Abstract
The purpose of this study is to identify and characterize individual sources of pollutants such as PM10, SO2, NOx, and CO in the urban area in Karadeniz (Turkey) using the bivariate polar plots method. In addition, the relationship between the meteorological conditions and the pollutants was determined based on correlation analysis in the region. Bivariate polar plots are a graphical method used to demonstrate the dependence of pollutant concentrations on wind direction measured at stations. Thanks to these graphics, resource types and properties can be determined. Wind flow and pollution data were used to provide information on wind and pollutant interactions in the study area. As a result of the study, it was founded that the main source of pollutants is intensive anthropogenic activities such as urban, street traffic, agricultural activities, and natural resources. It has been concluded that the highway in the region is not an important source of pollutants. In addition, the pollutant relations were examined with meteorological data, and it was discovered that temperature and relative humidity were effective for all pollutants.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alagha, O., & Tuncel, G. (2003). Evaluation of air quality over the black sea: Major ionic composition of rainwater. Water Air & Soil Pollution: Focus, 3(5–6), 87–96. https://doi.org/10.1023/A:1026049027446
Araujo, I., Costa, D., & De Moraes, R. (2014). Identification and characterization of particulate matter concentrations at construction jobsites. Sustainability, 6(11), 7666–7688. https://doi.org/10.3390/su6117666
Aung, W. Y., Noguchi, M., Pan-Nu Yi, E. E., Thant, Z., Uchiyama, S., Win-Shwe, T. T., Kunugita, N., & Mar, O. (2019). Preliminary assessment of outdoor and indoor air quality in yangon city, Myanmar. Atmospheric Pollution Research, 10(3), 722–730. https://doi.org/10.1016/j.apr.2018.11.011
Beddows, D. C. S., Harrison, R. M., Green, D. C., & Fuller, G. W. (2015). Receptor modelling of both particle composition and size distribution from a background site in London UK. Atmospheric Chemistry Physics, 15, 10107–10125. https://doi.org/10.5194/acp-15-10107-2015
Borrego, C., Monteiro, A., Ferreira, J., Miranda, A., Costa, A., Carvalho, A., & Lopes, M. (2008). Procedures for estimation of modelling uncertainty in air quality assessment. Environment International, 34(5), 613–620. https://doi.org/10.1016/j.envint.2007.12.005
Carslaw, D. (2019, November 12). The openair manual–open-source tools for analysing air pollution data. Manual for version. Retrieved March 15, 2020, from https://davidcarslaw.com/files/openairmanual.pdf
Carslaw, D. C., & Ropkins, K. (2012). Openair—an r package for air quality data analysis. Environmental Modelling & Software, 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008
Coelho, M. C., Fontes, T., Bandeira, J. M., Pereira, S. R., Tchepel, O., Dias, D., Elisa, S. A., Amorim, J. H., & Borrego, C. (2014). Assessment of potential improvements on regional air quality modelling related with implementation of a detailed methodology for traffic emission estimation. Science of the Total Environment, 470–471, 127–137. https://doi.org/10.1016/j.scitotenv.2013.09.042
Colvile, R., Woodfield, N., Carruthers, D., Fisher, B., Rickard, A., Neville, S., & Hughes, A. (2002). Uncertainty in dispersion modelling and urban air quality mapping. Environmental Science & Policy, 5(3), 207–220. https://doi.org/10.1016/S1462-9011(02)00039-4
Demirarslan, K. O. (2016). The effect of urban development on air quality and particle matter pollution in eastern black sea region cities. Journal of the Black Sea Studies, 52, 27–55. https://doi.org/10.12787/KARAM1120
Demirarslan, K. O., & Akıncı, H. (2016a). Determination of particulate matter dispersion in eastern black sea region using geographical information systems. Journal of Natural Hazards and Environment, 2(1), 30–45. https://doi.org/10.21324/dacd.29373
Demirarslan, K. O., & Akıncı, H. (2016b). Determination of sulphurdioxide (SO2) distribution in the eastern black sea region with geographical information system. Journal of Natural Hazards and Environment, 2(2), 81–99. https://doi.org/10.21324/dacd.91087
Elagha, O., Tuncel, G., & Tusun, S. (2001). Air quality of the black sea region: Local and long range transported pollutants. Eurasian Chemico-Technological Journal, 3(4), 273–279.
