Mapping urban air pollution using GIS: a regression-based approach

Briggs, David; Collins, Susan; Elliott, Paul; Fischer, Paul; Kingham, Simon; Lebret, Erik; Pryl, Karel; Reeuwijk, H. van; Smallbone, Kirsty; Veen, Andre Van Der

doi:10.1080/136588197242158

Cited by 571 publications

(345 citation statements)

References 33 publications

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“…First developed by Briggs et al (1997), LUR has been proved to have more advantages than other conventional approaches (Hoek et al 2008). By means of establishing a statistical relationship between pollutant concentrations measured at 20-100 sites and potential influencing factors such as land use, traffic, and population density, LUR is able to predict concentrations at unsampled locations throughout a given domain within the framework of GIS (Henderson et al 2007;Hoek et al 2008).…”

Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Section: Responsible Editor: Gerhard Lammelmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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“…Because NO 2 concentrations vary on a fine spatial scale, direct interpolation will result in systematic prediction errors related to emission sources in the area ( Stedman et al, 1997 ). On a smaller spatial scale, the SAVIAH study has found that regression -based methods using external information, such as distance to major roads, were superior to interpolation methods in explaining within-city variation of NO 2 concentrations (Briggs et al, 1997 ). In a UK study (Stedman et al, 1997 ), the percentage of urban and suburban land cover at two spatial scales ( 100 and 25 km 2 around the sites) and estimated emission from major road vehicle sources in an area of 4 km 2 around the site were used as predictors in a regression model for NO 2 .…”

Section: Urban Backgroundmentioning

confidence: 99%

“…In the SAVIAH study, individual exposure estimates for study subjects were generated based on concentration measurements at a limited number of sites and prediction of these concentrations using data on explanatory variables available in a GIS ( Briggs et al, 1997 ). Regression models were developed using factors such as traffic intensity near the sites, distance to major roads, population density, and sampling height to explain the measured concentrations.…”

Section: Introductionmentioning

confidence: 99%

“…Regression models were developed using factors such as traffic intensity near the sites, distance to major roads, population density, and sampling height to explain the measured concentrations. This regression model was next applied to the addresses of the study subjects (Briggs et al, 1997 ). A similar approach has been applied to describe the variation of the NO 2 concentration in a dense network spread over the UK ( Stedman et al, 1997 ).…”

Section: Introductionmentioning

confidence: 99%

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Estimation of long-term average exposure to outdoor air pollution for a cohort study on mortality

Hoek

Fischer

Brandt

et al. 2001

J Expo Sci Environ Epidemiol

Self Cite

138

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Recent prospective cohort studies have suggested that long -term exposure to low levels of particulate matter ( PM ) air pollution is associated with increased mortality due to, especially, cardio -pulmonary disease. Exposure to ambient air pollution was estimated mostly as city average concentrations, assuming homogenous exposure within the city. We used an ongoing cohort study -The Netherlands Cohort Study ( NLCS ) on diet and cancer -to investigate the relationship between traffic -related air pollution and mortality. The baseline data collection took place in 1986. A study was conducted to develop methods for exposure assessment and evaluate the contrast in exposure to air pollution within the cohort. Assessment of long -term exposure to two traffic -related air pollutants, Black Smoke ( BS ) and Nitrogen Dioxide ( NO 2 ) , consisted of separate estimation of regional background, urban background, and local traffic contributions at the home address. Interpolation of concentration data from a routine monitoring network was used to estimate the regional background concentration. A regression model relating degree of urbanization to air pollution was used to allow for differences between different towns / neighborhoods of cities. Distance to major roads was calculated to characterize local traffic contributions, using a Geographic Information System ( GIS ) . Interpolation resulted in reasonably precise regional background estimation when distant sites were not used and distance squared was used as the weight. Cross -validation showed that prediction errors were about 15% of the range in regional background concentration. Urban and local scales contributed significantly to the contrast within the cohort. Prediction errors for estimating the urban background were about 25% of the range in background concentrations. When the developed model was applied to the study cohort, there was substantial contrast in estimated exposure to BS and NO 2 . About 90% of the study population lived 10 years or more at its 1986 home address -supporting the use of the estimated concentration at the 1986 address as a relevant exposure variable.

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“…In lieu of using home-specific outdoor measurements to determine ambient-generated pollutant exposures (which would be nearly as labor-intensive as indoor monitoring), factors generated from Geographic Information Systems (GIS), such as distance from road, population density, and land use can be used in combination with central site monitoring data to estimate ambient exposures (Briggs et al 1997;Brauer et al 2003). Questionnaire (e.g., opening of windows, air conditioning usage) and/or property assessment data on individual building characteristics can then be used to estimate residential ventilation patterns (Long et al 2001;Setton et al 2005) that potentially affect the influence of ambient concentrations and indoor sources (Abt et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

Baxter

Clougherty

Paciorek³

et al. 2007

Atmospheric Environment

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Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and/or factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system (GIS), and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO 2 ) and fine particulate matter (PM 2.5 ) in 43 low SES residences across multiple seasons from 2003 -2005. Elemental carbon concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally-located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM 2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO 2 with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50m buffer of a home and distance from a truck route as important contributors to indoor levels of NO 2 and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and

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Mapping urban air pollution using GIS: a regression-based approach

Cited by 571 publications

References 33 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Estimation of long-term average exposure to outdoor air pollution for a cohort study on mortality

Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data

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