Introduction
Wind power has been the fastest-growing energy source and one of the most
rapidly expanding industries around the globe. To achieve sustainable
development, establish an “environment-friendly society”, and reduce
emissions of CO2 and other air pollutants, considerable efforts have
been made in China to develop and expand wind power generation in the past
decade. China's wind power has increased 100 % from 2006 to 2010. By
2015, the total installed capacity of wind power became the largest globally
with the capacity of 140 GW (GWEC, 2016). It is projected that wind power
capacity in the nation will reach 200 GW by 2020, 400 GW by 2030, and
1000 GW by 2050. In 2016, the wind power capacity accounted for 4 % of
total national electricity consumption. It is expected that wind power will
become one of five main power sources and meet 17 % of the total
electricity demand in China in the mid-21st century (IEA, 2011).
Extensive field and modeling studies have demonstrated that a relatively
large-scale wind farm could alter the local meteorological and climate
conditions. From a dynamic perspective, a large-scale wind farm can be
approximately regarded as a sink of kinetic energy (KE) and source of
turbulent kinetic energy (TKE). Turbulence generated by wind turbine rotors
could create eddies that can enhance vertical mixing of momentum, reducing
the wind speed at the turbine hub height level (Baidya et al., 2004; Baidya,
2011; Barrie and Kirk-Davidoff, 2010). The wind farm-induced turbulence can
also alter the vertical mixing which can markedly affect the vertical
distribution of temperature and humidity (Baidya et al., 2004; Baidya, 2011).
Coupled atmosphere–ocean climate model has predicted that the global
distribution of wind farms could increase air temperature by up to
1 ∘C in inland wind farms and cool down temperature near the ground
surface by 1 ∘C in offshore wind farms (Keith et al., 2004).
Ocean–atmosphere heat fluxes would increase in response to increasing
turbulence produced by wind farms (Barrie and Kirk-Davidoff, 2010).
Nevertheless, although the effects of wind farms on meteorology have been
observed and simulated, overall the net impact of wind power on global
surface temperatures may be overlooked (Wang and Prinn, 2010). Satellite
remote sensing and model simulations confirmed that the degree of variations
in the surface temperature altered by large-scale wind farms were not
significant compared to the benefit from wind power in the emission reduction
of CO2 and other greenhouse gases (Barrie and Kirk-Davidoff, 2010; Keith
et al., 2004; Baidya, 2011; Zhou et al., 2012).
As a clean energy source, a wind farm does not release any harmful chemicals
into the air and hence has not received particular attention in the
scientific community compared to its negative environmental impacts on
wildlife, its noise and visual impact (Saidur et al., 2011; Magoha, 2002;
Loss et al., 2013), and meteorological and climate conditions. Wind farms
could alter the underlying surface characteristics and disturb winds and
turbulence near and within the wind farms by enhancing the surface roughness
length through the layout of wind turbines and the spinning condition of the wind turbine rotors. These changes mostly occur near the surface or
the atmospheric boundary layer where the levels of air pollutants are
highest. As a result, the wind power operation might affect the atmospheric
transport and diffusion of an air pollutant released from its industrial and
mobile sources near the wind farm. Furthermore, considering the fast
expansion of wind energy industry in the past and future, a question may
arise: would the increasing number of wind farms perturb local, regional, and
national air pollution forecasting?
Location of Gansu province (shaded yellow area, a) and wind
farms in Jiuquan City (b). Black cross represents YWF and GWF and
black dots show Yumen City (40∘16′ N, 97∘02′ E),
Guazhou (40∘31′ N, 95∘42′ E), and Jiayuguan City
(39∘48′ N, 98∘18′ E), where JISCO is located.
The effect of the wind farm on air pollution depends on several factors,
including the source locations, proximity and strength, wind speed and
direction, and wind turbine size and layout in the wind
farm. It is not straightforward to measure the perturbation of an air
pollutant induced by a wind farm. As an alternative, the present study made
use of a coupled weather forecast and atmospheric chemistry model to
simulate the air pollution within and around a large-scale wind farm subject
to a typical atmospheric transport event of air pollutants emitted from a
point source near the wind farm, aiming to (1) assess and quantify the
temporal evolution and spatial distribution of the air pollutant within and
around the wind farm, (2) evaluate the wind and turbulent fields that drive
the spatiotemporal variation of the air pollutant over the wind farm, and
(3) identify primary characteristics of the air pollutant in the wind farm
under a specific mesoscale circulation over the wind farm. Results are
reported below.
