Araştırma Makalesi
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Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey

Yıl 2025, Cilt: 11 Sayı: 2, 406 - 417, 27.07.2025
https://doi.org/10.21324/dacd.1599855

Öz

Air is an essential requirement for living things to survive. Air pollution is defined as the foreign substances in the air reaching a quantity and density above normal. Regular monitoring and analysis of air pollution is an indispensable step for the measures and policies to be taken. In recent years, the use of open-source software in the evaluation and analysis of air quality data has increased. The R software-based Openair Package developed by the Environmental Research Group at King's College London. This package is used to analyze air quality data, identify potential pollutant sources, and identify correlations and statistical relationships between parameters. In this study, the hourly pollutant parameter (PM, SO2, and NOx) measured from two air quality stations between 2018 and 2022 in Kocaeli, were analyzed temporally and statistically with the Openair Package. The study found that the primary sources of pollutants are anthropogenic activities such as vehicle traffic, fossil fuels used for heating, and natural resources. As a result of the analysis, a decrease in PM concentrations and an increase in SO2 concentrations were observed during the study period. In addition, it was determined that traffic density has a linear relationship with the NOx parameter.

Kaynakça

  • Agustine, I., Yulinawati, H., Gunawan, D., & Suswantoro, E. (2018). Potential impact of particulate matter less than 10 micron (PM10) to ambient air quality of Jakarta and Palembang. IOP Conference Series: Earth and Environmental Science, 106(1), Article 012057. https://doi.org/10.1088/1755-1315/106/1/012057
  • Akyürek, Ö., Arslan, O., & Karademir, A. (2013, 11–13 Kasım). Spatial analysis of air pollution parameters SO₂ and PM₁₀ with GIS: A case study of Kocaeli province [Bildiri sunumu]. TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara.
  • Badida, P., & Jayaprakash, J. (2022). Variations in local, transported, and exposure risks of PM2.5 pollution: Insights from long-term monitoring data in mega coastal city. Human and Ecological Risk Assessment, 28(10), 1146–1174.
  • 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 and Physics, 15(17), 10107–10125. https://doi.org/10.5194/acp-15-10107-2015
  • Beringui, K., Justo, E. P. S., De Falco, A., Santa-Helena, E., Rocha, W. F. C., Deroubaix, A., & Gioda, A. (2022). Assessment of air quality changes during COVID-19 partial lockdown in a Brazilian metropolis: From lockdown to economic opening of Rio de Janeiro, Brazil. Air Quality, Atmosphere and Health, 15(7), 1205–1220. https://doi.org/10.1007/s11869-021-01127-2
  • Bousiotis, D., Beddows, D. C. S., Singh, A., Haugen, M., Diez, S., Edwards, P. M., Boies, A., Harrison, R. M., & Pope, F. D. (2022). A study on the performance of low-cost sensors for source apportionment at an urban background site. Atmospheric Measurement Techniques, 15(13), 4047–4061. https://doi.org/10.5194/amt-15-4047-2022
  • Carslaw, D. C. (2019). The openair manual — open-source tools for analysing air pollution data (Issue November). https://davidcarslaw.com/files/openairmanual.pdf
  • Carslaw, D. C., & Beevers, S. D. (2013). Characterising and understanding emission sources using bivariate polar plots and k-means clustering. Environmental Modelling and Software, 40, 325–329. https://doi.org/10.1016/j.envsoft.2012.09.005
  • Carslaw, D. C., Beevers, S. D., Ropkins, K., & Bell, M. C. (2006). Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmospheric Environment, 40(28), 5424–5434. https://doi.org/10.1016/j.atmosenv.2006.04.062
