Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China
Abstract
:1. Introduction
2. Data Source and Datasets Stationarity Test
2.1. Data Source
2.2. Datasets Stationarity Test
- (1)
- For the original time-series datasets of PM2.5, PM10 and SO2 concentration, the null hypothesis was rejected, which means that their data series were stationary and there was no unit root. Therefore, these datasets passed the ADF test.
- (2)
- For the original time-series datasets of CO and O3 concentration, the null hypothesis was rejected, and these datasets passed the ADF test at a 95% confidence level. Therefore, these datasets were stationary at a 95% confidence level.
- (3)
- For the original time-series data of NO2 concentration, the null hypothesis was accepted (which can be seen from the ADF test results before the data transformation in Table 1), so there existed a unit root. Therefore, these datasets were nonstationary.
3. Temporal Distribution of Six Representative Pollutions
3.1. The Correlation Relationship between AQI and Six Representative Pollutants
3.2. Pollutant Concentration Diurnal Characteristics
4. The Entropy Weight of Six Pollutants
5. Conclusions
- (1)
- The variation of residents’ travel behavior has brought down the concentrations of particulate matter. However, it has not changed the characteristics of the bimodal distribution of their concentrations. It will cause their concentration peaks to appear about an hour later in the morning and about an hour earlier in the evening. It is also one of the important factors in determining when the peak of SO2 concentration occurs. It will change the concentration distribution of O3 and NO2 from unimodal to bimodal. Additionally, it will cause a peak of NO2 concentration to appear two hours earlier in the evening.
- (2)
- The variation of residents’ travel behavior can promote a concentration reduction of particulate matter and gaseous contaminants. However, the dispersion of the hourly measured concentration of SO2, NO2, and PM2.5 has increased. Additionally, the dispersion of the other three pollutants including CO, PM10 and O3 is declining. Because SO2 and O3 have obvious discrete data values. Therefore, special attention should be given to gaseous contaminants, especially for the change in SO2 and O3 concentration, even if vehicles on the road are no longer as busy as usual and residents’ travel behavior is no longer the same.
- (3)
- The decline rates of the average concentrations of six representative pollutants in case two are significant. Among them, the decrease rate of NO2 and PM10 are the most considerable. The decrease rate of NO2 concentration is 61.05%, and the decrease rate of PM10 concentration is 53.68%. On the contrary, the concentration of O3 has increased significantly, and the growth rate of its average concentration has reached 9.82%.
- (4)
- Air quality is mainly determined by four pollutants, including NO2, O3, PM2.5, and PM10. At the same time, CP and EP were found before the changes in residents’ travel behavior. However, air quality has improved significantly, and the concentration of pollutants that have posed a potential threat to human health decreased significantly after the changes in residents’ travel behavior. The air quality is optimal in the first week, followed by the second and third weeks with fewer travels. Since the average hourly concentration of O3 has increased compared to that in case one, special attention should be paid to the development trend of O3.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Ouyang, X.; Xia, M.; Shen, X.; Zhan, Y. Pollution characteristics of 15 gas- and particle-phase phthalates in indoor and outdoor air in Hangzhou. J. Environ. Sci. 2019, 86, 107–119. [Google Scholar] [CrossRef] [PubMed]
