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- Evaluating urban green and blue spaces with space-based multi-sensor
datasets for sustainable development
Abstract: Urban green and blue spaces refer to the natural and semi-natural areas within a city or urban area. These spaces can include parks, gardens, rivers, lakes, and other bodies of water. They play a vital role in the sustainability of cities by providing a range of ecosystem services such as air purification, carbon sequestration, water management, and biodiversity conservation. They also provide recreational and social benefits, such as promoting physical activity, mental well-being, and community cohesion. Urban green and blue spaces can also act as buffers against the negative impacts of urbanization, such as reducing the heat island effect and mitigating the effects of stormwater runoff. Therefore, it is important to maintain and enhance these spaces to ensure a healthy and sustainable urban environment. Assessing urban green and blue spaces with space-based multi-sensor datasets can be a valuable tool for sustainable development. These datasets can provide information on the location, size, and condition of green and blue spaces in urban areas, which can be used to inform decisions about land use, conservation, and urban planning. Space-based sensors, such as satellites, can provide high-resolution data that can be used to map and monitor changes in these spaces over time. Additionally, multi-sensor datasets can be used to gather information on a variety of environmental factors, such as air and water quality, that can impact the health and well-being of urban residents. This information can be used to develop sustainable solutions for preserving and enhancing urban green and blue spaces. This study examines how urban green and blue infrastructures might improve sustainable development. Space-based multi-sensor datasets are used to estimate urban green and blue zones for sustainable development. This work can inform sustainable development research at additional spatial and temporal scales.
PubDate: 2023-03-17
- How do taxi usage patterns vary and why' A dynamic spatiotemporal
analysis in Beijing
Abstract: Existing studies lack attention to taxi usage dynamics, considering its trip proportion over other travel modes and its influencing factors at fine spatiotemporal resolutions. To fill these gaps, we propose a method for examining taxi usage in a grid of 1 km × 1 km cells per hour during a one-day cycle in Beijing. This method measures the differences between taxi trips from taxi trajectory data and mobile signaling data in the same week in January 2017. To explain the spatiotemporal variation in taxi usage, multiple linear models were used to investigate taxi usage dynamics with alternative transport modes, socioeconomic factors, and built environments. In summary, this study proposes to develop an indicator to measure taxi usage using multiple data sources. We confirm that taxi usage dynamics exist in both temporal and spatial dimensions. In addition, the effects of taxi usage factors vary over each hour in a one-day cycle. These findings are useful for urban planning and transport management, in which the dynamic interactions between taxi demand and distribution of facilities should be included.
PubDate: 2023-03-06
- Are older adults living in compact development more active' –
Evidence from 36 diverse regions of the United States
Abstract: With the population of older adults growing globally, this study asks the question: are older adults living in compact developments more active than those living in sprawling developments' Older adults can be deemed more active if they travel more in total or travel more by non-auto travel modes (such as walking, transit). By analyzing disaggregated data from 36 regions of the United States, this study finds that older adults living in compact neighborhoods do not travel more in total but travel more by walking and public transportation than those living in sprawling neighborhoods. In addition, older adults travel less, are more auto-dependent, and make more home-based-nonwork trips, compared to younger adults. Older adults with lower income travel less than those with higher income. Older adults living in compact neighborhoods with the lowest income level generate the highest number of transit trips. It is important for planners and policy makers to not only create built environments that support older adults’ travel needs, but also to avoid social inequity.
PubDate: 2023-03-02
- Uncovering the spatiotemporal evolution of the service industry based on
geo-big-data- a case study on the bath industry in China
Abstract: The bath industry has multiple attributes, such as economic, health, and cultural communication. Therefore, exploring this industry's spatial pattern evolution is crucial to forming a healthy and balanced development model. Based on POI (Points of Interest) and population migration data, this paper uses spatial statistics and radial basis function neural network to explore the spatial pattern evolution and influencing factors of the bath industry in mainland China. The results show that: (1) The bath industry presents a strong development pattern in the north, south-northeast, and east-northwest regions and weak development in the rest of the country. As a result, the spatial development of new bath space is more malleable. (2) The input of bathing culture has a guiding role in developing the bath industry. The growth of market demand and related industries has a specific influence on the development of the bath industry. (3) Improving the bath industry's adaptability, integration, and service level are feasible to ensure healthy and balanced development. (4) Bathhouses should improve their service system and risk management control during the pandemic.
