The Effects of Anthropogenic Heat Release on Urban Meteorology and Implication for Haze Pollution in the Beijing-Tianjin-Hebei Region

Liu, Ruiting; Han, Zhiwei

doi:https://doi.org/10.1155/2016/6178308

Advances in Meteorology

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Abstract Introduction Data and Methods Results and Discussion Conclusions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2016 | Article ID 6178308 | https://doi.org/10.1155/2016/6178308

The Effects of Anthropogenic Heat Release on Urban Meteorology and Implication for Haze Pollution in the Beijing-Tianjin-Hebei Region

Ruiting Liu^1,2and Zhiwei Han¹

Academic Editor: Harry D. Kambezidis

Received16 May 2016

Accepted29 Sept 2016

Published25 Oct 2016

Abstract

In this study, the effect of anthropogenic heat release (AHR) on meteorological variables and atmospheric diffusion capability and implication for haze pollution in the Beijing-Tianjin-Hebei region in January 2013 were investigated by using Weather Research and Forecasting (WRF) model with an urban canopy model (UCM) and an AHR scheme. The comparison with observation demonstrated the WRF/UCM model taking AHR into account apparently improved meteorological prediction, especially for surface air temperature at 2 m (T2). The model also exhibited a better performance for planetary boundary layer (PBL) height. This study revealed that AHR from cities exerted a significant impact on meteorology by generally increasing surface air temperature and wind speed, decreasing relative humidity, and elevating PBL height and near surface turbulent kinetic energy (TKE), which could consequently reduce surface pollutant concentration and mitigate haze pollution by enhancing atmospheric instability and turbulent mixing and reducing aerosol hygroscopic growth.

1. Introduction

In recent decades, large scale urbanization has developed rapidly, resulting in significant changes in local/regional environment and climate. Urbanization brings about changes in underlying surface and releases of anthropogenic heat and anthropogenic pollutants into the atmosphere, consequently altering urban air quality and boundary layer meteorology. Urban heat island (UHI) can be generated due to changes in land use (including land types and characteristics) and land surface processes during urbanization [1–3].

Anthropogenic heat release (AHR) is produced by human activities and spreads to surrounding atmosphere. AHR is generated from many kinds of sources, with major sources from human metabolism, vehicle, and energy consumption in buildings including electricity and heating fuels [4]. A large amount of anthropogenic heat flux provides additional energy in urban area and further alters urban thermal environment. Oke [5] indicated that AHR was one of contributors to UHI. Feng et al. [6] found the influence of AHR on surface air temperature was greater than the influence of underlying surface change in winter across China. Chen et al. [7] suggested that AHR was an important factor in global climate change that should not be ignored on a global scale.

The anthropogenic heat flux depends on climate, population density, and intensity of industrial and commercial activities [8]. Recently, some studies have used energy statistics data to estimate more realistic AHR values. Sailor and Lu [4] explored the diurnal and seasonal variation of AHR in six large US cities, indicating a larger value in winter (maximum 75 W m⁻²) than that in summer (maximum 60 W m⁻²) and a typical diurnal variation with a daytime peak slightly larger than that during nighttime. Offerle et al. [9] reported an average of 32 W m⁻² from October to March in a downtown area of Lodz, Poland. Ferreira et al. [10] found a typical diurnal variation of AHR in Sao Paulo due to traffic waste heat release, with three peaks in early morning (19.9 W m⁻²), at noontime (19.6 W m⁻²), and in late afternoon (20.3 W m⁻²), respectively. AHR has been incorporated into numerical models to estimate its impacts. Taha [11] found that AHR may create a heat island of up to 2~3°C in urban center during both daytime and nighttime. Fan and Sailor [8] performed numerical experiments with MM5 (Mesoscale Model Version 5) to study the impacts of AHR on urban climate in Philadelphia. Their results showed a large air temperature increase during nighttime (2~3°C) and changes in nocturnal PBL structure. Urano et al. [12] took into account vertical distribution of AHR in a mesoscale climate model and found its impact occurred mainly at nighttime. Best and Grimmond [13] found that the inclusion of AHR in surface energy balance was important for urban meteorological modeling. Recently, some studies also simulated long-term AHR impact on a global scale. Allen et al. [14] estimated a diurnal range of 0.7~3.6 W m⁻² for global mean urban AHR using a large scale urban consumption of energy (LUCY) model. Flanner [15] incorporated AHR into global climate models and simulated significant increases in annual mean temperature and PBL height over grid cells of AHR exceeding 3.0 W m⁻².

