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The Influence of Climatological Variables on Particulate Matter and Sulphur Dioxide

Yıl 2023, Cilt: 38 Sayı: 1, 13 - 24, 30.03.2023
https://doi.org/10.21605/cukurovaumfd.1273675

Öz

Hava kirleticilerin tahmin edilmesi, insan sağlığı üzerindeki etkilerinin artması ve çevre sorunları nedeniyle önemli bir konu haline gelmiştir. Bu makale, Çoklu Doğrusal Regresyon yöntemine dayalı istatistiksel yaklaşım yoluyla hava kirletici konsantrasyonlarını tahmin etmek için bir tahmin modeli oluşturmayı amaçlamaktadır. Analiz, Kırıkkale'de bulunan izleme istasyonunda hava kirleticilerin günlük konsantrasyon değerlerini ve bulutluluk, rüzgar hızı, yağış, bağıl nem ve hava sıcaklığı gibi iklimsel değişkenleri içermektedir. İklim elemanlarının hava kirleticileri üzerindeki etkisi, regresyon analizi yöntemi kullanılarak istatistiksel açıdan önemli olarak tanımlanmıştır (%5’ten küçük önem düzeyi). Değerlendirilen iklimsel değişkenler arasında, partikül madde için adımsal regresyon modellerinde en sık seçilen değişkenler bulutluluk, yağış ve bağıl nem olurken, kükürt dioksit için en çok bağıl nem ve minimum hava sıcaklığı seçilmiştir.

Kaynakça

  • Aas, W., Mortier, A., Bowersox, V., Cherian, R., Faluvegi, G., Fagerli, H., Hand, J., Klimont, Z., Galy-Lacaux, C., Lehmann, C.M.B., Myhre, C.L., Myhre, G., Olivié, D., Sato, K., Quaas, J., Rao, P.S.P., Schulz, M., Shindell, D., Skeie, R.B., Stein, A., Takemura, T., Tsyro, S., Vet, R., Xu, X., 2019. Global and Regional Trends of Atmospheric Sulfur. Sci. Rep., 9(1), 1-11, https://doi.org/10.1038/s41598-018-37304-0.
  • Theys, N., Hedelt, P., De Smedt, I., Lerot, C., Yu, H., Vlietinck, J., Pedergnana, M., Arellano, S., Galle, B., Fernandez, D., Carlito, C.J.M., Barrington, C., Taisne, B., Delgado-Granados, H., Loyola, D., Van Roozendael, M., 2019. Global Monitoring of Volcanic SO2 Degassing with Unprecedented Resolution From TROPOMI Onboard Sentinel-5 Precursor. Sci. Rep., 9, 2643, https://doi.org/10.1038/s41598-019-39279-y.
  • Baklanov, A., Molina, L.T., Gauss, M., 2016. Megacities, Air Quality and Climate. Atmospheric Environment, 126, 235–249. https://doi.org/10.1016/j.atmosenv.2015.11.059.
  • Chen, R., Huang, W., Wong, C.M., Wang, Z., Thach, T.Q., Chen, B., Kan, H., 2012. Short-Term Exposure to Sulfur Dioxide and Daily Mortality in 17 Chinese Cities: The China Air Pollution and Health Effects Study (CAPES). Environmental Research, 118, 101-106.
  • Fischer, P.H., Marra, M., Ameling, C.B., Janssen, N., Cassee, F.R., 2011. Trends in Relative Risk Estimates for the Association Between Air Pollution and Mortality, 1992-2006. Environmental Research, 111(1), 94-100.
  • Deryugina, T., Heutel, G., Miller, N.H., Molitor, D., Reif, J., 2019. The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction. Am. Econ. Rev., 109, 4178-4219.
  • Nascimento, A., SantosI, M., Mill, J., Bottoni, J., Costa, N., Anselmo, V., 2017. Association Between the Concentration of Fine Particles in the Atmosphere and Acute Respiratory Diseases in Children. Revista de Saúde Pública, 51, 1-10.
  • Saunders, R., Waught, D., 2015. Variability and Potential Sources of Summer PM2.5 in the Northeastern United States. Atmospheric Environment, 117, 259-270.
  • Künzli, N., Kaiser, R., Medina, S., Studnicka, M., Chanel, O., Filliger, P., Herry, M., Horak Jr, F., Puybonnieux-Texier, V., Quenel, P., Schneider, J., Seethaler, R., Vergnaud, J.C., Sommer, H., 2000. Public-Health Impact of Outdoor and Traffic-Related Air Pollution: A European Assessment. Lancet, 356(9232), 795-801.
  • Finzi, G., Tebaldi, G., 1982. A Mathematical Model for Air Pollution Forecast and Alarm in an Urban Area. Atmospheric Environment, 16, 2055-2059.
  • Ziomass, I.C., Melas, D., Zerefos, C.S., Bais, A.F., Paliatsos, A.G., 1995. Forecasting Peak Pollutant Levels From Meteorological Variables. Atmospheric Environment, 29(24), 3703-3711.
  • Polydoras, G.N., Anagnostopoulos, J.S., Bergeles, G., 1998. Air Quality Predictions: Dispersion Model vs Box-Jenkins Stochastic Models. An Implementation and Comparison for Athens, Greece. Applied Thermal Engineering, 18, 1037-1048.
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İklimsel Değişkenlerin Partikül Madde ve Kükürt Dioksit Üzerindeki Etkisi

