Title Page

ABSTRACT

Contents

ABBREVIATION 9

CHAPTER 1. INTRODUCTION 12

1.1. Background 12

1.2. Research objectives 16

1.3. Particulate matter 16

1.4. Prediction 17

1.5. ARIMA MODEL 18

CHAPTER 2. LITERATURE REVIEW 20

2.1. Current situation 20

2.2. Deep Learning Models for PM2.5 Prediction in UB 21

2.3. Research on Urban Air Pollution in Ulaanbaatar 21

CHAPTER 3. RESEARCH METHODOLOGY 23

3.1. Study area 23

3.2. DATA AND METHOD 26

CHAPTER 4. FINDINGS AND DISCUSSION 27

4.1. Air quality index 27

4.2. Monthly contribution 31

4.3. CORRELATION PM10 AND PM2.5 33

4.4. Prediction of Trend using ARIMA 34

4.5. Prediction of PM2.5 and PM10 pollutants concentration using ARIMA 37

CHAPTER 5. CONCLUSION 41

REFERENCES 43

국문초록 47

Table 1. Breakpoints 28

Table 2. Correlation number between PM10 and PM2.5 33

Figure 1. PM2.5 particles in the cold season average monthly content, 2018-2023 14

Figure 2. PM10 particles in the cold season average monthly content, 2018-2023 14

Figure 3. Nitrogen dioxide in the cold season average monthly content, 2018-2023 14

Figure 4. Sulfur dioxide monthly in the cold season average content, 2018-2023 15

Figure 5. During wintertime in Ulaanbaatar central area 15

Figure 6. PM10 and PM2.5 17

Figure 7. Map of the Ulaanbaatar city 25

Figure 8. Variation of air quality index from 2020 to 2022 30

Figure 9. Monthly contribution of pollutants to AQI during the period from 2020 to 2022 32

Figure 10. Correlation between PM10 and PM2.5 33

Figure 11. ACF and PACF before calibration (20,0,0) - (a) PM2.5 and (b) PM10 34

Figure 12. ACF and PACF before calibration (25,0,0) - (a) PM2.5 and (b) PM10 35

Figure 13. ACF and PACF before calibration (30,0,0) - (a) PM2.5 and (b) PM10 36

Figure 14. Prediction of (a) PM2.5 and (b) PM10 concentration for Ulaanbaatar city (20,0,0) 38

Figure 15. Prediction of (a) PM2.5 and (b) PM10 concentration for Ulaanbaatar city (25,0,0) 39

Figure 16. Prediction of (a) PM2.5 and (b) PM10 concentration for Ulaanbaatar city (30,0,0) 40