PubDate: 2023-02-27
- Delay in timing and spatial reorganization of rainfall due to
urbanization- analysis over India’s smart city Bhubaneswar
Abstract: Bhubaneswar is the first designed ‘smart city’ in India and has experienced rapid urbanization since 2000. The question undertaken in this study is to assess if there is a change in the rainfall over this rapidly urbanizing region, and if so, what are the characteristics of the change' The broader intent is to understand if the change in urbanization and rainfall are interlinked' The India Meteorological Department (hourly station and daily gridded) and Tropical Rainfall Measurement Mission (3-hourly) datasets are analyzed for the 1980–2018 period (39 years) for different seasons separately. Wavelet and trend analysis reveal that precipitation intensity has increased over the study period. The assessments of the hourly rainfall data show an interesting feature. There is a decrease in the midnight to early-morning rain, with a corresponding increase in the late-afternoon to midnight rainfall. The increase in the rainfall is preferentially downwind and on the east side of the city. A supervised classified land use land cover map of the Bhubaneswar region is developed for 1980, 1990, 2000, 2010, and 2019 using Landsat imagery to compute the urban sprawl. The urban area and population density over Bhubaneswar is increasing with time. Analysis of the LULC and rainfall data indicates that the rainfall over urban regions and the shift in the timing of rains to evenings is highly correlated with the urban sprawl.
PubDate: 2023-02-23
- Impact of COVID-19 on online grocery shopping discussion and behavior
reflected from Google Trends and geotagged tweets
Abstract: People express opinions, make connections, and disseminate information on social media platforms. We considered grocery-related tweets as a proxy for grocery shopping behaviors or intentions. We collected data from January 2019 to January 2022, representing three typical times of the normal period before the COVID-19 pandemic, the outbreak period, and the widespread period. We obtained grocery-related geotagged tweets using a search term index based on the top 10 grocery chains in the US and compiled Google Trends online grocery shopping data. We performed a topic modeling analysis using the Latent Dirichlet Allocation (LDA), and verified that most of the collected tweets were related to grocery-shopping demands or experiences. Temporal and geographical analyses were applied to investigate when and where people talked more about groceries, and how COVID-19 affected them. The results show that the pandemic has been gradually changing people’s daily shopping concerns and behaviors, which have become more spread throughout the week since the pandemic began. Under the causal impact of COVID-19, people first experienced panic buying groceries followed by pandemic fatigue a year later. The normalized tweet counts show a decrease of 40% since the pandemic began, and the negative causal effect can be considered statistically significant (p-value = 0.001). The variation in the quantity of grocery-related tweets also reflects geographic diversity in grocery concerns. We found that people in non-farm areas with less population and relatively lower levels of educational attainment tend to act more sensitively to the evolution of the pandemic. Utilizing the COVID-19 death cases and consumer price index (CPI) for food at home as background information, we proposed an understanding of the pandemic’s impact on online grocery shopping by assembling, geovisualizing, and analyzing the evolution of online grocery behaviors and discussion on social media before and during the pandemic.
PubDate: 2023-02-22
- Out-of-school hours care places in Xi’an City of China: location choice,
spatial relationships, and influencing factors
Abstract: s Affected by the burden reduction policy, out-of-school hours care places have become a hot issue of social concern. Taking Xi’an out-of-school hours care places as a research case, this paper discusses its location choice, spatial relationships and influencing factors using methods such as text analysis, spatial analysis, and mathematical statistics. The results show that: (1) the distribution of out-of-school hours care places in Xi’an is closely related to the community and schools. The names mostly use words such as “sunshine,” “teacher,” and “love,” which are mainly distributed on the lower floors (one to three floors), of which the first floor accounts for the largest proportion. (2) The high-value areas of out-of-school hours care places are mainly concentrated in the Lianhu District, Yanta District, Xincheng District, and the north Chang’an District. Their distribution has obvious directionality, showing a “northeast-southwest” trend, and the global spatial autocorrelation is positively correlated. (3) The spatial pattern of out-of-school hours care places is basically consistent with that of primary and secondary schools, and most of them are located within 1000 m of it. (4) The influencing factors mainly include the distribution of primary and secondary schools, residential areas, population density, house rent, and policies.
