The MIX inventory is developed for the years 2008 and 2010 to support the Model Inter-Comparison Study for Asia (MICS-Asia) and the Task Force on Hemispheric Transport of Air Pollution (TF HTAP) by a ...mosaic of up-to-date regional emission inventories. Emissions are estimated for all major anthropogenic sources in 29 countries and regions in Asia. We conducted detailed comparisons of different regional emission inventories and incorporated the best available ones for each region into the mosaic inventory at a uniform spatial and temporal resolution. Emissions are aggregated to five anthropogenic sectors: power, industry, residential, transportation, and agriculture. We estimate the total Asian emissions of 10 species in 2010 as follows: 51.3 Tg SO2, 52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC (non-methane volatile organic compounds), 28.8 Tg NH3, 31.7 Tg PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2. Emissions from China and India dominate the emissions of Asia for most of the species. We also estimated Asian emissions in 2006 using the same methodology of MIX. The relative change rates of Asian emissions for the period of 2006–2010 are estimated as follows: −8.1 % for SO2, +19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC, +1.7 % for NH3, −3.4 % for PM10, −1.6 % for PM2.5, +5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Model-ready speciated NMVOC emissions for SAPRC-99 and CB05 mechanisms were developed following a profile-assignment approach. Monthly gridded emissions at a spatial resolution of 0.25° × 0.25° are developed and can be accessed from http://www.meicmodel.org/dataset-mix.
We present a global tabulation of black carbon (BC) and primary organic carbon (OC) particles emitted from combustion. We include emissions from fossil fuels, biofuels, open biomass burning, and ...burning of urban waste. Previous “bottom‐up” inventories of black and organic carbon have assigned emission factors on the basis of fuel type and economic sector alone. Because emission rates are highly dependent on combustion practice, we consider combinations of fuel, combustion type, and emission controls and their prevalence on a regional basis. Central estimates of global annual emissions are 8.0 Tg for black carbon and 33.9 Tg for organic carbon. These estimates are lower than previously published estimates by 25–35%. The present inventory is based on 1996 fuel‐use data, updating previous estimates that have relied on consumption data from 1984. An offset between decreased emission factors and increased energy use since the base year of the previous inventory prevents the difference between this work and previous inventories from being greater. The contributions of fossil fuel, biofuel, and open burning are estimated as 38%, 20%, and 42%, respectively, for BC, and 7%, 19%, and 74%, respectively, for OC. We present a bottom‐up estimate of uncertainties in source strength by combining uncertainties in particulate matter emission factors, emission characterization, and fuel use. The total uncertainties are about a factor of 2, with uncertainty ranges of 4.3–22 Tg/yr for BC and 17–77 Tg/yr for OC. Low‐technology combustion contributes greatly to both the emissions and the uncertainties. Advances in emission characterization for small residential, industrial, and mobile sources and top‐down analysis combining field measurements and transport modeling with iterative inventory development will be required to reduce the uncertainties further.
Recent regulatory policies in East Asia reduce ozone precursors, but these changes are spatially and temporally nonuniform. This study investigates variations in the long‐term trends of tropospheric ...NO2, HCHO, and HCHO/NO2 ratios to diagnose ozone sensitivity to changes in NOx and volatile organic compound using the Ozone Monitoring Instrument (OMI). Using an adaptive‐degree polynomial filter, we identify extremums of time series of NO2 to determine when and how NO2 change. Due to the regulations in China, trends which were predominantly upward turned downward. The years undergoing these changes primarily happened in 2011 and 2012. OMI column densities, however, suggest that NOx sources in South Korea, the Pearl River Delta (PRD), Taiwan, and Japan have not consistently decreased. Specifically, as Chinese exports of NO2 started subsiding, increasing trends in NO2 columns over several Korean cities, including Seoul, become evident. To quantify the changes in NOx emissions from summertime 2010 to 2014, we conduct a 3D‐Var inverse modeling using a regional model with MIX‐Asia inventory and estimate NOx emissions (in 2010 and 2014) for the PRD (1.6 and 1.5 Gg/d), the Yangtze River Delta (3.9 and 3.0 Gg/d), north China (15.6 and 14.3 Gg/d), South Korea (1.6 and 1.5 Gg/d), and Japan (2.7 and 2.6 Gg/d). OMI HCHO shows upward trends in East Asia resulting from anthropogenic effects; however, the magnitudes are negative in the PRD, Japan, North Korea, and Taiwan. OMI HCHO/NO2 ratios reveal that while South Korea, Japan, and the south of China have undergone toward more NOx‐sensitive regime, areas around the Bohai Sea have become more NOx saturated.
Key Points
Reduction in NO2 in north China occurring predominantly in 2011 and 2012 reveals the local NO2 enhancements in South Korea
Reduction in tropospheric NO2 columns in Japan, Taiwan, South Korea, and the Pearl River Delta has slowed or reversed in recent years.
Predominant upward trends of HCHO total columns except the PRD, Japan, Taiwan and North Korea.
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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 of PM2.5 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 concentrations 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 conditions: 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 population 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 domestic anthropogenic BC emissions (effectiveness = 5%).
