Journal of Information Technology and Computer Science Volume 7, Number 2, August 2022, pp. 160-171
Journal Homepage: www.jitecs.ub.ac.id
The Dynamics of Urban Heat Island and
Anthropogenic Emissions in Bekasi before and during the COVID-19 Pandemic using
Landsat 8 and Sentinel-5P
Ramanatalia Parhusip*1, Iqbal Putut Ash Shidiq 2, Jarot Mulyo Semedi 3
1,2,3University of Indonesia,Depok
{1ramanatalia.parhusip, 2iqbalputut, 3jarot.mulyo}@ui.ac.id
*Corresponding Author
Received 26 August 2022; accepted 29 August 2022
Abstract. The rise in temperature in urban areas resulting in UHI formation is thought to be significantly driven by anthropogenic emissions due to human activities. During the COVID-19 pandemic, the Indonesian government issued the Large-Scale Social Restrictions (PSBB) and Community Activities Restrictions Enforcement (PPKM) policy. Bekasi Regency is part of the Jabodetabek megapolitan that applied strict PSBB and PPKM treatment during the pandemic. The decreasing industrial activity and traffic volume are expected to reduce air pollutants and thermal radiation. The research method uses processed satellite imagery from Sentinel 5P to get anthropogenic emissions concentrations (NO2 and SO2) and Landsat 8 to get land surface temperature (LST). The results showed that Bekasi had a slight decrease in the concentration of anthropogenic emissions during COVID-19 pandemic 2020, then increased during COVID-19 pandemic 2021. The areas affected by urban heat islands increased steadily during the COVID-19 pandemic. Therefore, when the concentration of anthropogenic emissions rises, the UHI ascends.
Keywords: Anthropogenic Emissions, Urban Heat Island, Landsat Imagery, Sentinel-5P, Bekasi
1 Introduction
The increase in COVID-19 cases in West Java, especially Bekasi, has caused the government to implement the PSBB dan PPKM. As reported by [1], there have been 46 positive cases since 13 April 2020. To reduce the COVID-19 cases, the Bekasi government implemented PSBB regulations, which began on 15 April – 12 May 2020 [2]. During the pandemic COVID-19 that occurred in Bekasi, the government established two social restriction policies, namely PSBB in 2020 and PPKM in 2021 [22]. Based on its implementation, PSBB implements work from home 100% for all economic driving sectors, while PPKM provides leeway for essential and critical sectors to work from the office 100% [23]. Bekasi regency is one of the buffer areas of the state capital and one of the largest industrial areas in West Java [3]. The Ministry of Industry (2016) noted that about 9,496 ha of industrial area and 4,000 factories operate in the Bekasi Regency [4].
According to Ferdinand (2016) and Wakhid (2018), 70-80% of urban air pollution is caused by vehicle gas emissions, where waste energy is released into the
Ramanatalia., et al., Journal of Information Tecnology and Computer Science: ... 161 atmosphere, thus warming the temperature in the urban environment [5][6].
Anthropogenic heat resulting from energy consumption and air pollution is a driving factor in the rising temperature in urban areas [18][7]. Air pollution comes from particulates, water vapor, industrial emissions, domestic emissions, and vehicles. This alters the urban net radiation wave budget by reducing the incidence flux of shortwave radiation, re-emitting longwave radiation from the urban surface downwards to where the ground holds the radiation, and absorbing longwave radiation from the urban surface, effectively warming the temperature [24]. The increasing urban temperature conditions in the surrounding area due to air pollution and anthropogenic heat from community activities such as mobility and vehicles influenced the formation of urban heat islands [7]. In general, Urban Heat Island (UHI) is often caused by land-use change [8][9], an increase in the number of vehicles and population [10][11], physical materials of urban buildings [12], industrial activity [13][14], urban activity [15]. Landsat 8 imagery is used to assist in detecting surface temperature distribution in relation to urban heat islands, as has been studied Landsat 8 data is used to calculate the LST, vegetation density index, building density index, as well as estimating the phenomenon of UHI in the semiarid region in Iran [12]. Sentinel-5P imagery is capable of multispatiotemporally estimating greenhouse gas emissions, such as in research in Gauteng Province and Jakarta, where anthropogenic emissions measurements using Sentinel-5P data [7][26].
