2026-10-18T23:16:24Zhttps://ebyu-rdlopen.rawdatalibrary.net/oai/request

oai:ebyu-rdlopen.rawdatalibrary.net:ebyu/108332026-10-18T20:43:23Zopenairedrivercom_ebyu_10000col_ebyu_10001

Application of Time Series Methodologies for Robust Forecasting of Atmospheric Pollutant Concentrations: PM10, SO2, NO2, NOx, and O3 in an Urban EnvironmentKatipoğlu, Okan MertElshaboury, NehalKartal, VeysiErtugay, NeşeKilinc, Huseyin CaganŞenocak, SerkanPande, Chaitanya Baliramair pollutionair quality predictionartificial intelligencedeep learningfeature selectionErzincanair qualitynitrogen dioxideparticulate matterpollution controlresource managementIn recent years, the concentration of air pollutants has risen significantly due to urbanization and increased transportation. Erzincan province, due to its geographical location, is one of the region's most severely impacted by air pollution. Consequently, it is essential to accurately estimate air quality parameters to effectively analyze and manage the associated health risks. This study utilized a time series methodology to examine hourly air quality parameters, including particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), nitrogen oxides (NOx), and ozone (O3) throughout the year 2022 in Erzincan province. Various predictive techniques, such as feed-forward neural networks, group method of data handling, long short-term memory (LSTM), and least squares boosting tree methods, were employed for the analysis. For estimating air quality parameters, values delayed by up to 4 h—determined as the effective time period through correlation analysis—were utilized. Prediction performance was evaluated using eight different statistical parameters and visualization techniques. Notably, air quality parameters can be effectively estimated using historical data, with the LSTM model demonstrating superior performance compared to other models in this estimation. Furthermore, the most accurate predictions for O3 values were achieved using the LSTM algorithm, yielding an R2 of 0.93 and a root mean square error (RMSE) of 6.02. The findings of this study can aid policymakers in developing water resource management strategies, pollution control policies, and measures to combat climate change. © 2025 Wiley Periodicals LLC.John Wiley and Sons Inc2025Articlehttps://rdlopen.rawdatalibrary.net/r/ebyu/10833https://doi.org/10.1002/tqem.702061088-1913enEnvironmental Quality Managementinfo:eu-repo/semantics/openAccessinfo:eu-repo/semantics/articlehttps://ebyu-rdlopen.rawdatalibrary.net/tr/items/ebyu/10833