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Kusak L, Kucukali UF. Investigating the relationship between COVID-19 shutdown and land surface temperature on the Anatolian side of Istanbul using large architectural impermeable surfaces. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2023:1-38. [PMID: 37362976 PMCID: PMC10221754 DOI: 10.1007/s10668-023-03397-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 05/18/2023] [Indexed: 06/28/2023]
Abstract
Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat 8 OLI/TIRS imagery images over the years 2016-2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures.
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Affiliation(s)
- Lutfiye Kusak
- Department of Geomatics Engineering, Mersin University, Mersin, Turkey
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2
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Lyu F, Wang S, Han SY, Catlett C, Wang S. An integrated cyberGIS and machine learning framework for fine-scale prediction of Urban Heat Island using satellite remote sensing and urban sensor network data. URBAN INFORMATICS 2022; 1:6. [PMID: 37522136 PMCID: PMC9458483 DOI: 10.1007/s44212-022-00002-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 05/01/2022] [Accepted: 06/22/2022] [Indexed: 11/29/2022]
Abstract
Due to climate change and rapid urbanization, Urban Heat Island (UHI), featuring significantly higher temperature in metropolitan areas than surrounding areas, has caused negative impacts on urban communities. Temporal granularity is often limited in UHI studies based on satellite remote sensing data that typically has multi-day frequency coverage of a particular urban area. This low temporal frequency has restricted the development of models for predicting UHI. To resolve this limitation, this study has developed a cyber-based geographic information science and systems (cyberGIS) framework encompassing multiple machine learning models for predicting UHI with high-frequency urban sensor network data combined with remote sensing data focused on Chicago, Illinois, from 2018 to 2020. Enabled by rapid advances in urban sensor network technologies and high-performance computing, this framework is designed to predict UHI in Chicago with fine spatiotemporal granularity based on environmental data collected with the Array of Things (AoT) urban sensor network and Landsat-8 remote sensing imagery. Our computational experiments revealed that a random forest regression (RFR) model outperforms other models with the prediction accuracy of 0.45 degree Celsius in 2020 and 0.8 degree Celsius in 2018 and 2019 with mean absolute error as the evaluation metric. Humidity, distance to geographic center, and PM2.5 concentration are identified as important factors contributing to the model performance. Furthermore, we estimate UHI in Chicago with 10-min temporal frequency and 1-km spatial resolution on the hottest day in 2018. It is demonstrated that the RFR model can accurately predict UHI at fine spatiotemporal scales with high-frequency urban sensor network data integrated with satellite remote sensing data.
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Affiliation(s)
- Fangzheng Lyu
- cyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Shaohua Wang
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094 China
- International Research Center of Big Data for Sustainable Development Goals, Beijing, 100094 China
| | - Su Yeon Han
- cyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Charlie Catlett
- Computing, Environment, and Life Sciences, Argonne National Laboratory, Chicago, IL USA
| | - Shaowen Wang
- cyberGIS Center for Advanced Digital and Spatial Studies, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Urbana, IL USA
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The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14102327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.
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Ulpiani G, Duhirwe PN, Yun GY, Lipson MJ. Meteorological influence on forecasting urban pollutants: Long-term predictability versus extreme events in a spatially heterogeneous urban ecosystem. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 814:152537. [PMID: 34942240 DOI: 10.1016/j.scitotenv.2021.152537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/29/2021] [Accepted: 12/15/2021] [Indexed: 06/14/2023]
Abstract
With the escalation of heat- and pollution-related threats in cities across the globe, timely counteractions and emergency procedures are vital, which calls for accurate co-prediction of urban heat and air quality under both standard conditions and under extreme events. In this study, we used historical hourly data recorded at 9 sites across the Sydney metropolitan area to test the performance of long short-term memory (LSTM) forecasting architectures in predicting 5 urban pollutants based on different combinations of meteorological inputs and considering standard, bushfire, and pandemic lockdown conditions. We demonstrate that, in most cases and even in a fast-growing city, there is no significant benefit achieved by including extra predictors to temperature and humidity, when adequate forecasting techniques capable of learning long-term dependencies are used. Further, in agreement with previous studies, we provide evidence of ozone's higher responsiveness to all weather parameters and thus enhanced predictability and PM10's lower predictability as compared to all other considered urban pollutants. The prediction accuracy tends to be comparable between standard conditions and bushfire events. However, the predictability significantly declines under anomalies in anthropogenic patterns and urban metabolic rates as those recorded during the pandemic. The inclusion of local emission sources and anthropogenic factors in the input dataset is considered necessary for NO and PM10 to properly predict urban air quality, especially under human-related extreme conditions.
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Affiliation(s)
| | - Patrick Nzivugira Duhirwe
- Department of Architectural Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea
| | - Geun Young Yun
- Department of Architectural Engineering, Kyung Hee University, 1 Seocheon-dong, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea
| | - Mathew J Lipson
- Australian Research Council Centre of Excellence for Climate System Science, UNSW Sydney, Australia
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Impact of Urbanization on Urban Heat Island Intensity in Major Districts of Bangladesh Using Remote Sensing and Geo-Spatial Tools. CLIMATE 2022. [DOI: 10.3390/cli10010003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.