Because of the rapid economic and industrial development in China, urbanization has been accelerating over the past 30 years which draws increasing attentions of scientific community. Feng et al. [16] estimated the values of annual mean AHR to be 23.6 W m⁻², 44.5 W m⁻², and 33.1 W m⁻² in urban areas of the Pearl River Delta, the Yangtze River Delta, and the Beijing-Tianjin-Hebei region, respectively. Through sensitivity simulations, Lin et al. [17] suggested that AHR played an important role in boundary layer development and UHI intensity in Taipei, especially during nighttime and early morning. Chen et al. [18] estimated the daily mean contribution ratios of AHR to UHI intensity were 54.5% and 43.6% in winter and summer, respectively. Zhang et al. [19] found that AHR contributed to nearly 75% of UHI intensity and its impact on near surface relative humidity and wind speed was evident in the Pearl River Delta. However, relevant studies on AHR in China were still limited and the above studies mainly focused on AHR’s impact on urban meteorology.

Anthropogenic heat release in China reaches maximum in winter due to increasing energy consumption; meanwhile, haze pollution occurs most frequently in winter due to the combined effects of larger emission amount and stronger atmospheric stability. So, it is valuable to explore the impact of urban AHR on meteorology and turbulent diffusion, which plays an important role in haze formation and evolution. This study aims to investigate the effect of AHR on meteorology in the Beijing-Tianjin-Hebei region in January 2013 by using the WRF/UCM together with AHR parameterization. The new aspect in this study is to further explore the changes induced by AHR in atmospheric diffusion and mixing capacity during haze event and its implication for haze pollution.

2. Data and Methods

The meteorological model used in this study is the Weather Research and Forecasting (WRF) model version 3.5 with the ARW dynamic core [20]. Two nested domains are selected (Figure 1(a)), with a center at (35°N, 115°E) and horizontal resolutions of 27 km (d01: 160 × 140) and 9 km (d02: 76 × 91), respectively. D02 (Figure 1(b)) covers the Beijing-Tianjin-Hebei region, one of the biggest urban agglomerations in China, which contains a population of 104 million. Figure 1(c) shows the spatial distribution of all land use types in d02. The land use dataset used in WRF model is retrieved from MODerate resolution Imaging Spectroradiometer (MODIS). There are 30 eta levels from the surface to 50 hpa. Initial and boundary conditions are provided by the 6 hourly and 1° × 1° Global Final Analysis (FNL) data from the National Centers for Environmental Prediction (NCEP). Simulation period is from 0000 UTC 27 December 2012 to 1800 UTC 31 January 2013, with the first five days as spin-up period. The physical options used in model simulation include the Mellor-Yamada-Janjic (MYJ) planetary boundary layer (PBL) scheme [21], Kessler microphysical scheme, new Kain-Fritsch cumulus scheme, CAM shortwave and longwave radiation [22], and the Noah land surface model (LSM) coupled with a single layer urban canopy model (UCM) [23]. The UCM takes urban geometry into account to describe more realistically the exchanges of energy, momentum, and vapor between urban surface and atmosphere. It divides the urban surface into three types (roof, wall, and road) and considers the influence of shadowing from buildings and the reflection of radiation. The urban parameters in this study are updated based on recent observational studies in China megacities. The mean building height is updated from 7.5 m to 18 m following Miao et al. [24], which is derived from field measurement around a 325 m meteorological tower in downtown Beijing. The fraction of nonnatural vegetation and surface albedo is set to 0.9 and 0.2, respectively, based on previous observations in Beijing [25, 26].

