Introduction
Nearly all energy used for human purposes can eventually turn into
anthropogenic heat (AH) within Earth's land–atmosphere system (Flanner,
2009; Chen et al., 2012). According to the distinctive human activities all
over the world, this heat flux might vary spatially and temporally. On the
global scale, the averaged value of AH flux has been estimated to be only
0.028 W m-2. But it can reach up to 0.39, 0.68 and 0.22 W m-2,
respectively, over the continental United States, western Europe and China
(Flanner, 2009). In the densely populated and economically vibrant urban
areas, the AH fluxes have been reported to typically range from 20 to
70 W m-2 (Crutzen, 2004; Sailor and Lu, 2004; Fan and Sailor, 2005;
Pigeon et al., 2007; Lee et al., 2009), whereas the fluxes might occasionally
exceed the value of 100 W m-2 as well (Quah and Roth, 2012; Xie et
al., 2015). Under some extreme conditions, the magnitude of AH fluxes in
cities can be a substantial heat source equivalent to the daily mean solar
forcing (Ichinose et al., 1999; Hamilton et al., 2009; Iamarino et al.,
2012), with a high value of 1590 W m-2 reported in the densest part of
Tokyo at the peak of air-conditioning demand (Ichinose et al., 1999).
Consequently, accurate prediction of AH emissions is always a key issue that
can improve our understanding of human impacts on urban climate and
environment.
Anthropogenic heat can increase turbulent fluxes in sensible and latent heat,
which might result in the atmosphere reserving more energy (Oke, 1988). Thus,
the abovementioned heat fluxes exhausted from human activities in cities can
exert a significant influence on the dynamics and thermodynamics of urban
boundary layer (Ichinose et al., 1999; Block et al., 2004; Fan and Sailor,
2005; Chen et al., 2009, 2012; Bohnenstengel et al., 2014), and thereby
change the surface meteorological conditions (Khan and Simpson, 2001; Block
et al., 2004; Fan and Sailor, 2005; Ferguson and Woodbury, 2007; Chen et al.,
2009; Zhu et al., 2010; Menberg et al., 2013; Wu and Yang, 2013; Feng et al.,
2014; Bohnenstengel et al., 2014). Most previous studies of AH have focused
on these effects. For instance, some researchers have found that AH
strengthens the vertical movement of urban surface air flow, changes the
urban heat island circulation, and makes the urban boundary layer more
turbulent and unstable (Ichinose et al., 1999; Block et al., 2004; Fan and
Sailor, 2005; Chen et al., 2009; Bohnenstengel et al., 2014). Others showed
that AH in cities can result in significant and extensive warming, and tend
to cause urban air temperatures to increase by several degrees (Fan and
Sailor, 2005; Ferguson and Woodbury, 2007; Chen et al., 2009; Zhu et al.,
2010; Menberg et al., 2013; Wu and Yang, 2013; Feng et al., 2014;
Bohnenstengel et al., 2014). Moreover, Feng et al. (2014) reported that AH
enhances the convergence of water vapor and rainfall amounts over urbanized
areas, and changes the regional precipitation patterns to some extent. Urban
air quality and local meteorological condition are inextricably linked.
Therefore, all the findings above are likely to have important implications
for air quality in urban areas as well. However, in the past, few researchers
paid attention to this issue, and only a couple of studies have estimated the
effects of AH on air pollutants (Ryu et al., 2013; Yu et al., 2014; Yang et
al., 2014).
Over the past decades, along with the accelerated urbanization process and
rapid economic development, many cities in China have been suffering the
successive deterioration of air quality (Xie et al., 2014). Located in the
coastal region in East China, the Yangtze River Delta (YRD) region also
experienced a rapid urban expansion with the urbanization rate as high as
70 % and suffered from air pollution (Liao et al., 2015). Consequently,
several previous studies have tried to figure out the effects of urbanization
on the severe atmospheric environmental problems in this region. For example,
by using the WRF/Chem model, Wang et al. (2009) quantified that the urban
sprawl in the YRD region has caused surface O3 to increase by
2.9–4.2 % during the daytime and by 4.7–8.5 % at night. Employing
the WRF/CMAQ model, Li et al. (2011) showed that O3 and haze problems
had become an important issue due to the increase in urban land use. Liao et
al. (2015) further quantified the increase in O3 and the decrease in
PM10 (or NOx) related to the urban expansion. Kang et al. (2014)
discussed the impact of Shanghai urban land surface forcing on downstream
city meteorology. Zhu et al. (2015) further studied this impact on O3
chemistry. However, the above studies only took the expansion of urban land
use into account. We still need to know how the excessive anthropogenic heat
from urban expansion impacts on urban climate and air quality. Among previous
studies, a couple of researchers have tried to fill the knowledge gap. For
instance, He et al. (2007) incorporated AH into a PBL (planetary boundary
layer) model for Nanjing 2002 and found a temperature increase
(0.5–1 ∘C) at night. Wang et al. (2015) reported that AH can cause
notable warming in almost the whole YRD, which is more significant in winter
than in summer. These studies only focused on the effects of AH on local
meteorological fields. Till now, no studies have evaluated the influence of
AH on air quality over the YRD region.
Spatial distribution of gross domestic product (a) and
population (b) in 2010 over the region between (117∘ E,
28∘ N) and (123∘ E, 34∘ N) with the resolution of
2.5 arcmin.
The main purpose of this study is to improve our understanding of the
influence mechanism of anthropogenic heat on the atmospheric environment,
especially in the typically polluted areas of China such as the YRD region.
In this paper, we focus on (1) quantifying the spatial and temporal
distribution of AH emissions in the YRD region, (2) implementing the gridded
AH data in the modified WRF/Chem model with improved AH flux
parameterization, and (3) evaluating the impacts of AH fluxes on
meteorological conditions and air quality over the YRD region. Detailed
descriptions about the estimating method for anthropogenic heat flux over the
YRD region, the adopted air quality model with configuration, and the observation data for model evaluation are given in Sect. 2. The main results,
including the spatial and temporal distribution of AH, the performance of
WRF/Chem, and the exact impacts of AH on urban climate and air quality are
presented in Sect. 3. In the end, a summary is given in Sect. 4.
Methodology
Anthropogenic heat flux modeling
We estimate the AH fluxes during the period from 1990 to 2010 over the area
between (117∘ E, 28∘ N) and (123∘ E,
34∘ N), which covers the YRD region including Shanghai, southern
Jiangsu province and northern Zhejiang province (shown in Fig. 1). In order
to get the spatial distribution, this study area is also gridded as 144 rows
and 144 columns with the grid spacing of 2.5 arcmin (approximately 4 km).
