Downloads

Additional Files

Zhang, T., Chen, Y., & Li, G. Synergistic Effects of the Urban-Rural Divide on Outdoor Airborne Bioaerosol Diffusion: A Case Study in the Monsoon Region of China. Global Environment Science. 2024. doi: Retrieved from https://www.sciltp.com/journals/ges/article/view/336
Forthcoming Issue
Copyright (c) 2024 by the authors.
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Article

Synergistic Effects of the Urban-Rural Divide on Outdoor Airborne Bioaerosol Diffusion: A Case Study in the Monsoon Region of China

Ting Zhang 1,*, Yuying Chen 2,3 and Guiying Li 2,3

1 College of Civil Engineering, Liaoning Technical University, Fuxin 123000, China

2 Guangdong-Hong Kong-Macao Joint Laboratory for Contaminants Exposure and Health, Guangdong Key Laboratory of
  Environmental Catalysis and Health Risk Control, Institute of Environmental Health and Pollution Control, Guangdong
  University of Technology, Guangzhou 510006, China

3 Guangdong Engineering Technology Research Center for Photocatalytic Technology Integration and Equipment,
  Guangzhou Key Laboratory of Environmental Catalysis and Pollution Control, School of Environmental Science and
  Engineering, Guangdong University of Technology, Guangzhou 510006, China

* Correspondence: memchem@163.com

Received: 6 March 2024; Revised: 30 June 2024; Accepted: 20 August 2024; Published: 10 September 2024

 

Abstract: Globally, air pollution is intensifying in both urban and rural environments due to rapid urbanization and industrialization. It is widely accepted that human activities, particularly mining, contribute to increased concentrations of fine particulate matter in the atmosphere, leading to serious concerns regarding the associated health risks. In this study, the characteristics of outdoor aerosols containing bacteria and fungi were determined from on-site samples that were analyzed using sequencing techniques as well as modelling at both artificial and natural locations in two cities (Fuxin, FX; Guangzhou, GZ) in China. The interaction between urban and rural anthropogenic activities had a synergistic effect on the distribution of airborne microorganisms. At locations with artificial surfaces, which were characterized by higher population densities, the concentration of Firmicutes, including Streptococcus pneumoniae and Aspergillus fumigatus, which are colonizers of the human respiratory tract, was 1.25 times greater than that of the on-site monitored total airborne microbes (TAM) concentration. In the natural wetland area with a lower population density, the coarse-, medium-, and fine- bioaerosols accounted for 16%, 49%, and 35% of the TAM concentration, respectively. When a concentration ratio was used to describe the airborne bioaerosol (x: C/C′), the community distribution was found to vary between artificial and natural environments in FX. This was attributed to the contributions of agricultural and traffic activity. The accumulation and atmospheric diffusion of aerosols, particularly in areas with low wind speeds, led to the presence of inhalable (0.65‒2.1 μm) bioaerosols. Through a modeling-based analysis, elevated x values indicated a regional deterioration in air quality due to aerosol emissions and their spatial dispersion. This ratio was highlighted by the significant abundance of Ascomycota observed at transportation infrastructure sites in GZ. The study suggests the existence of health risks associated with regional atmospheric bioaerosols, with the risks varying across the urban-rural divide.

Keywords:

urban air quality bioaerosol community atmospheric dispersion modelling risk assessment

