Designing sensor networks to resolve spatio-temporal urban temperature variations: fixed, mobile or hybrid?

Environmental Research Letters - Tập 14 Số 7 - Trang 074022 - 2019
Jiachuan Yang1,2, Elie Bou‐Zeid1
1Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey, United States of America
2Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Kowloon, Hong Kong, People’s Republic of China

Tóm tắt

Abstract The spatio-temporal variability of temperatures in cities impacts human well-being, particularly in a large metropolis. Low-cost sensors now allow the observation of urban temperatures at a much finer resolution, and, in recent years, there has been a proliferation of fixed and mobile monitoring networks. However, how to design such networks to maximize the information content of collected data remains an open challenge. In this study, we investigate the performance of different measurement networks and strategies by deploying virtual sensors to sample the temperature data set in high-resolution weather simulations in four American cities. Results show that, with proper designs and a sufficient number of sensors, fixed networks can capture the spatio-temporal variations of temperatures within the cities reasonably well. Based on the simulation study, the key to optimizing fixed sensor location is to capture the whole range of impervious fractions. Randomly moving mobile systems consistently outperform optimized fixed systems in measuring the trend of monthly mean temperatures, but they underperform in detecting mean daily maximum temperatures with errors up to 5 °C. For both networks, the grand challenge is to capture anomalous temperatures under extreme events of short duration, such as heat waves. Here, we show that hybrid networks are more robust systems under extreme events, reducing errors by more than 50%, because the time span of extreme events detected by fixed sensors and the spatial information measured by mobile sensors can complement each other. The main conclusion of this study concerns the importance of optimizing network design for enhancing the effectiveness of urban measurements.

Từ khóa


Tài liệu tham khảo

Brandsma, 2012, Measurement and statistical modeling of the urban heat island of the City of Utrecht (the Netherlands), J. Appl. Meteor. Climatol., 51, 1046, 10.1175/JAMC-D-11-0206.1

Chen, 2012, Sky view factor analysis of street canyons and its implications for daytime intra‐urban air temperature differentials in high‐rise, high‐density urban areas of Hong Kong: a GIS‐based simulation approach, Int. J. Climatol., 32, 121, 10.1002/joc.2243

Grimmond, 2007, Urbanization and global environmental change: local effects of urban warming, Geogr. J., 173, 83, 10.1111/j.1475-4959.2007.232_3.x

Heusinkveld, 2014, Spatial variability of the Rotterdam urban heat island as influenced by urban land use, J. Geophys. Res. Atmos., 119, 677, 10.1002/2012JD019399

Honjo, 2015, Network optimization for enhanced resilience of urban heat island measurements, Sustain. Cities Soc., 19, 319, 10.1016/j.scs.2015.02.004

Ivajnšič, 2014, Geographically weighted regression of the urban heat island of a small city, Appl. Geogr., 53, 341, 10.1016/j.apgeog.2014.07.001

Johansson, 2014, Instruments and methods in outdoor thermal comfort studies—the need for standardization, Urban Clim., 10, 346, 10.1016/j.uclim.2013.12.002

Koskinen, 2010, The Helsinki testbed: a mesoscale measurement, research, and service platform, Bull. Amer. Meteor. Soc., 92, 325, 10.1175/2010BAMS2878.1

Leconte, 2015, Using local climate zone scheme for UHI assessment: evaluation of the method using mobile measurements, Build. Environ., 83, 39, 10.1016/j.buildenv.2014.05.005

Li, 2013, Synergistic interactions between urban heat islands and heat waves: the impact in cities is larger than the sum of its parts, J. Appl. Meteor. Climatol., 52, 2051, 10.1175/JAMC-D-13-02.1

Li, 2013a, Development and evaluation of a mosaic approach in the WRF-Noah framework, J. Geophys. Res. Atmos., 118, 918, 10.1002/2013JD020657

Li, 2011, Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China, Remote Sens. Environ., 115, 3249, 10.1016/j.rse.2011.07.008

Li, 2013b, Relation between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution?, Landscape Urban Plan., 114, 1, 10.1016/j.landurbplan.2013.02.005

Llaguno-Munitxa, 2018, Sensing and information technologies for the environment (SITE); hardware and software innovations in mobile sensing applications

