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INDIVIDUAL CASE STUDYANALYSIS - ASSIGNMENT 2Student: Tran Hoang LongS-ID: SClass group: SGS-GLecturer: Ha Thanh NguyenSemester: Semester B 2021Word Count: 2998 (Excludingcover page, table of contents,references, appendices)Sarkar, S., Chauhan, A., Kumar, R., &Singh, R. P. (2019). Impact of deadl...

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INDIVIDUAL CASE STUDY ANALYSIS - ASSIGNMENT 2 Student: Tran Hoang Long S-ID: S3878257 Class group: SGS-G15 Lecturer: Ha Thanh Nguyen Semester: Semester B 2021 Word Count: 2998 (Excluding cover page, table of contents, references, appendices)

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Sarkar, S., Chauhan, A., Kumar, R., & Singh, R. P. (2019). Impact of deadly dust storms (May 2018) on air quality, meteorological, and atmospheric parameters over the northern parts of India. GeoHealth, 3 , 6 7 –80. Sarkar, S., Chauhan, A., Kumar, R., & Singh, R. P. (2019). Impact of deadly dust storms (May 2018) on air quality, meteorological, and atmospheric parameters over the northern parts of India. GeoHealth, 3 , 6 7 –80 Table of Contents I. Introduction..................................................................................................................................2 II. Descriptive Statistics and Probability.........................................................................................2 1. Probability................................................................................................................................2 a. Test of statistical dependence...............................................................................................3 b. Country category identification...........................................................................................3 2. Descriptive Statistics...............................................................................................................3 a. Measure of Central Tendency..............................................................................................4 b. Measure of Variation............................................................................................................4 III. Confidence Intervals..................................................................................................................5 1. Calculation...............................................................................................................................5 2. Discussion on assumptions......................................................................................................5 3. Discussion on the confidence interval results..........................................................................6 IV. Hypothesis Testing.....................................................................................................................6 1. Trend of the world average annual mean exposure.................................................................6 2. Hypothesis Testing...................................................................................................................6 3. Discussion on hypothesis testing results..................................................................................8

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V. Conclusion...................................................................................................................................9 VI. References...............................................................................................................................10 VII. Appendices.............................................................................................................................12

I. Introduction Global description: According to the World Health Organization 2016, over 90% of people worldwide live in polluted air places (Pirlea & Huang 2019). The leading causes of ambient air pollution include vehicles, coal-fired power plants, industrial emissions, and human activity, based on the State of the Air 2019 report (Pirlea & Huang 2019). Besides, Sarkar et al. (2019) found that air quality is also affected by dust storms, deserts and causes severe consequences for human health. Especially, air pollution is the main reason for mortality in low-to-middle-income nations (Netula 2021). Nearly 60% of deaths in the WHO's Southeast Asia and Western Pacific areas are attributable to air pollution, with 90% of cases ascribed to cardiovascular illness, stroke, cellular breakdown in the lungs, and intense respiratory diseases (Geneva 2016). Dr. Flavia Bustreo - an associate chief general of Who, states that women, youngsters, and seniors are the most vulnerable to the impacts of air contamination (Worland 2016). Why is it critical to check to mean annual openness to air pollution? Monitoring exposure to air pollution is essential for estimating health impacts and calculating disease burden from ambient air pollution (World Health Organization 2018). Therefore, concentrations in air pollutants will significantly reduce, helping to reduce the health burden, greenhouse gas emissions, and impacts of global warming (World Health Organization 2010). Furthermore, according to the UNFCCC, this is part of SDG 13, which is urgent action against climate change to relieve poverty, prosper a healthy planet, and pass it on to the next generation (Figueres 2015). Hence, as a target of SDG, monitoring exposure to air pollution will considerably decrease the quantity of passings and diseases and adverse effects on the environment by 2030 (Elder 2016). 2

Relationship between GNI and mean annual exposure to air pollution: It tends to be a significant association between atmospheric pollution levels, suicide rates, and climate change. The change in GNI per capita has important implications for temperature, suicide, and future annual studies are needed to determine the link between suicide and air pollution (Heo, Lee & Bell 2021). According to World Health Organization 2019, most suicides are estimated to occur in low-and center income nations. According to the latest global data from WHO, air pollution kills 7 million individuals per year, mainly in emerging countries (Watts 2018). Dr. Ghebreyesus, a WHO's executive director, said that the poor suffer the most from air pollution (Watts 2018). II. Descriptive Statistics and Probability 1. Probability The case study incorporates an aggregate of 44 nations, which are categorized into three gatherings relying upon their Gross National Income (GNI). Countries Low-income (LI)