  • Carslaw, D. C., & Ropkins, K. (2012). Openair – An R package for air quality data analysis. Environmental Modelling and Software, 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008
  • Chaurasia, R., & Mohan, M. (2022). Estimation of background concentration of ambient pollutants for Delhi NCT region. Atmospheric Pollution Research, 13(7), Article 101476. https://doi.org/10.1016/j.apr.2022.101476
  • Dang, D., Shu, M., Nguyen, T., Hsu, B., & Pham, K. (2017). Using Open-Air Package for statistic of air quality data: Study in Kaohsiung, Taiwan. Global Journal of Advanced Engineering Technologies and Sciences, 4(4), 53–59.
  • Demirarslan, K. O., & Akıncı, H. (2018). CBS ve hava kalitesi verileri kullanılarak Marmara Bölgesi’nin kış sezonunda hava kalitesinin değerlendirilmesi. Doğal Afetler ve Çevre Dergisi, 4(1), 11–27. https://doi.org/10.21324/dacd.344564
  • Demirarslan, K. O., & Zeybek, M. (2022). Conventional air pollutant source determination using bivariate polar plot in Black Sea, Turkey. Environment, Development and Sustainability, 24(2), 2736–2766. https://doi.org/10.1007/s10668-021-01553-3
  • Governership of Kocaeli. (2025). Kocaeli’s population was 2 million 102 thousand 907. http://www.kocaeli.gov.tr/kocaelinin-nufusu-2-milyon-102-bin-907-oldu
  • 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
  • Habeebullah, T. M., Munir, S., Zeb, J., & Morsy, E. A. (2022). Modelling the effect of COVID-19 lockdown on air pollution in Makkah, Saudi Arabia with a supervised machine learning approach. Toxics, 10(5), Article 225. https://doi.org/10.3390/toxics10050225
  • Huzlík, J., Hegrová, J., Effenberger, K., Ličbinský, R., & Brtnický, M. (2020). Air quality in Brno City parks. Atmosphere, 11(5), Article 510. https://doi.org/10.3390/ATMOS11050510
  • Iskandaryan, D., Di Sabatino, S., Ramos, F., & Trilles, S. (2022). Exploratory analysis and feature selection for the prediction of nitrogen dioxide. AGILE: GIScience Series, 3(2), 1–11. https://doi.org/10.5194/agile-giss-3-6-2022
  • Lamprecht, C. (2023). Meteorological weather and climate data [Data set]. https://meteostat.net/en/station/LTBQ0?t=2021-01-01/2021-12-31
  • Lv, Y., Tian, H., Luo, L., Liu, S., Bai, X., Zhao, H., Lin, S., Zhao, S., Guo, Z., Xiao, Y., & Yang, J. (2022). Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities. Atmospheric Pollution Research, 13(6), Article 101452. https://doi.org/10.1016/j.apr.2022.101452
  • Marangoz, M., & Akçam, G. (2023). The effect of the COVID-19 pandemic on the sectors: A conceptual evaluation. Hitit Journal of Economics and Politics, 3(1), 20–46. https://doi.org/10.5281/zenodo.1366522
  • Matthias, V., Quante, M., Arndt, J. A., Badeke, R., Fink, L., Petrik, R., Feldner, J., Schwarzkopf, D., Link, E. M., Ramacher, M. O. P., & Wedemann, R. (2021). The role of emission reductions and the meteorological situation for air quality improvements during the COVID-19 lockdown period in central Europe. Atmospheric Chemistry and Physics, 21(18), 13931–13971. https://doi.org/10.5194/acp-21-13931-2021
  • Ministry of Environment Urbanization and Climate Change. (2023). National Air Quality Monitoring Network. [Data set]. https://sim.csb.gov.tr/STN/STN_Report/StationDataDownloadNew
  • Ngo, T. H., Pan, W. C., & Waits, A. (2022). Reduction in aviation volume due to COVID-19 and changes in air pollution near the international airport in Taiwan. Aerosol and Air Quality Research, 22(4), 1–9. https://doi.org/10.4209/aaqr.210297
