Compilation of a GIS based high spatially and temporally resolved emission inventory for the greater Istanbul area

Markakis, Konstantinos; Im, Ulas; Unal, Alper; Melas, Dimitrios; Yenigun, Orhan; Incecik, Selahattin
January 2012
Atmospheric Pollution Research;Jan2012, Vol. 3 Issue 1, following p112
Academic Journal
Emission inventories are a fundamental input to atmospheric chemical transport models (CTMs). As the latter become increasingly demanding, modern inventories began to provide much more information (high spatial and temporal disaggregation, more chemical compounds etc). In this study we present a computational approach, an emission processing kernel that is used to compile a high spatially and temporally resolved emission inventory for the anthropogenic sources covering the Greater Istanbul Area (GIA) for the reference year 2007. The emission processor is used to produce emissions for a 92 × 57 km area covering the GIA with 2 km grid resolution. The emission inventory has high temporal resolution, covering monthly, weekly and diurnal processing and includes CO, NOx, SOx, NH3, and chemically speciated PM10, PM2.5 and NMVOCs emissions. PM10 and PM2.5 are chemically split into organic carbon, elemental carbon, sulfates, nitrates, ammonium and other particles while NMVOCs are chemically speciated into 23 chemical compounds. The compilation process includes the use of various activity information and statistical data that were gathered from local official authorities and experts, measurements, published studies for the region or extracted from pre-existing databases. The results indicate that the road transport sector is the main contributor to the emissions in the area, whereas residential combustion (for SOx) and solvent use (for NMVOCs) are also important source categories. Industrial combustion is found out to be the main SOx emitter. The temporal calculations show that monthly distributions follow the seasonal variation for most of the pollutants with higher emissions in winter time. Diurnal calculations show that the profile fits with the rush hours due to the highest contribution of traffic emissions.


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