GNI Under $1000

Middle-income (MI)

Between $1000 and $12500

High-income (HI)

More than $12500

Table 1: country classifications by group dependent on Gross National Income in 2017 (Unit: current US$ per capita) Furthermore, there are two distinct groups which are defined by the mean yearly exposure of this test, with 33 micrograms as norm. The contingency table below presents the following figures: High Mean Annual Exposure (H) Low Mean Annual Exposure (L) Total LI 1 4 5 MI 9 19 28 HI 1 10 11 Total 11 33 44 Table 2: table of contingencies depending on income levels and regardless of whether individuals are presented to air pollution in 2017 a. Test of statistical dependence Here is the mathematical proof to compare the elevation of income countries with mean annual exposure P(H/HI) and the rate of determination for all high altitude countries, which is P(H). The purpose of the work was to determine whether annual income and exposure were statistically dependent or independent. P (H/HI) = 1/11 = 0.09 P (H) = 11/44 = 0.25 3

Þ P (H | HI) P (H); (0.09

According to the above estimate, the probability of high mean annual exposure for high-income nations is not equal to high mean annual exposure for all countries. Therefore, national income and average yearly exposure are statistically dependent events. It indicates that the moderate yearly exposure has an impact on the worth of a country's income. b. Country category identification To determine the country categories most likely to be exposed to average annual air pollution, the study uses probabilities based on different proportions of three different country groups: high-income, middle-income and low-income. P (H / HI) = 1/11 = 0.09 = 9% P (H / MI) = 9/28 = 0.32 = 32% P (H / LI) = 1/5 = 0.2 = 20%

Þ P (H / MI) > P (H / LI) > P (H / HI)

As the estimation above, countries with a middle-income have a 32% chance of being exposed to average air pollution per year. This demonstrates that middle-income nations are more vulnerable to air pollution than the other two categories of countries. 2. Descriptive Statistics Min >,, 17.895 38.53 > 26.975 2 upper Middle (MI) 7.8 > -15.51 90.87 > 67.57 outliers High (HI) 6.48 > -1.0225 60.75 > 31.1175 Table 3: outliers test of three country group in 2017 (unit: micrograms per cubic meter) a. Measure of Central Tendency High-income countires Middle-income countries Low-income countries Mean 18.169 28.957 24.784 Median 13.43 23.925 22.54 Mode Table 4: table of contingencies depending on income levels and regardless of whether individuals are presented to air pollution (unit: micrograms per cubic meter) Because there is no mode in all three categories of nations, mean and median are the two most effective replacements, according to table 4. However, there are two outliers in the above calculation table (table 3); the mean will not be chosen because it is easily affected by outliers. Therefore, the only suitable measure of central tendency is the median. Table 4 shows that the median for middle-income nations is the highest, at 23.925 micrograms per cubic meter. The median of low-income nations is lower than middle-income countries, although the difference is negligible (22.54 micrograms). Finally, high-income nations have just 13.43 micrograms, 1.78 and 1.67 times less than middle- and low-income countries, respectively. Hence, a comparison of nations' medians illustrates that low-and center incomes countries are more probable than 4

wealthy countries, particularly middle-income countries, to be harmed by air pollution. Low- and middle-income nations should increase their per capita income (GNI) to decrease the danger of air contamination. Moreover, the people's health and national economy are both seriously affected. As a result, these governments may decrease loan interest rates and give financial support to low-income households, according to the United Nations Environment Programme (Steiner n.d.). b. Measure of Variation High-income Middle-income Low-income Range 54.27 83.07 20.55 Sample Variance 229.184 358.605 63.473 Interquartile Range (IQR) 8.035 20.77 2.27 Standard Deviation 15.138 18.936 7.967 Coefficient of Variation 83.32% 65.39% 32.14% Table 5: Variation of each country category on Mean Annual Exposure in 2017 (unit: micrograms per cubic meter) There are outliers in all three groups where factors such as range, standard deviation, or the coefficient of variation are primarily sensitive to outliers. IQR plays a vital role in determining if the data is truly an outlier, so it is the most suitable tool because of not being affected by any outliers (Taylor 2018). Regarding table 5, the IQR of middle-income countries accounts for 20.77 micrograms per cubic meter which is significantly more than low-income countries and high-income countries. In particular, the IQR of middle-income countries is 9.14 times more than those of low-income countries and more than double that of high-income countries. It indicates that air pollution has the most significant impact on middle-income countries' economic activities, health care, or value of life concerns. In addition to improving the average income for people, middle-income countries need to take measures to effectively consider moderate exposure to the effects of climate change and air pollution. These countries should conserve and sustainably manage the economic value of their forests and ecological services to address climate change (Steiner n.d.). Besides, the combination of technologies such as using wind, electricity in residential and industrial areas is also an ideal solution to air pollution (Jacobson 2009). III. Confidence Intervals 1. Calculation To compute the confidence intervals for the global average of mean annual air pollution exposure, 95% is assumed for the estimate. The data table is below: Significance level Confidence level Population standard deviation Sample standard deviation Sample mean Sample size 5

