Investigation of the chemical nature of precipitation and source apportionment of its constituents in Tehran metropolis, Iran.

Precipitation is a key process for purifying the atmosphere of pollutants. However, precipitation chemistry is also a significant environmental catastrophe on a global scale. Tehran Metropolitan Area, Iran's capital, is one of the world's most polluted cities. Nonetheless, little effort has been paid to determining the chemical composition of precipitation in this polluted metropolis. The chemical components and likely sources of trace metals and water-soluble ions in precipitation samples collected from 2021 to 2022 at an urban location in Tehran, Iran, were investigated in this study. The pH of the rainwater samples varied from 6.330 to 7.940 (mean 7.313, volume weighted mean (VWM) 7.523). The following is the order of the VWM concentration of main ions: Ca2+ > HCO3- > Na+ >SO42- > NH4+ > Cl- > NO3- > Mg2+> K+> F-. Furthermore, we discovered that VWM concentrations for trace elements are modest, with the exception of Sr (39.104 eq L-1). The primary neutralizing species for precipitation acidity were Ca2+ and NH4+. Vertical feature mask (VFM) diagrams derived from cloud-aerosol lidar and infrared pathfinder satellite observation (CALIPSO) track data indicated that polluted dust was the most common pollutant in the Tehran sky that might contribute significantly to the neutralization of precipitation. A study of species concentration ratios in seawater and the earth's crust indicated that virtually all Se, Sr, Zn, Mg2+, NO3-, and SO42- were anthropogenic. While Cl- was largely obtained from sea salt, K+ was obtained from both the earth's crust and the sea, with the earth's crust playing a larger role in K+. The earth's crust, aged sea salt, industry, and combustion processes were all verified as sources of trace metals and water-soluble ions by positive matrix factorization analysis.

Study of mid-latitude retrieval XCO2 greenhouse gas: Validation of satellite-based shortwave infrared spectroscopy with ground-based TCCON observations.

Carbon dioxide (CO2) is a major greenhouse gas. This study investigated the performance of three common algorithms, namely NIES, ACOS, and Remo Tec (SRFP). These algorithms were compared using GOSAT observation satellite data with reference data obtained from TCCON during the period 2009-2021. According to statistical evaluation, the SRFP and NIES algorithms achieved the lowest and highest correlation values of the 13 year (2009_2021) average of all sites, respectively. The average bias error values of NIES and ACOS was estimated to be less than that of SRFP approximately 0.5 ppm, while the bias within SRFP was of about 2 ppm. Comparing the RMSE and CRMS error values showed that the highest and lowest error values were related to the SRFP and NIES algorithms respectively, which were 0.37-1.67 and ppm 1.46-7.9. The researchers also compared them with monthly time changes based on ground measurements, and observed a time series of CO2 concentration changes that well matched the trend of gas concentration values at ground stations obtained by NIES algorithm. The results showed that in most cases NIES was an effective algorithm to retrieve carbon dioxide gas concentrations, allowing the researchers to identify the main sources of greenhouse gas emissions in different areas. The clustering result in the study area showed that the continental CO2 columnar concentration has a specific seasonal cycle, with the maximum and minimum values appearing in winter-early spring and spring-late summer, respectively. In conclusion, cluster analysis can classify the surface CO2 column concentration values and determine the spatial distribution pattern of CO2.

Development of reduction scenarios for criteria air pollutants emission in Tehran Traffic Sector, Iran.

Transport-related pollution as the main source of air pollution must be reduced in Tehran mega-city. The performance of various developed scenarios including BAU (Business As Usual) as baseline scenario, ECV (Elimination of carburetor equipped Vehicle), NEM (New Energy Motorcycles), HES (Higher Emission Standard), VCR (Vehicle Catalyst Replacement), FQE (Fuel Quality Enhancement), DPF (Diesel Particulate Filter) and TSA (Total Scenarios Aggregation) are evaluated by International Vehicle Model up to 2028. In the short term, the ECV, VCR, and FQE scenarios provided high performance in CO, VOCs and NOx emissions control. Also FQE has an excellent effect on SOx emission reduction (86%) and DPF on PM emissions (20%). In the mid-term, the VCR, ECV, and FQE scenarios were presented desirable mean emission reduction on CO, VOCs, and NOx. Moreover, NOx emission reduction of DPF scenario is the most common (14%). Again FQE scenario proves to have great effect on SOx emission reduction in mid-term (86%), DPF and HES scenarios on PM (DPF: 49% and HES: 17%). Finally for the long term, VCR, ECV, FQE, and NEM scenarios were shown good performance in emission control on CO, VOCs and NOx. For SOx only FQE has a good effect in all time periods (FQE: 86%) and DPF and HES scenarios have the best effect on PM emission reduction respectively (DPF: 51% and HES: 27%) compared with BAU scenario. However, DPF scenario increases 12% SOx emission in long-term (2028). It can be generally concluded that VCR and ECV scenarios would achieve a significant reduction on gaseous pollutants emission except for SOx in general and FQE scenarios have desirable performance for all gaseous pollutants in the short term and also for SOx and VOCs in long term. In addition, the DPF and HES would be desirable scenario for emission control on PM in Tehran Traffic Sector.