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Article

Impact of Anthropogenic Heat on Urban Environment: A Case Study of Singapore with High-Resolution Gridded Data

by
Ao Wang
1,
Xian-Xiang Li
1,2,*,
Rui Xin
1 and
Lup Wai Chew
3
1
School of Atmospheric Sciences, Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-Sen University, Zhuhai 519082, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China
3
Department of the Built Environment, National University of Singapore, Singapore 119077, Singapore
*
Author to whom correspondence should be addressed.
Atmosphere 2023, 14(10), 1499; https://doi.org/10.3390/atmos14101499
Submission received: 17 August 2023 / Revised: 23 September 2023 / Accepted: 26 September 2023 / Published: 28 September 2023
(This article belongs to the Special Issue Heat Waves: Perspectives from Observations, Reanalysis and Modeling)
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Abstract

:
Anthropogenic heat (AH) emissions have great impacts on urban climate. AH is usually spatially heterogeneous and depends on the urban land use type. Studies using high-resolution gridded data that can resolve spatially heterogeneous AH are still scarce. The present study uses AH data of a high spatial resolution of 200 m by 200 m and a temporal resolution of 1 h to investigate the impact of AH in Singapore in April 2016, particularly regarding the relative contribution of individual AH components. The WRF model coupled with a single-layer urban canopy model is employed. The WRF model can predict the 2-m air temperature and 2-m relative humidity with good agreement with the observation data, while the simulated 10-m wind speed has relatively large deviation from the observation data. The largest spatially averaged temperature increases caused by total AH (QF), AH from buildings (QB) and AH from traffic (QV) are 1.44 °C, 1.44 °C and 1.35 °C, respectively. The effects of AH on sensible heat flux and boundary layer height are largely consistent, with both QF and QB exhibiting significant effects at night, while the effects of QV are small. The effect of AH on the local circulations (sea and land breezes) in Singapore is small, while its effect on the urban heat island (UHI) circulations is more pronounced. Due to the UHI circulations, the sum of the effects on local temperatures caused by QB and QV may exceed that by QF in some areas. This finding can guide comprehensive mitigation measures of AH by not only focusing on land use type but also on the contribution of individual AH components, in order to ameliorate the impacts of urban overheating.

1. Introduction

Anthropogenic heat (AH) refers to the heat emitted into the atmosphere by human activities, which mainly consists of energy consumption from buildings, traffic, industries and human metabolism [1]. Increasing urban population and energy consumption induced by rapid urbanization inevitably increase AH generation in urban environments [2]. AH can bring about negative consequences, such as intensifying the urban heat island (UHI) phenomenon [3,4,5] and threatening the physical and mental health of urban dwellers [6,7,8]. Despite the current trend and awareness to improve energy efficiency and reduce energy consumption, AH cannot be eliminated and will continue to affect the urban environment [9,10,11,12,13].
Numerous studies have shown that AH can cause significant changes in the thermal environment in urban areas. Ichinose et al. [14] discussed the impact of increasing AH on the urban climate of Tokyo. The influence of AH in winter was greater than that in summer. A reduction of 50% in the energy consumption of hot water supply and 100% of space cooling would result in a reduction in outdoor air temperature by 0.5 °C. AH from urban environments can increase near-surface temperatures by 2 °C in summer and 3 °C in winter [15,16,17]. AH from air conditioning systems can contribute to the UHI effect by increasing the average urban air temperature by 0.2–2.5 °C [18]. Similar results were obtained in Madrid [19], Paris [20] and Singapore [21], where AH can contribute to the temperature increase in urban areas by up to 2.0 °C, 2.0 °C and 2.2 °C, respectively. Besides air conditioning systems, some studies have shown that increased traffic flow and commuting distance will increase energy consumption and thereby increase AH from traffic [1,22]. Industrial activities usually generate a large amount of AH, and as a result, industrial areas tend to be warmer than the surrounding areas [23,24].
Although many existing studies of AH have been reported in the literature, there is a major gap in the literature on the quantitative assessment of the impact of individual components of AH on urban environments. Most of the existing studies on the effects of AH used the “look-up table” method [25], i.e., assigning the same AH to the same urban land use type, which cannot account for the spatial heterogeneity in many real urban environments. Previous studies focusing on Singapore have also been adopting the “look-up table” method. Li et al. [26] validated a mesoscale model with high-resolution grids of 300 m for urban parameterizations in Singapore with observational data. The validated model was subsequently used to study the UHI mitigation potential of cool roof and greenery in Singapore [27], the effects of evapotranspiration [28,29] and the interaction of UHI with the heat wave in April 2016 in Singapore [30]. With the availability of gridded AH data for different cities, some studies started to investigate the effect of spatiotemporally heterogeneous AH on urban environments [31,32,33,34,35]. However, these studies did not distinguish the impacts of different sources of AH, mainly due to the fact that the AH data they used had no such detailed information. Among the individual components of AH, AH emissions from buildings have been studied the most, where the Building Energy Model is coupled with numerical weather prediction models [21,36,37]. Other AH components studied include AH from traffic [38,39] and industry [40]. Only a few numerical studies have compared contributions from different components of AH [41].
The brief literature review identifies a research gap and discusses the lack of research focusing on different AH components with realistic inputs (e.g., using gridded data which can account for the spatiotemporal heterogeneity in urban environments). This paper aims to investigate the relative contributions of AH from buildings, from traffic and from human metabolism using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model (SLUCM). The innovation of the current work is to use high-spatiotemporal-resolution gridded AH data in a WRF model to quantitatively assess the impact of heterogeneous AH from different sources on the urban thermal environment and local circulations.