The entry and exit of workers into the area, either by private or public vehicles, can trigger UHI formation [16]. The enactment of PSBB and PPKM decreased the use of vehicles and industrial processes so that anthropogenic heat and air pollution were reduced [17][7]. This restriction of community movement provides an opportunity to discuss the impact of reducing the UHI phenomenon due to the decrease in anthropogenic emission concentration in the Bekasi Regency. Based on this background, urban heat island research and anthropogenic emission dynamics before and during the COVID-19 pandemic in the Bekasi Regency are needed to understand its relationship.
2 Methods
2.1 Study Area
Bekasi Regency has a total area of 1,273.9 km2 and administratively consists of 23 subdistricts. Bekasi regency is one of Indonesia's largest industrial areas and part of the Jabodetabek area, where the industrial sector contributes significantly to the GDRB of Bekasi Regency. In addition, it is regulated in Perda Kabupaten Bekasi No. 12 Tahun 2011, approximately 23,437ha of Bekasi regency area intended for industrial areas.
Refer to the data of the Dinas Cipta Karya & Tata Ruang Kab. Bekasi, land use in the form of settlements is 26.98%, and industry is 8.28% of the total area of Bekasi Regency. In order to facilitate industrial and economic activities, transportation is essential and strategic, so it was recorded during the period 2006-2017, there was an increase in the volume of motor vehicles in Bekasi regency but not balanced with the increase in road length [3]. Such conditions significantly contribute to the waste of fuel.
2.2 Materials
In this study, UHI was obtained from interpreting the value of land surface temperature using the UHI threshold method. Meanwhile, the concentration parameters of air pollutants used are nitrogen dioxide and sulfur dioxide. Nitrogen dioxide represents emissions from gasoline-fueled motor vehicles, while sulfur dioxide is from industry and other diesel and sulfur-fueled vehicles. This study uses Landsat 8
162 Journal Volume 7, Number 2, August 2022, pp 160-171 OLI/TIRS and Sentinel-5P TROPOMI satellite imagery, divided into three periods of data; 16 April 2019 – 15 April 2020, 16 April 2020 – 15 April 2021, 16 April 2021 – 15 February 2022. All satellite imagery data were acquired and processed using Google Earth Engine (GEE). Landsat 8 data is used to calculate land surface temperature, vegetation density index, building density index, and estimate the UHI phenomenon in semi-arid areas in Iran [12]. The Sentinel-5P data can observe greenhouse gas emissions with multi-spatiotemporal. It is supported by the presence of a high-resolution spectrometer system operating in the ultraviolet to the shortwave infrared range with seven different spectral bands [26]. In addition, Sentinel-5P has a higher spatial resolution than other weather observation satellites, which is 0.01 degrees or about 1.11 km, with daily observations [20].
2.3 Methods
Data processing in this study utilizes cloud-based computing on the GEE platform. The method of interpreting Land Surface Temperature (LST) uses the brightness value. In this method, it is assumed that the water content in the atmosphere is constant for a small area, so the atmospheric conditions can be considered uniform, and the influence of atmospheric conditions on the radiance temperature is negligible [25]. Using the threshold method, LST from Landsat 8 imageries identifies the UHI phenomenon [18]. Calculation of the UHI threshold method with the formula in the following equation:
T > µ + 0,5 α (1)
0 < T ≤ µ + 0,5 α (2)
T = LST; µ = LST average value; α = LST standard deviation
UHI = Tmean – (µ + 0,5α) (3)
Tmean = LST (oC); µ = LST average; α = LST standard deviation
Positive temperature difference results represent UHI. Negative values represent non-UHI.
SO2 and NO2 emission data were acquired from Sentinel-5 satellite imageries using cloud-based computing on GEE. Computing is done to convert data from level 2 (L2) to level 3 (L3) so that the data is not aggregated on one granule [20] by selecting Sentinel-5P OFFL image dataset and NRTI SO2 with Channel 'troposheric_SO2_column_number_density'; and NO2 with Channel 'troposheric_NO2_column_number_ density' according to the period required, and calculation of the average value of the emissions of these gases.
Pearson product Moment (PPM) correlation test and simple linear regression were used to determine the anthropogenic emissions against urban heat islands. The level of significance used in this study is 95% (α = 0,05).
