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Both measurements and modelling of air pollution in the Arctic are difficult. Yet with the Arctic warming at nearly four times the global average rate, and changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. This study examines the simulations of atmospheric concentrations of methane, carbon monoxide and ozone in the Arctic by 11 models. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). Mixing ratios of trace gases are modelled at three-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1 and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by -9.6 % for CH4, -21 % for CO and -18 % for O3. Results for CH4 are relatively consistent across the four years, whereas CO has a maximum negative bias in the spring and minimum in the summer, and O3 has a maximum difference centred around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model-location comparisons.

Global reactive nitrogen (N) deposition has more than tripled since 1860 and is expected to remain high due to food production and fossil fuel consumption. Global sulfur emissions have been decreasing worldwide over the last 30 years, but many regions are still experiencing unhealthily high levels of deposition. We update the 2010 global deposition budget for reactive nitrogen and sulfur components with new regional wet deposition measurements from Asia, improving the ensemble results of 11 global chemistry transport models from the second phase of the United Nations Economic Commission for Europe's Task Force on Hemispheric Transport of Air Pollution (HTAP II). The observationally adjusted global N deposition budget is 114.5 Tg N, representing a minor increase of 1 % from the model-only derived values, and the adjusted global sulfur deposition budget is 88.9 Tg S, representing a 6.5 % increase from the modeled values, using an interpolation distance of 2.5∘. Regionally, deposition adjustments can be up to ∼ 73 % for nitrogen and 112 % for sulfur. Our study demonstrates that a global measurement–model fusion approach can improve N and S deposition model estimates at a regional scale, with sufficient availability of observations; however, in large parts of the world, alternative approaches need to be explored. The analysis presented here represents a step forward toward the World Meteorological Organization's goal of global fusion products for accurately mapping harmful air pollution deposition.

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.

The outflow of the East Asian haze (EAH) has attracted much attention in recent years. For downstream areas, it is meaningful to understand the impact of crucial upstream sources and the process analysis during transport. This study evaluated the impact of PM2.5 from the three largest industrial regions on the Asian continent, namely the Bohai Rim industrial region (BRIR), Yangtze River Delta industrial region (YRDIR), and Pearl River Delta industrial region (PRDIR), in Taiwan and discussed the processes during transport with the help of air quality modeling. The simulation results revealed that the contributions of monthly average PM2.5 from BRIR and YRDIR were 0.7–1.1 and 1.2–1.9 µg m−3 (∼ 5 % and 7.5 % of the total concentration) in Taiwan, respectively, in January 2017. When the Asian anticyclone moved from the Asian continent to the western Pacific, e.g., on 9 January 2017, the contributions from BRIR and YRDIR to northern Taiwan could reach daily averages of 8 and 11 µg m−3. The transport of EAH from BRIR and YRDIR to low-latitude regions was horizontal advection (HADV), vertical advection (ZADV), and vertical diffusion (VDIF) over the Bohai Sea and East China Sea. Over the Taiwan Strait and the northern South China Sea, cloud processes (CLDS) were the major contribution to PM2.5 due to a high relative humidity environment. Along the transport from high-latitude regions to low-latitude regions, aerosol chemistry (AERO) and dry deposition (DDEP) were the major removal processes. When the EAH intruded into northern Taiwan, the major processes for the gains of PM2.5 in northern Taiwan were HADV and AERO. The stronger the EAH, the more the EAH could influence central and southern Taiwan. Although PRDIR is located downstream of Taiwan under northeasterly wind, the PM2.5 from PRDIR could be lifted upward above the boundary layer, allowing it to move eastwards. When the PM2.5 plume moved over Taiwan and was blocked by mountains, PM2.5 could be transported downward, via boundary layer mixing (VDIF), as it was further enhanced by the passing cold surge. In contrast, for the simulation of July 2017, the influence from the three industrial regions was almost negligible unless there was a special weather system, such as thermal lows which may have carried pollutants from PRDIR to Taiwan, but this occurrence was rare.

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.

