Urban Informal Housing and Surface Urban Heat Island Intensity

Abstract

Urbanization leads to the densification of built-up areas, and thereby increases surface heat island intensity which is one of the growing concerns in the rapidly urbanizing cities. Another notable aspect of cities like Mumbai is the uncontrolled growth of informal slum housing clusters, which have emerged as a significant urban built form in the landscape of cities. This study presents a case of Mumbai that aims to explore the linkages between slum housing-here referred as 'slum urban form' (SUF)-and surface urban heat island (SUHI) supported by spatial-statistical analysis. The magnitude of the impact of urban form on SUHI, measured by land surface temperature (LST), is examined using Cohen's d index, which measures the effect size for two groups-SUF and 'formal' housing-on LST. The results confirm a 'large' effect indicating a significant difference in mean LST between the two groups. The spatial analysis reveals a statistically significant spatial clustering of LST and SUF (p-value < 0.05), and bivariate local indicator of spatial association (LISA) confirms that the spatial association of SUF is surrounded by 'high' LST (Moran I: 0.49). The exploratory spatial analysis indicates that the contribution of SUF in elevating SUHI intensity is more than the formal housing areas and has increased vulnerability to heat stress. The results were validated on the ground using environmental sensors, which confirms the susceptibility of SUF to heat stress.

Urban Heat Island studies: Current status in India and a comparison with the International studies

“…The Brst paper on heat island intensity found was based on the temperature increase in Pune, Maharashtra in the year 2000 (Deosthali 2000). As mentioned above, majority of the studies are concentrated on the capital city, New Delhi (Mohan et al 2012;Pandey et al 2012;Babazadeh and Kumar 2015;Aslam et al 2017) and the other cities where UHI studies were carried out are Bangalore (Gopalakrishnan et al 2003;Ramachandra and Kumar 2010; Ambinakudige 2011), Mumbai (Grover and Singh 2015;Mehrotra 2018), Chennai (Gopalakrishnan et al 2003;Amirtham 2016), Pune (Deosthali 2000; Nesarikar-Patki and Raykar-Alange 2012), Hyderabad (Gopalakrishnan et al 2003), Ahmedabad (Joshi et al 2015;Goswami et al 2016), Visakhapatnam (Devi 2006), Ernakulam (Baby and Arya 2016;Thomas et al 2014), Bhopal (Gupta et al 2009), Bhubaneswar (Swain et al 2016), Noida (Kikon et al 2016), Kanpur (Chakraborty et al 2016), Lucknow (Singh et al 2017), Bathinda (Sharma and Pandey 2015), Guwahati (Borbora and Das 2014), Jaipur (Gupta 2012), Kolkata (Ali Gazi and Mondal 2018), Thiruchirappalli (Kannamma and Sundaram 2015) and Uttarakhand (Kumar and Singh 2016).…”

“…But in some studies, the UHI intensity is estimated for a few months(Deosthali 2000;Pandey et al 2012;Borbora and Das 2014;Sharma and Pandey 2015;Amirtham 2016;Baby and Arya 2016;Aslam et al 2017;Shastri et al 2017) Mohan et al (2012Mohan et al ( , 2013,Grover and Singh (2015). and for even a single day as done byAhmad et al (2011),Kannamma and Sundaram (2015),Kumar et al (2017a) andMehrotra (2018).Using the Landsat 5 TM, the built-up and vegetated areas temperature for Delhi on 5…”

Health effects of heat vulnerability in Rio de Janeiro: a validation model for policy applications

“…The types of heat waves that Brazil experiences can lead to increased risk of heat-related deaths [4,12,24,29], and numbers of deaths are more likely due to climate change [7,46]. Furthermore, the urban heat island effect (where urban areas are hotter than surrounding rural areas) exacerbates heat waves [64,65], especially in very densely populated areas [45]. Unfortunately for researchers, there are relatively few populated areas with hyperlocal areas spanning extreme socioeconomic disparity.…”

Tree Cover of Accra’s Neighbourhoods—a Green Divide

“…The residents of affluent neighbourhoods are more likely to profit from tree services than those in less affluent ones who, however, tend to be more susceptible to the negative effects of poor urban living conditions. Low-income neighbourhoods are located in areas more prone to flooding (Amoako and Inkoom 2018), temperatures can get higher (Mehrotra et al 2018), and air pollution is more severe (Dionisio et al 2010). Focussing on indicators of urban forest structure at the city level might, intentionally or unintentionally, conceal spatial inequalities, but this 'green variation' needs to be explicitly addressed if tree planting is promoted as a means to mitigate some of the negative effects of rapid, unplanned urban growth and climate change.…”