2. Study Area, Data and Methods

2.1. Study Area

Singapore is a tropical island state located in Southeast Asia between 1°09′ N to 1°29′ N and 103°36′ E to 104°25′ E, with a total land area of 726 km2. In recent decades, Singapore has been undergoing rapid urbanization and development, with a population density reaching 7688 people/km2 in 2022 [42]. The land use/land cover status of Singapore is shown in Figure 1b, in which the land use classification is based on the WRF-modified MODIS-IGBP classification scheme [41]. Despite being a highly urbanized island state, Singapore has preserved many of its green spaces, as shown in the large area of evergreen forest in the west and central Singapore in Figure 1b.
The annual average temperature in Singapore is about 27.5 °C, with small monthly variations (less than 2 °C) and the hottest months being April and May [43,44]. Daily maximum temperatures range from 31 to 33 °C and daily minimum temperatures range from 23 to 25 °C. Relative humidity throughout the day (during periods without rain) usually varies between 60% and 90% but can achieve 100% on rainy days. The total annual precipitation is high and can reach 2190 mm [45].
The annual variations in regional climate caused by the Asian monsoon have a great impact on cloud cover, surface wind speed and wind direction [46]. The northeast monsoon season (from December to March) is associated with the highest monthly rainfall and weaker winds, and the southwest monsoon season (from June to September) corresponds to a relatively drier period [47]. The two inter-monsoon seasons are characterized by varying wind directions and low wind speeds. Under these conditions, sea breezes can develop during the day, and the UHI phenomenon is more pronounced at night [27]. Our study period (April 2016) was within the inter-monsoon season and experienced high temperatures for consecutive days (i.e., heat waves) [30].

2.2. Data

2.2.1. AH Data

The AH data were obtained from the comprehensive, spatiotemporally high-resolution AH database of Singapore developed by He et al. [48]. The total AH (QF) consists of AH from buildings (QB), AH from traffic (QV) and AH from human metabolism (QM), but QB does not include the AH from industrial buildings due to the lack of energy consumption data of industrial buildings. In this study, only data for April 2016 were used.
Figure 2 shows the daily variation in the hourly mean and maximum values for different components of AH in April 2016. From the hourly variation patterns of different components of AH, it can be judged that QF at night is mostly contributed by QB; while during the day, QB and QV are basically the same. In terms of mean values, QM is the smallest most of the time, with QV being larger than QM. The mean QF, QV and QM reach the peak at the same time at 1700 local time (LT), while the mean QB reaches the peak at 2100 LT. However, in terms of the maximum values, the maximum QV is much smaller than the maximum QM, which indicates that QV is very dispersed, whereas QM is relatively concentrated.
Figure 3 shows the spatial distribution of different AH components at the time of the maximum hourly mean in Figure 2a. The distributions of QF, QB and QM are consistent with the distribution of residential and industrial/commercial areas in Figure 1 (AH from the Western industrial area is not accounted for here), while QV is associated with the distribution of major roads and highways in Singapore. Since QM is small, its impact is negligible relative to the impacts of QV and QB, and so we only focus on QV and QB in this paper.

