The global increase in population has led to higher emissions from livestock and synthetic fertilizers. This study investigates the impact of agricultural ammonia emissions on NH3 concentrations and provides insights into PM2.5 levels and their components in agriculturally intensified areas. We developed a bottom-up emission inventory focused on fertilizer application over croplands and livestock, instead of relying on the EMEP database. This approach utilized an improved spatial and temporal distribution of these emissions. We compared annual total NH3 emissions from livestock and fertilizer, estimated at 598.5 kt and 187.2 kt in the EMEP inventory (Base case), and 245.2 kt and 536 kt in the bottom-up inventory (Scenario case). Using the CMAQ modelling framework, we estimated atmospheric concentrations for both cases and evaluated the model results by comparing them with IASI-NH3 satellite retrievals. This comparison revealed significant differences in column concentrations between the Base and Scenario cases, with the Scenario case showing substantial improvement. Over a period of seven months, which contributed 80 % of the annual agricultural emissions for the Scenario case, the domain averages of NH3 were 3.02 × 1015, 4.15 × 1015, and 4.17 × 1015 molecules/cm2 for the Base and Scenario cases and IASI-NH3, respectively. The Scenario case closely matched IASI measurements, indicating a more accurate representation of NH3 emissions and concentrations. This enhanced reliability underscores the effectiveness of the bottom-up inventory approach. Additionally, using the CMAQ model, we found that in the IASI hotspots, the averages were 1.67 μg/m3 for sulfate, 0.57 μg/m3 for nitrate, and 0.62 μg/m3 for ammonium, with a total PM2.5 mean of 10.45 μg/m3.

In the summer of 2021, unusually intense wildfires happened in southwestern Turkey, especially in Antalya and Mugla with destroying effects on forests, wildlife, and communities residing there. This study aims to understand the air quality impacts of these wildfires. A time period (June-September 2021) covering pre-, fire, and post-periods was investigated using NO2 ground-based observations and TROPOMI CO, HCHO, and NO2 satellite retrievals along with VIIRS FRP. Eight fire regions were selected for detailed analysis in Antalya (An-1-2), Mugla (Mu-1-4), and Mersin (Me-1-2). The highest fire intensity was found in An-1 and followed by Mu-1. CO also showed strongest signals over An-1 (6.73 x 10(18) molecules/cm(2)) with highest levels (1.44 x 10(19) molecules/cm(2)) in the downwind of the wildfires. NO2 showed fire signals in or in very close proximity to the fire regions with strongest signal and largest impact area in An-1 (>8.75 x 10(16) molecules/cm(2)). HCHO showed a different pattern due to HCHO undergoing chemical production and loss in the wildfire plume. HCHO highest levels were not observed over the fire regions, but inside the transported plume with maximum levels for An-1 (3.60 x 10(16) molecules/cm(2)) and for An-2 (3.23 x 10(16) molecules/cm(2)). CO and NO2 increase continued not only in fire, but also in post-fire period, whereas HCHO levels indicated decreases in post-fire compared to pre-fire period. The increases in column concentrations in fire period ranges from 17.6 to 123.1% for CO, 40.6-116.5% for HCHO and 34.4-294.5% for NO2. Higher increases were observed over the Mediterranean Sea especially for CO. Correlations with FRP indicated highest correlations with CO and NO2 and low correlations with HCHO. This study showed that the capability of sparse, ground-level air quality monitoring stations to capture wildfire impacts are limited in the region, because the plume transport was driven by wind direction and wildfire smoke plumes are elevated due to buoyancy. Satellite retrievals are better for capturing the wildfire plume transport and estimating the overall air quality impacts of wildfires.

In this study, NH3 and NMVOC emissions from agricultural activities were updated and compared with EMEP inventory for a region known for agricultural activities in Turkey. An advanced air quality model, CMAQ, was used to determine the spatio-temporal distribution of NH3 and NMVOC, which have seasonal patterns. Model was performed for base (EMEP 2018 emissions) and scenario (updated 2019 emissions) cases and 2018 summer months with high-resolution grid size. Moreover, secondary particle formation over the domain was identified. This study is significant due to the agriculture emissions to be updated and re-gridded using high-resolution CORINE land use data and examined of pollutants which are climate dependent. EMEP agriculture emissions were 955.92 kt/annual and 486.60 kt/annual (Turkey totals), and 284.35 kt/annual and 227.54 kt/annual (Turkey emissions in the domain) for NH3 and NMVOCs, respectively; updated agriculture emissions were found as 579.58 kt/annual and 289.97 kt/annual (Turkey totals), and 195.01 kt/annual and 98.66 kt/annual (Turkey emissions in the domain) for NH3 and NMVOC. Not only the emissions were different in overall quantities, but also there were spatial differences in concentrations between cases. For NH3, the largest source of which is agriculture, monthly averaged concentration differences were found up to 60% in some areas of the domain due to the distribution of total emissions over agricultural areas unlike the EMEP spatial distribution methodology. VOC emissions were overestimated in EMEP inventory; thus, a difference had also become in concentrations between two cases. Highest concentrations were found in July for PM2.5, PM2.5(OC), PM2.5(SO4), and PM2.5(NH4).