The anthropogenic heat flux QF (W m-2) is the rate at which
waste energy is discharged by human activities to the surroundings (Iamarino
et al., 2012). In urban areas, it usually consists of the heat flux derived
from energy consumption in buildings (QF,B), from the
transportation sector (QF,T) and from human metabolism
(QF,M) (Grimmond, 1992; Sailor and Lu, 2004; Allen et al., 2011;
Iamarino et al., 2012; Quah and Roth, 2012). Three general approaches have
been recognized to estimate these terms (Sailor, 2011), including the
building energy modeling approach for the building sector (Kikegawa et al.,
2003), the closure of the energy budget (Offerle et al., 2005), and the use
of statistics on energy consumption (Sailor and Lu, 2004; Flanner, 2009;
Hamilton et al., 2009; Lee et al., 2009; Allen et al., 2011; Iamarino et al.,
2012; Quah and Roth, 2012). The third method, which is also called the
top-down energy inventory method, was the most common approach and was widely
applied in AH flux predictions in China (Chen et al., 2012; Lu et al., 2014;
Xie et al., 2015). Based on these previous investigations, QF in
this study is calculated by the following equation:
QF=QF,I+QF,B+QF,T+QF,M,
where QF,I represents the heat emitted from the industry sector
(W m-2).
The three nested modeling domains (a) and MODIS urban
land-use category data set used in D03, with the locations of the four
meteorology observation sites (b). SH, HZ, NJ and HF in
panel (b) represent Shanghai, Hangzhou, Nanjing and Hefei,
respectively. Line AB denotes the location of the vertical cross section used
in Figs. 9 and 12.
According to the second law of thermodynamics, most energy used for human
economy is immediately dissipated as heat, other energy temporarily stored as
electrical, mechanical, chemical or gravitational potential energy can
finally transform to high entropy thermal energy as well, and only a
negligible portion (≪ 1 %) might be converted to radiation and
escape into space (Flanner, 2009). So, it is reasonable to assume that all
non-renewable primary energy consumption is dissipated thermally in Earth's
atmosphere. From another perspective, in this study, the gridded AH data are
finally incorporated into the Single Layer Urban Canopy Model, SLUCM (Kusaka
and Kimura, 2004; Chen et al., 2011), in which we do not need to strictly
distinguish between different sources of AH. As a result, QF,I+QF,B+QF,T at each grid can be estimated on the basis
of energy consumption from non-renewable sources (coal, petroleum, natural
gas, and electricity) by using the following equation:
QF,I+QF,B+QF,T=η×εs×Cs/(t×A),
where Cs is the primary energy consumption that has been
converted to standard coal (t) at a grid. εs is the
calorific value of standard coal (the conversion factor from primary energy
consumption to heat), which is recommended to be 29 271 kJ kg-1 in
many previous studies (Chen et al., 2012; Lu et al., 2014; Xie et al., 2015).
η is the efficiency of heat release in different sectors, with the
typical value of 60 % for electricity or heat-supply sector and 100 %
for other sectors (Lu et al., 2014). t is the time duration of used
statistic data, and is set to be 365 (days in a
year) × 24 × 3600 = 31 536 000 s in this study. A
represents the area of a grid, which is about 4×4 km2. To
quantify the values of Cs, the authoritative statistics of annual
standard coal consumption from 1990 to 2010 at provincial level are firstly
obtained from China Statistical Yearbooks and the yearbooks in Shanghai,
Jiangsu and Zhejiang. Then, the total provincial energy consumption is
apportioned to each grid according to population density and converted to
annual-mean gridded energy flux. The population density with the resolution
of 2.5×2.5 arcmin in 1990, 1995, 2000, 2005 and 2010 can be
downloaded from Columbia University's Socioeconomic Data and Applications
Center. That for 2010 is shown in
Fig. 1b for example.
With respect to the heat flux generated by the human metabolism
(QF,M), the grid value is computed as
QF,M=Pg×(Md×16+Mn×8)/24,
where Pg is the population at a grid. Md and
Mn represent the average human metabolic rate (W person-1)
during the daytime and nighttime. The 16, 8 and 24 are the hours of daytime,
nighttime and a whole day, respectively. Following the previous research work
(Sailor and Lu, 2004; Chen et al., 2012; Lu et al., 2014; Xie et al., 2015),
we assume that the sleeping metabolic rate Mn for a typical man
is 75 W, and the average daytime metabolic rate Md in urban
areas is 175 W.
Air quality model and configuration
WRF/Chem version 3.5 is applied to investigate the impacts of AH fluxes on
climate and air quality over the YRD region. WRF/Chem is a new generation of
air quality modeling system developed at the National Center for Atmospheric
Research (NCAR), in which the meteorological component (WRF) and the air
quality component (Chem) are fully coupled using the same coordinates and
physical parameterizations. The feedbacks between meteorology and air
pollutants are included in the model. It has been proven to be a reliable
tool in simulating air quality from city scale to meso scale in China (Liu et
al., 2013; Yu et al., 2014; Liao et al., 2014, 2015).
As shown in Fig. 2a, three nested domains are used in this study, with the
grid spacing of 81, 27 and 9 km, respectively. The outermost domain
(Domain 1, D01) covers most of East Asia and South Asia, the second domain
(Domain 2, D02) covers the central–eastern part of China, and the finest
domain (Domain 3, D03) centered at Nanjing covers the entire YRD region
(Fig. 2b). For all domains, from the ground level to the top pressure of
50 hPa, there are 36 vertical sigma layers, with about 10 in the PBL. The
height of the lowest level is about 25 m.
The grid settings, physics and chemistry options used in this study
for WRF/Chem.