Yıl 2023, Cilt: 38 Sayı: 1, 13 - 24, 30.03.2023
https://doi.org/10.21605/cukurovaumfd.1273675

Öz

The prediction of air pollutants has become an important issue because of the increasing effects on human health and environmental problems. This paper intends to build up predicting model for estimating air pollutants concentrations through a statistical approach based on the Multiple Linear Regression method. The analysis contains the daily concentration values of air pollutants and climatological variables such as cloudiness, wind speed, precipitation, relative humidity and air temperature at the monitoring station located in Kırıkkale. The influence of climate elements on air pollutants was defined using the regression analysis method as statistically significant (significance level smaller than 5%). Among the assessed climatological variables, cloudiness, precipitation and relative humidity were the most frequently chosen variables in stepwise regression models for particulate matter whereas relative humidity and minimum air temperature were the most for sulphur dioxide.

Kaynakça

  • Aas, W., Mortier, A., Bowersox, V., Cherian, R., Faluvegi, G., Fagerli, H., Hand, J., Klimont, Z., Galy-Lacaux, C., Lehmann, C.M.B., Myhre, C.L., Myhre, G., Olivié, D., Sato, K., Quaas, J., Rao, P.S.P., Schulz, M., Shindell, D., Skeie, R.B., Stein, A., Takemura, T., Tsyro, S., Vet, R., Xu, X., 2019. Global and Regional Trends of Atmospheric Sulfur. Sci. Rep., 9(1), 1-11, https://doi.org/10.1038/s41598-018-37304-0.
  • Theys, N., Hedelt, P., De Smedt, I., Lerot, C., Yu, H., Vlietinck, J., Pedergnana, M., Arellano, S., Galle, B., Fernandez, D., Carlito, C.J.M., Barrington, C., Taisne, B., Delgado-Granados, H., Loyola, D., Van Roozendael, M., 2019. Global Monitoring of Volcanic SO2 Degassing with Unprecedented Resolution From TROPOMI Onboard Sentinel-5 Precursor. Sci. Rep., 9, 2643, https://doi.org/10.1038/s41598-019-39279-y.
  • Baklanov, A., Molina, L.T., Gauss, M., 2016. Megacities, Air Quality and Climate. Atmospheric Environment, 126, 235–249. https://doi.org/10.1016/j.atmosenv.2015.11.059.
  • Chen, R., Huang, W., Wong, C.M., Wang, Z., Thach, T.Q., Chen, B., Kan, H., 2012. Short-Term Exposure to Sulfur Dioxide and Daily Mortality in 17 Chinese Cities: The China Air Pollution and Health Effects Study (CAPES). Environmental Research, 118, 101-106.
  • Fischer, P.H., Marra, M., Ameling, C.B., Janssen, N., Cassee, F.R., 2011. Trends in Relative Risk Estimates for the Association Between Air Pollution and Mortality, 1992-2006. Environmental Research, 111(1), 94-100.
  • Deryugina, T., Heutel, G., Miller, N.H., Molitor, D., Reif, J., 2019. The Mortality and Medical Costs of Air Pollution: Evidence from Changes in Wind Direction. Am. Econ. Rev., 109, 4178-4219.
  • Nascimento, A., SantosI, M., Mill, J., Bottoni, J., Costa, N., Anselmo, V., 2017. Association Between the Concentration of Fine Particles in the Atmosphere and Acute Respiratory Diseases in Children. Revista de Saúde Pública, 51, 1-10.
