Ozone Pollution in Chinese Cities: Spatiotemporal Variations and Their Relationships with Meteorological and Other Pollution Factors (2016–2020)
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Dataset
2.2.1. Air Quality Data
2.2.2. Meteorological Data
2.3. Methodology
2.3.1. Evaluation Standard
2.3.2. Multiple Linear Regression
2.3.3. Global Moran’s I Index
2.3.4. Getis–Ord Index
3. Analysis of Spatiotemporal Characteristics and Influencing Factors
3.1. Analysis of Temporal and Spatial Changes
3.1.1. Temporal and Spatial Changes in the Annual Average Ozone Concentration
3.1.2. Temporal and Spatial Changes in the Seasonal Average Ozone Concentration
3.1.3. Temporal and Spatial Changes in the Monthly Average Ozone Concentration
3.2. Analysis of the Relationships between O3 and Other Pollution Factors and Changes in Hourly Concentrations
3.3. Analysis of the Relationships between O3 and Meteorological Factors
4. Spatial Agglomeration Characteristics of O3 Concentrations in Chinese Cities
4.1. Spatial Autocorrelation Test of O3 Concentrations
4.2. Spatial Agglomeration Characteristics of O3 Concentrations
5. Conclusions
- (1)
- The O3 concentrations exhibited the same change trend year over year. However, the O3 concentrations in major urban agglomerations continued to rise in 2019. Seasonally, O3 has strong spatial characteristics and can be latitudinally divided into three regions from south to north. On a monthly scale, many cities exhibited O3 concentrations exceeding the national standard (160 μg·m−3) from April to September, with the peak appearing in June.
- (2)
- Cities with heavy O3 pollution were concentrated in the northern part of CC, southern NC and EC. Over time, the number of cities with O3 concentrations above 160 μg·m−3 has gradually increased, with the highest value being discovered in 2018, accounting for 50% of the total number of cities. In terms of monthly O3 concentrations, the number of cities with O3 concentrations above 160 μg·m−3 was relatively high from April to September, and most of these cities were concentrated in CC, EC and NC.
- (3)
- O3 displays significant negative correlations with NO2, PM2.5 and PM10, and its correlation coefficient with NO2 is the highest (with an r value of −0.399). The correlations between O3 and various meteorological factors vary both seasonally and spatially. GST and SSD show a significant positive correlation with O3 in all regions. PRS and RHU show significant negative correlations in most regions, while WIN and PRE show significant positive correlations in most regions. The effects of GST and PRS are greater in spring and autumn than in summer and winter. PRE is negatively correlated with the O3 concentration in summer and negatively correlated in the other seasons. For RHU, WIN and SSD, their correlations with O3 do not significantly differ from season to season.
- (4)
- The concentrations of O3 in Chinese cities have significant spatial agglomeration characteristics. Seasonal differences in the O3 concentration are large, and periodic changes are obvious. The hotspot cities in spring are distributed mainly in the southern part of NEC, NC, EC and northern CC. The distribution of summer hotspot cities further expands from the east coast to the inland areas. In autumn, the scope of hotspot cities expands further and spreads to the southeast coast, mainly in the southern part of NC, EC, CC and SC. The scope of winter hotspot cities shrinks sharply but continues to expand south, with cities appearing mainly in SWC, SC, EC and SC.