Materials and methods
Locations of wind farm and major emission source
The location of the selected wind farm in this study is illustrated in
Fig. 1. This wind farm extends from Yumen (40∘16′ N,
97∘02′ E) to Guazhou (40∘31′ N, 95∘42′ E) in
Jiuquan, located in the west end of the Hexi Corridor, Gansu Province,
northwestern China (Fig. 1a). Given its huge wind energy resources, Jiuquan
region has been termed “the Land Three Gorges” (the Three Gorges being the
largest hydroelectric power station in the world). The Jiuquan wind farm,
which consists of Yumen wind farm (YWF) and Guazhou wind farm (GWF), has been
ranked as the largest wind farm in the world (Fig. 1b). The total cumulative
wind power energy was about 12 GW in 2015 and is projected to reach 13.6 GW
by 2020. The wind turbine hub height in the YWF and GWF ranges from 70 to
90 m and rotor diameter ranges from 83 to 113 m (CCER, 2015). This
large-scale wind farm covers an area about 2000 km2. The underlying
surfaces over the YWF and GWF are almost entirely covered by the Gobi Desert
and bare lands with only few residential areas. The terrain height in the
wind farm ranges from 1.2 to 2 km above the sea level. Both YWF and GWF are
located closely in the suburb of Jiuquan and Jiayuguan, the two largest
cities in the Hexi Corridor. The largest emission source of air pollutants
proximate to the Jiuquan wind farm (YWF and GWF) is the Jiuquan Iron &
Steel Group Co., Ltd. (JISCO), located in Jiayuguan City
(39∘48′ N, 98∘18′ E), about 110 km southeast of the
YWF (Fig. 1b). This company is ranked as the largest iron and steel complexes
in northwestern China and one of the top 50 iron and steel companies in the
world.
WRF-Chem model setup and configuration
We applied WRF-Chem model v3.7
(http://www2.mmm.ucar.edu/wrf/users/wrfv3.7/wrf_model.html) to simulate
the meteorological field and atmospheric chemistry. The WRF-Chem (the Weather
Research and Forecasting model coupled with Chemistry) is a new-generation
air quality model with its air quality component (Chem) and meteorological
component (WRF) being fully coupled in an “online” approach (Grell et al.,
2005). The physical options in WRF-Chem v3.7 include the Lin microphysics
scheme (Lin et al., 1983), the Rapid Radiative Transfer Model (RRTM) longwave
radiation scheme (Mlawer et al., 1997), Goddard shortwave scheme (Kim and
Wang, 2011), revised MM5 M-O surface layer scheme (Beljaars, 1994; Chen and
Dudhia, 2001), YSU (Yonsei University) boundary layer scheme (Hong et al.,
2006), new Grell cumulus scheme (Grell and Devenyi, 2002), and Unified Noah
land surface model (Chen and Dudhia, 2001). The chemical options include
Madronich TUV, F-TUV, and Fast-J (Fast et al., 2006) photolysis scheme,
modified CB05 gas-phase chemistry scheme with updated chlorine chemistry
(Yarwood et al., 2005), several photo chemical mechanisms by RADM2 (Middleton
et al., 1990), CBMZ, SAPRC, MEGAN biogenic emission scheme (Guenther et al.,
2012), and three aerosol modules, MADE/SORGAM, MOSAIC, and a simple aerosol
module from GOCART.
Nested model domains, including large domain d01 (upper-left
panel), the medium size domain (d02, marked by a white box) covering
Guangzhou and Yumen wind farms, and the fine domain d03 marked by the red box
in upper-left panel covering Yumen wind farm only. The blue shaded area is
Jiuquan City. In the d01 domain, the GWF and YWF are also indicated. These
two wind farms are marked by the black cross. The lower-right panel shows
the enlarged d03 area. The red arrow line indicates the transect along which
the concentrations cross sections are generated (see Sect. 3). The white box
represents the d03 domain covering YWF and its surrounding region.
We used the anthropogenic emissions from HTAP_V2 (Task Force on Hemispheric
Transport of Air Pollution, 2012). This emission inventory consists of the
gridded emission data and grid maps of CH4, CO, SO2, NOx,
NMVOC, NH3, PM10, PM2.5, BC, and OC at a 0.1∘ latitude
× 0.1∘ longitude resolution. The global grid maps are a joint
effort from the USEPA, the MICS-Asia group, EMEP/TNO, and the REAS and the
EDGAR groups. The bio-emission calculated by MEGAN V2.1 has a spatial
resolution of 1 km (Model of Emissions of Gases and Aerosols from Nature,
2011). The FNL reanalysis data with 0.25∘ × 0.25∘
(latitude × longitude) provided by the National Centers for
Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR)
were used as initial and lateral boundary conditions.
Three nested domains at 10, 3.3, and 1.1 km resolutions were set up.
The first domain (d01) with 10 km spacing and an area of 850 km × 750 km covers Gansu Province and part of Xinjiang
Province. The second domain (d02) with 3.3 km spacing and an area of 413 km × 253 km covers Guazhou and Yumen wind farm. The third
domain (d03) with 1.1 km spacing and an area of 124 km × 124 km covers Yumen wind farm only. The spatial configurations of
these three model domains are illustrated in Fig. 2. The fine domain lateral
boundary conditions for the meteorological variables and air pollutants are
interpolated from the coarse domain prediction. Two-way nesting is then
optionally achieved by having the fine grid solution replace the coarse grid
solution for those grid nodes that lie within the fine nest domain. The
model has 28 eta levels with the top of 100 hPa. The vertical resolution is
much denser near the surface with 13 eta levels in the lowest 1 km of the
model atmosphere (about 10, 40, 75, 100, 130 m, etc.) so as to achieve
more accurate simulations of meteorology and atmospheric chemistry in the
planetary boundary layer (PBL).
