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.
Haze is a serious air pollution problem in China, especially in Beijing and surrounding areas, affecting visibility, public health and regional climate. In this study, the Weather Research and ...Forecasting-Chemistry (WRF-Chem) model was used to simulate PM2.5 (particulate matters with aerodynamic diameter ≤2.5μm) concentrations during the 2013 severe haze event in Beijing, and health impacts and health-related economic losses were calculated based on model results. Compared with surface monitoring data, the model results reflected pollution concentrations accurately (correlation coefficients between simulated and measured PM2.5 were 0.7, 0.4, 0.5 and 0.6 in Beijing, Tianjin, Xianghe and Xinglong stations, respectively). Health impacts assessments show that the PM2.5 concentrations in January might cause 690 (95% confidence interval (CI): (490, 890)) premature deaths, 45,350 (95% CI: (21,640, 57,860)) acute bronchitis and 23,720 (95% CI: (17,090, 29,710)) asthma cases in Beijing area. Results of the economic losses assessments suggest that the haze in January 2013 might lead to 253.8 (95% CI: (170.2, 331.2)) million US$ losses, accounting for 0.08% (95% CI: (0.05%, 0.1%)) of the total 2013 annual gross domestic product (GDP) of Beijing.
•Health impacts of the 2013 Beijing haze event are estimated.•Health-related economic losses are also calculated.•The PM2.5 concentrations in January 2013 might cause 690 deaths.•This haze event might lead to 253.8 million US$ losses.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM ≤ 2.5 μm in diameter) precursors, posing significant health risks among large, densely settled ...populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.
•Health risks of emissions from power plants in China and India are estimated using the state-of-the-science framework.•Both mortality burdens and years of life lost (YLL) are calculated for each province/state in China and India.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The Model Inter-Comparison Study for Asia (MICS-Asia)
phase III was conducted to promote understanding of regional air quality and
climate change in Asia, which have received growing attention due to ...the
huge amount of anthropogenic emissions worldwide. This study provides an
overview of acid deposition. Specifically, dry and wet deposition of the
following species was analyzed: S (sulfate aerosol, sulfur dioxide
(SO2), and sulfuric acid (H2SO4)), N (nitrate aerosol,
nitrogen monoxide (NO), nitrogen dioxide (NO2), and nitric acid
(HNO3)), and A (ammonium aerosol and ammonia (NH3)). The wet
deposition simulated by a total of nine models was analyzed and evaluated
using ground observation data from the Acid Deposition Monitoring Network in
East Asia (EANET). In the phase III study, the number of observation sites
was increased from 37 in the phase II study to 54, and southeast Asian
countries were newly added. Additionally, whereas the analysis period was
limited to representative months of each season in MICS-Asia phase II, the
phase III study analyzed the full year of 2010. The scope of this overview
mainly focuses on the annual accumulated deposition. In general, models can
capture the observed wet deposition over Asia but underestimate the wet
deposition of S and A, and show large differences in the wet deposition of N.
Furthermore, the ratio of wet deposition to the total deposition (the sum of
dry and wet deposition) was investigated in order to understand the role of
important processes in the total deposition. The general dominance of wet
deposition over Asia and attributions from dry deposition over land were
consistently found in all models. Then, total deposition maps over 13
countries participating in EANET were produced, and the balance between
deposition and anthropogenic emissions was calculated. Excesses of
deposition, rather than of anthropogenic emissions, were found over Japan,
northern Asia, and southeast Asia, indicating the possibility of long-range
transport within and outside of Asia, as well as other emission sources. To
improve the ability of models to capture the observed wet deposition, two
approaches were attempted, namely, ensemble and precipitation adjustment.
The ensemble approach was effective at modulating the differences in
performance among models, and the precipitation-adjusted approach
demonstrated that the model performance for precipitation played a key role
in better simulating wet deposition. Finally, the lessons learned from the
phase III study and future perspectives for phase IV are summarized.
