Crowdsourcing Urban Air Temperature Data for Estimating Urban Heat Island and Building Heating/Cooling Load in London

Benjamin, Kit; Luo, Zhiwen; Wang, Xiaoxue

doi:10.3390/en14165208

Cited by 18 publications

(4 citation statements)

References 37 publications

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“…CrowdQC is a statistically-based QC with four main and three optional QC levels that are applied sequentially, removing erroneous data based on the assumption that the whole crowd of CWS knows more than each individual station ("wisdom of the crowd"). Since its release, CrowdQC has successfully been applied in a number of studies to qualitycontrol CWS ta data for further analyses (e.g., Fenner et al, 2019;Feichtinger et al, 2020;Venter et al, 2020Venter et al, , 2021Vulova et al, 2020;Benjamin et al, 2021;Potgieter et al, 2021;Zumwald et al, 2021). Its large-scale applicability was only recently demonstrated by the study of Venter et al (2021), using CrowdQC to quality-control data from >50,000 CWS in 342 urban regions in Europe for a summer month.…”

Section: Introductionmentioning

confidence: 99%

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

Fenner

Bechtel

Demuzere

et al. 2021

Front. Environ. Sci.

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In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

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Section: Introductionmentioning

confidence: 99%

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

Fenner

Bechtel

Demuzere

et al. 2021

Front. Environ. Sci.

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“…A recent study used PWS to examine the cooling efficiencies of tree cover in European cities, finding that trees have a smaller impact on air than surface temperatures, and may not have a cooling effect in all cities [15]. Studies have used PWS, for example, to examine the impacts of land cover on temperatures [5,15,[26][27][28], model air temperatures [29][30][31], as an input to building physics models [32], and to validate and bias-correct urban climate simulations [24,33].…”

Section: Introductionmentioning

confidence: 99%

The potential of urban trees to reduce heat-related mortality in London

Taylor,

Simpson,

Brousse

et al. 2024

Environ. Res. Lett.

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Increasing temperatures and more frequent heatwave events pose threats to population health, particularly in urban environments due to the Urban Heat Island (UHI) effect. Greening, in particular planting trees, is widely discussed as a means of reducing heat exposure and associated mortality in cities. This study aims to use data from Personal Weather Stations (PWS) across the Greater London Authority to understand how urban temperatures vary according to tree canopy coverage and estimate the heat-health impacts of London’s urban trees. Data from Netatmo PWS from 2015 – 2022 were cleaned, combined with official Met Office temperatures, and spatially linked to tree canopy coverage and built environment data. A Generalized Additive Model was used to predict daily average urban temperatures under different tree canopy coverage scenarios for historical and projected future summers, and subsequent health impacts estimated. Results show areas of London with higher canopy coverage have lower urban temperatures, with average maximum daytime temperatures 0.8 °C and minimum temperatures 2.0 °C lower in the top decile versus bottom decile canopy coverage during the 2022 heatwaves. We estimate that London’s urban forest helped avoid 153 heat attributable deaths from 2015-2022 (including 16 excess deaths during the 2022 heatwaves), representing around 16% of Urban Heat Island-related mortality. Increasing tree coverage 10% in-line with the London Strategy would have reduced UHI-related mortality by a further 10%, while a maximal tree coverage would have reduced it 55%. By 2061-2080, under RCP8.5, we estimate that London’s current tree planting strategy can help avoid an additional 23 heat-attributable deaths a year, with maximal coverage increasing this to 131. Substantial benefits would also be seen for carbon storage and sequestration. Results of this study support increasing urban tree coverage as part of a wider public health effort to mitigate high urban temperatures.

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“…Historical UHI data indicate London has experienced rising temperatures since the late 19th century, with hotter summers and a widening temperature gap with its surrounding areas. [2]. After the Industrial Revolution, rapid urban population growth, extensive construction and road paving, industrial activities, and anthropogenic heat discharge have escalated.…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Analysis of the Impact of Summer Forest Dynamics on Urban Heat Island Effect in Yan’an City

Wang,

Chen,

Wang

et al. 2024

Sustainability

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In this study, MODIS land products and China land cover datasets were used to extract normalized difference vegetation index, land surface temperature, and vegetation cover type in Yan’an City during the summers of 2017–2022. On this basis, analysis of spatial change and correlation were carried out as a way to study the mitigation effect on urban heat islands in Yan’an City with forest. The study showed that: (1) The coverage of normalized difference vegetation index over 0.4 in summer in Yan’an City increased from 59.38% to 69.12%, and the vegetation showed good growth conditions. It has a spatial distribution pattern of more in the south and less in the north. (2) The proportion of the urban heat island in Yan’an City increased from 15.51% to 16.86%. Urban heat island intensity fluctuated year by year, with the maximum urban heat island intensity of 6.26 °C appearing in 2019. It has a spatial distribution pattern of less in the south and less in the north. The transition rate of temperature field grade from low to high is 73.32%, and the transition rate to low is only 0.31%. (3) There is a negative correlation between land surface temperature and normalized difference vegetation index in Yan’an City. Vegetation has a mitigating effect on the UHI and the best cooling effect among the vegetation is shown by forest. The cooling effect of forest in Yan’an City is attenuated by an increase in distance, and the effective range is greater than 1000 m. In this study, the regulation effect of forest on the urban heat island was obtained by digging deeper into the intrinsic connection between spatial change in vegetation cover and land surface temperature change in Yan’an City. It provides an important reference for the formulation of meteorological protection policy as well as the promotion of sustainable development of the urban ecological environment and is of guiding significance for future urban planning and ecological construction.

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Crowdsourcing Urban Air Temperature Data for Estimating Urban Heat Island and Building Heating/Cooling Load in London

Cited by 18 publications

References 37 publications

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

The potential of urban trees to reduce heat-related mortality in London

Multi-Temporal Analysis of the Impact of Summer Forest Dynamics on Urban Heat Island Effect in Yan’an City

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