Wind farm parameterization scheme
Two wind farm parameterization schemes were adopted to parameterize winds
and turbulence fields forced by the wind turbines across the wind farm. The
first one is the surface roughness length parameterization. In this scheme,
a wind farm can be seen to increase underlying surface obstacles which
reduce the wind speed in the hub height, featured by the increase in the
aerodynamic roughness length (Baiyda et al., 2004; Keith et al., 2004;
Oerlemans et al., 2007). Some of the previous model studies were conducted
by increasing the surface roughness lengths to quantify the aerodynamic
effect of wind turbines on wind and turbulence profiles (Frandsen, 1992;
Baidya et al., 2004; Keith et al., 2004). We adopted a similar approach to
enhance the roughness lengths over the GWF and YWF. To do so, we replaced
the land use types and surface roughness lengths defined by
LU_INDEX and LANDUSEF variables in the geo-data of the WPS by
a land-use-type scheme which takes into account typical land surface
characteristics in northwestern China (Zhang and Zhao, 2015) and estimated
effective roughness lengths in wind farm parameterization. In this
parameterization scheme, the roughness lengths in the wind farm were
calculated using the Lettau roughness length equation (Lettau, 1965):
z0=0.5h∗SSSL,
where z0 is the roughness length in meters, and h∗ is the average vertical
extent of the roughness elements or effective obstacle height (m). In our
case, h∗ is the height of the wind turbine rotor. SS in Eq. (1) is the
average silhouette area (m2) of the average obstacle or the vertical
cross-section area presented to the wind by one wind turbine, and SL is the density of roughness element. Here SL can be expressed as
SL=A/N, where A is the area of the wind farm, and N is the number of
wind turbines (Porté-Agel et al., 2014; Rooijmans, 2004; Frandsen,
2007). For YWF, h∗ is taken as 113 m (wind turbine height), SS is taken
as 10 029 m2, and SL is taken as 375 000 m2. The resulted
z0 is 1.51 m. We shall use this value as a typical roughness length to
represent the underlying surface characteristics for the YWF. Knowing that
bare land and Gobi Desert are dominant underlying surface of YWF and its
surrounding region, the roughness length on this surface was taken as 0.01 m
outside the wind farm in model scenario simulations except for the control
model run in which this surface roughness length was applied in entire d03
model domain (see below).
The second wind farm parameterization is the wind turbine drag force scheme,
developed by Fitch et al. (2012), which was extended from Blahak et al. (2010) in their modeling of the conversion of KE from atmosphere wind flow
(Fitch et al., 2012; Blahak et al., 2010). This scheme has been implemented
in WRF model. The turbine drag force scheme was developed subject to the
Mellor–Yamada–Nakanishi–Niino (MYNN) turbulence scheme (Mellor and
Yamada, 1974; Nakanishi and Niino, 2009). The Fitch scheme takes into
account the effects of the wind turbines on the atmospheric flow by adding a
momentum sink to the wind flow and transferring the fraction of the KE from
the atmosphere into electricity and TKE. The KE is quantified by a thrust
coefficient CT which depends on the wind speed and the specification of
the wind turbine. The electricity converted by KE is calculated by the power
coefficient CP with change in the wind speed and varies between
17 and 75 % of CT. Both coefficients CT and CP can be
obtained from a wind energy manufacturer. This approach assumes that the
mechanical and electrical losses are negligible, so KE could be
transferred to TKE, given by CTKE=CT-CP. The wind turbine drag force parameterization scheme reads
Fdrag=12CTVρVVA,∂KEcellijk∂t=∂∂tρijkVijk22zk+1-zkΔxΔy,∂Pijk∂t=12NtijCPVijkVijk3Aijkzk+1-zk,∂TKEijk∂t=12NtijCTKEVijkVijk3Aijkzk+1-zk,
where V=(u,v) is the horizontal velocity vector, ρ is the
air density, Nt is the density of wind turbines, A=(π/4)D2 is the cross-sectional rotor area (where D is the diameter of the
turbine rotor), i, j,k are the number of grids in three-dimensional
space (x, y, z), Δx and Δy are the horizontal grid
spacing, and zk is the height of vertical coordinate. In the present
study, the thrust coefficient CT=0.16, the turbine hub height is 90 m, the rotor blade
diameter is 113 m, and the nominal power of turbine is taken as 2.0 MW. These
parameters are defined and implemented in the WRF files to parameterize the
wind turbine profiles. It is worth noting that the wind turbine could both
act as an obstacle to enhance the surface roughness and as a sink of
momentum which results in the momentum loss through both surface friction
and spinning wind turbine rotors. The two parameterization schemes used in
the present study have, to some extent, similar physical background.
A case study
From 19 to 24 November 2016, a strong cold wave occurred in northern China.
