Statistical analysis of urban air-pollution data in the Athens basin area, Greece

Katsoulis, B. D.; Pnevmatikos, John D.

doi:10.1504/ijep.2009.021815

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…For example, (Koo et al, 2020), underscored the efficacy of ARIMA in predicting PM10 levels in Malaysia, demonstrating the model's capacity to capture the seasonal variances in air pollutant concentrations. Similarly, (Katsoulis and Pnevmatikos, 2009), successfully applied ARIMA models to predict daily PM10 concentrations in Athens, Greece, showcasing the model's adaptability across diverse environmental settings. Comparative analyses, such as the study by (Peralta et al, 2022), which evaluated neural networks against ARIMA models for air pollution forecasting in Santiago, Chile, revealed that despite neural networks' marginally better accuracy, ARIMA models' simplicity and interpretability render them a practical option for air quality prediction.…”

Section: Introductionmentioning

confidence: 99%

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

Ramadan,

Abuelgasim,

Al Hosani

2024

Front. Environ. Sci.

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This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

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Section: Introductionmentioning

confidence: 99%

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

Ramadan,

Abuelgasim,

Al Hosani

2024

Front. Environ. Sci.

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“…The possibility of breakdowns and industrial disasters in thermal heating plants imposes an additional need to get to know the impact of weather conditions on particulate matters (Poykio, Nurmesniemi, and Keiski, 2008). Prevailing weather conditions, or those forecast at the time of increased pollution, can significantly determine the type of impact and consequences of pollution on the shorter and longer run (Katsoulis and Pneymatikos, 2009;Astel et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Sustainable urban development and industrial pollution

Petrovic¹,

Lečić²,

Pavlović³

2016

Industrija

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Time Series Analysis of the Pollutants Imissions in Urban Areas

et al. 1970

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Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

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Statistical analysis of urban air-pollution data in the Athens basin area, Greece

Cited by 3 publications

References 29 publications

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

Sustainable urban development and industrial pollution

Time Series Analysis of the Pollutants Imissions in Urban Areas

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