Forecasting Particulate Pollution in an Urban Area: From Copernicus to Sub-Km Scale

Pappa, Areti; Kioutsioukis, Ioannis

doi:10.3390/atmos12070881

Cited by 16 publications

(14 citation statements)

References 42 publications

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“…Results obtained by multilinear linear regression models were slightly worse for both tests, mean differences between Random Forests models were equal to 3 µg•m −3 for RMSE. As mentioned before, the main motivation for using Random Forests was to determine which meteorological parameters should be considered for further analysis, but a comparison of the accuracy of our forecasts with similar models (both physical and based on machine learning) shows also the good predictive potential of such an approach [60][61][62].…”

Section: Random Forests Analysesmentioning

confidence: 93%

Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography

et al. 2022

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This study presents the assessment of the quantitative influence of atmospheric circulation on the pollutant concentration in the area of Kraków, Southern Poland, for the period 2000–2020. The research has been realized with the application of different statistical parameters, synoptic meteorology tools, the Random Forests machine learning method, and multilinear regression analyses. Another aim of the research was to evaluate the types of atmospheric circulation classification methods used in studies on air pollution dispersion and to assess the possibility of their application in air quality management, including short-term PM10 daily forecasts. During the period analyzed, a significant decreasing trend of pollutants’ concentrations and varying atmospheric circulation conditions was observed. To understand the relation between PM10 concentration and meteorological conditions and their significance, the Random Forests algorithm was applied. Observations from meteorological stations, air quality measurements and ERA-5 reanalysis were used. The meteorological database was used as an input to models that were trained to predict daily PM10 concentration and its day-to-day changes. This study made it possible to distinguish the dominant circulation types with the highest probability of occurrence of poor air quality or a significant improvement in air quality conditions. Apart from the parameters whose significant influence on air quality is well established (air temperature and wind speed at the ground and air temperature gradient), the key factor was also the gradient of relative air humidity and wind shear in the lowest troposphere. Partial dependence calculated with the use of the Random Forests model made it possible to better analyze the impact of individual meteorological parameters on the PM10 daily concentration. The analysis has shown that, for areas with a diversified topography, it is crucial to use the variability of the atmospheric circulation during the day to better forecast air quality.

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Section: Random Forests Analysesmentioning

confidence: 93%

Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography

et al. 2022

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“…The subject of air quality study and prediction is a very important research area [1][2][3]. As reported by the authors [4], air pollution prediction methods can be divided into statistical, numerical, neural network and hybrid models.…”

Section: Introductionmentioning

confidence: 99%

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

et al. 2022

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Predicting the status of particulate air pollution is extremely important in terms of preventing possible vascular and lung diseases, improving people’s quality of life and, of course, actively counteracting pollution magnification. Hence, there is great interest in developing methods for pollution prediction. In recent years, the importance of methods based on classical and more advanced neural networks is increasing. However, it is not so simple to determine a good and universal method due to the complexity and multiplicity of measurement data. This paper presents an approach based on Deep Learning networks, which does not use Bayesian sub-predictors. These sub-predictors are used to marginalize the importance of some data part from multisensory platforms. In other words—to filter out noise and mismeasurements before the actual processing with neural networks. The presented results shows the applied data feature extraction method, which is embedded in the proposed algorithm, allows for such feature clustering. It allows for more effective prediction of future air pollution levels (accuracy—92.13%). The prediction results shows that, besides using standard measurements of temperature, humidity, wind parameters and illumination, it is possible to improve the performance of the predictor by including the measurement of traffic noise (Accuracy—94.61%).

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“…One of the main environmental challenges, apart from GHG emissions (and their consequences), has been air pollution caused by mineral or anthropogenic particulates [1] which cause adverse effects on humans and the environment [2][3][4][5]. The types of air pollution with the most detrimental influence on people's health are: particulate matter (PM) [3,[5][6][7], nitrogen dioxide (NO 2 ), ozone (O 3 ) and sulphur dioxide (SO 2 ) [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Awareness of Air Pollution and Ecosystem Services Provided by Trees: The Case Study of Warsaw City

2021

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One of the consequences of the constant urban development in numerous countries is a growing concentration of air pollution, which adversely affects both the environment and people’s health. One of the ways of changing this negative trend is to maintain green areas and trees within cities, as they serve many ecosystem functions, including biological absorption of particles and other types of air pollution. This article provides the findings of a study carried out among the residents of Warsaw, the capital of Poland, in order to assess social awareness of air pollution and the importance of trees. The study of the residents’ awareness was supplemented with the assessment of the parameters of the trees’ capacity for pollution absorption in selected locations performed with the help of the i-Tree Eco tool, which allowed the authors to compare the residents’ impressions on the role of trees in the process of absorption of pollution with their actual potential. The analyses showed that the majority of city residents are concerned with the problem of air in the city, but at the same time failing to notice its negative impact on their health. The majority of respondents were not aware of the role the trees play in the process of pollution absorption, suggesting that there is a real need for raising social awareness of functions served by trees and green areas in urban spaces. The comparison of the city residents’ opinions on the importance of trees in the process of pollution absorption with objective data obtained with the help of i-Tree Eco tool shows that the majority of people’s impressions of pollution absorption by trees in urban areas is correct.

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Forecasting Particulate Pollution in an Urban Area: From Copernicus to Sub-Km Scale

Cited by 16 publications

References 42 publications

Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography

Random Forests Assessment of the Role of Atmospheric Circulation in PM10 in an Urban Area with Complex Topography

Self-Powered Wireless Sensor Matrix for Air Pollution Detection with a Neural Predictor

Awareness of Air Pollution and Ecosystem Services Provided by Trees: The Case Study of Warsaw City

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