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Su MA, Ngarambe J, Santamouris M, Yun GY. Empirical evidence on the impact of urban overheating on building cooling and heating energy consumption. iScience 2021; 24:102495. [PMID: 34113830 PMCID: PMC8169988 DOI: 10.1016/j.isci.2021.102495] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/05/2021] [Accepted: 04/26/2021] [Indexed: 11/25/2022] Open
Abstract
A primary contributor to urban overheating is the urban heat island (UHI) formed due to increased urbanization. The adverse effects of UHI on building energy use are substantial and well documented. However, such effects are typically demonstrated through numerical simulations which are susceptible to modeling uncertainties and lack of validation resulting in a pressing research gap. Here, for the first time, we conduct a large-scale assessment to demonstrate the devastating impact of UHI on building energy consumption using real building energy use data. We find empirical evidence correlating UHI with building energy use; changes in average UHI intensity of 0.5 K correspond to an increase in monthly cooling energy consumption in a range of 0.17 kWh/m2–1.84 kWh/m2. The study validates theoretical evidence on the impact of UHI on building energy and proposes a highly innovative methodology to assess the impact of overheating on the energy balance of cities. Real energy data used to study the effect of local climates on building energy use. Cooling energy rises between 0.17 and 1.84 kWh/m2 due to 0.5 K urban overheating. Heating energy drops between 0.06 and 1.19 kWh/m2 due to 0.5 K urban overheating. Urban overheating effects are more exacerbated during the cooling period.
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Affiliation(s)
- Mi Aye Su
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea
| | - Jack Ngarambe
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea
| | - Mat Santamouris
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea.,Facaulty of Built Environment, University of New South Wales, Sydney, Australia
| | - Geun Young Yun
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea
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Ngarambe J, Joen SJ, Han CH, Yun GY. Exploring the relationship between particulate matter, CO, SO 2, NO 2, O 3 and urban heat island in Seoul, Korea. JOURNAL OF HAZARDOUS MATERIALS 2021; 403:123615. [PMID: 32771816 DOI: 10.1016/j.jhazmat.2020.123615] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/24/2020] [Accepted: 07/30/2020] [Indexed: 06/11/2023]
Abstract
Urban environments face two challenging problems that are parallel in nature but yet with compelling potential synergistic interactions; urban heat island (UHI) and air pollution. We explore these interactions using in-situ temperature and air pollution data collected from 13 monitoring stations for nine years. Through regression analysis and analysis of variance (ANOVA) tests, we found that carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) show positive correlations with UHI intensity (UHII). At the same time, Ozone (O3) was negatively correlated with UHII. Moreover, there was a substantial seasonal effect on the strength of the correlations between UHI and air pollution, with some air pollutants showing strong associations with UHI during certain seasons (i.e., winter and autumn). The strongest interactions were observed for NO2 (R² = 0.176) and PM10 (R² = 0.596) during the wintertime and for SO2 (R² = 0.849), CO (R² = 0.346), PM2.5 (R² = 0.695) and O3 (R² = 0.512) during autumn. Understanding such interactions is essential for urban climate studies and our study provides a basis for scientific discussions on integrative mitigation strategies for both UHI and air pollution in Seoul city.
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Affiliation(s)
- Jack Ngarambe
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Soo Jeong Joen
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Choong-Hee Han
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, South Korea
| | - Geun Young Yun
- Department of Architectural Engineering, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, South Korea.
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Ngarambe J, Nganyiyimana J, Kim I, Santamouris M, Yun GY. Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics. PLoS One 2020; 15:e0243571. [PMID: 33284850 PMCID: PMC7721160 DOI: 10.1371/journal.pone.0243571] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Accepted: 11/23/2020] [Indexed: 11/18/2022] Open
Abstract
The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.
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Affiliation(s)
- Jack Ngarambe
- Department of Architectural Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeongi-do, Republic of Korea
| | - Jacques Nganyiyimana
- Department of Architectural Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeongi-do, Republic of Korea
| | - Inhan Kim
- Department of Architecture, Kyung Hee University, Giheung-gu, Yongin-si, Gyeongi-do, Republic of Korea
| | - Mat Santamouris
- Department of Architectural Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeongi-do, Republic of Korea
- Faculty of Built Environment, University of New South Wales, Sydney, New South Wales, Australia
| | - Geun Young Yun
- Department of Architectural Engineering, Kyung Hee University, Giheung-gu, Yongin-si, Gyeongi-do, Republic of Korea
- * E-mail:
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Marvuglia A, Koppelaar R, Rugani B. The effect of green roofs on the reduction of mortality due to heatwaves: Results from the application of a spatial microsimulation model to four European cities. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109351] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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