•Source contributions to PM2.5 in South Korea are investigated during the KORUS-AQ.•Source contributions by regions vary greatly depending on synoptic meteorological conditions.•Chinese contributions are up to ~68% of PM2.5 in surface air in South Korea.•An enhanced contribution from domestic sources (57%) occurs for the blocking period.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Lithium batteries are receiving considerable attention as storage devices in the renewable energy and sustainable road transport fields. However, low-cost, long-life lithium batteries with higher ...energy densities are required to facilitate practical application. Here we report a lithium-ion battery that can be cycled at rates as high as 10 C has a life exceeding 500 cycles and an operating temperature range extending from -20 to 55 °C. The estimated energy density is 260 W h kg(-1), which is considerably higher than densities delivered by the presently available Li-ion batteries.
The air quality in Republic of Korea, especially in cities such as Seoul, has been a serious public health concern over the years. The key pollutant in the atmosphere leading to poor air quality in ...Korea is fine particulate matter (PM2.5). Here, we use a 3-D global chemistry model (GEOS-Chem) to conduct source attribution to PM2.5 in Korea from international and domestic emissions. The modeling was done for 2015 and 2016 to account for different meteorological conditions. We ran the GEOS-Chem model for both years, conducted model evaluation using ground and aloft observations, and then conducted sensitivity simulations without domestic anthropogenic emissions and Chinese anthropogenic emissions, respectively. Results show that the Chinese influence on PM2.5 in Korea varies from month to month with the highest contribution during spring when observed concentrations are also the highest. Chinese contributions to PM2.5 concentrations in South Korea reach a maximum of up to ~60% in January and February and gradually decrease until August when they reach a minimum at about 20%. On an annual basis, our analysis estimated that in 2016, Chinese anthropogenic emissions contributed 45% to PM2.5 in South Korea. The 2016 contribution from China was generally 3–5% lower than in 2015 because of emissions reductions in China. Compared to the Chinese contribution, the rest of the world contributions (which also include contributions from natural emissions worldwide) were minor except for summer in the South Sea.
•A global model estimated Chinese and domestic contributions to PM2.5 in Korea.•Influence from China on PM2.5 in Korea was the highest during winter and spring.•Contributions from China were ~60% in January/February and ~20% in August.•Domestic contributions were also the highest during winter months.
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In this work, we investigate the NOx emissions inventory in Seoul, South Korea, using a regional ozone monitoring instrument (OMI) NO2 product derived from the standard NASA product. We first develop ...a regional OMI NO2 product by recalculating the air mass factors using a high-resolution (4 km × 4 km) WRF-Chem model simulation, which better captures the NO2 profile shapes in urban regions. We then apply a model-derived spatial averaging kernel to further downscale the retrieval and account for the subpixel variability. These two modifications yield OMI NO2 values in the regional product that are 1.37 times larger in the Seoul metropolitan region and >2 times larger near substantial point sources. These two modifications also yield an OMI NO2 product that is in better agreement with the Pandora NO2 spectrometer measurements acquired during the South Korea–United States Air Quality (KORUS-AQ) field campaign. NOx emissions are then derived for the Seoul metropolitan area during the KORUS-AQ field campaign using a top-down approach with the standard and regional NASA OMI NO2 products. We first apply the top-down approach to a model simulation to ensure that the method is appropriate: the WRF-Chem simulation utilizing the bottom-up emissions inventory yields a NOx emissions rate of 227±94 kt yr−1, while the bottom-up inventory itself within a 40 km radius of Seoul yields a NOx emissions rate of 198 kt yr−1. Using the top-down approach on the regional OMI NO2 product, we derive the NOx emissions rate from Seoul to be 484±201 kt yr−1, and a 353±146 kt yr−1 NOx emissions rate using the standard NASA OMI NO2 product. This suggests an underestimate of 53 % and 36 % in the bottom-up inventory using the regional and standard NASA OMI NO2 products respectively. To supplement this finding, we compare the NO2 and NOy simulated by WRF-Chem to observations of the same quantity acquired by aircraft and find a model underestimate. When NOx emissions in the WRF-Chem model are increased by a factor of 2.13 in the Seoul metropolitan area, there is better agreement with KORUS-AQ aircraft observations and the recalculated OMI NO2 tropospheric columns. Finally, we show that by using a WRF-Chem simulation with an updated emissions inventory to recalculate the air mass factor (AMF), there are small differences (∼8 %) in OMI NO2 compared to using the original WRF-Chem simulation to derive the AMF. This suggests that changes in model resolution have a larger effect on the AMF calculation than modifications to the South Korean emissions inventory. Although the current work is focused on South Korea using OMI, the methodology developed in this work can be applied to other world regions using TROPOMI and future satellite datasets (e.g., GEMS and TEMPO) to produce high-quality region-specific top-down NOx emissions estimates.