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Najafpoor, A.; Jonidi, A.A.; Dousti, S. Trend Analysis of Air Quality Index Criteria Pollutants (Co, NO2, SO2, PM10 and O3) Concentration Changes in Tehran Metropolis and Its Relationship with Meteorological Data, 2001–2009. J. Health Field 2017, 3, 17–26. [Google Scholar]
- Nwaogazie, I.L.; Wilson, A.H.; Henshaw, T. Modeling the Effect of Atmospheric Stability, Nitrogen Oxide and Carbon Monoxide on the Formation of Ozone: A Case of Ogba-Egbema-Ndoni Local Government Area in Nigeria. Int. J. Civ. Eng. 2016, 7, 111–121. [Google Scholar]
- Qu, Z.; Henze, D.K.; Cooper, O.R.; Neu, J.L. Impacts of Global Nox Inversions on NO2 and Ozone Simulations. Atmos. Chem. Phys. 2020, 20, 13109–13130. [Google Scholar] [CrossRef]
- Stohl, A.; Williams, E.; Wotawa, G.; Kromp-Kolb, H. A European Inventory of Soil Nitric Oxide Emissions and the Effect of These Emissions on the Photochemical Formation of Ozone. Atmos. Environ. 1996, 30, 3741–3755. [Google Scholar] [CrossRef]
- Wang, P.; Chen, Y.; Hu, J.; Zhang, H.; Ying, Q. Attribution of Tropospheric Ozone to No_X and Voc Emissions: Considering Ozone Formation in the Transition Regime. Environ. Sci. Technol. 2019, 53, 1404–1412. [Google Scholar] [CrossRef] [PubMed]
- Kaser, L.; Peron, A.; Graus, M.; Striednig, M.; Wohlfahrt, G.; Juráň, S.; Karl, T. Interannual Variability of Terpenoid Emissions in an Alpine City. Atmos. Chem. Phys. 2022, 22, 5603–5618. [Google Scholar] [CrossRef]
- Ordonez, C.; Garrido-Perez, J.M.; Garcia-Herrera, R. Early Spring near-Surface Ozone in Europe During the COVID-19 Shutdown: Meteorological Effects Outweigh Emission Changes. Sci. Total Environ. 2020, 747, 141322. [Google Scholar] [CrossRef]
- Alexis, N.E.; Lay, J.C.; Hazucha, M.; Harris, B.; Hernandez, M.L.; Bromberg, P.A.; Kehrl, H.; Diaz-Sanchez, D.; Kim, C.; Devlin, R.B.; et al. Low-Level Ozone Exposure Induces Airways Inflammation and Modifies Cell Surface Phenotypes in Healthy Humans. Inhal. Toxicol. 2010, 22, 593–600. [Google Scholar] [CrossRef] [Green Version]
- Lefohn, A.S.; Malley, C.S.; Smith, L.; Wells, B.; Gerosa, G. Tropospheric Ozone Assessment Report: Global Ozone Metrics for Climate Change, Human Health, and Crop/Ecosystem Research. Elem. Sci. Anthr. 2018, 6, 28. [Google Scholar] [CrossRef] [Green Version]
- Faridi, S.; Shamsipour, M.; Krzyzanowski, M.; Kunzli, N.; Amini, H.; Azimi, F.; Malkawi, M.; Momeniha, F.; Gholampour, A.; Hassanvand, M.S.; et al. Long-Term Trends and Health Impact of Pm2.5 and O-3 in Tehran, Iran, 2006–2015. Environ. Int. 2018, 114, 37–49. [Google Scholar] [CrossRef]
- Castell, J.F. Impacts De Lozone Sur Lagriculture Et Les Forts Et Estimation Des Cots Conomiques Ozone Impacts on Agriculture and Forests and Economic Losses Assessment. Pollut. Atmosphérique 2019, 229–230, 142–152. [Google Scholar]
- Flaum, J.B.; Rao, S.T.; Zurbenko, I.G. Moderating the Influence of Meteorological Conditions on Ambient Ozone Concentrations. J. Air Waste Manag. Assoc. 1996, 46, 35–46. [Google Scholar] [CrossRef] [PubMed]
- Liu, P.; Song, H.; Wang, T.; Wang, F.; Zhao, H. Effects of Meteorological Conditions and Anthropogenic Precursors on Ground-Level Ozone Concentrations in Chinese Cities. Environ. Pollut. 2020, 262, 114366. [Google Scholar] [CrossRef] [PubMed]
- Tong, N.Y.O.; Leung, D.Y.C.; Liu, C.H. A Review on Ozone Evolution and Its Relationship with Boundary Layer Characteristics in Urban Environments. Water Air Soil Pollut. 2011, 214, 13–36. [Google Scholar] [CrossRef]