  • Oleniacz, R., Gorzelnik, T., & Bogacki, M. (2021). Impact of urban, suburban and industrial background on air pollution levels of dust substances in north-eastern part of Krakow (Poland). IOP Conference Series: Earth and Environmental Science, 642(1), Article 012013. https://doi.org/10.1088/1755-1315/642/1/012013
  • R Core Team. (2019). R: A language and environment for statistical computing (Version 4.2.2). R Foundation for Statistical Computing.
  • Republic of Turkey Ministry of Industry and Technology. (2019). Kocaeli province industry status report. https://kosano.org.tr/wp-content/uploads/2020/02/KSO-faaliyet-raporu-2019.pdf
  • Ropkins, K., & Carslaw, D. C. (2012). Openair – Data analysis tools for the air quality community. R Journal, 4(1), 20–29. https://doi.org/10.32614/rj-2012-003
  • Ropkins, K., Tate, J. E., Walker, A., & Clark, T. (2022). Measuring the impact of air quality related interventions. Environmental Science: Atmospheres, 500–516. https://doi.org/10.1039/d1ea00073j
  • Rosianu, A. M., Leru, P. M., Stefan, S., Iorga, G., & Marmureanu, L. (2022). Six-year monitoring of atmospheric pollen and major air pollutant concentrations in relation with meteorological factors in Bucharest, Romania. Romanian Reports in Physics, 74(2), 1–15.
  • Shen, F., Hegglin, M. I., Luo, Y., Yuan, Y., Wang, B., Flemming, J., Wang, J., Zhang, Y., Chen, M., Yang, Q., & Ge, X. (2022). Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China. Climate and Atmospheric Science, 5(1), Article 54. https://doi.org/10.1038/s41612-022-00276-0
  • Sidjabat, F. M., Hakiki, R., & Wikaningrum, T. (2019). Air quality monitoring in industrial estate (case study: Jababeka Industrial Estate, Cikarang). Journal of Environmental Engineering and Waste Management, 4(2), 50–58.
  • Szulecka, A., Oleniacz, R., & Rzeszutek, M. (2017). Functionality of openair package in air pollution assessment and modeling – A case study of Krakow. Ochrona Środowiska i Zasobów Naturalnych, 28(2), 22–27. https://doi.org/10.1515/oszn-2017-0009
  • Uria-Tellaetxe, I., & Carslaw, D. C. (2014). Conditional bivariate probability function for source identification. Environmental Modelling and Software, 59, 1–9. https://doi.org/10.1016/j.envsoft.2014.05.002
  • Wang, M., Zhang, Z., Yuan, Q., Li, X., Han, S., Lam, Y., Cui, L., Huang, Y., Cao, J., & Lee, S. C. (2022). Slower than expected reduction in annual PM2.5 in Xi’an revealed by machine learning-based meteorological normalization. Science of the Total Environment, 841, Article 156740. https://doi.org/10.1016/j.scitotenv.2022.156740
  • World Health Organization. (2022). Air pollution. https://www.who.int/health-topics/air-pollution#tab=tab_1
  • World Health Organization. (2025). WHO COVID-19 dashboard. https://data.who.int/dashboards/covid19/cases?m49=001&n=o
  • Yener, İ., & Demirarslan, K. O. (2022). Determining the factors affecting air quality in Marmara, Turkey, and assessing it using air quality indices. Doğal Afetler ve Çevre Dergisi, 8(2), 383–395. https://doi.org/10.21324/dacd.1081167
  • Yener, İ., & Demirarslan, K. O. (2024). Did Turkey experience reductions in air pollution during the COVID-19 lockdown and partial lockdown? Doğal Afetler ve Çevre Dergisi, 10(1), 179–191. https://doi.org/10.21324/dacd.1339741
  • Yorkor, B., Leton, T. G., & Ugbebor, J. N. (2021). Analysis of temporal variations of air pollutant concentrations in Ogoni Area, Niger Delta Region, Nigeria. Asian Journal of Environment & Ecology, November, 63–73. https://doi.org/10.9734/ajee/2021/v16i430260
  • Yulinawati, H., Khairani, T., & Siami, L. (2021). Analysis of indoor and outdoor particulate (PM2.5) at a women and children’s hospital in West Jakarta. IOP Conference Series: Earth and Environmental Science, 737(1), Article 012057. https://doi.org/10.1088/1755-1315/737/1/012067