3 Results and Discussion
3.1 LST and Urban Heat Island
Fig. 1 shows the LST results for Bekasi Regency before and during the COVID- 19 pandemic. The LST indicated was increasing when the COVID-19 outbreak happens, especially in Tambun Selatan, Cikarang Barat, Cikarang Utara, Karang Bahagia, Kedung Waringin, Tarumajaya, Babelan dan Setu. Settlements and industrial areas dominate these areas. Table 1 indicated that the temperature of Bekasi was increasing. The average temperature increased by 0,6oC from 25,3oC to 25,9oC in 2020, then by 0,1oC from 25,9oC to 26oC in 2021. Fig. 1 shows that the temperature
Ramanatalia., et al., Journal of Information Tecnology and Computer Science: ... 163 distribution in the area in the three periods was mostly about 25-30oC (moderate).
Figure 1. Temporal Map of Land Surface Temperature
Table 1.Temporal LST Value
Years LST min LST max LST avg STD
2019 15 36.7 25.3 1.73
2020 14.3 35.3 25.9 2.16
2021 6.6 36.5 26 2.31
The transect lines A - B and C-D are drawn to obtain a more detailed LST distribution pattern. Points A, B, C, and D were selected based on the range of anthropogenic emission concentration values between high to low and the type of land use representing built-up areas, vegetation, and water bodies. Based on the transect line extraction results obtained by grouping surface temperature based on land use, where the temperature will rise if it is in areas with high to low concentrations of anthropogenic emissions and settlements, vacant land, and industry (Fig. 2). At the same time, the temperature will drop if it is in areas with low concentrations of anthropogenic emissions and paddy fields, vegetation, and water bodies. It can be seen that the high surface temperature is in the area of high concentration, and the use of land in the form of settlements and industries, such as the cross-section of the A-B axis at a distance of 0-10,000m indicates the surface temperature is between 26-35oC. Then, at a distance of 10,000 – 15,000m, the surface temperature drops because the concentration of emissions is low, and there are paddy fields. As for the C-D axis, at a distance of 0-5,000m, the surface temperature is between 24 - 28oC because it is in a low concentration area and paddy fields, water bodies, and vacant land. Then at a distance of 5,000 – 15,000m, the surface temperature rises between 26 – 35oC because of the high concentration of emissions at that distance and the presence of settlements, vacant land, and industry. Then, at 20,000m, the surface temperature drops due to regions with low emission concentrations and the presence of water bodies.
The UHI phenomenon is determined based on the calculation of LST. The results of UHI map during the observation period are overlaid to obtain the dynamics of UHI in Bekasi before and during COVID-19 pandemic. Based on the results of processing, a classification scheme of categories of UHI dynamics is obtained, which is divided into 6 (six) classes. Fig. 3 shows that Bekasi has a UHI which is formed by
164 Journal Volume 7, Number 2, August 2022, pp 160-171 14,2% of the total area between 2019-2021. Some areas of new UHI are present in the settlements land use such as in the Tarumajaya, Tambun Utara, Tambun Selatan, Cikarang Barat, Cikarang Utara, Cikarang Pusat, Serang Baru, Setu, and Cibarusah Subdistricts.
Figure 2. Transect of LST
Figure 3. Temporal Map of Urban Heat Island
In addition, Bekasi also has diminishing UHI. The diminishing UHI occurred in industries, settlements and roads, also ponds, about 9,3% of the area of diminishing UHI. The consecutive UHI are appears during the observation period were found in areas with built-up land cover such as in Tambun Selatan, Cikarang Barat, Cikarang Utara, Cikarang Selatan, Setu, and Sukatani Subdistricts. Meanwhile, the oscillating UHI also covers part of the area in Bekasi with built-up and vegetated land cover such as in Cabangbungin, Pebayuran, Sukawangi, Tambelang, Sukakarya, Cikarang Timur and Serang Baru Subdistricts, there are oscillating UHI covering areas of 7,5% of the total area (Fig. 3).
3.2 NO2 Distributions over Bekasi Regency
Fig. 4 shows the NO2 distributions before and during the COVID-19 pandemic.
