Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA) Model in Hunan Province, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Sources
2.3. Research Methods
2.3.1. Data Pre-Processing
2.3.2. ARIMA Model
3. Results and Discussion
3.1. Evaluation of the Quality of Atmospheric Environment in Hunan Province
3.2. Predicted Results of Atmospheric Quality
3.2.1. Construction of ARIMA Models
3.2.2. The Predictive Results Based on the ARIMA Model
- (1)
- Prediction curves of PM2.5 and PM10
- (2)
- Prediction curve of O3
- (3)
- Prediction curve of NO2
- (4)
- Prediction curve of SO2
- (5)
- Prediction curve of CO
3.3. Discussion
3.4. Model Validation
4. Conclusions
- (1)
- The forecasting results of PM10, PM2.5, SO2, and CO were developed well with an R2 of 0.817~0.918 based on ARIMA model; meanwhile, the forecasting results of NO2 and O3 were less than satisfactory with an R2 of 0.697~0.723.
- (2)
- During the period of 2016–2023, the ambient air quality of Hunan province was maintained at a Class II range (AQI = 51–100). Meanwhile, PM10, PM2.5, and O3 in Hunan Province stably matched the Class II standard of ambient air quality for China; the rest of other air pollutants met the Class I standard.
- (3)
- PM10 and PM2.5 are major air containments for impacting the ambient air quality of Hunan Province; meanwhile, O3 is an air pollutant of major concern in the near future for Hunan Province; the contamination levels of NO2, SO2, and CO are in relatively safe ranges.
- (4)
- The ARIMA model prediction method is simple and convenient, and it can be widely used in outdoor air quality assessment.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
ARIMA | Autoregressive integrated moving average |
AQI | Air quality index |
PM10 | Respirable particulate matter |
PM2.5 | Fine particulate matter |
O3-8 h | Average of the maximum consecutive eight-hour ozone concentration in 24 h daily |
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Parameter | Pre-Interpolation | Post-Interpolation | ||||||
---|---|---|---|---|---|---|---|---|
Number of Cases | Number of Missing Data | Number of Cases | Number of Outliers | Min | Max | Mean | SD | |
PM10 | 40,487 | 427 | 39,000 | 1914 | 4 | 145 | 53.51 | 28.53 |
PM2.5 | 40,608 | 306 | 39,121 | 1793 | 2 | 102 | 35.23 | 20.75 |
O3 | 40,908 | 6 | 40,591 | 323 | 3 | 185 | 82.15 | 35.81 |
NO2 | 40,908 | 6 | 39,607 | 1307 | 1 | 50 | 20.76 | 10.07 |
SO2 | 40,906 | 8 | 38,448 | 2466 | 1 | 22 | 8.86 | 4.63 |
CO | 40,908 | 6 | 40,106 | 808 | 0.1 | 1.6 | 0.81 | 0.28 |
Parameter | Unit | Spring | Summer | Autumn | Winter | Mean Value |
---|---|---|---|---|---|---|
PM10 | μg/m3 | 53.8 ± 11.7 | 35.1 ± 6.9 | 57.6 ± 13.5 | 84.6 ± 21.3 | 57.8 ± 20.4 |
PM2.5 | μg/m3 | 33.7 ± 7.3 | 20.5 ± 4.1 | 37.3 ± 9.0 | 63.9 ± 13.6 | 38.9 ± 18.2 |
O3 | μg/m3 | 87.3 ± 14.3 | 94.0 ± 11.3 | 92.3 ± 25.5 | 58.4 ± 9.9 | 83.0 ± 16.6 |
NO2 | μg/m3 | 22.0 ± 4.4 | 13.8 ± 2.4 | 23.3 ± 5.8 | 28.9 ± 7.2 | 22.0 ± 6.2 |
SO2 | μg/m3 | 10.5 ± 4.1 | 9.1 ± 2.5 | 10.3 ± 3.0 | 11.0 ± 5.3 | 10.2 ± 0.8 |
CO | mg/m3 | 0.8 ± 0.1 | 0.7 ± 0.1 | 0.8 ± 0.1 | 1.0 ± 0.2 | 0.8 ± 0.1 |
Parameter | PM10 | PM2.5 | O3 | NO2 | SO2 | CO |
---|---|---|---|---|---|---|
ARIMA (p,d,q) | (2,0,2) | (2,0,2) | (2,1,2) | (2,1,2) | (3,1,3) | (2,1,2) |
R2 | 0.817 | 0.896 | 0.723 | 0.697 | 0.852 | 0.918 |
MSE | 9.763 | 5.928 | 11.442 | 4.274 | 1.508 | 0.049 |
MAE | 7.529 | 4.363 | 8.611 | 3.244 | 1.157 | 0.037 |
p value | 0.952 | 0.945 | 0.964 | 0.976 | 0.919 | 0.971 |
Parameter | PM10 | PM2.5 | O3 | NO2 | SO2 | CO |
---|---|---|---|---|---|---|
Unit | μg/m3 | μg/m3 | μg/m3 | μg/m3 | μg/m3 | mg/m3 |
2024 | 45.7 | 32.3 | 87.9 | 20.4 | 6.5 | 0.7 |
2025 | 42.5 | 30.6 | 87.9 | 22.3 | 5.2 | 0.7 |
2026 | 39.2 | 28.9 | 87.9 | 21.9 | 4.0 | 0.7 |
2027 | 35.9 | 27.2 | 87.9 | 21.8 | 2.7 | 0.7 |
2028 | 32.7 | 25.5 | 87.9 | 21.8 | 1.4 | 0.7 |
Indicator | Accuracy | R2 | ARIMA (p,d,q) |
---|---|---|---|
PM10 | 92.4% | 0.817 | (2,0,2) |
PM2.5 | 90.0% | 0.896 | (2,0,2) |
O3 | 92.4% | 0.723 | (2,1,2) |
NO2 | 67.7% | 0.697 | (2,1,2) |
SO2 | 82.9% | 0.852 | (3,1,3) |
CO | 94.1% | 0.918 | (2,1,2) |
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Gao, W.; Xiao, T.; Zou, L.; Li, H.; Gu, S. Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA) Model in Hunan Province, China. Sustainability 2024, 16, 8471. https://doi.org/10.3390/su16198471
Gao W, Xiao T, Zou L, Li H, Gu S. Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA) Model in Hunan Province, China. Sustainability. 2024; 16(19):8471. https://doi.org/10.3390/su16198471
Chicago/Turabian StyleGao, Wenyuan, Tongjue Xiao, Lin Zou, Huan Li, and Shengbo Gu. 2024. "Analysis and Prediction of Atmospheric Environmental Quality Based on the Autoregressive Integrated Moving Average Model (ARIMA) Model in Hunan Province, China" Sustainability 16, no. 19: 8471. https://doi.org/10.3390/su16198471