Temporal variations of atmospheric black carbon and its relation to other pollutants and meteorological factors at an urban traffic site in Istanbul

Şahin, Ülkü Alver; Onat, Burcu; Akın, Özcan; Ayvaz, Coşkun; Uzun, Burcu; Mangır, Nizamettin; Doğan, Muhammet; Harrison, Roy M.

doi:10.1016/j.apr.2020.03.009

Cited by 30 publications

(11 citation statements)

References 79 publications

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“…Moreover, before the analyses in this study, all data were checked for robustness; to detect anomalous records; for irregular, zero, negative values; and for outliers by a manual inspection of the data for quality control. Many researchers have used these data and described the processing of measurements in Istanbul (Capraz et al, 2016;Yurtseven et al, 2018;Guclu et al, 2019;Onay et al, 2019, Flores et al, 2017; a detailed measurement description can be found in Sahin et al (2020).…”

Section: Air Pollution Measurements In Istanbulmentioning

confidence: 99%

“…Polar plots and diurnal variation plots were created using the open air package of R version 3.1.2 (R Core Team, 2015). Detailed descriptions of the statistical techniques used in this study can be found in Sahin et al (2020). Table 2 shows the descriptive statistics for the parameters at the urban and traffic sites in Istanbul based on daily values from 1-15 March 2020 (before COVID-19 measures) and 16-31 March 2020 (after COVID-19 measures).…”

Section: Statistical Analysesmentioning

confidence: 99%

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The Effects of COVID-19 Measures on Air Pollutant Concentrations at Urban and Traffic Sites in Istanbul

Şahin

2020

Aerosol Air Qual. Res.

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Since December 2019, most countries have been working to stop the spread of SARS-CoV-2, the virus responsible for COVID-19. These measures, which include restricting movement, have environmental consequences. This study assessed the impact of COVID-19 measures on air pollutant concentrations measured in urban areas and traffic stations on both the European and Asian sides of Istanbul during March 2020. Significant reductions in pollutants: 32-43% (PM 10), 19-47% (PM 2.5), 29-44% (NO 2), 40-58% (CO) and 34-69% (SO 2) were calculated. The clearest reductions at the traffic stations were in NO 2 which originates primarily from vehicle exhaust. The reduction of NO 2 at the traffic station on the European side was found higher comparing the Asian side. The average concentrations of NO 2 , PM 2.5 , PM 10 and CO during peak traffic hours were significantly (p < 0.01) decreased under COVID-19 measures. The results indicate that due to the measures taken in Istanbul and across Turkey and to control the spread of the virus, anthropogenic activities such as industry, vehicle traffic and sea transport have decreased, and consequently, air pollution has been significantly reduced. These pollutant levels demonstrate the anthropogenic contribution to air pollution and can inform clean air actions in Istanbul and in others cities throughout the world.

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Section: Air Pollution Measurements In Istanbulmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

The Effects of COVID-19 Measures on Air Pollutant Concentrations at Urban and Traffic Sites in Istanbul

Şahin

2020

Aerosol Air Qual. Res.

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“…They proposed that the long-range transport of influenza viruses is possible by combustion derived particles, which have pores providing a suitable space for the virus settlement. This strong assumption may also be valid for our case since a high incidence of daily COVID-19 were observed in Zonguldak and Tekirdağ cities where extreme coal and industrial particles were reported (48-50) as well as in İstanbul—the biggest metropolitan city of Turkey suffering from traffic and industry-related air pollution (51, 52).…”

Section: Discussionmentioning

confidence: 59%

Existence of SARS-CoV-2 RNA on ambient particulate matter samples: A nationwide study in Turkey

Kayalar

Arı

Babuççu

et al. 2021

Preprint

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Coronavirus disease 2019 (COVID-19) is caused by the SARS-CoV-2 virus and has been affecting the world since the end of 2019. Turkey is severely affected with the first case being reported on March 11th 2020. Ambient particulate matter (PM) samples in various size ranges were collected from 13 sites including urban and urban background locations and hospital gardens in 10 cities across Turkey between the 13th of May and the 14th of June, 2020 to investigate a possible presence of SARS-CoV-2 RNA on ambient PM. A total of 155 daily samples (TSP, n=80; PM2.5, n=33; PM2.5-10, n=23; PM10, n=19; and 6 size segregated, n=48) were collected using various samplers in each city. The N1 gene and RdRP gene expressions were analyzed for the presence of SARS-CoV-2 as suggested by the Centers for Disease Control and Prevention (CDC). According to RT-PCR and 3D-RT-PCR analysis, dual RdRP and N1 gene positivity were detected in 20 (9.8 %) of the samples. The highest percentage of virus detection on PM samples was from hospital gardens in Tekirdag, Zonguldak, and Istanbul, especially in PM2.5 mode. Samples collected from two urban sites were also positive. Findings of this study have suggested that SARS CoV2 may be transported by ambient particles especially at sites close to the infection hot-spots. However, whether this has an impact on the spread of the virus infection remains to be determined.

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“…Nevertheless, these examples mostly look into one specific sector or in a combination with other factors, such as pollution, traffic congestion, etc. There are some new studies that look into temporal variations (Sahin et al, 2020), but none considers time variable from the sector-based approach. This study aims to address this research gap from a modeling analysis approach.…”

Section: State Of the Artmentioning

confidence: 99%

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

Cheshmehzangi¹,

Ardakani²

2021

Front. Sustain. Cities

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This paper studies the important factor of sector-based time variable, which is critical to urban mobility patterns in an urban environment. In particular, this study analyzes urban traffic optimization based on modeling analysis of a simulated urban environment. In doing so, we develop and assess the urban traffic model based on three key components of agents, urban map, and mobility pattern. We develop an urban sample based on a medium-to-large city in China, which is represented by Manhattan grid pattern layout. By developing a homogeneous urban layout, we distribute the urban blocks of various sectors across the sample model. Through simulation studies, we model urban traffic based on “conventional operation hours” and “proposed operation hours” of all sectors. This urban traffic model is used to study the impact of the proposed approach on urban traffic based on two measured metrics of end-to-end delay (ETE) and Agent queue count (AQC). By suggesting a new sector-based time variable, we then evaluate the urban traffic model based on multiple active agent ratio. The findings from this simulation uncover the importance of sector-based time variable in optimizing urban traffic.

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Temporal variations of atmospheric black carbon and its relation to other pollutants and meteorological factors at an urban traffic site in Istanbul

Cited by 30 publications

References 79 publications

The Effects of COVID-19 Measures on Air Pollutant Concentrations at Urban and Traffic Sites in Istanbul

The Effects of COVID-19 Measures on Air Pollutant Concentrations at Urban and Traffic Sites in Istanbul

Existence of SARS-CoV-2 RNA on ambient particulate matter samples: A nationwide study in Turkey

Urban Traffic Optimization Based on Modeling Analysis of Sector-Based Time Variable: The Case of Simulated Ningbo, China

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