Organic aerosols (OAs) in the atmosphere affect Earth’s energy budget by not only scattering but also absorbing solar radiation due to the presence of the so-called “brown carbon” (BrC) component. ...However, the absorptivities of OAs are not represented or are poorly represented in current climate and chemical transport models. In this study, we provide a method to constrain the BrC absorptivity at the emission inventory level using recent laboratory and field observations. We review available measurements of the light-absorbing primary OA (POA), and quantify the wavelength-dependent imaginary refractive indices (k OA, the fundamental optical parameter determining the particle’s absorptivity) and their uncertainties for the bulk POA emitted from biomass/biofuel, lignite, propane, and oil combustion sources. In particular, we parametrize the k OA of biomass/biofuel combustion sources as a function of the black carbon (BC)-to-OA ratio, indicating that the absorptive properties of POA depend strongly on burning conditions. The derived fuel-type-based k OA profiles are incorporated into a global carbonaceous aerosol emission inventory, and the integrated k OA values of sectoral and total POA emissions are presented. Results of a simple radiative transfer model show that the POA absorptivity warms the atmosphere significantly and leads to ∼27% reduction in the amount of the net global average POA cooling compared to results from the nonabsorbing assumption.
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IJS, KILJ, NUK, PNG, UL, UM
Since the mid-1990s a new generation of Earth-observing satellites has been able to detect tropospheric air pollution at increasingly high spatial and temporal resolution. Most primary emitted ...species can be measured by one or more of the instruments. This review article addresses the question of how well we can relate the satellite measurements to quantification of primary emissions and what advances are needed to improve the usability of the measurements by U.S. air quality managers. Built on a comprehensive literature review and comprising input by both satellite experts and emission inventory specialists, the review identifies several targets that seem promising: large point sources of NOx and SO2, species that are difficult to measure by other means (NH3 and CH4, for example), area sources that cannot easily be quantified by traditional bottom-up methods (such as unconventional oil and gas extraction, shipping, biomass burning, and biogenic sources), and the temporal variation of emissions (seasonal, diurnal, episodic). Techniques that enhance the usefulness of current retrievals (data assimilation, oversampling, multi-species retrievals, improved vertical profiles, etc.) are discussed. Finally, we point out the value of having new geostationary satellites like GEO-CAPE and TEMPO over North America that could provide measurements at high spatial (few km) and temporal (hourly) resolution.
•Comprehensive review of studies of satellite data applied to emissions estimation.•Overview of retrievals for eight major tropospheric air pollutants.•Techniques to enhance the usefulness of satellite retrievals.•Identification of target source categories for satellite data application.•Recommendations on ways to improve the usability of satellite retrievals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Planned geostationary satellites will provide aerosol optical depth (AOD) retrievals at high temporal and spatial resolution which will be incorporated into current assimilation systems that use ...low‐Earth orbiting (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) AOD. The impacts of such additions are explored in a real case scenario using AOD from the Geostationary Ocean Color Imager (GOCI) on board of the Communication, Ocean, and Meteorology Satellite, a geostationary satellite observing northeast Asia. The addition of GOCI AOD into the assimilation system generated positive impacts, which were found to be substantial in comparison to only assimilating MODIS AOD. We found that GOCI AOD can help significantly to improve surface air quality simulations in Korea for dust, biomass burning smoke, and anthropogenic pollution episodes when the model represents the extent of the pollution episodes and retrievals are not contaminated by clouds. We anticipate future geostationary missions to considerably contribute to air quality forecasting and provide better reanalyses for health assessments and climate studies.