Items
Contents
Dimensions (x,y)
(85,75), (76,70), (76,70)
Grid size (km)
81, 27, 9
Time step (s)
360
Microphysics
Purdue Lin microphysics scheme (Lin et al., 1983)
Longwave radiation
RRTM scheme (Mlawer et al., 1997)
Shortwave radiation
Goddard scheme (Kim and Wang, 2011)
Cumulus parameterization
Kain–Fritsch scheme, only for D01 and D02 (Kain, 2004)
Land surface
Noah land surface model (Chen and Dudhia, 2001)
Planetary boundary layer
Mellor–Yamada–Janjic scheme (Janjic, 1994)
Urban canopy model
SLUCM (Kusaka and Kimura, 2004)
Gas-phase chemistry
CBM-Z (Zaveri and Peters, 1999)
Aerosol module
MOSAIC using 8 sectional aerosol bins (Zaveri et al., 2008)
Two simulation cases are conducted. One incorporates the urban canopy model
with the gridded AH fluxes that are estimated in Sect. 2.1 (referred to as
the ADDAH case hereafter). The other only one applies the same model but
ignores the contribution of AH (referred to as the NONAH case hereafter). To
exclude the uncertainty conceivably caused by different configurations, all
the physical schemes, chemical schemes and emission inventory are the same in
both the NONAH and ADDAH simulations. Thus, the difference between the
modeling results of NONAH and ADDAH can demonstrate the impacts of
anthropogenic heat. In the YRD region, January and July can be representative
of the dry and wet seasons, respectively (Liao et al., 2015). Consequently,
two time periods are chosen for simulations and analysis. One is from
00:00 UTC 1 January to 00:00 UTC 1 February 2010, and the other is from
00:00 UTC 1 July to 00:00 UTC 1 August 2010, which also matches the time
when observation data are available. The monthly averaged difference between
ADDAH and NONAH can be calculated by the following algorithm:
ADDAH-NONAH=∑t=1744(VADDAH,t-VNONAH,t)744,
where VADDAH,t and VNONAH,t are the hourly
modeling outputs of variable V (meteorological factors or air pollutants)
from ADDAH and NONAH, respectively. The monthly averaged differences of
variables are calculated grid by grid. To guarantee the differences of one
variable are statistically significant, a Student's t test is carried out
based on the data set from NONAH and ADDAH for each grid. At one grid, if the
difference is non-significant under the 95 % confidence level, we can
assert that the AH flux cannot significantly change the meteorology or air
quality at this grid (Zhuang et al., 2013a, b; Liao et al., 2015).
The detailed options for the physical and chemical parameterization schemes
used in this study are shown in Table 1. The major selected physical options
include the Purdue Lin microphysics scheme, the RRTM (Rapid Radiative
Transfer Model) longwave radiation scheme, the Goddard shortwave radiation
scheme, the Kain–Fritsch cumulus parameterization scheme, the Noah/LSM (Land
Surface Model) scheme and the MYJ (Mellor–Yamada–Janjic) PBL scheme.
Specifically, SLUCM (coupled with Noah/LSM) is adopted for better simulating
the urban effect on meteorological conditions and pollutant distribution. The
30 s MODIS 20 category land data sets (Fig. 2b) are used to replace the
default USGS (U.S. Geological Survey) land-use data, because USGS data are
too outdated to illustrate the intensive land cover change over the YRD
region. The default values for urban canopy parameters in SLUCM, such as
building morphometry, urban fraction and roughness length, are replaced by
the typical values in the YRD region as well, following the work of He et
al. (2007) and Liao et al. (2015). The initial meteorological fields and
boundary conditions (forced every 6 h) are from NCEP global reanalysis data
with 1∘ × 1∘ resolution.
With respect to the major chemical options, the CBM-Z gas-phase chemistry
scheme and the MOSAIC aerosol scheme are chosen. CBM-Z (Carbon-Bond Mechanism
version Z) contains 55 prognostic species and 134 reactions (Zaveri and
Peters, 1999). In MOSAIC (Model for Simulating Aerosol Interactions and
Chemistry), the aerosol size distribution is divided into eight discrete size
bins (Zaveri et al., 2008). Besides aerosol direct and indirect effects
through interaction with atmospheric radiation, photolysis, and microphysics,
routines are also taken into account in our simulations. The modeling results
from the MOZART-4 global chemistry transport model are used to provide the
initial chemical state and boundary conditions as described by Liao et
al. (2015). The anthropogenic emissions are mainly from the inventory
developed for the NASA INTEX-B mission (Zhang et al., 2009), and modified for
simulations in the YRD region (Liao et al., 2014, 2105). The ammonia emission
and biomass burning emissions, which are not contained in the INTEX-B
inventory, are obtained from the inventory developed for TRACE-P (Streets et
al., 2003). For the Shanghai area, we use the additional
1 km × 1 km source emission compiled by the Shanghai Environmental
Monitoring Center during EXPO 2010 (Wang et al., 2012). The biogenic
emissions are estimated by using MEGAN2.04 (Guenther et al., 2006).
Methodology for incorporating gridded AH emission data
Within the Single Layer Urban Canopy Model, SLUCM, the AH for each grid is
determined by the fixed AH value for the urban land-use category, the fixed
temporal diurnal pattern and the urban fraction value on each grid (Chen et
al., 2011). AH with its diurnal variation is generally considered by adding
them to the sensible heat flux from the urban canopy layer by the following
equation:
QH=FV×QHV+FU×(QHU+FixAH),
where QH is the total sensible heat flux. FV and FU are
the fractional coverage of natural and urban surfaces, respectively.
QHV is the sensible heat flux from the Noah LSM for natural
surfaces, and QHU is that from SLUCM for artificial surfaces.
FixAH represents the fixed AH value for all urban areas (Chen
et al., 2011). In the ADDAH simulation case of this study, we basically
follow Eq. (4) but incorporate the gridded AH data (QF) to
replace the fixed AH value (FixAH) in order to consider the
spatial distribution of AH fluxes. The data estimated in Sect. 2.1 with the
resolution of about 4 km are re-projected to Domain 3 (9 km) by the
latitude and longitude of each grid. To account for temporal variability, the
annual-mean AH fluxes in 2010 over the modeling area are further scaled with
weighting functions dependent on local time of day (td) and time
of year (my):
QF(td,my)=QF×wd(td)×wy(my),
where the diurnal cycles of wd are obtained from the work of He
et al. (2007) for the YRD region (shown in Fig. 3). According to the findings
of Sailor and Lu (2004) and Flanner (2009), the values of wy for January
and July are set to be 1.2 and 0.8, respectively.
Diurnal variation of anthropogenic heat flux based on He et
al. (2007), applied as weights to the annual-mean flux.
Evaluation method and relevant observation data
Meteorological and chemical observation records are used to evaluate the
model performance in this study. The mean bias (MB), root mean square error
(RMSE) and correlation coefficient (CORR) between observation and the ADDAH
model results are used to verify model performance. In statistics, they are
usually defined as
MB=1N∑i=1N(Si-Oi),RMSE=1N∑i=1N(Si-Oi)2,CORR=∑i=1N(Si-Sm)(Oi-Om)∑i=1N(Si-Sm)2∑i=1N(Oi-Om)2,
where Si is the simulation and Oi is the observation. Sm and
Om are the average values of simulations and observations, respectively.
In general, the model performance is acceptable if the values of MB and RMSE
are close to 0 and those of CORR are close to 1.
