Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Udriștioiu, Mihaela Tinca; MGHOUGHI, Youness EL; Yıldızhan, Hasan

doi:10.21203/rs.3.rs-1881325/v1

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…The results showed that air humidity played a minor role in the accumulation of PM10 levels, as the correlation coefficient was below 0.27. We reached a similar conclusion in a previous study for Craiova using data from five uRADMonitor ® sensors [58]. However, it is critical to state that air humidity could significantly impact PM10 concentration at seasonal levels…”

Section: Correlation Between Pm10 Concentrations and Meteorological P...supporting

confidence: 89%

A Community-Based Sensor Network for Monitoring the Air Quality in Urban Romania

et al. 2023

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Air quality, especially particulate matter pollution levels in urban areas, is an essential academic and social topic due to its association with health issues and climate change. In Romania, an increasing awareness of urban communities and the availability of low-cost sensors has led to the development of an independent monitoring network currently distributed in over 194 cities and towns. The uRADMonitor® network consists of 630 sensors measuring PM10 and PM2.5 concentration levels. The spatial distribution of the sensors complements the national air quality network with sensors in residential areas, intense traffic zones, and industrial areas. The data are available through a user-friendly web-based platform from uRADMonitor®. Based on data collected in 2021, we present an analysis of PM10 pollution levels in Romania’s five most populated urban areas by employing five annual statistical indicators recommended by the European Environmental Agency. For the case of Timișoara, we also compare the data measured by independent sensors with those from the national monitoring network. The results highlight the usefulness of our community-based network as it complements the national one.

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Section: Correlation Between Pm10 Concentrations and Meteorological P...supporting

confidence: 89%

A Community-Based Sensor Network for Monitoring the Air Quality in Urban Romania

et al. 2023

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“…According to the statistical analysis of the effect of PM 10 concentrations on air temperature [55], a strong correlation was observed between the temperature and PM 10 due to the measurements made in the Caribbean Basin. A good correlation (−0.553) between PM 10 and temperature was found in Craiova by Udristioiu et al (2023) [56,57] using an independent sensor network as an alternative to the governmental network for the citizens' benefit. Moreover, the independent sensor network agreed with those from the official monitoring network and produced data when the official monitoring had missing values.…”

Section: Resultsmentioning

confidence: 95%

From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey

Pekdogan,

Udriștioiu,

Yildizhan

et al. 2024

Sensors

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Air pollution significantly threatens human health and natural ecosystems and requires urgent attention from decision makers. The fight against air pollution begins with the rigorous monitoring of its levels, followed by intelligent statistical analysis and the application of advanced machine learning algorithms. To effectively reduce air pollution, decision makers must focus on reducing primary sources such as industrial plants and obsolete vehicles, as well as policies that encourage the adoption of clean energy sources. In this study, data analysis was performed for the first time to evaluate air pollution based on the SPSS program. Correlation coefficients between meteorological parameters and particulate matter concentrations (PM1, PM2.5, PM10) were calculated in two urban regions of Romania (Craiova and Drobeta-Turnu Severin) and Turkey (Adana). This study establishes strong relationships between PM concentrations and meteorological parameters with correlation coefficients ranging from −0.617 (between temperature and relative humidity) to 0.998 (between PMs). It shows negative correlations between temperature and particulate matter (−0.241 in Romania and −0.173 in Turkey) and the effects of humidity ranging from moderately positive correlations with PMs (up to 0.360 in Turkey), highlighting the valuable insights offered by independent PM sensor networks in assessing and improving air quality.

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“…In this study, first, the Kolmogorov-Smirnov test was used to verify the normality of the distribution of continuous variables. It was evaluated whether there was a significant difference between the countries' data, and the Mann-Whitney U Test was applied [56]. The Pearson correlation coefficient was used for the variables for the correlation analysis.…”

Section: Discussionmentioning

confidence: 99%

“…In general, this type of sensor underestimates the PM concentrations. [55,56]. Even though the method used by the SMOGGIE-PM sensor is different than the one used by the National Environmental Agencies, SMOGGIE-PM underestimates the values of PM concentrations.…”

Section: Sensors and Datasetsmentioning

confidence: 99%

A Multi-Country Statistical Analysis Covering Turkey, Slovakia, and Romania in an Educational Framework

Pekdogan,

Udriștioiu,

Puiu

et al. 2023

Sustainability

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This paper uses hierarchical regression analysis, a statistically robust method, to explore the correlations between two meteorological parameters and three particulate matter concentrations. The dataset is provided by six sensors located in three cities from three countries, and the measurements were taken simultaneously for three months at each minute. Analyses and calculations were performed with the Statistical Package for the Social Sciences (SPSS). The results underscore that the complexity of air pollution dynamics is affected by the location even when the same type of sensors is used, and emphasize that a one-size-fits-all approach cannot effectively address air pollution. The findings are helpful from three perspectives: for education, to show how to handle and communicate a solution for local communities’ issues about air pollution; for research, to understand how easy a university can generate and analyze open-source data; and for policymakers, to design targeted interventions addressing each country’s challenges.

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Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Cited by 4 publications

References 20 publications

A Community-Based Sensor Network for Monitoring the Air Quality in Urban Romania

A Community-Based Sensor Network for Monitoring the Air Quality in Urban Romania

From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey

A Multi-Country Statistical Analysis Covering Turkey, Slovakia, and Romania in an Educational Framework

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