Hava Kirliliği Ölçümlerine Farklı Bir Bakış

Abstract

Bu çalışmanın amacı Karabük ilindeki hava kirliliğini tespit etmektir. Bu amaçla tarafımızdan yeni bir hava kirliliği ölçüm cihazı tasarlanmış ve üretilmiştir. Ölçüm cihazı SO2, CO2, CO, CH4, NOX, O3, PM2,5 ve VOC kirletici parametreleri ile birlikte birçok atmosferik parametreyi de aynı zamanlı olarak ölçebilmektedir. Ölçümler Haziran 2021' den başlayarak bir yıllık süre için yapılmıştır. Ölçüm noktalarının sayısı 50 olarak belirlenmiş ve 8 portatif ekipman ile anlık olarak ölçülmüştür. Bu ölçüm değerleri kullanılarak saatlik ve aylık ortalama değerler hesaplanmıştır. Ortalamanın hesaplanması için birçok istatistiksel analiz yapılmış ve doğru ortalama değer istatistisel analizler ile belirlenmiştir. Ortalama (geometrik, harmonik, kök, çeyrekler arası, Winsorized metodu), medyan, orta aralık, çarpıklık ve basıklık analizleri yapılarak en doğru günlük ve aylık ortalama değerler, ölçümlerdeki uç değerlerin veri setlerinden çıkarılması ile hesaplanmıştır. Bu analizler aralıklı ölçüm değerlerinin ortalamasını bulmak için oldukça önemlidir. Veri analizlerine göre konsantrasyonlar, ölçüm süresi boyunca önemli ölçüde değişmektedir. En yüksek konsantrasyon, sırasıyla 186,4, 170, 204,9 ve 265 µg/m3 değerleriyle SO2, CO, NOX ve PM2,5 için gözlenmiştir. Bütün bu değerler standardların üzerindedir ve insan sağlığı için tehlikelidir. Yükseklik, sıcaklıklar, atmosferik basınç ve rüzgar, atmosfer kirliliği için hassas parametrelerdir. Karabük ilinde ölçüm noktalarının çoğu çoklu kirlilik kaynaklarından etkilenmektedir. Dağılım diyagramları da bu gerçeği desteklemektedir. Kış aylarında kirlilik önemli ölçüde artmaktadır. Ancak O3 ve VOC parametreleri diğer kirleticilere göre farklı bir eğilim göstermektedir. Bu iki parametrenin konsantrasyonu bahar mevsiminde sırasıyla %78,1 ve %43,2 oranında artmaktadır. Atmosferdeki sıcaklık artışına bağlı olarak oluşan fotokimyasal reaksiyonlar sonunda bu parametrelerin konsantrasyonlarının arttığı görülmektedir.

Keywords

• Hava Kirliliği
• İstatistiksel Analizler
• Kriging
• CBS haritalama
• Yeni ölçüm modülü

A Different Perspective on Air Pollution Measurements

Abstract

This study aims to determine the air pollution in Karabük province. For this purpose, a new equipment has been designed. The equipment can measure the SO2, CO2, CO, CH4, NOX, O3, PM2.5, and VOC pollution alongside with many atmospheric parameters. The measurement period has been decided to be one year starting from June 2021. The measurement period was one year, starting from June 2021. The measurements were taken at fifty points with 8 portable intermittent equipment. Then hourly and monthly averages were calculated. The calculation of the averages depends on many statistical analyses. The mean (geometric, harmonic, root, interquartile, Winsorized), median, midrange, Skewness, and Kurtosis analyses were done to obtain correct daily, and monthly averages. These analyses are necessary to comment on the intermittent measurement averages. The analyses of the collected data showed that the concentrations are changing considerably through the measurement period. The highest concentration was observed for the SO2, CO, NOX, and PM2.5 with respective values of 186.4, 170, 204.9, and 265 µg/m3. All these values are dangerous for human health. Elevation, temperatures, atmospheric pressure, and wind are sensitive parameters for atmospheric pollution. In Karabük province, most of the measurement points are affected by multi-pollution sources. The scatter diagrams also support this fact. During winter months, the pollution increases instantly. However, O3 and VOC parameters show different trends as compared to other pollutants. The concentration of these two parameters, namely O3 and VOC, increases during spring months. The O3 and VOC increase by 78.1%, and 43.2%, respectively due to photochemical reactions in the atmosphere in spring.