Malings, 2018, Surface heat assessment for developed environments: optimizing urban temperature monitoring, Build. Environ., 141, 143, 10.1016/j.buildenv.2018.05.059

Mallick, 2013, Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India, Adv. Space Res., 52, 639, 10.1016/j.asr.2013.04.025

Marjovi, 2015, High resolution air pollution maps in urban environments using mobile sensor networks, 10.1109/DCOSS.2015.32

Mead, 2013, The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks, Atmos. Environ., 70, 186, 10.1016/j.atmosenv.2012.11.060

Meehl, 2004, More intense, more frequent, and longer lasting heat waves in the 21st century, Science, 305, 994, 10.1126/science.1098704

Muller, 2013, Sensors and the city: a review of urban meteorological networks, Int. J. Climatol., 33, 1585, 10.1002/joc.3678

, 2012

Peng, 2012, Surface urban heat island across 419 global big cities, Environ. Sci. Technol., 46, 696, 10.1021/es2030438

Pigeon, 2006, Urban thermodynamic island in a coastal city analysed from an optimized surface network, Boundary-Layer Meteorol., 120, 315, 10.1007/s10546-006-9050-z

Ramamurthy, 2017, Impact of heatwave on a megacity: an observational analysis of New York City during July 2016, Environ. Res. Lett., 12, 10.1088/1748-9326/aa6e59

Song, 2017, The hysteresis effect on surface-air temperature relation and its implications to urban planning: an examination in Phoenix, Arizona, USA, Landscape Urban Plan., 167, 198, 10.1016/j.landurbplan.2017.06.024

Stewart, 2011, A systematic review and scientific critique of methodology in modern urban heat island literature, Int. J. Climatol., 31, 200, 10.1002/joc.2141

Sun, 2003, Estimation of land surface temperature from a geostationary operational environmental satellite (GOES-8), J. Geophys. Res. Atmos., 108, 10.1029/2002JD002422

Svensson, 2004, Sky view factor analysis–implications for urban air temperature differences, Meteorol. Appl., 11, 201, 10.1017/S1350482704001288

Tan, 2014, Urban integrated meteorological observations: practice and experience in Shanghai, China, Bull. Amer. Meteor. Soc., 96, 85, 10.1175/BAMS-D-13-00216.1

Tewari, 2019, Interaction of urban heat islands and heat waves under current and future climate conditions and their mitigation using green and cool roofs in New York city and Phoenix, Arizona, Environ. Res. Lett., 14, 10.1088/1748-9326/aaf431

Theeuwes, 2014, Seasonal dependence of the urban heat island on the street canyon aspect ratio, Quarterly J. Royal Meteorolo. Soc., 140, 2197, 10.1002/qj.2289

Vincent, 2012, A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis, J. Geophys. Res. Atmos., 117, 10.1029/2012JD017859

Wang, 2013, A coupled energy transport and hydrological model for urban canopies evaluated using a wireless sensor network, Quarterly J. Royal Meteorolo. Soc., 139, 1643, 10.1002/qj.2032

Wu, 2015, Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature, Remote Sens. Environ., 156, 169, 10.1016/j.rse.2014.09.013

Yang, 2018, Should cities embrace their heat islands as shields from extreme cold?, J. Appl. Meteor. Climatol., 57, 1309, 10.1175/JAMC-D-17-0265.1

Yang, 2019, Scale dependence of the benefits and efficiency of green and cool roofs, Landscape Urban Plan., 185, 127, 10.1016/j.landurbplan.2019.02.004

Yang, 2015, Enhancing hydrologic modelling in the coupled Weather Research and Forecasting–Urban modelling system, Boundary-Layer Meteorol., 155, 87, 10.1007/s10546-014-9991-6

Yuan, 2007, Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote Sens. Environ., 106, 375, 10.1016/j.rse.2006.09.003

Zakšek, 2012, Downscaling land surface temperature for urban heat island diurnal cycle analysis, Remote Sens. Environ., 117, 114, 10.1016/j.rse.2011.05.027

Zhao, 2012, A review on the prediction of building energy consumption, Renew. Sustain. Energy Rev., 16, 3586, 10.1016/j.rser.2012.02.049