Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China.

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated historical and future PM2.5 concentrations and associated OP using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from WRF weather forecast model. Considering different future socio-economic pathways and emission scenario assumptions, we quantified how the contribution from various anthropogenic emission sectors will change under these scenarios. Results show that compared to the CESM_SSP2-4.5_CLE scenario (based on moderate radiative forcing and Current Legislation Emission), the CESM_SSP1-2.6_MFR scenario (based on sustainability development and Maximum Feasible Reductions) is projected to yield greater environmental and health benefits in the future. Under the CESM_SSP1-2.6_MFR scenario, annual average PM2.5 concentrations (OP) are expected to decrease to 30 (0.8 nmolmin-1m-3) in almost all regions by 2030, which will be 65 % (67 %) lower than that in 2010. From a long-term perspective, it is anticipated that OP in the Fen-Wei Plain region will experience the maximum reduction (82.6 %) from 2010 to 2049. Largely benefiting from the effective control of PM2.5 in the region, it has decreased by 82.1 %. Crucially, once emission reduction measures reach a certain level (in 2040), further reductions become less significant. This study also emphasized the significant role of secondary aerosol formation and biomass-burning sources in influencing OP during both historical and future periods. In different scenarios, the reduction range of OP from 2010 to 2049 is estimated to be between 71 % and 85 % by controlling precursor emissions involved in secondary aerosol formation and emissions from biomass burning. Results indicate that strengthening the control of anthropogenic emissions in various regions are key to achieving air quality targets and safeguarding human health in the future.

'Source-specific' air pollution and risk of stroke in Denmark.

BACKGROUND: Long-term air pollution is a risk factor for stroke. Which types and sources of air pollution contribute most to stroke in populations is unknown. We investigated whether risk of stroke differed by type and source of air pollution. METHODS: We selected all persons aged >50 years and living in Denmark in the period 2005-17. We estimated running 5-year mean residential air-pollution concentrations of particulate matter <2.5 µm (PM2.5), ultrafine particles (UFP), elemental carbon (EC) and nitrogen dioxide (NO2). Pollutants were modelled as total air pollution from all emission sources, as well as apportioned into contributions from non-traffic and traffic sources. Hazard ratios (HRs) and CIs were estimated by using Cox proportional hazards models, adjusting for area-level and personal demographic and socio-economic covariates. We identified all primary strokes from hospital and mortality registers. RESULTS: The cohort numbered 2 million people and 94 256 cases of stroke. Interquartile ranges (IQR) of air pollution were associated with risk of stroke with HRs of 1.077 (95% CI: 1.061-1.094, IQR: 1.85 µg/m3) for PM2.5, 1.039 (1.026-1.052, IQR: 4248 particles/cm3) for UFP, 1.009 (1.001-1.018, IQR: 0.28 µg/m3) for EC and 1.028 (1.017-1.040, IQR: 7.15 µg/m3) for NO2. Traffic sources contributed little to the total exposure. HRs associated with air pollution from traffic were close to the null, whereas non-traffic sources tended to be associated with HRs higher than those for total air pollution, e.g. for non-traffic PM2.5, the HR was 1.091 (1.074-1.108). CONCLUSIONS: Air pollution, including UFP, was associated with risk of stroke. The risk appeared attributable mainly to air pollution from non-traffic sources.

Exposure to ambient air pollution and lipid levels and blood pressure in an adult, Danish cohort.

