The Effects of Industry Increase and Urbanization on Air Pollutants in Turkey: A Nonlinear Air Quality Model

Kılınç, Betül Kan

doi:10.15666/aeer/1704_98899903

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Guo et al [15,16], Hu et al [17,18] used support vector machine algorithm to predict air quality. Currently, machine learning (ML) in artificial intelligence (AI) is the most popular implementation method, while deep learning (DL) is a branch of machine learning (ML) and one of the most popular machine learnings (ML) [19,20]. Deep learning theory is expanding rapidly.…”

Section: Related Workmentioning

confidence: 99%

Air Quality Prediction Based on Wavelet Analysis and Machine Learning

Duan

Ren²

2022

SPEE

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This thesis takes the historical weather time series of Chongqing as experimental samples. Firstly, this thesis uses wavelet transform to organize the data, and then divides the sample data into training and test sets to verify the accuracy of the evaluation of the Naive Bayes Model. Secondly, the Naive Bayes Model is compared with currently used machine learning models such as SVM, XGBoost, bagging, and random forest. Finally, the results show that the Naive Bayes Model has high stability and accuracy for the air quality assessment of Chongqing, and it can be applied to the evaluation of urban ambient air quality.

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Section: Related Workmentioning

confidence: 99%

Air Quality Prediction Based on Wavelet Analysis and Machine Learning

Duan

Ren²

2022

SPEE

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“…Studies in Türkiye have investigated the impact of industrial growth, urbanization, and climate change on air pollutants. These studies have focused on determining the causes of air pollution and analyzing long-term changes in atmospheric pollutants, particularly in large cities such as Istanbul [14,15]. As a result, it is known that industrial activities can significantly degrade the air quality of cities.…”

Section: Introductionmentioning

confidence: 99%

Unveiling the Air Quality Impacts of Municipal Solid Waste Disposal: An Integrative Study of On-Site Measurements and Community Perceptions

Pekdogan,

Yildizhan,

Ameen

2024

Atmosphere

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This study examines air quality conditions in and around a classroom located in the Sarıçam/Adana region of Türkiye, near the campus of Adana Alparslan Türkeş Science and Technology University and the Sofulu municipal solid waste (MSW) facility. This academic setting was strategically chosen due to its proximity to the waste facility. The study aims to provide a comprehensive view of the environmental and social impacts of solid waste management through a methodological approach that combines quantitative on-site measurements and qualitative survey studies. Findings from measurements and surveys underline the significant and measurable impacts of MSW facilities on the ambient air quality of university residents. The analysis revealed a marked increase in concentrations of key pollutants, including carbon monoxide (CO), hydrogen sulfide (H2S), dust, and methane (CH4). At sampling point N1, H2S levels rose from 0 ppm in July to 13 ppm in November. Methane increased from 0.2% to 2.5% of the Lower Explosive Limit (LEL) at the same point, although it remained within safety limits. Additionally, CO levels showed a 40% increase, and dust concentration levels rose from 0.21 mg/m3 to 2.36 mg/m3 from summer to winter, indicating a seasonal variation likely influenced by the landfill’s operational dynamics, as well as changes in temperature and relative humidity. In particular, the results indicate high concentrations of CO, H2S and dust, which are directly related to air quality degradation. The study also sheds light on the impacts of these waste disposal facilities on the general well-being and health of the university community, particularly on students and staff. In addition to these findings, the study highlights a general lack of awareness in the university community about the impacts of MSW facilities on air quality. This highlights the need for increased education and information dissemination. The results support the development of comprehensive and effective strategies, including technical solutions and public awareness initiatives, to mitigate the impacts of these facilities on residential areas. In conclusion, the impacts of MSW facilities on air quality should be seen as a multidimensional issue that requires a holistic approach addressing environmental, health, social, and educational dimensions.

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“…Air pollution is mainly caused by natural sources and human activities, and air pollution caused by human activities usually lasts for a long time and is widespread. Industrialization and urbanization pose the major threats to the quality of the air we breathe [4]. Fortunately, air pollution is a preventable risk, and reducing pollution at its source can yield rapid and substantial improvements.…”

Section: Introductionmentioning

confidence: 99%

Air Quality Research Based on B-Spline Functional Linear Model: A Case Study of Fujian Province, China

Xu,

You,

Wen

et al. 2023

Applied Sciences

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It is generally accepted that air quality is closely related to human health. In this study, to investigate the dynamic characteristics of air quality and explore the driving factors of air pollution, the Air Quality Index (AQI) and concentration data of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) were fitted to functional curves using the B-spline basis function. Compared with discrete data, functional data can better express the dynamic characteristics of data and reduce information loss. Additionally, functional clustering based on the principal component coefficient was established to analyze the spatiotemporal dynamic characteristics of air quality, and a functional linear model was established to analyze the relationship between pollutants and anthropogenic factors. The results showed that air pollutants in Fujian Province were found to have certain temporal and spatial heterogeneity, among which the seasonal characteristics of NO2 and O3 (high in summer, low in winter) were opposite to those of the other pollutants considered. The spatial distribution of air pollution was low (high) pollution in inland (coastal) areas, and the primary air pollutants in Fujian Province were PM10 and PM2.5. The functional linear model indicated that anthropogenic factors (e.g., vehicle numbers and emissions of industrial NOX emissions) were found to have a notable impact on air pollutants. The findings of this study could act as a reference in support of air pollution control.

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The Effects of Industry Increase and Urbanization on Air Pollutants in Turkey: A Nonlinear Air Quality Model

Cited by 5 publications

References 15 publications

Air Quality Prediction Based on Wavelet Analysis and Machine Learning

Air Quality Prediction Based on Wavelet Analysis and Machine Learning

Unveiling the Air Quality Impacts of Municipal Solid Waste Disposal: An Integrative Study of On-Site Measurements and Community Perceptions

Air Quality Research Based on B-Spline Functional Linear Model: A Case Study of Fujian Province, China

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