The energy demand is increasing day by day, although the installation and operation of coal-fired power plants slowly fade out throughout the world, it is still increasing in Turkey. Air pollutant emissions from public power sector, mainly composed of power plants contribute significantly to Turkey national totals (70.4% SO2, 38.9% NOx, 9.8% PM2.5). Along with the emission estimation, the temporal and spatial distribution of these emissions are also crucial for accurate simulations with low uncertainty in air quality modeling. Sector-specific profiles are widely used as temporal profiles to input hourly emissions for modeling. In this study, a new temporal profile was established using hourly electricity generation of a large-capacity coal-fired power plant: Afsin Elbistan Power Plant (AEPP). The WRF/CMAQ model was run for two cases; one with sector-specific temporal profile (base case) and another with facility-specific temporal profile (scenario case). EMEP 2018 emissions were used and model was run for 2018 summer months (JJA) with high-resolution 4 × 4 km2 grid size. Temporal profile comparisons for the summer period indicated maximum differences between 00.00 and 07.00 h with higher ratios for scenario case. Day of the week comparison showed consistently high differences on the weekends, especially on Sundays (average of +40.4% for JJA) with base case always allocating lower emissions on Saturdays and Sundays than scenario case. The largest daily differences were observed between 14 and 22 July where the highest was on 22 July (+91%) which were again weekend days. Hourly concentrations over AEPP showed the highest differences in July with scenario case being usually higher. The peak concentrations and concentration differences usually occur around midnight (00.00). The days with the highest hourly differences were on 18 July (1614 ppbV for SO2, 26 ppbV for NO2, and 9 ppbV for VOC) for Layer 7, which is around midnight.

This study focuses on investigation the spatial distribution of pollutants in Çorlu Stream is the highest industrialized tributary of Ergene River which is one of the most polluted rivers in Turkey. A total of 250 conventional, metal and micropollutants were scanned at eleven sampling locations for four seasons in Çorlu Stream of Ergene River. At these locations, total of 126, 124, 99 and 107 pollutants were detected at least one sampling location in summer, fall, winter and spring, respectively. Four micropollutants which are associated with textile industry were detected in every location in all seasons. Cluster Analysis found four main clusters for sampling locations from most polluted to low polluted, and six cluster for pollutants ranging from conventional to unique pollutants. Principal Component Analysis identified six components for every season explaining more than 80% of the variation in each. PCA results indicated the impact of textile, leather, metal industries and appliance-electronics production in the Çorlu Region.

Climate change has several impacts on our Earth. Even though wildfires are natural processes to sustain structure of an ecosystem, there is a significant increase in the global fire cases and their extent in the recent years caused by the climate change. These wildfires have important impacts on air quality, climate and relatedly public health. Copernicus Atmospheric Monitoring Service (CAMS) indicated that Siberia, North America, and the Mediterranean regions are greatly impacted by wildfires and the intensities of these fires are expressed as Fire Radiative Power (FRP). Effect of wildfires can also be observed with gas pollutant satellite retrievals of CO, NO2, and HCHO which is an important volatile organic carbon (VOC).Turkey was challenged with wildfires that result in the destruction of forests, the death of animals and devastating impacts on local people in 2021. CAMS Global Fire Assimilation System (GFAS) indicated that the worst fire case observed in Turkey compared with other Mediterranean countries. Global Forest Watch fire counts showed that, fire counts reached up to 695 and 385 in summer (between June-August) 2021 for Antalya and Mugla provinces, respectively. However, fire counts did not exceed 165 fires in the summer season for either Antalya or Mugla in the last five years. Moreover, there was a significant increase in fires in the forested lands for Mersin province as well. Fire counts reached up to 171 per day (31st August) in Antalya province and fire smokes were observable from MODIS Corrected Reflectance images in the fire period. In addition, air pollutants caused by these fires were observable with high resolution TROPOMI retrievals.In this study, multi-pollutant satellite retrievals were used to investigate the wildfires air quality impacts on the Southwestern Turkey. VIIRS S-NPP Fire Radiative Power product and TROPOMI CO, NO2, and HCHO, products were used to analyze impacts of these extreme wildfire cases. Products were processed spatially and temporally for two months (July-August 2021). A specific attention was given on period of 28th July-12th August. A 1×1 km2 gridded domain covering the impacted region was selected to investigate the spatial distribution of these pollutants. 29th and 31st of July were the days where the impacts of wildfires were analyzed specifically. Wind speed and direction were used to understand the relation between meteorological conditions and the pollution distribution caused by the wildfires. Aerosol signals will be also investigated using MODIS aerosol optical depth (AOD) and TROPOMI aerosol index (AI) retrievals.

This study focuses on the spatial distribution and temporal changes of NO2 and SO2 pollution over large point sources using high-resolution TROPOMI retrievals, and aims to find a correlation between the retrievals and electricity production. SO2 retrievals showed highest signals over power plants, whereas NO2 retrievals showed highest signals over large cities. Daily and monthly time series of NO2 and SO2 for the selected nine coal power plants were compared with electricity production for two years. Correlations between NO2, SO2, and electricity production were calculated for daily and monthly averages for every power plant. Then, the monthly averages with more than 23 days data (~75%, May-October) were used for correlation analysis. Highest correlations were observed for lignite-fired Afsin Elbistan Power Plant with highest total capacity in Turkey. The monthly correlations for May-October were found as 0.71 for NO2, 0.84 for SO2 for Afsin Elbistan Power Plant, as 0.45 for NO2 and 0.43 for SO2 for all selected power plants. Temporal changes in electricity production can be captured on monthly-basis, however, the correlations were lower on daily-basis. Point sources located close to land-sea boundaries and multiple sources located in the same region were poorly captured using satellite retrievals.