α (1 - a) * 100% � S n

0.05 95% Unknown 17.489 25.786 44

Table 6: Statistics summary table of normal mean yearly exposure to polluted air around the world in 2017 (unit: micrograms per cubic meter) As the population standard deviation is not provided, the sample standard deviation is substituted. In this situation, the student's t-Table is applied instead of the z-Table. ● t-Table Degree of freedom: d x f = n – 1 = 43

Þ t-critical value: t = ± 2.0167

Significance level: a = 0.05 ● Confidence Interval: Þ 20.468 ≤ � ≤ 31.103  The calculation indicates that that the worldwide ordinary of mean yearly openness to air pollution ranges between 20.468 and 31.103 micrograms per cubic with 95% confidence. 2. Discussion on assumptions Table 6 shows that the sample size (n) is 44, larger than the usual sample size of 30, implying that the Central Limit Theorem (LCT) is pertinent and the sampling distribution is ordinarily issued. Hence, there is no assumption required despite the unknown population standard deviation. 3. Discussion on the confidence interval results In cases where the population standard deviation is known, the z-value will be used because of having the appropriate sample size and population standard deviation. We have the formula following the recognized population standard deviation: The population and the standard deviation (S) measure change but still vary significantly from sample to sample. They represent the distinction between parameter (population standard deviation) and statistical (sample standard deviation). Furthermore, the sample standard deviation is more significant than the population because it has a sizeable sample-based variability (Taylor 2019). Therefore, it leads to a lot of uncertainty in the statistical data (Anderson 2014). As the confidence interval is smaller, the uncertainty also decreases because the confidence interval is a measure of tension and excludes other parameter values (Gelman & Greenland 2019). According to Hazra 2017, the critical z-value is determined by the level of confidence. From the formula above, the confidence level is proportional to the z-value, which means that increasing the z value also widens the confidence interval, leading to less precise results. On the other hand, the margin of error (e) is determined by the sample size, so the length of the sample is considerable, then the width of the confidence interval will also be narrowed (Hazra 2017). Therefore, a lower confidence level will ensure more accurate results and more minor errors. 6

IV. Hypothesis Testing 1. Trend of the world average annual mean exposure As indicated by the World Health Organization 2016, the world standard yearly mean openness to air pollution amounts to 45.2 micrograms per cubic meter, while the calculation from the previous part indicates that the yearly mean exposure ranged from 20,468 to 31,103 micrograms in 2017. In comparing annual mean exposure levels between 2016 and 2017, there is a substantial drop from 45.2 to the maximum confidence interval of 31.103, roughly 1.45 times. Furthermore, according to the State of Global Air n.d., global average PM2.5 exposure declined marginally from 2010 to 2019. Therefore, we anticipate that world normal yearly mean openness to air contamination will keep on falling later on, which is likewise a decent sign for worldwide wellbeing and the economy. 2. Hypothesis Testing Significance level α 0.05 Confidence level (1 - a) * 100% 95% Population standard deviation � Unknown Sample standard deviation S 17.489 Population mean � 45.2 Sample mean 25.786 Sample size n 44 Table 7: Statistics summary table of hypothesis testing (unit: micrograms per cubic meter) Step 1: Check the CLT: the sample size n is 44 that is greater than the standard 30, so the Central Limit Theorem is applicable, indicating that the sampling distribution of mean is normally distributed. Step 2: State null and alternative hypotheses: Null hypothesis Alternative hypothesis