As to AHR emission in China, Feng et al. [6] estimated a daily mean value of 28 W m⁻² for high intensity residential based on an annual mean AHR estimate of cities in China and urban classification method. For annual mean AHR, it was calculated with energy data, including energy consumption, utility efficiency, and calorific values. Meanwhile, the diurnal variation of urban AHR is taken into account in the WRF/UCM based on observational data in Beijing [27], with higher value during daytime and two peaks at 08:00 and 17:00 LST (Figure 2(a)). The AHR diurnal variation in Beijing is found to be similar to the default one in the WRF/UCM. The hourly value of AHR during a day is derived through multiplying daily maximum 50 W m⁻² (daily mean 28 W m⁻² divided by daily mean scaling factor) by hourly scaling factor (the ratio of hourly AHR to daily maximum AHR). Figure 2(b) shows the spatial distributions of AHR at 12:00 LST in the domain. Large values (about 37 W m⁻²) occurred in the centers of Beijing and Tianjin, with decreasing magnitude toward suburban and rural areas. AHR is added to the surface energy balance equation of the model in a form of sensible heat.

(a)

(b)

To investigate the effect of AHR on meteorological variables, two sensitivity model simulations are conducted. One is the base case without AHR; the other is case 1 by considering AHR in urban areas. The difference between the two cases reflects the AHR-induced meteorological changes. The observed meteorological data in January 2013 is derived from China Meteorological Administration (http://data.cma.cn/site/index.html), and the observed PM_2.5 concentration is converted from air quality index (AQI) in Beijing (http://www.bjepb.gov.cn/).

3. Results and Discussion

3.1. Model Validation

The day to day variations of the observed and simulated meteorological variables in the two cases for January 2013 in Beijing are presented in Figures 3(a)–3(d). High and low pressure occurred alternatively in the study period, which were well reproduced by both the base case and case 1. Surface air temperature exhibited an increasing trend up to 0°C at the end of January. It was noticed that T2 in case 1 was higher than that in base case due to the AHR effect and was closer to observation. It is impressive that WS10 was mostly less than 2 m s⁻¹ during the study period, implying an unfavorable condition for pollutant diffusion. The model was able to simulate WS10 variation reasonably well and AHR tended to generally increase WS10 with a relatively smaller magnitude compared with T2. There was a large variation in RH2, ranging from 20% during a cold front passage to above 90% under a weak low pressure system, implying a potentially large effect on haze and visibility. AHR generally led to a decrease of RH2 in case 1, but the agreement with observation between base case and case 1 was not discernible.

(a)

(b)

(c)

(d)

(e)

The statistical comparison for T2, WS10, and RH2 at the 5 sites (Figure 1(c)) is shown in Table 1. In general, WRF was able to reproduce the variation of ground meteorological variables reasonably well, with correlation coefficients (COR) being 0.85 for T2, about 0.70 for WS10 and RH2 for all sites. However, it was apparent that T2 in Beijing was predicted to be much lower than the observation in base case, with mean bias (MB) and normalized mean bias (NMB) of −2.5°C and 59%, respectively. By taking AHR into account, T2 prediction was closer to observation in case 1, with MB and NMB reduced to −0.7°C and 19%. Such improvement occurred at all sites, resulting in an overall MB and NMB of −0.1°C and 3%, demonstrating the significant influence of AHR and the necessity to include AHR in prediction of urban meteorology. It is also noteworthy that AHR tended to increase wind speed in urban areas and slightly improve WS10 prediction regarding the reduced MB (from −0.1 to −0.02 m s⁻¹) and NMB (−3% to −1%). WRF generally predicted a larger RH2 in base case, but a lower RH2 in case 1 due to increasing T2 by AHR; the overall performances for RH2 were comparable between the two cases.

