Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław

Brunello, Andrea; Kamińska, Joanna; Marzano, Enrico; Montanari, Angelo; Sciavicco, Guido; Turek, Tomasz

doi:10.1007/978-3-030-30278-8_45

Cited by 7 publications

(7 citation statements)

References 9 publications

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“…That is why it can be regarded as constituting an unsustainable action in economic terms. Similar situation can be observed in environmental terms, where new urban development degrades valuable environmental resources [14][15][16], or social, where migration can cause social conflicts [17]. The level of imbalance between supply and demand in land use planning was highlighted by the authors of the report Analysis of the Status and Conditions of Spatial Planning Work in Municipalities at the end of 2017.…”

Section: Introductionmentioning

confidence: 73%

Spatial Absorbency Assessment for Sustainable Land Development

Broża

Birnbaum

Castro

et al. 2020

GaEE

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Sustainable land use development requires a smart decision‑making process based on appropriate spatial design patterns. However, the pro‑investment policy of many municipalities often results in disproportions between the de‑mand and supply of investment areas, which means that spatial development is not realized in a sustainable way. In order to avoid the overconsumption of land for investment purposes, central government in Poland introduced a requirement to assess land balance, which includes the calculation of spa‑tial absorbency at the level of local urban planning. The aim of this work is to develop the concept of spatial absorbency assessment. In order to select best practices, document reviews of local spatial policies in Kalisz, Łódź, Toruń and Gdańsk were performed. Based on the review, a method for spatial absorbency assessment was presented. The applicability of the proposed solution is pre‑sented by its use on the case of the Racibórz municipality. The visualization shows what kind of geoinformation is required to perform an assessment by means of the developed method. The results of the calculations present the final feedback that is received by local authorities. The work was carried out us‑ing ArcGIS software. The obtained results may constitute the basis for further multi‑dimensional analyses as part of sustainable land development planning at the local level.

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Section: Introductionmentioning

confidence: 73%

Spatial Absorbency Assessment for Sustainable Land Development

Broża

Birnbaum

Castro

et al. 2020

GaEE

View full text Add to dashboard Cite

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“…While explicit models can be used for forecasting, it is not their focus, considering that their forecasting horizons are limited to the maximum lag in the model; time series forecasting models, on the other hand, allow long-term predictions, at the expenses of the interpretability of the models. Different, yet related, approaches include, 37 in which a modified decision tree learner has been used to model air pollution using lagged versions of the predicting variable in the form of univariate time series. Lagged models, and more in general, models that include consideration on the past values of the predictors, have been mainly used in dealing with issues related to the analysis of factors affecting health and human life.…”

Section: Time Series and Lagged Modelsmentioning

confidence: 99%

Temporal Aspects in Air Quality Modeling—A Case Study in Wrocław

Kamińska

Lucena-Sánchez

Sciavicco

2020

Air, Soil and Water Research

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Anthropogenic environmental pollution is a known and indisputable issue, and the importance of searching for reliable mathematical models that help understanding the underlying process is witnessed by the extensive literature on the topic. In this article, we focus on the temporal aspects of the processes that govern the concentration of pollutants using typical explanatory variables, such as meteorological values and traffic flows. We develop a novel technique based on multiobjective optimization and linear regression to find optimal delays for each variable, and then we apply such delays to our data to evaluate the improvement that can be obtained with respect to learning an explanatory model with standard techniques. We found that optimizing delays can, in some cases, improve the accuracy of the final model up to 15%.

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“…However, in their experiments, they did not take traffic intensity into account. Brunello et al [ 14 ] investigated temporal information management to assess the relationships between air pollutants, including

and

, and road traffic. In all of these studies, thanks to the direct link between road traffic and air pollutants, road traffic was used to predict air pollution.…”

Section: Introductionmentioning

confidence: 99%

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

Awan

Minerva

Crespi

2020

Sensors

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Traffic flow forecasting is one of the most important use cases related to smart cities. In addition to assisting traffic management authorities, traffic forecasting can help drivers to choose the best path to their destinations. Accurate traffic forecasting is a basic requirement for traffic management. We propose a traffic forecasting approach that utilizes air pollution and atmospheric parameters. Air pollution levels are often associated with traffic intensity, and much work is already available in which air pollution has been predicted using road traffic. However, to the best of our knowledge, an attempt to improve forecasting road traffic using air pollution and atmospheric parameters is not yet available in the literature. In our preliminary experiments, we found out the relation between traffic intensity, air pollution, and atmospheric parameters. Therefore, we believe that addition of air pollutants and atmospheric parameters can improve the traffic forecasting. Our method uses air pollution gases, including C O , N O , N O 2 , N O x , and O 3 . We chose these gases because they are associated with road traffic. Some atmospheric parameters, including pressure, temperature, wind direction, and wind speed have also been considered, as these parameters can play an important role in the dispersion of the above-mentioned gases. Data related to traffic flow, air pollution, and the atmosphere were collected from the open data portal of Madrid, Spain. The long short-term memory (LSTM) recurrent neural network (RNN) was used in this paper to perform traffic forecasting.

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Assessing the Role of Temporal Information in Modelling Short-Term Air Pollution Effects Based on Traffic and Meteorological Conditions: A Case Study in Wrocław

Cited by 7 publications

References 9 publications

Spatial Absorbency Assessment for Sustainable Land Development

Spatial Absorbency Assessment for Sustainable Land Development

Temporal Aspects in Air Quality Modeling—A Case Study in Wrocław

Improving Road Traffic Forecasting Using Air Pollution and Atmospheric Data: Experiments Based on LSTM Recurrent Neural Networks

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