Population density and urban air quality

Borck, Rainald; Schrauth, Philipp

doi:10.1016/j.regsciurbeco.2020.103596

Cited by 112 publications

(68 citation statements)

References 48 publications

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“…The figure reveals the positive correlation of population density with AQI (correlation coefficient, r = 0.50, p < 0.05), PM2.5 (r = 0.59, p < 0.01), and NO2 (r = 0.55, p < 0.05). Borck and Schrauth (2021) argued the effect of population density on urban air pollution in over 400 German districts. They found that increasing population density by one percent increases NO2 by 0.25 percent, PM10 by 0.07 percent, and AQI with an elasticity of 0.11-0.13.…”

Section: Resultsmentioning

confidence: 99%

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Benchrif

Wheida

Tahri

et al. 2021

Sustainable Cities and Society

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Implemented quarantine due to the ongoing novel coronavirus (agent of COVID-19) has an immense impact on human mobility and economic activities as well as on air quality. Since then, and due to the drastic reduction in pollution levels in cities across the world, a large discussion has been magnetized regarding if the lockdown is an adequate alternative counter-measure for enhancing air quality. This paper aimed at studying the Air Quality Index (AQI), PM2.5, and tropospheric NO2 levels in three lockdown phases (before, during, and after) among 21 cities around the world. Simple before/after comparison approach was carried out to capture the declining trend in air pollution levels caused by the lockdown restrictions. The results showed that the frequency distribution for NO2 is more variable than that for PM2.5, and the distribution is flatter from 2020 to the baseline 2018-2019 period. Besides, AQI, in most of the cities, has varied from high to mild pollution during the lockdown and was moderate before. Although during the lockdown, a reduction of 3 to 58% of daily NO2 concentrations was observed across the cities, an increase was detected in three cities including Abidjan (1%), Conakry (3%), and Chengdu (10%). Despite this mixed trend, the NO2 time series clearly showed the effect of the unlocking phase where the NO2 levels increased in almost all cities. Similarly, PM2.5 concentrations have increased in the post-lockdown period, with 50% of the cities reporting significant positive differences between the lock and the unlock phase. Then, the levels of PM2.5 were higher at the pre-lockdown phase than at any other time exhibiting a “U” shape. In addition, during Ramadan, it was noted that altered patterns of daily activities in some Islamic cities have a significant negative impact on air quality.

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Section: Resultsmentioning

confidence: 99%

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Benchrif

Wheida

Tahri

et al. 2021

Sustainable Cities and Society

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“…To conduct this investigation, we utilized the largest collection of Google Street View images that has been leveraged for public health research to characterize neighborhood environments. In examining associations between built environment characteristics and COVID cases, we controlled for demographic compositional characteristics of areas and population density, which has previously been utilized in econometric studies as a proxy for air pollution and other factors found with greater prevalence in urban areas [ 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

Nguyen

Huang

Kumar

et al. 2020

IJERPH

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The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents’ risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

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“…In another systematic study on 109 districts in Germany, which included 51 urban districts, Borck and Schrauth [14] found that a 1% increase in population density increased PM 2.5 concentrations by a modest 0.08%. Using an authoritative survey, Ahlfeldt and Pietrostefani [27] cited both studies by Carozzi and Roth [13] and Borck and Schrauth [14], and recommended +0.13 as the elasticity for pollution reduction. In short, the impact of high-density cities on PM 2.5 pollution was positive in Chinese studies.…”

Section: Literature Review Of Pm 25 Concentrations In Urban Centersmentioning

confidence: 97%

“…In terms of developed countries, PM 2.5 is not as critical of an environmental pollution issue for cities in developed countries [2]. Thus, only a few socio-economic studies on PM 2.5 concentrations for cities in developed countries have been published recently on cities in the U.S. [13] and Germany [14]. A group of related studies on air pollution in developed countries such as the U.S. and Canada have also been published [16][17][18][19][20][21].…”

Section: Literature Review Of Pm 25 Concentrations In Urban Centersmentioning

confidence: 99%

“…In contrast to the many studies on urban pollution, only a few socio-economic studies on PM 2.5 concentrations in cities in developed countries have been published in recent years [13,14]. The current study fills this important gap in the urban pollution literature for developed economies by focusing on 254 cities in the U.S., Germany, Japan, France, U.K. and Spain.…”

Section: Introductionmentioning

confidence: 98%

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Impact of Income, Density, and Population Size on PM2.5 Pollutions: A Scaling Analysis of 254 Large Cities in Six Developed Countries

Kim

Chang

Kim

2021

IJERPH

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Despite numerous studies on multiple socio-economic factors influencing urban PM2.5 pollution in China, only a few comparable studies have focused on developed countries. We analyzed the impact of three major socio-economic factors (i.e., income per capita, population density, and population size of a city) on PM2.5 concentrations for 254 cities from six developed countries. We used the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model with three separate data sets covering the period of 2001 to 2013. Each data set of 254 cities were further categorized into five subgroups of cities ranked by variable levels of income, density, and population. The results from the multivariate panel regression revealed a wide variation of coefficients. The most consistent results came from the six income coefficients, all of which met the statistical test of significance. All income coefficients except one carried negative signs, supporting the applicability of the environmental Kuznet curve. In contrast, the five density coefficients produced statistically significant positive signs, supporting the results from previous studies. However, we discovered an interesting U-shaped distribution of density coefficients across the six subgroups of cities, which may be unique to developed countries with urban pollution. The results from the population coefficients were not conclusive, which is similar to the results of previous studies. Implications from the results of this study for urban and national policy makers are discussed.

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Population density and urban air quality

Cited by 112 publications

References 48 publications

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Air quality during three covid-19 lockdown phases: AQI, PM2.5 and NO2 assessment in cities with more than 1 million inhabitants

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases

Impact of Income, Density, and Population Size on PM2.5 Pollutions: A Scaling Analysis of 254 Large Cities in Six Developed Countries

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