The outflow of East Asian haze (EAH) has gathered much attention in recent years. For downstream areas, it is meaningful to understand the impact of crucial upstream sources and the process analysis during transport. This study evaluated the impact of PM2.5 from the three biggest industrial regions in Asian continent: Bohai Rim industrial region (BRIR), Yangtze River Delta industrial region (YRDIR), and Pearl River Delta industrial region (PRDIR) on Taiwan and discussed the processes during transport with the help of air quality modeling. The simulation results revealed the contributions of monthly average PM2.5 from BRIR and YRDIR were 0.7∼1.1 µg m−3 and 1.2∼1.9 µg m−3 (∼5 % and 7.5 % of total concentration) on Taiwan, respectively in January 2017. When the Asian anticyclone moved from Asian continent to the West Pacific, e.g. on Jan 9th 2017, the contributions from BRIR and YRDIR to northern Taiwan could reach 6∼8 and 9∼12 µg m−3. The transport of EAH from BRIR and YRDIR to low latitude regions was horizontal advection (HADV), vertical advection (ZADV), and vertical diffusion (VDIF) over Bohai Sea and East China Sea. Over Taiwan Strait and northern South China Sea, cloud processes (CLDS) was the major production process of PM2.5 due to high relative humidity environment. Along the transport from high latitude regions to low latitude regions, Aerosol chemistry (AERO) and Dry deposition (DDEP) were the major removal processes. When the EAH intruded northern Taiwan, the major production processes of PM2.5 at northen Taiwan were HADV and AERO. The stronger the EAH was the easier the EAH could influence central and southern Taiwan. Although PRDIR was located at the downstream of Taiwan under northeast wind, the PM2.5 from PRDIR could transport upward above boundary layer and moved eastwards. When the PM2.5 plume moved overhead Taiwan, PM2.5 could transport downward via boundary layer mixing (VDIF) and further enhanced by the passing cold surge. In contrast, for the simulation of July 2017, the influence from three industrial regions was almost negligible unless there was special weather system like thermal lows, which may carried pollutants from PRDIR to Taiwan, but the occurrence was rare.

Despite significant progress in improving chemical transport models (CTMs), applications of these modeling endeavours are still subject to the large and complex model uncertainty. Model Inter-Comparison Study for Asia III (MICS-Asia III) provides the opportunity to assess the capability and uncertainty of current CTMs in East Asia applications. In this study, we evaluated the multi-model simulations of nitrogen dioxide (NO2), carbon monoxide (CO) and ammonia (NH3) over China under the framework of MICS-Asia III. Compared with MICS-Asia II, modeling results were provided by a larger number of independent groups from different countries/regions and covered a longer period of time (one-full year). Furthermore, most of these groups used a common emission inventory, common meteorological inputs, and the same modeling domain and horizontal resolutions. New observations over North China Plain (NCP) and Peral River Delta (PRD) were also available in MICS-Asia III, allowing model evaluations over highly industrialized regions. The results show that most models well captured the monthly and spatial patterns of NO2 in NCP though NO2 levels were slightly underestimated. Relatively poor performance in NO2 simulations was found in PRD with larger root mean square error and lower spatial correlation coefficients, possibly due to the relative coarse model resolutions. All models significantly underpredicted CO concentrations both in NCP and PRD, with annual mean concentrations 65.4 % and 61.4 % underestimated by the ensemble mean respectively. Such large underestimations suggest that CO emissions might be underestimated in current emission inventory. In contrast to the good skills in simulating the monthly variations of NO2 and CO concentrations, all models failed to reproduce the observed monthly variations of NH3 concentrations in NCP. Most models mismatched the observed peak in July and showed negative correlation coefficients with observations, which may be closely related to the uncertainty in the monthly variations of NH3 emissions and the NH3 gas-aerosol partitioning. Finally, inter-comparisons of these model results were conducted to quantify the impacts of model uncertainty on the simulations of these gases, which are shown 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.

This study minimizes total cost for single-feedstock supply chains of two dedicated energy crops, perennial switchgrass and biomass sorghum, in Tennessee using a spatial optimization model. Greenhouse gas emissions from the transport of feedstock to the conversion facility were estimated for respective feedstock supply chains. Results show that different demand for land types from two feedstocks and the geographically diverse landscape across the state affect the economics of bioenergy crops supply chains and feedstock transportation emissions. Switchgrass is more suitable than biomass sorghum for biofuel production in Tennessee based on the supply chains cost and feedstock hauling emissions.

International audience; Predicting the human-health effects of reducing atmospheric emissions of nitrogen oxide (NOx) emissions from power plants, motor vehicles, and other sources is complex because of nonlinearity in the relevant atmospheric processes. We estimate the health impacts of changes in fine particulate matter (PM2.5) and ozone concentrations that result from control of NOx emissions alone and in conjunction with other pollutants in and outside the mega-city of Shanghai, China. The Community Multiscale Air Quality (CMAQ) Modeling System is applied to model the effects on atmospheric concentrations of emissions from different economic sectors and geographic locations. Health impacts are quantified by combining concentration-response functions from the epidemiological literature with pollutant concentration and population distributions. We find that the health benefits per ton of emission reduction are more sensitive to the location (i.e., inside vs. outside of Shanghai) than to the sectors that are controlled. For eastern China, we predict between 1 and 20 fewer premature deaths per year per 1,000 tons of NOx emission reductions, valued at $300-$6,000 per ton. Health benefits are sensitive to seasonal variation in emission controls. Policies to control NOx emissions need to consider emission location, season, and simultaneous control of other pollutants to avoid unintended consequences.