The above comparison and statistics indicate that the meteorological prediction can be significantly improved by considering AHR in urban areas of north China, especially for near surface air temperature, suggesting the importance to incorporate AHR in weather/climate models to represent meteorology and human activity more realistically.

3.2. The Effect of AHR on Meteorological Variables

Figure 4(a) shows the monthly mean differences (case 1 minus base case) in T2 and wind vector. It is impressive that AHR caused an apparent T2 increase in Beijing, Tianjin, and other cities in the domain; T2 increased to some extent in suburbs as well. The maximum increase of T2 over Beijing was about 2.1°C, with larger increase (2.4°C) during nighttime than that during daytime (1.6°C). Meanwhile, AHR induced slight convergence of wind, blowing from suburb to urban areas.

(a)

(b)

(c)

(d)

(e)

(f)

AHR led to an increase of wind speed (Figure 4(b)) in urban areas, with a maximum up to 0.25 m s⁻¹ in the downtowns of Beijing and Tianjin. RH2 (Figure 4(c)) decreased while considering AHR mainly due to increased saturated vapor pressure with T2 increase given little change in specific humidity, with a maximum decrease of 9.0% in Beijing and Tianjin, and the decrease at night (12.0%) was larger than that during daytime (5.6%). PBL height increased considerably in urban areas due to AHR, with a maximum increment up to 180 m (percent change of 45%) in downtown Beijing. The increase in PBL height was larger during daytime than nighttime. The sensible heat flux increased by about 22.0 W m⁻² in most cities, with larger increment (36.0 W m⁻²) during the daytime than that at nighttime (12.0 W m⁻²), consistent with the diurnal variation of AHR emission. AHR induced a little increase of latent heat flux in urban areas, with a much smaller magnitude (0.3 W m⁻²) than that of sensible heat flux.

The above analysis demonstrates that AHR significantly modifies surface energy balance and results in considerable changes in the distribution and magnitude of meteorological variables, which could consequently affect atmospheric diffusion and haze pollution.

3.3. The Impact of AHR on Meteorology during Haze Episode and Implication for Haze Pollution

Given the large impact of AHR on monthly mean meteorology discussed above, it is interesting to explore the effect of AHR on meteorology during haze episode and a severe haze episode was selected. Figure 3(e) shows that the Beijing-Tianjin-Hebei region experienced several haze episodes in January 2013. The severest haze pollution occurred on 12 January, when daily mean concentration of PM_2.5 was up to 404 μg m⁻³. It was noticed that the high PM_2.5 concentration was closely associated with low pressure, small wind speed, and high relative humidity, suggesting the potentially significant effect of meteorology on haze pollution. The base case simulated daily mean surface wind field and relative humidity in the Beijing-Tianjin-Hebei region on 11 and 12 January are shown in Figure 5. On 11 January, northwest wind prevailed in Beijing, Tianjin, and northern Hebei province, with WS10 of 3 m s⁻¹ and RH2 of 40~70% in Beijing. On 12 January, southerly wind dominated southern Hebei province and encountered northwesterly wind in Beijing, resulting in a small wind less than 2 m s⁻¹. RH2 increased to about 80% simultaneously in portions of southeastern Beijing. The meteorological condition on 12 January was very favorable for haze occurrence compared with 11 January.

(a)

(b)

In terms of daily mean (figure not shown), AHR consistently caused an increase in T2 and a decrease in RH2 in Beijing and Tianjin on 12 January, with maximums up to 2.5°C and 13.0%, respectively. Wind speed generally increased in most urban areas of Beijing and Tianjin but decrease also occurred in some areas south and west of Beijing city due to interaction between background wind and AHR-induced wind. AHR caused a maximum PBL height increase of 210 m in downtown Beijing and Tianjin, well corresponding to the areas of maximum temperature increase.

