Assessment of urban surface thermal environment using MODIS with a population-weighted method: a case study

Yang, Yuanjian; Gao, Zhiqiu; Tao, Shi; Wang, Hong; Li, Yubin; Zhang, Ning; Zhang, Hongqun; Huang, Yong

doi:10.1080/14498596.2017.1422155

Cited by 14 publications

(14 citation statements)

References 55 publications

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“…The UHI also affects the structure and movement of cloud systems (Changnon and Westcott 2002, Kug and Ahn 2013, Pinto et al 2013. The diurnally and seasonally varying UHI is affected by many factors, such as weather and climatic regimes, urban impervious surfaces, anthropogenic heat, air pollution, and urban 3D structure (Oke 1982, Morris and Simmonds 2000, Kim and Baik 2002, Gedzelman et al 2003, Ryu and Baik 2012, Yang Y et al 2019. It is well established that cities are the largest sources of anthropogenic heat emissions as by-products from industrial and human activities.…”

Section: Introductionmentioning

confidence: 99%

Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

Han

et al. 2020

Preprint

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Abstract. The urban heat island intensity (UHII) is the temperature difference between urban areas and their rural surroundings. It is commonly attributed to changes in the underlying surface structure caused by urbanization. Air pollution caused by aerosol particles can affect the UHII by changing the surface energy balance and atmospheric thermodynamic structure. By analyzing satellite data and ground-based observations collected from 2001 to 2010 at 35 cities in China and using the WRF-Chem model, we found that aerosols have very different effects on daytime UHII in different seasons: reducing the UHII in summer, but increasing the UHII in winter. The seasonal contrast in the spatial distribution of aerosols between the urban centers and the suburbs lead to a spatial discrepancy in aerosol radiative effect (SD-ARE). Additionally, different stability of the planetary boundary layer induced by aerosol is closely associated with a dynamic effect (DE) on the UHII. SD-ARE reduces the amount of radiation reaching the ground and changes the vertical temperature gradient, whereas DE increases the stability of the planetary boundary layer and weakens heat release and exchange between the surface and the PBL. Both effects exist under polluted conditions, but their relative roles are opposite between the two seasons. It is the joint effects of the SD-ARE and the DE that drive the UHII to behave differently in different seasons, which is confirmed by model simulations. In summer, the UHII is mainly affected by the SD-ARE, and the DE is weak, and the opposite is the case in winter. This finding sheds a new light on the impact of the interaction between urbanization-induced surface changes and air pollution on urban climate.

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Section: Introductionmentioning

confidence: 99%

Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

Han

et al. 2020

Preprint

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“…Urbanization changes land use and land cover over the urban area, leading to distinctive features in the thermal and hydrological properties (Feng et al, 2015; Yang et al, 2019). Thus, urbanization is characterized by the great differences in temperature and humidity between urban and surrounding rural areas, which are well known as the urban heat island (UHI) (Manley, 1958).…”

Section: Introductionmentioning

confidence: 99%

Urbanization in an Underdeveloped City—Nanning, China and its Impact on a Heavy Rainfall Event in July

Xiao

Wang

Ming-ce

et al. 2020

Earth and Space Science

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A rapid urbanization process is undergoing in China, exerting significant impacts on local weather and climate. However, rare study pays attention to the underdeveloped regions. An underdeveloped city of Nanning, in southwestern China, is experiencing considerable urbanization. The present urban area of Nanning is 2.8 times larger than that in 1980. Using the Weather Research and Forecasting model coupled with an urban canopy model, two experiments with different urban land use are designed to investigate impacts of Nanning urbanization on a rainstorm occurred in July 2009. Results show simulated precipitation can be significantly improved by the experiment with urbanization effects included. Urbanization leads to a warmer and drier surface, which is favored for the formation of near-surface low pressure and wind convergence over the city. But the warm-dry air near urban surface cannot lead to deep convective activity, resulting in urban-enhanced precipitation is dominated by the large-scale precipitation. Such enhanced large-scale precipitation has a positive contribution to atmospheric latent heat release, inducing a heating source in middle troposphere over the urban area. Then the cyclonic circulation around Nanning is intensified according to thermal adaption theory. The enhanced cyclonic circulation is beneficial for stronger moisture convergence and warm-moist southwesterly flow transports into Guangxi from tropical oceans. Vertical motion is amplified by the coastal orographic uplift effect, and wind convergence over the Nanning downstream is also enhanced. Therefore, the convective precipitation dominantly enhances over the coastal and city's downstream regions, which is different from the urban-increased large-scale precipitation in the Nanning City area.

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“…In recent years, high-precision night-time-light (NTL) data from satellites [DMSP/OLS (Defense Meteorological Satellite Program/Operational Line scan System) and NPP-VIIRS (National Polar-orbiting Partnership, NPP; Visible Infrared imaging Radiometer Suite, VIIRS)] have been used to estimate the spatial distribution of the population [35][36][37][38][39], because the information from such satellite imagery is closely related to human activities. Random-forest (RF) model is a non-parametric method that can model complex nonlinear relationships between predictions and heterogeneous predictor variables.…”

Section: Introductionmentioning

confidence: 99%

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

Wang

Gao

et al. 2019

Remote Sensing

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To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

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Assessment of urban surface thermal environment using MODIS with a population-weighted method: a case study

Cited by 14 publications

References 55 publications

Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

Opposite Effects of Aerosols on Daytime Urban Heat Island Intensity between Summer and Winter

Urbanization in an Underdeveloped City—Nanning, China and its Impact on a Heavy Rainfall Event in July

High-Spatial-Resolution Population Exposure to PM2.5 Pollution Based on Multi-Satellite Retrievals: A Case Study of Seasonal Variation in the Yangtze River Delta, China in 2013

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