Keywords

• Air Pollution
• Statistical Analyses
• Kriging
• GIS mapping
• A new equipment

References

1. [1] Liu, H., Li, J., Sun, Y., Wang, Y. and Zhao, H., “Estimation method of carbon emissions in the embodied phase of low carbon building”, Advances in Civil Engineering, 2020: 1–9, (2020).
2. [2] Demares, F., Gibert, L., Creusot, P., Lapeyre, B. and Proffit, M., “Acute ozone exposure impairs detection of floral odor, learning, and memory of honey bees, through olfactory generalization”, Science of the Total Environment, 827: 1–11, (2022).
3. [3] Torkayesh, A.E., Alizadeh, R., Soltanisehat, L., Torkayesh, S.E. and Lund, P.D., “A comparative assessment of air quality across European countries using an integrated decision support model”, Socio-Economic Planning Sciences, 81: 1–14, (2021).
4. [4] Manisalidis, I., Stavropoulou, E., Stavropoulos, A. and Bezirtzoglou, E., “Environmental, and health impacts of air pollution: a review”, Front. Public Health, 8: 1–13, (2020).
5. [5] Castell, N., Dauge, F.R., Schneider, P., Vogt, M., Lerner, U., Fishbain, B., Broday, D. and Bartonova, A., “Can commercial low-cost sensor platforms contribute to air quality monitoring, and exposure estimates?”, Environment International, 99: 293–302, (2017).
6. [6] Piaskowska-Silarska, M., Pytel, K., Gumuła, S. and Hudy, W., “Evaluation of the impact of meteorological conditions on the amount of air pollution in Krakow”, E3S Web of Conferences, 108: 1–8, (2019).
7. [7] Celikkaya, N., Fullerton, M. and Fullerton, B., “Use of low-cost air quality monitoring devices for assessment of road transport related emissions”, Transportation Research Procedia, 41: 762–781, (2019).
8. [8] Chang, D., Zeng, J. and Wang, X., “Effects, and influence factors of regional based air pollution control mechanism: an econometric analysis”, International Journal of Environmental Science, and Technology, (2022).