2.2.2. Meteorological Observation Data

The performance of the WRF model was evaluated by comparing it with the observation data from a dense network of 13 weather stations across Singapore. These stations are maintained by the Meteorological Service Singapore (MSS) under the National Environment Agency and provide real-time weather data on the MSS website (www.weather.gov.sg/home, accessed on 23 September 2023). In this study, only the data in April 2016 were used. The locations of the 13 weather stations are shown in Figure 1b. The meteorological observation data include 2-m air temperature, 2-m relative humidity (RH) and 10-m wind speed with a time resolution of 1 h.

2.3. WRF Model Setting

The WRF model V3.8.1 [49] coupled with SLUCM was employed in the present study, as this model has been successfully validated in our previous studies using near-surface air temperature, relative humidity, wind, rainfall and energy balance data [26,30,50]. The simulation starts at 0000 UTC, 31 March 2016, and ends at 1500 UTC, 30 April 2016, with the first 16 h used as spin-up time. The initial and boundary conditions were derived from 6-hourly, 0.25° resolution data from the NCEP Global Data Assimilation System (GDAS) [51].
The model was set up with a total of five one-way nested domains, and the extent of which is shown in Figure 1a. The horizontal resolutions (number of grids) of the five domains are 24.3 km (76 × 76), 8.1 km (79 × 91), 2.7 km (112 × 112), 0.9 km (112 × 112) and 0.3 km (211 × 130), respectively, and the temporal resolutions are 81, 27, 9, 3 and 1 s, respectively. Previous validated studies in Singapore using this high-resolution configuration have confirmed that the simulations are accurate with good agreement to the meteorological observation data [26,30].
The NOAH land surface model [52] provides surface sensible and latent heat fluxes and surface temperature as the lower boundary conditions for the atmospheric model. Other parameterization schemes employed in this study include the Mellor–Yamada–Janjić boundary layer scheme, the Goddard microphysical scheme, the RRTM scheme long-wave radiation scheme, the Dudhia short-wave radiation scheme, the Monin–Obukhov near-surface scheme and the Kain–Fritsch cumulus scheme (for d01 and d02 only) [30].
In order to study the different effects of AH from different sources on the urban environment, four sets of experiments were carried out, and the experimental settings are shown in Table 1.

3. WRF Model Validation

The model validation focuses on comparing the model-simulated 2-m air temperature, 2-m RH, and 10-m wind speed with the meteorological observation data. According to Tewari et al. [53], the weather stations can be classified either as urban stations or rural stations. S24, S42, S44, S50, S104, S109, S111, S115 and S116 are urban stations (indicated as “(U)” in Figure 4, Figure 5 and Figure 6), while S106, S108, S121 and S122 are rural stations (indicated as “(R)” in Figure 4, Figure 5 and Figure 6).

3.1. 2-m Temperature

Figure 4 compares the simulated 2-m air temperature from Case 2 to the observation data, with the solid blue line representing the simulated values, the black dots representing the observed values and the black error bars representing ±1 standard deviation. Figure 4 shows that the simulated results at seven stations (S44, S50, S104, S106, S109, S121 and S122) can largely match the observations, while the simulated results at another five stations (S24, S43, S111, S115 and S116) match the observations from 1000 to 2100 LT but are lower than the observations at other times, and the simulated results at S108 are consistent with observations only from 1500 to 2000 LT but are significantly lower at all the other times. Overall, the simulated temperature variations in all stations capture the diurnal patterns well. The underestimation of temperature at night and in the early morning may be due to the simplistic representation of urban buildings in the SLUCM, which leads to the underestimation of the building’s heat storage capacity in daytime and the heat release at night.

3.2. 2-m Relative Humidity

Figure 5 compares the simulated 2-m RH from Case 2 to the observation data, with the elements in the figure having the same meaning as in Figure 4. The observed 2-m RH is only available for 10 stations (fraction of missing data < 30%). The simulated results of RH are consistent with observations at S24, S43, S109, S111 and S116: underestimated at night at S104 and S106, overestimated at night at S108 and S115 and underestimated in the afternoon at S50. The reason for the underestimation may be that the WRF model assumes the entire vegetation cover of the urban grid to be grassland, ignoring the contribution from trees [29] and vegetation components with high uncertainty in soil moisture, as well as neglecting the latent heat sources of AH.

3.3. 10-m Wind Speed

Figure 6 compares the simulated 10-m wind speed from Case 2 to the observation data, with the elements in the figure having the same meaning as in Figure 4. The simulated wind speeds only match the observations at S24, S104 and S116, while the simulated wind speeds at the other stations are overestimated, except for S106, where the wind speeds are underestimated in the early morning. Overall, the simulated results for wind speed deviate significantly from the observations. The reasons may be because of the poor representation of rural aerodynamic characteristics. The results show that wind speeds at urban stations are closer to observations than at rural stations, suggesting that wind resistance is better estimated in urban areas than in rural areas.