BACKGROUND: Air pollution is a well-recognized risk factor for cardiovascular disease. However, the mechanistic pathways underlying the association are not completely understood. Hence, further studies are required to shed light on potential mechanisms, through which air pollution may affect the development from subclinical to clinical cardiovascular disease. OBJECTIVES: To investigate associations between short-term exposure to air pollution and high-density lipoprotein (HDL), non-high density lipoprotein (non-HDL), systolic and diastolic blood pressure. METHODS: The study was conducted among 32,851 Danes from the Diet, Cancer and Health - Next Generations cohort, who had a blood sample taken and blood pressure measured. We measured HDL and non-HDL in the blood samples. We modelled exposure to fine particulate matter (PM2.5), ultrafine particles (UFP), elemental carbon (EC) and nitrogen dioxide (NO2) in time-windows from 24 h up to 90 days before blood sampling. Pollutants were modelled as total air pollution from all sources, and apportioned into contributions from non-traffic and traffic sources. We analyzed data using linear and logistic regression, with adjustment for socio-economic and lifestyle factors. RESULTS: Air pollution exposure over 24 h to 30 days was generally adversely associated with lipid profile and blood pressure, e.g. for 30-day UFP-exposure, adjusted ß-estimates were: -0.025 (-0.043; -0.006) for HDL, 0.086 (0.042; 0.130) for non-HDL, 2.45 (1.70; 3.11) for systolic and 1.56 (1.07; 20.4) for diastolic blood pressure, per 10,000 particles/cm3. The strongest associations were found for the non-traffic components of air pollution, and among those who were overweight/obese. DISCUSSION: In this large study of air pollution and lipid levels and blood pressure, we found that 24-h to 30-day PM2.5, UFP, EC and NO2 concentrations were generally adversely associated with lipid profile and blood pressure, two important cardiovascular risk factors. The study suggests potential pathways, through which air pollution could affect the development of cardiovascular disease.

Present-day and future PM2.5 and O3-related global and regional premature mortality in the EVAv6.0 health impact assessment model.

We used the EVAv6.0 system to estimate the present (2015) and future (2015-2050) global PM2.5 and O3-related premature mortalities, using simulated surface concentrations from the GISS-E2.1-G Earth system model. The PM2.5-related global premature mortality is estimated to be 4.3 and 4.4 million by the non-linear and linear models, respectively. Ischemic heart diseases are found to be the leading cause of PM2.5-related premature deaths, contributing by 35% globally. Both long-term and short-term O3-related premature deaths are estimated to be around 1 million, globally. Overall, PM2.5 and O3-related premature mortality leads to 5.3-5.4 million premature deaths, globally. The global burden of premature deaths is mainly driven by the Asian region, which in 2015 contributes by 75% of the total global premature deaths. An increase from 6.2% to 8% in the PM2.5 relative risk as recommended by the WHO leads to an increase of PM2.5-related premature mortality by 28%, to 5.7 million. Finally, bias correcting the simulated PM2.5 concentrations in 2015 leads to an increase of up to 73% in the global PM2.5-related premature mortality, leading to a total number of global premature deaths of up to 7.7 million, implying the necessity of bias correction to get more robust health burden estimates. PM2.5 and O3-related premature mortality in 2050 decreases by up to 57% and 18%, respectively, due to emission reductions alone. However, the projected increase and aging of the population leads to increases of premature mortality by up to a factor of 2, showing that the population exposed to air pollution is more important than the level of air pollutants, highlighting that the population dynamics should be considered when setting up health assessment systems.

Air pollution, road traffic noise and lack of greenness and risk of type 2 diabetes: A multi-exposure prospective study covering Denmark.

OBJECTIVE: Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective. METHODS: We estimated 5-year residential mean exposure to fine particles (PM2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO2) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates. RESULTS: In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO2 remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO2, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149). CONCLUSIONS: Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.

Contribution of fine particulate matter to present and future premature mortality over Europe: A non-linear response.

The World Health Organization estimates that around 7 million people die every year from exposure to fine particles (PM2.5) inpolluted air. Here, the number of premature deaths in Europe from different diseases associated to the ambient exposure to PM2.5 have here been studied both for present (1991-2010) and future periods (2031-2050, RCP8.5 scenario). This contribution combines different state-of-the-art approaches (use of high-resolution climate/chemistry simulations over Europe for providing air quality data; use of different baseline mortality data for specific European regions; inclusion of future population projections and dynamical changes for 2050 obtained from the United Nations (UN) Population Projections or use of non-linear exposure-response functions) to estimate the premature mortality due to PM2.5. The mortality endpoints included in this study are Lung Cancer (LC), Chronic Obstructive Pulmonary Disease (COPD), Cerebrovascular Disease (CEV), Ischemic Heart Disease (IHD), Lower Respiratory Infection (LRI) and other Non-Communicable Diseases (other NCDs). Different risk ratio and baseline mortalities for each disease end each age range have been estimated individually. The results indicate that the annual excess mortality rate from fine particulate matter in Europe is 904,000 [95% confidence interval (95% CI) 733,100-1,067,800], increasing by 73% in 2050s (1,560,000; 95% CI 1,260,000-1,840,000); meanwhile population decreases from 808 to 806 million according to the UN estimations. The results show that IHD is the main cause of premature mortality in Europe associated to PM2.5 (around 48%) both for the present and future periods. Despite several marked regional differences, premature deaths associated to all the endpoints included in this study will increase in the future period due to the climate penalty but especially because of changes in the population projected and its aging.

