High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review
01 Nov 2019-Asia-pacific Journal of Atmospheric Sciences (Korean Meteorological Society)-Vol. 55, Iss: 4, pp 539-556
TL;DR: In this paper, the authors present an organized review of the broad aspects related to urban air quality modeling such as urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models.
Abstract: According to World Health Organization, 9 out of 10 people breathe polluted air and the ambient air pollution accounts for nearly 4.2 million early deaths worldwide. There is an urgent need for scientific management of urban air systems. Mathematical modeling of air quality helps the researchers and urban authorities in devising scientific management plans for mitigation of the associated impacts. We present an organized review of the broad aspects related to urban air quality modeling such as – urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models. The paper also discusses about the influence of urban land scape features on air quality, accuracy of emission inventory and model validation methods. The present review provides a vantage point to the researchers in the emerging field of high resolution urban air quality modeling for devising the location specific mitigation plans for the scientific management of the clean air.
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01 Feb 2015
TL;DR: In this article, the authors illustrate the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.
136 citations
TL;DR: In this paper, the authors provide a perspective regarding the opportunity available in addressing the urban air quality management (UAQM) issues using smart city framework in the context of "urban computing".
Abstract: Cities foster economic growth. However, growing cities also contribute to air pollution and climate change. The paper provides a perspective regarding the opportunity available in addressing the urban air quality management (UAQM) issues using smart city framework in the context of ‘urban computing’. Traditionally, UAQM has been built on sparse regulatory monitoring, enhanced with satellite data and forecast models. The ‘Fourth Industrial Revolution’ (4IR) technologies such as Internet of Things (IoT), big data, artificial intelligence, smartphones, social and cloud computing are reshaping urban conglomerates, worldwide. Cities can harness these ubiquitous technologies in concert with traditional methods for betterment of air quality governance and to improve quality of life. This paper discusses the role of urban computing in UAQM through a review of scientific publications and ‘grey literature’ from technical reports of governments, international organizations and institutional websites. It provides an interdisciplinary knowledge repository on urban computing applications for air quality functions. It highlights the potential of integrated technologies in enabling data driven, strategic and real-time mitigation governance actions and helping citizens to take informed decisions. It recommends ‘fit for the purpose’ multitechnology framework for UAQM services in emerging smart cities.
58 citations
TL;DR: In this article, the authors quantified the flood mitigation service of green spaces and estimated the tangible economic damage to the built infrastructure in the Hyderabad metropolitan city, India using the Integrated Valuation of Ecosystem Services and Trade-offs model.
Abstract: Urban floods have become more frequent across the globe. The transformation of the urban landscape with increased concretization and dwindling green cover has resulted in excess run-off generation thereby causing the flash floods. The protective role and ecosystem benefits of urban green spaces needs to be quantified so that it will unlock the possibilities of integrating natural capital thinking into policymaking. Hence in this study, we quantified the flood mitigation service of green spaces and estimated the tangible economic damage to the built infrastructure in the Hyderabad metropolitan city, India using the Integrated Valuation of Ecosystem Services and Trade-offs model. The analysis was carried out for 2-years and 5-years design precipitation of 1 h duration. Results show that 44–50% of the precipitation is retained by the urban green and open spaces. With an increase of 13% in the rainfall intensity (from 2-years to 5-years), the run-off volume has increased by 21%, while the run-off retained has increased only by 5%, which indicates that even slight increase in rainfall intensity results in huge run-off generation that causes commensurate economic damages. The economic damage due to flood inundation of the built infrastructure is estimated to be 1.39 million USD using the unit cost method. Overall, the indicator of run-off retention service is quantified as 4.25E + 13 and 4.46E + 13 for the 2-years and 5-years return period precipitation, respectively. The structural and non-structural flood mitigation measures are also enumerated along with the limitations of the model.
34 citations
TL;DR: In this paper, the authors used high-resolution climate/chemistry simulations over Europe for providing air quality data; use of different baseline mortality data for specific European regions; inclusion of future population projections and dynamical changes for 2050 obtained from the United Nations (UN) Population Projections or use of nonlinear exposure-response functions to estimate the premature mortality due to PM2.5.
24 citations
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
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TL;DR: The Technical Note series provides an outlet for a variety of NCAR manuscripts that contribute in specialized ways to the body of scientific knowledge but which are not suitable for journal, monograph, or book publication.
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9,022 citations
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