Impact of Programming Language on Air Quality Estimation

Emre Dalkılıç; Şükrü Dursun

Impact of Programming Language on Air Quality Estimation

Abstract

The world has started to gain extra awareness about human health and environmental health after the coronavirus outbreak. In parallel with the increasing environmental awareness, components such as the use of natural resources and the possibility of causing global environmental problems to have started to play an effective role in decision-making processes rather than the financial side of the projects that come to the agenda. States carry out various environmental policies through their ministries, such as preparing legislation on air quality protection and sources affecting air pollution, odor emissions, determining targets, principles, policies and strategies, determining, implementing and having implemented procedures, principles and criteria for the creation of air pollution maps and the preparation of clean air action plans. However, the current situation is no longer sufficient for policymaking, and it is necessary to foresee the future and take steps in this direction. Being able to see today through the eyes of tomorrow provides great convenience in combating problems before they reach the threshold of a crisis in military, political and economic terms as well as environmental terms. Machine learning, a sub-branch of computer science developed in the early 20th century from digital learning and pattern recognition studies in artificial intelligence, is a system that investigates the operability and writing of algorithms that can learn as a structural function and make predictions on data. Written algorithms are designed to learn, instead of following program instructions to the letter, to create data-based predictions from the inputs provided to the system and to act as a decision maker. In the future, there is a need for algorithms that can be written using programming languages to predict air pollution and to determine its effects on public health. Today, using machine learning methods to predict air pollution has become more popular with data and data processing capabilities, which are among the most invaluable capitals. In this study, studies on predictability of air pollution with programming languages will be presented.

Keywords

References

Angra S, Ahuja S, (2017) Machine learning and its applications: A review. Paper presented at the 2017 international conference on big data analytics and computational intelligence (ICBDAC). https://doi.org/10.1109/ICBDACI.2017.8070809
Bellinger C, Mohomed Jabbar MS, Zaïane O, Osornio-Vargas A, (2017) A systematic review of data mining and machine learning for air pollution epidemiology. BMC Public Health, 17, 1-19. https://doi.org/10.1186/s12889-017-4914-3 Castelli M, Clemente FM, PopovičA, Silva S, Vanneschi L, (2020) A machine learning approach to predict air quality in California. Complexity, 2020(1), 8049504. https://doi.org/10.1155/2020/8049504
Chollet F, Chollet F, (2021) Deep learning with Python: Simon and Schuster. https://sourestdeeds.github.io/pdf/Deep%20Learning%20with%20Python.pdf
Dokuz Y, Bozdağ A, Gökçek B, (2020) Hava Kalitesi Parametrelerinin Tahmini ve Mekansal Dağilimi Için Makine Öğrenmesi Yöntemlerinin Kullanilmasi. Niğde Ömer Halisdemir Ün. Müh. Bil. Der., 9(1), 37-47. https://dergipark.org.tr/tr/pub/ngumuh/issue/52170/654092
Domingos, P. (2012) A few useful things to know about machine learning. Communications of the ACM, 55(10), 78-87. https://dl.acm.org/doi/pdf/10.1145/2347736.2347755
Garip E, (2017) OECD ülkelerindeki CO2 emisyonunun makina öğrenmesi ile tahmin edilmesi. İstanbul Medeniyet Üni. Fen Bil. Enst. Müh. Tezler. https://acikbilim.yok.gov.tr/bitstream/handle/20.500.12812/630311/yokAcikBilim_10157532.pdf
Gültepe Y, (2019) Makine öğrenmesi algoritmaları ile hava kirliliği tahmini üzerine karşılaştırmalı bir değerlendirme. Av. Bil. ve Tek. Derg. (16), 8-15. https://dergipark.org.tr/tr/pub/ejosat/issue/45333/530347
Irmak ME, Aydilek İB, (2019) Hava kalite indeksinin tahmin başarısının artırılması için topluluk regresyon algoritmalarının kullanılması. Academic platform-J. Engin. & Sci., 7(3), 507-514. https://dergipark.org.tr/tr/pub/apjes/issue/44190/478038

