Journal of Atmospheric Chemistry最新文献

In this study, the aerosol removal coefficients based on ⁷Be, ²¹⁰Pb and ²¹⁰Po radionuclides in the urban air, in Lodz, Poland, were investigated over 3 years, between May 2014 and December 2017. Results representing the summer/warm and winter/cold seasons were applied to quantity and quality estimates of aerosol removal processes. The values for the removal processes were closely dependent on the meteorological conditions; therefore, a set of nine meteorological parameters was employed in the analysis. The multiple regression method was applied to explain the relationship between the removal coefficients of aerosols and independent factors identified using Principal Component Analysis.

The seasonal variation of particulate matter and its relationship with meteorological parameters were measured at five different residential sites in Delhi. Sampling was carried out for one year including all three seasons (summer, monsoon, and winter). The yearly average concentration of particulate matter (PM_2.5) was 135.16 ± 41.34 µg/m³. The highest average values were observed in winter (208.44 ± 43.67 µg/m³) and the lowest during monsoon season (80.29 ± 39.47 µg/m³). The annual average concentration of PM_2.5 was found to be the highest at the Mukherjee Nagar site (242.16 µg/m³ ) during the winter and lowest at (Jawaharlal Nehru University) JNU (35.65 µg/m³) during the monsoon season. The strongest correlation between PM mass and a meteorological parameter was a strong negative correlation with temperature (R2=0.55). All other parameters were weakly correlated (R2<0.2) with PM mass.

Observations of particulate matter less than 10 µm (PM₁₀) were conducted from January to December in 2015 in the Ciuc basin, Eastern Carpathians, Romania. Daily concentrations of PM₁₀ ranged from 10.90 to 167.70 µg/m³, with an annual mean concentration of 46.31 µg/m³, which is higher than the European Union limit of 40 µg/m³. Samples were analyzed for a total of 21 elements. O, C and Si were the most abundant elements accounting for about 85% of the PM₁₀ mass. Source identification showed that the elemental composition of PM₁₀ is represented by post volcanic activity, crustal origin, and anthropogenic sources, caused by the resuspension of crustal material, sea salt and soil dust. The average PM₁₀ composition was 72.10% soil, 20.92% smoke K, 13.84% salt, 1.53% sulfate and 1.02% organic matter. The back-trajectory analysis showed that the majority of PM₁₀ pollution comes from the West, Southwest and South.

Pre and Post-Monsoon levels of ambient SO₂, NO₂, PM_2.5 and the trace metals Fe, Cu, etc. were measured at industrial and residential regions of the Kochi urban area in South India for a period of two years. The mean PM_2.5, SO₂ and NO₂ concentrations across all sites were 38.98 ± 1.38 µg/m³, 2.78 ± 0.85 µg/m³ and 11.90 ± 4.68 µg/m³ respectively, which is lower than many other Indian cities. There was little difference in any on the measured species between the seasons. A few sites exceeded the NAAQS (define acronym and state standard) and most of the sites exceeded WHO (define acronym and state standard) standard for PM_2.5. The average trace metal concentrations (ng/m³) were found to be Fe (32.58) > Zn (31.93) > Ni (10.13) > Cr (5.48) > Pb (5.37) > Cu (3.24). The maximum concentration of trace metals except Pb were reported in industrial areas. The enrichment factor, of metals relative to crustal material, indicated anthropogenic dominance over natural sources for the trace metal concentration in Kochi’s atmosphere. This work demonstrates the importance of air quality monitoring in this area.

Black carbon (BC) along with PM_2.5 (fine particular matters) plays an important role in the assessment health effect of human beings. Winter season campaign measurements carried out for BC concentrations by using 7 different wavelengths such as 370, 470, 520, 590, 660, 880, and 950 nm, handy aethalometer (AE-33, Magee Scientific, USA), at two different locations i.e., National Institute of Technology, Jamshedpur (NIT J) and Sakchi, Jamshedpur (SAK J), in eastern India. During the study period, the mass concentration of BC varies from 4.19 µgm⁻³ to 15.36 µgm⁻³, with an average mean of 8.88 ± 2.40 µgm⁻³ in NIT J and SAK J, the mass concentration of BC varies from 6.3 µgm⁻³ to 13.48 µgm⁻³, with an average mean of 10.29 ± 1.58 µgm⁻³. However, the concentration of PM_2.5 varies from 102.98 µgm⁻³to 198.21 µgm⁻³, with an average mean of 155.82 ± 29.98 µgm⁻³ in NIT J and SAK J, the concentration of PM_2.5 varies from 110.83 µgm⁻³ to 207.65 µgm⁻³, with an average mean of 169.14 ± 22.40 µgm⁻³. It was reported that SAK J has a higher BC concentration compared to NIT J. This was due to heavy traffic load and dense population in SAK J. Backward Trajectories were seen that the airborne particulate matter came from differerajeshnt directions. According to the diagnostic ratio analysis of BC, it was observed that most of the BC mass concentrations come from fossil-fuel (69.70%) followed by wood-burning (30.30%) in a particular place. The overall health risk assessment of BC concentration observed during the study period was 26.70, 13.95, 24.95 and 51.32 at NIT J as well as 32.07, 16.72, 29.95 and 61.87 at SAK J, the passive cigarettes comparable concerning the risk of CVM, LC, LBW, and PLEDSC, respectively.