Ramanatalia., et al., Journal of Information Tecnology and Computer Science: ... 165 This area's primary sources of NO2 are motor vehicle emissions and industrial processing. Before the COVID-19 pandemic, the study area was dominated by low to moderate concentrations. Low NO2 concentration is distributed in the vegetated land use and ponds, such as in the northern, eastern, and southernmost parts of the Bekasi Regency. Meanwhile, moderate to high concentrations of NO2 tend to distribute on built-up land use (urban settlements, industrial areas, and highways), such as in the sub- districts of Tambun Selatan, Cikarang Barat, Setu, and Cikarang Selatan. The image processing results show that NO2 concentration ranges between 4 x 10-5 to 22,4 x 10- 5 mol/m2, with a concentration average of 7,4 x 10-5 mol/m2 (Table 3).
Figure 4. NO2 Tropospheric Column and Distribution before and during the COVID-19 Pandemic
During the COVID-19 pandemic 2020, the concentration of NO2 was slightly decreased to 7,3 x 10-5 from 7,4 x 10-5 mol/m2, with a range of value between 4,43 x 10-5 to 15 x 10-5 mol/m2 (Table 3). Fig. 4 shows that the decrease in NO2 was evident in land-use areas in settlements, industries, and highways. Moderate to low concentrations of NO2 were dominating. It can be assumed that social restrictions influence the decrease in the concentration of NO2 in Bekasi. Then during the PPKM 2021, there is a slight increase in the NO2 total column densities value in Bekasi. The concentration values are in the range between 4,43 x 10-5 to 20 x 10-5 mol/m2, with an average of 7,9 x 10-5 mol/m2. Fig. 4 shows that Tambun Selatan, Cikarang Barat, and Setu are the main subdistricts in the distribution and column densities NO2. Those areas have high concentrations to moderate, caused by vehicle emissions, industrial and factories processing, and industrial areas containing power plants. The implementation of PPKM is given more loosely than PSBB 2020. In PPKM, work from home is only required for non-essential sectors, while important and critical sectors such as industry, transportation, and logistics are allowed to work from the office (WFO) 100%. So, some industries started to operate again and made high traffic again, and mobility increased compared to the previous year, increasing NO2 emissions and more emissions [7].
Table 3. Statistics for NO2, Before and During COVID-19 Pandemic Years NO2 min
(mol/m2)
NO2 max (mol/m2)
NO2 avg
(mol/m2) STD 2019 4,46 x 10-5 22,4 x 10-5 7,4 x 10-5 1,91 x 10-5 2020 4,43 x 10-5 15 x 10-5 7,3 x 10-5 1,6 x 10-5 2021 4,43 x 10-5 20 x 10-5 7,9 x 10-5 2 x 10-5
166 Journal Volume 7, Number 2, August 2022, pp 160-171 A-B and C-D transect lines are drawn to determine and obtain a more detailed NO2 distribution pattern (Fig. 5). If the average concentration of NO2 is high, the number of vehicles, industries, and households are many, and vice versa [21]. This is also seen in the A-B and C-D lines, where high NO2 concentrations are found in built- up land use (settlements and industries). Meanwhile, low NO2 concentration groupings are found in areas with land use still in the form of paddy fields and water bodies.
Figure 5. Transect of NO2
3.3 SO2 Distributions over Bekasi Regency
Fig. 6 shows the SO2 distributions before and during the COVID-19 pandemic in Bekasi. This area's primary sources of SO2 are industrial processing and coal-fired power stations. Before the COVID-19 pandemic, the SO2 hotspot was observed in the Cikarang Selatan subdistrict, where the primary sources of SO2 are processing industries and manufacturing. Moderate to low concentrations were dominating the study area. Before the COVID-19 pandemic, low to moderate concentrations are spread over the Bekasi Regency, especially in paddy fields and ponds. Meanwhile, high SO2
concentration is only found in Cikarang Selatan, where there is a large industrial area.
EIJP Industrial Estate which is dominated by large factories as the main source of SO2
comes from the processing and manufacturing industries. The image processing results show that SO2 concentration ranges between 4,66 x 10-6 to 575 x 10-6 mol/m2,with an average of 289,8 x 10-6 mol/m2 (Table 4).