Figure 6 shows the AHR induced near surface meteorological changes during daytime (10:00–14:00 LST) and nighttime (22:00–02:00 LST). It was clearly seen that, in the urban areas of Beijing and Tianjin, the T2 increase at nighttime (~3.0°C) was larger than that during daytime (about 2.2°C). The RH2 decreased by about 21.0% at nighttime, larger than that during daytime (10.0%) (figure not shown). The hygroscopic growth of hydrophilic aerosols with increasing relative humidity (RH) may significantly reduce surface visibility [28]; in this regard, the decrease in RH2 induced by AHR should mitigate visibility reduction. During the daytime, in the urban areas of Beijing and Tianjin, wind speed consistently increased by ~0.7 m s⁻¹, with larger increase in Tianjin than in Beijing, whereas during nighttime, the AHR induced increase was smaller (~0.3 m s⁻¹). It was noticed that, at night, in some areas south of Beijing and southern Hebei province, wind speed decreased slightly by 0.1~0.3 m s⁻¹, which was mainly due to the interaction between background wind and AHR induced wind. In southern Hebei, AHR induced a slight south wind of about 0.5 m s⁻¹ (Figure 6(b)), which was opposite to the background north wind of 4~5 m s⁻¹ (figure not shown), resulting in a reduced wind speed in these regions.

(a)

(b)

(c)

(d)

PBL height variation exerts a direct impact on distribution and level of surface air pollutant through mixing and dilution effect [29]. Previous study [30] indicated a strong negative correlation between surface PM_2.5 concentration and mixing depth, decreasing PM_2.5 concentration with increasing mixing height and vice versa. Hence it is useful to gain more insight into PBL height variation during the haze episode.

Figure 7 shows the model simulated hourly variation of PBL height on 11–13 January in downtown Beijing. The PBL height in the MYJ scheme is defined as the height at which turbulent kinetic energy (TKE) decreases to below 0.1 m² s⁻² [31]. It was striking that the PBL height reduced to about 400 m during the daytime on 12 January, which tended to restrict pollutants within a shallower surface layer and consequently caused accumulation and increase of PM_2.5 concentration. The simulated daytime PBL heights averaged over 10:00–15:00 LST on 12 January were 260 m and 374 m in the base case and case 1, respectively. Sun et al. [32] reported a corresponding PBL height of about 450 m retrieved from backscattering coefficient by depolarization lidar during the same time period. It was noteworthy that, in base case, the model using MYJ scheme tended to underestimate PBL height (260 m) during the daytime, whereas PBL height increased (374 m) by considering AHR and was closer to observation, indicating the importance of AHR in prediction of urban PBL height. The AHR effect on PBL height at nighttime was small. The percent increase of PBL height due to AHR during the daytime on 12 January was about 44%, suggesting a significant impact of AHR on pollutant mixing and diffusion.

The vertical cross sections of the simulated TKE and air temperature along 39.9°N (Figure 1(b)) across downtown Beijing at 14:00 and 02:00 LST in the base case and case 1 are shown in Figure 8. Due to weaker turbulence, TKE in Beijing (116.2°E to 116.6°E) during nighttime was smaller than that during daytime in both the base case and case 1. Figure 8(a) shows, during the daytime, the near surface TKE in Beijing in the base case was in a range of 0.18 m² s⁻²~0.24 m² s⁻² with air temperature of −1.0°C, while, considering AHR (Figure 8(c)), surface air temperature increased to above 0°C, with TKE increasing up to 0.32 m² s⁻². In the vertical, TKE > 0.20 m² s⁻² extended to 400 m, resulting in the corresponding PBL height increase, and at the same time, the near surface vertical velocity was increased by about 0.01 m s⁻¹ over Beijing (not shown). It was also noticed that TKE about ~0.30 m² s⁻² occurred at altitude < 500 m in the eastern parts of the domain, which was caused by strong wind shear. At nighttime, the surface air temperature was −7.0°C, with lower TKE of 0.14 m² s⁻²~0.20 m² s⁻² in the base case (Figure 8(b)), whereas AHR led to an increase of TKE up to 0.28 m² s⁻² in case 1 (Figure 8(d)). TKE is a direct indicator of atmospheric turbulence and mixing capability; the strengthened TKE due to AHR during both daytime and nighttime indicated a stronger turbulent diffusion, which should result in a decrease of surface pollutant concentration.