3.4. Quantitative Error Analysis

Figure 4, Figure 5 and Figure 6 qualitatively compare the simulated results with observation data. To quantify the errors between the simulated results with observation data, the mean bias error (MBE), mean absolute error (MAE) and root mean square error (RMSE) were calculated using the following equations, where x i and y i represent the simulated and observed values for each station at the same time, respectively:
M B E = ∑ i = 1 n ( x i − y i ) n
M A E = ∑ i = 1 n | x i − y i | n
R M S E = 1 n ∑ i = 1 n ( x i − y i ) 2
The statistical analysis of observations compared with Case 2 simulation is shown in Table 2. Note that S44, S121 and S122 have more than 30% missing values of RH and are therefore excluded in the comparison. The MBEs show that the temperature and RH are underestimated, while the wind speeds are overestimated. The air temperature MAEs of urban stations are 1.32–1.74 °C, which are smaller than those of rural stations (1.25–2.16 °C), and the RMSEs of urban stations (1.73–1.93 °C) are also lower than those of rural stations (1.62–2.43 °C). The RH MAEs of urban stations (6.10–10.84%) are greater than those of rural stations (7.57–7.94%), and the RMSEs of urban stations (8.53–14.03%) are also greater than those of rural stations (9.37–10.35%). The wind speed MAEs of urban stations (1.08–1.96 m s−1) are smaller than those of rural stations (1.35–2.34 m s−1), and the RMSEs of urban stations (1.40–2.35 m s−1) are also smaller than those of rural stations (1.87–2.99 m s−1). Table 2 shows that S108 and S115 have large errors, presumably due to their locations, as S108 is close to a highway and S115 is close to a power station. Except for these two stations, the simulation results of other stations have small errors and are in good agreement with the observation data. Therefore, the WRF model is overall considered to be satisfactory.
When compared with our previous study using AH data based on the “look-up table” method [30], the present study shows some improvements in RMSE. The urban station (S44) has an RMSE of 1.89 °C in temperature and 1.46 m s−1 in wind speed, comparable with that of 1.6 °C in temperature and 1.6 m s−1 in wind speed [30]. On the other hand, the rural station (S106) exhibits an RMSE of 1.62 °C in temperature and 1.87 m s−1 in wind speed, slightly improved from that of 1.8 °C in temperature and 1.9 m s−1 in wind speed [30]. It is interesting to note that using gridded AH not only favorably impacts air temperature, but also improves the performance of wind speed. This is consistent with observations in previous studies [33].

4. Results and Discussion

4.1. The Effect of AH on Temperature

In order to investigate the effect of different sources of AH on temperature in each grid, we analyzed the differences between each cases and Case 1 at each hour throughout the entire simulated period. The differences of 2-m air temperature between the simulated results of Case 1 and Case 2 represent the effect of QF. The effects of QB and QV were calculated in the same way. The largest temperature increase in each grid in April 2016 is displayed in Figure 7. It can be seen that the largest temperature increases by different AH components are 1–2 °C in nearly 60% of the grids, and the largest temperature increases are less than 3 °C in more than 97% of the grids. These magnitudes of increase in temperature are consistent with previous studies focusing on Singapore [41,54]. Taking the spatial average over Singapore, the largest temperature increases caused by QF, QB and QV are 1.44 °C, 1.44 °C and 1.35 °C, respectively. The impact of QF, QB and QV on local temperature could be different, as the effect of QF is not a simple addition of the effects of QB, QV and QM. In other words, the impacts of individual components of AH cannot be estimated by taking their differences with QF. The largest temperature increase caused by only QB or QV in some grids may exceed that of QF, which will be further analyzed in Section 4.4.
While the largest temperature increase indicates the highest impact of each component of AH, it is also necessary to study the average temperature increase as an indication of the overall average impact of different components of AH. The monthly-averaged diurnal cycles of hourly temperatures in April 2016 were first calculated for all cases. The effects of AH were then estimated by taking the differences in the diurnal cycles of hourly temperatures between each cases and Case 1. The differences between the simulated results of Case 1 and Case 2 represent the effect of QF. The effects of QB and QV were calculated in the same way. Next, the largest differences in the diurnal cycles of hourly temperatures in each grid, as well as the corresponding times (in hour LT) where these largest differences occurred, were recorded. The spatial and temporal distributions of the largest temperature increases in the monthly-averaged diurnal cycles of hourly temperatures are shown in Figure 8. Figure 8(a1–a3) show that the main impacted areas of AH are in the southeast of Singapore, and the spatial distribution of the impact of QB is concentrated in areas with high residential density, while the impact of QV is distributed along the highways, consistent with the trends reported in Singh et al. [41] and Mussetti et al. [55]. QF causes the largest temperature increase of over 0.3 °C in 23.9% of the grids, where some grids achieve the largest temperature increase of 1.7 °C; QB causes the largest temperature increase of over 0.3 °C in 3.96% of the grids, where some grids achieve the largest temperature increase of 1.5 °C; and QV causes the largest temperature increase of over 0.3 °C in 1.54% of the grids, where some grids achieve the largest temperature increase of 0.5 °C. Figure 8(b1–b3) plot the time (in hour LT) when the largest temperature increases are obtained. It can be seen that the time of the largest temperature increase for both QF and QV is concentrated at 0800 LT, which takes up 43% of the grids in Figure 8(b1) and 33% in Figure 8(b3); the time corresponding to the largest temperature increase due to QB is more evenly distributed, while the effects of QV are primarily in the morning and the afternoon around the peak traffic hours. These results are expected, as during the daytime, solar radiation is the dominant heat source and AH will only cause large temperature increases outside of the daytime (from sunset to sunrise) [56].