An anticyclone featured by a surface high-pressure system moved from western
Siberia to northern China. This system forced the change in the prevailing
wind direction from westerly wind to easterly and southeasterly wind across
the western Hexi Corridor on the south of the anticyclone. The air quality in
Jiuquan City was deteriorated during this period, characterized by the rapid
increase in atmospheric levels of several criteria air pollutants sampled at
the Jiuquan air monitoring station which was operated by Ministry of
Environmental Protection of China (http://www.zhb.gov.cn/). Given that
both YWF and GWF are located in the northwest of Jiuquan City and in the
north of Jiayuguan City, where JISCO is located, heavy air pollutants from
the JISCO were delivered to the two wind farms. We then performed extensive
model investigations subject to the four model scenarios to assess
numerically the spatiotemporal variation of air pollution in the YWF during
this cold wave episode and heavy air pollution event. The target chemical
selected in the present modeling investigation is NO2. Although NOx
(nitrogen oxide) as a precursor gas often receives more concern, since
NOx = NO + NO2 and NO (nitric oxide) can be quickly
oxidized to NO2 in the ambient air, NOx is considered to
approximately be equal to NO2. In addition, NO2 is on the list of
ambient air quality standards and measured routinely at air quality
monitoring stations across China. These data can then be used to verify
modeled air concentrations. While hourly sulfate dioxide (SO2)
concentrations were also available, its atmospheric level was lower than
NO2 due to the mandatory implementation of flue-gas desulfurization at
JISCO, the major emission source of air pollutants in this region.
To identify and quantify the influence of the YWF on air pollutants within
and around this large-scale wind farm, we performed four model scenario runs.
The first model scenario (S1) is the control run in which the YWF was not
taken into consideration. Rather, we simply assigned the roughness length
value of 0.01 m throughout the model fine domain (d03) including the YWF
area. In the second model scenario (S2), the YWF was parameterized by the
roughness length z0=1.51 m which was calculated by Eq. (1), and in
the rest of the fine model grids, z0 was taken as 0.01 m. In the third
model scenario (S3), the YWF was parameterized by the drag force approach
(Fitch et al., 2012) and the distance between two wind turbines is set to
500 m. The last model scenario (S4) also made use of the drag force approach
to parameterize the YWF, but the turbine density was extended from 500 m to
1 km. In the YWF, the distances between wind turbines are not uniform but
range from 300 to 1000 m
(http://cdm.ccchina.gov.cn/zyDetail.aspx?newsId=58797&TId=169, last
access: 28 November 2017). We chose the 500 and 1000 m distances to examine
the effects of typical distribution of wind turbines across the YWF on
modeled NO2 air concentrations and the roughness lengths. The setup of
the two distances also highlighted the responses of simulated air
concentrations to the wind turbine density in a wind farm.
The modeled NO2 air concentrations and meteorological variables (wind
speed and temperature) have been validated against available measurement
data. The modeled NO2 air concentrations from the four model scenarios
were compared with the monitored air concentrations from 00:00 UTC 19 November
to 00:00 UTC 21 November 2016, at the Jiuquan Air Quality Monitoring
Station operated by the Jiuquan Environmental Protection Agency. Overall,
the model results from the four modeling scenarios agree reasonably well with
the measured data, as shown in Fig. S1 and Table S1 in the Supplement. Details are presented
in the Supplement.
As an operational weather forecasting model, the WRF model has been
evaluated extensively. In the present study, we further compared WRF
simulated winds and temperatures from the four modeling scenarios with measured
data near the surface at the three routine weather stations within the fine
model domain. These are Mazongshan (52323), Dunhuang (52418), and Jiuquan
(52533) stations. The detailed evaluations of WRF modeled
winds and temperatures are presented in the Supplement (text, Fig. S2–S4).
WRF-Chem simulated hourly NO2 air concentrations (ppmv) and
vector winds at the first model level above the surface (∼ 10 m) at
06:00, 12:00, and 20:00 UTC, 19 November, and 04:00 UTC, 20 November, in
the fine domain (d03) from the control run (S1). The YWF is encircled by the
black dashed line. Two white stars highlight two model grids at which the
modeled NO2 vertical profiles within and outside the YWF are compared
(Figs. 11 and 13 and corresponding discussions). The one grid (44, 52) is
located within the wind farm and the other one (50,48) is located outside the
YWF. The magnitude of reference wind speed at 10 m s-1 is shown in the
upper-right panel.
Results
NO2 in YWF without wind farm parameterization
Figure 3 shows simulated NO2 air concentrations (ppmv) superimposed
by the vector winds (m s-1) at the first model vertical level
(∼ 10 m) across the fine domain (d03) at 06:00, 12:00, and 20:00 UTC,
19 November, and 04:00 UTC, 20 November, from the model control run (model
scenario 1, S1), respectively. At 06:00 UTC (local time 14:00), 19 November,
weak easterly winds prevailed over most of the model domain, except in the
south of the domain where northerly wind component prevailed (Fig. 3a). At
this time, NO2 levels were low. At 12:00 UTC, the southeasterly winds
extending from the industrial source region (JISCO) to the YWF started to
build up, which delivered NO2 from JISCO region to YWF (Fig. 3b). This
southeasterly wind regime became stronger at 20:00 UTC, enhancing the
atmospheric transport of NO2 to the YWF, characterized by increasing
NO2 levels in the northwest of the JISCO and the YWF (Fig. 3c). The
maximum NO2 levels were observed in the wind farm between 20:00 to 23:00 UTC. Along with the change in wind direction from southeast to northeast at
04:00 UTC, 20 November, NO2 concentrations declined considerably
compared to 20:00 UTC, 19 November (Fig. 3d). Accordingly, Fig. 4
illustrates the vertical cross section of
hourly NO2 concentrations predicted by the control scenario run from 19:00 to 22:00 UTC, 19 November, along the
transect across the fine domain (d03), highlighted by the red arrow line in
Fig. 2. At 19:00 UTC, the NO2 plume extended from 0 to 25 km and moved
from southeast to northwest along the transect of YWF (Fig. 2). Relatively
lower concentrations can be identified near the upwind interface of YWF
(5–7 km, Fig. 4a), in line with of the pollutants moved towards the
northwest. By the next 2 h at 21:00 and 22:00 UTC, the plume had moved to
the upwind border of YWF (Fig. 4c, d), and remained there. The levels of
NO2 slightly increased from 19:00 UTC (Fig. 4b, c). The results are in
line with the horizontal advance of NO2 concentrations near the
surface, as shown in Fig. 3c.