(a)

(b)

(c)

(d)

The above results suggest the AHR-induced changes in meteorology and turbulence activity are generally favorable for pollutant diffusion and mixing and for weakening of aerosol hygroscopic growth, which could mitigate haze pollution in the study domain.

4. Conclusions

In this paper, a scheme for anthropogenic heat release was incorporated into WRF/UCM and applied to investigate the effects of AHR on meteorological variables, diffusion, and mixing capability, which has important implication for haze pollution in the Beijing-Tianjin-Hebei region. By taking AHR into account, meteorological prediction, especially for surface air temperature, had been improved significantly, and the model performed better for PBL height, which was a key factor controlling pollutant diffusion. In terms of monthly mean, AHR tended to increase near surface air temperature and wind speed by 2.1°C and 0.25 m s⁻¹, to decrease relative humidity by 9.0% in the cities of Beijing and Tianjin. For the severest haze episode on 12 January, AHR induced the increases in daily mean T2, WS10, and RH2 in Beijing city of 2.5°C, 0.2 m s⁻¹, and −13.0%, respectively. It was noteworthy that, during haze episode, PBL height decreased to about 400 m, which was quite unfavorable for vertical diffusion. AHR tended to increase PBL height by about 44% during the daytime, suggesting an enhanced atmospheric instability and vertical mixing. Near the surface, TKE was much larger during the daytime than during the nighttime, with a maximum up to 0.32 m² s⁻². AHR led to TKE increase throughout the day, with percent change being about 50% and 33% during the daytime and nighttime, respectively. The results from this study demonstrated the significant effect of AHR on meteorological variables by increasing surface air temperature and wind speed, decreasing relative humidity, as well as enhancing PBL height and TKE, which were mainly favorable for pollutant diffusion and weakening of aerosol hygroscopic growth and could consequently lead to a decrease of surface pollutant level and an increase of visibility. We are aware that pollutants are released together with anthropogenic heat during energy consumption; this study demonstrated that the AHR induced changes in meteorology and turbulence were generally favorable for mitigation of haze pollution, which were rarely considered in previous air quality modeling. Besides, by considering AHR, the model represented urban meteorology and turbulent diffusion more realistically and accurately, suggesting the necessity to couple this process into weather/climate model.

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this article.

Acknowledgments

This study was supported by the National Natural Science Foundation of China (no. 41375151) and the Jiangsu Collaborative Innovation Center for Climate Change.