Hava Kirletici Konsantrasyonlarının Zamansal Değişikliğinin Analizi; Kocaeli Örneği

Yıl 2025, Cilt: 11 Sayı: 2, 406 - 417, 27.07.2025
https://doi.org/10.21324/dacd.1599855

Öz

Hava, canlıların yaşamlarını sürdürebilmeleri için temel bir gereksinimdir. Hava kirliliği, havadaki yabancı maddelerin normalin üzerinde bir miktar ve yoğunluğa ulaşması olarak tanımlanmaktadır. Hava kirliliğinin düzenli olarak izlenmesi ve analiz edilmesi, alınacak önlemler ve politikalar için vazgeçilmez bir adımdır. Son yıllarda hava kalitesi verilerinin değerlendirilmesi ve analizinde açık kaynak kodlu yazılımların kullanımı artmıştır. King's College London'daki Çevre Araştırma Grubu tarafından R yazılımı tabanlı Openair Paketi geliştirilmiştir. Bu paket, hava kalitesi verilerini analiz etmek, potansiyel kirletici kaynaklarını belirlemek ve parametreler arasındaki korelasyonları ve istatistiksel ilişkileri tespit etmek için kullanılmaktadır. Bu çalışmada, Kocaeli'de 2018-2022 yılları arasında iki hava kalitesi istasyonundan ölçülen saatlik kirletici parametreler (PM, SO2 ve NOx) Openair Paketi ile zamansal ve istatistiksel olarak analiz edilmiştir. Çalışma, kirleticilerin birincil kaynaklarının araç trafiği, ısınma için kullanılan fosil yakıtlar ve doğal kaynaklar gibi antropojenik faaliyetler olduğunu ortaya koymuştur. Analiz sonucunda, çalışma dönemi boyunca PM konsantrasyonlarında azalma, SO2 konsantrasyonlarında ise artış gözlemlenmiştir. Ayrıca trafik yoğunluğunun NOx parametresi ile doğrusal bir ilişki içinde olduğu tespit edilmiştir.