With respect to observed meteorological data, four observation sites are
selected, which are NJ (32.00∘ N, 118.80∘ E) located in
Nanjing, HF (31.87∘ N, 117.23∘ E) in Hefei, HZ
(30.23∘ N, 120.16∘ E) in Hangzhou, and SH
(31.40∘ N, 121.46∘ E) in Shanghai, respectively (marked in
Fig. 2b). Their time series of 2 m temperature, 10 m wind speed and 2 m
relative humidity in January and July of 2010 can be obtained from hourly
records of the atmospheric sounding data set compiled by the University of
Wyoming. In order to evaluate the model performance of chemical fields,
hourly chemical series of PM10 and O3 during the modeling period
are acquired from the Caochangmen (CCM) site. CCM is located in the central
and highly residential area of Nanjing (32.06∘ N,
118.74∘ E), and is run by the Nanjing Environmental Monitoring
Center. The assurance/quality control (QA / QC) procedures at CCM
strictly follow the national standards.
Results and discussions
Spatial and temporal distribution of anthropogenic heat flux in
the YRD region
Using the methodology outlined above in Sect. 2.1, we construct the spatial
distribution of anthropogenic heat fluxes over the YRD region from 1990 to
2010 with a 5-year interval. Figure 4 illustrates the gridded distribution in
1995, 2000, 2005 and 2010 (the magnitude and spatial distribution pattern in
1990 are similar to 1995). Obviously, big cities, such as Shanghai, Nanjing,
and Hangzhou, have the largest values among neighboring areas from the early
1990s till now. Before 2000, except for some megacities, AH fluxes are
generally less than 2.5 W m-2 in most parts of the YRD region.
However, after 2000, the AH fluxes are more than 5 W m-2 in many
areas, with the high values over 25 W m-2 centrally appearing along
the Yangtze River, around Lake Taihu and beside Hangzhou Bay. The temporal
variation of the spatial pattern fits in well with the economic boom in the
YRD region over the past decades.
Estimates of annual-mean anthropogenic heat fluxes resulting from
the consumption of non-renewable energy sources (coal, petroleum, natural
gas, and electricity) and human metabolism between (117∘ E,
28∘ N) and (123∘ E, 34∘ N) with the resolution of
2.5 arcmin for 1995 (a), 2000 (b), 2005 (c) and
2010 (d), respectively.
The statistics of annual average anthropogenic heat flux in
different administrative districts over the YRD region (W m-2).
Province or municipality
This study
Previous results (year)
References
1990
1995
2000
2005
2010
Shanghai
Regional
5.47
7.85
9.2
12.39
14.45
16.54 (2008)
Chen et al. (2012)
16.10 (2010)
Lu et al. (2014)
Downtown
42
60.8
71.6
96.9
113.5
117.7 (2010)
Lu et al. (2014)
Jiangsu
Regional
0.68
0.94
0.99
1.83
2.61
2.32 (2008)
Chen et al. (2012)
Downtown
5.1
9.5
12.5
28.6
50.2
40 (Nanjing, 2007)
He et al. (2007)
20–70 (2010)
Lu et al. (2014)
Zhejiang
Regional
0.33
0.54
0.73
1.25
1.63
1.60 (2008)
Chen et al. (2012)
Downtown
2.7
7.4
12.1
25.1
39.3
50 (Hangzhou, 2007)
He et al. (2007)
20–70 (2010)
Lu et al. (2014)
Regional represents the average value over the whole area of an
administrative district, while Downtown represents the high value in the city
center.
Being the largest city, Shanghai always has the highest anthropogenic heat
emissions in the YRD region. As shown in Table 2, the annual-mean value over
the whole administrative district is 5.47 W m-2 in 1990 and
14.45 W m-2 in 2010, with the annual growth of 0.45 W m-2. In
recent years, the AH fluxes in the city center of Shanghai have exceeded
100 W m-2, which is comparable to those in the most crowded
megacities, such as Tokyo (Ichinose et al., 1999), Hong Kong (Flanner, 2009),
London (Hamilton et al., 2009; Iamarino et al., 2012) and Singapore (Quah and
Roth, 2012). The annual-mean values in the downtown area are much higher than
the regional ones. With respect to Jiangsu province and Zhejiang province,
the AH fluxes there also increase from 0.68 and 0.33 W m-2 in 1990 to
2.61 and 1.63 W m-2 in 2010. The regional annual-mean values in
Jiangsu higher than those in Zhejiang can be attributed to the fact that
there are more large state-owned enterprises (including petrochemical
companies and power plants) in Jiangsu. Furthermore, the AH fluxes in the
urban areas of Jiangsu and Zhejiang range from 20 to 50 W m-2 in
recent decades. These high values are close to those in Toulouse of France
(Pigeon et al., 2007), Seoul of Korea (Lee et al., 2009), and some large US
cities (Sailor and Lu, 2004; Fan and Sailor, 2005).
The statistics of meteorological conditions from the ADDAH
simulation at four sites.
Varsa
Sitesb
January
July
Meanc
MB
RMSE
CORRd
Meanc
MB
RMSE
CORRd
OBSe
SIMf
OBSe
SIMf
T2 (∘C)
NJ
3.5
5.1
1.6
2.2
0.92
28.2
30.2
2.0
2.0
0.83
HZ
5.7
7.4
1.7
1.9
0.93
28.7
30.5
1.8
2.2
0.80
HF
3.6
5.1
1.5
2.2
0.91
28.9
30.6
1.7
2.1
0.76
SH
5.6
6.7
1.1
1.6
0.94
28.8
29.5
0.7
1.7
0.85
RH2 (%)
NJ
65
53
-12
14
0.74
76
68
-9
10
0.71
HZ
67
60
-7
10
0.83
74
70
-4
17
0.71
HF
71
51
-20
13
0.75
88
69
-19
12
0.62
SH
70
64
-6
11
0.79
76
72
-4
11
0.77
WS10 (m s-1)
NJ
2.6
3.1
0.5
1.2
0.61
2.9
3.2
0.3
1.3
0.53
HZ
2.5
2.6
0.1
1.0
0.69
2.4
2.5
0.1
1.3
0.34
HF
2.6
2.9
0.3
1.1
0.67
2.3
2.7
0.4
1.2
0.40
SH
4.1
3.8
-0.3
1.2
0.78
4.1
3.6
-0.5
1.2
0.66
a Vars represents the variables, including
temperature at 2 m (T2), relative humidity at 2 m (RH2) and wind
speed at 10 m (WS10). b Sites indicates the observation
meteorological sites used in this study, including NJ in Nanjing, HF in
Hefei, HZ in Hangzhou and SH in Shanghai. c Mean represents the
average value. d CORR indicates the correlation coefficients,
with statistical significance at the 95 % confidence level.
e OBS represents the observation data. f SIM
indicates the simulation results from WRF/Chem.
In 2010, nearly all areas of the YRD region had AH fluxes of more than
2.5 W m-2 (shown in Fig. 4d). High fluxes generally occur in and
around the cities, such as Shanghai, Nanjing, Hangzhou, Yangzhou, Zhenjiang,
Taizhou, Changzhou, Wuxi, Suzhou, Nantong, Huzhou, Jiaxing, Shaoxing, and
Ningbo, with typical values of 113.5, 50.2 and 39.3 W m-2 in the urban
areas of Shanghai, Jiangsu and Zhejiang, respectively (shown in Table 2).