References

R. Hamdi and G. Schayes, “Sensitivity study of the urban heat island intensity to urban characteristics,” International Journal of Climatology, vol. 28, no. 7, pp. 973–982, 2008.
View at: Publisher Site | Google Scholar
C. L. Zhang, F. Chen, S. G. Miao, Q. C. Li, X. A. Xia, and C. Y. Xuan, “Impacts of urban expansion and future green planting on summer precipitation in the Beijing metropolitan area,” Journal of Geophysical Research Atmospheres, vol. 114, no. 2, Article ID D02116, 2009.
View at: Publisher Site | Google Scholar
S. Miao, F. Chen, M. A. LeMone, M. Tewari, Q. Li, and Y. Wang, “An observational and modeling study of characteristics of urban heat island and boundary layer structures in Beijing,” Journal of Applied Meteorology and Climatology, vol. 48, no. 3, pp. 484–501, 2009.
View at: Publisher Site | Google Scholar
D. J. Sailor and L. Lu, “A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas,” Atmospheric Environment, vol. 38, no. 17, pp. 2737–2748, 2004.
View at: Publisher Site | Google Scholar
T. R. Oke, Boundary Layer Climates, Routledge, London, UK, 1987.
J.-M. Feng, Y.-L. Wang, Z.-G. Ma, and Y.-H. Liu, “Simulating the regional impacts of urbanization and anthropogenic heat release on climate across China,” Journal of Climate, vol. 25, no. 20, pp. 7187–7203, 2012.
View at: Publisher Site | Google Scholar
B. Chen, L. Dong, G. Shi, L.-J. Li, and L.-F. Chen, “Anthropogenic heat release: estimation of global distribution and possible climate effect,” Journal of the Meteorological Society of Japan, vol. 92A, pp. 157–165, 2014.
View at: Publisher Site | Google Scholar
H. Fan and D. J. Sailor, “Modeling the impacts of anthropogenic heating on the urban climate of Philadelphia: a comparison of implementations in two PBL schemes,” Atmospheric Environment, vol. 39, no. 1, pp. 73–84, 2005.
View at: Publisher Site | Google Scholar
B. Offerle, C. S. B. Grimmond, and K. Fortuniak, “Heat storage and anthropogenic heat flux in relation to the energy balance of a central European city centre,” International Journal of Climatology, vol. 25, no. 10, pp. 1405–1419, 2005.
View at: Publisher Site | Google Scholar
M. J. Ferreira, A. P. de Oliveira, and J. Soares, “Anthropogenic heat in the city of São Paulo, Brazil,” Theoretical and Applied Climatology, vol. 104, no. 1-2, pp. 43–56, 2011.
View at: Publisher Site | Google Scholar
H. Taha, “Urban climates and heat islands: albedo, evapotranspiration, and anthropogenic heat,” Energy and Buildings, vol. 25, no. 2, pp. 99–103, 1997.
View at: Publisher Site | Google Scholar
A. Urano, T. Ichinose, and K. Hanaki, “Thermal environment simulation for three dimensional replacement of urban activity,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 81, no. 1–3, pp. 197–210, 1999.
View at: Publisher Site | Google Scholar
M. J. Best and C. S. Grimmond, “Investigation of the impact of anthropogenic heat flux within an urban land surface model and PILPS-urban,” Theoretical and Applied Climatology, vol. 126, no. 1-2, pp. 51–60, 2016.
View at: Publisher Site | Google Scholar
L. Allen, F. Lindberg, and C. S. B. Grimmond, “Global to city scale urban anthropogenic heat flux: model and variability,” International Journal of Climatology, vol. 31, no. 13, pp. 1990–2005, 2011.
View at: Publisher Site | Google Scholar
M. G. Flanner, “Integrating anthropogenic heat flux with global climate models,” Geophysical Research Letters, vol. 36, no. 2, Article ID L02801, 2009.
View at: Publisher Site | Google Scholar
J. M. Feng, J. Wang, and Z. W. Yan, “Impact of anthropogenic heat release on regional climate in three vast urban agglomerations in China,” Advances in Atmospheric Sciences, vol. 31, no. 2, pp. 363–373, 2014.
View at: Publisher Site | Google Scholar
