Sürdürülebilir Kalkınma Amaçları
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- MakaleAçık Erişim2022Scopus SJRQ1How Covid-19 pandemic and partial lockdown decisions affect air quality of a city? The case of Istanbul, Turkey
Celik, Erkan; Gul, Muhammet
The world is currently struggling with a new type of coronavirus (2019-nCoV) pandemic that first appeared in Wuhan, China, and then spread to almost all countries. As in other countries of the world, public authorities in Turkey are implementing many preventive and mitigating partial lockdown (PL) actions against the virus's effects. Some decisions and policies implemented before and after March 11, 2020, when the first virus case has been identified, have reduced people and traffic circulation, which has also turned into some improvements in air quality. At this point, this study aims to investigate how this pandemic affects the air quality of a metropolis. A case study of the city of Istanbul, the most affected city with more than half of Turkey's cases, is performed. In our analysis, we observe, compare, and discuss the impact of the COVID-19 pandemic and PL decisions on Istanbul city's air quality. We consider the particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NO), nitrogen oxides (NOx), and ozone (O3) concentrations. We used data from 19 air monitoring stations (AMSs) and obtained improvements in the air quality for the pandemic period. In summary, the concentration levels in PM10, NO2, NO, and NOx result in a clear decline in pandemic times compared to the normal times in Istanbul. On the other hand, a non-homogenous trend for SO2 and CO concentrations is observed for different AMSs. A partial increase in O2 concentration is obtained in the comparison of before and during the PL period.
AtıfScopus15WoS13OpenAlex22
İncele - MakaleAçık Erişim2017Scopus SJRQ2Identifying key factors of rail transit service quality: An empirical analysis for Istanbul
Isikli, Erkan; Aydin, Nezir; Celik, Erkan; Gumus, Alev Taskin
Providing a high quality of service in public transportation is essential to reduce dissatisfactions stemming from traffic congestion and noise. Public transport providers need to find ways to dilute the effects of immoderate use of private cars in big cities while maintaining a sufficient level of customer satisfaction. This study aimed to identify the key service quality (SQ) factors that drive passenger satisfaction in Istanbul’s rail transit (RT) system using data obtained from an extensive survey conducted by the Istanbul Public Transportation Co. A total of 11,116 passengers who used rail transport from May 15-June 3, 2012, and June 17-July 3, 2013, were interviewed in person. The relative importance of the SQ factors was assessed so that service provision could be prioritized and the enhancement of passenger satisfaction can be achieved employing several social choice techniques. The results indicate that, from an overall perspective, waiting time, crowdedness in cars, and fare are the SQ factors that best reflect the public good.
AtıfScopus31OpenAlex36İncele - MakaleYalnız künye2016Scopus SJRQ1An artificial neural network-based earthquake casualty estimation model for Istanbul city
Gul, Muhammet; Guneri, Ali Fuat
Earthquakes over the world cause serious damages for people and the economy. The establishment of an appropriate model to estimate potential losses or injuries in earthquake disasters is fundamental to decrease their impacts and losses and effectively respond and mitigate. Turkey as a country that experienced many earthquakes in the last century and had serious human and financial losses needs a comprehensive knowledge of consequences of devastating earthquakes to be able to plan for the future. Artificial neural networks (ANNs) have abilities to solve and analyse complex relations as an appropriate method to estimate number of injuries. In this study, an ANN model is built up for the earthquake casualty prediction, which takes earthquake occurrence time, earthquake magnitude, and population density as the predictors and employs 21 Mw > 5 earthquake disasters occurred in Turkey from 1975 as samples for the training of the network. The model was then tested on a study region consisting of four districts in Istanbul which is estimated to have the highest injury rate according to the earlier reports and generated estimations of the expected number of injured people. Results show that 99.9 % of the variability in the number of injured people is predictable with using this ANN model. Comparison of actual values and estimated output values in the ANN model was also found apparently very close to each other. According to the test case study results, when the value of earthquake magnitude is 6.5 Mw, number of injured people has increased in a sharp trend compared to the previous magnitude value (6 Mw) for all districts. Estimated number of injured people in daytime is obtained higher than at night when the earthquake magnitude is 5 and 5.5 Mw. The highest value in estimated number of injured people has emerged in Fatih district as 7241 for a 7.5 Mw daytime earthquake. On conclusion, it was deduced that the model can reveal accurate estimation of casualties and it can provide information in order to develop mitigation policies, especially in earthquake emergency service management.
AtıfWoS32OpenAlex46İncele