Long-term trend of ambient air PM10, PM2.5, and O3 and their health effects in Tabriz city, Iran, during 2006–2017

Barzeghar, Vahideh; Sarbakhsh, Parvin; Hassanvand, Mohammad Sadegh; Faridi, Sasan; Gholampour, Akbar

doi:10.1016/j.scs.2019.101988

Cited by 98 publications

(39 citation statements)

References 52 publications

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“…Air pollution remains one of the most important public health concerns across the globe, contributing substantial premature mortality with greater impact in developing nations (Mannucci and Franchini, 2017;Barzeghar et al, 2020). As per World Health Organization (WHO), it has been estimated that 4.2 million premature deaths globally are linked to ambient air pollution.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Air Quality Changes in the Four Metropolitan Cities of India during COVID-19 Pandemic Lockdown

Bedi

Dhaka

Vijay

et al. 2020

Aerosol Air Qual. Res.

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In view of emerging threat of COVID-19 pandemic, stringent lockdown measures have been implemented in India since 25 th March, 2020. The present study aims to assess the changes in air quality before and during lockdown in the four major metropolitan cities of India viz., Delhi, Mumbai, Kolkata and Chennai. The data on major air quality parameters and meteorological parameters was collected for 15 days before lockdown (i.e., March 10 th-March 24 th , 2020) and 15 days after implementation of lockdown (25 th March-April 8 th , 2020). The lockdown measures reflected a significant reduction in air pollutants, most significant fall was estimated for NO 2 (29.3-74.4%) while the least reduction was noticed for SO 2. On the contrary, levels of ground level ozone were found to be increased (except in Mumbai), could be related to the lower utilization of O 3 owing to decrease of NO x in the environment. Since, the lockdown period has been extended, therefore further reduction of most pollutants is expected. Among the various metrological parameters, significant increase in diurnal temperature was observed at Delhi, Kolkata and Mumbai, however relative humidity has changed significantly only in Mumbai. In the absence of any major violent meteorological phenomena in India during recent months the significant difference in air quality parameters could be majorly attributed to the effect of lockdown measures. However, the regional differences may be because of the local emission of pollutants and the prevailing effects of secondary pollutants. These observations highlight the contribution of anthropogenic sources in air pollution, therefore, sustainable polices for mitigation of air pollution are essential in India.

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Section: Introductionmentioning

confidence: 99%

Assessment of Air Quality Changes in the Four Metropolitan Cities of India during COVID-19 Pandemic Lockdown

Bedi

Dhaka

Vijay

et al. 2020

Aerosol Air Qual. Res.

View full text Add to dashboard Cite

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“…Current situation of the city is generated by the geographical characteristics, meteorological factors, and residential constructions in the direction of the wind entering the city. It should be noted that based on the results, there was a fluctuated variation for PM10 and O3 concentrations from to in Tabriz [25].…”

Section: Discussionmentioning

confidence: 80%

Clustering the Concentrations of PM10 and O3: Application of Spatio-temporal Model-based Clustering

Saeipourdizaj

Musavi

Gholampour

et al. 2021

Preprint

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Air pollution data are large-scale dataset which can be analyzed in low scales by clustering to recognize the pattern of pollution and have simpler and more comprehensible interpretation. So, this study aims to cluster the days of year 2017 according to the hourly O3 and PM10 amounts collected from four stations of Tabriz by using spatio-temporal mixture model-based clustering (STMC). Besides, mixture model-based clustering with temporal dimension (TMC) and mixture model-based clustering without considering spatio-temporal dimensions (MC) were utilized to compare with STMC. To evaluate the efficiency of these three models and obtain the optimal number of clusters in each model, BIC and ICL criteria were used. According to BIC and ICL, STMC outperforms TMC and MC. Three clusters for O3 and four clusters for PM10 were selected as the optimal number of clusters to fit STMC models. Regarding PM10, the average concentration was the highest in cluster 4. Regarding O3, all summer days were in cluster 3 and the average concentration of this cluster was the highest. Cluster 2 had the lowest concentration with a high difference from clusters 1 and 3 and its average temperature was the lowest. Autumn days make up about 84% of this cluster. The clustering of polluted and clean days into separate groups and observing the effect of meteorological factors on the amount of concentration in each cluster clearly prove the efficiency of the model. Results of STMC showed that efficiency of clustering in air pollution data increases by considering both spatio-temporal dimensions.

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“…The study performed data pre-processing to maintain the consistency of the raw dataset containing 2372 observations. To process the checking and removal of spatiotemporal outliers from raw data, the Z scores method was used before the calculation of statistical parameters, in consistency with previous studies (Barzeghar et al 2020). Firstly, the series data were transformed into Z scores.…”

Section: Data Pre-processingmentioning

confidence: 99%

Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh

Shahriar

Kayes

Hasan

et al. 2020

Air Qual Atmos Health

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Atmospheric particle pollution causes acute and chronic health effects. Predicting the concentrations of PM 2.5 and PM 10 , therefore, is a prerequisite to avoid the consequences and mitigate the complications. This research utilized the machine learning (ML) models such as linear-support vector machine (L-SVM), medium Gaussian-support vector machine (M-SVM), Gaussian process regression (GPR), artificial neural network (ANN), random forest regression (RFR), and a time series model namely PROPHET. Atmospheric NO X , SO 2 , CO, and O 3 , along with meteorological variables from Dhaka, Chattogram, Rajshahi, and Sylhet for the period of 2013 to 2019, were utilized as exploratory variables. Results showed that the overall performance of GPR performed better particularly for Dhaka in predicting the concentration of both PM 2.5 and PM 10 while ANN performed best in case of Chattogram and Sylhet for predicting PM 2.5. However, in terms of predicting PM 10 , M-SVM and RFR were selected respectively. Therefore, this study recommends utilizing "ensemble learning" models by combining several best models to advance application of ML in predicting pollutants' concentration in Bangladesh.

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Long-term trend of ambient air PM10, PM2.5, and O3 and their health effects in Tabriz city, Iran, during 2006–2017

Cited by 98 publications

References 52 publications

Assessment of Air Quality Changes in the Four Metropolitan Cities of India during COVID-19 Pandemic Lockdown

Assessment of Air Quality Changes in the Four Metropolitan Cities of India during COVID-19 Pandemic Lockdown

Clustering the Concentrations of PM10 and O3: Application of Spatio-temporal Model-based Clustering

Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh

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