Fu, B., Kurisu, K., Hanaki, K., & Che, Y. (2019). Influential factors of public intention to improve the air quality in china. Journal of Cleaner Production, 209, 595–607. https://doi.org/10.1016/j.jclepro.2018.10.192
Garza-Galindo, R., Morton-Bermea, O., Hernandez-Alvarez, E., Ordonez-Godınez, S. L., Amador-Munoz, O., Beramendi-Orosco, L. E., Retama, A., Miranda, J., & Perez, I. R. (2019). Spatial and temporal distribution of metals in PM2.5 during 2013: Assessment of wind patterns to the impacts of geogenic and anthropogenic sources. Environmental Monitoring and Assessment, 191(3), 165. https://doi.org/10.1007/s10661-019-7251-4
Giorgi, F., & Meleux, F. (2007). Modelling the regional effects of climate change on air quality. Comptes Rendus Geoscience, 339(11–12), 721–733. https://doi.org/10.1016/j.crte.2007.08.006
Grange, S. K., Lewis, A. C., & Carslaw, D. C. (2016). Source apportionment advances using polar plots of bivariate correlation and regression statistics. Atmospheric Environment, 145, 128–134. https://doi.org/10.1016/j.atmosenv.2016.09.016
Gualtieri, G., Camilli, F., Cavaliere, A., De Filippis, T., Di Gennaro, F., Di Lonardo, S., Dini, F., Gioli, B., Matese, A., Nunziati, W., Rocchi, L., Toscano, P., Vagnoli, C., & Zaldei, A. (2017). An integrated low-cost road traffic and air pollution monitoring platform to assess vehicles’ air quality impact in urban areas. Transportation Research Procedia, 27, 609–616. https://doi.org/10.1016/j.trpro.2017.12.043
Hirono, Y., & Nonaka, K. (2014). Effects of application of lime nitrogen and dicyandiamide on nitrous oxide emissions from green tea fields. Soil Science and Plant Nutrition, 60(2), 276–285. https://doi.org/10.1080/00380768.2014.890015
Hopke, P. K. (1991). Receptor modeling for air quality management (Vol. 7). Elsevier.
Huzlík, J., Hegrová, J., Effenberger, K., Ličbinskỳ, R., & Brtnickỳ, M. (2020). Air quality in Brno City parks. Atmosphere, 11(5), 510. https://doi.org/10.3390/ATMOS11050510
Ilten, N., & Selici, A. T. (2008). Investigating the impacts of some meteorological parameters on air pollution in Balikesir Turkey. Environmental Monitoring and Assessment, 140(1), 267–277. https://doi.org/10.1007/s10661-007-9865-1
Jury, M. R. (2020). Meteorology of air pollution in Los Angeles. Atmospheric Pollution Research, 11(7), 1226–37. https://doi.org/10.1016/j.apr.2020.04.016.
Kara, G. (2012). Effect of meteorological to urban air pollutants: The case of Konya. Selcuk University Journal of Engineering, Science and Technology, 27, 73–86.
Kentisbeer, J., Leeson, S. R., Malcolm, H. M., Leith, I. D., Braban, C. F., & Cape, J. N. (2014). Patterns and source analysis for atmospheric mercury at Auchencorth Moss Scotland. Environmental Science: Processes and Impacts, 16(5), 1112–1123.
Kim, D., Chen, Z., Zhou, L. F., & Huang, S. X. (2018). Air pollutants and early origins of respiratory diseases. Chronic Diseases and Translational Medicine, 4(2), 75–94. https://doi.org/10.1016/j.cdtm.2018.03.003
Mallet, M. D. (2021). Meteorological normalisation of PM10 using machine learning reveals distinct increases of nearby source emissions in the Australian mining town of Moranbah. Atmospheric Pollution Research, 12, 23–35. https://doi.org/10.1016/j.apr.2020.08.001
Mardoyan, A., & Braun, P. (2015). Analysis of Czech subsidies for solid biofuels. International Journal of Green Energy, 12(4), 405–408. https://doi.org/10.1080/15435075.2013.841163
Maroušek, J. (2014). Novel technique to enhance the disintegration effect of the pressure waves on oilseeds. Industrial Crops and Products, 53, 1–5. https://doi.org/10.1016/j.indcrop.2013.11.048
Maroušek, J., Bartoš, P., Filip, M., Kolář, L., Konvalina, P., Maroušková, A., & Zoubek, T. (2020). Advances in the agrochemical utilization of fermentation residues reduce the cost of purpose-grown phytomass for biogas production. Energy Sources, Part a: Recovery, Utilization, and Environmental Effects. https://doi.org/10.1080/15567036.2020.1738597
McLaren, J., & Williams, I. D. (2015). The impact of communicating information about air pollution events on public health. Science of the Total Environment, 538, 478–491. https://doi.org/10.1016/j.scitotenv.2015.07.149