- Ba, A.N.; Verdin, A.; Cazier, F.; Garcon, G.; Thomas, J.; Cabral, M.; Dewaele, D.; Genevray, P.; Garat, A.; Allorge, D.; et al. Individual exposure level following indoor and outdoor air pollution exposure in Dakar (Senegal). Environ. Pollut. 2019, 248, 397–407. [Google Scholar] [CrossRef] [PubMed]
- Shen, F.; Zhang, L.; Jiang, L.; Tang, M.; Gai, X.; Chen, M.; Ge, X. Temporal variations of six ambient criteria air pollutants from 2015 to 2018, their spatial distributions, health risks and relationships with socioeconomic factors during 2018 in China. Environ. Int. 2020, 137, 105556. [Google Scholar] [CrossRef] [PubMed]
- Liu, J.; Yang, P.; Lv, W.; Liu, A. Comprehensive assessment grade of air pollutants based on human health risk and ANN method. Procedia Eng. 2014, 84, 715–720. [Google Scholar]
- Shah, S.; Sook, K.; Park, H.; Hong, Y.; Kim, Y.; Kim, B.; Chang, N.; Kim, S.; Kim, Y.; Kim, B.; et al. Environmental pollutants affecting children’ s growth and development: Collective results from the MOCEH study, a multi-centric prospective birth cohort in Korea. Environ. Int. 2020, 137, 105547. [Google Scholar] [CrossRef]
- Kim, D.; Chen, Z.; Zhou, L.-F.; Huang, S.-X. Air pollutants and early origins of respiratory diseases. Chronic Dis. Transl. Med. 2018, 4, 75–94. [Google Scholar] [CrossRef]
- Chen, J.; Xie, Y.; Li, W. Health Impact Assessment of Beijing’s Residents in Exposure of Chief Air Pollutants from 2010 to 2015 Based on Energy Consumption Scenarios. Procedia Environ. Sci. 2013, 18, 277–282. [Google Scholar] [CrossRef] [Green Version]
- Kim, H.; Kim, H.; Lee, J.T. Effect of air pollutant emission reduction policies on hospital visits for asthma in Seoul, Korea; Quasi-experimental study. Environ. Int. 2019, 132, 104954. [Google Scholar] [CrossRef]
- Ialongo, I.; Fioletov, V.; McLinden, C.; Jåfs, M.; Krotkov, N.; Li, C.; Tamminen, J. Application of satellite-based sulfur dioxide observations to support the cleantech sector: Detecting emission reduction from copper smelters. Environ. Technol. Innov. 2018, 12, 172–179. [Google Scholar] [CrossRef]
- Amini, H.; Trang Nhung, N.T.; Schindler, C.; Yunesian, M.; Hosseini, V.; Shamsipour, M.; Hassanvand, M.S.; Mohammadi, Y.; Farzadfar, F.; Vicedo-Cabrera, A.M.; et al. Short-term associations between daily mortality and ambient particulate matter, nitrogen dioxide, and the air quality index in a Middle Eastern megacity. Environ. Pollut. 2019, 254, 113121. [Google Scholar] [CrossRef]
- Pozzer, A.; Bacer, S.; Sappadina, S.D.Z.; Predicatori, F.; Caleffi, A. Long-term concentrations of fine particulate matter and impact on human health in Verona, Italy. Atmos. Pollut. Res. 2019, 10, 731–738. [Google Scholar] [CrossRef]
- Norbäck, D.; Lu, C.; Zhang, Y.; Li, B.; Zhao, Z.; Huang, C.; Zhang, X.; Qian, H.; Sun, Y.; Wang, J.; et al. Sources of indoor particulate matter (PM) and outdoor air pollution in China in relation to asthma, wheeze, rhinitis and eczema among pre-school children: Synergistic effects between antibiotics use and PM10 and second hand smoke. Environ. Int. 2019, 125, 252–260. [Google Scholar] [CrossRef] [PubMed]
