ZijlemaCerinCirach2024

Methods: 

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Ambient air pollutants (NO 2 , NO x , PM 2.5 , PM 10 , PM coarse , and PM2.5absorbance [a measure of blackness of the filter and marker of elemental carbon]) were estimated with Land Use Regression (LUR) models for Barcelona developed in the context of the ESCAPE project 

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In this study we measured some pollutants in the air including nitrogen dioxide, nitric oxide. We also measured for particulate matter (PM), this includes PM that is less than 2.5 micrometres, PM that is less than 10 micrometres, and coarse PM that is less than 10 PM and more than 2.5 PM. We also tested the colour on 2.5 PM filters; this involves looking for/testing the blackness of the filter. We then used these with an algorithm to help measure pollution in densely populated areas. 

Introduction: 

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Built environment characteristics, for example density, transport infrastructure and green space can influence morbidity directly and indirectly through behavior (e.g., walking, cycling, car use) and environmental exposures (e.g., traffic-related air pollution and noise exposure) 

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There are many environmental things within cities that can have a positive or negative affect on metal health and morbidity; these include direct effects such as density of the city, the type of transportation options, and access to green space. However, it can also indirectly affect behaviour like if you chose to walk, use your car, or ride a bike. Often there are also other environmental affects like exposure to noise pollution, such as from vehicles and other loud noises.

Results: 

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Curvilinear associations were observed for a number of built environment attributes and the environmental expo sures, indicating inverted U-shape or J-shape associations. For example, population density and the associations with the air pollutants start positive, reach a plateau around 3500 inhabitants/km2 and are followed by a descending curve 

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We observed our data formed a bit of a curve when graphed. These built environmental attributes often formed a U or a J shape when graphed. An example of this would be testing for density of population and the air pollutants. They would start in the positive than plateau at about 3500 people per square kilometre and then it descends. 

Discussion: 

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The unexpected results in women may be due to residual confounding . Areas with higher concentrations of air pollution may be a proxy for better access to commercial destinations that may have a positive influence on women’s mental health. 

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Our studies showed some interesting and unexpected results in women. We believe this is from some of the exposure remaining even after we tried to control for it; even though we knew about it. We found that some places with higher air pollution may also be a place like a commercial destination that has a positive effect of the mental health of women. 

Future Directions: 

• What future research should follow up on this work? 

Any future research should focus on replicating this study in other cities or in rural areas to see if the results stay the same or change. You could also follow up with see if changing any of the environmental things have a positive or negative effect and if it does how much. 

Difficult Material: 

• What did you not understand about this paper that someone else may be able to help you with? Or, if you understood everything, what did you find most challenging to understand? 

I didn’t understand many of the terms and acronyms until I looked them up. If someone were to help me, I would appreciate them helping me with that.