Air pollution in central European capital (Budapest) via self-made passive samplers and Sentinel-3B SYN satellite images

Neckel, Alcindo; Oliveira, Marcos L.S.; Maculan, Laércio Stolfo; Bodah, Brian William; Gonçalves, Affonso Celso; Silva, Luis

doi:10.1016/j.uclim.2022.101384

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…[22] It is obvious that due to the inversions that occur in winter, transported dust, smoke, and soot accumulated and contributed to the increase in pollution levels in the city. [23] Despite a general reduction in air pollution trends, specific stations continue to exhibit higher levels compared to others. This phenomenon underscores the significance of the station's air quality context.…”

Section: F I G U R Ementioning

confidence: 99%

Short‐term predictions of PM₁₀ and NO₂ concentrations in urban environments based on ARIMA search grid modeling

Houria,

Abderrahmane,

Kenza

et al. 2024

CLEAN Soil Air Water

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Air pollution poses a persistent challenge for urban management departments and policymakers due to its significant health and economic impacts. Various cities worldwide have implemented diverse strategies and initiatives to enhance air quality monitoring and modeling standards. However, the outcomes of these efforts often manifest over the long term, leading to a preference for short‐term statistical methods. The autoregressive integrated moving average (ARIMA) search grid modeling approach has gained widespread use for forecasting air quality. This paper presents a comprehensive time series analysis conducted to predict air quality in urban areas of Budapest, Hungary, with a focus on nitrogen dioxide (NO2) and particulate matter (PM10), using air quality data spanning from 2018 to 2022 for four monitoring categories: Urban traffic, industrial background, urban background, and suburban background. The study employs the ARIMA search grid method to forecast concentrations of these pollutants at multiple air quality monitoring stations based on Akaike information criteria (AIC) and the Bayesian information criteria (BIC) criteria along with the results of augmented Dickey–Fuller (ADF) test. The results demonstrate varying levels of forecast accuracy across different stations, indicating the model's effectiveness in short‐term predicting of air quality. These findings are essential for assessing the reliability of air quality forecasts in Budapest and can inform decisions regarding air quality management and the development of strategies to address air pollution and particulate matter concerns in the region.

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Section: F I G U R Ementioning

confidence: 99%

Short‐term predictions of PM₁₀ and NO₂ concentrations in urban environments based on ARIMA search grid modeling

Houria,

Abderrahmane,

Kenza

et al. 2024

CLEAN Soil Air Water

View full text Add to dashboard Cite

show abstract

Detection of atmospheric aerosols and terrestrial nanoparticles collected in a populous city in southern Brazil

Bortoluzzi,

Neckel,

Bodah

et al. 2023

Environ Sci Pollut Res

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Environmental impact evaluation of low-carbon concrete incorporating fly ash and limestone

Thorne,

Bompa,

Funari

et al. 2024

Cleaner Materials

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Air pollution in central European capital (Budapest) via self-made passive samplers and Sentinel-3B SYN satellite images

Cited by 7 publications

References 38 publications

Short‐term predictions of PM₁₀ and NO₂ concentrations in urban environments based on ARIMA search grid modeling

Short‐term predictions of PM₁₀ and NO₂ concentrations in urban environments based on ARIMA search grid modeling

Detection of atmospheric aerosols and terrestrial nanoparticles collected in a populous city in southern Brazil

Environmental impact evaluation of low-carbon concrete incorporating fly ash and limestone

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Air pollution in central European capital (Budapest) via self-made passive samplers and Sentinel-3B SYN satellite images

Cited by 7 publications

References 38 publications

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

Short‐term predictions of PM10 and NO2 concentrations in urban environments based on ARIMA search grid modeling

Detection of atmospheric aerosols and terrestrial nanoparticles collected in a populous city in southern Brazil

Environmental impact evaluation of low-carbon concrete incorporating fly ash and limestone

Contact Info

Product

Resources

About

Short‐term predictions of PM₁₀ and NO₂ concentrations in urban environments based on ARIMA search grid modeling

Short‐term predictions of PM₁₀ and NO₂ concentrations in urban environments based on ARIMA search grid modeling