Neighborhood Air Quality, Respiratory Health, and Vulnerable Populations in Compact and Sprawled Regions

Schweitzer, Lisa; Zhou, Jiangping

doi:10.1080/01944363.2010.486623

Cited by 160 publications

(122 citation statements)

References 18 publications

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“…A limited number of studies have empirically evaluated the direct associations between urban form and the emission or concentration of air pollution (Stone 2008;Schweitzer and Zhou 2010;Bechle et al 2011;Clark et al 2011). Although it is generally assumed that the relationship between land use and vehicle travel holds implications for tailpipe emissions and air quality, only a handful of studies has sought to statistically link urban form to vehicle emissions directly (Stone 2008).…”

Section: Environmental Impacts Of Sprawlmentioning

confidence: 99%

“…Schweitzer and Zhou (2010) took a finer scale approach and found that neighborhoods in less sprawling US metropolitan areas, generally exhibited lower concentrations of O 3 , but higher O 3 and PM 2.5 exposures due to a greater concentration of people living in areas with higher air pollutant concentrations. Through an analysis of 111 U.S. Census defined urban areas, Clark et al (2011) found that those with more centralized populations were associated with lower population-weighted PM 2.5 and O 3 concentrations, whereas those with higher population densities were associated with significantly higher populationweighted PM 2.5 concentrations.…”

Section: Environmental Impacts Of Sprawlmentioning

confidence: 99%

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Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Kashem

Irawan

Wilson

2014

Int. J. Environ. Sci. Technol.

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Previous urban sprawl studies have typically taken a cross-sectional approach without examining how sprawling urban areas are performing over time. Longitudinal studies of individual or household travel behavior and built-environment preference have made some progress in this direction, but very few studies have explored the longitudinal interaction of urban form with transportation and environmental outcomes. This study begins to fill this gap by evaluating the transportation and environmental impact dynamics of several key dimensions of urban sprawl: density, land use mix, centrality, and street connectivity. The central hypothesis is that while the built environment is durable and development patterns change slowly, the impacts of urban sprawl are dynamic rather than static and are accelerated in more sprawling cities relative to less sprawling cities. To test this hypothesis, a panel dataset of 7 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) was developed for 60 Metropolitan Statistical Areas of the US and a hybrid modeling framework that incorporates fixed and random effects is applied to evaluate different transportation and environmental outcomes over time. We found that the influence of urban centrality or the strength of urban subcenters on the travel pattern of commuters and transit users is dynamic. This suggests that people living in cities with stronger centers drive less and use public transportation more over time. For environmental outcomes, we found that cities with higher density have experienced a significant decrease in ambient ozone and PM 2.5 concentrations after controlling city-specific variables.

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Section: Environmental Impacts Of Sprawlmentioning

confidence: 99%

Section: Environmental Impacts Of Sprawlmentioning

confidence: 99%

Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Kashem

Irawan

Wilson

2014

Int. J. Environ. Sci. Technol.

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“…It has demonstrated that the mass concentrations of total suspended particulate (TSP), PM10 and PM 2.5 vary in different areas within a same city (10)(11)(12)(13). Researches particularly prove the significant influences of land use types (14)(15)(16)(17)(18) and building density (19)(20)(21), green space (10), water body (22), location of highway entrance (23), traffic patterns (24,25) and street scale (26,27) upon particulate matter concentration and distribution.…”

Section: Introductionmentioning

confidence: 99%

Correlation analysis of lung cancer and urban spatial factor: based on survey in Shanghai