Kampa M, Castanas E, (2008) Human health effects of air pollution. Environ. Pollut., 151(2), 362-367. https://doi.org/10.1016/j.envpol.2007.06.012
Kaplan Y, Saray U, Azkeskin E, (2014) Hava kirliliğine neden olan PM10 ve SO2 maddesinin yapay sinir ağı kullanılarak tahmininin yapılması ve hata oranının hesaplanması. AKU J. Sci.Eng.14 (2014) 025201 (1-6) https://dergipark.org.tr/tr/pub/akufemubid/issue/1611/20175
Köktürk F, Ankarali H, Sümbüloğlu V, (2009) Veri madenciliği yöntemlerine genel bakış. Turkiye Klinikleri J. Biostat., 1(1), 20-25. https://www.turkiyeklinikleri.com/article/en-veri-madenciligi-yontemlerine-genel-bakis-53374.html
Lary DJ, Faruque FS, Malakar N, Moore A, Roscoe B, Adams ZL, Eggelston Y, (2014) Estimating the global abundance of ground level presence of particulate matter (PM2.5). Geospatial health, 8(3), S611. https://doi.org/10.4081/gh.2014.292
LeCun Y, Bengio Y, Hinton G, (2015) Deep learning. Nature, 521(7553), 436-444. https://www.nature.com/articles/nature14539
Li W, Kong D, Wu J, (2017) A new hybrid model FPA‐SVM considering Cointegration for particular matter concentration forecasting: A case study of Kunming and Yuxi, China. Comput. Intellig. & Neurosci., 2017(1), 2843651. https://doi.org/10.1155/2017/2843651
Özel G, (2019) Markov zinciri kullanarak Ankara ili için hava kirliliği tahmini. Int. J. Sci. & Tech. Res., 3(2), 144-151. https://dergipark.org.tr/tr/pub/bilgesci/article/546317
Rybarczyk Y, Zalakeviciute R, (2016) Machine learning approach to forecasting urban pollution. Paper presented at the 2016 IEEE Ecuador Technical Chapters Meeting (ETCM). https://doi.org/10.1109/ETCM.2016.7750810
Shinde PP, Shah S, (2018) A review of machine learning and deep learning applications. Paper presented at the 2018 Fourth international conference on computing communication control and automation (ICCUBEA). https://doi.org/10.1109/ICCUBEA.2018.8697857
Singh A, Thakur N, Sharma A, (2016) A review of supervised machine learning algorithms. Paper presented at the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7724478
Sotomayor-Olmedo A, Aceves-Fernández MA, Gorrostieta-Hurtado E, Pedraza-Ortega C, Ramos-Arreguín JM, Vargas-Soto JE, (2013) Forecast urban air pollution in Mexico City by using support vector machines: A kernel performance approach. Int. J. Intel. Sci. 3, (3) 126-135 http://dx.doi.org/10.4236/ijis.2013.33014
Toros H, Bağış S, (2017) Hava kirlilik modellerinde kullanılacak emisyon envanteri oluşturulması için yaklaşımlar ve İstanbul hava kirliliği dağılımı örneği. Çukurova Ün. Müh.-Mim. Fak. Der., 32(2), 1-12. https://dergipark.org.tr/tr/pub/cukurovaummfd/issue/32170/357001
Turgut D, Temiz İ, (2015) Ankara'daki hava kirliliği için zaman serileri analizi ve tahmin: box-jenkins yaklaşımı. Alphanum. J., 3(2), 131-138. https://dergipark.org.tr/tr/pub/alphanumeric/article/20803
Varınca, K. B., Güneş, G., & Ertürk F, (2008) Hava kirleticilerinin insan sağlığı ve iklim değişikliği üzerine etkileri. Ulusal Hava Kalitesi Sempozyumu UHAKS, Konya. https://www.academia.edu/1424169/Hava_Kirleticilerinin_%C4%B0nsan_Sa%C4%9Fl%C4%B1%C4%9F%C4%B1_ve_%C4%B0klim_De%C4%9Fi%C5%9Fikli%C4%9Fi_%C3%9Czerine_Etkileri
Vong, C.-M., Ip, W.-F., Wong PK, Yang JY, (2012) Short‐term prediction of air pollution in Macau using support vector machines. Journal of Control Science and Engineering, 2012(1), 518032. https://doi.org/10.1155/2012%2F518032
Yılmaz M., Toros H., İncecik, Öztürk, Z., Kirkil, G., Öztaş, D, Beba, HE. (2020) Dilovası Hava Kirliliğinin Trafik Emisyonları Açısından Değerlendirilmesi. Ulusal Çevre Bil. Araş. Der., 3(1), 43-51. https://dergipark.org.tr/tr/pub/ucbad/issue/54360/738990
Zhan Y, Luo Y, Deng X, Chen H, Grieneisen ML, Shen X, Zhang M, (2017) Spatiotemporal prediction of continuous daily PM2. 5 concentrations across China using a spatially explicit machine learning algorithm. Atmos. Environ., 155, 129-139. http://dx.doi.org/10.1016/j.atmosenv.2017.02.023