Modelling black carbon absorption of solar radiation: combining external and internal mixing assumptions.

An accurate simulation of the absorption properties is key for assessing the radiative effects of aerosol on meteorology and climate. The representation of how chemical species are mixed inside the particles (the mixing state) is one of the major uncertainty factors in the assessment of these effects. Here we compare aerosol optical properties simulations over Europe and North America, coordinated in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII), to 1 year of AERONET sunphotometer retrievals, in an attempt to identify a mixing state representation that better reproduces the observed single scattering albedo and its spectral variation. We use a single post-processing tool (FlexAOD) to derive aerosol optical properties from simulated aerosol speciation profiles, and focus on the absorption enhancement of black carbon when it is internally mixed with more scattering material, discarding from the analysis scenes dominated by dust. We found that the single scattering albedo at 440 nm (ω 0,440) is on average overestimated (underestimated) by 3-5 % when external (core-shell internal) mixing of particles is assumed, a bias comparable in magnitude with the typical variability of the quantity. The (unphysical) homogeneous internal mixing assumption underestimates ω 0,440 by ~ 14 %. The combination of external and core-shell configurations (partial internal mixing), parameterized using a simplified function of air mass aging, reduces the ω 0,440 bias to -1/-3 %. The black carbon absorption enhancement (E abs) in core-shell with respect to the externally mixed state is in the range 1.8-2.5, which is above the currently most accepted upper limit of ~ 1.5. The partial internal mixing reduces E abs to values more consistent with this limit. However, the spectral dependence of the absorption is not well reproduced, and the absorption Ångström exponent AAE 675 440 is overestimated by 70-120 %. Further testing against more comprehensive campaign data, including a full characterization of the aerosol profile in terms of chemical speciation, mixing state, and related optical properties, would help in putting a better constraint on these calculations.

Evaluation of impact of residential heating on air quality of megacity Istanbul by CMAQ.

Residential heating is one of the major sectors that contribute to emissions of harmful air pollutants in urban areas. In Istanbul, local sources are the main contributors of particulate matter levels. To quantify the contributions of residential heating sector to ambient particulate matter concentrations, we have developed an up to date spatially distributed high-resolution emissions inventory based on local activity data. The air quality simulations were conducted using the CMAQ (version 5.2). Our study results showed that our high-resolution emissions of the residential heating sector significantly improve the spatial distribution and concentration of air pollutants (SO2, PM10, PM2.5) for Istanbul. Air quality model simulations with our high-resolution emissions underestimated PM10 concentrations throughout the study episode on average by only 4.16% with a mean bias of 2.23 µg/m-3 while base inventory underestimated PM10 concentrations on average by 35.1% with a mean bias of 18.91 µg/m-3. Results show that our spatially distributed high-resolution emissions inventory produces more realistic results for Istanbul during wintertime when residential heating has the most influence on local air pollution.

Modeled deposition of nitrogen and sulfur in Europe estimated by 14 air quality model systems: evaluation, effects of changes in emissions and implications for habitat protection.