9. [9] Xie, X., Semanjski, I., Gautama, S., Tsiligianni, E., Deligiannis, N., Rajan, R.T., Pasveer, F. and Philips, W., “A review of urban air pollution monitoring, and exposure assessment methods”, ISPRS Int. J. Geo-Inf., 6(12): 1–21, (2017).
10. [10] Karaduman Er I., Çorlu T., Yıldırım M.A., Ateş A. and Acar S., “SnO2 ve Zn0.50Sn0.50O sensörlerinin düşük no gaz konsantrasyonu algılama özellikleri”, Politeknik Dergisi, 23(4): 1189–1196, (2020).
11. [11] Cavaliere, A., Carotenuto, F., Di Gennaro, F., Gioli, B., Gualtieri, G., Martelli, F., Matese, A., Toscano, P., Vagnoli, C. and Zaldei, A., “Development of low-cost air quality stations for next generation monitoring networks: calibration, and validation of PM2.5, and PM10 sensors”, Sensors, 18(9): 1–20, (2018).
12. [12] Cao, T. and Thompson, J.E., “Portable, ambient PM2.5 sensor for human, and/or animal exposure studies”, Analytical Letters, 50(4): 712–723, (2017).
13. [13] Zaldei, A., Camilli, F., De Filippis, T., Di Gennaro, F., Di Lonardo, S., Dini, F., Gioli, B., Gualtieri, G., Matese, A., Nunziati, W., Rocchi, L., Toscano, P. and Vagnoli, C., “An integrated low-cost road traffic, and air pollution monitoring platform for next citizen observatories”, Transportation Research Procedia, 27: 609–616, (2017).
14. [14] Jo, B. and Khan, R.M.A., “An internet of things system for underground mine air quality pollutant prediction based on azure machine learning”, Sensors, 18(4): 930–950, (2018).
15. [15] Ali, S., Kullayappa, G.R., Saritha, V. and Kumar, C.M., “Design, and development of wireless meteorological system for measuring air pollutants at ındoor, and outdoor environments”, MAPAN-Journal of Metrology Society of India, 37: 611–623, (2022).
16. [16] İncirci N., and Ekmekci İ., “Determining the location of the urban transport interchanges based on the geographic information system: the case study for Istanbul” Politeknik Dergisi, 24(3): 1121–1128, (2021).
17. [17] Yilmaz A.E. and Aktas Altunay S., “Mean, and standard deviation for open-ended grouped data”, Politeknik Dergisi, *(*): *, (*).
18. [18] Choi, H.J., Roh, Y.M., Lim, Y.W., Lee, Y.J. and Kim, K.Y., “Land-use regression modeling to estimate NO2, and VOC concentrations in Pohang city, South Korea”, Atmosphere, 13(4): 1–12, (2022).
19. [19] Dharmendra Singh, D., Dahiya, M. and N, anda, C., “Geospatial view of air pollution, and health risk over north ındian region in covid-19 scenario”, Journal of the Indian Society of Remote Sensing, 50(6): 1145–1162, (2022).
20. [20] Huang, Y., Yan, Q. and Zhang, C., “Spatial–temporal distribution characteristics of PM2.5 in China in 2016”, Journal of Geovisualization, and Spatial Analysis, 2: 1–18, (2018).
21. [21] Karim, B. and Shokrinezhad, B., “Spatial variation of ambient PM2.5, and PM10 in the industrial city of Arak, Iran: a l, and-use regression”, Atmospheric Pollution Research, 12(12): 1–9, (2021).
22. [22] Chang, F.J., Chang, L.C., Kang, C.C., Wang, Y.S. and Huanga, A., “Explore spatio-temporal PM2.5 features in northern Taiwan using machine learning techniques”, Science of the Total Environment, 736: 1–14, (2020).
23. [23] Wang, J., Wang, Z., Deng, M., Zou., H. and Wang, K., “Heterogeneous spatiotemporal copula-based kriging for air pollution prediction”, Transactions in GIS, 25(6): 3210–3232, (2021).
24. [24] Belkhiri, L., Tiri, A. and Mouni, L., “Spatial distribution of the groundwater quality using kriging, and co-kriging interpolations”, Groundwater for Sustainable Development, 11: 1–9, (2020).
25. [25] https://www.epa.gov/criteria-air-pollutants/naaqs-table, Environmental Protection Agency, “NAAQS Table”,
26. [26] https://www.mevzuat.gov.tr/File/GeneratePdf?mevzuat No=12188&mevzuatTur=KurumVeKurulusYonetmeligi&mevzuatTertip=5, T.C. Cumhurbaşkanlığı Mevzuat Bilgi Sistemi, “Hava Kalitesi Değerlendirme ve Yönetimi Yönetmeliği”, (2022).
27. [27] Prakash, S. and Mukhopadhyay, A.K., “A mixed weibull method for reliability analysis of tricone roller bits in blasthole drilling”, Journal of Mining Science, 54(5): 763–772, (2018).
28. [28] Bahonar, E., Chahardowli, M., Ghalenoei, Y. and Simjoo, M., “New correlations to predict oil viscosity using data mining techniques”, Journal of Petroleum Science, and Engineering, 208: 1–16, (2022).
29. [29] Lemini, R., Attwood, K., Pecenka, S., Grego, J., Spaulding, A.C., Nurkin, S., Colibaseanu, D.T. and Gabriel, E., “Stage II–III colon cancer: a comparison of survival calculators”, Journal of Gastrointestinal Oncology, 9(6): 1091–1098, (2018).
30. [30] Darlington, R.D. and Hayes, A.F., “Regression analysis, and linear models: concepts, applications, and implementation”, The Guilford Press, (2017).
31. [31] Gomathy, V., Janarthanan, K., F., Al-Turjman, Sıtharthan, R., Rajesh, M., Vengatesan, K. and Reshma, T.P., “Investigating the spread of coronavirus disease via edge-ai, and air pollution correlation”, ACM Transactions on Internet Technology, 21(4): 1–10, (2021).
32. [32] USEPA, “Guidance on the use of models, and other analyses for demonstrating attainment of air quality goals for ozone, PM2.5, and regional haze”, National Service Center for Envir. Pub., (2007).
33. [33] Chunga, C.J., Hsiehb, Y.Y. and Linc, H.C., “Fuzzy inference system for modeling the environmental risk map of air pollutants in Taiwan”, Journal of Environmental Management, 246: 808–820, (2019).
34. [34] Parveen, N., Siddiqui, L., Sarif, M.N., Islam, M.S., Khanam, N. and Mohibul, S., “Industries in Delhi: air pollution versus respiratory morbidities”, Process Safety, and Environmental Protection, 152: 495–512, (2021).
35. [35] Xu, S., Zou, B., Xiong, Y., Wan, N., Feng, H., Hu, C. and Lin, Y., “High spatiotemporal resolution mapping of PM2.5 concentrations under a pollution scene assumption”, Journal of Cleaner Production, 326: 1–14, (2021).
36. [36] Beauchamp, M., Malherbe, L., Fouquet, C., Letinois, L. and Tognet, F., “A polynomial approximation of the traffic contributions for krigingbased interpolation of urban air quality model”, Environmental Modelling & Software, 105: 132–152, (2018).
37. [37] Sun, Y., Jin, F., Zheng, Y., Ji, M. and Wang, H., “A new ındicator to assess public perception of air pollution based on complaint data”, Appl. Sci., 11(4): 1–17, (2021).
38. [38] Goodchild, M.F., “Reimagining the history of GIS”, Annals of GIS, 24(1): 1–8, (2018).
39. [39] Kirby, R.S., Delmelle, E.,and Eberth, J.M., “Advances in spatial epidemiology, and geographic information systems”, Annals of Epidemiology, 27(1): 1–9, (2017).
40. [40] IPCC, “2013 Revised Supplementary Methods, and Good Practice Guidance Arising From the Kyoto Protocol”, Intergovern. Panel on CC, (2013).