature (T ), relative humidity (RH),

and outside wind speed (u) were used as regression variables,

and wind direction (q) was considered as a class variable or

level for modelling ACH, Ea and Em. The interaction effect

b i o s y s t em s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8272

between q and u was also included to account for different

effects of wind speed depending on the wind direction.

Because T and RH were not fixed during the measurement

period, they were added in the model as co-variables,

although the main target was to investigate the effects of u

and q on ACH, Ea and Em.

The ACH, Ea, and Em data were pre-processed by a common

logarithm (base 10) for better fitting of the models. The

equations take the form of Eq. (8) for ACH, Ea and Em,

�log10ACH=Ea=Em

�ij¼ mþ qi þ a$uþ b$Tþ c$RHþ di$uþ 3ij (8)

where ACH is the air change per hour (i.e., in the j-th hour), m

is the intercept value, qi is the fixed level of influences for the

specific wind directions, u is the external wind speed (m s�1),

T is the indoor air temperature (�C), RH is the indoor relative

humidity (%), a, b, c, and di are the coefficients or fixed effects

of u, T, RH, interaction of u and q, respectively, 3 is the

residual of estimate, Ea/m is the emission of gas (g LU�1 h�1),

and the subscripts ‘a’ and ‘m’ stand for NH3 and CH4 gas,

respectively.

The fixed values that include the intercept (m) togetherwith

the wind direction effect (q), i.e., (m þ qi) can be presented as

a single estimate and the individual effects of u and interac-

tion effects of u and q, i.e., (a þ di) can also be presented as

a single estimate as in Eq. (9) for better understanding and

model evaluation.

�log10ACH=Ea=Em

�ij¼ ai þ b$Tþ c$RHþ bi$uþ 3ij (9)

Hourly data for all dependent variables in the same time

span were used for modelling purposes. ANCOVA was con-

ducted to determine parameter (T, RH, u, q, and u � q) effects

on ACH, Ea and Em. The hypothesis of the ANCOVAmodel was

that there is no effect of T, RH, u, q and u � q at a significance

level of a ¼ 0.05.

3. Results and discussion

3.1. Climate conditions

The mean values and the standard deviations of external

wind speed, air temperature and relative humidity inside and

outside the building for four groups of wind direction are

presented in Table 1. The values calculated for each group

contained between 62 and 68 data points (averaged hourly),

including both daytime and night-time measurement data.

Table 1 e Climate conditions inside and outside the naturallydirection.

Wind directionrange, �

No of data Wind speed,m s�1

Indoortemperature, �C

0e10 62 0.7 � 0.8 17 � 4.6

85e95 65 1.8 � 1.1 16 � 4.8

175e185 68 1.3 � 1.0 19 � 4.2

265e275 63 0.9 � 0.9 19 � 2.9

Mean � standard deviation.

There were no significant differences between mean indoor

and outdoor temperature and relative humidity because the

side wall curtains and doors were fully open during the

measurement period. The absolute values of temperature

difference between the indoor and outdoor air were mainly

less than 2.5 �C. There were large standard deviations in the

temperature and relative humidity because of large variations

in these parameters during daytime and night-time, and there

were also variations across the days. Relatively higher wind

speed was observed in the wind direction range of 85�e95�

compared to others.

3.2. Effect of external wind speed and wind direction

3.2.1. Sampling point concentrationAs expected, wind speed and wind directions influenced NH3

and CH4 concentrations at different sampling points (see

Fig. 5). Among the 12 sampling points, 2 outdoor sampling

points (SP1 and SP7) and 4 indoor sampling points (SP5, SP6,

SP9 and SP10) (Fig. 3) are presented in figures to show themain

effects clearly (Fig. 5).

Fig. 5a(i) and (ii) show greater differences of NH3 and CH4

concentration among the sampling points at lower wind

speed. The differences decreased with the increase of wind

speeds. The general trend of concentration reduction was

expected with the increase of wind speed due to dilution and

flushing (Saha, Zhang, Ni, 2011; Ye et al., 2008); however, in the

case of NH3 (Fig. 5a(i)) a different behaviour was observed at

wind speeds between 0 and 1 m s�1, where the NH3 concen-

tration increased with increase of wind speed. This phenom-

enon can be explained by the presence of manure storage

tanks on the south-eastern side and animal houses in

northern and north-eastern sides as NH3 sources, which

might increase NH3 concentration at the sampling points.

