Difference between revisions of "ISSS608 2017-19 T1 Assign HyderAli Conclusion"

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<b><font size = 6; color="#3a2e29"> Air Pollution in Sofia City </font></b>
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<b><font size = 6; color="#3a2e29"> A Visualization Approach to Air Pollution in Sofia </font></b>
 
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<div style="border-style: solid; border-width:0; background: #c8bdb9; padding: 7px; font-weight: bold; text-align:left; line-height: wrap_content; text-indent: 20px; font-size:20px; font-family:Century Gothic;border-bottom:5px solid white; border-top:5px solid black"><font color= #000000>Conclusion (Task 3 Answer)</font></div>
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<div style="border-style: solid; border-width:0; background: #c8bdb9; padding: 7px; font-weight: bold; text-align:left; line-height: wrap_content; text-indent: 20px; font-size:20px; font-family:Century Gothic;border-bottom:5px solid white; border-top:5px solid black"><font color= #000000>Insights</font></div>
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''In this third task, you are required to reveal the relationships between the factors mentioned below and the air quality measure detected in Task 1 and Task 2.  Limit your response to no more than 5 images and 600 words.
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* Local energy sources.  For example, according to [http://unmaskmycity.org/project/sofia/ Unmask My City], a global initiative by doctors, nurses, public health practitioners, and allied health professionals dedicated to improving air quality and reducing emissions in our cities, Bulgaria’s main sources of PM10, and fine particle pollution PM2.5 (particles 2.5 microns or smaller) are household burning of fossil fuels or biomass, and transport. 
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* Local meteorology such as temperature, pressure, rainfall, humidity, wind etc
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* Local topography
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* Complex interactions between local topography and meteorological characteristics.
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* Transboundary pollution for example the haze that intruded into Singapore from our neighbours.''
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== Relationship between Pollutant Levels and Local Meteorology ==
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In this section, we will investigate the relationship between pollutant levels and weather factors such as temperature, pressure, rainfall, humidity and wind speed during high pollutant periods between November to January.
  
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We hypothesize that the Rose-crested Blue Pipits may have been affected by certain activities at their old hangout location at the alleged dumping site. From Task 1 we see they had been found there in large concentrations pre-2014, had stopped singing in 2014 which shows they may have been under distress, and had moved away from there since 2015. These observations point to the high possibility that something bad had happened at that location in year 2014 which caused them to move away.
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The correlation scatter plot in the Official Air Quality Station data shows that the weather factors such as precipitation, wind speed and dew point temperature are highly uncorrelated because the Pearson's r values are very low. PM<sub>10</sub> concentration levels has also a negative correlation (very weak) with all the weather factors indicating that the pollutant levels are generally inversely related to the weather factors. Since the correlation among pollutant and weather factors in Official Air Quality data are extremely low (<0.20), it's a strong indication that air pollution in Sofia is more likely caused by human intervention than natural phenomenon activities. <br>
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[[File: corr_plot_1.png|800px]] <br>
  
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On top of these observations centered around the Pipit, it was also found that another 7 species of the 19 species had obvious changes in their numbers and/or their spatial distribution in years 2014 and 2015, and these 7 species do not live near the dumping site. It seems the problem is more than just the alleged dumping site and the Rose-crested Blue Pipits, as nearly half of the species had shown signs of moving away or reductions in numbers and spatially these effects are observed throughout the Preserve, not just at the alleged dumping site.
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Analyzing the correlation scatter plot in the Citizen Science Air Quality data, it clearly shows that P1 (most likely PM<sub>2.5</sub>) and P2 (most likely PM<sub>10</sub>) are strongly positively correlated with a Pearson's r value = 0.934. The positive correlation between P1 and P2 pollutants indicates that P1 and P2 may be emitted by the same sources, or one may be emitted by the transformation of another through some type of chemical mechanism. To determine the specific reasons, a combined physical and chemical analysis of pollutants is desirable. In addition, the correlation between P1/P2 and the weather factors were very low (<0.10) supporting the previous finding that the air pollution in Sofia is more likely linked to human activities than any natural phenomenon. <br>
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[[File: corr_plot_2.png|800px]] <br>
  