  • Saunders, R., Waught, D., 2015. Variability and Potential Sources of Summer PM2.5 in the Northeastern United States. Atmospheric Environment, 117, 259-270.
  • Künzli, N., Kaiser, R., Medina, S., Studnicka, M., Chanel, O., Filliger, P., Herry, M., Horak Jr, F., Puybonnieux-Texier, V., Quenel, P., Schneider, J., Seethaler, R., Vergnaud, J.C., Sommer, H., 2000. Public-Health Impact of Outdoor and Traffic-Related Air Pollution: A European Assessment. Lancet, 356(9232), 795-801.
  • Finzi, G., Tebaldi, G., 1982. A Mathematical Model for Air Pollution Forecast and Alarm in an Urban Area. Atmospheric Environment, 16, 2055-2059.
  • Ziomass, I.C., Melas, D., Zerefos, C.S., Bais, A.F., Paliatsos, A.G., 1995. Forecasting Peak Pollutant Levels From Meteorological Variables. Atmospheric Environment, 29(24), 3703-3711.
  • Polydoras, G.N., Anagnostopoulos, J.S., Bergeles, G., 1998. Air Quality Predictions: Dispersion Model vs Box-Jenkins Stochastic Models. An Implementation and Comparison for Athens, Greece. Applied Thermal Engineering, 18, 1037-1048.
  • Orellano, P., Reynoso, J., Quaranta, N., 2021. Short-Term Exposure to Sulphur Dioxide (SO2) and All-Cause and Respiratory Mortality: A Systematic Review and Meta-Analysis. Environ. Int., 150, Article 106434, https://doi.org/10.1016/j.envint.2021.106434.
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  • Barnaba, F., Romero, N.A., Bolignano, A., Basart, S., Renzi, M., Stafoggia, M., 2022. Multiannual Assessment of the Desert Dust Impact on Air Quality in Italy Combining PM10 Data with Physics-Based and Geostatistical Models. Environment International, 163, 107204.
  • Sharma, E., Deo, R.C., Soar, J., Prasad, R., Parisi, A.V., Raj, N., 2022. Novel Hybrid Deep Learning Model for Satellite Based PM10 Forecasting in the Most Polluted Australian Hotspots. Atmospheric Environment, 279, 119111.
  • Sánchez-Ccoyllo, O.R., Ordoñez-Aquino, C.G., Muñoz, A.G., Llacza, A., Andrade, M.F., Liu, Y., Reategui-Romero, W., Brasseur, G., 2018. Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016. International Journal of Applied Engineering Research: IJAER, 13(11), 10129-10141.
  • Feng, X., Zhang, X., Wang J., 2023. Update of SO2 Emission Inventory in the Megacity of Chongqing, China by Inverse Modeling. Atmospheric Environment, 294, 119519. https://doi.org/10.1016/j.atmosenv.2022.119519.
  • Ju, T., Lei, M., Guo, G., Xi, J., Zhang, Y., Xu, Y., Lou, Q., 2022. A New Prediction Method of Industrial Atmospheric Pollutant Emission Intensity Based on Pollutant Emission Standard Quantification. Frontiers of Environmental Science & Engineering, 17, Article number: 8.
  • Jeong, G.-R., Baker, B., Campbell, P.C., Saylor, R., Pan, L., Bhattacharjee, P.S., Smith, S.J., Tong, D., Tang, Y., 2023. Updating and Evaluating Anthropogenic Emissions for NOAA’s Global Ensemble Forecast Systems for Aerosols (GEFS-Aerosols): Application of An SO2 Bias-Scaling Method. Atmosphere, 14(2), 234. https://doi.org/10.3390/atmos14020234.
  • Opio, R., Mugume, I., Nakatumba-Nabende, J., Mbogga, M., 2023. Modeling The Atmospheric Dispersion of SO2 from Mount Nyiragongo. Journal of African Earth Sciences, 197, 104771.