Vertical cross section of hourly NO2 concentration on the
transect across the fine domain (d03) simulated by the control run (S1) at
19:00, 20:00, 21:00, and 22:00 UTC on 19 November. The transect is
highlighted by the red arrow line in Fig. 2. Terrain height is shown by brown
shading, and the x axis indicates the length of the transect (km) across
the fine model domain d03 and YWF, bounded by black dashed line, extending from
5 to 25 km.
NO2 in YWF due to roughness changes
Using the wind farm roughness length parameterization (z0=1.51 m), we performed the second model scenario run. Figure 5 shows the modeled
hourly NO2 concentrations at the same time as indicated in Fig. 3.
Compared to the results from the control run, similar spatial patterns of
NO2 from the model scenarios 1 and 2 can be observed, characterized by
northwest transport of NO2 towards the YWF from its major industrial
source to the southeast of YWF. However, the second model scenario run
accounting for the roughness changes forced by the wind turbine setup
appeared to yield higher NO2 concentrations. Considering that the
atmospheric transport often dominates the spatial distribution of NO2
under prevailing winds, to identify the influence of the wind farm on
NO2 air concentrations, we simply estimated the concentration
differences between the two model scenarios including and excluding the wind
farm. Figure 6 illustrates the differences of NO2 concentrations
between the two model scenarios runs (S2 minus S1). As shown, the positive
concentration differences indicating higher concentrations from the S2
model run were found in the upwind and border region of the YWF and
negative differences manifesting lower concentrations were identified within
the YWF, particularly at 12:00 and 20:00 UTC. The mean positive concentration
difference in the upwind region of the YWF is 0.009 ppmv. The estimated
fraction (Cs2-Cs1)/Cs1×100 %, where Cs1
and Cs2 are mean concentrations from the S1 and S2, is 23 %. The
negative concentration difference within the YWF is -0.009 ppmv, and the
ratio of the mean concentration from S2 to that from the control run (S1) is
-33 %. These results suggest that the wind farm parameterized by the
aerodynamic roughness change resulted in lower concentrations within the
wind farm and higher concentrations in the upstream region.
Same as Fig. 3 but for the roughness change parameterization (S2)
using the roughness length parameterization. The white star stands for the
model grids within the wind farm (44, 52) and outside the wind farm (44, 48)
for subsequent discussions (Fig. 9).
Differences of modeled NO2 concentrations (ppmv) between S2
model run and control run (S1) at 06:00, 12:00, and 20:00 UTC on 19 November and
04:00 UTC on 20 November. The wind field is the same as that shown in
Fig. 3,
and YWF is encircled by black dashed line. The differences were calculated by
S2 - S1. The deep blue and red dashed lines encircled relatively higher
and lower values of the concentration differences.
The vertical cross section of hourly NO2 concentrations, simulated by
S2 model run, from 19:00 to 22:00 UTC, 19 November, along the transect in the
fine domain d03 (Fig. 2) is shown in Fig. 7. Although the maximum
concentrations simulated by the S2 run were lower than that from the control
run (S1), particularly within the wind farm, the plumes from the S2 run
expanded to the upwind locations of the YWF. This can be seen from the
NO2 vertical cross sections at 20:00, 21:00, and 22:00 UTC on 19 November
(Fig. 7b–d) which show plume extension from 0 to 20 km compared to the
modeled NO2 plumes in the control run. This is particularly evidenced at
20:00 and 21:00 UTC, agreeing with the horizontal distribution of NO2
near the surface (Fig. 6). Figure 8 shows the differences of modeled cross
sections of NO2 concentrations between the first and second model
scenario runs (S2 minus S1). In general, higher NO2 concentration
differences simulated from the S2 run can be observed at the upwind and
interface of the extended YWF, especially at 0–9 km locations. Lower
NO2 differences were observed within the YWF and its downstream region,
manifesting again the influences of the wind farm on the spatial
distribution of NO2 concentration. The negative differences became more
obvious at 21:00 and 22:00 UTC. This likely resulted from
stronger easterly and southeasterly wind after 20:00 UTC (Fig. 3) which
speeds up the atmospheric transport of NO2 from the upstream region to
the wind farm.
Same as Fig. 4 but for S2 model run using the roughness change
parameterization scheme. The wind farm is bounded by black dashed line.
Cross section of the difference of modeled NO2 air
concentrations between S1 and S2 model runs (S2 - S1) at 19:00, 20:00,
21:00, and 22:00 UTC, 19 November 2016, along a transect across YWF, as
shown by the red arrow line in Fig. 2.