To improve PM2.5 predictions in Northeast Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations. In contrast to the ...conventional method of BEC estimation that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. By taking the emission uncertainty into account, we found that the standard deviations in the BEC were significantly increased. The standard deviations became around three times larger than those in the conventional method at the surface. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over Northeast Asia using the Community Multiscale Air Quality (CMAQ) model initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1535 sites in China were assimilated every 6 h during the campaign period of the Korea-United States Air Quality Study (KORUS-AQ) (1 May–14 June 2016). The data assimilation with the new BEC showed better agreement with the surface PM2.5 observations than with the BEC from the conventional method. Our method was also more consistent with the observations in 24-h PM2.5 predictions than the conventional method (specifically, with a ∼44% reduction of negative biases). We concluded that increased standard deviations, together with updated horizontal and vertical length scales in the new BEC, improved the data assimilation and short-term predictions of the surface PM2.5. This paper also suggests several research efforts to further improve the BEC for better short-term PM2.5 predictions in Northeast Asia.
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•A new background error covariance (BEC) for PM2.5 predictions was developed.•The new BEC considers the uncertainties in anthropogenic emissions.•Two different emission inventories were used to account for the uncertainty.•24-h PM2.5 predictions were improved with ∼44% fewer negative biases.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Spatiotemporal variations of ozone (O3) and
nitrogen oxide (NOx) mixing ratios from 14 state-of-the-art
chemical transport models (CTMs) are intercompared and evaluated with
O3 observations in East ...Asia, within the framework of the Model
Inter-Comparison Study for Asia Phase III (MICS-Asia III). This study was designed to
evaluate the capabilities and uncertainties of current CTMs simulations for
Asia and to provide multi-model estimates of pollutant distributions. These
models were run by 14 independent groups working in China, Japan,
South Korea, the United States and other countries/regions. Compared with the
previous phase of MICS-Asia (MICS-Asia II), the evaluation with observations
was extended from 4 months to 1 full year across China and the western Pacific Rim. In general, model performance levels for O3 varied widely
by region and season. Most models captured the key patterns of monthly and
diurnal variation of surface O3 and its precursors in the North China Plain
and western Pacific Rim but failed to do so for the Pearl River Delta. A
significant overestimation of surface O3 was evident from
May to September/October and from January to May over the North China Plain,
the western Pacific Rim and the Pearl River Delta. Comparisons drawn from
observations show that the considerable diversity in O3 photochemical
production partly contributed to this overestimation and to high levels of
inter-model variability in O3 for North China. In terms of O3
soundings, the ensemble average of models reproduced the vertical structure
for the western Pacific, but overestimated O3 levels to below 800 hPa
in the summer. In the industrialized Pearl River Delta, the ensemble average
presented an overestimation for the lower troposphere and an underestimation
for the middle troposphere. The ensemble average of 13 models for O3 did not always exhibit superior performance compared with certain
individual models in contrast with its superior value for Europe. This finding
suggests that the spread of ensemble-model values does not represent all of the
uncertainties of O3 or that most MICS-Asia III models missed key
processes. This study improved the performance of modeling O3 in March
at Japanese sites compared with MICS-Asia II. However, it overpredicted surface
O3 concentrations for western Japan in July, which was not found by
MICS-Asia II. Major challenges still remain with regard to identifying the
sources of bias in surface O3 over East Asia in CTMs.
Substantial mitigation of air pollutants emissions has been performed since 2013 around Beijing, and changes in the atmospheric characteristics have been expected over the downstream area of Beijing. ...In this study, both WRF-Chem simulation and on-site measurements were utilized for the Baengnyeong (island) supersite, one of the representative regional background sites located in the Yellow Sea, the entrance area of the long-range transport process in Korea. The changes in the chemical compositions of inorganic aerosols were examined for spring-time during the Chinese emission mitigation period from 2014 to 2016.
The measured ratio of ionic species to PM2.5 at the Baengnyeong supersite showed changes in aerosol inorganic chemical compositions from sulfate in 2014 to nitrate in 2015–2016. The modeling results also showed that nitrate was low in 2014 and significantly increased in 2015 and 2016, and the acidic aerosol condition had also changed toward a more neutralized status in both the simulation and the observations. The WRF-Chem modeling study further indicated that the sulfur was not neutralized in 2014. However, in 2015 and 2016, SO2 was more sufficiently neutralized as sulfur emissions were substantially reduced in China, while at the same time nitrate had begun to increase in such a ‘SO2–poor’ condition in Beijing area in China, and thus approaching more enhanced neutralization over the Yellow Sea area. The causes of the higher nitrate based on the modeled characteristics of the ammonia-sulfate-nitrate aerosol formation in response to the SO2 emissions reduction in China are also discussed in this paper.
Inorganic composition is changing from sulfate to nitrate, and aerosol acidity is approaching more enhanced neutralization over regional background area in Northeast Asia. Display omitted
•Inorganic components were examined over background area during Beijing's emission reduction.•Observations showed significant change in decreased sulfate and opposite trend in nitrate.•Aerosol acidity changed to enhanced nitrate neutralization through the emission reduction period.•More enhanced nitrate was also simulated by WRF-Chem mainly due to Beijing's emission mitigation.
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