Details

Primary Language

English

Subjects

Environmental Education and Extension

Journal Section

Review

Authors

Emre Dalkılıç ^*
0000-0003-1766-9790
Türkiye

Şükrü Dursun
0000-0001-9502-1178
Türkiye

Publication Date

March 31, 2025

Submission Date

March 13, 2025

Acceptance Date

March 19, 2025

Published in Issue

Year 2025 Volume: 20 Number: 1

IZ

https://izlik.org/JA89CW85AS

Cite

RIS / Bibtex

APA

Dalkılıç, E., & Dursun, Ş. (2025). Impact of Programming Language on Air Quality Estimation. Journal of International Environmental Application and Science, 20(1), 80-87. https://izlik.org/JA89CW85AS

AMA

1.Dalkılıç E, Dursun Ş. Impact of Programming Language on Air Quality Estimation. J. Int. Environmental Application & Science. 2025;20(1):80-87. https://izlik.org/JA89CW85AS

Chicago

Dalkılıç, Emre, and Şükrü Dursun. 2025. “Impact of Programming Language on Air Quality Estimation”. Journal of International Environmental Application and Science 20 (1): 80-87. https://izlik.org/JA89CW85AS.

EndNote

Dalkılıç E, Dursun Ş (March 1, 2025) Impact of Programming Language on Air Quality Estimation. Journal of International Environmental Application and Science 20 1 80–87.

IEEE

[1]E. Dalkılıç and Ş. Dursun, “Impact of Programming Language on Air Quality Estimation”, J. Int. Environmental Application & Science, vol. 20, no. 1, pp. 80–87, Mar. 2025, [Online]. Available: https://izlik.org/JA89CW85AS

ISNAD

Dalkılıç, Emre - Dursun, Şükrü. “Impact of Programming Language on Air Quality Estimation”. Journal of International Environmental Application and Science 20/1 (March 1, 2025): 80-87. https://izlik.org/JA89CW85AS.

JAMA

1.Dalkılıç E, Dursun Ş. Impact of Programming Language on Air Quality Estimation. J. Int. Environmental Application & Science. 2025;20:80–87.

MLA

Dalkılıç, Emre, and Şükrü Dursun. “Impact of Programming Language on Air Quality Estimation”. Journal of International Environmental Application and Science, vol. 20, no. 1, Mar. 2025, pp. 80-87, https://izlik.org/JA89CW85AS.

Vancouver

1.Emre Dalkılıç, Şükrü Dursun. Impact of Programming Language on Air Quality Estimation. J. Int. Environmental Application & Science [Internet]. 2025 Mar. 1;20(1):80-7. Available from: https://izlik.org/JA89CW85AS