1. Fig5. Aero_Jaco_effect_new2.png
    Title
    Sensitivity of HCHO column measurements to temporal variation of AMF
    Authors
    Kwon et al. (2017)
    Picture caption
    (a) Differences between AMFh and AMFm values and relative contributions to them by the temporal changes of (b) HCHO profiles, (c) aerosol optical properties, and (d) aerosol vertical distributions.
    Summary
    AMFh (hourly AMF) is smaller than AMFm (monthly AMF) over northeastern China (blue box), whereas the former is higher than the latter in the middle of eastern China (red box). Pronounced differences shown over China appear to correlate significantly with the effect of aerosols, whose optical properties (Fig. (c)) and vertical distributions (Fig. (d)) change with time. In particular, the decrease of AMF in the north results from decreased HCHO absorption within and below aerosol layers (a shielding effect). Aerosol profile effects are evident over the red box where the increment of AMF occurs. The resulting change of AMF is owing to HCHO above aerosol layers (an enhancement effect).
    1. 그림1.jpg
    Title
    Efficacy of dust aerosol forecasts for East Asia using the adjoint of GEOS-Chem with observations
    Authors
    Jaein I. Jeong, Rokjin J. Park
    Picture caption
    (top) Flowchart of the proposed adjoint framework, (bottom) Observed versus simulated hourly surface PM10 concentrations over the Korea Peninsula on May 24–27, 2007.
    Summary
    Based on our adjoint model constrained by observations for the whole period of each event, the reproduction of the spatial and temporal distributions of observations over East Asia was substantially improved. We then examine the efficacy of the data assimilation system for daily dust storm forecasts based on the adjoint model including previous day observations to update the initial condition of the forward model simulation for the next day. The forecast results successfully captured the spatial and temporal variations of ground-based observations in downwind regions, indicating that the data assimilation system with ground-based observations effectively forecasts dust storms, especially in downwind regions.
    1. FigB.PNG
    Title
    Dissimilar effects of two El Niño types on PM2.5 concentrations in East Asia
    Authors
    Jaein I. Jeong, Rokjin J. Park and Sang-Wook Yeh
    Picture caption
    Composite spatial patterns of anomalous seasonal surface PM2.5 concentration for C- and E-types
    Summary
    Based on the Oceanic Niño Index, 10 El Niño events occurred for the past three decades (1980‒2014). We then classified the 10 El Niño events into 6 central Pacific El Niño (C-type) and 4 eastern Pacific El Niño (E-type) to examine the different roles of two El Niño types in determining seasonal surface PM2.5 concentrations in East Asia. We find opposite impacts on the seasonal surface PM2.5 concentrations depending on two El Niño types, such that the surface PM2.5 concentrations during the E-type period are higher than the climatological mean value, especially in northern East Asia. The peak increase of as much as 20% occurs in winter and is sustained until the following spring. However, the C-type period shows a decrease in seasonal PM2.5 concentrations in northern East Asia compare to the climatological mean, and the peak decrease of as much as 10% occurs in the following spring. The different of two El Niño types also have dissimilar impacts on surface PM2.5 concentrations in southeastern China.
    1. 스크린샷 2019-02-20 오전 10.58.37.png
    Title
    Impacts of local vs. trans-boundary emissions on PM2.5 exposure in South Korea
    Authors
    Jinkyul Choi, Rokjin J. Park, et al.
    Picture caption
    Figure shows spatial distributions of contributions from the five most important emission sources to population exposure to PM2.5 in South Korea during the KORUS-AQ: (b) anthropogenic NH3, (c) NOx, (d) SO2, (e) OC, and (f) BC.
    Summary
    High concentrations of PM2.5 have become a serious environmental issue in South Korea, which ranked 1st or 2nd among OECD countries in terms of population exposure to PM2.5. Quantitative understanding of PM2.5 source attribution is thus crucial for developing efficient air quality mitigation strategies. Here we use a suite of extensive observations ofPM2.5 and its precursors concentrations during the international KORea-US cooperative Air Quality field study in Korea (KORUS-AQ) in May–June 2016 to investigate source contributions to PM2.5 in South Korea under various meteorological conditions. For the quantitative analysis, we updated a 3-D chemical transport model, GEOS-Chem, and its adjoint with the latest regional emission inventory and other recent findings. The updated model is evaluated by comparing against observed daily PM2.5 and its component con- centrations from six ground sites (Bangnyung, Bulkwang, Olympic park, Gwangju, Ulsan, and Jeju). Overall, simulated concentrations of daily PM2.5 and its components are in a good agreement with observations over the peninsula. We conduct an adjoint sensitivity analysis for simulated surface level PM2.5 concentrations at five ground sites (except for Bangnyung because of its small population) under four different meteorological con- ditions: dynamic weather, stagnant, extreme pollution, and blocking periods. Source contributions by regions vary greatly depending on synoptic meteorological conditions. Chinese contribution accounts for almost 68% of PM2.5 in surface air in South Korea during the extreme pollution period of the campaign, whereas an enhanced contribution from domestic sources (57%) occurs for the blocking period. Results from our sensitivity analysis suggest that the reduction of domestic anthropogenic NH3 emissions could be most effective in reducing po- pulation exposure to PM2.5 in South Korea (effectiveness=14%) followed by anthropogenic SO2 emissions from Shandong region (effectiveness=11%), domestic anthropogenic NOx emissions (effectiveness=10%), anthropogenic NH3 emissions from Shandong region (effectiveness=8%), anthropogenic NOx emissions from Shandong region (effectiveness=7%), domestic anthropogenic OC emissions (effectiveness=7%), and do- mestic anthropogenic BC emissions (effectiveness=5%).