IS428 AY2018-19T1 Chrysta Yuen Jia Lin

Problem and Motivation

Air pollution is an important risk factor for health in Europe and worldwide. A recent review of the global burden of disease showed that it is one of the top ten risk factors for health globally. Worldwide, an estimated 7 million people died prematurely because of pollution; in the European Union (EU) 400,000 people suffer a premature death. The Organisation for Economic Cooperation and Development (OECD) predicts that in 2050 outdoor air pollution will be the top cause of environmentally related deaths worldwide. In addition, air pollution has also been classified as the leading environmental cause of cancer.

In particular, air quality in Bulgaria is a big concern: measurements show that citizens all over the country breathe in air that is considered harmful to health. For example, concentrations of PM2.5 and PM10 are much higher than what the EU and the World Health Organization (WHO) have set to protect health. Bulgaria had the highest PM2.5 concentrations of all EU-28 member states in urban areas over a three-year average. For PM10, Bulgaria is also leading on the top polluted countries with 77 μg/m3on the daily mean concentration (EU limit value is 50 μg/m3).

According to the WHO, 60 percent of the urban population in Bulgaria is exposed to dangerous (unhealthy) levels of particulate matter (PM10).

With the huge amount of data collected, there is a need to build an interactive data visualization tool to assist the WHO and the government officials in Bulgaria to identify the areas with highly polluted air that is unfit for breathing.

Dataset Analysis & Transformation Process

Before analyzing the data, there is a need to do data preparation to make sense of the data. Under the Sofia Air data, there are 4 different zip files provided in the assignment with each own unique ways to process and make sense of the data. This particular section will be used to elaborate on the dataset analysis and its transformation process for each dataset, to prepare the data for import and analysis onto tableau.

EEA Data

Problem 1: The raw dataset (EEA Data) has numerous data(bg_x_xxx_year) located in different csv files as seen in Figure 1.

Figure 1

Solution 1: To successfully upload the data set onto Tableau, use the union function(figure 2) to include all the different csv files.

To integrate the metadata, innerjoin metadata and the union-ed bg data based on the variable: AirQualityEoiCode. This step helps to integrate both the bg_data and the metadata.

Figure 2

Problem 2: The raw dataset (EEA Data) has data of stations with limited number of yearly data.

As seen in Figure 3, the problematic data is highlighted with the purple border.

Figure 3

Solution 2: To prevent the data from affecting the rest of the dataset, it will be omitted .

As seen from Figure 3, the data file affected includes: Station 60881 and Station 9484. Both data file will be excluded from the visualization.

Air Tube Data

Problem 1: AirTube's data does not brings about the exact location as it is given in geohash format.

Solution 1: Determine the location of the data points by using the geohash package in R environment to convert the geohash format into longitude and latitude. Using the geohash package in R environment, convert the geohash as seen in Figure 5.

Figure 6

Task 1: Spatio-temporal Analysis of Official Air Quality

What does a typical day look like for Sofia city?
A typical day in Sofia city can be seen from the image in Figure 1. The concentration level is divided into 5 different concentration bins: A typical day in Sofia city from March to October is generally rated “Fair”; where a Fair grade is determine by a concentration level between 30-45um/g. However, a typical day in Sofia city from November to February is generally rated “Very Poor”; where a Very Poor grade is determine by a concentration level that is higher than 60.
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 to the environment?

Task 2: Spatio-temporal Analysis of Citizen Science Air Quality Measurements

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 behaviors of the sensors through analyzing the readings they capture?
Which part of the city shows relatively higher readings than others?
Are these differences time dependent?

Task 3

Context
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.
Reveal the relationships between the factors mentioned above and the air quality measure detected in Task 1 and Task 2.

Conclusion

Reference

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