  • Zateroglu, M.T., 2021a. Evaluating the Sunshine Duration Characteristics in Association with Other Climate Variables. European Journal of Science and Technology, Special Issue 29, 200-207, https://doi.org/ 10.31590/ejosat.1022639.
  • Zateroglu, M.T., 2021b. Statistical Models for Sunshine Duration Related to Precipitation and Relative Humidity. European Journal of Science and Technology, Special Issue 29, 208-213. https://doi.org/10.31590/ejosat.1022962.
  • Zateroglu, M.T., 2021c. Assessment of the Effects of Air Pollution Parameters on Sunshine Duration in Six Cities in Turkey. Fresenius Environmental Bulletin, 30(02A), 2251-2269.
  • Zateroglu, M.T., 2021d. The Role of Climate Factors on Air Pollutants (PM10 and SO2). Fresenius Environmental Bulletin, 30(11), 12029-12036.
  • Zateroglu, M.T., 2022. Modelling the Air Quality Index for Bolu, Turkey. Carpathian Journal of Earth and Environmental Sciences, 17(1), 119-130. https://doi.org/10.26471/cjees/ 2022/017/206.
  • Wang, J., Ogawa, S., 2015. Effects of Meteorological Conditions on PM2.5 Concentrations in Nagasaki, Japan. International Journal of Environmental Research and Public Health, 12, 9089-9101.
  • Pohjola, M., Kousa, A., Kukkonen, J. Härkönen, J., Karppinen, A., Aarnio, P., Koskentalo, T., 2002. The Spatial and Temporal Variation of Measured Urban PM10 and PM2.5 in the Helsinki Metropolitan. Water, Air and Soil Pollution, 2, 189–201.
  • Tai, A., Loretta, J., Daniel, J., 2010. Correlations Between Fine Particulate Matter (PM2.5) and Meteorological Variables in the United States: Implications for the Sensitivity of PM2.5 to Climate Change. Atmospheric Environment, 44, 3976–3984.
  • Radaideh, J., 2017. Effect of Meteorological Variables on Air Pollutants Variation in Arid Climates. Journal of Environmental & Analytical Toxicology, 7, 1-12.
  • Gorai, A., Tuluri, F., Tchounwou, P., Ambinakudige, S., 2015. Influence of Local Meteorology and NO2 Conditions on Ground-Level Ozone Concentrations in the Eastern Part of Texas, USA. Air Quality, Atmosphere and Health, 8, 81-96.
  • Shouquan, C., Campbell, M., Li, Q., Li, G., Auld, H., Day, N., Pengelly, D., Gingrich, S., Yap, D., 2007. A Synoptic Climatological Approach to Assess Climatic Impact on Air Quality in South-Central Canada. Part II: Future Estimates. Water, Air, and Soil Pollution, 182, 117-130.
  • Camalier, L., Cox, W., Dolwick, P., 2007. The Effect of Meteorology on Ozone in Urban Areas and Their Use in Assessing Ozone Trends. Atmospheric Environment, 41, 7127-7137.
  • Cuhadaroglu, B., Demirci, E., 1997. Influence of Some Meteorological Factors on Air Pollution in Trabzon City. Energy and Building, 25, 179-184.
  • Akpinar, E.K., Öztop, H.F., 2008. Evaluation of Relationship Between Meteorological Parameters and Air Pollutant Concentrations During the Winter Season in Elaziğ, Turkey. Environmental Monitoring and Assessment, 146, 211-224.
  • Akpinar, E.K., Akpinar S., Öztop, H.F., 2009. Statistical Analysis of Meteorological Factors and Air Pollution at Winter Months in Elaziğ, Turkey. Journal of Urban and Environmental Engineering, 3(1), 7-16.