PubDate: 2023-02-20
- The impacts of land cover spatial combination on nighttime light intensity
in 2010 and 2020: a case study of Fuzhou, China
Abstract: As human activities highly depend on the land resources and changed the land cover (LC) condition, the relationship between LC and nighttime light (NTL) intensity has been widely analyzed to support the foundation of NTL applications and help explain the drivers of urban economic development. However, previous studies always paid attention to the effect of each LC type on NTL intensity, with limited consideration of the joint effects of any two LC types. To fill this gap, this study measured the land cover spatial combination (LCSC) by using a spatial adjacency matrix, and then analyzed its impacts on NTL intensity based on an extreme gradient boosting (XGBoost) regression model with the assistant of sharpley additive explanations (SHAP) method. Our results presented that the LCSC can better (R2 of 82.4% and 98.1% in 2010 and 2020) explain the relationship between LC and NTL intensity with the traditional LC metrics (e.g., area and patch count), since the LCSC is much more sensitive to the diverse land functions. It is noteworthy that the impacts, as well as their dynamics, of LCSC between any two LC types on NTL intensity are various. LCSC associated with artificial surface contributed more to NTL intensity. In detail, the LCSC of water/wetland and artificial surface can increasingly promote the NTL intensity while the LCSC of grassland/forest and artificial surface has a decreasing or inverse U-shaped contribution to NTL intensity. Whereas LCSC associated with non-artificial surface were not conducive to the increase in NTL intensity due to high vegetation density. We also provided three implications to help further urbanization process and discussed the applications of LCSC.
PubDate: 2023-02-02
- Need for considering urban climate change factors on stroke,
neurodegenerative diseases, and mood disorders studies
Abstract: The adverse health impacts of climate change have been well documented. It is increasingly apparent that the impacts are disproportionately higher in urban populations, especially underserved communities. Studies have linked urbanization and air pollution with health impacts, but the exacerbating role of urban heat islands (UHI) in the context of neurodegenerative diseases has not been well addressed. The complex interplay between climate change, local urban air pollution, urbanization, and a rising population in cities has led to the byproduct of increased heat stress in urban areas. Some urban neighborhoods with poor infrastructure can have excessive heat even after sunset, increasing internal body temperature and leading to hyperthermic conditions. Such conditions can put individuals at higher risk of stroke by creating a persistent neuroinflammatory state, including, in some instances, Alzheimer’s Disease (AD) phenotypes. Components of the AD phenotype, such as amyloid beta plaques, can disrupt long-term potentiation (LTP) and long-term depression (LTD), which can negatively alter the mesolimbic function and thus contribute to the pathogenesis of mood disorders. Furthermore, although a link has not previously been established between heat and Parkinson’s Disease (PD), it can be postulated that neuroinflammation and cell death can contribute to mitochondrial dysfunction and thus lead to Lewy Body formation, which is a hallmark of PD. Such postulations are currently being presented in the emerging field of ‘neurourbanism’. This study highlights that: (i) the impact of urban climate, air pollution and urbanization on the pathogenesis of neurodegenerative diseases and mood disorders is an area that needs further investigation; (ii) urban climate- health studies need to consider the heterogeneity in the urban environment and the impact it has on the UHI. In that, a clear need exists to go beyond the use of airport-based representative climate data to a consideration of more spatially explicit, high-resolution environmental datasets for such health studies, especially as they pertain to the development of locally-relevant climate adaptive health solutions. Recent advances in the development of super-resolution (downscaled climate) datasets using computational tools such as convolution neural networks (CNNs) and other machine learning approaches, as well as the emergence of urban field labs that generate spatially explicit temperature and other environmental datasets across different city neighborhoods, will continue to become important. Future climate – health studies need to develop strategies to benefit from such urban climate datasets that can aid the creation of localized, effective public health assessments and solutions.