Comparing Fig. 4d with Fig. 1, we can easily find that the spatial
distribution of AH based on the population reflects the economic activities
in the YRD region as well, suggesting that our method is effective and the
results are reasonable. Moreover, as shown in Table 2, parts of our
conclusion can be supported by some other previous studies (He et al., 2007;
Chen et al., 2012; Lu et al., 2014; Xie et al., 2015). Therefore, the gridded
AH fluxes can be used in meso-scale meteorological and environmental modeling
to investigate their impacts on urban climate and air quality.
Model evaluation for WRF/Chem
Table 3 shows the statistical comparisons between meteorological observations
and the model results from both January and July simulations in the ADDAH
case. Mean values, MB, RMSE and CORR are all quantified for 2 m temperature
(T2), 2 m relative humidity (RH2) and 10 m wind speed (WS10)
at four grids where NJ, HF, HZ and SH are located. As shown in Table 3, the
correlation coefficients between observations and simulations (CORR) are over
0.9 in January and about 0.8 in July for T2, higher than 0.7 for
RH2 at most sites in both months, and close to 0.7 for WS10 in
January. So WRF/Chem simulates the urban meteorological conditions over the
YRD region quite well. With respect to T2, the modeling results are
slightly overvalued at all sites, which might be attributed to the
uncertainty caused by urban canopy and surface parameters (Kusaka and Kimura,
2004; Chen et al., 2011; Liao et al., 2015). But the level of overestimation
is acceptable, because the MB values of T2 are only 1.1–1.7 ∘C
in January and 0.7–2.0 ∘C in July, with the RMSE of T2
1.6–2.2 ∘C. The lowest value 0.7 ∘C for MB and the highest
value 0.94 for CORR illustrate the best T2 estimation at SH. For
RH2, compared with the observations, the simulation results are
underestimated at all sites. Though the worst simulation of RH2 occurs
at HF, the results are reasonable at the other three sites. We find that the
land-use data set cannot describe waters around HF well. In view of the fact
that HF is not in the central area of the YRD region, the deviation at HF
cannot introduce crucial uncertainty into our main conclusion. With regards
to WS10, the modeling values from the ADDAH case are slightly
overestimated at NJ, HF and HZ, whereas they are underestimated at SH. The MB
for WS10 is generally less than 0.5 m s-1, and the RMSE is less
than 1.3 m s-1. These over- or under-estimates are attributable to
near-surface wind speed being influenced by local underlying surface
characteristics more than other meteorological parameters. Further
improvement of urban canopy parameters might improve the simulations (Zhang
et al., 2010; Liao et al., 2015).
Figure 5 presents time series comparisons between the observation data of
O3 and PM10 at CCM and their modeling results from the ADDAH
simulation case. Obviously, WRF/Chem with gridded AH fluxes can capture the
diurnal variations and magnitude of these pollutants. For O3, the
correlation coefficient between observations and simulations (CORR) is 0.60
in January and 0.71 in July (statistically significant at the 95 %
confident level). The value of MB is -0.8 ppb in January and 7.0 ppb in
July, which can be explained by stronger solar radiation reaching the urban
surface in July, causing positive biases in T2 and thereby producing
more O3 within the PBL (Zhang et al., 2010; Liao et al., 2015). With
regards to PM10, the model prediction underestimates the concentration,
with MB being -19.9 µg m-3 in January and
-10.8 µg m-3 in July, respectively. This underestimate can
be partially ascribed to positive biases of T2, which induce an increase
in PBL height and cause PM10 dilution within the PBL (Liao et al.,
2015). Furthermore, uncertainties in emissions may also cause these biases.
Hourly variations of PM10 (µg m-3) and O3
(ppb) from the observation data and the ADDAH simulation results at the CCM
monitoring site in Nanjing for January (a) and July (b).
Liao et al. (2014) also simulated the same time periods in the YRD region by
running WRF/Chem with a fixed AH flux in SLUCM. They found that the default
SLUCM scheme tends to underestimate 2 m temperature in January but
overestimate it in July, and overestimate the wind speed in both months. As a
result, their chemical predictions are not so perfect either, with the CORR
of 0.44–0.52 for O3 and 0.19–0.33 for PM10. Compared with their
results, our simulations accounting for the temporal and spatial distribution
of AH improve the accuracy of the model results, and well predict the urban
climate and air quality.
Generally, the WRF/Chem with gridded AH fluxes has a relatively good
capability in simulating urban climate and air quality over the YRD region in
this study. Though the biases are still found, the difference between the
modeling results from NONAH and ADDAH can still quantify the impacts of
anthropogenic heat on meteorology and pollution, because all other conditions
are the same in both simulations.
Impacts of AH on meteorological conditions
Horizontal meteorology changes
Figure 6 presents the monthly averaged differences of the main meteorological
factors between ADDAH and NONAH (ADDAH-NONAH) over modeling Domain 3 (D03).
Differences that are non-significant under the 95 % confidence level
using a Student's t test have been masked out. Obviously, the emissions of
anthropogenic heat increase the sensible heat fluxes from the urban canopy
layer over the YRD region. As shown in Fig. 6a and b, the spatial patterns of
sensible heat changes in both January and July are similar to the spatial
distribution of AH fluxes (Fig. 4d). High values of variation
(> 10 W m-2) generally occur around megacities with a
positive magnitude. For instance, in Shanghai, due to the maximum AH fluxes
in the city center, the biggest increase in sensible heat flux for January
can be 82 W m-2, and the value is 75 W m-2 in July. In other
cities, such as Hangzhou, Changzhou and Nantong, high values over
20 W m-2 can be found in both months as well. In order to better
understand the different behavior during the daytime and at night, the
monthly averaged diurnal variations of these modeled meteorological factors
over the urban area of Shanghai in January and July are also calculated. As
illustrated in Fig. 7, the addition of AH fluxes leads to an obvious increase
in sensible heat flux (SHF) from 07:00 to 21:00 UTC, with the daily mean
increase of 22 W m-2 for January and 20.5 W m-2 for July. The
increases are insignificant at night because the AH fluxes are small during
this time. On account of AH and its diurnal variation only being added to the
sensible heat item, there are no significant differences between the ADDAH
and NONAH simulations for ground heat flux (GRDFLX) and latent heat flux
(LH). It is worth mentioning that many AH emission processes are related to
water vapor releasing, and thereby latent heat fluxes might be affected by
the human activities that release AH.