Figure 6. SO2 Tropospheric Column and Distribution before and during the COVID-19 Pandemic
Table 4. Statistics for SO2, Before and During COVID-19 Pandemic
Ramanatalia., et al., Journal of Information Tecnology and Computer Science: ... 167
During the COVID-
19 pandemic, SO2
distribution and total
column densities were
noticeable in several subdistricts with industries and settlements land use such as in Setu, Tambun Selatan, Cikarang Barat, and Cikarang Selatan, Cibitung, and some parts of Serang Baru (Fig. 6). High to moderate concentration dominates the study area.
Distribution and total column densities of SO2 increased to range values between 1,79 x 10-6 to 850 x 10-6 mol/m2, with a average 425,9 x 10-6 mol/m2 (Table 4). Then during the PPKM 2021, the SO2 concentration increased again in range values of SO2 between 2,13 x 10-6 to 1087 x 10-6 mol/m2, with an average of 546,07 x 10-6 mol/m2. Easing regulations from PSBB to PPKM, coal-fired power stations (such as Babelan Plant, Bekasi Power, PLTU GUGUKOSINDO), and industries returned to full operation. The full operation of the power plant impacts the increase in SO2 payload in the atmosphere [7]. The high to moderate concentration of SO2 observed in industries and urban settlements land use such as in Cikarang Barat, Cibitung, Tambun Selatan, Babelan, Sukawangi, Tambelang, Cikarang Timur, Cikarang Selatan, and some part area of Sukatani (Fig. 6).
Figure 7. Transect of SO2
Before the COVID-19 pandemic, the distribution of SO2 detected by the dominant image was spread in areas with land use still in the form of vegetation and ponds, while in settlements and industrial areas distance from 0-10,000 m, the current SO2 concentration value was only about 0-250 x 10-6 (A - B axis). Then the concentration at a 5,000 – 25,000m is about 0 – 150 x 10-6 (C - D axis). Although social restrictions were imposed during the COVID-19 pandemic, the number of SO2 column densities increased. At a distance of 0-7,000 m (axis A-B), there is an increase in the concentration of SO2 in industrial areas, and it tends to fall when in residential areas.
On the C-D axis, at a distance of 15,000 - 20,000m, SO2 concentration rises and gradually falls in settlements and paddy fields (Fig. 7). Then, during the PPKM 2021, the concentration of SO2 tends to increase in land use in the form of industry and paddy fields. This indicates that human activities on industrial land use and paddy fields are fully restored.
3.4 Statistic Analysis of UHI and Anthropogenic Emissions
Anthropogenic emissions were represented by NO2 and SO2 parameters, while Years SO2 min
(mol/m2)
SO2 max (mol/m2)
SO2 avg (mol/m2) 2019 4,66 x 10-6 575 x 10-6 289,8 x 10-6 2020 1,79 x 10-6 850 x 10-6 425,9 x 10-6 2021 2,13 x 10-6 1087 x 10-6 546,07 x 10-6
168 Journal Volume 7, Number 2, August 2022, pp 160-171 urban heat islands were explicitly based on LST. Pearson Product Moment (PPM) correlation test was conducted to determine the association between anthropogenic emissions to UHI. The PPM correlation test measures the strength of association and influence between anthropogenic emissions (NO2 and SO2) to LST. Then, a linear regression test will prove whether anthropogenic emissions affect LST.
Table 5. PPM & Linear Regression Results for NO2 and LST Over Three Years Years/
Parameters NO2
p-value R R2 B
2019 3,7 x 10-8 0,271 0,073 33.247,046 2020 8,87 x 10-15 0,375 0,14 49.269,301 2021 4,22 x 10-28 0,512 0,262 56.381,057
The PPM correlation test results show a significant association between NO2 to LST for 2019 – 2021 (Table 5). The alternate hypothesis (H1) was tested that there is a significant correlation between NO2 and LST at a 95% confidence level (α = 0,05).
