Investigating Correlations and Variations of Air Pollutant Concentrations under Conditions of Rapid Industrialization – Kocaeli (1987–2009)

Doğruparmak, Şenay Çetin; Özbay, Bilge

doi:10.1002/clen.201000478

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…SO 2 and NO x are two of the main air pollutants generated by combustion processes, and both have harmful effects on human health. Moreover, it was proven that both pollutants are correlated [46], which is consistent with the model in (1). Fortunately, pollution episodes are not very frequent and the trend is that they will become scarcer as technology advances.…”

Section: Estimation Algorithmsupporting

confidence: 85%

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

2020

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Predicting anomalous emission of pollutants into the atmosphere well in advance is crucial for industries emitting such elements, since it allows them to take corrective measures aimed to avoid such emissions and their consequences. In this work, we propose a functional location-scale model to predict in advance pollution episodes where two pollutants are involved. Functional generalized additive models (FGAMs) are used to estimate the means and variances of the model, as well as the correlation between both pollutants. The method not only forecasts the concentrations of both pollutants, it also estimates an uncertainty region where the concentrations of both pollutants should be located, given a specific level of uncertainty. The performance of the model was evaluated using real data of SO 2 and NO x emissions from a coal-fired power station, obtaining good results.

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Section: Estimation Algorithmsupporting

confidence: 85%

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

2020

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“…These correlations are also statistically significant. As we expect, sulphur dioxide and carbon monoxide correlate directly with PM10, considering that there are chemical processes that relate them (Doğruparmak and Özbay, 2011;Sharma and Sharma, 2016).…”

Section: Air Qualitymentioning

confidence: 81%

The demand for air quality: evidence from the housing market in Bogotá, Colombia

Carriazo

Gomez-Mahecha

2018

Envir. Dev. Econ.

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Identifying a demand function for air quality is a key input to calculate welfare measurements of pollution abatement policies. We defined intra-urban housing submarkets to apply a Second Stage hedonic pricing model that allowed us to identify an inverse demand function for PM10 reductions in Bogotá. The monthly benefits of compliance with the U.S. Environmental Protection Agency standard (50 μg/m3 – annual average), and the far more stringent World Health Organization standard (20 μg/m3 – annual average) are valued at US$12.16 and US$189.64 per household, respectively. These values, in turn, represent about 1.35 per cent and 21.04 per cent of the average household income. The hedonic model applied hereby shows that intra-urban housing submarkets are suitable for the identification of a demand function to be used by policy makers interested in evaluating non-marginal benefits (costs) from air quality improvements (deterioration).

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“…Specifically, sometimes pollution concentration can be high due to transported pollution produced elsewhere in the region. Various studies have been reported in the literature to investigate the correlation between different ambient air pollutants [57], [58].…”

Section: A Data Descriptionmentioning

confidence: 99%

Integrated Multiple Directed Attention-Based Deep Learning for Improved Air Pollution Forecasting

Dairi

Harrou

Khadraoui

et al. 2021

IEEE Trans. Instrum. Meas.

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In recent years, human health across the world is becoming concerned by a constant threat of air pollution, which causes many chronic diseases and premature mortalities. Poor air quality does not have only serious adverse effects on human health and vegetation, but also some major negative political, societal, and economic impacts. Hence, it is essential investing more effort on accurate forecasting of ambient air pollution to provide practical and relevant solutions, achieve acceptable air quality, and plan for prevention. In this work, we propose a flexible and efficient deep learning-driven model to forecast concentrations of ambient pollutants. The paper introduces first the traditional Variational AutoEncoder (VAE) and the attention mechanism to develop the forecasting modeling strategy based on the innovative Integrated Multiple Directed Attention Deep Learning architecture (IMDA). To assess the performance of the proposed forecasting methodology, experimental validation is then performed using air pollution data from four US states. Six statistical indicators have been used to evaluate the forecasting accuracy. A discussion of the results obtained finally demonstrates the satisfying performance of IMDA-VAE methods to forecast different pollutants in different locations. Furthermore, results indicate that the proposed IMDA-VAE model can effectively improve air pollution forecasting performance and outperforms the deep learning models, namely VAE, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), bidirectional LSTM, bidirectional GRU, and ConvLSTM. We also showed that the forecasting results of the proposed model surpass the performance of LSTM and GRU with the attention mechanism.

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Investigating Correlations and Variations of Air Pollutant Concentrations under Conditions of Rapid Industrialization – Kocaeli (1987–2009)

Cited by 19 publications

References 23 publications

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

The demand for air quality: evidence from the housing market in Bogotá, Colombia

Integrated Multiple Directed Attention-Based Deep Learning for Improved Air Pollution Forecasting

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