Vertical profiles of NO2 concentrations at two model grids at
(a) (44, 52) at 21:00 UTC on 19 November within the YWF and
(b) (44, 48) at 22:00 UTC on 19 November in the upstream of the YWF,
simulated by the control run (S1, blue solid line) and S2 run accounting for
the roughness changes in the wind farm (red dashed line).
Figure 9 shows vertical profiles of NO2 from the surface to the 1000 m
height, simulated from the control run (S1) and S2 run respectively at the
wind farm grid (44, 52) at 21:00 UTC, 19 November (Fig. 9a), and the upwind grid
(44, 48) at 22:00 UTC, 19 November (Fig. 9b), which is 5 km away from the YWF
marked by white star in Fig. 5a. Within the YWF (Fig. 9a), the S2 model
scenario yielded considerably lower concentration (red dash line) below the
wind turbine rotor height (∼ 50 m) and higher concentration
from this level to the 600 m height compared to that of the control run
(solid blue line). The modeled NO2 concentrations from the S2 run were
lower by up to 23 % than the NO2 level simulated from the control run.
At the upstream site (Fig. 9b), the S2 run simulated higher NO2
concentration almost throughout the atmospheric boundary layer with the
concentration level increasing by as much as 20 % near the surface and up to
64 % at the height of 130 m compared to the result from the control run.
These results are in line with the NO2 horizontal distributions and
cross sections obtained from the two model scenario runs.
NO2 in YWF by wind turbine drag force parameterization
To confirm the modeling results from the roughness change parameterization
for the wind farm, we replaced this parameterization scheme with the wind
turbine drag force parameterization (Eqs. 2–5). This scheme requires the
input of the wind turbine density subject to the layout of wind turbines. We
set the distance between wind turbines as 500 m in model scenario 3 (S3),
and in the subsequent numerical scenario run (S4) this distance was extended
to 1000 m.
Figure 10 shows the differences of hourly NO2 concentrations at 06:00,
12:00, and 20:00 UTC on 19 November and 04:00 UTC on 20 November at the first eta
level between S3 and S1 (S3 minus S1) on the same day. Again the NO2
concentrations within the YWF which were simulated by the wind turbine drag
force parameterization scheme were lower than that from the control run
(S1). The modeled mean concentration within the YWF by S3 was about 21 %
lower than that from the control run at 20:00 UTC. Mean concentrations at the
upwind locations outside the YWF were 13 % higher than that simulated by S1
model scenario. Overall, the values of the concentration differences between
the S3 and S1 model scenarios were smaller than the differences between S2
and S1. Higher concentrations were found in the south and southeast of the
YWF, differing somewhat from the result of S2, as shown in Fig. 6.
The vertical profiles of modeled NO2 concentrations at the two model
grids within (44, 52) and at the upwind site (50, 48) marked in Fig. 3a from
S3 and S1 are illustrated in Fig. 11. Lower concentrations at the wind farm
grid extending from the surface to the 75 m height were predicted by S3 run
with the strongest decline of 8 % compared to S1 control run (Fig. 11a).
Above this height, higher NO2 levels extended up to the 200 m height.
At the upwind site, the S3 model run also predicted significantly higher
NO2 concentration than that of S1, analogous to the result obtained by
using the roughness length parameterization scheme (Fig. 9b). The maximum
concentration in the vertical is about 27 % higher than that from the
control run.
Same as Fig. 6 but for the concentration differences ΔC
between S3 model run and the control run (S1), given by S3 - S1.
Vertical profiles of NO2 concentration from the surface to the
1000 m height from the control run (S1, solid blue line) and S3 run (red
dashed line) at (a) the YWF grid (44, 52) at 21:00 UTC on 19 November,
and (b) the upwind grid (50, 48) at 22:00 UTC on 19 November.
We further adopted a low density layout of wind turbines by increasing the
distance between two wind turbines from 500 to 1000 m (the fourth model
scenario, S4) and rerun the WRF-Chem with the same model setups and
configurations. Figure 12 shows NO2 concentration differences between S4
and S1 at 06:00, 12:00, and 20:00 UTC on 19 November and 04:00 UTC on
20 November, respectively. As seen, the spatial pattern of the concentration
differences subject to the lower density wind turbine setup (1000 m
distance) is almost identical to that from the higher density setup (500 m
distance). However, the mean NO2 concentration from S4 averaged over a
region in the YWF, encircled by the red dashed line, was about 16 % lower
than that from the control run, showing a weaker influence on the changes in
NO2 concentration, as compared to the 21 % decrease in the higher
wind turbine density setup (500 m spacing) from S3 run. At the upwind region
of the YWF encircled by the blue dashed line (Fig. 11), the mean NO2
concentration from the lower wind turbine density run (S4) was the same as
that from the higher density turbine setup (S3), both showing 13 %
increase in the mean NO2
concentrations from the control run (S1) compared to S3 and S4 models. This
is expected because the wind turbine setup is not applicable outside of the
wind farm.
Same as Fig. 6 but for concentration differences between S4 run and
the control run (S1), given by S4 - S1.
Vertical profiles of NO2 concentration from the first vertical
model level above the surface to the 1000 m height from the control run (S1,
solid blue line) and S4 run (red dashed line) at (a) the YWF grid
(44, 52) at 21:00 UTC on 19 November, and (b) the upwind grid (50, 48)
at 22:00 UTC on 19 November.
