Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach

Kanaroglou, Pavlos; Jerrett, Michael; Morrison, Jason; Beckerman, Bernardo; Arain, M. Altaf; Gilbert, Nicolas L.; Brook, Jeffrey R.

doi:10.1016/j.atmosenv.2004.06.049

Cited by 227 publications

(127 citation statements)

References 15 publications

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“…The LUR model development in the current study was similar to approaches used by Jerrett et al (2007) and Kanaroglou et al (2005) and involved development of a location-allocation model for choosing optimal monitoring locations (Buzzelli, 2008); deployment of air pollution monitors and measurement of pollution concentrations in locations in and around the City of London; and the application of LUR modeling to examine land use characteristics that contribute to NO 2 variability in London. The LUR model is subsequently used to estimate ambient concentrations throughout the city based on these land use characteristics.…”

Section: Methodsmentioning

confidence: 99%

Assessing the spatial distribution of nitrogen dioxide in London, Ontario

Oiamo

Luginaah

Buzzelli

et al. 2012

Journal of the Air & Waste Management Association

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Land use regression (LUR) models have been widely used to characterize the spatial distribution of urban air pollution and estimate exposure in epidemiologic studies. However, spatial patterns of air pollution vary greatly between cities due to local source type and distribution. London, Ontario, Canada, is a medium-sized city with relatively few and isolated industrial point sources, which allowed the study to focus on the contribution of different transportation sectors to urban air pollution. This study used LUR models to estimate the spatial distribution of nitrogen dioxide (NO 2 ) and to identify local sources influencing NO 2 concentrations in London, ON. Passive air sampling was conducted at 50 locations throughout London over a 2-week period in May-June 2010. NO 2 concentrations at the monitored locations ranged from 2.8 to 8.9 ppb, with a median of 5.2 ppb. Industrial land use, dwelling density, distance to highway, traffic density, and length of railways were significant predictors of NO 2 concentrations in the final LUR model, which explained 78% of NO 2 variability in London. Traffic and dwelling density explained most of the variation in NO 2 concentrations, which is consistent with LUR models developed in other Canadian cities. We also observed the importance of local characteristics. Specifically, 17% of the variation was explained by distance to highways, which included the impacts of heavily traveled corridors transecting the southern periphery of the city. Two large railway yards and railway lines throughout central areas of the city explained 9% of NO 2 variability. These results confirm the importance of traditional LUR variables and highlight the importance of including a broader array of local sources in LUR modeling. Finally, future analyses will use the model developed in this study to investigate the association between ambient air pollution and cardiovascular disease outcomes, including plaque burden, cholesterol, and hypertension.

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Section: Methodsmentioning

confidence: 99%

Assessing the spatial distribution of nitrogen dioxide in London, Ontario

Oiamo

Luginaah

Buzzelli

et al. 2012

Journal of the Air & Waste Management Association

Self Cite

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“…Location-allocation, a technique used in many of the major LUR studies Kanaroglou et al, 2005;Sahsuvaroglu et al, 2006;Henderson et al, 2007;Jerrett et al, 2007) makes use of a geographic information system and existing monitoring data to determine optimal locations for temporary monitoring sites to maximize their ability to represent spatial variations in pollutant concentrations. No methodology currently exists for determining the minimum number of sampling locations to perform LUR.…”

Section: Introductionmentioning

confidence: 99%

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

Poplawski

Gould

Setton

et al. 2008

J Expo Sci Environ Epidemiol

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Land use regression (LUR) is a method for predicting the spatial distribution of traffic-related air pollution. To facilitate risk and exposure assessment, and the design of future monitoring networks and sampling campaigns, we sought to determine the extent to which LUR can be used to predict spatial patterns in air pollution in the absence of dedicated measurements. We evaluate the transferability of one LUR model to two other geographically comparable areas with similar climates and pollution types. The source model, developed in 2003 to estimate ambient nitrogen dioxide (NO 2 ) concentrations in Vancouver (BC, Canada) was applied to Victoria (BC, Canada) and Seattle (WA, USA). Model estimates were compared with measurements made with Ogawa s passive samplers in both cities. As part of this study, 42 locations were sampled in Victoria for a 2-week period in June 2006. Data obtained for Seattle were collected for a different project at 26 locations in March 2005. We used simple linear regression to evaluate the fit of the source model under three scenarios: (1) using the same variables and coefficients as the source model; (2) using the same variables as the source model, but calculating new coefficients for local calibration; and (3) developing site-specific equations with new variables and coefficients. In Scenario 1, we found that the source model had a better fit in Victoria (R 2 ¼ 0.51) than in Seattle (R 2 ¼ 0.33). Scenario 2 produced improved R 2 -values in both cities (Victoria ¼ 0.58, Seattle ¼ 0.65), with further improvement achieved under Scenario 3 (Victoria ¼ 0.61, Seattle ¼ 0.72). Although it is possible to transfer LUR models between geographically similar cities, success may depend on the between-city consistency of the input data. Modest field sampling campaigns for location-specific model calibration can help to produce transfer models that are equally as predictive as their sources.

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“…For legislatory purposes, policymakers have been installing monitoring stations or systems across many cities [4,5]. As a consequence, current systems measure air pollution at a very low spatial resolution, e.g., only 22 stations covering a 50 × 50 km 2 (113 km 2 per station) in Beijing, 14 stations covering 1572 km 2 (112 km 2 per station) in London, and 61 stations (221 km 2 per station) in Flanders, Belgium.…”

Section: Introductionmentioning

confidence: 99%

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

Xie

Šemanjski

Gautama

et al. 2017

IJGI

142

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Abstract:The impact of urban air pollution on the environments and human health has drawn increasing concerns from researchers, policymakers and citizens. To reduce the negative health impact, it is of great importance to measure the air pollution at high spatial resolution in a timely manner. Traditionally, air pollution is measured using dedicated instruments at fixed monitoring stations, which are placed sparsely in urban areas. With the development of low-cost micro-scale sensing technology in the last decade, portable sensing devices installed on mobile campaigns have been increasingly used for air pollution monitoring, especially for traffic-related pollution monitoring. In the past, some reviews have been done about air pollution exposure models using monitoring data obtained from fixed stations, but no review about mobile sensing for air pollution has been undertaken. This article is a comprehensive review of the recent development in air pollution monitoring, including both the pollution data acquisition and the pollution assessment methods. Unlike the existing reviews on air pollution assessment, this paper not only introduces the models that researchers applied on the data collected from stationary stations, but also presents the efforts of applying these models on the mobile sensing data and discusses the future research of fusing the stationary and mobile sensing data.

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Establishing an air pollution monitoring network for intra-urban population exposure assessment: A location-allocation approach

Cited by 227 publications

References 15 publications

Assessing the spatial distribution of nitrogen dioxide in London, Ontario

Assessing the spatial distribution of nitrogen dioxide in London, Ontario

Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide

A Review of Urban Air Pollution Monitoring and Exposure Assessment Methods

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