Urban form and air pollution disperse: Key indexes and mitigation strategies

Yang, Junyan; Shi, Beixiang; Zheng, Yu; Shi, Yi; Xia, Geyang

doi:10.1016/j.scs.2019.101955

Cited by 65 publications

(46 citation statements)

References 32 publications

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“…The thermal environments of urban near surface spaces have remarkable effects on human outdoor activities and are affected by complex and diverse factors, including motor vehicles, air conditioner external units, reflex of glass curtain walls, thermal radiation of the hard ground and outer walls of buildings, and building shadows. In traditional studies on thermal environments based on actual satellite remote sensing imaging, the thermal environment of the urban canopy layer has actually been studied [25][26][27][28][29], and it has not been possible to determine the thermal environments in the near surface space accurately. However, actual measurement of near surface thermal environments is a very complex problem, in which the selections of the locations and heights of the measurement points are the main issues.…”

Section: Measurement Of Thermal Environmentsmentioning

confidence: 99%

“…However, the most existing studies on thermal environments (reflecting the characteristics of urban canopy layer), are based on air temperature data acquired via fixed-point meteorological observations and satellite remote sensing imaging by automatic meteorological stations in the professional observational networks operated by weather bureaus [25][26][27][28][29]. Such average temperature data can only reflect the conditions in the urban canopy layer and differ significantly from the urban near surface air temperature perceived by humans (as shown in Figure 1).…”

Section: Introductionmentioning

confidence: 99%

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Impacts of Urban Form on Thermal Environment Near the Surface Region at Pedestrian Height: A Case Study Based on High-Density Built-Up Areas of Nanjing City in China

et al. 2020

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The continuous worsening of urban thermal environments poses a severe threat to human health and is among the main problems associated with urban climate change and sustainable development. This issue is particularly severe in high-density built-up areas. Existing studies on the thermal environments (temperature data extracted from satellite remote sensing images) are mainly focused on urban canopy areas (airspace below the average height of trees or buildings) rather than the near surface region (at pedestrian height). However, the main outdoor activity space of urban residents is the area near surface region. Hence, this study aims to investigate the influence of urban form (i.e., building density, height, and openness) on thermal environment near the surface region. The high-density built-up areas of a typical megacity (i.e., Nanjing) in China were selected, and the thermal environments of 26 typical blocks were simulated using ENVI-met software. Temperature field measurements were carried out for simulation validation. On this basis, a classified and comparative study was conducted by selecting the key spatial form elements that affect thermal environments. The results showed that in actual high-density built-up areas, single urban form parameter does not determine the thermal environments near the urban surface but mainly affected by the use (function) of space. For this study, the overall thermal environment of a street block is optimal when the building density is between 40% and 50% and the average building height is between 8 and 17 stories. Nonetheless, the urban form can be improved to optimize the overall effects on building functions and thermal environments. Furthermore, function-specific urban form optimization strategies were proposed to optimize thermal environments according to specific functional needs.

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Section: Measurement Of Thermal Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impacts of Urban Form on Thermal Environment Near the Surface Region at Pedestrian Height: A Case Study Based on High-Density Built-Up Areas of Nanjing City in China

et al. 2020

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“…In addition, based on this information, responsible agency or authorities can plan and make decisions to solve the pollutants problem on a smaller scale. Furthermore, advanced 3D spatial analysis could be done as suggested by other researchers (Yang et al, 2020;Azri et al, 2016;Salleh et al, 2018;Keling et al 2017)…”

Section: Recommendationmentioning

confidence: 99%

Visualising Urban Air Quality Using Aermod, Calpuff and CFD Models: A Critical Review

Ridzuan

Ujang

Azri

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.

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“…Countries face increasingly severe air pollution challenges due to massive emissions from vehicles and industrial waste (Yang et al 2020 ). However, after the restricted human activates amidst COVID-19 lockdown, globally, a significant improvement has been observed in air quality, such as the USA (Pata 2020 ), India (Mahato et al 2020 ), Iran (Broomandi et al 2020 ), China (Bao and Zhang 2020 ; Balsalobre-Lorente et al 2020 ), Malaysia (Suhaimi et al 2020 ), Spain (Baldasano 2020 ), Brazil (Dantas et al 2020 ), and Japan (Ma and Kang 2020 ).…”

Section: Introductionmentioning

confidence: 99%

COVID-19 outbreak, lockdown, and air quality: fresh insights from New York City

Shehzad

Bilgili

Koçak

et al. 2021

Environ Sci Pollut Res

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The outbreak of the COVID-19 pandemic has adversely affected all aspects of life and poses a severe threat to human health and economic development. New York City administration enacted a strict isolation decision at the end of March 2020 to tackle the COVID-19, creating a unique opportunity to assess air quality. Therefore, we investigated the impact of the lockdown on air quality in New York City. We evaluated the air pollutants concentration, i.e., PM 2.5 , CO, NO 2 , SO 2 , and O 3, during the lockdown and compared them with pre-COVID-19. We explored the first phase of lockdown through a spatial approach, then formulated the air quality index (AQI) of each pollutant before and during the lockdown. Our findings revealed that (1) there was a significant decline in the concentration level of PM 2.5 from 10.3 to 4.0 μg/m 3 during phase one of lockdown. (2) NO 2 concentrations have been decreased by up to 52% in 1st phase of lockdown. (3) O 3 concentration has been increased by 44.4%. (4) Brooklyn, Manhattan, Queens, and Staten Island County encountered 18.75%, 55.62%, 47.14%, and 47% diminution in AQI due to lockdown as compared to 2018, respectively. Our key findings can provide critical environmental implications for policymakers, researchers, academics, and the US government.

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Urban form and air pollution disperse: Key indexes and mitigation strategies

Cited by 65 publications

References 32 publications

Impacts of Urban Form on Thermal Environment Near the Surface Region at Pedestrian Height: A Case Study Based on High-Density Built-Up Areas of Nanjing City in China

Impacts of Urban Form on Thermal Environment Near the Surface Region at Pedestrian Height: A Case Study Based on High-Density Built-Up Areas of Nanjing City in China

Visualising Urban Air Quality Using Aermod, Calpuff and CFD Models: A Critical Review

COVID-19 outbreak, lockdown, and air quality: fresh insights from New York City

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