Measurement and modelling of nitrogen dioxide (NO<sub>2</sub>) emissions: a marker for traffic-related air pollution in Doha, Qatar

Al-Naimi, Noora; Balakrishnan, P.; Goktepe, Ipek

doi:10.1080/19475683.2015.1057225

Cited by 11 publications

(7 citation statements)

References 15 publications

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Section: Introductionmentioning

confidence: 99%

“…NO 2 and SO 2 are dangerous air pollutants formed from the excessive combustion of nitrogen and sulfur-containing fossil fuels ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ; Chen et al, 2007 ; Liu et al, 2016 ). NO 2 and SO 2 are severe respiratory irritants implicated in the aggravation of asthma and respiratory inflammation ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ; Chen et al, 2007 ; Liu et al, 2016 ). Both NO 2 and SO 2 are also highly reactive with other compounds forming additional environmental pollution, such as PM 2.5 , PM 10 , and acid rain ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ; Chen et al, 2007 ; Liu et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

“…NO 2 and SO 2 are severe respiratory irritants implicated in the aggravation of asthma and respiratory inflammation ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ; Chen et al, 2007 ; Liu et al, 2016 ). Both NO 2 and SO 2 are also highly reactive with other compounds forming additional environmental pollution, such as PM 2.5 , PM 10 , and acid rain ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ; Chen et al, 2007 ; Liu et al, 2016 ). NO 2 is also a critical reactant in the chemical processes that form O 3 and photochemical smog ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

“…Both NO 2 and SO 2 are also highly reactive with other compounds forming additional environmental pollution, such as PM 2.5 , PM 10 , and acid rain ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ; Chen et al, 2007 ; Liu et al, 2016 ). NO 2 is also a critical reactant in the chemical processes that form O 3 and photochemical smog ( Salonen, Salthammer & Morawska, 2019 ; Al-Naimi, Balakrishnan & Goktepe, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea

Shen¹,

Valagolam²,

McCalla³

2020

PeerJ

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Amidst recent industrialization in South Korea, Seoul has experienced high levels of air pollution, an issue that is magnified due to a lack of effective air pollution prediction techniques. In this study, the Prophet forecasting model (PFM) was used to predict both short-term and long-term air pollution in Seoul. The air pollutants forecasted in this study were PM2.5, PM10, O3, NO2, SO2, and CO, air pollutants responsible for numerous health conditions upon long-term exposure. Current chemical models to predict air pollution require complex source lists making them difficult to use. Machine learning models have also been implemented however their requirement of meteorological parameters render the models ineffective as additional models and infrastructure need to be in place to model meteorology. To address this, a model needs to be created that can accurately predict pollution based on time. A dataset containing three years worth of hourly air quality measurements in Seoul was sourced from the Seoul Open Data Plaza. To optimize the model, PFM has the following parameters: model type, change points, seasonality, holidays, and error. Cross validation was performed on the 2017–18 data; then, the model predicted 2019 values. To compare the predicted and actual values and determine the accuracy of the model, the statistical indicators: mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and coverage were used. PFM predicted PM2.5 and PM10 with a MAE value of 12.6 µg/m3 and 19.6 µg/m3, respectively. PFM also predicted SO2 and CO with a MAE value of 0.00124 ppm and 0.207 ppm, respectively. PFM’s prediction of PM2.5 and PM10 had a MAE approximately 2 times and 4 times less, respectively, than comparable models. PFM’s prediction of SO2and CO had a MAE approximately five times and 50 times less, respectively, than comparable models. In most cases, PFM’s ability to accurately forecast the concentration of air pollutants in Seoul up to one year in advance outperformed similar models proposed in literature. This study addresses the limitations of the prior two PFM studies by expanding the modelled air pollutants from three pollutants to six pollutants while increasing the prediction time from 3 days to 1 year. This is also the first research to use PFM in Seoul, Korea. To achieve more accurate results, a larger air pollution dataset needs to be implemented with PFM. In the future, PFM should be used to predict and model air pollution in other regions, especially those without advanced infrastructure to model meteorology alongside air pollution. In Seoul, Seoul’s government can use PFM to accurately predict air pollution concentrations and plan accordingly.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea

Shen¹,

Valagolam²,

McCalla³

2020

PeerJ

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“…The main contributors to the air's high nitrogen level are agricultural activities and fossil fuel combustion in motor vehicles. Pollutants are measured mechanically using automatic chemiluminescence analyzer or diffusion tubes to determine the air's nitrogen level [7][8]. Several types of health effects have been discussed and studied in correlation to exposure to nitrogen oxides.…”

Section: Introductionmentioning

confidence: 99%

Pilot study of air quality index assessment of nitrogen pollutant using lichen as bioindicators in Jakarta and Depok, Indonesia

et al. 2020

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Indonesia needs an alternative air quality index considering that the ones currently in use were not precisely made for the environment and people’s genetic makeup in Indonesia. This study aims to determine if lichen can be used as an alternative index to assess Indonesia’s air quality by acting as a bioindicator. Lichen samples were taken at six locations, three in Gelora Bung Karno’s City Forest, Jakarta, and three at the Universitas Indonesia’s city forest, Depok. Lichens are identified according to the chemical test results and identification key. The amount of lichen obtained is calculated and converted into a NAQI chart to determine if the site’s status is clean air, at-risk, N-polluted, or very N-polluted location. Two of them are polluted by nitrogen from seven sampling areas, while the other five are heavily polluted. This data shows the air quality in all areas is insufficient and polluted. However, our findings are contradictory to nitrogen pollutant data from Meteorology Climatology and Geophysics Council (BMKG). Thus, in the future, there should be a calibrator when measuring air quality analysis using lichen.

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Occupational Exposure to Air Pollutants Among Professional Motorcyclists in Brazil Results in Higher Health Risk and Changing in Biochemical and Cardiovascular Parameters

Carvalho

Marmett

Amantéa

et al. 2022

Water Air Soil Pollut

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Measurement and modelling of nitrogen dioxide (NO₂) emissions: a marker for traffic-related air pollution in Doha, Qatar

Cited by 11 publications

References 15 publications

Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea

Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea

Pilot study of air quality index assessment of nitrogen pollutant using lichen as bioindicators in Jakarta and Depok, Indonesia

Occupational Exposure to Air Pollutants Among Professional Motorcyclists in Brazil Results in Higher Health Risk and Changing in Biochemical and Cardiovascular Parameters

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Measurement and modelling of nitrogen dioxide (NO2) emissions: a marker for traffic-related air pollution in Doha, Qatar

Cited by 11 publications

References 15 publications

Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea

Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM2.5, PM10, O3, NO2, SO2, CO) in Seoul, South Korea

Pilot study of air quality index assessment of nitrogen pollutant using lichen as bioindicators in Jakarta and Depok, Indonesia

Occupational Exposure to Air Pollutants Among Professional Motorcyclists in Brazil Results in Higher Health Risk and Changing in Biochemical and Cardiovascular Parameters

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Measurement and modelling of nitrogen dioxide (NO₂) emissions: a marker for traffic-related air pollution in Doha, Qatar

Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea

Prophet forecasting model: a machine learning approach to predict the concentration of air pollutants (PM_2.5, PM₁₀, O₃, NO₂, SO₂, CO) in Seoul, South Korea