4.2. The Effect of AH on Sensible Heat Flux

The spatial distributions of the differences in the sensible heat fluxes between Case 2, Case 3, Case 4 and Case 1 at 0200 LT and 1400 LT for each grid are shown in Figure 9. These two times were selected because they are representative of nighttime and daytime, respectively. It can be seen that the impacts of QF and QB on the sensible heat flux at 0200 LT are similar, reaching above 20 Wm−2 in the southeast urban area, which is due to the AH from air conditioners, while the impact of QV is negligible at the same time. This is expected, as the traffic volume is very low at this time. At 1400 LT, the impact of QB and QV on the sensible heat flux in the southeast urban area is mainly in the range of 20–30 Wm−2, while the impact of QF is roughly equivalent to the addition of QB and QV, and exceeds 30 Wm−2 in the urban area. Overall, we can see that the affected areas are concentrated in the urban areas at 0200 LT, and the affected areas become larger at 1400 LT. This is possibly due to the lower wind speed at night, which is not conducive for the dispersion of AH, whereas during daytime, the higher wind speed is able to disperse AH to a larger area [57,58]. As sensible heat flux and its dispersion are directly linked to UHI intensity, UHI mitigation should focus not only on AH reduction, but also its dispersion. For example, building permeability or building porosity [59,60] can be increased in urban planning to improve the ventilation in urban environments, allowing for more effective dispersion of AH.

4.3. The Effect of AH on the Planetary Boundary Layer Height

The spatial distributions of differences in the planetary boundary layer height between Case 2, Case 3, Case 4 and Case 1 at 0200 LT and 1400 LT are shown in Figure 10. The effect of AH on the planetary boundary layer height is similar to its effect on the sensible heat flux. At 0200 LT, QF is mainly contributed by QB, so that the affected area is similar to that of QB, which is mainly in the southeast urban area, and the increased planetary boundary layer height is 20–40 m. The effects of QV on the planetary boundary layer height are small, only reaching 20 m in a few grids. At 1400 LT, AH can raise the planetary boundary layer height by more than 200 m in very few grids and by 50–150 m in most areas, and there is little difference in the effects of different AH components. The result is consistent with the literature. For example, Xie et al. [61] found that AH can increase the planetary boundary layer height in Shanghai, China, by up to 160 m, while Liu et al. [62] also found a similar increase in Beijing, China, with increased AH.

4.4. Local Circulation and Urban Heat Island Circulation

Complex local circulations (sea/land breezes) often develop in Singapore because of differences in the specific heat capacity between the land and the sea. These local circulation currents are an important mechanism for the transport of air pollutants in coastal areas. The sea breeze usually forms in the afternoon, while the land breeze mainly forms at night [26]. UHI circulations are small circulations between the urban and rural area, which are caused by the UHI phenomenon and appear as air rises in urban areas and falls in rural areas [63].