The evaluation and intercomparison of air quality models is key to reducing model errors and uncertainty. The projects AQMEII3 and EURODELTA-Trends, in the framework of the Task Force on Hemispheric Transport of Air Pollutants and the Task Force on Measurements and Modelling, respectively (both task forces under the UNECE Convention on the Long Range Transport of Air Pollution, LTRAP), have brought together various regional air quality models to analyze their performance in terms of air concentrations and wet deposition, as well as to address other specific objectives. This paper jointly examines the results from both project communities by intercomparing and evaluating the deposition estimates of reduced and oxidized nitrogen (N) and sulfur (S) in Europe simulated by 14 air quality model systems for the year 2010. An accurate estimate of deposition is key to an accurate simulation of atmospheric concentrations. In addition, deposition fluxes are increasingly being used to estimate ecological impacts. It is therefore important to know by how much model results differ and how well they agree with observed values, at least when comparison with observations is possible, such as in the case of wet deposition. This study reveals a large variability between the wet deposition estimates of the models, with some performing acceptably (according to previously defined criteria) and others underestimating wet deposition rates. For dry deposition, there are also considerable differences between the model estimates. An ensemble of the models with the best performance for N wet deposition was made and used to explore the implications of N deposition in the conservation of protected European habitats. Exceedances of empirical critical loads were calculated for the most common habitats at a resolution of 100 × 100 m2 within the Natura 2000 network, and the habitats with the largest areas showing exceedances are determined. Moreover, simulations with reduced emissions in selected source areas indicated a fairly linear relationship between reductions in emissions and changes in the deposition rates of N and S. An approximate 20 % reduction in N and S deposition in Europe is found when emissions at a global scale are reduced by the same amount. European emissions are by far the main contributor to deposition in Europe, whereas the reduction in deposition due to a decrease in emissions in North America is very small and confined to the western part of the domain. Reductions in European emissions led to substantial decreases in the protected habitat areas with critical load exceedances (halving the exceeded area for certain habitats), whereas no change was found, on average, when reducing North American emissions in terms of average values per habitat.

Influence of anthropogenic emissions and boundary conditions on multi-model simulations of major air pollutants over Europe and North America in the framework of AQMEII3.

In the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3), and as contribution to the second phase of the Hemispheric Transport of Air Pollution (HTAP2) activities for Europe and North America, the impacts of a 20 % decrease of global and regional anthropogenic emissions on surface air pollutant levels in 2010 are simulated by an international community of regional-scale air quality modeling groups, using different state-of-the-art chemistry and transport models (CTMs). The emission perturbations at the global level, as well as over the HTAP2-defined regions of Europe, North America and East Asia, are first simulated by the global Composition Integrated Forecasting System (C-IFS) model from European Centre for Medium-Range Weather Forecasts (ECMWF), which provides boundary conditions to the various regional CTMs participating in AQMEII3. On top of the perturbed boundary conditions, the regional CTMs used the same set of perturbed emissions within the regional domain for the different perturbation scenarios that introduce a 20 % reduction of anthropogenic emissions globally as well as over the HTAP2-defined regions of Europe, North America and East Asia. Results show that the largest impacts over both domains are simulated in response to the global emission perturbation, mainly due to the impact of domestic emission reductions. The responses of NO2, SO2 and PM concentrations to a 20 % anthropogenic emission reduction are almost linear (~ 20 % decrease) within the global perturbation scenario with, however, large differences in the geographical distribution of the effect. NO2, CO and SO2 levels are strongly affected over the emission hot spots. O3 levels generally decrease in all scenarios by up to ~ 1 % over Europe, with increases over the hot spot regions, in particular in the Benelux region, by an increase up to ~ 6 % due to the reduced effect of NOx titration. O3 daily maximum of 8 h running average decreases in all scenarios over Europe, by up to ~ 1 %. Over the North American domain, the central-to-eastern part and the western coast of the US experience the largest response to emission perturbations. Similar but slightly smaller responses are found when domestic emissions are reduced. The impact of intercontinental transport is relatively small over both domains, however, still noticeable particularly close to the boundaries. The impact is noticeable up to a few percent, for the western parts of the North American domain in response to the emission reductions over East Asia. O3 daily maximum of 8 h running average decreases in all scenarios over north Europe by up to ~ 5 %. Much larger reductions are calculated over North America compared to Europe. In addition, values of the Response to Extra-Regional Emission Reductions (RERER) metric have been calculated in order to quantify the differences in the strengths of nonlocal source contributions to different species among the different models. We found large RERER values for O3 (~ 0.8) over both Europe and North America, indicating a large contribution from non-local sources, while for other pollutants including particles, low RERER values reflect a predominant control by local sources. A distinct seasonal variation in the local vs. non-local contributions has been found for both O3 and PM2.5, particularly reflecting the springtime long-range transport to both continents.

Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3.

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Initiative (AQMEII3). The modeled surface concentrations of O3, CO, SO2 and PM2.5 are used as input to the Economic Valuation of Air Pollution (EVA) system to calculate the resulting health impacts and the associated external costs from each individual model. Along with a base case simulation, additional runs were performed introducing 20 % anthropogenic emission reductions both globally and regionally in Europe, North America and east Asia, as defined by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2). Health impacts estimated by using concentration inputs from different chemistry-transport models (CTMs) to the EVA system can vary up to a factor of 3 in Europe (12 models) and the United States (3 models). In Europe, the multi-model mean total number of premature deaths (acute and chronic) is calculated to be 414 000, while in the US, it is estimated to be 160 000, in agreement with previous global and regional studies. The economic valuation of these health impacts is calculated to be EUR 300 billion and 145 billion in Europe and the US, respectively. A subset of models that produce the smallest error compared to the surface observations at each time step against an all-model mean ensemble results in increase of health impacts by up to 30 % in Europe, while in the US, the optimal ensemble mean led to a decrease in the calculated health impacts by ~ 11 %. A total of 54 000 and 27 500 premature deaths can be avoided by a 20 % reduction of global anthropogenic emissions in Europe and the US, respectively. A 20 % reduction of North American anthropogenic emissions avoids a total of ~ 1000 premature deaths in Europe and 25 000 total premature deaths in the US. A 20 % decrease of anthropogenic emissions within the European source region avoids a total of 47 000 premature deaths in Europe. Reducing the east Asian anthropogenic emissions by 20 % avoids ~ 2000 total premature deaths in the US. These results show that the domestic anthropogenic emissions make the largest impacts on premature deaths on a continental scale, while foreign sources make a minor contribution to adverse impacts of air pollution.

Two-scale multi-model ensemble: is a hybrid ensemble of opportunity telling us more?

In this study we introduce a hybrid ensemble consisting of air quality models operating at both the global and regional scale. The work is motivated by the fact that these different types of models treat specific portions of the atmospheric spectrum with different levels of detail, and it is hypothesized that their combination can generate an ensemble that performs better than mono-scale ensembles. A detailed analysis of the hybrid ensemble is carried out in the attempt to investigate this hypothesis and determine the real benefit it produces compared to ensembles constructed from only global-scale or only regional-scale models. The study utilizes 13 regional and 7 global models participating in the Hemispheric Transport of Air Pollutants phase 2 (HTAP2)-Air Quality Model Evaluation International Initiative phase 3 (AQMEII3) activity and focuses on surface ozone concentrations over Europe for the year 2010. Observations from 405 monitoring rural stations are used for the evaluation of the ensemble performance. The analysis first compares the modelled and measured power spectra of all models and then assesses the properties of the mono-scale ensembles, particularly their level of redundancy, in order to inform the process of constructing the hybrid ensemble. This study has been conducted in the attempt to identify that the improvements obtained by the hybrid ensemble relative to the mono-scale ensembles can be attributed to its hybrid nature. The improvements are visible in a slight increase of the diversity (4 % for the hourly time series, 10 % for the daily maximum time series) and a smaller improvement of the accuracy compared to diversity. Root mean square error (RMSE) improved by 13-16 % compared to G and by 2-3 % compared to R. Probability of detection (POD) and false-alarm rate (FAR) show a remarkable improvement, with a steep increase in the largest POD values and smallest values of FAR across the concentration ranges. The results show that the optimal set is constructed from an equal number of global and regional models at only 15 % of the stations. This implies that for the majority of the cases the regional-scale set of models governs the ensemble. However given the high degree of redundancy that characterizes the regional-scale models, no further improvement could be expected in the ensemble performance by adding yet more regional models to it. Therefore the improvement obtained with the hybrid set can confidently be attributed to the different nature of the global models. The study strongly reaffirms the importance of an in-depth inspection of any ensemble of opportunity in order to extract the maximum amount of information and to have full control over the data used in the construction of the ensemble.

