Spatio-temporal variations of tropospheric nitrogen dioxide in Turkey based on satellite remote sensing

Abstract

The satellite observations of NO2 acquire the total tropospheric column over an area while the current ground observations lack spatial and temporal coverage. In this study the Dutch Ozone Monitoring Instrument (OMI) NO2 (DOMINO) data product v2.0 for 2004 - 2019 period was used to analyze the spatial and temporal variations of NO2 in Turkey. Considering the seasonality characteristics of NO2, we have used pixel based Seasonal Kendall (S-K) test to investigate the trend of the change. The highest values of NO2 has been found at the metropolitan areas and perimeter of the high capacity power plants in the observed period. The monthly average concentrations of NO2 are higher in winter months due to the higher demand of heating and power usage. The S-K trend test results indicate a statistically negative trend at the largest cities such as Istanbul, Ankara and Izmir. However statistically significant positive trend has been found in some areas and Syrian border provinces in particular. Our results show that there is an abrupt change by 2011 in the tropospheric NO2 concentrations, same period when the first Syrian refugees have arrived after the political disorder. The dramatic change at the emission landscape of the NO2 in the region can be explained by changes in population concentration due to political circumstances.

Spatio-temporal variations of tropospheric nitrogen dioxide in South Mato Grosso based on remote sensing by satellite

“…The OMI measures atmospheric NO 2 daily since late 2004 with spatial resolution of 13 × 24 km in the nadir visualization, increasing the size to 24 × 135 km for widest viewing angles. Several studies have been performed using satellite observation data of tropospheric NO 2 columns (Richter et al 2005 ; Castellanos and Boersma 2012 ; Schneider and Van Der 2012 ; Lelieveld et al 2015 ; Khokhar et al 2015 ; Irie et al 2016 ; Cai et al 2018 ; Yanfang et al 2019 ; Yavaşlı 2020 ; Zheng et al 2018 ), and they show that its concentrations are dependent on economic, industrial, and other human-controlled activities but not correlated to emission policies to improve air quality. Several inventories on both global and regional scales of NO 2 concentrations normally use regression models for trending analysis.…”

Occurred Change in Air Pollution Parameters in the Process of Coronavirus (Covid-19) Measures: Example of the Eastern Mediterranean

“…Hava kirliliğine yönelik olarak Türkiye'de yapılan çalışmalarda daha çok zamana bağlı kirletici değişimlerinin (Erbaşlar ve Taşdemir, 2005) ve bunların birbirleriyle ilişkilerinin (Erbaşlar ve Taşdemir, 2007) regresyon modeli kullanılarak incelendiği görülür. Buna ek olarak gelişen teknolojilerle birlikte son dönemde uzaktan algılama teknolojileri kullanılarak hava kirliliği parametrelerinin Türkiye için hesaplandığı çalışmalar da literatürde mevcuttur (Yavaşlı, 2020;Kaplan ve Yiğit Avdan, 2020 (Büyüköztürk, 2012). Aynı deneklerin tekrarlı ölçümleri veya eşleştirilmiş örneklemlerden elde edilen ölçümler olduğunda kullanılabilen bu yöntem, çalışmada kirletici parametrelerin ölçüldüğü istasyonlarda farklı dönemlerde yani pandemi öncesi ve pandemi sırası şeklinde tekrarlanmıştır.…”

“…As for air pollution studies conducted in Turkey, more time-bound pollutants exchange of (Erbaşlar and Taşdemir, 2005) and their relationship with each other (Erbaşlar and Taşdemir, 2007) is seen to be examined using the regression model. For Turkey calculated air pollution parameters studies by using remote sensing are available in the literature in recent years (Yavaşlı, 2020;Kaplan ve Yiğit Avdan, 2020). The purpose of this study is to examine occurred change in air pollution parameters in the process of coronavirus measures the Eastern Mediterranean.…”