The vertical profiles of NO2 concentrations from the lowest model
vertical level above the surface to the 1000 m height at the model grid (44,
52) within the YWF and grid (50, 48) at the upwind site of the YWF from S4
and S1 model runs are illustrated in Fig. 13. Compared to the concentration
profiles as shown in Fig. 11, the lower wind turbine setup does not markedly
reduce the NO2 concentrations within the YWF. The S4 model predicted
merely a 4 % decline from the control run near the surface (Fig. 13a). This
scenario also yielded less significant increase in the NO2
concentration at the upwind site of the YWF than that from the higher
density turbine setup run with the maximum concentration increase by 20 %
from the control run, compared with the 27 % increase in the higher
turbine density simulation (S3).
Discussions
In this numerical case study, the Yumen–Guazhou wind farm, the world largest
wind farm located in the western Hexi Corridor, China, was parameterized by
the wind turbine-induced roughness change scheme and wind turbine drag force
scheme, to assess the potential influences of the wind farm on spatial
distribution of NO2 within and around the wind farm. Overall, by making
use of these two parameterization schemes, our modeling results predicted
higher NO2 concentrations at the immediate upstream and border regions
of the YWF and lower concentrations within the YWF. As previously mentioned,
a wind farm acts to increase the aerodynamic roughness lengths through two
mechanisms. First, the layout and array of wind turbines throughout the wind
farm alter underlying surface characteristics (roughness elements) enhancing
the roughness lengths within the wind farm. Second, because wind turbines
take out momentum proportional to the wind speed, the mean wind speed will be
reduced relative to the ambient wind in the wind farm (Emeis and Frandsen,
1993). From the well-known logarithmic wind law for neutral conditions in the
surface boundary layer (∼ 100 m), the reduction of wind speed implies
increasing aerodynamic roughness length (Ma and Daggupaty, 2000). As a
result, an internal boundary layer (IBL) may develop in which the flow
characteristics only depend on the new surface roughness. Outside the IBL the
flow is identical to the upwind flow (Garratt, 1994; Frandsen, 2007). Hence,
the presence of the IBL leads to a step change in the roughness length in the
interface between rough (in the wind farm) and smooth (outside the wind farm)
surfaces. Thus, the IBL is particularly evident in the upwind interface. For
an air pollutant coming from the upstream of the wind farm on land, the step
change in the roughness from the smooth upstream surface to the rough surface
over the wind farm could result in an “overshooting” of the surface stress
in the wind farm (Garratt, 1994), slowing down the concentration transport by
wind. This would lead to the accumulation of the air pollutants featured by a
step change in the concentration at the “edge” (interface) of the wind
farm. For the pollutant out of the wind farm to the downstream region, the
roughness changes from rough to smooth surface are expected to cause an
“undershooting” of the downstream stress which accelerates the pollutant
transport in the downwind edge of the wind farm.
Figure 14 is a schematic view of the IBL and the edge effect on an air
pollutant passing through a wind farm induced by the mechanic internal
boundary layer. In the figure, hi is the top of IBL, and
hss is a sublayer below hi in which the wind
(momentum) has to be adjusted to accommodate the new underlying surface. When
the air flow moves from a relatively smooth to rough surface, the wind speed
in the IBL will decrease (Garratt, 1994; Bradley, 1968; Elliot, 1958). This
deceleration of wind speed results in the accumulation of air pollution
(overshooting), characterized by increasing air concentration in the
immediate upwind of the wind farm.
We developed a simple model in the neutral surface boundary layer to address
the changes in the concentration of an air pollutant induced by the roughness
changes in a wind farm, given by
Δc=-FcκuhcDeff12lnzz0c-eff,
where Δc=c-c0, which is the gradient of air concentration of a
pollutant at z0c-eff and z height in the wind farm,
Fc (µg m-2 s-1) is a diffusive concentration
flux (= w′c′‾=u∗c∗), where u∗ is the
fraction velocity (m s-1), and c∗ is a turbulent scale for
concentration (µg m-3). uh is the wind speed
(m s-1) at the hub height of the wind farm, cDeff is an
effective drag coefficient by summing the surface drag coefficient within the
wind farm and the averaged wind turbine drag coefficient, κ is the
von Kármán constant (= 0.4), z is the height (0–100 m), and
z0c-eff is an effective roughness length (m) for concentration,
defined by
z0c-eff=0.1zexp-κκlnz00/hb2-ct12,
where z00 is an apparent roughness length, hb is the hub height, and
ct is the averaged wind turbine drag coefficient. Figure 15 displays the
vertical profiles of the concentration gradient in the neutral surface
boundary layer (0–100 m) within and outside the wind farm.
Considerably smaller concentration gradient can be seen within the wind farm
compared to that outside the wind farm, forced by increasing drag force
under the rough underlying surface in the wind farm.
Schematic view of the IBL and an air pollutant passing through a
wind farm. The IBL and PBL change from smaller
roughness length 0.01 m to large roughness length 1.51 m. The red dashed
line h indicates the PBL thickness, black solid line hi
indicates the IBL, the green dashed line hss indicates a
sublayer, u indicates the wind vector, and δs
indicates the upward displacement of PBL thickness change.
