Implications of climate variability and change on urban and human health: A review

Orimoloye, Israel R.; Mazinyo, Sonwabo Perez; Kalumba, Ahmed Mukalazi; Ekundayo, Olapeju Yewande; Nel, Werner

doi:10.1016/j.cities.2019.01.009

Cited by 82 publications

(45 citation statements)

References 153 publications

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“…Constant population growth and human activities result in significant changes in the atmospheric constituents [139]. Climate change and air pollution are two decisive consequences of these disturbances that directly impact the surrounding environment and human health [140], [141]. Therefore, it is essential to monitor and control air quality and climate conditions to avoid severe outcomes.…”

Section: G Atmosphere and Climatementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

662

216

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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.

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Section: G Atmosphere and Climatementioning

confidence: 99%

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Amani¹,

Ghorbanian

Ahmadi

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

662

216

View full text Add to dashboard Cite

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“… Lee et al, 2020 ). Human health issues related to the urban context was previously investigated ( Orimoloye, Mazinyo, Kalumba, Ekundayo, & Nel, 2019 ; Wang, 2020 ) and other work showed that property price reduced after pandemic ( Ambrus, Field, & Gonzalez, 2020 ). The change of residential-built environment characteristics due to pandemic were also discussed ( Spencer, Finucane, Fox, Saksena, & Sultana, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Study of COVID-19 pandemic in London (UK) from urban context

Ghosh

Nundy

Ghosh

et al. 2020

Cities

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COVID-19 transmission in London city was discussed in this work from an urban context. The association between COVID-19 cases and climate indicators in London, UK were analysed statistically employing published data from national health services, UK and Time and Date AS based weather data. The climatic indicators included in the study were the daily averages of maximum and minimum temperatures, humidity, and wind speed. Pearson, Kendall, and Spearman rank correlation tests were selected for data analysis. The data was considered up to two different dates to study the climatic effect (10th May in the first study and then updated up to 16th of July in the next study when the rest of the data was available). The results were contradictory in the two studies and it can be concluded that climatic parameters cannot solely determine the changes in the number of cases in the pandemic. Distance from London to four other cities (Birmingham, Leeds, Manchester, and Sheffield) showed that as the distance from the epicentre of the UK (London) increases, the number of COVID-19 cases decrease. What should be the necessary measure to be taken to control the transmission in cities have been discussed.

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“…Despite this, the partial correlation suggested the strong influence on HVI from hot environment depicted by land surface temperature and extremely hot days. Moreover, as temperature extremes and variability will remain important determinants of health [92,93], spatial distribution of HVI emphasizes the area with high risk and reveals great variation across urban areas, and thus could provide suggestions for heat alerts and developing emergency interventions.…”

Section: Discussionmentioning

confidence: 99%

Mapping Heat-Related Risks in Northern Jiangxi Province of China Based on Two Spatial Assessment Frameworks Approaches

Zheng

Zhang

Shi

et al. 2020

IJERPH

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Heat-health risk is a growing concern in many regions of China due to the more frequent occurrence of extremely hot weather. Spatial indexes based on various heat assessment frameworks can be used for the assessment of heat risks. In this study, we adopted two approaches—Crichton’s risk triangle and heat vulnerability index (HVI) to identify heat-health risks in the Northern Jiangxi Province of China, by using remote sensing and socio-economic data. The Geographical Information System (GIS) overlay and principal component analysis (PCA) were separately used in two frameworks to integrate parameters. The results show that the most densely populated community in the suburbs, instead of city centers, are exposed to the highest heat risk. A comparison of two heat assessment mapping indicates that the distribution of HVI highlights the vulnerability differences between census tracts. In contrast, the heat risk index of Crichton’s risk triangle has a prominent representation for regions with high risks. The stepwise multiple linear regression zero-order correlation coefficient between HVI and outdoor workers is 0.715, highlighting the vulnerability of this particular group. Spearman’s rho nonparametric correlation and the mean test reveals that heat risk index is strongly correlated with HVI in most of the main urban regions in the study area, with a significantly lower value than the latter. The analysis of variance shows that the distribution of HVI exhibits greater variety across urban regions than that of heat risk index. Our research provides new insight into heat risk assessment for further study of heat health risk in developing countries.

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Implications of climate variability and change on urban and human health: A review

Cited by 82 publications

References 153 publications

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

Study of COVID-19 pandemic in London (UK) from urban context

Mapping Heat-Related Risks in Northern Jiangxi Province of China Based on Two Spatial Assessment Frameworks Approaches

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