  • Luvsan, M.E., Shie, R.H., Purevdorj, T., Badarch, L., Baldorj, B., Chan, C.C., 2012. The Influence of Emission Sources and Meteorological Conditions on SO2 Pollution in Mongolia. Atmospheric Environment, 61, 542-549.
  • Çelik, M.B., Kadi, İ., 2007. The Relation Between Meteorological Factors and Pollutants Concentrations in Karabük City. G.U. Journal of Science, 20(4), 87-95.
  • Turalioglu, F.S., Nuhoglu, A., Bayraktar, H., 2005. Impacts of Some Meteorological Parameters on SO2 and TSP Concentrations in Erzurum, Turkey. Chemosphere, 59, 1633-1642.
  • Banerjee, T., Singh, S.B., Srivastava, R.K., 2011. Development and Performance Evaluation of Statistical Models Correlating Air Pollutants and Meteorological Variables at Pantnagar, India. Atmospheric Research, 99, 505-517.
  • Jegathesan, A. , Lokupitiya, E., Premasiri, S. 2022. Trends of Air Pollutants in Colombo City and Relationship with Meteorological Variables. International Journal of Environmental Pollution and Environmental Modelling, 5(2), 93-98. Retrieved from https://dergipark.org.tr/en/pub/ijepem/issue/71152/1102902.
  • Ramli, N., Abdul Hamid, H., Yahaya, A.S., Ul-Saufie, A.Z., Mohamed Noor, N., Abu Seman, N.A., Kamarudzaman, A.N., Deák, G., 2023. Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia. Atmosphere, 14, 311. https://doi.org/ 10.3390/atmos14020311.
  • Bose, A., Roy Chowdhury, I., 2023. Investigating the Association Between Air Pollutants’ Concentration and Meteorological Parameters in A Rapidly Growing Urban Center of West Bengal, India: A Statistical Modeling-Based Approach. Model. Earth Syst. Environ. https://doi.org/10.1007/s40808-022-01670-6.
  • Bridgman, H.A., Davies, T.D., Jickells, T., Hunova, I., Tovey, K., Bridges, K., Surapipith, V., 2002. Air Pollution in the Krusne Hory Region, Czech Republic During the 1990s. Atmospheric Environment, 36, 3375-3389.
  • Pearce, J.L., Beringer, J., Nicholls, N., Hyndman, R.J., Tapper, N.J., 2011. Quantifying the Influence of Local Meteorology on Air Quality Using Generalized Addictive Models. Atmospheric Environment, 45, 1328-1336.
  • Giri, D., Kirishna Murthy, V., Adhikary, P,R., 2008. The Influence of Meteorological Conditions on PM10 Concentrations in Katmandu Valley. International Journal of Environmental Resources, 2(1), 49-60.
  • Czarnecka, M., Kalbarczyk, R., Kalbarczyk, E., 2007. Variability in Particulate Matter Concentration Versus Precipitation in Pomerania Region. Polish Journal of Natural Science, 22(4), 645-659.
  • Abdullah, S., Ismail, M., Fong, S.Y., Mahfoodh, A., Ahmed, A.N., 2016. Evaluation for Long Term PM10 Concentration Forecasting Using Multi Linear Regression (MLR) and Principal Component Regression (PCR) Models. Environ. Asia, 9, 101–110.
  • Dominick, D., Juahir, H., Latif, M.T., Zain, S.M., Aris, A.Z., 2012. Spatial Assessment of Air Quality Patterns in Malaysia Using Multivariate Analysis. Atmos. Environ., 60, 172–181.