Moreira, T. C., de Oliveira, R. C., Amato, L. F., Kang, C. M., Saldiva, P. H., & Saiki, M. (2016). Intra-urban biomonitoring: Source apportionment using tree barks to identify air pollution sources. Environment International, 91, 271–275. https://doi.org/10.1016/j.envint.2016.03.005
Oduber, F., Calvo, A. I., Blanco-Alegre, C., Castro, A., Vega-Maray, A. M., Valencia-Barrera, R. M., Fernandez-Gonzalez, D., & Fraile, R. (2019). Links between re-cent trends in airborne pollen concentration, meteorological parameters and air pollutants. Agricultural and Forest Meteorology, 264, 16–26. https://doi.org/10.1016/j.agrformet.2018.09.023
Oğuz, K. (2020). Investigation of air quality and the effects of meteorological factors on air pollution in Nevşehir Province. Journal of Natural Hazards and Environment, 90, 391–404. https://doi.org/10.21324/dacd.686052
Peters, E., Kliestik, T., Musa, H., & Durana, P. (2020). Product decision-making information systems, real-time big data analytics, and deep learning-enabled smart process planning in sustainable industry 4.0. Journal of Self-Governance and Management Economics, 8(3), 16–22. https://doi.org/10.22381/JSME8320202
Qiu, G., Song, R., & He, S. (2019). The aggravation of urban air quality deterioration due to urbanization, transportation and economic development - panel models with marginal effect analyses across china. Science of the Total Environment, 651(Pt 1), 1114–1125. https://doi.org/10.1016/j.scitotenv.2018.09.219
R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, Retrieved March 15, 2020, from https://www.R-project.org/
Shu, M., Dang, D., Nguyen, T., Hsu, B., & Pham, K. (2017a). International journal of advance engineering and research the application of bivariate polar plots and k-means clustering to analysis air pollution in Taoyuan Taiwan. International Journal of Advance Engineering and Research Development, 4, 553–557.
Shu, M., Dang, D., Nguyen, T., Hsu, B., Pham, K., Nai, D., & Phong, H. (2017b). Using open-air package for statistic of air quality data: Study in Kaohsiung Taiwan. Global Journal of Advanced Engineering Technologies and Sciences, 4, 53–59.
Singh, K. P., Malik, A., Kumar, R., Saxena, P., & Sinha, S. (2008). Receptor modeling for source apportionment of polycyclic aromatic hydrocarbons in urban atmosphere. Environmental Monitoring and Assessment, 136(1), 183–196. https://doi.org/10.1007/s10661-007-9674-6
Sooktawee, S., Kanchanasuta, S., Boonyapitak, S., Patpai, A., & Piemyai, N. (2020). Distinguish potential source areas of PM2.5 and PM10 by statistical data analysis. IOP Conference Series: Earth and Environmental Science, 489, 1–7. https://doi.org/10.1088/1755-1315/489/1/012024
Szulecka, A., Oleniacz, R., & Rzeszutek, M. (2017). Functionality of openair package in air pollution assessment and modeling—a case study of krakow. Ochrona Srodowiska i Zasobow Naturalnych, 28(2), 22–27. https://doi.org/10.1515/oszn-2017-0009
Tezel, M. N., Sari, D., Ozkurt, N., & Keskin, S. S. (2019). Combined nox and noise pollution from road traffic in Trabzon Turkey. Science of the Total Environment, 696, 134044. https://doi.org/10.1016/j.scitotenv.2019.134044
Uria-Tellaetxe, I., & Carslaw, D. C. (2014). Conditional bivariate probability function for source identification. Environmental Modelling & Software, 59, 1–9. https://doi.org/10.1016/j.envsoft.2014.05.002
Wang, J., & Song, G. (2018). A deep spatial-temporal ensemble model for air quality prediction. Neuro Computing, 314, 198–206. https://doi.org/10.1016/j.neucom.2018.06.049
Xu, S., Fu, X., Ma, S., Bai, Z., Xiao, R., Li, Y., & Zhuang, G. (2014). Mitigating nitrous oxide emissions from tea field soil using bioaugmentation with a trichoderma viride biofertilizer. The Scientific World Journal. https://doi.org/10.1155/2014/793752
Acknowledgements
We would like to thank the Republic of Turkey Ministry of Environment and Urbanization for the data accessible online.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
See Tables
6,
7,
8 and
9.
Rights and permissions
About this article
Cite this article
Demirarslan, K.O., Zeybek, M. Conventional air pollutant source determination using bivariate polar plot in Black Sea, Turkey. Environ Dev Sustain 24, 2736–2766 (2022). https://doi.org/10.1007/s10668-021-01553-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10668-021-01553-3