- Chen, Y.; Song, Y.; Chen, Y.J.; Zhang, Y.; Li, R.; Wang, Y.; Qi, Z.; Chen, Z.F.; Cai, Z. Contamination profiles and potential health risks of organophosphate flame retardants in PM2.5 from Guangzhou and Taiyuan, China. Environ. Int. 2020, 134, 105343. [Google Scholar] [CrossRef]
- Lee, C.W.; Wu, C.H.; Chiang, Y.C.; Chen, Y.L.; Chang, K.T.; Chuang, C.C.; Lee, I.T. Carbon monoxide releasing molecule-2 attenuates Pseudomonas aeruginosa-induced ROS-dependent ICAM-1 expression in human pulmonary alveolar epithelial cells. Redox Biol. 2018, 18, 93–103. [Google Scholar] [CrossRef] [PubMed]
- Dey, S.; Chandra Dhal, G.; Mohan, D.; Prasad, R. Synthesis of silver promoted CuMnOx catalyst for ambient temperature oxidation of carbon monoxide. J. Sci. Adv. Mater. Devices 2019, 4, 47–56. [Google Scholar] [CrossRef]
- Li, A.; Pei, L.; Zhao, M.; Xu, J.; Mei, Y.; Li, R.; Xu, Q. Investigating potential associations between O3 exposure and lipid profiles: A longitudinal study of older adults in Beijing. Environ. Int. 2019, 133, 105135. [Google Scholar] [CrossRef]
- Zhong, M.; Chen, F.; Saikawa, E. Sensitivity of projected PM2.5- and O3-related health impacts to model inputs: A case study in mainland China. Environ. Int. 2019, 123, 256–264. [Google Scholar] [CrossRef]
- Yang, P.; Zhang, Y.; Wang, K.; Doraiswamy, P.; Cho, S.H. Health impacts and cost-benefit analyses of surface O3 and PM2.5 over the U.S. under future climate and emission scenarios. Environ. Res. 2019, 178, 108687. [Google Scholar] [CrossRef]
- Guo, J.; Zhao, M.; Xue, P.; Liang, X.; Fan, G.; Ding, B.; Liu, J.; Liu, J. New indicators for air quality and distribution characteristics of pollutants in China. Build. Environ. 2020, 172, 106723. [Google Scholar] [CrossRef]
- Sun, Z.; Yang, L.; Bai, X.; Du, W.; Shen, G.; Fei, J.; Wang, Y.; Chen, A.; Chen, Y.; Zhao, M. Maternal ambient air pollution exposure with spatial-temporal variations and preterm birth risk assessment during 2013–2017 in Zhejiang Province, China. Environ. Int. 2019, 133, 105242. [Google Scholar] [CrossRef]
- Ikram, M.; Yan, Z.J. Statistical Analysis of the Impact of AQI on Respiratory Disease in Beijing: Application Case 2009. Energy Procedia 2017, 107, 340–344. [Google Scholar] [CrossRef]
- Yerramilli, A.; Srinivas Challa, V.; Rao Dodla, V.B.; Myles, L.T.; Pendergrass, W.R.; Vogel, C.A.; Tuluri, F.; Baham, J.M.; Hughes, R.; Patrick, C.; et al. Simulation of surface ozone pollution in the Central Gulf Coast region during summer synoptic condition using WRF/Chem air quality model. Atmos. Pollut. Res. 2012, 3, 55–71. [Google Scholar] [CrossRef] [Green Version]
- Fiddes, S.L.; Pezza, A.B.; Mitchell, T.A.; Kozyniak, K.; Mills, D. Synoptic weather evolution and climate drivers associated with winter air pollution in New Zealand. Atmos. Pollut. Res. 2016, 7, 1082–1089. [Google Scholar] [CrossRef] [Green Version]
- Song, P.; Wanga, L.; Hui, Y.; Li, R. PM2.5 Concentrations Indoors and Outdoors in Heavy Air Pollution Days in Winter. Procedia Eng. 2015, 121, 1902–1906. [Google Scholar] [CrossRef] [Green Version]
- Contreras, L.; Ferri, C. Wind-sensitive interpolation of urban air pollution forecasts. Procedia Comput. Sci. 2016, 80, 313–323. [Google Scholar] [CrossRef] [Green Version]
