| 1. |
- Habermann, Mateus, 1981, et al.
(author)
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Land Use Regression Approach to Model NO2–Concentrations in a Swedish Medium-City
- 2018
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In: Environmental Pollution and Protection. - : Isaac Scientific Publishing Co., Ltd.. - 2519-1055 .- 2519-1063. ; 3:3, s. 71-89
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Journal article (peer-reviewed)abstract
- In order to visualize the geographical distribution of air pollution concentration realistically, we applied the Land Use Regression (LUR) model in the urban area of Gothenburg, Sweden. The concentration of NO2 was obtained by 25 passive air samplers during 7-20 May, 2001. Explanatory variables were estimated by GIS in buffers ranging from 50 to 500 m-radii. Linear regression was calculated, and the most robust were attained to the multiple linear regression. Additionally, the LUR model was compared with a dispersion model. The final model explained 81.7% of the variance of NO2 concentration with presence of sum of traffic within 150 m and altitude as predictor variables. Mann-Whitney Test did not exhibit significant difference between yearly concentrations of NO2 measured by regulatory measurement sites and measurements from passive samplers, thus LUR model was extrapolated for later years and mapped. The extrapolation indicated more elevated levels of pollution for the years 2003, 2006 and 2010. The results highlight the contribution of traffic on air quality and suggest that LUR modelling may explain the variations of atmospheric pollution with good accuracy. In addition, the model puts focus on spatial and temporal variability needed to describe retrospective exposure to air pollution in studies that evaluate health effects.
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| 2. |
- Habermann, Mateus, 1981, et al.
(author)
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Land use Regression as Method to Model Air Pollution. Previous Results for Gothenburg/Sweden
- 2015
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In: Procedia Engineering. - : Elsevier BV. - 1877-7058 .- 1877-7058. ; 115, s. 21-28
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Conference paper (peer-reviewed)abstract
- In the past 20 years, considerable progress has been made to improve urban air quality in the EU. However, road traffic still contributesconsiderably to the deterioration of urban air quality to below standards, which requires a method to measure properly and model pollutionlevels resulting from road traffic. In order to visualize the geographical distribution of pollution concentration realistically, we applied the LandUse Regression (LUR) model to the urban area of Gothenburg.The NO2 concentration was already obtained by 25 samplers through the urban area during 7-20 May, 2001. Predictive variables such asaltitude, density, roads types, traffic and land use were estimated by geographic information system in buffers ranging 50 to 500 m-radii. Linearregression (α=5%) between NO2 and every predictive variable was calculated, and the most robust variables and without collinearity variableswere selected to the multivariate regression model. The final formula was applied using Kriging in a grid map to estimate NO2 levels.The average of measurements was 23.5 μg/m³ (± 6.8 μg/m³) and 180 predictive variables were obtained. The final model explained 59.4% ofthe variance of NO2 concentration with presence of altitude and sum of traffic within 150 m around the sampler sites as predictor variables. Thecorrelation measured versus predicted levels of NO2 was r = 0.77 (p
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| 3. |
- Habermann, Mateus, 1981, et al.
(author)
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Requalificação urbana em áreas contaminadas na cidade de São Paulo
- 2014
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In: Estudos Avancados. - 1806-9592 .- 0103-4014. ; 28:82, s. 129-137
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Journal article (peer-reviewed)abstract
- Metropolitan areas face increasing problems of urban sprawl. Simultaneously suffer an emptying of properties and depending on the prior use, may exhibit contamination of soil and ground water. The article aims to evaluate the urban density, availability of sanitation infrastructure and socioeconomic status of the neighborhood to check the potential for urban redevelopment and reoccupation of contaminated areas in the city of São Paulo. Through GIS the Cetesb database for the year 2010 with the addresses of the contaminated areas was georeferenced and census tracts with information Censo/2010 as household density, access the sewage system, garbage collection, and income were dichotomized between those with and no contaminated areas. The differences between of median and quartiles of these variables were compared. The city had 1,190 registered as contaminated areas, distributed in 5.1% of its census tracts. Census tracts with contaminated areas have lower household density and greater access to the sewage system, solid waste collection and performance compared to sectors without contaminated area. The census tract with contaminated areas have higher sanitation infrastructure, however, have a lower density of households. Despite the effort to reoccupy these areas, there is still great potential for growth in these areas in the city.
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