The logistics required to supply biomass feedstock a refinery is crucial to the development of the cellulosic biofuel industry because of the importance of the quality and quantity and bulky nature associated with cellulosic feedstock to the biofuel conversion process. In addition, the potential social and environmental impact of biomass feedstock transportation has also received increasing attention due to the expansion of truck traffic on the current road system. This study applies a spatial-oriented mixed-integer mathematical programming model linked to a GIS resource model to generate a least cost solution of a typical feedstock harvest and logistic system for a potential biorefinery with the capacity of 50 million gallons per year. Moreover, U.S. EPA’s MOVES2010a was used to estimate the baseline emissions for 2010 with national scale option in study region and additional emissions generated from hauling those feedstock with project scale option. Results showed that the transportation cost accounted for nearly one-quarter of total plant gate costs of the large round bales. Also, it was estimated that the biorefinery received about 50,000 truckloads per year, hence creating annually 100,000 truck trips (or 274 truck trips per day) on the road linking the entrance of the biorefinery to the supply regions. The overall VMT increase resulting from additional feedstock truck traffics was 3.7 million miles and the emissions of NOX, CO2, PM10, and PM2.5 emissions increased by 0.32%, 0.13%, 0.60%, and 0.71%, respectively, in these 13 counties studied when comparing with the overall baseline emissions.

The logistics required to supply biomass feedstock a refinery is crucial to the development of the cellulosic biofuel industry because of the importance of the quality and quantity and bulky nature associated with cellulosic feedstock to the biofuel conversion process. In addition, the potential social and environmental impact of biomass feedstock transportation has also received increasing attention due to the expansion of truck traffic on the current road system. This study applies a spatial-oriented mixed-integer mathematical programming model linked to a GIS resource model to generate a least cost solution of a typical feedstock harvest and logistic system for a potential biorefinery with the capacity of 50 million gallons per year. Moreover, U.S. EPA’s MOVES2010a was used to estimate the baseline emissions for 2010 with national scale option in study region and additional emissions generated from hauling those feedstock with project scale option. Results showed that the transportation cost accounted for nearly one-quarter of total plant gate costs of the large round bales. Also, it was estimated that the biorefinery received about 50,000 truckloads per year, hence creating annually 100,000 truck trips (or 274 truck trips per day) on the road linking the entrance of the biorefinery to the supply regions. The overall VMT increase resulting from additional feedstock truck traffics was 3.7 million miles and the emissions of NOX, CO2, PM10, and PM2.5 emissions increased by 0.32%, 0.13%, 0.60%, and 0.71%, respectively, in these 13 counties studied when comparing with the overall baseline emissions.

The efficiency of supply chain system of lignocellulosic biomass (LCB) feedstock is crucial to the development of the cellulosic biofuel industry. Moreover, the potential environmental impact of LCB feedstock transportation has also received increasing attention lately. This study first applied a spatial-oriented mixed-integer mathematical programming model linked to a GIS resource model to generate a least-cost solution of alternative typical feedstock supply chain systems for a potential commercial scale biorefinery per year in east, central and west Tennessee. The EPA’s MOVES model was then used to estimate the baseline emissions for 2010 in the study region and additional emissions generated from hauling feedstock. Results showed that switchgrass is more suitable than energy sorghum for biofuel production in Tennessee based on feedstock plant-gate cost and hauling emissions. Also, the large square bale system outperformed the large round bale system in both economic and environmental indicators. Finally, the biorefinery with the most economic feedstock cost and the least feedstock hauling emission is suggested to be sited in Robertson County, TN. The emissions of NOx, CO2, PM10, and PM2.5 from feedstock hauling in related counties increased by 0.12%, 0.04%, 0.15%, and 0.18%, respectively, when comparing with the emissions produced by existing overall traffics.

9 pags., 5 figs. 1 tab., Oceans are the largest source of biogenic emissions to the atmosphere, including aerosol precursors like marine halocarbons and dimethyl sulfide (DMS). During the last decade, the CAMS-GLOB-OCE dataset has developed an analysis of daily emissions of tribromomethane (CHBr3), dibromomethane (CH2Br2), iodomethane (CH3I), and DMS, due to its increasingly recognized role on tropospheric chemistry and climate dynamics. The potential impacts of these compounds on air quality modeling remain, however, largely unexplored. The lack of a reliable and easy methodology to incorporate these marine emissions into air quality models is probably one of the reasons behind this knowledge gap. Therefore, this study describes a methodology to adapt the CAMS-GLOB-OCE dataset to be used as an input of the preprocessor software Sparse Matrix Operator Kernel Emissions (SMOKE). The method involves nine steps to update file attribute properties and to bilinearly interpolate compound emission fields. The procedure was tested using halocarbon and DMS emissions fields available within the CAMS-GLOB-OCE database for the Southern Ocean around Antarctica. We expect that this methodology will allow more studies to include the marine emissions of halocarbons and DMS in air quality studies., This work was supported by CONICYT-PIA-FONDEQUIP-FUNDACIÓN ALEMANA PARA LA INVESTIGACIÓN D.F.G (DFG190001), FONDECYT-REGULAR 1211338, and the supercomputing infrastructure at NLHPC (ECM-02).