PubDate: 2023-01-30
- Understanding cycling mobility: Bologna case study
Abstract: Understanding human mobility in touristic and historical cities is of the utmost importance for managing traffic and deploying new resources and services. In recent years, the need to enhance mobility has been exacerbated due to rapid urbanisation and climate changes. The main objective of this work is to study cycling mobility within the city of Bologna, Italy. We used six months dataset that consists of 320,118 self-reported bike trips. First, we performed several descriptive analysis to understand the temporal and spatial patterns of bike users for understanding popular roads and most favourite points within the city. The findings show how bike users present regular daily and weekly temporal patterns and the characteristics of their trips (i.e. distance, time and speed) follow well-known distribution laws. We also identified several points of interest in the city that are particularly attractive for cycling. Moreover, using several other public datasets, we found that bike usage is more correlated to temperature and precipitation and has no correlation to wind speed and pollution. We also exploited machine learning approaches for predicting short-term trips in the near future (that is for the following 10, 30, and 60 minutes), which could help local governmental agencies with urban planning. The best model achieved an R square of 0.91 for the 30-minute time interval, and a Mean Absolute Error of 2.52 and a Root Mean Squared Error of 3.88 for the 10-minute time interval.
PubDate: 2023-01-29
- Urban modification of heavy rainfall: a model case study for Bhubaneswar
urban region
Abstract: An increase in urbanization has been witnessed from 1980 to 2019 in Bhubaneswar, Odisha. The impact of this increase in urban areas on rainfall pattern and intensity has been assessed in this study. To evaluate these changes, four heavy rainfall events, such as 06th March 2017, 23rd May 2018, 20 – 22 July 2018, and 04 – 08 August 2018, have been simulated with 1980, 2000, and 2019 land use land cover (LULC) obtained from United States Geological Survey imageries. With these two LULC sensitivities, urban canopy model (UCM) experiments have also been carried out. These experiments suggest that incorporating corrected LULC is necessary for simulating heavy rainfall events using the Weather Research and Forecasting (WRF) model. Urbanization increases the rainfall intensity, and the spatial shift was more pronounced along the peripheral region of the city. The vertically integrated moisture flux analysis suggests that more moisture present over the area received intense rainfall. An increase in urbanization increases the temperature at the lower level of the atmosphere, which increases [planetary boundary layer height, local convection, and rainfall over the region. Contiguous Rain Area method analysis suggests that the 2019 LULC with single layer UCM predicts a better spatial representation of rainfall. This combination works well for all the four cases simulated.
PubDate: 2023-01-27
- Development of a composite regional vulnerability index and its
relationship with the impacts of the COVID-19 pandemic
Abstract: The interactions between vulnerability and human activities have largely been regarded in terms of the level of risk they pose, both internally and externally, for certain groups of disadvantaged individuals and regions/areas. However, to date, very few studies have attempted to develop a comprehensive composite regional vulnerability index, in relation to travel, housing, and social deprivation, which can be used to measure vulnerability at an aggregated level in the social sciences. Therefore, this research aims to develop a composite regional vulnerability index with which to examine the combined issues of travel, housing and socio-economic vulnerability (THASV index). It also explores the index’s relationship with the impacts of the COVID-19 pandemic, reflecting both social and spatial inequality, using Greater London as a case study, with data analysed at the level of Middle Layer Super Output Areas (MSOAs). The findings show that most of the areas with high levels of composite vulnerability are distributed in Outer London, particularly in suburban areas. In addition, it is also found that there is a spatial correlation between the THASV index and the risk of COVID-19 deaths, which further exacerbates the potential implications of social deprivation and spatial inequality. Moreover, the results of the multiscale geographically weighted regression (MGWR) show that the travel and socio-economic indicators in a neighbouring district and the related vulnerability indices are strongly associated with the risk of dying from COVID-19. In terms of policy implications, the findings can be used to inform sustainable city planning and urban development strategies designed to resolve urban socio-spatial inequalities and the potential related impacts of COVID-19, as well as guiding future policy evaluation of urban structural patterns in relation to vulnerable areas.