Additionally, the presence of manure storage tanks and

animal houses as obstacles might retard the dilution of NH3 in

the air. However, different airflow patterns at different

sampling point locations might affect point concentrations

(Kiwan, Brunsch, Ozcan, Berg, & Berckmans, 2010; Van

Buggenhout et al., 2009). Higher upwind outdoor gas concen-

tration results in higher concentrations inside the NVD

building. Moreover, the highest concentration of NH3 was

found at SP6 irrespective of wind velocity. SP6 was located

next to the milking parlour passage and was a site of greater

animal activity, which might accelerate the release of

ammonia.

ventilated dairy building at the specific ranges of wind

Outdoortemperature, �C

Indoorrelative

humidity, %

Outdoor relativehumidity, %

ACH

15 � 5.8 77 � 15.6 79 � 17.6 25 � 14.4

15 � 3.8 80 � 15.6 79 � 16.6 23 � 7.4

18 � 5.0 82 � 20.6 83 � 21.2 48 � 32.7

18 � 3.6 83 � 14.5 84 � 15.4 32 � 18.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

0 1 2 3 4

0 1 2 3 4

Wind speed, m s-1

a(i)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0-10

85

-9

5

17

5-185

26

5-275

NH

3concentratio

n, m

g m

-3

NH

3concentratio

n, m

g m

-3

Wind direction, degree

b(i)

0

5

10

15

20

25

4

Wind speed, m s-1

a(ii)

0

5

10

15

20

0-10

85-95

175-185

265-275

CH

4con

ce

ntr

ation

, m

g m

-3

CH

con

ce

ntr

ation

, m

g m

-3

Wind direction, degree

b(ii)

Fig. 5 e (a) Wind speed and (b) wind direction effects on ammonia (NH3) and methane (CH4) concentration at different

sampling points (SPs) (see Fig. (3)), where, >, *, B. 3, d and 6 represent SP1, SP5, SP6, SP7, SP9 and SP10 respectively.

b i o s y s t em s e ng i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8 273

Fig. 5 b(i) and (ii) show the concentration differences of NH3

and CH4, respectively, at different SPs for different wind

directions. In general, there are differences in point concen-

trations among the groups of wind directions. The depen-

dency of the measured point concentration on wind direction

is demonstrated by SP10. The measured concentrations of

NH3 and CH4 at SP10 were higher when wind was blowing

from the east (85�e95�) than from the west (265�e275�),although the mean temperature and relative humidity were

lower during the sampling time for thewind direction range of

85�e95�. Windmight sweep/transport gases from the east side

of the building and increase the concentration at SP10. The

results corroborate other studies (Shen, Zong, & Zhang, 2012;

Wu, Zhai, Zhang, & Nielsen, 2012) which also found that

sampling point concentrations are affected by variations in

wind direction. However, building orientation to the wind and

obstacles (the manure tank and the young stock house)

present at the eastern side might also be the cause of higher

concentration of NH3 and CH4 at SP10 even though the mean

wind speed was highest from the east (85�e95�) i.e., 1.8 m s�1

(Table 1). Nevertheless, the influence of variations of T and

RH over time and direction cannot be ignored, as we see

highest NH3 concentrations for the westerly wind direction

(265�e275�) (Table 1, Fig. 5b).

3.2.2. Air change per hourFig. 6a shows the calculated ACH, measured indoor T and RH

for different wind speeds at wind direction q ¼ 0�e10�. In

general, wind speed directly influences ACH. Air change per

hour was increased as outside wind speed increased (Fig. 6a).

This phenomenon is consistent with the study by Wu, Zhang,

and Kai (2012). The variations of ACH might be due to the

variations of T and RH during the measuring periods of pre-

sented data. Zhang et al. (2005) noted that wind effects will

contribute more to air exchange rate as wind speed increases,

although a temperature difference provides a buoyant force

that induces ventilation in livestock buildings. However,

studies have shown that heat stress (expressed in temper-

atureehumidity index) affects the activity and performance of

dairy cows (De Palo, Tateo, Padalino, Zezza, & Centoducati,

2005; Provolo & Riva, 2008). Higher animal activity means

higher CO2 emission (Pedersen et al., 2008). Therefore, the

calculated ACH might be varied due to the variations of T

and RH.