Kaynakça

  • Agustine, I., Yulinawati, H., Gunawan, D., & Suswantoro, E. (2018). Potential impact of particulate matter less than 10 micron (PM10) to ambient air quality of Jakarta and Palembang. IOP Conference Series: Earth and Environmental Science, 106(1), Article 012057. https://doi.org/10.1088/1755-1315/106/1/012057
  • Akyürek, Ö., Arslan, O., & Karademir, A. (2013, 11–13 Kasım). Spatial analysis of air pollution parameters SO₂ and PM₁₀ with GIS: A case study of Kocaeli province [Bildiri sunumu]. TMMOB Coğrafi Bilgi Sistemleri Kongresi, Ankara.
  • Badida, P., & Jayaprakash, J. (2022). Variations in local, transported, and exposure risks of PM2.5 pollution: Insights from long-term monitoring data in mega coastal city. Human and Ecological Risk Assessment, 28(10), 1146–1174.
  • 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 and Physics, 15(17), 10107–10125. https://doi.org/10.5194/acp-15-10107-2015
  • Beringui, K., Justo, E. P. S., De Falco, A., Santa-Helena, E., Rocha, W. F. C., Deroubaix, A., & Gioda, A. (2022). Assessment of air quality changes during COVID-19 partial lockdown in a Brazilian metropolis: From lockdown to economic opening of Rio de Janeiro, Brazil. Air Quality, Atmosphere and Health, 15(7), 1205–1220. https://doi.org/10.1007/s11869-021-01127-2
  • Bousiotis, D., Beddows, D. C. S., Singh, A., Haugen, M., Diez, S., Edwards, P. M., Boies, A., Harrison, R. M., & Pope, F. D. (2022). A study on the performance of low-cost sensors for source apportionment at an urban background site. Atmospheric Measurement Techniques, 15(13), 4047–4061. https://doi.org/10.5194/amt-15-4047-2022
  • Carslaw, D. C. (2019). The openair manual — open-source tools for analysing air pollution data (Issue November). https://davidcarslaw.com/files/openairmanual.pdf
  • Carslaw, D. C., & Beevers, S. D. (2013). Characterising and understanding emission sources using bivariate polar plots and k-means clustering. Environmental Modelling and Software, 40, 325–329. https://doi.org/10.1016/j.envsoft.2012.09.005
  • Carslaw, D. C., Beevers, S. D., Ropkins, K., & Bell, M. C. (2006). Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport. Atmospheric Environment, 40(28), 5424–5434. https://doi.org/10.1016/j.atmosenv.2006.04.062
  • Carslaw, D. C., & Ropkins, K. (2012). Openair – An R package for air quality data analysis. Environmental Modelling and Software, 27–28, 52–61. https://doi.org/10.1016/j.envsoft.2011.09.008
  • Chaurasia, R., & Mohan, M. (2022). Estimation of background concentration of ambient pollutants for Delhi NCT region. Atmospheric Pollution Research, 13(7), Article 101476. https://doi.org/10.1016/j.apr.2022.101476
  • Dang, D., Shu, M., Nguyen, T., Hsu, B., & Pham, K. (2017). Using Open-Air Package for statistic of air quality data: Study in Kaohsiung, Taiwan. Global Journal of Advanced Engineering Technologies and Sciences, 4(4), 53–59.
  • Demirarslan, K. O., & Akıncı, H. (2018). CBS ve hava kalitesi verileri kullanılarak Marmara Bölgesi’nin kış sezonunda hava kalitesinin değerlendirilmesi. Doğal Afetler ve Çevre Dergisi, 4(1), 11–27. https://doi.org/10.21324/dacd.344564
  • Demirarslan, K. O., & Zeybek, M. (2022). Conventional air pollutant source determination using bivariate polar plot in Black Sea, Turkey. Environment, Development and Sustainability, 24(2), 2736–2766. https://doi.org/10.1007/s10668-021-01553-3
  • Governership of Kocaeli. (2025). Kocaeli’s population was 2 million 102 thousand 907. http://www.kocaeli.gov.tr/kocaelinin-nufusu-2-milyon-102-bin-907-oldu
  • 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
  • Habeebullah, T. M., Munir, S., Zeb, J., & Morsy, E. A. (2022). Modelling the effect of COVID-19 lockdown on air pollution in Makkah, Saudi Arabia with a supervised machine learning approach. Toxics, 10(5), Article 225. https://doi.org/10.3390/toxics10050225
  • Huzlík, J., Hegrová, J., Effenberger, K., Ličbinský, R., & Brtnický, M. (2020). Air quality in Brno City parks. Atmosphere, 11(5), Article 510. https://doi.org/10.3390/ATMOS11050510
  • Iskandaryan, D., Di Sabatino, S., Ramos, F., & Trilles, S. (2022). Exploratory analysis and feature selection for the prediction of nitrogen dioxide. AGILE: GIScience Series, 3(2), 1–11. https://doi.org/10.5194/agile-giss-3-6-2022
  • Lamprecht, C. (2023). Meteorological weather and climate data [Data set]. https://meteostat.net/en/station/LTBQ0?t=2021-01-01/2021-12-31
  • Lv, Y., Tian, H., Luo, L., Liu, S., Bai, X., Zhao, H., Lin, S., Zhao, S., Guo, Z., Xiao, Y., & Yang, J. (2022). Meteorology-normalized variations of air quality during the COVID-19 lockdown in three Chinese megacities. Atmospheric Pollution Research, 13(6), Article 101452. https://doi.org/10.1016/j.apr.2022.101452
  • Marangoz, M., & Akçam, G. (2023). The effect of the COVID-19 pandemic on the sectors: A conceptual evaluation. Hitit Journal of Economics and Politics, 3(1), 20–46. https://doi.org/10.5281/zenodo.1366522
