Short-term, real-time forecasting of hourly ozone, NO2 and NO levels by means of multiple linear regression modelling

Ibarra-Berastegi, Gabriel; Ηλίας, Α.; Agirre, E.; Uria, J.

doi:10.1007/bf02987401

Cited by 12 publications

(4 citation statements)

References 2 publications

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“…Understanding the relationship of O 3 formation with VOCs and NOx is essential for O 3 control. Until now, several methods were developed to investigate O 3 -VOC-NO x sensitivity, such as VOCs/NO x [4], a linear regression analysis of O 3 with its precursors [5], air quality model based on emissions (EBM) [6], and a box model based on observations (OBM) [7]. Since O 3 -related chemical reactions are highly simplified in the VOCs/NO x and linear regression methods, these two methods are usually used to preliminarily identify O 3 sensitivity.…”

Section: Introductionmentioning

confidence: 99%

O3 Sensitivity and Contributions of Different NMHC Sources in O3 Formation at Urban and Suburban Sites in Shanghai

et al. 2020

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Ground-level ozone (O3) pollution is still one of the priorities and challenges for air pollution control in the Yangtze River Delta (YRD) region of China. Understanding the relationship of O3 with its precursors and contributions of different sources in O3 formation is essential for the development of an O3 control strategy. This study analyzed O3 sensitivity to its precursors using a box model based on online observations of O3, non-methane hydrocarbons (NMHCs), nitrogen oxides (NOx), and carbon monoxide (CO) at an urban site and a suburban site in Shanghai in July 2017. Anthropogenic sources of NMHCs were identified using the positive matrix factorization (PMF) receptor model, and then contributions of different sources in O3 formation were estimated by the observation-based model (OBM). The relative incremental reactivity (RIR) values calculated by the OBM suggest that O3 formation at the urban site was in the NMHC-limited regime, while O3 formation at the suburban site tended between the transition regime and the NMHC-limited regime. Vehicular emission and liquefied petrochemical gas (LPG) use or aged air mass were found to be the two largest contributors at the urban and suburban sites in July, followed by paint and solvent use, and the petrochemical industry. However, from the perspective of O3 formation, vehicular emission and paint and solvent use were the largest two contributors at two sites due to the higher RIR values for paint and solvent use. In addition, the influence of transport on O3 sensitivity was identified by comparing O3 sensitivity at the suburban site across two days with different air mass paths. The result revealed that O3 formation in Shanghai is not only related to local emissions but also influenced by emissions from neighboring provinces. These findings on O3–NMHC–NOX sensitivity, contributions of different sources in O3 formation, and influence of transport could be useful for O3 pollution control in the YRD region. Nevertheless, more quantitative analyses on transport and further evaluation of the uncertainty of the OBM are still needed in future.

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Section: Introductionmentioning

confidence: 99%

O3 Sensitivity and Contributions of Different NMHC Sources in O3 Formation at Urban and Suburban Sites in Shanghai

et al. 2020

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“…For the five pollutants analyzed, predictions are made using all the types of NNs [4][5][6][7][8][9][10]. However, it was necessary to analyze the effect that the spatial variability of pollutants' emissions have on the different models used at each location.…”

Section: Resultsmentioning

confidence: 99%

“…For this study, historical hourly records of the above mentioned variables corresponding to years 2000 and 2001 were available [4][5][6][7]. Data of year 2000 were used to build the different groups of candidate prognostic models while data belonging to year 2001 were reserved to test and select the best model.…”

Section: Databasementioning

confidence: 99%

Prediction of air pollution levels using neural networks: influence of spatial variability

Ibarra-Berastegi¹,

Ηλίας²,

Barona³

et al. 2008

WIT Transactions on Ecology and the Environment

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This work focuses on the prediction of hourly levels up to 8 hours ahead for five pollutants (SO 2 , CO, NO 2 , NO and O 3 ) and six locations in the area of Bilbao (Spain). To that end, 216 models based on neural networks (NN) have been built. Spatial variability for the five pollutants has been assessed using Principal Components Analysis and different behaviour has been detected for the nonreactive pollutant (SO 2 ) and the rest (CO, NO 2 , NO and O 3 ). This can be explained by the very local effects involved in the photochemical reactions. The inputs used to feed the NN models intended to predict forthcoming levels of these five pollutants, include a baseline based on autocorrelation plus a linear or nonlinear combination of different meteorological and traffic variables. The nature of these combinations is different depending on the sensor thus showing the importance of the spatial variability to build the models. The number of hourly cases, due to gaps in data predictions, can have a possible range from 11% to 38% depending on the sensor. Depending on the pollutant, location and number of hours ahead the prediction is made, different types of models have been selected. The use of these models based on NNs can provide Bilbao's air pollution network originally designed for diagnosis purposes, with short-term, real time forecasting capabilities. The performance of these models at the different sensors in the area range from a maximum value of R 2 =0.88 for the prediction of NO 2 1 hour ahead, to a minimum value of R 2 =0.15 for the prediction of ozone 8 hours ahead. These boundaries and the limitation in which the number of cases that predictions are possible represent the maximum forecasting capability that Bilbao's network can provide in real-life operating conditions.

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“…Bilbao's network is not an exception regarding these general trends (Ibarra-Berastegi et al [1][2][3][4][5]). Since 1977, throughout the whole ZONE B (Fig.…”

Section: Introductionmentioning

confidence: 99%

Identification of redundant sensors in an air pollution network using cluster analysis and SOM

Ibarra-Berastegi¹,

Sáenz²,

Ezcurra³

et al. 2010

WIT Transactions on Ecology and the Environment

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An air pollution network monitors -among others -the sulfur dioxide (SO 2 ) levels at 4 locations in Bilbao city (Spain) and surroundings. The main objective of this work was to develop a practical methodology to identify redundant sensors and evaluate the network's capability to correctly represent SO 2 fields throughout the whole area. The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area. To that purpose, the combination of two different techniques has been used: Self-Organizing Maps (SOM) and cluster analysis (CA). The results show that both techniques yield the same results, but the information obtained via SOM can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of CA and SOM is that they provide readily interpretable results.

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Short-term, real-time forecasting of hourly ozone, NO2 and NO levels by means of multiple linear regression modelling

Cited by 12 publications

References 2 publications

O3 Sensitivity and Contributions of Different NMHC Sources in O3 Formation at Urban and Suburban Sites in Shanghai

O3 Sensitivity and Contributions of Different NMHC Sources in O3 Formation at Urban and Suburban Sites in Shanghai

Prediction of air pollution levels using neural networks: influence of spatial variability

Identification of redundant sensors in an air pollution network using cluster analysis and SOM

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