The results of the measured wind speed and calculated

ACH in different wind directions are shown in Fig. 6b. Fig. 6b

demonstrates the strong dependency of ACH on wind direc-

tion. For example, the mean value of ACH for q in the range of

175�e185� (48 h�1 at the mean wind speed 1.3 m s�1) is 2.08

Fig. 6 e Air change per hour (ACH) at different (a) wind

speeds (u) at wind direction of 0�e10�, where D, 6, and^

indicate indoor air temperature (T), indoor relative

humidity (RH), and ACH, respectively. The line represents

the regression of ACH on wind speed ( y [ 13.13x D 16.28,

R2 [ 0.6828) and (b) wind directions, where symbol

lozenges represent the averages, the internal dashes

designate the medians, the upper and external dashes are

the maximal and minimal values within 1.5 times the

interquartile range, and open dots represent the single

value outside the 1.5 times the interquartile range.

Table 2 e F-test results for fixed effects of different parametersemissions for the four groups of randomly selected data.

Effect Num DF ACH (Pr > F ) Ammonia

A B C D A

q 3 0.0056 0.0011 0.0097 <0.0001 0.4507

u 1 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <

T 1 0.2318 0.765 0.4503 0.2897 <0.0001 <

RH 1 0.0012 0.03 0.0443 0.0006 0.9532

u$q 3 <0.0001 0.0023 0.0003 <0.0001 0.076

ACH is the air change per hour; q is thewind direction, �; T is the indoor air

wind speed, m s�1; A, B, C, D indicate four models of randomly selected da

b i o s y s t em s e n g i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8274

times higher than for q in the range of 85�e95� (23 h�1 at the

mean wind speed 1.8 m s�1). This result is consistent with

that reported in the study conducted by Van Hooff and

Blocken (2010). Air change per hour in other groups of wind

directions also shows larger deviations than for the q in the

range of 175�e185�. The lower ACH for all other groups than

for q in the range of 175�e185� can be explained by the

presence of other agricultural buildings and manure tanks

upwind of the NVD building. These obstacles provide some

shelter from the wind. ANCOVA shows that the parameters q,

RH, and u and the combination of q and u have significant

influence on ACH, whereas parameter T does not (Table 2).

There were no significant differences between outdoor and

indoor T (Table 1), which could be the reason why T did

not have significant influence on ACH. Our results are

partly consistent with those from Snell et al. (2003) who re-

ported that wind velocity has crucial importance for venti-

lation, whereas other climate factors have only a small,

usually insignificant influence on the ventilation in an NVD

building.

3.2.3. Gaseous emissionsAmmonia and methane emissions were calculated by multi-

plying the ventilation rate by the concentration difference

between the outdoor and indoor air. Methane emissions from

the NVD building were 5.0e7.5 times higher than NH3 emis-

sions at all wind speeds and wind directions (Fig. 7). These

results are in line with the results from previous studies

(Ngwabie et al., 2011; Samer et al., 2012; Snell et al., 2003; Wu,

Zhang et al., 2012). No clear pattern of the effects of wind

speed on gas emissions was observed in this study (Fig. 7a).

Increasing external wind speed should have increased floor

air velocity of NVD building, and hence increased NH3 emis-

sions, as described in other studies (Arogo, Zhang, Riskowski,

Christianson, & Day, 1999; Saha, Zhang, & Kai, 2012; Saha,

Zhang, & Ni, 2010), but this phenomenon was not observed

clearly in this study. The effects of wind speed might be

masked by other parameters (e.g. T, RH), which were not

constant (as shown in Fig. 6a as an example) during the

measurement period.

Wind direction (q) shows some divergences of NH3 and CH4

emissions among the wind direction groups (Fig. 7b). But the

effect was not significant for NH3 and CH4 emissions (P > 0.05)

as it was for ACH (P < 0.05) for all sets of data (Table 2). Our

on air change per hour (ACH) and ammonia and methane

emission (g LU�1 h�1)(Pr > F )

Methane emission (g LU�1 h�1)(Pr > F )

B C D A B C D

0.386 0.1818 0.4175 0.2957 0.3544 0.3797 0.8788

0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

0.9023 0.7583 0.9466 0.0151 0.0603 0.0029 0.0023

0.0171 0.0002 0.121 0.0088 0.0025 0.0098 0.1076

temperature, �C; RH is indoor air relative humidity, %; u is the external

ta sets of (2 þ 3 þ 4), (1þ 3þ 4), (1 þ 2þ 4), and (1 þ 2 þ 3) respectively.