  • Fong, S.Y., Abdullah, S., Ismail, M., 2018. Forecasting of Particulate Matter (PM10) Concentration Based on Gaseous Pollutants and Meteorological Factors for Different Monsoons of Urban Coastal Area in Terengganu. J. Sustain. Sci. Manag. Spec. Issue Number, 5, 3–18.
  • Ul-Saufie, A.Z., Yahaya, A.S., Ramli, N.A., Rosaida, N., Hamid, H.A., 2013. Future Daily PM10 Concentrations Prediction by Combining Regression Models and Feedforward Backpropagation Models with Principle Component Analysis (PCA). Atmos. Environ., 77, 621–630.
  • Ul-Saufie, A.Z., Yahaya, A.S., Ramli, N.A., Hamid, H.A., 2015. PM10 Concentrations Short Term Prediction Using Feedforward Backpropagation and General Regression Neural Network in a Sub-Urban Area. J. Environ. Sci. Technol., 8, 59–73.
  • Kumar, K., Pande, B.P., 2022. Air Pollution Prediction with Machine Learning: A Case Study of Indian Cities. Int. J. Environ. Sci. Technol., 1–16.
  • Tan, C., Chen, Q., Qi, D., Xu, L., Wang, J.A., 2022. Case Analysis of Dust Weather and Prediction of PM10 Concentration Based on Machine Learning at the Tibetan Plateau. Atmosphere, 13, 897. https://doi.org/ 10.3390/atmos13060897.
  • Abdullah, S., Ismail, M., Ahmed, A.N., Abdullah, A.M., 2019. Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support. Atmosphere, 10, 667.
  • Hamid, H.A., 2013. Probabilistic and Distribution Modelling for Predicting PM10 Concentration in Malaysia; Universiti Sains Malaysia: George Town, Malaysia.
  • Suleiman, A., Tight, M.R., Quinn, A.D., 2016. Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter. Environ. Model. Assess., 21, 731–750.
  • Qin, S., Liu, F., Wang, J., Sun, B., 2014. Analysis and Forecasting of the Particulate Matter (PM) Concentration Levels over Four Major Cities of China Using Hybrid Models. Atmos. Environ., 98, 665–675.
  • Chen, J., Yuan, C., Dong, S., Feng, J., Wang, H., 2023. A Novel Spatiotemporal Multigraph Convolutional Network for Air Pollution Prediction. Applied Intelligence, 1-14. Choi, H.S., Song, K., Kang, M., Kim, Y., Lee, K.K., Choi, H., 2022. Deep Learning Algorithms for Prediction of PM10 Dynamics in Urban and Rural Areas of Korea. Earth Science Informatics, 15(2), 845-853.
  • Shaziayani, W.N., Ahmat, H., Razak, T.R., Zainan Abidin, A.W., Warris, S.N., Asmat, A., Noor, N.M., Ul-Saufie, A.Z., 2022. A Novel Hybrid Model Combining the Support Vector Machine (SVM) and Boosted Regression Trees (BRT) Technique in Predicting PM10 Concentration. Atmosphere, 13, 2046.
  • Nejadkoorki, F., Baroutian S., 2012. Forecasting Extreme PM10 Concentrations Using Artificial Neural Networks. Int. J. Environ. Res. 6(1), 277–284.
  • Chaloulakou, A., Grivas, G., Spyrellis, N., 2003. Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment. J Air Waste Manage Assoc., 53(10),1183–1190.
  • Chen, Y., Shi, R., Shu, S., Gao, W., 2013. Ensemble and Enhanced PM10 Concentration Forecast Model Based on Stepwise Regression and Wavelet Analysis. Atmos Environ 74, 346–359.
  • Zaman, N.A.F.K., Kanniah, K.D., Kaskaoutis, D.G., 2017. Estimating Particulate Matter Using Satellite Based Aerosol Optical Depth and Meteorological Variables in Malaysia. Atmos. Res., 193, 142-162. https://doi.org/ 10.1016/j.atmosres.2017.04.019.