By adding more surface sensible heat into the atmosphere, the AH flux changes
can influence the 2 m air temperature (T2) as well. The patterns of the
monthly averaged T2 changes (Fig. 6c and d) are similar to those of SHF
(Fig. 6a and b). For city centers like Shanghai, Hangzhou and Nanjing, adding
AH can lead to an increase in T2 of over 1 ∘C in January and
over 0.5 ∘C in July, generating an enhanced urban heat island. The
maximum T2 changes usually occur in the city center of Shanghai, with
the typical values of 1.6 ∘C in January and 1.4 ∘C in July.
These findings are comparable to the values estimated in megacities all over
the world (Fan and Sailor, 2005; Ferguson and Woodbury, 2007; Chen et al.,
2009; Zhu et al., 2010; Menberg et al., 2013; Wu and Yang, 2013;
Bohnenstengel et al., 2014; Feng et al., 2014; Yu et al., 2014). Moreover,
the mean increase in T2 at night in January (1.2 ∘C) is larger
than that in the daytime (1.0 ∘C), whereas the increase during the
daytime and nighttime is always equal to 0.6 ∘C in July, suggesting
that AH can help to form a weakened diurnal T2 variation in winter.
The spatial distributions of monthly averaged differences for
sensible heat flux (SHF), air temperature at 2 m (T2), the height of
the planetary boundary layer (PBLH), and wind speed (WS10) at 10 m
between ADDAH and NONAH (ADDAH-NONAH). Panels (a), (c),
(e) and (g) show changes in January. Panels (b),
(d), (f) and (h) illustrate variations in July.
The arrows in panels (g) and (h) are the differences of
wind fields. Differences that are non-significant under the 95 %
confidence level (Student's t test) are masked out.
The monthly averaged diurnal variations of modeled meteorological
factors in January (a) and July (b) over the urban area of
Shanghai. NONAH and ADDAH represent the simulation cases with and without AH
fluxes, respectively. LH means latent heat. SHF indicates sensible heat flux.
GRDFLX represents heat flux from ground level. T2, RH2, WS10,
and PBLH indicate 2 m air temperature (∘C), 2 m relative humidity
(%), 10 m wind speed (m s-1) and the height of the planetary
boundary layer (m), respectively.
The vertical air movement in the PBL can be enhanced by the warming up of
surface air temperature, which might increase the height of the PBL (PBLH).
Consequently, the enhanced AH fluxes increase the PBLH by more than 50 m in
January and by more than 70 m in July over the YRD urban areas, with the
maximum changes (140 m for January and 160 m for July) occurring in
Shanghai (shown in Fig. 6e and f). In summer, the weather is more unstable
and the vertical convection is easy to form. So the adding of AH induces a
greater increase in PBLH in July. For both months, as shown in Fig. 7, the
daytime relative increase in PBLH (10–15 %) is smaller than that at
night (23–33 %), which can be attributed to the facts that the absolute
PBLH values are lower and the air temperature increases more during the
nighttime.
Figure 6g and h show the changes in wind components over the YRD region, and
demonstrate that AH can enhance the 10 m wind speed (WS10) in the urban
areas. The maximum increase is located in Shanghai, with the increments of
0.7 m s-1 (19 %) in January and 0.5 m s-1 (17 %) in
July. In other cities like Hangzhou and Nanjing, the added value is only
about 0.3 m s-1. Over the YRD region, an increase in WS10 is more
obvious in January (Fig. 6g) than in July (Fig. 6h), and is slightly higher
at night than in the daytime (Fig. 7). As mentioned in previous studies, the
above increase in wind speed can be ascribed to the strengthened urban-breeze
circulation caused by the enhanced AH fluxes (Chen et al., 2009; Ryu et al.,
2013; Yu et al., 2014), which can be further clarified by the surface
stronger convergence wind patterns occurring around the megacities shown in
Fig. 6g and h. The simulated divergence at the surface near cities decreases
by 0.07–0.23 s-1 in January and by 0.08–0.31 s-1 in July (not
shown), also providing further evidence that the convergence is enhanced in
these areas.
The strengthened urban-breeze circulation caused by adding AH can also
enhance the vertical movement of the atmosphere. As shown in Fig. 8a, the
simulated vertical velocity above the megacities on the 850 hPa layer
increases by about 2 cm s-1 in July, suggesting that the convection
movements that can transport moisture and pollutants from the surface to the
upper layer are strengthened in the urban areas. Thus, the spatial and
vertical distributions of moisture are modified. Figure 8c and d illustrate
the spatial plots for monthly averaged differences of 2 m relative humidity
(RH2) caused by adding AH (ADDAH-NONAH). The negative centers over the
cities (the AH centers) can be seen in both January (-2 to -8 %) and
July (-2 to -6 %), meaning the air near the surface became dryer.
More moisture transported into the mid-troposphere (the vertical profile is
discussed in Fig. 9g and h in detail) might enhance rainfall inside urban
areas as well. As shown in Fig. 8b, the increase in rainfall in July can be
72.4, 84.6 and 63.2 mm in Shanghai, Hangzhou and Ningbo, respectively.
However, because of the negligible accumulative precipitation in winter, the
increment in rainfall over the YRD region in January can be ignored (not
shown).
The spatial distributions of monthly averaged differences for
vertical wind velocity on the 850 hPa layer (w), surface accumulative
precipitation and 2 m relative humidity (RH2) between ADDAH and NONAH
(ADDAH-NONAH). Differences that are non-significant under the 95 %
confidence level (Student's t test) are masked out.
The vertical distribution of monthly averaged differences for air
temperature (T), vertical wind velocity (w), divergence (DIV), and water
vapor mixing ratio (QVAPOR) between ADDAH and NONAH (ADDAH-NONAH) from
surface to 1.5 km altitude along line AB (shown in Fig. 2b).
Panels (a), (c), (e) and (g) show changes
in January. Panels (b), (d), (f) and (h)
illustrate variations in July. Differences that are non-significant under the
95 % confidence level (Student's t test) are masked out.
Vertical meteorology changes
To better understand how AH changes the vertical and spatial distribution of
meteorology in the YRD region, we present changes (ADDAH - NONAH) in air
temperature (T), vertical wind velocity (w), divergence (DIV) and water
vapor mixing ratio (QVAPOR) along a cross section from (28.9∘ N,
118.1∘ E) to (31.8∘ N, 122.6∘ E) as shown by the
solid line AB in Fig. 2b. The vertical cross sections for T changes
(Fig. 9a and b) illustrate that adding AH leads to a significant increase in
air temperature near the surface around the cities (Shanghai and Hangzhou),
while the changes are close to 0 in the rural areas and free troposphere. The
monthly mean increment of T over Shanghai and Hangzhou at ground level in
January (0.7 ∘C) is bigger than that in July (0.4 ∘C),
which can be attributed to the fact that the relative increase in heat is
higher in January due to background heat fluxes being much lower in winter.