Wang

Zhao

et al. 2016

J. Thorac. Dis.

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Background: The density of particulate matter (PM) in mega-cities in China such as Beijing and Shanghai has exceeded basic standards for health in recent years. Human exposure to PMs has been identified as traceable and controllable factor among all complicated risk factors for lung cancer. While the improvement of air quality needs tremendous efforts and time, certain revision of PM's density might happen associated with the adjustment of built environment. It is also proved that urban built environment is directly relevant to respiratory disease. Studies have respectively explored the indoor and outdoor factors on respiratory diseases. More comprehensive spatial factors need to be analyzed to understand the cumulative effect of built environment upon respiratory system. This interdisciplinary study examines the impact of both indoor (including age of housing, interval after decoration, indoor humidity etc.) and outdoor spatial factors (including density, parking, green spaces etc.) on lung cancer. Methods: A survey of lung cancer patients and a control group has been conducted in 2014 and 2015. A total of 472 interviewees are randomly selected within a pool of local residents who have resided in Shanghai for more than 5 years. Data are collected including their socio-demographic factors, lifestyle factors, and external and internal residential area factors. Regression models are established based on collected data to analyze the associations between lung cancer and urban spatial factors. Results: Regression models illustrate that lung cancer presents significantly associated with a number of spatial factors. Significant outdoor spatial factors include external traffic volume (P=0.003), main plant type (P=0.035 for trees) of internal green space, internal water body (P=0.027) and land use of surrounding blocks (P=0.005 for residential areas of 7-9 floors, P=0.000 for residential areas of 4-6 floors, P=0.006 for business/ commercial areas over 10 floors, P=0.005 for business/commercial areas of 7-9 floors, P=0.043 for business/ commercial areas of 4-6 floors). Indoor spatial factors include age of housing (P=0.003) and indoor humidity (P=0.000). Conclusions: The findings support the hypothesis that both indoor and outdoor spatial factors are independently associated with lung cancer incidence. Certain principles based on the modeling results are proposed to revise the criteria for lung cancer screening of high-risk individuals. It also provides empirical evidence for urban planning and design to improve built environment with more thorough consideration of respiratory health.

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“…The frequent occurrence of PM 2.5 pollution offers a warning against rapid urban development, which also underscores the importance of constructing a scientific and reasonable city structure. Previous studies have shown that PM 2.5 concentrations in different urban spaces are variable [1,2], as they are affected by the urban spatial pattern [3], land development intensity [4,5], public green spaces [6], road grades, and traffic [7]. For example, Chan et al (2001) [8] found that the concentration of PM 2.5 in an urban industrial district, a residential area, and a business district of Hong Kong, was 77.6, 107.0, and 88.5 µg/m 3 , respectively.…”

Section: Introductionmentioning

confidence: 99%

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

2017

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Abstract:With the increase in urbanization and energy consumption, PM 2.5 has become a major pollutant. This paper investigates the impact of road patterns on PM 2.5 pollution in Beijing, focusing on two questions: Do road patterns significantly affect PM 2.5 concentrations? How do road patterns affect PM 2.5 concentrations? A land-use regression model (LUR model) is used to quantify the associations between PM 2.5 concentrations, and road patterns, land-use patterns, and population density. Then, in the condition of excluding other factors closely correlated to PM 2.5 concentrations, based on the results of the regression model, further research is conducted to explore the relationship between PM 2.5 concentrations and the types, densities, and layouts of road networks, through the controlling variables method. The results are as follows: (1) the regression coefficient of road patterns is significantly higher than the water area, population density, and transport facilities, indicating that road patterns have an obvious influence on PM 2.5 concentrations; (2) under the same traffic carrying capacity, the layout of "a tight network of streets and small blocks" is superior to that of "a sparse network of streets and big blocks"; (3) the grade proportion of urban roads impacts the road patterns' rationality, and a high percentage of branch roads and secondary roads could decrease PM 2.5 concentrations. These findings could provide a reference for the improvement of the traffic structure and air quality of Beijing.

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Neighborhood Air Quality, Respiratory Health, and Vulnerable Populations in Compact and Sprawled Regions

Cited by 160 publications

References 18 publications

Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Evaluating the dynamic impacts of urban form on transportation and environmental outcomes in US cities

Correlation analysis of lung cancer and urban spatial factor: based on survey in Shanghai

Influence of Road Patterns on PM2.5 Concentrations and the Available Solutions: The Case of Beijing City, China

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