Simple linear regression is used to explain the correlation as formulated below:
(2019) y = 0,073 + 33.247,046x (4)
(2020) y = 0,14 + 49.269,301x (5)
(2021) y = 0,262 + 56.381,057x (6)
In 2019, the association between NO2 and LST was directly proportional and had a low association. It means that the higher the concentration of NO2, it can be increasing the LST. The concentration of NO2 affects 7,3% of LST, while the rest is influenced by other variables not studied. The regression shows that for every addition of 1 x 10-5 mol/m2 of NO2 concentration in 2019, the LST increases by 0,33oC. In 2020, the association between NO2 and LST was low and directly proportional. It means that the higher the concentration of NO2, it can be increasing the LST. The concentration of NO2
affects 14% of LST, while the rest is influenced by other variables not studied. The regression shows that for every addition of 1 x 10-5 mol/m2 of NO2 concentration in 2020, the LST increases by 0,49oC. In 2021, the association between NO2 and LST was moderate and directly proportional. It means that the higher the concentration of NO2, it can be increasing the LST. The concentration of NO2 affects 26,2% of LST, while the rest is influenced by other variables not studied. The regression shows that for every addition of 1 x 10-5 mol/m2 of NO2 concentration in 2021, the LST increases by 0,56oC.
Table 6. PPM & Linear Regression Results for SO2 and LST Over Three Years Years/
Parameters
NO2
p-value R R2 B
2019 4,8 x 10-17 0,403 0,162 4.685,370 2020 1,7 x 10-11 0,382 0,108 2.766,737 2021 3,95 x 10-57 0,687 0,472 4.294,037
The PPM correlation test results show a significant association between SO2 to LST for 2019 – 2021 (Table 6). The alternate hypothesis (H1) was tested that there is a significant correlation between SO2 and LST at a 95% confidence level (α = 0,05).
Simple linear regression is used to explain the correlation as formulated below:
(2019) y = 0,162 + 4.685,370x (7)
(2020) y = 0,108 + 2.766,737x (8)
(2021) y = 0,472 + 4.294,037x (9)
In 2019, the association between SO2 and LST was directly proportional and had
Ramanatalia., et al., Journal of Information Tecnology and Computer Science: ... 169 a moderate association. It means that the higher the concentration of SO2, it can be increasing the LST. The concentration of SO2 affects 16,2% of LST, while the rest is influenced by other variables not studied. The regression shows that for every addition of 1 x 10-5 mol/m2 of SO2 concentration in 2019, the LST increases by 0,04oC. In 2020, the association between SO2 and LST was directly proportional and had a low association. It means that the higher the concentration of SO2, it can be increasing the LST. The concentration of SO2 affects 10,8% of LST, while the rest is influenced by other variables that are not studied. The regression shows that for every addition of 1 x 10-5 mol/m2 of SO2 concentration in 2020, the LST increases by 0,03oC. In 2021, the association between SO2 to LST will have a strong association and be directly proportional. It means that the higher the concentration of SO2, it can be increasing the LST. The concentration of SO2 affects 47,2% of LST, while the rest is influenced by other variables not studied. The regression shows that for every addition of 1 x 10-5 mol/m2 of SO2 concentration in 2021, the LST increases by 0,04oC.
4 Conclusion
The phenomenon of urban heat islands in the Bekasi Regency before and during the COVID-19 pandemic tends to spread in areas where land use is in industries and settlements. Urban heat islands before and during the COVID-19 pandemic showed a pattern that tends to be the same as anthropogenic emissions. When the concentration of anthropogenic emissions increases, the land surface temperature increases, forming the UHI phenomenon. During the COVID-19 pandemic, the UHI phenomenon experienced an increase compared to before.
Anthropogenic emissions in the Bekasi Regency before and during the COVID- 19 pandemic with high to moderate concentrations tend to spread in areas where land use is in the form of industry and settlements. In contrast, anthropogenic emissions with low concentrations tend to spread in areas where land use is still in the form of rice fields and ponds. During the COVID-19 pandemic and the implementation of PSBB, anthropogenic emissions decreased by about 0.4% compared to the conditions before the COVID-19 pandemic. Then during the PPKM, anthropogenic emissions again increased in concentration by about 2.7%.
Furthermore, we found a positive relationship between anthropogenic emissions and UHI. The results of the statistics test show that there is an association between anthropogenic emissions to urban heat islands. The anthropogenic emissions tended to have moderate relationships with UHI. The higher concentration of anthropogenic emissions can increase urban heat island.
Acknowledgments. This study is supported by Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, under the Publication with International Index (PUTI Q2) grant scheme no. NKB- 652/UN2.RST/HKP.05.00/2022.
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