HTAP2 multi-model estimates of premature human mortality due to intercontinental transport of air pollution and emission sectors.

Ambient air pollution from ozone and fine particulate matter is associated with premature mortality. As emissions from one continent influence air quality over others, changes in emissions can also influence human health on other continents. We estimate global air pollution-related premature mortality from exposure to PM2.5 and ozone, and the avoided deaths from 20% anthropogenic emission reductions from six source regions, North America (NAM), Europe (EUR), South Asia (SAS), East Asia (EAS), Russia/Belarus/Ukraine (RBU) and the Middle East (MDE), three global emission sectors, Power and Industry (PIN), Ground Transportation (TRN) and Residential (RES) and one global domain (GLO), using an ensemble of global chemical transport model simulations coordinated by the second phase of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP2), and epidemiologically-derived concentration-response functions. We build on results from previous studies of the TF-HTAP by using improved atmospheric models driven by new estimates of 2010 anthropogenic emissions (excluding methane), with more source and receptor regions, new consideration of source sector impacts, and new epidemiological mortality functions. We estimate 290,000 (95% CI: 30,000, 600,000) premature O3-related deaths and 2.8 million (0.5 million, 4.6 million) PM2.5-related premature deaths globally for the baseline year 2010. While 20% emission reductions from one region generally lead to more avoided deaths within the source region than outside, reducing emissions from MDE and RBU can avoid more O3-related deaths outside of these regions than within, and reducing MDE emissions also avoids more PM2.5-related deaths outside of MDE than within. Our findings that most avoided O3-related deaths from emission reductions in NAM and EUR occur outside of those regions contrast with those of previous studies, while estimates of PM2.5-related deaths from NAM, EUR, SAS and EAS emission reductions agree well. In addition, EUR, MDE and RBU have more avoided O3-related deaths from reducing foreign emissions than from domestic reductions. For six regional emission reductions, the total avoided extra-regional mortality is estimated as 6,000 (-3,400, 15,500) deaths/year and 25,100 (8,200, 35,800) deaths/year through changes in O3 and PM2.5, respectively. Interregional transport of air pollutants leads to more deaths through changes in PM2.5 than in O3, even though O3 is transported more on interregional scales, since PM2.5 has a stronger influence on mortality. For NAM and EUR, our estimates of avoided mortality from regional and extra-regional emission reductions are comparable to those estimated by regional models for these same experiments. In sectoral emission reductions, TRN emissions account for the greatest fraction (26-53% of global emission reduction) of O3-related premature deaths in most regions, in agreement with previous studies, except for EAS (58%) and RBU (38%) where PIN emissions dominate. In contrast, PIN emission reductions have the greatest fraction (38-78% of global emission reduction) of PM2.5-related deaths in most regions, except for SAS (45%) where RES emission dominates, which differs with previous studies in which RES emissions dominate global health impacts. The spread of air pollutant concentration changes across models contributes most to the overall uncertainty in estimated avoided deaths, highlighting the uncertainty in results based on a single model. Despite uncertainties, the health benefits of reduced intercontinental air pollution transport suggest that international cooperation may be desirable to mitigate pollution transported over long distances.

Attributing differences in the fate of lateral boundary ozone in AQMEII3 models to physical process representations.

