Study of a winter PM episode in Istanbul using the high resolution WRF/CMAQ modeling system

Markakis, K.; Ünal, Alper; Kındap, Tayfun; Poupkou, A.; İncecik, Selahattin; Yenigün, Orhan; Melas, Dimitrios; Theodosi, C.; Mihalopoulos, N.

doi:10.1016/j.atmosenv.2010.05.036

Cited by 64 publications

(29 citation statements)

References 38 publications

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“…However, it is difficult to extract all the parameters from current remote sensing sensors while the ground-observed meteorological data are space limited. The WRF model v3.4.1 [39][40][41] is therefore considered to extract the needed meteorological field information based on the initial and boundary conditions generated by WRF Processing System (WPS) using NCEP FNL Operational Model Global Tropospheric Analyses dataset of 1˝ˆ1˝resolution (http://rda.ucar.edu/dsszone/ds083.2/). The WRF model is a mesoscale numerical weather prediction system designed for both atmospheric research and operational forecast, and serves as a wide range of meteorological applications across scales from tens of meters to thousands of kilometers.…”

Section: Auxiliary Datamentioning

confidence: 99%

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

et al. 2016

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Abstract:Regional haze episodes have occurred frequently in eastern China over the past decades. As a critical indicator to evaluate air quality, the mass concentration of ambient fine particulate matters smaller than 2.5 µm in aerodynamic diameter (PM 2.5 ) is involved in many studies. To overcome the limitations of ground measurements on PM 2.5 concentration, which is featured in disperse representation and coarse coverage, many statistical models were developed to depict the relationship between ground-level PM 2.5 and satellite-derived aerosol optical depth (AOD). However, the current satellite-derived AOD products and statistical models on PM 2.5 -AOD are insufficient to investigate PM 2.5 characteristics at the urban scale, in that spatial resolution is crucial to identify the relationship between PM 2.5 and anthropogenic activities. This paper presents a geographically and temporally weighted regression (GTWR) model to generate ground-level PM 2.5 concentrations from satellite-derived 500 m AOD. The GTWR model incorporates the SARA (simplified high resolution MODIS aerosol retrieval algorithm) AOD product with meteorological variables, including planetary boundary layer height (PBLH), relative humidity (RH), wind speed (WS), and temperature (TEMP) extracted from WRF (weather research and forecasting) assimilation to depict the spatio-temporal dynamics in the PM 2.5 -AOD relationship. The estimated ground-level PM 2.5 concentration has 500 m resolution at the MODIS satellite's overpass moments twice a day, which can be used for air quality monitoring and haze tracking at the urban and regional scale. To test the performance of the GTWR model, a case study was carried out in a region covering the adjacent parts of Jiangsu, Shandong, Henan, and Anhui provinces in central China. A cross validation was done to evaluate the performance of the GTWR model. Compared with OLS, GWR, and TWR models, the GTWR model obtained the highest value of coefficient of determination (R 2 ) and the lowest values of mean absolute difference (MAD), root mean square error (RMSE), and mean absolute percentage error (MAPE).

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Section: Auxiliary Datamentioning

confidence: 99%

A Geographically and Temporally Weighted Regression Model for Ground-Level PM2.5 Estimation from Satellite-Derived 500 m Resolution AOD

et al. 2016

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“…shipping; Poupkou et al, 2008) or of total anthropogenic emission reduction scenarios on urban air quality . Im et al (2010) showed that under favorable meteorological conditions with low mixing heights and limited precipitation, wintertime exceedences in Istanbul can be attributed by more than 90 % to local anthropogenic sources. A European-wide intercomparison study of models with different spatial resolutions, ranging from 5 to 50 km, CityDelta, covered partly the East Mediterranean (Thunis et al, 2007) and evaluated that nitrogen oxides (NO x ) control measures lead to increases in O 3 levels in city centers.…”

Section: Introductionmentioning

confidence: 99%

Impacts of East Mediterranean megacity emissions on air quality

İm

Kanakidou

2012

Atmos. Chem. Phys.

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Abstract.Megacities are large urban agglomerations with intensive anthropogenic emissions that have significant impacts on local and regional air quality. In the present mesoscale modeling study, the impacts of anthropogenic emissions from the Greater Istanbul Area (GIA) and the Greater Athens Area (GAA) on the air quality in GIA, GAA and the entire East Mediterranean are quantified for typical wintertime (December 2008) and summertime (July 2008) conditions. They are compared to those of the regional anthropogenic and biogenic emissions that are also calculated. Finally, the efficiency of potential country-based emissions mitigation in improving air quality is investigated.The results show that relative contributions from both cities to surface ozone (O 3 ) and aerosol levels in the cities' extended areas are generally higher in winter than in summer. Anthropogenic emissions from GIA depress surface O 3 in the GIA by ∼ 60 % in winter and ∼ 20 % in summer while those from GAA reduce the surface O 3 in the GAA by 30 % in winter and by 8 % in summer. GIA and GAA anthropogenic emissions contribute to the fine particulate matter (PM 2.5 ) levels inside the cities themselves by up to 75 % in winter and by 50 % (GIA) and ∼ 40 % (GAA), in summer. GIA anthropogenic emissions have larger impacts on the domain-mean surface O 3 (up to 1 %) and PM 2.5 (4 %) levels compared to GAA anthropogenic emissions (< 1 % for O 3 and ≤ 2 % for PM 2.5 ) in both seasons. Impacts of regional anthropogenic emissions on the domain-mean surface pollutant levels (up to 17 % for summertime O 3 and 52 % for wintertime fine particulate matter, PM 2.5 ) are much higher than those from Istanbul and Athens together (∼ 1 % for O 3 and ∼ 6 % for PM 2.5 , respectively). Regional biogenic emissions are found to limit the production of secondary inorganic aerosol species in summer up to 13 % (non-sea-salt sulfate (nss-SO 2− 4 ) in rural Athens) due to their impact on oxidant levels while they have negligible impact in winter.Finally, the responses to country-based anthropogenic emission mitigation scenarios inside the studied region show increases in O 3 mixing ratios in the urban areas of GIA and GAA, higher in winter (∼ 13 % for GIA and 2 % for GAA) than in summer (∼ 7 % for GIA and < 1 % for GAA). On the opposite PM 2.5 concentrations decrease by up to 30 % in GIA and by 20 % in GAA with the highest improvements computed for winter. The emission reduction strategy also leads to domain-wide decreases in most primary pollutants like carbon monoxide (CO) or nitrogen oxides (NO x ) for both seasons. The results show the importance of long range transport of pollutants for the air quality in the East Mediterranean. Thus, improvements of air quality in the East Mediterranean require coordinated efforts inside the region and beyond.

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“…The sectoral distributions of these emissions is presented in Table 5. Im et al (2010) showed that wintertime PM 10 episodes can be explained almost entirely by the local anthropogenic sources. Kocak et al (2011) confirmed this finding by the PMF analysis conducted on the chemical composition data provided by Theodosi et al (2010).…”

Section: Emission Inventory Preparationsmentioning

confidence: 99%

“…Furthermore, in recent years, there are important efforts on higher resolution (in high spatial (2 × 2 km 2 ) and temporal resolutions) detailed emission inventories of anthropogenic sources studies. Markakis et al (2012) developed high resolution emission inventory for 2007 reference year as annual sectoral distribution of pollutants; and cited by Im et al (2010 and2011) and Kanakidou et al, (2011). The sectoral distributions of these emissions is presented in Table 5.…”

Section: Emission Inventory Preparationsmentioning

confidence: 99%