4.4.1. Local Circulation

The spatial distributions of the 10-m wind speed and direction for the four cases at 0200 LT and 1400 LT are given in Figure 11, with the colored contours showing the 2-m air temperature at the same time. It is evident that the wind at 0200 LT blows mainly from land to sea, with the wind speed being about 2 m s−1. At 1400 LT, the wind blows mainly from sea to land, with the wind speed being about 4 m s−1, and the wind speed decreases as the sea breeze penetrates deeper into the land, while there is a convergence of wind in the center of the island. By comparing different cases in Figure 11, it is found that the temperature at 0200 LT changes significantly after adding different AH components, and the most obvious nighttime UHI phenomena are observed in the southern part of Singapore in both Case 2 and Case 3. Nevertheless, the AH has very little effect on the 10-m wind speed and temperature at 1400 LT, as neither the wind nor the temperature are significantly different.

4.4.2. Urban Heat Island Circulation

The UHI circulations can be visualized with vertical winds. Figure 12 plots the vertical wind speeds for the four cases at 1400 LT at the location along 103.85° E. The vector arrows represent the wind direction and speed of the wind, and the colored contours represent the vertical wind speed, with positive values being upward. It can be seen that at 1400 LT, there is a significant updraft in the UHI circulations near 1.32° N, 1.36° N and 1.43° N, with updraft speeds exceeding 0.4 ms−1; furthermore, significant downdrafts exist near 1.35° N, 1.40° N and 1.46° N, with downdraft speeds being approximately 0.3 ms−1. The results of the four cases indicate that there is essentially no obvious difference among all of the cases in terms of horizontal wind speeds, and the effects of different components of AH on the vertical wind speeds are different, causing different strengths in UHI circulations.
Figure 13 shows the profiles of the difference in vertical wind speeds between Case 2, Case 3, Case 4 and Case 1 at 103.85° E at 1400 LT, with the elements in the figure having the same meaning as in Figure 12. From Figure 13a, it can be seen that QF plays a significant role in enhancing the updraft around 1.35° N, 1.37° N and 1.42° N, and the maximum increases in vertical wind speed are all over 0.2 m s−1; at 1.32° N, 1.36° N and 1.45° N, there are weaker effects on the updraft. Figure 13b shows that QB has relatively lower enhancement on the updraft, but significantly weakens the updraft near 1.36° N. Figure 13c indicates that QV enhances the vertical updraft mainly near 1.37° N, with an increase of less than 0.3 ms−1, while weakening the updraft near 1.32° N and enhancing the downdraft near 1.35° N.
Combining the results in this section with the effect of AH on temperature in Section 4.1, it can be speculated that when only QB or QV is added to the WRF model, the enhancement effect on the updrafts in urban areas is weaker and the AH will stay near the ground. As QF will significantly enhance the upward spread of AH, the vertical wind transports the AH away from the surface. As a result, the effect of QB or QV on local temperature is stronger than that of QF in some grids, while the areas impacted by QF are significantly larger than those impacted by QB and QV alone, since the UHI circulations will bring the transported AH back and heat up the surface in surrounding areas. Since the effects of QF may not be the sum of the effects from individual components of AH, care should be taken while estimating the net effect of these individual components, as such an approach may lead to inaccurate conclusions.

