A Deployment of Fine-Grained Sensor Network and Empirical Analysis of Urban Temperature

Thepvilojanapong, Niwat; Ono, T.; Tobe, Yoshito

doi:10.3390/s100302217

Cited by 26 publications

(30 citation statements)

References 18 publications

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“…Although the alternative approach of urban sensor networks for different monitoring purposes, such as air temperature or air pollution constantly improves (Rashid and Rehmani, 2016), many obstacles still remain, e.g. cost, scale, location settings (Thepvilojanapong et al, 2010), making remote sensing for multi-temporal and city-wide approaches so far indispensable.…”

Section: Discussionmentioning

confidence: 99%

Heat waves and urban heat islands in Europe: A review of relevant drivers

Ward

Lauf

Kleinschmit

et al. 2016

Science of The Total Environment

410

183

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Section: Discussionmentioning

confidence: 99%

Heat waves and urban heat islands in Europe: A review of relevant drivers

Ward

Lauf

Kleinschmit

et al. 2016

Science of The Total Environment

410

183

View full text Add to dashboard Cite

“…Recent deployments include personal environment monitoring [5], city monitoring [6], building monitoring [7], ocean exploration [8] and toxic gas monitoring [9]. …”

Section: Previous Workmentioning

confidence: 99%

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature

Bhandari

Bergmann

Jurdak

et al. 2017

Sensors

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Wireless sensor networks have gained significant traction in environmental signal monitoring and analysis. The cost or lifetime of the system typically depends on the frequency at which environmental phenomena are monitored. If sampling rates are reduced, energy is saved. Using empirical datasets collected from environmental monitoring sensor networks, this work performs time series analyses of measured temperature time series. Unlike previous works which have concentrated on suppressing the transmission of some data samples by time-series analysis but still maintaining high sampling rates, this work investigates reducing the sampling rate (and sensor wake up rate) and looks at the effects on accuracy. Results show that the sampling period of the sensor can be increased up to one hour while still allowing intermediate and future states to be estimated with interpolation RMSE less than 0.2 °C and forecasting RMSE less than 1 °C.

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“…As examples, the Lausanne Urban Canopy Experiment (LUCE) had installed 92 stations over 300 m × 400 m areas from October 2006 to April 2007 to determine the sensible heat flux according to the surface geometry and type in Switzerland [19]. And the USscan network had installed 200 sensors over 250 m × 430 m area from July to August 2007 to investigate the fine-gridded temperature pattern in Tokyo downtown [20]. Recently, 52 stations in a block with 500 m × 700 m in Sydney had been installed during two sunny days in summer to verify the precinct ventilation performance [21].…”

Section: Introductionmentioning

confidence: 99%

A Building-Block Urban Meteorological Observation Experiment (BBMEX) Campaign in Central Commercial Area in Seoul

et al. 2020

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High-resolution meteorological information is essential for attaining sustainable and resilient cities. To elucidate high-resolution features of surface and air temperatures in high-rise building blocks (BBs), a 3-dimensional BB meteorological observation experiment (BBMEX) campaign was designed. The campaign was carried out in a central commercial area in Seoul during a heat-wave event period (5−6 August) in 2019. Several types of fixed instrument were deployed, a mobile meteorological observation cart (MOCA) and a vehicle were operated periodically. The surface temperature was determined to be strongly dependent on the facial direction of a building, and sunlit or shade by surrounding obstacles. Considerable increases in surface temperature on the eastern facades of buildings before noon, on horizontal surfaces near noon, and on the western facades in the afternoon could provide more energy in BBs than over a flat surface. The air temperatures in the BB were higher than those at the Seoul station by 0.1−2.2 • C (1.1−1.9 • C) in daytime (night-time). The MOCA revealed that the surface and air temperatures in a BB could be affected by many complex factors, such as the structure of the BBs, shades, as well as the existence of facilities that mitigate heat stresses, such as ground fountains and waterways.Atmosphere 2020, 11, 299 2 of 21 in urban areas are higher than those in rural areas, is a well-known and prominent feature [5][6][7][8]. Higher temperatures in urban areas and relatively lower temperatures in rural areas induce urban-rural circulation over highly populated urban areas e.g., [9].Most UHI studies assume that urban and rural areas are composed of land cover with kilometer-scale horizontal homogeneities to explain the temperature difference between two areas [10,11]. In real scenarios, urban areas are composed of various land-cover blocks: compact high-rise, open high-rise, compact low-rise, open low-rise buildings, commercial or industrial blocks, urban parks, water bodies, and so on [3]. The height and density of buildings, and land cover types become the main criteria for classifying urban climate zones. These are key factors in determining the surface roughness and zero-plane displacement lengths for describing wind and sky views, as well as temperature inside or over building-blocks (BBs) [12].Recently, high-resolution meteorological information has become essential for attaining sustainable and resilient cities [13,14].

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A Deployment of Fine-Grained Sensor Network and Empirical Analysis of Urban Temperature

Cited by 26 publications

References 18 publications

Heat waves and urban heat islands in Europe: A review of relevant drivers

Heat waves and urban heat islands in Europe: A review of relevant drivers

Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature

A Building-Block Urban Meteorological Observation Experiment (BBMEX) Campaign in Central Commercial Area in Seoul

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