  • Matthias, V., Quante, M., Arndt, J. A., Badeke, R., Fink, L., Petrik, R., Feldner, J., Schwarzkopf, D., Link, E. M., Ramacher, M. O. P., & Wedemann, R. (2021). The role of emission reductions and the meteorological situation for air quality improvements during the COVID-19 lockdown period in central Europe. Atmospheric Chemistry and Physics, 21(18), 13931–13971. https://doi.org/10.5194/acp-21-13931-2021
  • Ministry of Environment Urbanization and Climate Change. (2023). National Air Quality Monitoring Network. [Data set]. https://sim.csb.gov.tr/STN/STN_Report/StationDataDownloadNew
  • Ngo, T. H., Pan, W. C., & Waits, A. (2022). Reduction in aviation volume due to COVID-19 and changes in air pollution near the international airport in Taiwan. Aerosol and Air Quality Research, 22(4), 1–9. https://doi.org/10.4209/aaqr.210297
  • Oleniacz, R., Gorzelnik, T., & Bogacki, M. (2021). Impact of urban, suburban and industrial background on air pollution levels of dust substances in north-eastern part of Krakow (Poland). IOP Conference Series: Earth and Environmental Science, 642(1), Article 012013. https://doi.org/10.1088/1755-1315/642/1/012013
  • R Core Team. (2019). R: A language and environment for statistical computing (Version 4.2.2). R Foundation for Statistical Computing.
  • Republic of Turkey Ministry of Industry and Technology. (2019). Kocaeli province industry status report. https://kosano.org.tr/wp-content/uploads/2020/02/KSO-faaliyet-raporu-2019.pdf
  • Ropkins, K., & Carslaw, D. C. (2012). Openair – Data analysis tools for the air quality community. R Journal, 4(1), 20–29. https://doi.org/10.32614/rj-2012-003
  • Ropkins, K., Tate, J. E., Walker, A., & Clark, T. (2022). Measuring the impact of air quality related interventions. Environmental Science: Atmospheres, 500–516. https://doi.org/10.1039/d1ea00073j
  • Rosianu, A. M., Leru, P. M., Stefan, S., Iorga, G., & Marmureanu, L. (2022). Six-year monitoring of atmospheric pollen and major air pollutant concentrations in relation with meteorological factors in Bucharest, Romania. Romanian Reports in Physics, 74(2), 1–15.
  • Shen, F., Hegglin, M. I., Luo, Y., Yuan, Y., Wang, B., Flemming, J., Wang, J., Zhang, Y., Chen, M., Yang, Q., & Ge, X. (2022). Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China. Climate and Atmospheric Science, 5(1), Article 54. https://doi.org/10.1038/s41612-022-00276-0
  • Sidjabat, F. M., Hakiki, R., & Wikaningrum, T. (2019). Air quality monitoring in industrial estate (case study: Jababeka Industrial Estate, Cikarang). Journal of Environmental Engineering and Waste Management, 4(2), 50–58.
  • Szulecka, A., Oleniacz, R., & Rzeszutek, M. (2017). Functionality of openair package in air pollution assessment and modeling – A case study of Krakow. Ochrona Środowiska i Zasobów Naturalnych, 28(2), 22–27. https://doi.org/10.1515/oszn-2017-0009
  • Uria-Tellaetxe, I., & Carslaw, D. C. (2014). Conditional bivariate probability function for source identification. Environmental Modelling and Software, 59, 1–9. https://doi.org/10.1016/j.envsoft.2014.05.002
  • Wang, M., Zhang, Z., Yuan, Q., Li, X., Han, S., Lam, Y., Cui, L., Huang, Y., Cao, J., & Lee, S. C. (2022). Slower than expected reduction in annual PM2.5 in Xi’an revealed by machine learning-based meteorological normalization. Science of the Total Environment, 841, Article 156740. https://doi.org/10.1016/j.scitotenv.2022.156740
  • World Health Organization. (2022). Air pollution. https://www.who.int/health-topics/air-pollution#tab=tab_1
  • World Health Organization. (2025). WHO COVID-19 dashboard. https://data.who.int/dashboards/covid19/cases?m49=001&n=o
  • Yener, İ., & Demirarslan, K. O. (2022). Determining the factors affecting air quality in Marmara, Turkey, and assessing it using air quality indices. Doğal Afetler ve Çevre Dergisi, 8(2), 383–395. https://doi.org/10.21324/dacd.1081167
  • Yener, İ., & Demirarslan, K. O. (2024). Did Turkey experience reductions in air pollution during the COVID-19 lockdown and partial lockdown? Doğal Afetler ve Çevre Dergisi, 10(1), 179–191. https://doi.org/10.21324/dacd.1339741
  • Yorkor, B., Leton, T. G., & Ugbebor, J. N. (2021). Analysis of temporal variations of air pollutant concentrations in Ogoni Area, Niger Delta Region, Nigeria. Asian Journal of Environment & Ecology, November, 63–73. https://doi.org/10.9734/ajee/2021/v16i430260
  • Yulinawati, H., Khairani, T., & Siami, L. (2021). Analysis of indoor and outdoor particulate (PM2.5) at a women and children’s hospital in West Jakarta. IOP Conference Series: Earth and Environmental Science, 737(1), Article 012057. https://doi.org/10.1088/1755-1315/737/1/012067
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Hava Kirliliği Modellemesi ve Kontrolü, Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme
Bölüm Araştırma Makalesi
Yazarlar