The warming of air temperature near the surface in cities, as well as the
rise in PBLH in these areas (Fig. 6e and f), can generate an enhanced urban
heat island. As shown in Fig. 9c and d, the vertical wind velocities above
Shanghai and Hangzhou increase with added values of 0.3–0.7 cm s-1 in
both months, whereas w in the rural areas decreases by about
-0.3 m s-1 in January and -0.5 cm s-1 in July, suggesting
that there is enhanced upward movement in cities and enhanced downward
movement in the countryside. We also analyze the divergence changes along the
cross section including Shanghai and Hangzhou (Fig. 9e and f). It can be seen
that adding AH decreases DIV from the surface to 750 m and increases DIV at
higher levels, which means that there is a stronger convergence wind pattern
in the lower PBL and a more divergent wind pattern in the higher PBL. This
change implies that the atmosphere is more unstable, and intends to promote
the development of deep convection in the troposphere. Consequently, impacted
by the strengthened urban-breeze circulation, more moisture is transported
from the surface to the upper levels (over 1 km), with a 0.6 g kg-1
decrease in QVAPOR at the ground level and a 0.1 g kg-1 increase for
the upper PBL in July as presented in Fig. 9g and h. Furthermore, the
abovementioned vertical changes in w, DIV and QVAPOR are only restricted to
the air column over the AH emission centers (Shanghai and Hangzhou) in
January, while the changes are distributed widely (the adding AH fluxes can
impact wider areas) in July. This seasonal difference can be ascribed to the
fact that the atmosphere is more stagnant in winter and more convective in
summer.
The spatial distributions of monthly averaged differences for
PM10 between ADDAH and NONAH (ADDAH-NONAH). Differences that are
non-significant under the 95 % confidence level (Student's t test) are
masked out.
Impacts of AH on air pollutants
Changes in surface PM10 and O3
Adding AH changes spatial and vertical meteorology conditions, and thereby
undoubtedly affects the transportation and dispersion of air pollutants. Due
to PM10 being the main pollutant in the YRD region (Wang et al., 2012;
Xie et al., 2014; Liao et al., 2015), it is chosen as an indicator to show
the changes in primary air pollutants in this study. Figure 10 illustrates
the influence of AH on PM10 spatial distribution in typical months of
winter and summer (differences that are non-significant at the 95 %
confidence level using a t test are masked out). Results show that
PM10 in the lowest modeling layer is reduced at all times around the
cities, especially in Shanghai, Nanjing and Hangzhou. The maximum decrease
usually appears in Shanghai, with the monthly mean reduction of
29.3 µg m-3 (24.5 %) in January and
26.6 µg m-3 (18.8 %) in July. Compared with the
distribution of AH emissions (Fig. 4) and meteorology changes (Fig. 6), the
reduction in surface PM10 should be mainly related to the increase in
PBLH, the rise in surface wind speed and the enhanced upward movement of air,
because these modifications of meteorological conditions caused by adding AH
over the urban areas can facilitate PM10 transport and dispersion within
the urban boundary layer. Furthermore, on account of the precipitation around
the cities increasing by 15–30 %, the wet scavenging can contribute to
the reductions in the surface PM10 concentrations as well.
The spatial distribution of O3 concentration can also be influenced by
the changes in meteorological conditions due to the adding of AH. It should
be noted that the increase in wind speed might facilitate O3 transport,
and the rise in PBLH can lead to O3 dilution within the planetary
boundary layer. Thus, the surface O3 concentrations are seemingly
reduced. However, unlike PM10, O3 is a secondary air pollutant
formed by a series of complex chemical reactions involving oxides of nitrogen
(NOx= NO + NO2) and volatile organic compounds (VOCs), so only
considering the factors affecting O3 transport and dispersion is not
sufficient. In fact, O3 changes are different from those of PM10.
As illustrated in Fig. 11a and b, the increases in the surface O3 level
can be seen in both January and July over the YRD region, with large increase
centers occurring in megacities. In January (Fig. 11a), the maximum O3
difference appears in Shanghai, with the monthly mean increment of 2.5 ppb
(18 %). In July (Fig. 11b), the highest O3 change occurs in
Hangzhou, with the added value of 4 ppb (15 %). In the surrounding areas
of these high-value centers, the increase in O3 associated with the
introduction of AH can be over 0.5 ppb in January and more than 1 ppb in
July. This change pattern and the magnitude are consistent with the findings
reported in Beijing (Yu et al., 2014) and Seoul (Ryu et al., 2013).
Chemical direct and indirect effects should play a more important role in
O3 changes than other physical influencing factors. On the one hand, the
rise in air temperature (Fig. 6c and d) can directly accelerate O3
formation by increasing the chemical reaction rates, and thereby directly
increase the O3 level at the surface. On the other hand, O3 changes
are inextricably influenced by the changes in NOx (indirect chemical
effects). Similarly to other primary air pollutants (such as PM10),
NOx at ground level are reduced in both January and July due mainly to
the increase in PBLH, surface wind speed and upward air movement caused by
adding AH (Fig. 11c and d). It was reported that the O3 formation over
the cities in the YRD region is sensitive to VOC (Xie et al., 2014), which
means that a decrease in surface NOx might lead to a slight increase in
O3 during the daytime. At night, when the process of NOx titration
(O3+ NO → O2+ NO2) supersedes the O3
sensitivity to be the governing factor of O3 chemistry, less NOx
can only consume less O3 as well. Consequently, the decrease in NOx
at the ground can result in the increase in O3. This indirect function
might be clearly illustrated in the vertical distribution of O3 changes
in Sect. 3.4.2.
The spatial distributions of monthly averaged differences for
O3 and its precursor NOx between ADDAH and NONAH (ADDAH-NONAH).
Differences that are non-significant under the 95 % confidence level
(Student's t test) are masked out.