Non-methane volatile organic compounds (NMVOCs) play key roles in local and regional atmospheric chemistry as precursors for the production of ozone and secondary organic aerosols. Ambient air C₂-C₅ NMVOCs were measured at a tropical forest site in the central Western Ghats and urban site of Udaipur in India during the late monsoon period of 2016–17 and 2015, respectively. In the Western Ghats, air samples were collected from the protected Bhagwan Mahaveer Sanctuary. Ethene, propene, and isoprene were the dominant biogenic compounds with mean concentrations of 4.8 ± 2, 1.6 ± 0.66 and 1.05 ± 0.43 ppb, respectively. The concentrations of anthropogenic compounds such as propane and pentane were significantly lower than those of light alkenes. The contributions of ethene and propene among different NMVOCs were ~ 44 and 14%, respectively. However, the contributions of isoprene were highly variable of 3–22%. The tight correlation (r² = 0.90) between the mixing ratios of ethene and propene and their ratio indicates their common formation and emission mechanisms. The molar emission ratio of ethene/propene (2.9 ± 0.17 ppb ppb⁻¹) was comparable to those measured at other biogenic sites of Asia while higher than those reported for mid-latitude sites. The concentrations of light alkenes and isoprene at the Western Ghats were 4–5 times higher than those measured in an urban environment in the same season. The higher ozone formation potentials and Propylene-Equivalent concentrations of alkenes and isoprene than those of other NMVOCs indicate important implications of biogenic emissions on ozone photochemistry in the forest regions of India.

Graphical abstract

Ethanol concentrations measured in 178 event-based wet deposition samples collected at five Atmospheric Integrated Research Monitoring Network (AIRMoN) sites in the Eastern US between February 2018 to January 2019 ranged from below the detection limit of 19 nM to 4160 nM. The volume weighted average ethanol concentration at each site ranged from 237 nM to 1375 nM. No significant correlation was observed between ethanol and any analytes (NH₄⁺, Cl⁻, SO₄²⁻, NO₃⁻, Ca²⁺, Na⁺, Mg²⁺, K⁺, PO₄³⁻ and H⁺) at all sites in the study, likely due to differences in atmospheric residence time and emission sources. Significant seasonal variations of ethanol were not observed for any sites, however notably higher concentrations in the winter vs. summer and growing vs. nongrowing seasons suggest photochemical dynamics play a substantial role in seasonal atmospheric concentrations. The AIRMoN concentrations were combined with previous measured ethanol wet deposition data to produce an updated empirical-based global wet deposition ethanol flux of 3.7 ± 1.8 Tg/yr (n = 1051). The carbon isotopic composition of a subset of samples ranged from −25.8 to −15.7‰ with an average of (−20.4 ± 4.0‰, n = 6). Isotope mixing model results indicate an approximately equivalent contribution of biogenic (55.2 ± 14.4%) and anthropogenic (44.8 ± 14.4%) sources of ethanol to the atmosphere over all collections sites. Results provide atmospheric scientists, environmental chemists and policy makers with baseline U.S. atmospheric ethanol concentrations in order to help determine the impact of future ethanol fuel production and to help quantify the wet deposition ethanol sink.

Surfactants exist in atmospheric aerosols mixed with inorganic salts and can significantly influence the formation of cloud droplets due to bulk–surface partitioning and surface tension depression. To model these processes, we need continuous parametrizations of the concentration dependent properties of aqueous surfactant–salt solutions for the full composition range from pure water to pure surfactant or salt. We have developed density functions based on the pseudo-separation method and Young’s mixing rule for apparent partial molal volumes for solutions that mimic atmospheric droplets of marine environments. The developed framework requires only model parameters from binary water–salt and water–surfactant systems and includes the effect of salinity on micellization with composition-dependent functions for the critical micelle concentration (CMC). We evaluate different models and data available in the literature to find the most suitable representations of the apparent partial molal volume of sodium chloride (NaCl) in aqueous solutions and the CMC of selected atmospheric and model surfactants in pure water and aqueous NaCl solutions. We compare model results to experimental density data, available in the literature and obtained from additional measurements, for aqueous solutions containing one of the ionic surfactants sodium octanoate, sodium decanoate, sodium dodecanoate or sodium dodecylsulfate mixed with NaCl in different relative ratios. Our model follows the experimental trends of increasing densities with increasing surfactant concentrations or increasing surfactant–salt mixing ratios both, below and above the CMC, capturing the effect of the inorganic salt on the surfactant micellization.