Difference between revisions of "IS428 AY2018-19T1 Gokarn Malika Nitin"
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==Task 1: Spatio-temporal Analysis of Official Air Quality== | ==Task 1: Spatio-temporal Analysis of Official Air Quality== | ||
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| + | Characterize the past and most recent situation with respect to air quality measures in Sofia City. What does a typical day look like for Sofia city? Do you see any trends of possible interest in this investigation? What anomalies do you find in the official air quality dataset? How do these affect your analysis of potential problems in the environment? | ||
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| + | Your submission for this questions should contain no more than 10 images and 1000 words. | ||
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| + | Firstly, we are looking at only EEA Data from 2013 to 2018. By looking at the data as a whole, we identified that all stations have missing values from the period of 1 Jan 2017 to 28 November 2017. | ||
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| + | ''' INSERT THE FIRST GRAPH HERE ''' | ||
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| + | From this simple plot, we are able to identify that there is a pattern in the increase of the concentration of PM10. This means that there could be an interesting reason for the cause. Thus I decided to explore what is the current standards for PM10 to be considered unhealthy. Sofia City is located in Bulgaria, which is part of EU, thus I referenced to their standards of air quality from this [http://ec.europa.eu/environment/air/quality/standards.htm link]. From [https://www.epa.vic.gov.au/your-environment/air/air-pollution/pm10-particles-in-air this link] we can further categorize the PM Air quality into different categories. Firstly 50μg/m3 measured daily is the limit for Bulgaria with a 35 exceedences each year. Thus we need to generate a graph that can clearly pinpoint on which day the concentration exceeds and when are the days where people in Sofia city can enjoy breathing healthy air. | ||
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| + | ''' INSERT THE CATEGORIES HERE ''' | ||
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| + | I used the above categorization as my Color Scaling to visualize how a typical day in Sofia City looks like. | ||
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| + | ''' INSERT THE HEATMAP HERE ''' | ||
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| + | By categorizing the concentration, we can identify that actually, Sofia City is facing a high level of concentration of PM10. Surprisingly, other than the spikes in January and December, Sofia City is also facing a high concentration of pollutant across the years except for June. | ||
| − | + | ''' INSERT THE Control Plot HERE ''' | |
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| + | Although Heatmap can highlight the seriousness of pollution Sofia is facing, but using Control Plot, we can use it to identify the underlying pattern and interesting insight from this graph. You can notice that every year during 24th December and between 18th to 24th January, there is a significant rise in the concentration of PM10 in Sofia. Could this be a coincidence or a reason behind this. I look up the [http://www.parliament.bg/en/24 national holidays] of Bulgaria and try to identify to see other Festive Seasons also have a significant rise other than Christmas Season, but in this case there isn't. | ||
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| + | ''' INSERT THE Final Dashboard HERE ''' | ||
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| + | Reference for Task 1: | ||
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Your submission for this questions should contain no more than 10 images and 1000 words. | Your submission for this questions should contain no more than 10 images and 1000 words. | ||
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==Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements == | ==Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements == | ||
Revision as of 05:40, 11 November 2018
Contents
Problem and Motivation
Dataset Analysis and Transformation Process
Task 1: Spatio-temporal Analysis of Official Air Quality
Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements
Using appropriate data visualisation, you are required will be asked to answer the following types of questions:
- Characterize the sensors’ coverage, performance and operation. Are they well distributed over the entire city? Are they all working properly at all times? Can you detect any unexpected behaviours of the sensors by analyzing the readings they capture? Limit your response to no more than 4 images and 600 words.
- Now turn your attention to the air pollution measurements themselves. Which part of the city shows relatively higher readings than others? Are these differences time-dependent? Limit your response to no more than 6 images and 800 words.
Task 3
Urban air pollution is a complex issue. There are many factors affecting the air quality of a city. Some of the possible causes are:
- 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.
In this third task, you are required to reveal the relationships between the factors mentioned above and the air quality measure detected in Task 1 and Task 2. Limit your response to no more than 5 images and 600 words.
Software
- Tableau - for visualization of the various tasks
- Python - for geocoding