Vertical changes in PM10 and O3
Figure 12 shows the vertical plots on cross-sectional line AB (presented in
Fig. 2b) for the changes in chemical species impacted by adding AH
(ADDAH-NONAH). Differences that are non-significant at the 95 %
confidence level using a t test have been masked out. For the primary air
pollutants such as PM10 and NOx, the AH fluxes can decrease their
concentrations near the surface. As shown in Fig. 12a and b, in the
atmosphere below 300 m above Shanghai and Hangzhou, the concentrations of
PM10 decrease by 2.3–16.2 µg m-3 in January and by
2.1–15.8 µg m-3 in July, respectively. Surface NOx
concentrations near Shanghai and Hangzhou can be reduced by over 15 ppb in
both months as well (Fig. 12c and d). Meanwhile, it was also found that there
are increases in PM10 and NOx concentrations at the upper levels
over the cities. For instance, the added values of PM10 and NOx can
be more than 3 µg m-3 and 3 ppb at about 1 km above the
surface in January, respectively. This vertical changing pattern for primary
chemical species is quite similar to that for water vapor (Fig. 9g and h),
indicating that this is a reflection of the change in vertical transport
patterns in the region due to AH (Yu et al., 2014). It should be noted that
the maximum vertical changes in air pollutants in Hangzhou usually occur at
about 1 km above the surface, whereas those in Shanghai generally appear at
higher levels (> 1 km), implying that more surface air
pollutants in Shanghai might be transported into higher levels due to higher
AH emissions in this biggest city in the YRD region. Furthermore, Fig. 13
shows the vertical profiles of the changes for PM10, NOx and
O3 caused by adding AH over Shanghai. In winter, the large increases in
PM10 and NOx appear at 500 to 1500 m above the surface. But the
maximum increases usually occur at more than 1.5 km above the surface in
summer. This phenomenon can be attributed to the fact that the atmosphere is
more convective in summer than in winter.
In contrast to the primary air pollutants, O3 changes show increases
near the surface and decreases at the upper levels over the urban areas.
Figure 12e and f illustrates that the increases in O3 concentrations are
limited within 400 m above the surface over the cities, with the high values
of 2.6 ppb in January and 4.2 ppb in July. As mentioned in Sect. 3.4.1,
this may be the result of both the increase in O3 production caused by a
higher surface temperature and the decrease in O3 depletion resulting
from less surface NO. With respect to O3 concentrations from 400 m to
1.5 km above the surface, they generally decrease with the reduction in
values of more than 1 ppb in both January and July. Comparing Fig. 12e and f
with Fig. 12c and d, we believe that the increases in NOx concentrations
at these upper levels can lead to the depletion of O3, because of the
VOC-sensitive O3 chemistry in the daytime and NOx titration at
night in this region. In some previous studies on the O3 variations
induced by urban land use, researchers also found that O3 chemical
production is increased at the surface around big cities in summer (Liao et
al., 2015; Zhu et al., 2015) and in winter (Liao et al., 2015). However, it
was also found that the averaged daytime O3 in the upper PBL could
significantly increase by 20–40 ppbv because of strong urban heat island
circulation in the summer of Shanghai (Zhu et al., 2015). This result implies
that the vertical transport of O3 caused by urban land use should be
stronger than that caused by AH. Thus, more upward O3 can compensate for
the depletion of O3 at upper levels.
The vertical distribution of monthly averaged differences for
PM10, NOx and O3 between ADDAH and NONAH (ADDAH-NONAH) from
surface to 1.5 km altitude along line AB (shown in Fig. 2b).
Panels (a), (c) and (e) show changes in January.
Panels (b), (d) and (f) illustrate variations in
July. Differences that are non-significant under the 95 % confidence
level (student t test) are masked out.
Conclusions
Anthropogenic heat (AH) emissions from human activities caused by
urbanization can affect the city environment. In this paper, we especially
address its impacts on meteorological conditions and air pollution over the
cities in the YRD region. Firstly, based on the energy consumption and the
gridded population data, we estimate the spatial distribution of AH fluxes by
a top-down energy inventory method. Secondly, the gridded AH data with the
seasonal and the diurnal variation are added to the sensible heat flux in the
modified WRF/Chem. Finally, the WRF/Chem is applied to investigate the
impacts of AH. Two simulation cases are conducted. One incorporates the
Single Layer Urban Canopy Model (SLUCM) with the gridded AH fluxes, while the
other ignores the contribution of AH.
The results show that the AH flux in the YRD region has increased continually
since 1990. During the period between 1990 and 2010, the annual-mean values
of AH fluxes over Shanghai, Jiangsu and Zhejiang have increased from 5.47 to
14.45 W m-2, 0.68 to 2.61 W m-2, and 0.33 to 1.63 W m-2,
respectively. High AH fluxes generally occur in and around the cities. The
typical values of AH in 2010 over the urban areas of Shanghai, Jiangsu and
Zhejiang can reach 113.5, 50.2 and 39.3 W m-2, respectively.
The vertical profiles of monthly averaged differences for PM10,
NOx and O3 between ADDAH and NONAH (ADDAH-NONAH) over Shanghai.
The model results of WRF/Chem fit the observational meteorological conditions
and air quality very well. Inclusion of the AH can enhance the urban heat
island in the cities over the YRD region. The 2 m air temperature can
increase by more than 1 ∘C in January and by over 0.5 ∘C in
July. The PBL heights can increase, with the maximum changes of 140 m for
January and 160 m for July in Shanghai. The strengthened urban-breeze
circulation that resulted from adding the AH can enhance the 10 m wind speed
and the vertical air movement as well. Thus, more moisture is transported
from the surface to the upper levels, with a 0.6 g kg-1 decrease at
the ground level and a 0.1 g kg-1 increase for the upper PBL in July,
which might induce the accumulative precipitation to increase by 15–30 %
in Shanghai, Nanjing and Hangzhou.
Influenced by the modifications of meteorological conditions, the spatial and
vertical distribution of air pollutants is modified. With respect to the
primary air pollutants (PM10 and NOx), their transport and
dispersion in PBL can be facilitated by the increases in PBLH, surface wind
speed and upward air movement, which causes the decreases in concentrations
near the surface and the increases at the upper levels. Usually, PM10
can be reduced by 2–16 µg m-3 within 300 m above the
surface of the cities, and added over 3 µg m-3 in the upper
PBL. However, surface O3 concentrations increase in the urban areas,
with maximum changes of 2.5 ppb in January and 4 ppb in July. Besides the
rise in air temperature directly accelerating the surface O3 formation,
the decrease in NOx at the ground can also result in the increase in
surface O3 due to the VOC-sensitive O3 chemistry in the daytime and
NOx titration at night in this region. Furthermore, O3
concentrations at higher levels are reduced by about 1 ppb due mainly to the
increase in NO, and the impacts of AH are not only limited to the urban
centers, but are also extended regionally.
Impact of anthropogenic heat emission on urban climate and air quality is
undoubtedly an important and complex scientific issue. Our results show that
the meteorology and air pollution predictions in and around large urban areas
are highly sensitive to the anthropogenic heat inputs. Consequently, for
further understanding of urban atmospheric environment issues, good
information on land use, detailed urban structure of the cities and more
studies of the anthropogenic heat release should be better considered.
Data availability
The population density data in 1990, 1995,
2000, 2005 and 2010 are available at http://sedac.ciesin.columbia.edu/gpw/index.jsp.
The observed meteorological data at four observation sites are accessible from http://weather.uwyo.edu/.