Forecasting PM10 concentrations using time series models: a case of the most polluted cities in Turkey

Çekim, Hatice Öncel

doi:10.1007/s11356-020-08164-x

Cited by 26 publications

(11 citation statements)

References 46 publications

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“…The methodology used in this research for processing the available data is based on specialized papers published in recent years by a number of researchers concerned with the stage reached in the implementation of the SDGs at national, regional or international level [29,62,63]. It is obvious that different methodologies can be used; however, in terms of the trend forecasting function, the variant chosen in this case proves to be the most suitable, both in terms of relevance and the ease with which it can be reproduced and extended to different levels of analysis [64][65][66][67].…”

Section: Methodsmentioning

confidence: 99%

Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030

et al. 2020

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In this period of extreme changes in our society, issues related to the health and well-being of citizens are considered essential for the future of a united and prosperous Europe. Achieving the Sustainable Development Goals (SDGs) at EU level by 2030 requires hard work done in a transformative way in order to implement a set of coherent, evidence-informed policies that address health, well-being and all their determinants throughout the course of life and across all sectors of government and society. The objective of this paper is to assess the stage of fulfillment of all SDG targets in relation to health and well-being at EU level, based on the current trend of each indicator, for each EU member country. Based on the Eurostat SDG data set for 2007–2018, the individual trends were forecast using the AAA (Holt-Winters) version of the exponential smoothing (ETS) algorithm. The research results are surprising, on the one hand showing the possibility that some targets will be reached, but also indicating that a large percentage of targets will not be reached if the current trend is continued, especially due to disruptive change generated by the current pandemic. There is a need to increase the involvement of all member states, but also ensure a deeper involvement at the level of EU institutions, to provide full support for meeting the targets proposed by the 2030 Agenda, ensuring prosperity and health for all European citizens, and becoming a model for all the states of the world.

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Section: Methodsmentioning

confidence: 99%

Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030

et al. 2020

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“…The wavelet transform is a commonly used tool in the field of signal decomposition. This method obtains different modes of signals by constructing an orthogonal wavelet filter bank, which can adapt to data with non-stationary characteristics (Cekim 2020;Gilles 2013). The signal decomposition method is included in Definition 1.…”

Section: Step3 Trend Fittingmentioning

confidence: 99%

Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle Pollutants in Multiple Scenarios

Long

Pan

et al. 2021

Preprint

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Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high performance algorithms. The framework proposed in this paper provides a general process including five basic tasks, and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework, and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation.

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“…There are two kinds of statistical prediction models: linear and nonlinear. The commonly-used linear statistical prediction models, which is based on the supposed linearity of real-world observed data, are autoregressive moving average (ARMA) (Graupe et al 1975), and autoregressive integrated moving average (ARIMA) (Cekim 2020, Jian et al 2012. Considerning the nonlinearity of real-world observed data, the conventional nonlinear statistical prediction methods are machine learning (ML) models.…”

Section: Introductionmentioning

confidence: 99%

Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

Zhang

Zhao

et al. 2022

Preprint

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The acceleration of industrialization and urbanization has recently brought about serious air pollution problems, which threaten human health and lives, the environmental safety, and sustainable social development. Air quality prediction is an effective approach for providing early warning of air pollution and supporting cleaner industrial production. However, existing approaches have suffered from a weak ability to capture long-term dependencies and complex relationships from time series PM2.5 data. To address this problem, this paper proposes a new deep learning model called temporal difference-based graph transformer networks (TDGTN) to learn long-term temporal dependencies and complex relationships from time series PM2.5 data for air quality PM2.5 prediction. The proposed TDGTN comprises of encoder and decoder layers associated with the developed graph attention mechanism. In particular, considering the similarity of different time moments and the importance of temporal difference between two adjacent moments for air quality prediction, we first construct graph-structured data from original time series PM2.5 data at different moments without explicit graph structure. Then, based on the constructed graph, we improve the self-attention mechanism with the temporal difference information, and develop a new graph attention mechanism. Finally, the developed graph attention mechanism is embedded into the encoder and decoder layers of the proposed TDGTN to learn long-term temporal dependencies and complex relationships from a graph prospective on air quality PM2.5 prediction tasks. To verify the effectiveness of the proposed method, we conduct air quality prediction experiments on two real-world datasets in China, such as Beijing PM2.5 dataset ranging from 01/01/2010 to 12/31/2014 and Taizhou PM2.5 dataset ranging from 01/01/2017 to 12/31/2019. Compared with other air quality forecasting methods, such as autoregressive moving average (ARMA), support vector regression (SVR), convolutional neural network (CNN), long short-term memory (LSTM), the original Transformer, our experiment results indicate that the proposed method achieves more accurate results on both short-term (1 hour) and long-term (6, 12, 24, 48 hours) air quality prediction tasks.

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Forecasting PM10 concentrations using time series models: a case of the most polluted cities in Turkey

Cited by 26 publications

References 46 publications

Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030

Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030

Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle Pollutants in Multiple Scenarios

Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

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Forecasting PM10 concentrations using time series models: a case of the most polluted cities in Turkey

Cited by 26 publications

References 46 publications

Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030

Assessing the Achievement of the SDG Targets for Health and Well-Being at EU Level by 2030

Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle&nbsp;Pollutants in Multiple Scenarios

Temporal Difference-based Graph Transformer Networks for Air Quality PM2.5 Prediction: A Case Study in China

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Product

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Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle Pollutants in Multiple Scenarios