- Wu, J.; Li, C.; Ma, Z.Q.; Sun, Z.B.; Dong, F. Influence of Meteorological Conditions on Ozone Pollution at Shangdianzi Station Based on Weather Classification. Huan Jing Ke Xue = Huanjing Kexue 2020, 41, 4864–4873. [Google Scholar] [PubMed]
- Fan, L.P.; Fu, S.; Wang, X.; Fu, Q.Y.; Jia, H.H.; Xu, H.; Qin, G.M.; Hu, X.; Cheng, J.P. Spatiotemporal Variations of Ambient Air Pollutants and Meteorological Influences over Typical Urban Agglomerations in China during the COVID-19 Lockdown. J. Environ. Sci. 2021, 106, 26–38. [Google Scholar] [CrossRef] [PubMed]
- Li, L.; Wang, K.; Li, S.; Feng, X.; Zhang, L. Lst-Net: Learning a Convolutional Neural Network with a Learnable Sparse Transform. In European Conference on Computer Vision; Springer: Cham, Switzerland, 2020; pp. 562–579. [Google Scholar]
- Wang, Z.B.; Li, J.X.; Liang, L.W. Spatio-Temporal Evolution of Ozone Pollution and Its Influencing Factors in the Beijing-Tianjin-Hebei Urban Agglomeration. Environ. Pollut. 2020, 256, 113419. [Google Scholar] [CrossRef]
- Zhang, J.X.; Zhang, Y.; Zhu, S.Y. Variation of Total Ozone over China for 30 Years Analyzed by Multi-Source Satellite Remote Sensing Data. Geo. Spat. Inf. Sci. 2014, 16, 971–978. [Google Scholar]
- Tan, K.C.; Lim, H.S.; Jafri, M.Z. Satellite Remote Sensing of Total Column Ozone over Peninsular Malaysia. Presented at the 6th IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, 25–27 September 2016; pp. 374–379. [Google Scholar]
- Fu, Y.; Liao, H. Simulation of the Interannual Variations of Biogenic Emissions of Volatile Organic Compounds in China: Impacts on Tropospheric Ozone and Secondary Organic Aerosol. Atmos. Environ. 2012, 59, 170–185. [Google Scholar] [CrossRef]
- Hu, J.; Chen, J.; Ying, Q.; Zhang, H. One-Year Simulation of Ozone and Particulate Matter in Chinausing Wrf/Cmaq Modeling System. Atmos. Chem. Phys. 2016, 16, 10333–10350. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Z.; Yao, M.; Wu, W.; Zhao, X.; Zhang, J. Spatiotemporal Assessment of Health Burden and Economic Losses Attributable to Short-Term Exposure to Ozone During 2015–2018 in China. J. Atmos. Sci. 2020, 21, 1–18. [Google Scholar]
- Mao, J.; Wang, L.; Lu, C.; Liu, J.; Wang, Y. Meteorological Mechanism for a Large-Scale Persistent Severe Ozone Pollution Event over Eastern China in 2017. J. Environ. Sci. 2020, 92, 187–199. [Google Scholar] [CrossRef] [PubMed]
- Zhuang, Y.; Li, R.Y.; Yang, H.; Chen, D.L.; Chen, Z.Y.; Gao, B.B.; He, B. Understanding Temporal and Spatial Distribution of Crop Residue Burning in China from 2003 to 2017 Using Modis Data. Remote Sens. 2018, 10, 390. [Google Scholar] [CrossRef] [Green Version]
- Geary, R.C. The Contiguity Ratio and Statistical Mapping. Inc. Stat. 1954, 5, 115–146. [Google Scholar] [CrossRef]
- Goodchild, M.; Haining, R.; Wise, S. Integrating Gis and Spatial Data Analysis: Problems and Possibilities. Int. J. Geogr. Inf. Syst. 1992, 6, 407–423. [Google Scholar] [CrossRef]
- Moran, P.A.P. The Interpretation of Statistical Maps. J. R. Stat. Soc. Ser. A Stat. Soc. 1948, 10, 243–251. [Google Scholar] [CrossRef]
- Ord, J.K. Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geogr. Anal. 1995, 27, 286–306. [Google Scholar] [CrossRef]
- Getis, A. Spatial Interaction and Spatial Autocorrelation: A Cross-Product Approach. Environ. Plan. A 2010, 23, 1269–1277. [Google Scholar] [CrossRef]