Özer Akyürek 0000-0002-5179-0191

Gönderilme Tarihi 11 Aralık 2024
Kabul Tarihi 7 Şubat 2025
Yayımlanma Tarihi 27 Temmuz 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 11 Sayı: 2

Kaynak Göster

APA Akyürek, Ö. (2025). Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey. Doğal Afetler ve Çevre Dergisi, 11(2), 406-417. https://doi.org/10.21324/dacd.1599855
AMA Akyürek Ö. Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey. Doğ Afet Çev Derg. Temmuz 2025;11(2):406-417. doi:10.21324/dacd.1599855
Chicago Akyürek, Özer. “Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey”. Doğal Afetler ve Çevre Dergisi 11, sy. 2 (Temmuz 2025): 406-17. https://doi.org/10.21324/dacd.1599855.
EndNote Akyürek Ö (01 Temmuz 2025) Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey. Doğal Afetler ve Çevre Dergisi 11 2 406–417.
IEEE Ö. Akyürek, “Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey”, Doğ Afet Çev Derg, c. 11, sy. 2, ss. 406–417, 2025, doi: 10.21324/dacd.1599855.
ISNAD Akyürek, Özer. “Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey”. Doğal Afetler ve Çevre Dergisi 11/2 (Temmuz2025), 406-417. https://doi.org/10.21324/dacd.1599855.
JAMA Akyürek Ö. Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey. Doğ Afet Çev Derg. 2025;11:406–417.
MLA Akyürek, Özer. “Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey”. Doğal Afetler ve Çevre Dergisi, c. 11, sy. 2, 2025, ss. 406-17, doi:10.21324/dacd.1599855.
Vancouver Akyürek Ö. Analysis of Temporal Changes of Air Pollutants Concentrations in Kocaeli, Turkey. Doğ Afet Çev Derg. 2025;11(2):406-17.

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