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From Task 2 we found that only two out of fifteen test files were recordings of the Rose-crested Blue Pipit, and both these files were recorded at locations far away from the usual hangout locations of the species. In addition, some of the fifteen test files do not appear to be recorded in the Preserve, as their spectrograms did not match any of the 19 species identified and provided by Mistford College. Overall, the set provided by Kasios did not support their claim that the Pipits are being found across the Preserve.
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== Relationship between P1 and P2 ==
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As many species started showing signs of change in 2014, we should investigate the movement of vehicles and visitors in the Preserve in the year 2014, considering the fact that in mini-challenge 1 2017 the dataset was only from May 1 2015 to May 1 2016. As the effects from negative externalities are not only constrained to around the dumping site but also observed throughout the Preserve, this may be related to the finding in mini-challenge 2 from 2017 that Methylosmolene was detected in their smokestack emissions, and if that was true, that could be causing the air in the Preserve to be undesirable for the birds to live in. Therefore investigations should also be carried out about the smokestack emissions and general air quality in the Preserve.
 
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The current collection strategy does not ensure an equal number of recordings being collected each period, which makes it difficult to support or refute the claim that the Pipits or any species are still living happily in the Preserve because it is compounded by the fact that some years have more recordings than other years overall. What seems like a reduction in species numbers during spatial analysis could actually be due to the reduction in recordings for those years. Moving forward, a consistent methodology for bird recording collection should be adopted. Explore the possibility of replacing the human collectors with sensors, so that the sensors can be configured to collect recordings at equal time intervals of the day, month and year.
 
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Banner image credit to: [https://www.flickr.com/photos/23660854@N07/24385545393 Marshal Hedin]
 

Revision as of 20:21, 15 November 2018

AP HA.png A Visualization Approach to Air Pollution in Sofia

Overview

Task 1 Insights

Task 2 Insights

Task 3 Insights


Insights

In this third task, you are required to reveal the relationships between the factors mentioned below and the air quality measure detected in Task 1 and Task 2. Limit your response to no more than 5 images and 600 words.

  • Local energy sources. For example, according to Unmask My City, a global initiative by doctors, nurses, public health practitioners, and allied health professionals dedicated to improving air quality and reducing emissions in our cities, Bulgaria’s main sources of PM10, and fine particle pollution PM2.5 (particles 2.5 microns or smaller) are household burning of fossil fuels or biomass, and transport.
  • Local meteorology such as temperature, pressure, rainfall, humidity, wind etc
  • Local topography
  • Complex interactions between local topography and meteorological characteristics.
  • Transboundary pollution for example the haze that intruded into Singapore from our neighbours.

Relationship between Pollutant Levels and Local Meteorology

In this section, we will investigate the relationship between pollutant levels and weather factors such as temperature, pressure, rainfall, humidity and wind speed during high pollutant periods between November to January.

The correlation scatter plot in the Official Air Quality Station data shows that the weather factors such as precipitation, wind speed and dew point temperature are highly uncorrelated because the Pearson's r values are very low. PM10 concentration levels has also a negative correlation (very weak) with all the weather factors indicating that the pollutant levels are generally inversely related to the weather factors. Since the correlation among pollutant and weather factors in Official Air Quality data are extremely low (<0.20), it's a strong indication that air pollution in Sofia is more likely caused by human intervention than natural phenomenon activities.
Corr plot 1.png

Analyzing the correlation scatter plot in the Citizen Science Air Quality data, it clearly shows that P1 (most likely PM2.5) and P2 (most likely PM10) are strongly positively correlated with a Pearson's r value = 0.934. The positive correlation between P1 and P2 pollutants indicates that P1 and P2 may be emitted by the same sources, or one may be emitted by the transformation of another through some type of chemical mechanism. To determine the specific reasons, a combined physical and chemical analysis of pollutants is desirable. In addition, the correlation between P1/P2 and the weather factors were very low (<0.10) supporting the previous finding that the air pollution in Sofia is more likely linked to human activities than any natural phenomenon.
Corr plot 2.png

Relationship between P1 and P2

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