PubDate: 2023-01-16
- Modeling of emergency support capacity and optimization of delivery
service system for urban living materials under uncertain situations: a
case study of Xi’an City during COVID-19 epidemic
Abstract: The severe acute respiratory syndrome coronavirus 2 (COVID-19) pandemic has brought a heavy burden and severe challenges to the global economy and society, forcing different countries and regions to take various preventive and control measures ranging from normal operations to partial or complete lockdowns. Taking Xi’an city as an example, based on multisource POI data for the government’s vegetable storage delivery points, logistics terminal outlets, designated medical institutions, communities, etc., this paper uses the Gaussian two-step floating catchment area method (2SFCA) and other spatial analysis methods to analyze the spatial pattern of emergency support points (ESPs) and express logistics terminals in different situations. It then discusses construction and optimization strategies for urban emergency support and delivery service systems. The conclusions are as follows. (1) The ESPs are supported by large-scale chain supermarkets and fresh supermarkets, which are positively related to the population distribution.The spatial distribution of express logistics terminals is imbalanced, dense in the middle while sparse at the edges. 90% of express terminals are located within a 500 m distance of communities, however, some terminals are shared, which restrict their ability to provide emergency support to surrounding residents. (2) In general, accessibility increases as the number of ESPs increases; under normal traffic, as the distance threshold increases, the available ESPs increase but accessibility slightly decreases; with a traffic lockdown, the travel distance of residents is limited, and as ESPs increase, accessibility and the number of POIs covered significantly increase. (3) The spatial accessibility of the ESPs has a “dumbbell-shaped” distribution, with highest accessibility in the north and south, higher around the second ring road, slightly lower in the center, and lowest near the third ring road at east and west. (4) With the goal of “opening up the logistics artery and unblocking the distribution microcirculation”, based on “ESPs + couriers + express logistics terminals + residents”, this paper proposes to build and optimize the urban emergency support and delivery service system to improve the comprehensive ability of the city to cope with uncertain risks.
PubDate: 2022-12-24
DOI: 10.1007/s43762-022-00076-5
- Intercity mobility pattern and settlement intention: evidence from China
Abstract: Floating population is an important group in the emerging urbanization process. This group promotes long-term settlement, which is a significant driving force increasing the urbanization level of countries. This study analyzed the changes in population mobility between Chinese cities and the willingness of the floating population to settle down. The analyses were based on data obtained from the China Migrants Dynamic Survey (CMDS) in 2017, and the China Seventh Census 2020. Spatial econometric models were constructed for in-depth research. The result showed that: ① the floating population migrated mainly from the central region to the surrounding cities, and their long-term settlement intention presented a spatial pattern of "high in the east, low in the west, and local concentration." ②the long-term settlement intention significantly negatively affected the urban floating population. City economic level, public service capacity, and environmental quality significantly positively or negatively influence the number of the floating population. For promoting more floating population to become urban residents, management of the group should be strengthened, construction level of the urban economy, society, and ecology improved, and the willingness of the group to settle for an extended time encouraged.
PubDate: 2022-12-14
DOI: 10.1007/s43762-022-00075-6
- Developing a smart tool for integrated climate action planning (ICLAP
2050) in Asia-Pacific Cities
Abstract: In light of the growing global environmental challenges, smart cities need to serve as testing workshops or labs to smartly tackle complex cross-sectional issues like jobs, seamless mobility, safety and security, sustained growth, while responding to the impending climate change too. This necessitates for developing a smart model or tool that integrates such varied but crucial climate concerns of a city into its direct decision-making and long-term planning. In this research, we conduct a literature review to have an overview of the state-of-the-affairs on urban climate planning in Asia-Pacific Cities covering China, Japan, India, Philippines, Singapore and Thailand. This is followed by an intensive theoretical understanding on the need of having a smart tool in urban climate action planning. This includes the study of recent urban climate metrics and tools, their different typologies based on key purpose, method, sectoral and geographical scope, and challenges and gaps in formulating smart urban climate tools. We then introduce the conceptual framework for integrated climate action planning (ICLAP) tool that transects spatial, statistical and bibliometric methods. We establish applicability of ICLAP in case of Indian cities by discerning climate vulnerabilities, GHG trends and relevant urban climate solutions. The paper eventually culminates with major scientific findings and policy recommendations, essentially underscoring more intensive and wider application of ICLAP like smart urban climate tools in local decision making and national urban policies duly supported by international scientific collaborations.