References

  1. Winkler, K.; Fuchs, R.; Rounsevell, M.; Herold, M. Global land use changes are four times greater than previously estimated. Nat. Commun. 2021, 12, 2501.
  2. Dadashpoor, H.; Azizi, P.; Moghadasi, M. Land use change, urbanization, and change in landscape pattern in a metropolitan area. Sci. Total Environ. 2019, 655, 707–719.
  3. Zhou, W.; Yu, W.; Qian, Y.; Han, L.; Pickett, S.T.A.; Wang, J.; Li, W.; Ouyang, Z. Beyond city expansion: Multi-scale environmental impacts of urban megaregion formation in China. Natl. Sci. Rev. 2022, 9, nwab107.
  4. Espey, J.; Parnell, S.; Revi, A. The transformative potential of a Global Urban Agenda and its lessons in a time of crisis. NPJ Urban Sustain. 2023, 3, 15.
  5. Pandey, B.; Brelsford, C.; Seto, K.C. Infrastructure inequality is a characteristic of urbanization. Proc. Natl. Acad. Sci. 2022, 119, e2119890119.
  6. Yu, H.; Zahidi, I. Environmental hazards posed by mine dust, and monitoring method of mine dust pollution using remote sensing technologies: An overview. Sci. Total Environ. 2023, 864, 161135.
  7. Maus, V.; Giljum, S.; Gutschlhofer, J.; da Silva, D.M.; Probst, M.; Gass, S.L.B.; Luckeneder, S.; Lieber, M.; McCallum, I. A global-scale data set of mining areas. Sci. Data 2020, 7, 289.
  8. Worlanyo, A.S.; Jiangfeng, L. Evaluating the environmental and economic impact of mining for post-mined land restoration and land-use: A review. J. Environ. Manag. 2021, 279, 111623.
  9. Sonter, L.J.; Ali, S.H.; Watson, J.E.M. Mining and biodiversity: Key issues and research needs in conservation science. Proc. R. Soc. B Biol. Sci. 2018, 285, 20181926.
  10. Hara, K.; Zhang, D. Bacterial abundance and viability in long-range transported dust. Atmos. Environ. 2012, 47, 20–25.
  11. Zhang, T.; Chen, Y.; Cai, Y.; Yu, Y.; Liu, J.; Shen, X.; Li, G.; An, T. Abundance and cultivable bioaerosol transport from a municipal solid waste landfill area and its risks. Environ. Pollut. 2023, 320, 121038.
  12. Xie, Z.; Fan, C.; Lu, R.; Liu, P.; Wang, B.; Du, S.; Jin, C.; Deng, S.; Li, Y. Characteristics of ambient bioaerosols during haze episodes in China: A review. Environ. Pollut. 2018, 243, 1930–1942.
  13. Li, Z.; Ma, Z.; van der Kuijp, T.J.; Yuan, Z.; Huang, L. A review of soil heavy metal pollution from mines in China: Pollution and health risk assessment. Sci. Total Environ. 2014, 468–469, 843–853.
  14. Fan, P.; Lu, X.; Yu, B.; Fan, X.; Wang, L.; Lei, K.; Yang, Y.; Zuo, L.; Rinklebe, J. Spatial distribution, risk estimation and source apportionment of potentially toxic metal(loid)s in resuspended megacity street dust. Environ. Int. 2022, 160, 107073.
  15. Šantl-Temkiv, T.; Sikoparija, B.; Maki, T.; Carotenuto, F.; Amato, P.; Yao, M.; Morris, C.E.; Schnell, R.; Jaenicke, R.; Pöhlker, C.; et al. Bioaerosol field measurements: Challenges and perspectives in outdoor studies. Aerosol Sci. Technol. 2020, 54, 520–546.
  16. Akimbekov, N.S.; Digel, I.; Tastambek, K.T.; Marat, A.K.; Turaliyeva, M.A.; Kaiyrmanova, G.K. Biotechnology of Microorganisms from Coal Environments: From Environmental Remediation to Energy Production. Biology 2022, 11, 1306.
  17. Labouyrie, M.; Ballabio, C.; Romero, F.; Panagos, P.; Jones, A.; Schmid, M.W.; Mikryukov, V.; Dulya, O.; Tedersoo, L.; Bahram, M.; et al. Patterns in soil microbial diversity across Europe. Nat. Commun. 2023, 14, 3311.
  18. Wanjun, T.; Qingxiang, C.A.I. Dust distribution in open-pit mines based on monitoring data and fluent simulation. Environ. Monit. Assess. 2018, 190, 632.