  • Lin, C.-Y., Chiang, M.-L., Lin, C.-Y., 2016. Empirical Model for Evaluating PM10 Concentration Caused by River Dust Episodes. Int. J. Environ. Res. Public Health, 13, 553. https://doi.org/10.3390/ijerph13060553.
  • Millionis, A.E., Davies, T.D., 1994. Regression and Stochastic Models for Air Pollution – I. Review, Comments and Suggestions. Atmospheric Environment, 28(17), 2801-2810.
  • Robeson, S.M., Steyn, D.G., 1990. Evaluation and Comparison of Statistical Forecast Models for Daily Maximum Ozone Concentrations. Atmospheric Environment, 24B(2), 303-312.
  • Ryan, W.F., 1995. Forecasting Severe Ozone Episodes in The Baltimore Metropolitan Area. Atmospheric Environment, 29(17), 2387-2398. Shi, J.P., Harrison, R.M., 1997. Regression Modelling of Hourly NOx and NO2 Concentrations in Urban Air in London. Atmospheric Environment, 31(24), 4081-4094.
  • Witz, S., Moore, A.B., 1981. Effect of Meteorology on the Atmospheric Concentrations of Traffic-Related Pollutants at A Los Angeles Site. JAPCA, 31, 1098-1101.
  • Ocak, S., Demircioglu, N., 2002. Erzurum İli Kasım 1995-Nisan 1996 Hava Kalitesi Profili. I. Ulusal Çevre Sorunları Sempozyumu, Ataturk Univ. Çevre Sorunları Araştırma Merkezi, Erzurum.
  • Turalıoglu, F.S., Nuhoglu, A., Bayraktar, H., 2005. Impacts of Some Meteorological Parameters on SO2 and TSP Concentrations in Erzurum, Turkey. Chemosphere, 59, 1633-1642.
  • Katsoulis, B.D., 1996. The Relationship Between Synoptic, Mesoscale and Microscale Meteorological Parameters During Poor Air Quality Events in Athens, Greece. The Science of the Total Environment, 181, 13-24.
  • Elminir, H.K., 2005. Dependence of Urban Air Pollutants on Meteorology. The Science of the Total Environment, 350, 225-237.
  • Chiu, K.H., Sree, U., Tseng, S.H., Wu, C-H., Lo, J-G., 2005. Differential Optical Absorption Spectrometer Measurement of NO2, SO2, O3, HCHO and Aromatic Volatile Organics in Ambient Air of Kaohsiung Petroleum Refinery in Taiwan. Atmospheric Environment, 39, 941-955.
  • Spellman, G., 1999. An Application of Artificial Neural Networks to the Prediction of Surface Ozone Concentrations in The United Kingdom. Applied Geography, 19, 123-136.
  • Chan, J.C., Hanna, S.R., 2004. Air Quality Performance Evaluation. Meteorology and Atmospheric Physics, 87, 167-196.
  • Ilten, N., Selici, A.T., 2008. Investigating the Impact of Some Meteorological Parameters on Air Pollution in Balikesir, Turkey. Environmental Monitoring Assessment, 140, 267-277.
Toplam 81 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Mine Tülin Zateroğlu Bu kişi benim 0000-0002-1050-6174

Yayımlanma Tarihi 30 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 38 Sayı: 1

Kaynak Göster

APA Zateroğlu, M. T. (2023). The Influence of Climatological Variables on Particulate Matter and Sulphur Dioxide. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 38(1), 13-24. https://doi.org/10.21605/cukurovaumfd.1273675

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