Fig. 7 e Ammonia (NH3) and methane (CH4) emissions at

different (a) wind speeds at 0�e10� wind direction, where

plus and triangle indicate NH3 and CH4 emissions

respectively; and (b) wind directions, where 0e10, 85e95,

175e185, 265e275 indicate wind direction ranges in

degrees, symbol lozenges represent the averages, the

internal dashes designate the medians, the upper and

external dashes are the maximal and minimal values

within 1.5 times the interquartile range, and open dots

represent the single value outside the 1.5 times the

interquartile range.

b i o s y s t em s e ng i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8 275

statistical analysis also shows that u and T have substantial

effects on NH3 and CH4 emissions (Table 2).

This finding can be explained by the sources of the gases

and the level of air speed close to that emission source.

Ammonia is mainly emitted from manure and urine on the

floor (Jungbluth,Hartung,&Brose, 2001;Monteny, Groenestein,

& Hilhorst, 2001). Increases in the external wind speed might

increase air speed at floor level (Saha et al., 2012) and ACH,

therefore significantly affected NH3 emission. In contrast,

direct influence of ambient weather conditions (wind,

temperature etc) on CH4 emission cannot be expected since

CH4 and CO2 originated mainly from the cow (Jungbluth et al.,

2001). Increasing wind speed significantly affected ACH

(Fig. 6a, Table 2), as explained in section 3.2.2, hence would

affect CH4 emissions significantly as calculated by Eq. (6) and in

Table 2.

The temperature of the manure is of vital importance

because NH3 is almost exclusively emitted from manure

(Jungbluth et al., 2001). Ngwabie et al. (2011) found that NH3

emissions increased with increasing indoor air temperature.

However, higher temperatures lead to higher ventilation rates

(Saha, Zhang, Kai et al., 2010; Zhang et al., 2005), and conse-

quently higher NH3 and CH4 emissions. Aswe see in Table 1, at

mean external temperature of 18 �C (wind speed 0.9 m s�1,

wind direction 265�e275�), ACH was higher than at mean

external temperature 15 �C (wind speed 0.7 m s�1, wind

direction 0�e10�), therefore giving higher NH3 and CH4 emis-

sion (Fig. 7b).

Relative humidity had a strong effect on CH4 emissions in

the NVD building, except random data sets group B (data sets

1 þ 3 þ 4) (P ¼ 0.06). The cause is largely unknown and needs

further investigation. However, the effect of RH might be

related to the changes in animal activities because of heat

stress (expressed in temperatureehumidity index) (Provolo &

Riva, 2008).

The interaction effect of u and q showed significant effects

on NH3 and CH4 emissions (P > 0.05), but not in all group of

classified data sets (Table 2). This interaction effect might be

due to the presence of obstacles and emission sources upwind

of the NVD building. However, long term measurement data

including all wind directions and all seasons might give us

a clear answer about u and q effects on emissions.

3.3. Statistical modelling

The estimates and standard error of four groups for each

parameter are given in Table 3. In Table 3, a1 to a4 estimates

are fixed values that include the intercept (m) togetherwith the

wind direction effect (q), i.e., (m þ ai) and b1 to b4 estimates are

coefficients, which include the individual effects of u and

interaction effects of u and q, i.e., (a þ bi). For example, for

wind direction 0�e10�, Eq. (10), Eq. (11), and Eq. (12) can be used

to predict ACH, Ea, and Em, respectively.

log10ACH ¼ 1:4974� 0:0029T� 0:0029RHþ 0:1942u (10)

log10Ea ¼ �0:7677þ 0:0409Tþ 0:0001RHþ 0:0422u (11)

log10Em ¼ 0:9409þ 0:0072T� 0:0013RHþ 0:0284u (12)

The model-predicted versus measured log10 ACH, log10 Ea,

and log10 Em are given in Fig. 8aec respectively. Pearson

correlation coefficients (r) of predicted versusmeasuredvalues

of log10 ACH, log10 Ea, and log10 Em are 0.8227, 0.6666, and 0.6960

respectively. Pearson correlation coefficient indicates linear

association between two variables, in these cases measured

versus predicted variables. Among the three models, the log10ACH model was a better fit than the log10 Ea and log10 Emmodels. The large variation from the 45� straight line (Fig. 8)

indicates that there might be other unknown factors that

affected ACH and emissions. However, the models are only

based on the data for warmer periods, and for the generic

model, experiments should be conducted year round by taking

into account the side wall opening, animal activities and

management practices together with other factors (e.g. T, RH,

u, q, etc.) mentioned in this study. Therefore, intensive

Table 3 e Solution of fixed effects of different variables and factor level with estimate and standard error of estimates for the mixed model of air change per hour (ACH),ammonia emission (Ea), and methane emission (Em), Eq. (9).

