A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5

Michanowicz, Drew R.; Shmool, Jessie L.C.; Tunno, Brett; Tripathy, Sheila; Gillooly, Sara; Kinnee, Ellen; Clougherty, Jane E.

doi:10.1016/j.atmosenv.2016.01.045

Cited by 55 publications

(38 citation statements)

References 33 publications

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“…We used Inverse Distance Weighted (IDW) to interpolate the air pollution data from the 35 monitoring stations to general spatial maps for spatial comparison. This method for spatial interpolation was applied to characterize spatial distributions of pollutant concentrations in some studies [43][44][45][46]. The IDW-based interpolation maps of PM 2.5 and O 3 were produced using a random 70% of points [15] and validated with the remaining 30% of data.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Zhao

Zheng

2018

Sustainability

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This study analyzed the spatiotemporal variations in PM 2.5 and O 3 , and explored their interaction in the summer and winter seasons in Beijing. To this aim, hourly PM 2.5 and O 3 data for 35 air quality monitoring sites were analyzed during the summer and winter of 2016. Results suggested that the highest PM 2.5 concentration and the lowest O 3 concentration were observed at traffic monitoring sites during the two seasons. A statistically significant (p < 0.05) different diurnal variation of PM 2.5 was observed between the summer and winter seasons, with higher concentrations during daytime summer and nighttime winter. Diurnal variations of O 3 concentrations during the two seasons showed a single peak, occurring at 16:00 and 15:00 in summer and winter, respectively. PM 2.5 presented a spatial pattern with higher concentrations in southern Beijing than in northern areas, particularly evident during wintertime. On the contrary, O 3 concentrations presented a decreasing spatial trend from the north to the south, particularly evident during summer. In addition, we found that PM 2.5 concentrations were positively correlated (p < 0.01, r = 0.57) with O 3 concentrations in summer, but negatively correlated (p < 0.01, r = −0.72) with O 3 concentrations in winter.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Zhao

Zheng

2018

Sustainability

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“…Integrated PM 2.5 samples were collected using Harvard Impactors (Air Diagnostics and Engineering, Inc, Harrison, ME) and analyzed for BC using reflectometry (EEL43M Smokestain Reflectometer; Diffusion Systems, Ltd, London, UK). We combined data from both seasons to develop hybrid land‐use regression models, which estimate spatial variation in air pollution as a function of geographic information system (GIS)‐based source covariates (eg, traffic density, industrial emissions), and output from an AERMOD emissions dispersion model, which better accounts for the impact of meteorology and local topography on local source emissions . Patients addresses were geocoded in GIS using a composite address locator designed to maximize positional accuracy, and residence‐specific pollution exposures were estimated as the average value from the spatial model within the 300 meters surrounding each home.…”

Section: Methodsmentioning

confidence: 99%

“…We combined data from both seasons to develop hybrid land-use regression models, which estimate spatial variation in air pollution as a function of geographic information system (GIS)-based source covariates (eg, traffic density, industrial emissions), and output from an AERMOD emissions dispersion model, which better accounts for the impact of meteorology and local topography on local source emissions. 20 Patients addresses were geocoded in GIS using a composite address locator designed to maximize positional accuracy, and residence-specific pollution exposures were estimated as the average value from the spatial model within the 300 meters surrounding each home. Finally, to estimate longterm exposures during a 5-year window (2009-2013), we temporally adjusted all exposure estimates using average daily concentrations for that period from an EPA regulatory monitor, centrally located within the original sampling domain.…”

Section: Ambient Air Quality Sampling and Exposure Assignmentmentioning

confidence: 99%

Association of air pollutants, airborne occupational exposures, and chronic rhinosinusitis disease severity

Velasquez

Moore

Boudreau

et al. 2019

Int Forum Allergy Rhinol

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Background Previous work has shown that chronic rhinosinusitis (CRS) severity may be associated with particulate matter 2.5 (PM2.5) and black carbon (BC) in CRS patients without nasal polyps (CRSsNP). Data regarding occupational exposures, however, are lacking. We assessed the impact of PM2.5, BC, as well as occupational airborne exposure on CRS disease severity. Methods Patients with CRS with nasal polyps (CRSwNP), CRSsNP, and aspirin‐exacerbated respiratory disease (AERD) were identified from an institutionwide database. Spatial modeling from 37 pollutant monitoring sites in Allegheny County was used to estimate exposures. Patient occupations using the 2010 Standard Occupation Classification (SOC10) and airborne occupation exposures to vapors, gases, dusts, fumes, fibers and mists (VGDFFiM) or diesel fumes were recorded. Disease severity was measured by modified Lund‐Mackay score (LMS), systemic corticosteroid therapy, and incidence of functional endoscopic sinus surgery (FESS). Results Two hundred thirty‐four patients were included (CRSwNP, n = 113; CRSsNP, n = 96; AERD, n = 25). The prevalence of AERD among those with CRSwNP was 18%. Patients exposed to VGDFFiM or diesel fumes required higher steroid doses vs nonexposed patients (p = 0.015 and p = 0.03, respectively); patients with VGDFFiM levels >5% were more likely to undergo FESS vs nonexposed patients (p = 0.0378). There was no difference in PM2.5 and BC with regard to disease severity and FESS between CRSwNP, CRSsNP, and AERD patients. Steroid use was significantly higher in CRSwNP and AERD vs CRSsNP (p = 0.001). LMS was significantly higher in AERD as compared with CRSwNP and CRSsNP (p = 0.001). Conclusion Occupational airborne exposure to VGDFFiM correlated with increased prevalence of FESS and need for corticosteroids in CRS patients. There was no difference in PM2.5 and BC levels and disease severity outcome measures between CRS subtypes in this subset.

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“…It should be mentioned that one of the limitations of this study is the propagation of the intrinsic error of the dispersion model in the HFIS model. This limitation is common in nearly all studies which integrate the dispersion and regression models to overcome their drawbacks [26,[28][29][30]65]. However, these studies reveal that, despite this limitation, especially when a specific source of pollution is to be modeled, applying the dispersion models in generating the regression model contributes to model accuracy improvement.…”

Section: Scenarios Evaluationsmentioning

confidence: 88%

“…This dispersion model was used in numerous EHIAs for modeling the pollution caused by a specific source of emission [59][60][61][62][63][64][65].…”

Section: Hierarchical Fuzzy Inference Model For Modeling Traffic Relamentioning

confidence: 99%

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

2017

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Abstract:Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM 2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM 2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM 2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners.

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A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5

Cited by 55 publications

References 33 publications

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

Association of air pollutants, airborne occupational exposures, and chronic rhinosinusitis disease severity

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

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