Improvement of the anthropogenic emission rate estimate in Ulaanbaatar, Mongolia, for 2020-21 winter.

Ulaanbaatar (UB), the fast-growing capital of Mongolia, is known for its world's worst level of particulate matter (PM) concentrations in winter. However, current anthropogenic emission inventories over the UB are based on data from more than fifteen years ago, and satellite observations are scarce because UB is in high latitudes. During the winter of 2020-21, the first period of the Fine Particle Research Initiative in East Asia considering the National Differences (FRIEND), several times higher concentrations of PM in UB compared to other urban sites in East Asia were observed but not reproduced with a chemical transport model mainly due to the underestimated anthropogenic emissions. Therefore, we devised a method for sequentially adjusting emissions based on the reactivity of PM precursors using ground observations. We scaled emission rates for the inert species (CO, elemental carbon (EC), and organic carbon (OC)) to reproduce their observed ambient concentrations, followed by SO2 to reproduce the concentration of SO42-, which was examined to have the least uncertainty based on the abundance of observed NH3, and finally NO and NH3 for NO3-, and NH4+. This improved estimation is compared to regional inventories for Asia and suggests more than an order of magnitude increase in anthropogenic emissions in UB. Using the improved emission inventory, we were able to successfully reproduce independent observation data on PM2.5 concentrations in UB in December 2021 from the U.S. Embassy. During the campaign period, we found more than 50% of the SO42-, NO3-, and NH4+ increased in UB due to the improvement could travel to Beijing, China (BJ), and about 20% of the SO42- could travel to Noto, Japan (NT), more than 3000 km away. Also, the anthropogenic emissions in UB can effectively increase OC, NO3-, and NH4+ concentrations in BJ when Gobi dust storms occur.

Parameterization of below-cloud scavenging for polydisperse fine mode aerosols as a function of rain intensity.

The below-cloud aerosol scavenging process by precipitation is one of the most important mechanisms to remove aerosols from the atmosphere. Due to its complexity and dependence on both aerosol and raindrop sizes, wet scavenging process has been poorly treated, especially during the removal of fine particles. This makes the numerical simulation of below-cloud scavenging in large-scale aerosol models unrealistic. To consider the slip effects of submicron particles, a simplified expression for the diffusion scavenging was developed by approximating the Cunningham slip correction factor. The derived analytic solution was parameterized as a simple power function of rain intensity under the assumption of the lognormal size distribution of particles. The resultant approximated expression was compared to the observed data and the results of previous studies including a 3D atmospheric chemical transport model simulation. Compared with the default GEOS-Chem coefficient of 0.00106R0.61 and the observation-based coefficient of 0.0144R0.9268, the coefficient of a and b in Λm = aRb spread in the range of 0.0002- 0.1959 for a and 0.3261- 0.525 for b over a size distribution of GSD of 1.3-2.5 and a geometric mean diameter of 0.01- 2.5 µm. Overall, this study showed that the scavenging coefficient varies widely by orders of magnitude according to the size distribution of particles and rain intensity. This study also demonstrated that the obtained simplified expression could consider the theoretical approach of aerosol polydispersity. Our proposed analytic approach showed that results can be effectively applied for reduced computational burden in atmospheric modeling.

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 µg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

Speciation and source identification of organic compounds in PM10 over Seoul, South Korea.

Seventy three individual organic compounds in the atmospheric particulate matter with an aerodynamic diameter of less than or equal to a nominal 10 µm (PM10) over Seoul were identified and quantified from April 2010 to April 2011 using gas chromatography/mass spectrometry (GC/MS). These organic compounds were classified into five groups, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), mono-carboxylic acids, di-carboxylic acids (DCAs), and sugars based on their chemical structures and properties. The organic compounds showed higher seasonal average concentrations from fall to winter than from spring to summer due to source strength, except some organic compounds among mono-carboxylic acids, DCAs, sugars such as undecanoic acid, methylmalonic acid, and fructose. Through qualitative data analysis using seasonal concentration variations and relevant diagnostic parameters, it was found that (1) anthropogenic sources such as combustion of fossil fuel and biomass burning attributed more to the formation of the organic aerosols than biogenic sources, and (2) the ambient level of n-alkanes, PAHs, and some compounds of DCAs and sugars was elevated in winter due to the increased primary emissions and larger transport from outside of the organic compounds in winter.

Relationship between particulate matter measured by optical particle counter and mortality in Seoul, Korea, during 2001.

This study was performed to examine the relationship between particulate matter exposure and mortality in Seoul, Korea, during the year 2001. Particulate matter data were collected using an optical particle counter (OPC) and national monitoring stations in Seoul. The size-resolved aerosol number concentrations of particles 0.3-25 microm in diameter and mass concentrations of PM10 (particulate matter less than 10 microm in diameter) and PM2.5 (less than 2.5 microm in diameter) were measured. Meteorological data such as air temperature and relative humidity were provided by the Korea Meteorological Administration. Daily mortality was analyzed using a generalized additive Poisson model, with adjustment for the effects of seasonal trend, air temperature, humidity, and day of the week as confounders, in a nonparametric approach. We used S-Plus for all analyses. Model fitness, using loess smoothing, was based on stringent convergence criteria to minimize the default convergence criteria in the S-Plus generalized additive models module. The IQR (interquartile range) increase of fine particle (10.21 number/cm3 [the total number of particles per cubic centimeter]) and respiratory particle (10.38 number/cm3) number concentration were associated with a 5.73% (5.03%-6.45%) and a 5.82% (5.13%-6.53%) increase in respiratory disease-associated mortality, respectively. Mortality effects in the elderly (aged over 65 years) were increased by more than 0.51% to 2.59%, and the relative risks of respiratory-related and cardiovascular-related mortality were increased by 0.51% to 1.06% compared with all-cause mortality. These findings support the hypothesis that air pollution is harmful to sensitive subjects, such as the elderly, and has a greater effect on respiratory- and cardiovascular-related mortality than all-cause mortality. However, our results using OPC data did not support the hypothesis that PM2.5 would have more adverse health effects than PM10 in number concentration but not in mass concentration.