5. Conclusions

This paper employed the coupled WRF/SLUCM model to simulate the urban environment of Singapore in April 2016 by incorporating high-spatial–temporal-resolution gridded anthropogenic heat (AH) data to account for heterogeneity in urban environments. The results were compared with observations from 13 weather stations across Singapore to validate the WRF model. The effects of different components of AH, namely the total AH (QF), AH from buildings (QB) and AH from traffic (QV), on the urban environment were analyzed. The conclusions are as follows:
(1)
The results of 2-m temperature and 2-m relative humidity are consistent with the observation data from weather stations, but there is a larger bias of 10-m wind speed.
(2)
QF causes the largest temperature increase of over 0.3 °C in 23.9% of the grids, where some grids exhibit the largest temperature increase of 1.7 °C; QB causes the largest temperature increase of over 0.3 °C in 3.96% of the grids, where some grids exhibit the largest temperature increase of 1.5 °C; and QV causes the largest temperature increase of over 0.3 °C in 1.54% of the grids, where some grids exhibit the largest temperature increase of 0.5 °C.
(3)
The effects of AH on sensible heat fluxes and the planetary boundary layer height are generally consistent. Both QF and QB increase the sensible heat flux by about 20 Wm−2 and the planetary boundary layer height by 20–40 m in urban areas at night, while the effects of QV are small. In the daytime, QF increases the sensible heat flux in urban areas by more than 30 Wm−2, with QB and QV both contributing 20–30 Wm−2, and the effects on the planetary boundary layer height by different AH components are quite similar.
(4)
The effect of AH on the local circulations in Singapore is very small, while the effect on the UHI circulations is more pronounced. AH significantly enhances the vertical updrafts in urban areas. With little change in horizontal wind speed, the effects of QB or QV on the urban environment can be greater than that of QF in some grids.
Many existing studies of AH have focused on AH from buildings. The present study demonstrates that besides AH from buildings, AH from traffic can also contribute significantly to temperature changes in urban areas and therefore cannot be ignored. Due to the nonlinear interaction between different AH components, the sum of the effects caused by individual AH components may exceed that caused by the total AH in some areas. This finding calls for comprehensive mitigation measures of AH by not only focusing on land use type but also on the contribution of individual AH components, in order to ameliorate the impacts of urban overheating.
There are several limitations in the present study. First, the AH data used in this paper did not include AH emissions from industrial areas due to the unavailability of data, resulting in a low simulated temperature in the industrial areas. Second, the contribution of AH from human metabolism was small and its effect was not investigated separately. This may not be the case and needs to be further studied. Third, we employed the single-layer urban canopy model (SLUCM) as it had been validated in our previous studies. Although not guaranteed to perform better than the SLUCM [63], the multi-layer urban canopy model allows for higher resolution within the urban canopy layer and has the potential to improve the simulation results.

Author Contributions

Conceptualization, A.W., X.-X.L. and L.W.C.; methodology, A.W., X.-X.L. and R.X.; validation, A.W. and X.-X.L.; formal analysis, A.W., X.-X.L. and L.W.C.; investigation, A.W. and R.X.; writing—original draft preparation, A.W., X.-X.L. and L.W.C.; writing—review and editing, A.W., X.-X.L. and L.W.C.; visualization, A.W. and R.X.; supervision, X.-X.L.; project administration, X.-X.L.; funding acquisition, X.-X.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No. 42088101), Guangdong Basic and Applied Basic Research Foundation (Nos. 2021A1515011890 and 2021B0301030007) and the National Research Foundation Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program and Intra-CREATE Seed Grant (NRF2018-ITS003-022).

