Surface heat assessment for developed environments: Optimizing urban temperature monitoring. (15th August 2018)

• Record Type: Journal Article
• Authors: Malings, Carl; Pozzi, Matteo; Klima, Kelly; Bergés, Mario; Bou-Zeid, Elie; Ramamurthy, Prathap
• Is Part Of: Building and environment. Volume 141(2018)
• Page Start: 143
• Page End: 154
• Publication Date: 2018-08-15
• Subjects: Entropy -- Value of information -- Sensor placement -- Optimization methods -- Temperature -- Urban areas; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2018.05.059
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: The urban heat island effect, exacerbated by rising average surface temperatures due to climate change, can lead to adverse impacts on city populations. Fine resolution modeling of the spatial and temporal distribution of extreme heat risk within a city can improve the strategies used to mitigate this risk, such as the issuance of targeted heat advisories to city residents. In this paper, we combine a recently developed method for probabilistic modeling of urban temperatures with previously developed vulnerability assessments, and then implement sensor placement optimization techniques to guide temperature monitoring in urban areas. A variety of metrics are used to optimize the placement of temperature measures to best support decision-making for monitoring and responding to extreme heat risk. This optimal sensor placement methodology is demonstrated for the city of Pittsburgh, PA, resulting in several proposed temperature monitoring schemes based on the various sensor performance metrics investigated. We quantitatively and qualitatively compare these schemes to identify the relative merits of each proposed metric. Highlights: Surface temperature has been modeled probabilistically for a case study city. This model is combined with vulnerability assessments to optimize sensing. Entropy, prediction error, and value of information metrics guide sensor placement. Different metrics lead to different optimized sensing schemes for a city. Value of information best supports heat advisory issuance for risk reduction.