Dependentvariables

Effect Winddirection (q)

Estimates � SE Pearson correlationcoefficient

A B C D M

log10 ACH a1 0�e10� 1.5616 � 0.1406 1.4414 � 0.1565 1.4436 � 0.1523 1.5431 � 0.1364 1.4974 � 0.1465 0.8227 P < 0.0001 N ¼ 224

a2 85�e95� 1.5771 � 0.1538 1.3907 � 0.1667 1.4318 � 0.1624 1.5614 � 0.1489 1.4902 � 0.1579

a3 175�e185� 1.7114 � 0.1494 1.5842 � 0.1631 1.5774 � 0.1574 1.7513 � 0.1438 1.6561 � 0.1534

a4 265�e275� 1.6675 � 0.1492 1.5085 � 0.1577 1.5126 � 0.1556 1.6289 � 0.1433 1.5794 � 0.1515

T �0.0042 � 0.0035 �0.0012 � 0.0039 �0.0028 � 0.0038 �0.0036 � 0.0034 �0.0029 � 0.0037

RH �0.0035 � 0.0010 �0.0024 � 0.0011 �0.0022 � 0.0011 �0.0034 � 0.0009 �0.0029 � 0.0010

b1 0�e10� 0.2036 � 0.0283 0.1845 � 0.0244 0.1800 � 0.0238 0.2088 � 0.0238 0.1942 � 0.0251

b2 85�e95� 0.0678 � 0.0228 0.0856 � 0.0227 0.0915 � 0.0228 0.0728 � 0.0229 0.0794 � 0.0228

b3 175�e185� 0.2069 � 0.0203 0.1828 � 0.0208 0.2166 � 0.0192 0.1932 � 0.0199 0.1999 � 0.0201

b4 265�e275� 0.1944 � 0.0247 0.1897 � 0.0261 0.1961 � 0.0239 0.2137 � 0.0279 0.1985 � 0.0257

log10 Ea a1 0�e10� �0.7474 � 0.236 �0.8217 � 0.2916 �0.7626 � 0.2499 �0.7391 � 0.2441 �0.7677 � 0.2554

a2 85�e95� �0.7835 � 0.2581 �0.988 � 0.3105 �0.8880 � 0.2665 �0.8145 � 0.2665 �0.8685 � 0.2754

a3 175�e185� �0.6647 � 0.2507 �0.8513 � 0.3037 �0.7158 � 0.2582 �0.6615 � 0.2573 �0.7233 � 0.2675

a4 265�e275� �0.6746 � 0.2503 �0.8635 � 0.2937 �0.8248 � 0.2553 �0.7166 � 0.2564 �0.7698 � 0.2639

T 0.0378 � 0.0059 0.0458 � 0.0074 0.0400 � 0.0062 0.0401 � 0.0061 0.0409 � 0.0064 0.6666 P < 0.0001 N ¼ 224

RH �0.0001 � 0.0018 0.0003 � 0.0020 0.0005 � 0.0018 �0.0001 � 0.0017 0.0001 � 0.0018

b1 0�e10� 0.1078 � 0.0475 0.0046 � 0.0455 �0.0041 � 0.0390 0.0604 � 0.0426 0.0422 � 0.0436

b2 85�e95� 0.1341 � 0.0382 0.1259 � 0.0422 0.1443 � 0.0374 0.1290 � 0.0411 0.1333 � 0.0397

b3 175�e185� 0.0879 � 0.0341 0.0910 � 0.0388 0.1007 � 0.0315 0.0759 � 0.0356 0.0889 � 0.0349

b4 265�e275� 0.2217 � 0.0414 0.2213 � 0.0486 0.2483 � 0.0394 0.2011 � 0.0499 0.2231 � 0.0448

log10 Em a1 0�e10� 0.9269 � 0.0656 0.9097 � 0.0792 0.9615 � 0.0678 0.9655 � 0.0636 0.9409 � 0.0690

a2 85�e95� 0.9092 � 0.0717 0.8729 � 0.0844 0.9320 � 0.0722 0.9555 � 0.0695 0.9174 � 0.0745

a3 175�e185� 0.9471 � 0.0697 0.9175 � 0.0825 0.9702 � 0.0700 0.9722 � 0.0671 0.9518 � 0.0723

a4 265�e275� 0.9507 � 0.0696 0.9082 � 0.0798 0.9676 � 0.0692 0.9731 � 0.0668 0.9499 � 0.0714

