Difference between revisions of "ISSS608 2018-19 T1 Assign HyderAli Conclusion"
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* Complex interactions between local topography and meteorological characteristics. | * Complex interactions between local topography and meteorological characteristics. | ||
* Transboundary pollution for example the haze that intruded into Singapore from our neighbours.'' | * Transboundary pollution for example the haze that intruded into Singapore from our neighbours.'' | ||
| − | == Relationship between | + | == Relationship between Pollutant Levels and Local Meteorology == |
| − | In this section, we | + | 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. |
| − | [[File: | + | |
| + | 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> | ||
| + | [[File: corr_plot_1.png|800px]] <br> | ||
| + | |||
| + | 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> | ||
| + | [[File: corr_plot_2.png|800px]] <br> | ||
== Relationship between P1 and P2 == | == Relationship between P1 and P2 == | ||
Revision as of 13:01, 14 November 2018
A Visualization Approach to Air Pollution in Sofia
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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.
- 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.
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.