Increasing emphasis has been placed on characterizing the contributions and the uncertainties of ozone imported from outside the US. In chemical transport models (CTMs), the ozone transported through lateral boundaries (referred to as LB ozone hereafter) undergoes a series of physical and chemical processes in CTMs, which are important sources of the uncertainty in estimating the impact of LB ozone on ozone levels at the surface. By implementing inert tracers for LB ozone, the study seeks to better understand how differing representations of physical processes in regional CTMs may lead to differences in the simulated LB ozone that eventually reaches the surface across the US. For all the simulations in this study (including WRF/CMAQ, WRF/CAMx, COSMO-CLM/CMAQ, and WRF/DEHM), three chemically inert tracers that generally represent the altitude ranges of the planetary boundary layer (BC1), free troposphere (BC2), and upper troposphere-lower stratosphere (BC3) are tracked to assess the simulated impact of LB specification. Comparing WRF/CAMx with WRF/CMAQ, their differences in vertical grid structure explain 10 %-60 % of their seasonally averaged differences in inert tracers at the surface. Vertical turbulent mixing is the primary contributor to the remaining differences in inert tracers across the US in all seasons. Stronger vertical mixing in WRF/CAMx brings more BC2 downward, leading to higher BCT (BCT = BC1+BC2+BC3) and BC2/BCT at the surface in WRF/CAMx. Meanwhile, the differences in inert tracers due to vertical mixing are partially counteracted by their difference in sub-grid cloud mixing over the southeastern US and the Gulf Coast region during summer. The process of dry deposition adds extra gradients to the spatial distribution of the differences in DM8A BCT by 5-10 ppb during winter and summer. COSMO-CLM/CMAQ and WRF/CMAQ show similar performance in inert tracers both at the surface and aloft through most seasons, which suggests similarity between the two models at process level. The largest difference is found in summer. Sub-grid cloud mixing plays a primary role in their differences in inert tracers over the southeastern US and the oceans in summer. Our analysis of the vertical profiles of inert tracers also suggests that the model differences in dry deposition over certain regions are offset by the model differences in vertical turbulent mixing, leading to small differences in inert tracers at the surface in these regions.

Seasonal ozone vertical profiles over North America using the AQMEII3 group of air quality models: model inter-comparison and stratospheric intrusions.

This study evaluates simulated vertical ozone profiles produced in the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII3) against ozonesonde observations in North America for the year 2010. Four research groups from the United States (US) and Europe have provided modeled ozone vertical profiles to conduct this analysis. Because some of the modeling systems differ in their meteorological drivers, wind speed and temperature are also included in the analysis. In addition to the seasonal ozone profile evaluation for 2010, we also analyze chemically inert tracers designed to track the influence of lateral boundary conditions on simulated ozone profiles within the modeling domain. Finally, cases of stratospheric ozone intrusions during May-June 2010 are investigated by analyzing ozonesonde measurements and the corresponding model simulations at Intercontinental Chemical Transport Experiment Ozonesonde Network Study (IONS) experiment sites in the western United States. The evaluation of the seasonal ozone profiles reveals that, at a majority of the stations, ozone mixing ratios are underestimated in the 1-6 km range. The seasonal change noted in the errors follows the one seen in the variance of ozone mixing ratios, with the majority of the models exhibiting less variability than the observations. The analysis of chemically inert tracers highlights the importance of lateral boundary conditions up to 250 hPa for the lower-tropospheric ozone mixing ratios (0-2 km). Finally, for the stratospheric intrusions, the models are generally able to reproduce the location and timing of most intrusions but underestimate the magnitude of the maximum mixing ratios in the 2-6 km range and overestimate ozone up to the first kilometer possibly due to marine air influences that are not accurately described by the models. The choice of meteorological driver appears to be a greater predictor of model skill in this altitude range than the choice of air quality model.

Evaluation and error apportionment of an ensemble of atmospheric chemistry transport modeling systems: multivariable temporal and spatial breakdown.

Through the comparison of several regional-scale chemistry transport modeling systems that simulate meteorology and air quality over the European and North American continents, this study aims at (i) apportioning error to the responsible processes using timescale analysis, (ii) helping to detect causes of model error, and (iii) identifying the processes and temporal scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition, and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overallsense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance, and covariance) can help assess the nature and quality of the error. Each of the error components is analyzed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intraday) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emission and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high interdependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. This will require evaluaion methods that are able to frame the impact on error of processes, conditions, and fluxes at the surface. For example, error due to emission and boundary conditions is dominant for primary species (CO, particulate matter (PM)), while errors due to meteorology and chemistry are most relevant to secondary species, such as ozone. Some further aspects emerged whose interpretation requires additional consideration, such as the uniformity of the synoptic error being region- and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

Spatial and temporal analysis of black carbon aerosols in Istanbul megacity.