Key Points
Geostationary AOD data improves skill of current air quality predictionsImprovements are found for multiple types of pollution events on Northeast AsiaIt serves as a real case scenario support for planned geostationary missions
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The impacts of black carbon (BC) and particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) emissions from different source sectors (e.g., transportation, power, industry, ...residential, and biomass burning) and geographic source regions (e.g., Europe, North America, China, Russia, central Asia, south Asia, and the Middle East) to Arctic BC and PM2.5 concentrations are investigated through a series of annual sensitivity simulations using the Weather Research and Forecasting – sulfur transport and deposition model (WRF-STEM) modeling framework. The simulations are validated using observations at two Arctic sites (Alert and Barrow Atmospheric Baseline Observatory), the Interagency Monitoring of Protected Visual Environments (IMPROVE) surface sites over the US, and aircraft observations over the Arctic during spring and summer 2008. Emissions from power, industrial, and biomass burning sectors are found to be the main contributors to the Arctic PM2.5 surface concentration, with contributions of ∼ 30 %, ∼ 25 %, and ∼ 20 %, respectively. In contrast, the residential and transportation sectors are identified as the major contributors to Arctic BC, with contributions of∼ 38 % and ∼ 30 %. Anthropogenic emissions are the most dominant contributors (∼ 88 %) to the BC surface concentration over the Arctic annually; however, the contribution from biomass burning is significant over the summer (up to ∼ 50 %). Among all geographical regions, Europe and China have the highest contributions to the BC surface concentrations, with contributions of ∼ 46 % and ∼ 25 %, respectively. Industrial and power emissions had the highest contributions to the Arctic sulfate (SO4) surface concentration, with annual contributions of ∼ 43 % and ∼ 41 %, respectively. Further sensitivity runs show that, among various economic sectors of all geographic regions, European and Chinese residential sectors contribute to∼ 25 % and ∼ 14 % of the Arctic average surface BC concentration. Emissions from the Chinese industry sector and European power sector contribute ∼ 12 % and ∼ 18 % of the Arctic surface sulfate concentration. For Arctic PM2.5, the anthropogenic emissions contribute > ∼ 75 % at the surface annually, with contributions of ∼ 25 % from Europe and ∼ 20 % from China; however, the contributions of biomass burning emissions are significant in particular during spring and summer. The contributions of each geographical region to the Arctic PM2.5 and BC vary significantly with altitude. The simulations show that the BC from China is transported to the Arctic in the midtroposphere, while BC from European emission sources are transported near the surface under 5 km, especially during winter.
Despite the significant progress in improving chemical transport models (CTMs), applications of these modeling endeavors are still subject to large and complex model uncertainty. The Model ...Inter-Comparison Study for Asia III (MICS-Asia III) has provided the opportunity to assess the capability and uncertainty of current CTMs in East Asian applications. In this study, we have evaluated the multi-model simulations of nitrogen dioxide (NO2), carbon monoxide (CO) and ammonia (NH3) over China under the framework of MICS-Asia III. A total of 13 modeling results, provided by several independent groups from different countries and regions, were used in this study. Most of these models used the same modeling domain with a horizontal resolution of 45 km and were driven by common emission inventories and meteorological inputs. New observations over the North China Plain (NCP) and Pearl River Delta (PRD) regions were also available in MICS-Asia III, allowing the model evaluations over highly industrialized regions. The evaluation results show that most models captured the monthly and spatial patterns of NO2 concentrations in the NCP region well, though NO2 levels were slightly underestimated. Relatively poor performance in NO2 simulations was found in the PRD region, with larger root-mean-square error and lower spatial correlation coefficients, which may be related to the coarse resolution or inappropriate spatial allocations of the emission inventories in the PRD region. All models significantly underpredicted CO concentrations in both the NCP and PRD regions, with annual mean concentrations that were 65.4 % and 61.4 % underestimated by the ensemble mean. Such large underestimations suggest that CO emissions might be underestimated in the current emission inventory. In contrast to the good skills for simulating the monthly variations in NO2 and CO concentrations, all models failed to reproduce the observed monthly variations in NH3 concentrations in the NCP region. Most models mismatched the observed peak in July and showed negative correlation coefficients with the observations, which may be closely related to the uncertainty in the monthly variations in NH3 emissions and the NH3 gas–aerosol partitioning. Finally, model intercomparisons have been conducted to quantify the impacts of model uncertainty on the simulations of these gases, which are shown to increase with the reactivity of species. Models contained more uncertainty in the NH3 simulations. This suggests that for some highly active and/or short-lived primary pollutants, like NH3, model uncertainty can also take a great part in the forecast uncertainty in addition to the emission uncertainty. Based on these results, some recommendations are made for future studies.
Heat waves and air pollution extremes exert compounding effects on human health and food security and may worsen under future climate change. On the basis of reconstructed daily O3 levels in China ...and meteorological reanalysis, we found that the interannual variability of the frequency of summertime co-occurrence of heat wave and O3 pollution in China is regulated mainly by a combination of springtime warming in the western Pacific Ocean, western Indian Ocean, and Ross Sea. These sea surface temperature anomalies impose influences on precipitation, radiation, etc., to modulate the co-occurrence, which were also confirmed with coupled chemistry–climate numerical experiments. We thus built a multivariable regression model to predict co-occurrence a season in advance, and correlation coefficient could reach 0.81 (P < 0.01) for the North China Plain. Our results provide useful information for the government to take actions in advance to mitigate damage from these synergistic costressors.
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