- Leogrande, S.; Alessandrini, E.R.; Stafoggia, M.; Morabito, A.; Nocioni, A.; Ancona, C.; Bisceglia, L.; Mataloni, F.; Giua, R.; Mincuzzi, A.; et al. Industrial air pollution and mortality in the Taranto area, Southern Italy: A difference-in-differences approach. Environ. Int. 2019, 132, 105030. [Google Scholar] [CrossRef]
- Hao, Y.; Zheng, S.; Zhao, M.; Wu, H.; Guo, Y.; Li, Y. Reexamining the relationships among urbanization, industrial structure, and environmental pollution in China—New evidence using the dynamic threshold panel model. Energy Rep. 2020, 6, 28–39. [Google Scholar] [CrossRef]
- Barnes, J.H.; Chatterton, T.J.; Longhurst, J.W.S. Emissions vs exposure: Increasing injustice from road traffic-related air pollution in the United Kingdom. Transp. Res. Part D Transp. Environ. 2019, 73, 56–66. [Google Scholar] [CrossRef]
- Wang, X.; Zou, C.; Wang, X.; Liu, X. Human and Ecological Risk Assessment: An International Impact of vehicular exhaust emissions on pedestrian health under different traffic structures and wind speeds. Hum. Ecol. Risk Assess. Int. J. 2019, 1–17. [Google Scholar] [CrossRef]
- Ilango, V.; Subramanian, R.; Vasudevan, V. Statistical data mining approach with asymmetric conditionally volatility model in financial time series data. Int. J. Soft Comput. 2013, 8, 252–260. [Google Scholar]
- Lian, L.; Ma, H. FDI and Economic Growth in Western Region of China and Dynamic Mechanism: Based on Time-Series Data from 1986 to 2010. Int. Bus. Res. 2013, 6, 180–186. [Google Scholar] [CrossRef] [Green Version]
- Cochran, J.J.; Cox, L.A.; Keskinocak, P.; Kharoufeh, J.P.; Smith, J.C.; Perry, M.B. The Exponentially Weighted Moving Average. Wiley Encycl. Oper. Res. Manag. Sci. 2010, 6, 1–9. [Google Scholar]
- Du, X.; Chen, R.; Meng, X.; Liu, C.; Niu, Y.; Wang, W.; Li, S.; Kan, H.; Zhou, M. The establishment of National Air Quality Health Index in China. Environ. Int. 2020, 138, 105594. [Google Scholar] [CrossRef] [PubMed]
- Wen, J.; Chen, Y. Variation characteristics of sulfur dioxide in Guangzhou in recent years. Guan. Chem. Indu. 2011, 39, 134–135. [Google Scholar]
- Tian, C.; Wang, F.; Zhu, D.; Ma, C.; Chen, W. Research on influencing factors of sulfur dioxide concentrations on both sides of traffic trunk line. World China Bus. Econo. Yearbook 2013, 6, 70–71. [Google Scholar]
- Ye, J. Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients of interval-valued intuitionistic fuzzy sets. Appl. Math. Model. 2010, 34, 3864–3870. [Google Scholar] [CrossRef]
- Li, X.; Wang, K.; Liu, L.; Xin, J.; Yang, H.; Gao, C. Application of the entropy weight and TOPSIS method in safety evaluation of coal mines. Procedia Eng. 2011, 26, 2085–2091. [Google Scholar] [CrossRef] [Green Version]
- Delgado, A.; Romero, I. Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A case study of a mining project in Peru. Environ. Model. Softw. 2016, 77, 108–121. [Google Scholar] [CrossRef]
- Cheng, C.H.; Mon, D.L. Evaluating weapon system by Analytical Hierarchy Process based on fuzzy scales. Fuzzy Sets Syst. 1994, 63, 1–10. [Google Scholar] [CrossRef]
- Lin, J. Divergence Measures Based on the Shannon Entropy. IEEE Trans. Inf. Theory 1991, 37, 145–151. [Google Scholar] [CrossRef] [Green Version]
- Bruhn, J.; Lehmann, L.E.; Röpcke, H.; Bouillon, T.W.; Hoeft, A. Shannon entropy applied to the measurement of the electroencephalographic effects of desflurane. Anesthesiology 2001, 95, 30–35. [Google Scholar] [CrossRef] [PubMed]
