The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

Arain, M. Altaf; Blair, Rose; Finkelstein, Norm; Brook, Jeffrey R.; Sahsuvaroglu, Talar; Beckerman, Bernardo; Zhang, Leiming; Jerrett, Michael

doi:10.1016/j.atmosenv.2006.11.063

Cited by 151 publications

(79 citation statements)

References 13 publications

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“…A variety of models and relevant variables to be included have been proposed to study the relationships between airborne concentrations and distance from the source [27][28][29]. Overall, our work indicates how cubic splines can be used to identify complex relations between proximity to point sources of air pollution and observed extent of contamination, and identified batteries as an important source of benzene emissions that was missed in previous analysis of the same data.…”

Section: Discussionmentioning

confidence: 84%

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Dinu¹,

Chen²,

Burstyn³

2010

TOENVIRJ

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Abstract:The level and determinants of airborne concentrations were estimated by collecting air samples at 1206 fixed sites across a geographic area associated with primary oil and gas industry in the rural western Canada, in the provinces of Alberta, north-east British Columbia, and central and southern Sasketchewan from April 2001 to December 2002. Benzene concentrations integrated over one calendar month were determined using passive organic vapor monitors. Previous work applied linear mixed effects models to identify the determinants of airborne benzene concentrations, in particular the proximity to oil and gas facilities. We present results of a more flexible model using cubic splines to accommodate nonlinearities in the effects of determinants of airborne benzene concentrations, as well as time. Benzene concentrations exhibited monotonically increasing time trends for the months from July through December, and monotonically decreasing time trends corresponding to the months from December to July. We illustrated here how cubic splines can be used to identify complex relations between proximity to point sources of air pollution and observed extent of contamination, during the study period, and identified batteries as an important source of benzene emissions that was missed in previous analysis of the same data. These findings contribute to better understanding how positioning oil and gas facilities impacts air quality.

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Section: Discussionmentioning

confidence: 84%

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Dinu¹,

Chen²,

Burstyn³

2010

TOENVIRJ

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“…In each case, we selected monitoring Arain et al (2007) for Toronto (Canada), and by Kim et al (2012) building to the peak. In addition, morning traffic peaks coincide with peaks of [PM 10 ], but the afternoon traffic peak shows a comparatively lower level of [PM 10 ].…”

Section: Temporal Variation Of Air Pollution and Assimilative Capacitymentioning

confidence: 99%

Air pollution dynamics and the need for temporally differentiated road pricing

Coria

Bonilla

Grundström

et al. 2015

Transportation Research Part A: Policy and Practice

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In this paper we investigate the effects of the temporal variation of pollution dispersion, traffic flows and vehicular emissions on pollution concentration and illustrate the need for temporally differentiated road pricing through an application to the case of the congestion charge in Stockholm, Sweden. By accounting explicitly for the role of pollution dispersion on optimal road pricing, we allow for a more comprehensive view of the economy-ecology interactions at stake, showing that price differentiation is an optimal response to the physical environment. Most congestion charges in place incorporate price bans to mitigate congestion. Our analysis indicates that, to ensure compliance with air quality standards, such price variations should also be a response to limited pollution dispersion.

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“…Meteorology is well known to be an important factor contributing to air quality (Arain et al, 2007;Elminir, 2005;Mandurino et al, 2009;Seaman, 2000;Sousa et al, 2008). It is extremely important to consider the effect of meteorological conditions on atmospheric pollution, since they clearly influence dispersion capability in the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

Barrón-Adame

Cortina-Januchs

Vega-Corona

et al. 2012

Expert Systems with Applications

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Keywords:Air pollution Meteorological variables Artificial neural networks Self-Organizing Maps (SOM) Clustering Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (S0 2 ) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of S0 2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.

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The use of wind fields in a land use regression model to predict air pollution concentrations for health exposure studies

Cited by 151 publications

References 13 publications

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Modeling the Shape of the Dependency of Airborne Benzene Concentration in the Air on Distance to Primary Oil and Gas Facilities

Air pollution dynamics and the need for temporally differentiated road pricing

Unsupervised system to classify SO2 pollutant concentrations in Salamanca, Mexico

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