; � 45.2 ; � 45.2 (claim)

Step 3: Choose rejection region: lower-tailed test is applied with the alternative hypothesis containing “. 2. Anderson, A 2014, Business statistics for dummies, John Wiley & Sons, Inc., NJ. 3. Bill, K 2017, How can type 1 and type 2 errors be minimized?, Socratic, viewed 17 August 2021, . 4. Elder, M 2016, Application of SDGs to Air Pollution, Institute for Global Environmental Strategies, viewed 14 August 2021, . 5. Figueres, C 2015, Goal 13: Taking Urgent Action to Combat Climate Change - SDGs and the Paris Climate Agreement, United Nations, viewed 14 August 2021, . 6. Fienberg, S 2005, Contingency Tables and Log-Linear Models, Encyclopedia of Social Measurement, vol. 1, pp. 499-506. 7. Frost, J n.d., Degrees of Freedom in Statistics, Statistics By Jim, viewed 17 August 2021, < https://statisticsbyjim.com/hypothesis-testing/degrees-freedom-statistics/>. 8. Gelman, A & Greenland, S 2019, ‘Are confidence intervals better termed “uncertainty intervals”?’, thebmj, viewed 16 August 2021, . 9. Geneva 2016, WHO releases country estimates on air pollution exposure and health impact, World Health Organization, viewed 13 August 2021, .

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10. Hazra, A 2017, ‘Using the confidence interval confidently’, Journal of Thoracic Disease, vol. 9(10), pp. 4125-4130. 11. Heo, S, Lee, W & Bell, M 2021, Suicide and Associations with Air Pollution and Ambient Temperature: A Systematic Review and Meta-Analysis, International Journal of Environmental Research and Public Health, vol 18, pp. 76-99. 12. Jacobson, M 2009, ‘Review of solutions to global warming, air pollution, and energy security’, the journal of Energy & Environmental Science, vol. 2, pp. 148-173. 13. Netula, O 2021, Health and economic impact of air pollution, Journal of Huazhong University of Science and Technology, vol. 50, pp. 1671-4512. 14. Pernet, C 2016, Null hypothesis significance testing: a short tutorial, F1000Research, vol. 3, pp. 621. 15. Pirlea, F & Huang, W 2019, The global distribution of air pollution, The World Bank, viewed 13 August 2021, . 16. Rumsey, D 2016, Statistics for dummies, 2nd edn, Wiley Publishing, Inc., IN. 17. Salkind, N $ Frey, B 2019, Statistics for People Who (Think They) Hate Statistics, 7th edn, University of Kansas, USA. 18. Sarkar, S, Chauhan, A, Kumar, R & Singh, R 2019, ‘Impact of deadly dust storms (May 2018) on air quality, meteorological, and atmospheric parameters over the northern parts of India’, GeoHealth, vol. 3, pp. 67–80. 19. State of Global Air n.d., PM2.5 Exposure: Fine-particle outdoor air pollution remains high across much of the world, State of Global Air, viewed 17 August 2021, < https://www.stateofglobalair.org/air/pm>. 20. Steiner, A n.d., The UN Role In Climate Change Action: Taking The Lead Towards A Global Response, United Nations, viewed 15 August 2021, . 21. Taylor, C 2018, What Is the Interquartile Range Rule?, ThoughtCo., viewed 15 August 2021, < https://www.thoughtco.com/what-is-the-interquartile-range-rule-3126244>. 22. Taylor, C 2019, Differences Between Population and Sample Standard Deviations, ThoughtCo., viewed 16 August 2021, . 23. Watts, J 2018, Air pollution inequality widens between rich and poor nations, The Guardian, viewed 14 August 2021, <

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https://www.theguardian.com/environment/2018/may/01/air-pollution-inequality-widensbetween-rich-and-poor-nations>. 24. Worland, J 2016, Unsafe Air-Pollution Levels Affect 9 in 10 People Globally, Report Says, Time, viewed 13 August 2021, . 25. World Health Organization 2010, Exposure to air pollution: a major public health concern, World Health Organization, viewed 14 August 2021, . 26. World Health Organization 2018, Exposure to ambient air pollution from particulate matter for 2016, World Health Organization, viewed 14 August 2021, . 27. World Health Organization 2019, Suicide in the world: Global Health Estimates, World Health Organization, viewed 14 August 2021, .

VII. Appendices Appendix 1: Country lists of data set Country

Iceland...