- Mensi, W.; Aloui, C.; Hamdi, M.; Ngugen, D.K. Grude oil market efficiency: An empirical investigation via the Shannon entropy. Inernation. Econom. 2012, 129, 119–137. [Google Scholar]
- Balzter, H.; Tate, N.J.; Kaduk, J.; Harper, D.; Page, S.; Morrison, R.; Muskulus, M.; Jones, P. Multi-scale entropy analysis as a method for time-series analysis of climate data. Climate 2015, 3, 227–240. [Google Scholar] [CrossRef] [Green Version]
- Zhang, G.; Lu, Y.; Han, S.; Zheng, Y.; Lin, G.; Liu, Y.; Lin, X. Development of dynamic vehicle exhaust emission inventory based on traffic big data: A case study of Guangzhou inner ring. Environ. Pollut. Preven. 2018, 6, 724–726. [Google Scholar]
Before the Data Transformation | After the Data Transformation | |
---|---|---|
Test Statistic | −1.273999 | −5.592696 |
p-Value | 0.640982 | 0.000001 |
Critical Value (1%) | −3.436442 | −3.436364 |
Critical Value (5%) | −2.864230 | −2.864195 |
Critical Value (10%) | −2.568202 | −2.568184 |
AQI | PM2.5 | PM10 | SO2 | NO2 | CO | O3 | |
---|---|---|---|---|---|---|---|
Coefficients | |||||||
Case one | 0.939462 | 0.978872 | 0.707992 | 0.796998 | 0.125977 | 0.018185 | |
Case two | 0.994004 | 0.986916 | 0.491164 | 0.601048 | 0.130621 | 0.053570 |
Average Concentration | PM2.5 (µg/m3) | PM10 (µg/m3) | SO2 (µg/m3) | NO2 (µg/m3) | CO (µg/m3) | O3 (µg/m3) |
---|---|---|---|---|---|---|
Case one | 32.28 | 61.38 | 6.86 | 52.22 | 0.98 | 40.14 |
Case two | 21.77 | 28.43 | 4.99 | 20.34 | 0.74 | 44.08 |
Decline rates | 32.56% | 53.68% | 27.26% | 61.05% | 24.49% | −9.82% |
PM2.5 | PM10 | SO2 | NO2 | CO | O3 | |
---|---|---|---|---|---|---|
Case one | 0.120497 | 0.145691 | 0.143843 | 0.157466 | 0.095655 | 0.336848 |
Case two | 0.145131 | 0.132877 | 0.295273 | 0.216554 | 0.083962 | 0.126202 |
Weight adjustment | ↑ | ↓ | ↑ | ↑ | ↓ | ↓ |
IAQI | Pollutant Concentration Thresholds | Air Quality Categories | |||||
---|---|---|---|---|---|---|---|
SO2 24-h Average (µg/m3) | NO2 24-h Average (µg/m3) | CO 24-h Average (mg/m3) | O3 8-h Moving Average(µg/m3) | PM2.5 24-h Average (µg/m3) | PM10 24-h Average (µg/m3) | ||
0 | 0 | 0 | 0 | 0 | 0 | 0 | Excellent |
50 | 50 | 40 | 2 | 100 | 35 | 50 | Excellent |
100 | 150 | 80 | 4 | 160 | 75 | 150 | Good |
150 | 475 | 180 | 14 | 215 | 115 | 250 | Light pollution |
200 | 800 | 280 | 24 | 265 | 150 | 350 | Moderate pollution |
300 | 1600 | 565 | 36 | 800 | 250 | 420 | Heavy pollution |
400 | 2100 | 750 | 48 | 1000 | 350 | 500 | Severe pollution |
500 | 2620 | 940 | 60 | 1200 | 500 | 600 | Severe pollution |
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Wang, X.; Zou, C.; Wang, L. Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China. Int. J. Environ. Res. Public Health 2020, 17, 4947. https://doi.org/10.3390/ijerph17144947
Wang X, Zou C, Wang L. Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China. International Journal of Environmental Research and Public Health. 2020; 17(14):4947. https://doi.org/10.3390/ijerph17144947
Chicago/Turabian StyleWang, Xiaoxia, Chao Zou, and Luqi Wang. 2020. "Analysis on the Temporal Distribution Characteristics of Air Pollution and Its Impact on Human Health under the Noticeable Variation of Residents’ Travel Behavior: A Case of Guangzhou, China" International Journal of Environmental Research and Public Health 17, no. 14: 4947. https://doi.org/10.3390/ijerph17144947