Vertical profile of concentration gradient in the neutral boundary
layer. The wind speed at the hub height was set as 4 m s-1, the
surface roughness length was set as 0.01 m, and hub height as 60 m.
Concentrations were taken as 100 µg m-3 at the 1.5 m height
and 80 µg m-3 at the 10 m height.
An interesting feature in the vertical profiles of the simulated NO2
air concentrations in the presence of the YWF by the two parameterization
schemes (Figs. 9a, 11a, and 13a) is the lower NO2 level
below the hub height (0–70 m) and the higher level above the hub height
compared with NO2 concentration simulated by the control run (the YWF
was not taken into consideration). It has been reported that wind farms
could significantly slow down the wind speed at the turbine hub height level
and the turbulence generated by wind turbine rotors create eddies which
enhance vertical mixing of momentum and scalars (Baiyda et al., 2004). As a
result, there may be a wind speed deficit in the neutral boundary layer.
The modeled NO2 concentration profiles in the YWF as shown in Figs. 9a,
11a, and 13a are likely associated with the vertical mixing of air
concentrations. Nevertheless, the magnitude of the air concentration deficit
in the neutral boundary layer within the wind farm simulated in this model
investigation depends on wind farm parameterization. The roughness change
parameterization yielded the largest concentration deficit, whereas the
turbine drag force parameterization with the low wind turbine density
produced a moderate deficit. In the immediate upwind region of the YWF, the
two parameterization schemes all predicted notably higher concentrations in
the vertical up to 450–600 m height, manifesting significant “edge effect”
and the overshooting signature. We wish to point out that here we only
discuss the wind profiles over the wind farm in the neutral boundary layer.
The diurnal changes in NO2 concentrations presented in the last section
took place in the stratified (non-neutral) atmosphere. However, since the
wind profiles in the stable and unstable boundary layer can be treated as a
departure from the neutral condition, our interpretations for the “edge
effect” should hold for the non-neutral conditions.
It is worthwhile to note that the identification of the “edge effect” or
overshooting in the immediate upwind and the undershooting in the downwind
region of the wind farm largely depends on the proper location of upstream
emission sources and downstream wind farm which should be aligned with the
wind direction. Figure S5 displays the wind field from the control run and
the differences (ΔV) between the perturbed wind fields by the wind
farm parameterizations (Fig. S5b–d) and the wind field from the control run
(Fig. S5a) at 20:00 UTC on 19 November at the fourth model level
(∼ 100 m). This vertical level is the nearest level to the hub height
(70–93 m). At this level, the wind speed should exhibit largest reduction
within the wind farm (Emeis, 2010; Frandsen, 2007; Barrie and Kirk-Davidoff,
2010). As shown, the background wind field in the model domain simulated by
the control run (model scenario 1) generated easterly and southeasterly winds
across the fine model domain (d03) with stronger easterly winds in the north,
except for those model grids near the south boundary of the domain where
westerly wind prevailed. Analogous to the previous findings (Fitch et al.,
2012), all three wind farm parameterization schemes yield lower wind speed,
as shown by -ΔV across the YWF, particularly in the roughness change
parameterization scheme. Outside the YWF, the wind turbine parameterization
yielded very small ΔV (Fig. S5c, d). The roughness change
parameterization also predicted -ΔV across the YWF but positive
ΔV on the south and north lateral boundaries. This feature has also
been simulated by Fitch et al. (2012). Figure S3 illustrates the modeled TKE
overlapped with vector winds at 20:00 UTC on 19 November at the fourth model
level (∼ 100 m). All three wind farm parameterization schemes
predicted largest TKE in the northwestern YWF (Fig. S6b–d) as compared to
non-wind farm (control run) simulations in which no significantly higher TKE
was observed (Fig. S6a), corresponding nicely to the largest wind speed
deficit and concentration reduction (Figs. 6 and 9). The result is also in
line with the TKE field in a relatively smaller wind farm reported by Fitch
et al. (2012).
It is also noted that if a large-scale wind farm could disturb atmospheric
dispersion of an air pollutant and is located near a city, it may influence
the spatiotemporal distribution of the pollutant over the city. This would
depend on how far the influence of the edge effect could be extended to the
surrounding region of a large-scale wind farm. The edge effect of an
internal boundary layer can be estimated via a “fetch–height ratio”
(Garratt, 1994). In micro-meteorology, such a ratio is typically about
1 : 100 from the rough to smooth surface. In the smooth-to-rough surface case,
the fetch–height ratio is approximately 2 times greater than that in the
rough to smooth case (Garratt, 1994). This suggests that if the mean
obstacle height of the YWF is equivalent to the wind turbine hub height
(∼ 100 m), the fetch over which the edge effect could be
extended would be 10 km. If the westerly wind prevails in winter, and
knowing that the roughness changes from rough to smooth surface would
accelerate the pollutant transport in the downwind edge of the wind farm, we
would expect that the eastward transport of air pollutants might influence
downwind residential areas, such as Jiuquan and Jiayuguan City in our case
(Fig. 1b), located in the downstream of the YWF. However, given that there
were no significant emission sources in the upstream of the YWF under the
westerly wind regime, the edge effect on the air quality in these two
largest cities in the Hexi Corridor was negligible.