T 0.0071 � 0.0016 0.0081 � 0.0020 0.0070 � 0.0017 0.0067 � 0.0016 0.0072 � 0.0017 0.6960 P < 0.0001 N ¼ 224

RH �0.0012 � 0.0005 �0.0010 � 0.0006 �0.0015 � 0.0004 �0.0014 � 0.0004 �0.0013 � 0.0005

b1 0�e10� 0.0402 � 0.0132 0.0268 � 0.0123 0.0208 � 0.0106 0.0259 � 0.0111 0.0284 � 0.0118

b2 85�e95� 0.0295 � 0.0106 0.0286 � 0.0115 0.0286 � 0.0101 0.0163 � 0.0107 0.0258 � 0.0107

b3 175�e185� 0.0144 � 0.0095 0.0160 � 0.0105 0.0223 � 0.0085 0.0197 � 0.0093 0.0181 � 0.0094

b4 265�e275� 0.0633 � 0.0115 0.0785 � 0.0132 0.0636 � 0.0107 0.0536 � 0.0130 0.0647 � 0.0121

A, B, C, D indicate four models of randomly selected data sets of (2þ 3þ 4), (1þ 3þ 4), (1þ 2þ 4), and (1þ 2þ 3) respectively.M indicates mean value of model estimate A, B, C, and D. a1 to a4 estimates

are the fixed values that include the intercept (m) together with the wind direction effect (q) values for four different wind direction level; T is the indoor air temperature (�C); RH is indoor air relative

humidity (%); u is the external wind speed, m s�1; b1 to b4 estimates are co-efficient of the interaction effect of u and q; SE is the standard error.

bio

systems

engin

eerin

g114

(2013)267e278

276

Fig. 8 e Predicted values versus measured values (a) air

change per hour (ACH), (b) ammonia emission (Ea), and (c)

methane emission (Em) where , D, 3, and 6 indicate

randomly selected data set of groups 1, 2, 3, and 4

respectively.

b i o s y s t em s e ng i n e e r i n g 1 1 4 ( 2 0 1 3 ) 2 6 7e2 7 8 277

experiments in the lab (e.g. scalemodel in boundary layerwind

tunnel) and long-time measurement including all seasons at

full scale are required to establish a good empirical model.

4. Conclusions

A study of the influence of wind speed and wind direction on

sampling point concentrations, ACH and NH3 and CH4 emis-

sions from an NVD building has been presented. The results

have demonstrated that the combination of wind speed and

wind direction has a significant influence on sampling point

concentrations of NH3 and CH4, ACH, and NH3 and CH4

emissions. ACH increased with the increase of external wind

speed in the NVD building. Depending on the wind speed and

direction, the value of ACH estimated with two different

approach flow conditions (i.e., wind from two different

directions) could vary by 100% or more. Wind speed had

a significant influence onNH3 emission. The indirect influence

of wind speed on CH4 emission (P < 0.001) might be due to

variation of ACH by wind speed. In the NVD building, wind

speed and wind direction together with surrounding obsta-

cles, emission sources, and other climate parameters (e.g. T

and RH) need to be taken into account, both for interpreting

results and for model development. Excluding the wind

direction and the surrounding rural environment of an NVD

building can lead to an incorrect estimation of ACH,which can

affect emissions estimation for the NVD building.

The ammonia and methane emissions model did not give

a good fit, indicating an effect from other unknown factors.

However, experiments in a controlled environment (e.g. scale

model in boundary layer wind tunnel) are needed to provide

precise information about how ACH and emissions are

affected by wind speed and wind direction together with

surrounding obstacles and other emission sources.

Acknowledgements

The authors would like to acknowledge U. Stollberg, K.

Schroter and D. Werner, technicians at the Department of

Engineering for LivestockManagement at the Leibniz Institute

for Agricultural Engineering Potsdam-Bornim (ATB) in

Germany, for their technical and logistical support during the

measurements. Furthermore, we gratefully acknowledge the

contribution of O. Tober at the Institute for Animal Produc-

tion, State Institute for Agriculture and Fishery MV, Dum-

merstorf, Germany, for his technical and logistical support

during the measurements. We would like to thank the anon-

ymous reviewers for their efforts put into the review of the

manuscript and their helpful comments.

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