- Gong, K.; Li, L.; Li, J.; Qin, M.; Wang, X.; Ying, Q.; Liao, H.; Guo, S.; Hu, M.; Zhang, Y.; et al. Quantifying the Impacts of Inter-City Transport on Air Quality in the Yangtze River Delta Urban Agglomeration, China: Implications for Regional Cooperative Controls of Pm2.5 and O3. Sci. Total Environ. 2021, 779, 146619. [Google Scholar] [CrossRef]
- Varotsos, K.V.; Giannakopoulos, C.; Tombrou, M. Ozone-Temperature Relationship During the 2003 and 2014 Heatwaves in Europe. Reg. Environ. Chang. 2019, 19, 1653–1665. [Google Scholar] [CrossRef]
- Tang, G.; Li, X.; Wang, Y.; Xin, J.; Ren, X. Surface Ozone Trend Details and Interpretations in Beijing, 2001–2006. Atmos. Chem. Phys. 2009, 9, 8813–8823. [Google Scholar] [CrossRef] [Green Version]
- Parrish, D.D.; Trainer, M.; Holloway, J.S.; Yee, J.E.; Warshawsky, M.S.; Fehsenfeld, F.C.; Forbes, G.L.; Moody, J.L. Relationships between Ozone and Carbon Monoxide at Surface Sites in the North Atlantic Region. J. Geophys. Res. 1998, 103, 13357–13376. [Google Scholar] [CrossRef]
- Li, Z.; Yu, S.C.; Wang, L.Q.; Mehmood, K.; Liu, W.P. Suppression of Convective Precipitation by Elevated Man-Made Aerosols Is Responsible for Large-Scale Droughts in North China. Proc. Natl. Acad. Sci. USA 2018, 115, 8327–8328. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kraemer, H.C.; Bloch, D.A. A Goodness-of-Fit Approach to Inference Procedures for the Kappa Statistic: Confidence Interval Construction, Significance-Testing and Sample Size Estimation. Stat. Med. 2010, 11, 1511–1519. [Google Scholar] [CrossRef] [PubMed]
- Lin, I.K. A Concordance Correlation-Coefficient to Evaluate Reproducibility. Biometrics 1989, 45, 255–268. [Google Scholar] [CrossRef] [PubMed]
- Zhou, X.L.; Liang, J.Q. Pollution Characteristics of Ozone in Urban Area of Foshan and Correlation between Ozone and Meteorological Factors. Environ. Monit. Assess. 2016, 8, 39–44. [Google Scholar]
- Sun, W.; Hess, P.; Liu, C. The Impact of Meteorological Persistence on the Distribution and Extremes of Ozone. Geophys. Res. Lett. 2017, 44, 1545–1553. [Google Scholar] [CrossRef]
- Zhan, C.; Xie, M.; Liu, J.; Wang, T.; Li, M. Surface Ozone in the Yangtze River Delta, China: A Synthesis of Basic Features, Meteorological Driving Factors and Health Impacts. J. Geophys. Res. Atmos. 2021, 126, e2020JD033600. [Google Scholar] [CrossRef]
- Chen, Z.; Zhuang, Y.; Xie, X.; Chen, D.; Cheng, N.; Yang, L.; Li, R. Understanding Long-Term Variations of Meteorological Influences on Ground Ozone Concentrations in Beijing During 2006–2016. Environ. Pollut. 2019, 245, 29–37. [Google Scholar] [CrossRef]
- Kumari, S.; Jayaraman, G.; Ghosh, C. Analysis of Long-Term Ozone Trend over Delhi and Its Meteorological Adjustment. Int. J. Environ. Sci. Technol. 2013, 10, 1325–1336. [Google Scholar] [CrossRef] [Green Version]
- Huang, L.S.; Smith, R.L. Meteorologically-Dependent Trends in Urban Ozone. Environmetrics 2015, 10, 103–118. [Google Scholar] [CrossRef]
- Chen, Z.; Li, R.; Chen, D.; Zhuang, Y.; Li, M. Understanding the Causal Influence of Major Meteorological Factors on Ground Ozone Concentrations across China. J. Clean. Prod. 2019, 242, 118498. [Google Scholar] [CrossRef]
- Bei, N.; Lei, W.; Zavala, M.; Molina, L.T. Ozone Predictabilities Due to Meteorological Uncertainties in the Mexico City Basin Using Ensemble Forecasts. Atmos. Chem. Phys. 2010, 10, 6295–6309. [Google Scholar] [CrossRef] [Green Version]