  19. Wu, T.; Boor, B.E. Urban aerosol size distributions: A global perspective. Atmos. Chem. Phys. 2021, 21, 8883–8914.
  20. Chen, P.-C.; Lin, Y.-T. Exposure assessment of PM2.5 using smart spatial interpolation on regulatory air quality stations with clustering of densely-deployed microsensors. Environ. Pollut. 2022, 292, 118401.
  21. Faisal, A.-A.; Rahman, M.M.; Haque, S. Retrieving spatial variation of aerosol level over urban mixed land surfaces using Landsat imageries: Degree of air pollution in Dhaka Metropolitan Area. Phys. Chem. Earth Parts A/B/C 2022, 126, 103074.
  22. Liu, L.; Zhang, X.; Gao, Y.; Chen, X.; Shuai, X.; Mi, J. Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects. J. Remote Sens. 2021, 2021, 5289697.
  23. Zhao, Y.; Zhu, Z. ASI: An artificial surface Index for Landsat 8 imagery. Int. J. Appl. Earth Obs. Geoinf. 2022, 107, 102703.
  24. Zuo, J.; Rameezdeen, R.; Hagger, M.; Zhou, Z.; Ding, Z. Dust pollution control on construction sites: Awareness and self-responsibility of managers. J. Clean. Prod. 2017, 166, 312–320.
  25. Edwards, D.P.; Sloan, S.; Weng, L.; Dirks, P.; Sayer, J.; Laurance, W.F. Mining and the African Environment. Conserv. Lett. 2014, 7, 302–311.
  26. Li, J.; Carlson, B.E.; Yung, Y.L.; Lv, D.; Hansen, J.; Penner, J.E.; Liao, H.; Ramaswamy, V.; Kahn, R.A.; Zhang, P.; et al. Scattering and absorbing aerosols in the climate system. Nat. Rev. Earth Environ. 2022, 3, 363–379.
  27. Sharma, R.; Kumar, A. Analysis of seasonal and spatial distribution of particulate matters and gaseous pollutants around an open cast coal mining area of Odisha, India. Environ. Sci. Pollut. Res. 2023, 30, 39842–39856.
  28. Zhao, X.; Huang, J.; Lu, J.; Sun, Y. Study on the influence of soil microbial community on the long-term heavy metal pollution of different land use types and depth layers in mine. Ecotoxicol. Environ. Saf. 2019, 170, 218–226.
  29. Zhang, J.; Fu, M.; Hassani, F.P.; Zeng, H.; Geng, Y.; Bai, Z. Land Use-Based Landscape Planning and Restoration in Mine Closure Areas. Environ. Manag. 2011, 47, 739–750.
  30. Gutierrez-Velez, V.H.; Gilbert, M.R.; Kinsey, D.; Behm, J.E. Beyond the ‘urban’ and the ‘rural’: Conceptualizing a new generation of infrastructure systems to enable rural–urban sustainability. Curr. Opin. Environ. Sustain. 2022, 56, 101177.
  31. Korek, M.; Johansson, C.; Svensson, N.; Lind, T.; Beelen, R.; Hoek, G.; Pershagen, G.; Bellander, T. Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden. J. Expo. Sci. Environ. Epidemiol. 2017, 27, 575–581.
  32. Ma, X.; Gao, J.; Longley, I.; Zou, B.; Guo, B.; Xu, X.; Salmond, J. Development of transferable neighborhood land use regression models for predicting intra-urban ambient nitrogen dioxide (NO2) spatial variations. Environ. Sci. Pollut. Res. 2022, 29, 45903–45918.
  33. Ghosh, B.; Lal, H.; Srivastava, A. Review of bioaerosols in indoor environment with special reference to sampling, analysis and control mechanisms. Environ. Int. 2015, 85, 254–272.
  34. Zhao, Y.; Zhang, J.; Wang, S.; Yu, L.; Yu, H.; Wang, Y.; Feng, L. Efficacy of 75% alcohol in pretreatment of the Andersen sampler in trapping maximum airborne microbes. Aerobiologia 2021, 37, 171–178.
  35. Zhang, Z.; Wang, K. The Synoptic to Decadal Variability in the Winter Surface Wind Speed Over China by the Weather Regime View. Geophys. Res. Lett. 2021, 48, e2020GL091994.