Black carbon (BC) is an important component of particulate matter due to its effects on human health and climate. In this study, we present the first BC concentrations measured in the Istanbul megacity (~15 million inhabitants). Two measurement campaigns have been conducted to measure BC and fine particulate matter (PM2.5) concentrations at four locations, characterized by different traffic densities. In the first campaign, BC daily mean concentrations have been found to be between 4 µg/m(3) and 10 µg/m(3). In the second campaign, BC and PM2.5 have been measured at the site with the highest traffic density for an entire year. Annually averaged BC contributes by 38 ± 14% to the PM2.5 levels (annual average BC: 13 µg/m(3) and PM2.5: 36 µg/m(3)). Diurnal variations of BC concentrations followed those of traffic density (correlation coefficient of 0.87). These measurements are essential to identify the sources of BC and PM2.5 concentrations in Istanbul and develop mitigation measures.

Analysis of surface ozone and nitrogen oxides at urban, semi-rural and rural sites in Istanbul, Turkey.

Ozone (O(3)) mixing ratios were measured at three different sites (urban/traffic, semi-rural and rural/island) in Istanbul from September 2007 to December 2009 in order to determine the diurnal, monthly and seasonal variations of O(3) and nitrogen oxides (NO(x)) and to study the local and regional impacts. This is the first study that evaluates the O(3) levels in semi-rural and rural sites in Istanbul in addition to the urban sites. The diurnal O(3) variations are generally characterized by afternoon maxima (64 ppb at the urban, 80 ppb at the semi-rural and 100 ppb at the rural site) and the nighttime minimum being more pronounced at the polluted urban site. The monthly mean O(3) mixing ratios start to increase in March, reaching their maximum values in August for the urban (~25 ppb) and semi-rural sites (30 ppb). However, at the rural site, the monthly mean O(3) levels reach their maximum value in June (35 ppb). The O(3) mixing ratios for weekends were higher than those on weekdays at each site by up to 28%, possibly due to changes in VOC sensitivity and reduction in NO(x) levels. In order to better understand and characterize the relationship between air masses and O(3) levels, cluster analysis was applied to the back-trajectories calculated by the HYSPLIT model for the semi-rural site. The analyses clearly showed that major transport is characterized by northern and western clusters, particularly from the Eastern Europe and the Mediterranean region, as well as recirculation over Istanbul due to high pressure systems leading to accumulated levels of O(3). The results clearly suggest that extended measurement networks from urban to rural sites should be considered for a more comprehensive evaluation of O(3) levels.

The impact of anthropogenic and biogenic emissions on surface ozone concentrations in Istanbul.

Surface ozone concentrations at Istanbul during a summer episode in June 2008 were simulated using a high resolution and urban scale modeling system coupling MM5 and CMAQ models with a recently developed anthropogenic emission inventory for the region. Two sets of base runs were performed in order to investigate for the first time the impact of biogenic emissions on ozone concentrations in the Greater Istanbul Area (GIA). The first simulation was performed using only the anthropogenic emissions whereas the second simulation was performed using both anthropogenic and biogenic emissions. Biogenic NMVOC emissions were comparable with anthropogenic NMVOC emissions in terms of magnitude. The inclusion of biogenic emissions significantly improved the performance of the model, particularly in reproducing the low night time values as well as the temporal variation of ozone concentrations. Terpene emissions contributed significantly to the destruction of the ozone during nighttime. Biogenic NMVOCs emissions enhanced ozone concentrations in the downwind regions of GIA up to 25ppb. The VOC/NO(x) ratio almost doubled due to the addition of biogenic NMVOCs. Anthropogenic NO(x) and NMVOCs were perturbed by ±30% in another set of simulations to quantify the sensitivity of ozone concentrations to the precursor emissions in the region. The sensitivity runs, as along with the model-calculated ozone-to-reactive nitrogen ratios, pointed NO(x)-sensitive chemistry, particularly in the downwind areas. On the other hand, urban parts of the city responded more to changes in NO(x) due to very high anthropogenic emissions.