C.-Y. Lin, F. Chen, J. C. Huang et al., “Urban heat island effect and its impact on boundary layer development and land-sea circulation over northern Taiwan,” Atmospheric Environment, vol. 42, no. 22, pp. 5635–5649, 2008.
View at: Publisher Site | Google Scholar
Y. Chen, W. M. Jiang, N. Zhang, X. F. He, and R. W. Zhou, “Numerical simulation of the anthropogenic heat effect on urban boundary layer structure,” Theoretical and Applied Climatology, vol. 97, no. 1-2, pp. 123–134, 2009.
View at: Publisher Site | Google Scholar
N. Zhang, X. Wang, Y. Chen, W. Dai, and X. Wang, “Numerical simulations on influence of urban land cover expansion and anthropogenic heat release on urban meteorological environment in Pearl River Delta,” Theoretical and Applied Climatology, vol. 126, no. 3, pp. 469–479, 2016.
View at: Publisher Site | Google Scholar
W. C. Skamarock, J. B. Klemp, J. Dudhia et al., “A description of the advanced research WRF version 3,” NCAR Tech Note NCAR/TN-4751STR, 2008.
View at: Google Scholar
Z. I. Janjic, “The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes,” Monthly Weather Review, vol. 122, no. 5, pp. 927–945, 1994.
View at: Publisher Site | Google Scholar
W. D. Collins, P. J. Rasch, B. A. Boville et al., “Description of the NCAR community atmosphere model (CAM 3.0),” NCAR Tech Note NCAR/TN-464STR, 2004.
View at: Google Scholar
H. Kusaka and F. Kimura, “Coupling a single-layer urban canopy model with a simple atmospheric model: impact on urban heat island simulation for an idealized case,” Journal of the Meteorological Society of Japan, vol. 82, no. 1, pp. 67–80, 2004.
View at: Publisher Site | Google Scholar
S. G. Miao, J. X. Dou, F. Chen, J. Li, and A. G. Li, “Analysis of observations on the urban surface energy balance in Beijing,” Science China Earth Sciences, vol. 55, no. 11, pp. 1881–1890, 2012.
View at: Publisher Site | Google Scholar
S. P. Sun, R. Z. Gu, and J. Zhang, “Inhalable particulate matter (PM10) related to different green Cover percentage and greenbelt types in Beijing,” Chinese Landscape Architecture, vol. 20, no. 3, pp. 77–79, 2004.
View at: Google Scholar
X. L. Li, B. G. Bi, and Z. C. Li, “Simulation study of formation mechanism of winter urban boundary layer structure over Beijing area,” Acta Meteorologica Sinica, vol. 63, no. 6, pp. 889–902, 2005.
View at: Google Scholar
H. Tong, H. Z. Liu, J. G. Sang, and F. Hu, “The impact of urban anthropogenic heat on Beijing heat environment,” Climatic and Environmental Research, vol. 9, no. 3, pp. 409–421, 2004 (Chinese).
View at: Google Scholar
J. Li, Z. Han, and R. Zhang, “Influence of aerosol hygroscopic growth parameterization on aerosol optical depth and direct radiative forcing over East Asia,” Atmospheric Research, vol. 140-141, pp. 14–27, 2014.
View at: Publisher Site | Google Scholar
D. J. Jacob and D. A. Winner, “Effect of climate change on air quality,” Atmospheric Environment, vol. 43, no. 1, pp. 51–63, 2009.
View at: Publisher Site | Google Scholar
J. P. Dawson, P. J. Adams, and S. N. Pandis, “Sensitivity of PM2.5 to climate in the Eastern US: a modeling case study,” Atmospheric Chemistry and Physics, vol. 7, no. 16, pp. 4295–4309, 2007.
View at: Publisher Site | Google Scholar
H. H. Shin and S.-Y. Hong, “Intercomparison of planetary boundary-layer parametrizations in the WRF model for a single day from CASES-99,” Boundary-Layer Meteorology, vol. 139, no. 2, pp. 261–281, 2011.
View at: Publisher Site | Google Scholar
Y. L. Sun, Q. Jiang, Z. F. Wang et al., “Investigation of the sources and evolution processes of severe haze pollution in Beijing in January 2013,” Journal of Geophysical Research: Atmospheres, vol. 119, no. 7, pp. 4380–4398, 2014.
View at: Publisher Site | Google Scholar

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Copyright © 2016 Ruiting Liu and Zhiwei Han. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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