  36. Xu, Y.; Liu, Y.; Chen, R.; Meng, Y.; Li, K.; Fu, C. Study on the spatio-temporal evolution characteristics and driving mechanism of China’s carbon emissions. Humanit. Soc. Sci. Commun. 2023, 10, 786.
  37. Xu, M.; Chang, C.-P.; Fu, C.; Qi, Y.; Robock, A.; Robinson, D.; Zhang, H. Steady decline of east Asian monsoon winds, 1969–2000: Evidence from direct ground measurements of wind speed. J. Geophys. Res. Atmos. 2006, 111, D24111.
  38. Mousavi, S.E.; Delgado-Saborit, J.M.; Adivi, A.; Pauwels, S.; Godderis, L. Air pollution and endocrine disruptors induce human microbiome imbalances: A systematic review of recent evidence and possible biological mechanisms. Sci. Total Environ. 2022, 816, 151654.
  39. Wong, P.-Y.; Lee, H.-Y.; Chen, Y.-C.; Zeng, Y.-T.; Chern, Y.-R.; Chen, N.-T.; Candice Lung, S.-C.; Su, H.-J.; Wu, C.-D. Using a land use regression model with machine learning to estimate ground level PM2.5. Environ. Pollut. 2021, 277, 116846.
  40. Xue, S.; Liu, X.; Li, Y.; Liu, B.; Tu, Q.; Li, C. Pathogenic Bacterial Communities of Dust in a Coal Mine. Front. Environ. Sci. 2022, 10, 857744.
  41. Pincetl, S. Cities in the age of the Anthropocene: Climate change agents and the potential for mitigation. Anthropocene 2017, 20, 74–82.
  42. Kameoka, S.; Motooka, D.; Watanabe, S.; Kubo, R.; Jung, N.; Midorikawa, Y.; Shinozaki, N.O.; Sawai, Y.; Takeda, A.K.; Nakamura, S. Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1–V2 and V3–V4 primer sets. BMC Genom. 2021, 22, 527.
  43. Yang, K.; Li, L.; Xue, S.; Wang, Y.; Liu, J.; Yang, T. Influence factors and health risk assessment of bioaerosols emitted from an industrial-scale thermophilic biofilter for off gas treatment. Process Saf. Environ. Prot. 2019, 129, 55–62.
  44. Akila, M.; Earappa, R.; Qureshi, A. Ambient concentration of airborne microbes and endotoxins in rural households of southern India. Build. Environ. 2020, 179, 106970.
  45. Lü, H.; Wen, S.; Feng, Y.; Wang, X.; Bi, X.; Sheng, G.; Fu, J. Indoor and outdoor carbonyl compounds and BTEX in the hospitals of Guangzhou, China. Sci. Total Environ. 2006, 368, 574–584.
  46. Borghi, F.; Spinazzè, A.; Mandaglio, S.; Fanti, G.; Campagnolo, D.; Rovelli, S.; Keller, M.; Cattaneo, A.; Cavallo, D.M. Estimation of the Inhaled Dose of Pollutants in Different Micro-Environments: A Systematic Review of the Literature. Toxics 2021, 9, 140.
  47. Cheng, Y.; Ma, N.; Witt, C.; Rapp, S.; Wild, P.S.; Andreae, M.O.; Pöschl, U.; Su, H. Face masks effectively limit the probability of SARS-CoV-2 transmission. Science 2021, 372, 1439–1443.
  48. Medema, G.J.; Teunis, P.F.M.; Havelaar, A.H.; Haas, C.N. Assessment of the dose-response relationship of Campylobacter jejuni. Int. J. Food Microbiol. 1996, 30, 101–111.
  49. Seppey, C.V.W.; Lara, E.; Broennimann, O.; Guisan, A.; Malard, L.; Singer, D.; Yashiro, E.; Fournier, B. Landscape structure is a key driver of soil protist diversity in meadows in the Swiss Alps. Landsc. Ecol. 2023, 38, 949–965.
  50. Hanson, C.A.; Fuhrman, J.A.; Horner-Devine, M.C.; Martiny, J.B.H. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nat. Rev. Microbiol. 2012, 10, 497–506.
  51. Martiny, J.B.H.; Bohannan, B.J.M.; Brown, J.H.; Colwell, R.K.; Fuhrman, J.A.; Green, J.L.; Horner-Devine, M.C.; Kane, M.; Krumins, J.A.; Kuske, C.R.; et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Microbiol. 2006, 4, 102–112.