- Zhao, W.; Gao, B.; Liu, M.; Lu, Q.; Fan, S.J. Impact of Meteorological Factors on the Ozone Pollution in Hong Kong. Huan Jing Ke Xue = Huanjing Kexue 2019, 40, 55–66. [Google Scholar] [PubMed]
- Kang, S.M.; Polvani, L.M.; Fyfe, J.C.; Sigmond, M. Impact of Polar Ozone Depletion on Subtropical Precipitation. Science 2011, 332, 951–954. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Juran, S.; Sigut, L.; Holub, P.; Fares, S.; Klem, K.; Grace, J.; Urban, O. Ozone Flux and Ozone Deposition in a Mountain Spruce Forest Are Modulated by Sky Conditions. Sci. Total Environ. 2019, 672, 296–304. [Google Scholar] [CrossRef]
- Bloomfield, P.; Royle, J.A.; Steinberg, L.J.; Yang, Q. Accounting for Meteorological Effects in Measuring Urban Ozone Levels and Trends. Atmos. Environ. 1996, 30, 3067–3077. [Google Scholar] [CrossRef]
Value | Spring | Summer | Autumn | Winter | Year | |
---|---|---|---|---|---|---|
Moran’s I | 2016 | 0.17 | 0.23 | 0.40 | 0.17 | 0.24 |
2017 | 0.36 | 0.27 | 0.49 | 0.24 | 0.34 | |
2018 | 0.30 | 0.28 | 0.69 | 0.21 | 0.37 | |
2019 | 0.31 | 0.31 | 0.58 | 0.26 | 0.36 | |
2020 | 0.37 | 0.30 | 0.68 | 0.18 | 0.38 | |
Z[I] | 2016 | 17.92 | 24.71 | 45.39 | 29.4 | 29.36 |
2017 | 32.73 | 36.54 | 57.21 | 42.66 | 42.29 | |
2018 | 30.40 | 35.12 | 73.13 | 37.20 | 43.96 | |
2019 | 34.75 | 40.00 | 69.15 | 46.55 | 47.61 | |
2020 | 35.99 | 37.58 | 66.87 | 29.64 | 42.52 |
Value | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Moran’s I | 2016 | 0.03 | 0.11 | 0.05 | 0.19 | 0.14 | 0.17 | 0.23 | 0.22 | 0.5 | 0.12 | 0.27 | 0.28 |
2017 | 0.25 | 0.25 | 0.14 | 0.36 | 0.32 | 0.27 | 0.21 | 0.21 | 0.38 | 0.55 | 0.38 | 0.30 | |
2018 | 0.25 | 0.16 | 0.14 | 0.33 | 0.25 | 0.31 | 0.24 | 0.23 | 0.63 | 0.67 | 0.41 | 0.10 | |
2019 | 0.27 | 0.04 | 0.21 | 0.19 | 0.27 | 0.30 | 0.28 | 0.24 | 0.53 | 0.73 | 0.51 | 0.30 | |
2020 | 0.10 | 0.07 | 0.14 | 0.18 | 0.24 | 0.25 | 0.28 | 0.24 | 0.62 | 0.58 | 0.43 | 0.26 | |
Z[I] | 2016 | 0.03 | 0.11 | 0.05 | 0.19 | 0.14 | 0.17 | 0.23 | 0.22 | 0.5 | 0.12 | 0.27 | 0.28 |
2017 | 0.25 | 0.25 | 0.14 | 0.36 | 0.32 | 0.27 | 0.21 | 0.21 | 0.38 | 0.55 | 0.38 | 0.30 | |
2018 | 0.25 | 0.16 | 0.14 | 0.33 | 0.25 | 0.31 | 0.24 | 0.23 | 0.63 | 0.67 | 0.41 | 0.10 | |
2019 | 0.27 | 0.04 | 0.21 | 0.19 | 0.27 | 0.30 | 0.28 | 0.24 | 0.53 | 0.73 | 0.51 | 0.30 | |
2020 | 0.10 | 0.07 | 0.14 | 0.18 | 0.24 | 0.25 | 0.28 | 0.24 | 0.62 | 0.58 | 0.43 | 0.26 |
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Ge, Q.; Zhang, X.; Cai, K.; Liu, Y. Ozone Pollution in Chinese Cities: Spatiotemporal Variations and Their Relationships with Meteorological and Other Pollution Factors (2016–2020). Atmosphere 2022, 13, 908. https://doi.org/10.3390/atmos13060908
Ge Q, Zhang X, Cai K, Liu Y. Ozone Pollution in Chinese Cities: Spatiotemporal Variations and Their Relationships with Meteorological and Other Pollution Factors (2016–2020). Atmosphere. 2022; 13(6):908. https://doi.org/10.3390/atmos13060908
Chicago/Turabian StyleGe, Qiang, Xusheng Zhang, Kun Cai, and Yang Liu. 2022. "Ozone Pollution in Chinese Cities: Spatiotemporal Variations and Their Relationships with Meteorological and Other Pollution Factors (2016–2020)" Atmosphere 13, no. 6: 908. https://doi.org/10.3390/atmos13060908
APA StyleGe, Q., Zhang, X., Cai, K., & Liu, Y. (2022). Ozone Pollution in Chinese Cities: Spatiotemporal Variations and Their Relationships with Meteorological and Other Pollution Factors (2016–2020). Atmosphere, 13(6), 908. https://doi.org/10.3390/atmos13060908