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. (a) WRF simulation domain settings. (b) Land use/land cover of Singapore and the spatial distribution of weather stations in Singapore.
Figure 1. (a) WRF simulation domain settings. (b) Land use/land cover of Singapore and the spatial distribution of weather stations in Singapore.
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Figure 2. Daily variation in (a) hourly mean values and (b) hourly maximum values of different AH components of Singapore in April 2016.
Figure 2. Daily variation in (a) hourly mean values and (b) hourly maximum values of different AH components of Singapore in April 2016.
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Figure 3. Spatial distribution of different components of AH of Singapore in April 2016 at the time of the maximum hourly mean.
Figure 3. Spatial distribution of different components of AH of Singapore in April 2016 at the time of the maximum hourly mean.
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Figure 4. Model validation of 2-m air temperature. “(U)” represents urban stations while “(R)” represents rural stations.
Figure 4. Model validation of 2-m air temperature. “(U)” represents urban stations while “(R)” represents rural stations.
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Figure 5. Model validation of 2-m RH. “(U)” represents urban stations while “(R)” represents rural stations.
Figure 5. Model validation of 2-m RH. “(U)” represents urban stations while “(R)” represents rural stations.
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Figure 6. Model validation of 10-m wind speed. “(U)” represents urban stations while “(R)” represents rural stations.
Figure 6. Model validation of 10-m wind speed. “(U)” represents urban stations while “(R)” represents rural stations.
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Figure 7. Spatial distribution of the largest temperature increases due to different components of AH. (a) Effect of QF, (b) effect of QB, (c) effect of QV.
Figure 7. Spatial distribution of the largest temperature increases due to different components of AH. (a) Effect of QF, (b) effect of QB, (c) effect of QV.
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Figure 8. (a1–a3) Spatial distribution of the largest temperature increases in the monthly-averaged diurnal profiles due to different components of AH. (b1–b3) Time of the largest temperature increases due to different components of AH. (a1,b1) Effect of QF, (a2,b2) effect of QB, (a3,b3) effect of Qv.
Figure 8. (a1–a3) Spatial distribution of the largest temperature increases in the monthly-averaged diurnal profiles due to different components of AH. (b1–b3) Time of the largest temperature increases due to different components of AH. (a1,b1) Effect of QF, (a2,b2) effect of QB, (a3,b3) effect of Qv.
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Figure 9. The impact of AH on sensible heat flux (HFX) at 0200 LT (a,c,e) and 1400 LT (b,d,f). (a,b) Effect of QF, (c,d) effect of QB, (e,f) effect of QV.
Figure 9. The impact of AH on sensible heat flux (HFX) at 0200 LT (a,c,e) and 1400 LT (b,d,f). (a,b) Effect of QF, (c,d) effect of QB, (e,f) effect of QV.
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Figure 10. The impact of AH on the planetary boundary layer height (PBLH) at 0200 LT (a,c,e) and 1400 LT (b,d,f). (a,b) Effect of QF, (c,d) effect of QB, (e,f) effect of QV.
Figure 10. The impact of AH on the planetary boundary layer height (PBLH) at 0200 LT (a,c,e) and 1400 LT (b,d,f). (a,b) Effect of QF, (c,d) effect of QB, (e,f) effect of QV.
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Figure 11. Spatial distribution of 10-m wind speed and 2-m air temperature at 0200 and 1400 LT.
Figure 11. Spatial distribution of 10-m wind speed and 2-m air temperature at 0200 and 1400 LT.
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Figure 12. Vertical wind speeds along 103.85°E at 1400 LT for different cases, along with the wind vector in each case. The thick black line along the x axis indicates the extent of Singapore.
Figure 12. Vertical wind speeds along 103.85°E at 1400 LT for different cases, along with the wind vector in each case. The thick black line along the x axis indicates the extent of Singapore.
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Figure 13. Profiles of vertical wind speed differences along 103.85° E at 1400 LT, along with the wind vector in each case. (a) Effect of QF, (b) effect of QB, (c) effect of Qv.
Figure 13. Profiles of vertical wind speed differences along 103.85° E at 1400 LT, along with the wind vector in each case. (a) Effect of QF, (b) effect of QB, (c) effect of Qv.
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Table 1. WRF mode simulation scheme settings.
Table 1. WRF mode simulation scheme settings.
QBQVQM
Case 1×××
Case 2√√√
Case 3√××
Case 4×√×
Table 2. Statistical analysis of observations compared with Case 2 simulation. “(U)” represents urban stations while “(R)” represents rural stations.
Table 2. Statistical analysis of observations compared with Case 2 simulation. “(U)” represents urban stations while “(R)” represents rural stations.
StationTemperature (°C)RH (%)Wind Speed (m s−1)
MBEMAERMSEMBEMAERMSEMBEMAERMSE
S24 (U)−0.741.381.73−2.456.128.570.081.081.40
S43 (U)−0.831.521.83−0.946.108.530.841.191.50
S44 (U)0.171.371.89---0.801.161.46
S50 (U)0.081.321.82−5.937.6510.891.171.371.74
S104 (U)−0.281.431.88−9.8710.8414.030.091.111.45
S106 (R)−0.381.251.62−6.747.9410.350.531.351.87
S108 (R)−1.962.162.435.257.579.372.222.342.99
S109 (U)−0.091.431.91−1.797.3810.551.071.241.64
S111 (U)−0.201.401.78−1.077.309.441.301.632.06
S115 (U)−1.111.631.931.447.348.991.711.962.35
S116 (U)−1.031.491.80−5.437.509.780.411.161.54
S121 (R)−0.321.501.94---1.561.682.04
S122 (R)−0.501.742.15---1.411.531.95
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Wang, A.; Li, X.-X.; Xin, R.; Chew, L.W. Impact of Anthropogenic Heat on Urban Environment: A Case Study of Singapore with High-Resolution Gridded Data. Atmosphere 2023, 14, 1499. https://doi.org/10.3390/atmos14101499

AMA Style

Wang A, Li X-X, Xin R, Chew LW. Impact of Anthropogenic Heat on Urban Environment: A Case Study of Singapore with High-Resolution Gridded Data. Atmosphere. 2023; 14(10):1499. https://doi.org/10.3390/atmos14101499

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Wang, Ao, Xian-Xiang Li, Rui Xin, and Lup Wai Chew. 2023. "Impact of Anthropogenic Heat on Urban Environment: A Case Study of Singapore with High-Resolution Gridded Data" Atmosphere 14, no. 10: 1499. https://doi.org/10.3390/atmos14101499

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