  52. Vicuña, R.; González, B. The microbial world in a changing environment. Rev. Chil. De Hist. Nat. 2021, 94, 2.
  53. Katiyar, V. Assessment of indoor air micro-flora in selected schools. Adv. Environ. Res. 2013, 2, 61–80.
  54. Li, Y.; Lu, R.; Li, W.; Xie, Z.; Song, Y. Concentrations and size distributions of viable bioaerosols under various weather conditions in a typical semi-arid city of Northwest China. J. Aerosol Sci. 2017, 106, 83–92.
  55. Lladó Fernández, S.; Větrovský, T.; Baldrian, P. The concept of operational taxonomic units revisited: Genomes of bacteria that are regarded as closely related are often highly dissimilar. Folia Microbiol. 2019, 64, 19–23.
  56. He, Y.; Caporaso, J.G.; Jiang, X.-T.; Sheng, H.-F.; Huse, S.M.; Rideout, J.R.; Edgar, R.C.; Kopylova, E.; Walters, W.A.; Knight, R.; et al. Stability of operational taxonomic units: An important but neglected property for analyzing microbial diversity. Microbiome 2015, 3, 20.
  57. Roswell, M.; Dushoff, J.; Winfree, R. A conceptual guide to measuring species diversity. Oikos 2021, 130, 321–338.
  58. Wei, K.; Zheng, Y.; Li, J.; Shen, F.; Zou, Z.; Fan, H.; Li, X.; Wu, C.-Y.; Yao, M. Microbial aerosol characteristics in highly polluted and near-pristine environments featuring different climatic conditions. Sci. Bull. 2015, 60, 1439–1447.
  59. Menut, L.; Siour, G.; Bessagnet, B.; Couvidat, F.; Journet, E.; Balkanski, Y.; Desboeufs, K. Modelling the mineralogical composition and solubility of mineral dust in the Mediterranean area with CHIMERE 2017r4. Geosci. Model Dev. 2020, 13, 2051–2071.
  60. Cavender-Bares, J.; Schneider, F.D.; Santos, M.J.; Armstrong, A.; Carnaval, A.; Dahlin, K.M.; Fatoyinbo, L.; Hurtt, G.C.; Schimel, D.; Townsend, P.A.; et al. Integrating remote sensing with ecology and evolution to advance biodiversity conservation. Nat. Ecol. Evol. 2022, 6, 506–519.
  61. Han, Y.; Li, L.; Wang, Y.; Ma, J.; Li, P.; Han, C.; Liu, J. Composition, dispersion, and health risks of bioaerosols in wastewater treatment plants: A review. Front. Environ. Sci. Eng. 2020, 15, 38.
  62. Liu, Z.; Zhuang, W.; Hu, L.; Rong, R.; Li, J.; Ding, W.; Li, N. Experimental and numerical study of potential infection risks from exposure to bioaerosols in one BSL-3 laboratory. Build. Environ. 2020, 179, 106991.
  63. Sheikhnejad, Y.; Aghamolaei, R.; Fallahpour, M.; Motamedi, H.; Moshfeghi, M.; Mirzaei, P.A.; Bordbar, H. Airborne and aerosol pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics. Sustain. Cities Soc. 2022, 79, 103704.
  64. Peng, S.; Chen, Q.; Liu, E. The role of computational fluid dynamics tools on investigation of pathogen transmission: Prevention and control. Sci. Total Environ. 2020, 746, 142090.
  65. Fairs, A.; Agbetile, J.; Bourne, M.; Hargadon, B.; Monteiro, W.R.; Morley, J.P.; Edwards, R.E.; Wardlaw, A.J.; Pashley, C.H. Isolation of Aspergillus fumigatus from sputum is associated with elevated airborne levels in homes of patients with asthma. Indoor Air 2013, 23, 275–284.
  66. Leleu, C.; Menotti, J.; Meneceur, P.; Choukri, F.; Sulahian, A.; Garin, Y.J.-F.; Denis, J.-B.; Derouin, F. Bayesian Development of a Dose-Response Model for Aspergillus fumigatus and Invasive Aspergillosis. Risk Anal. 2013, 33, 1441–1453.
  67. Wang, M.; Kong, W.; Marten, R.; He, X.-C.; Chen, D.; Pfeifer, J.; Heitto, A.; Kontkanen, J.; Dada, L.; Kürten, A.; et al. Rapid growth of new atmospheric particles by nitric acid and ammonia condensation. Nature 2020, 581, 184–189.