PubDate: 2022-12-13
DOI: 10.1007/s43762-022-00074-7
- Urbanization diseconomies in China: roles of temporary migrant workers in
foreign direct investment location
Abstract: This paper investigates whether temporary migrant workers still attract foreign direct investment (FDI) in China nowadays after they played a strong magnet role for FDI in the last century. This paper tests the hypothesis that foreign firms reduce investments to avoid urbanization diseconomies from temporary migrants when China is experiencing rapid urbanization in the 2000s, with the urbanization rate raised from 36% in 2000 to 59% in 2017. This research employs spatial statistics and analyses to examine the change in the spatial inequality of temporal migrant workers and FDI. This research also uses regression models to investigate whether temporary migrant workers still attract foreign direct investment (FDI) in China nowadays. Temporary migrants are increasingly concentrated in the Pearl River Delta, the Yangtze River Delta, and the Bohai Rim Region of the eastern region, and Chengdu in the western region. The results indicate that a one-person increase in temporary migrant workers is associated with 259 dollars decrease in FDI, suggesting that FDI might reduce with increased migrants to avoid urbanization diseconomies from these cities, helping policymakers develop urbanization and migration policies to optimize labor allocation and promote industrial upgrading, developing peripheral cities.
PubDate: 2022-12-08
DOI: 10.1007/s43762-022-00068-5
- Using quantum computing to solve the maximal covering location problem
Abstract: In this article, we present the process and results of using quantum computing (QC) to solve the maximal covering location problem proposed by Church and ReVelle. With this contribution, we seek to lay the foundations for other urban and regional scientists to begin to consider quantum technologies. We obtained promising results, but it is clear that there is a need for more capable devices with more qubits and less susceptibility to electronic noise to solve instances that currently cannot be optimally solved by traditional solvers. We foresee that QC will be of common use in urban and regional science and its applications in the years to come.
PubDate: 2022-12-05
DOI: 10.1007/s43762-022-00070-x
- Land use impacts of implementing a bus rapid transit system: case of
Beirut southern corridor
Abstract: This study investigates and forecasts the effects of implementing a newly proposed Bus Rapid Transit (BRT) system in Lebanon on the urban land use evolution between the years 2019 and 2049. It contributes to the emerging scientific literature by proposing a technique intended to identify the potential urban land use impacts of BRT. The identification of these impacts as part of the feasibility study for the BRT is considered important for policymakers, local officials, and urban planners. The impacts are identified by conducting the Analytical Hierarchy Process, based on data collected via survey and interviews with real estate experts. The outcomes show that implementing a BRT service complemented with bus feeder services will: (i) reshape the urban fabric, in proximity to BRT routes and particularly around the stations, by triggering the Transit-Oriented Development and increasing the attractivity of urban development by 6 to 9% according to the distance from BRT route; and (ii) increase the attractivity of urban development projects by 11% in areas distant from the highway if these areas are characterized by high coverage of bus feeder services, low possibility of an increase in estate prices, and medium to high public acceptance of the proposed BRT.
PubDate: 2022-11-28
DOI: 10.1007/s43762-022-00072-9
- Impact of urban parameterization and integration of WUDAPT on the severe
convection
Abstract: Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction.
PubDate: 2022-11-11
DOI: 10.1007/s43762-022-00071-w
- A modelling study on quantifying the impact of urbanization and regional
effects on the wintertime surface temperature over a rapidly-growing
tropical city
Abstract: Climate change and sustainability are among the most widely used terms among policymakers and the scientific community in recent times. However, climate action or steps to sustainable growth in cities in the global south are mostly borrowed from general studies at a few large urban agglomerations in the developed world. There are very few modeling studies over south Asia to understand and quantify the impact of climate change and urbanization on even the most primary meteorological variable, such as temperature. Such quantifications are difficult to estimate due to the non-availability of relevant long-term observational datasets. In this modeling study, an attempt is made to understand the urban heat island (UHI), its transition, and the segregation of regional climate change effects and urbanization over the rapidly growing tier 2 tropical smart city Bhubaneswar in India. The model is able to simulate the UHI for both land surface temperature, called the SUHI, and 2-m air temperature, called UHI, reasonably well. Their magnitudes were ~ 5 and 2.5°C, respectively. It is estimated that nearly 60–70% of the overall air and 70–80% of the land surface temperature increase during nighttime over the city between the period 2004 and 2015 is due to urbanization, with the remaining due to the regional/non-local effects.
PubDate: 2022-11-04
DOI: 10.1007/s43762-022-00067-6
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