Hot emission model for mobile sources: application to the metropolitan region of the city of Santiago, Chile

Depending on the final application, several methodologies for traffic emission estimation have been developed. Emission estimation based on total miles traveled or other average factors is a sufficient approach only for extended areas such as national or worldwide areas. For road emission control and strategies design, microscale analysis based on real-world emission estimations is often required. This involves actual driving behavior and emission factors of the local vehicle fleet under study. This paper reports on a microscale model for hot road emissions and its application to the metropolitan region of the city of Santiago, Chile. The methodology considers the street-by-street hot emission estimation with its temporal and spatial distribution. The input data come from experimental emission factors based on local driving patterns and traffic surveys of traffic flows for different vehicle categories. The methodology developed is able to estimate hourly hot road CO, total unburned hydrocarbons (THCs), particulate matter (PM), and NO(x) emissions for predefined day types and vehicle categories.     

Spatial surrogates for the disaggregation of CORINAIR emission inventories

CORINAIR atmospheric emission inventories are frequently used input data for air quality models with a domain situated in Europe. In CORINAIR emission inventories, sources are broken down over 11 major source categories. This paper presents spatial surrogates for the disaggregation of CORINAIR atmospheric emission inventories for input of air pollutants and particulate matter to grid or polygon based air quality model domains inside Europe. The basis for the disaggregation model was the CLC2000 land cover data to which statistical weights were added. Weights were population census data for residential emissions, employment statistics for agricultural and industrial area emissions, livestock statistics for ammonia emissions and annual aircraft movements for emissions realized by air transport. Additional road and off-road network information was used to disaggregate emissions realized by traffic. A comparison of top down produced emission estimates with spatially resolved national emission data for The Netherlands and the United Kingdom gave confidence in the present spatial surrogates as a tool for the top down production of atmospheric emission maps. Explained variance at a spatial resolution of 5 km was >70% for CO, NMVOC and NO_x, >60% for PM10 and almost 50% for SO₂.     

Emission inventory for the road transport sector in Sardinia (Italy)

Atmospheric emission inventories are important tools for studying air quality and to set up possible remediation plans in areas characterised by nonattainment of the limit values established by legislation. In industrialised countries a considerable fraction of the emissions is due to road traffic, in particular in urban areas. For this reason emissions from road traffic must be estimated as accurately as possible, a task that can be performed, for the European vehicle fleet, thanks to the availability of the COPERT III methodology. This methodology is powerful and accurate, even if its algorithms can be difficult to apply in a regional emission inventory; moreover the collection of the necessary input data requires a lot of resources and time. This paper describes the road traffic emission inventory estimated for Region Sardinia (Italy) with a bottom-up approach. The estimation has been done by means of a software tool (EMITRA—EMIssions from road TRAnsport) which implements the COPERT III methodology. The resulting emission inventory has been compared against another emission inventory for Sardinia and against emission inventories for other Italian regions, to evaluate its reliability.     

Resolving local-scale emissions for modeling air quality near roadways

A large body of literature published in recent years suggests increased health risk due to exposure of people to air pollution in close proximity to roadways. As a result, there is a need to more accurately represent the spatial concentration gradients near roadways to develop mitigation strategies. In this paper, we present a practical, readily adaptable methodology, using a "bottom-up" approach to develop a detailed highway vehicle emission inventory that includes emissions for individual road links. This methodology also takes advantage of geographic information system (GIS) software to improve the spatial accuracy of the activity information obtained from a Travel Demand Model. In addition, we present an air quality modeling application of this methodology in New Haven, CT. This application uses a hybrid modeling approach, in which a regional grid-based model is used to characterize average local ambient concentrations, and a Gaussian dispersion model is used to provide texture within the modeling domain because of spatial gradients associated with highway vehicle emissions and other local sources. Modeling results show substantial heterogeneity of pollutant concentrations within the modeling domain and strong spatial gradients associated with roadways, particularly for pollutants dominated by direct emissions.     

Sub-region (district) and sector level SO2 and NOx emissions for India: assessment of inventories and mitigation flexibility

Sub-regional and sector level distribution of SO₂ and NO_x emissions inventories for India have been estimated for all the 466 Indian districts using base data for years 1990 and 1995. Although, national level emissions provide general guidelines for assessing mitigation alternatives, but significant regional and sectoral variability exist in Indian emissions. Districts reasonably capture this variability to a fine grid as 80% of these districts are smaller than 1°×1° resolution with 60% being smaller than even 1/2°×1/2°. Moreover, districts in India have well-established administrative and institutional mechanisms that would be useful for implementing and monitoring measures. District level emission estimates thus offer a finer regional scale inventory covering the combined interests of the scientific community and policy makers. The inventory assessment methodology adopted is similar to that prescribed by the Intergovernmental Panel on Climate Change (IPCC) for greenhouse gas (GHG) emissions. The sectoral decomposition at district level includes emissions from fossil fuel combustion, non-energy emissions from industrial activities and agriculture. Total SO₂ and NO_x emissions from India were 3542 and 2636 Gg, respectively (1990) and 4638 and 3462 Gg (1995) growing at annual rate of around 5.5%. The sectoral composition of SO₂ emissions indicates a predominance of electric power generation sector (46%). Power and transport sector emissions equally dominate NO_x emissions contributing nearly 30% each. However, majority of power plants are situated in predominantly rural districts while the latter are concentrated in large urban centers. Mitigation efforts for transport sector NO_x emissions would therefore be higher. The district level analysis indicates diverse spatial distribution with the top 5% emitting districts contributing 46.5 and 33.3% of total national SO₂ and NO_x emissions, respectively. This skewed emission pattern, with a few districts, sectors and point sources emitting significant SO₂ and NO_x, offers mitigation flexibility to policy makers for cost-effective mitigation.     

A revised estimate of copper emissions from road transport in UNECE-Europe and its impact on predicted copper concentrations

Comparisons of measured and model-predicted atmospheric copper concentrations show a severe underestimation of the observed concentrations by the models. This underestimation may be (partly) due to underestimated emissions of copper to air. Since the phase out of asbestos brake lining material, the composition of brake lining material has changed and may contain up to ∼15% copper. This makes brake wear from vehicles potentially an important source of atmospheric (particulate) copper concentrations. In this paper, we reassess the copper emissions due to exhaust emissions and brake wear from road transport. Overall, our reassessments result in an estimate of total copper emission to air in UNECE-Europe of 4.0–5.5 ktonnes yr⁻¹, which is substantially higher than the previous estimate of 2.8 ktonnes yr⁻¹. Copper concentrations over Europe are calculated with the LOTOS-EUROS model using the revised emission data as model input. The results show that the revised emission estimates are a major step towards gap closure of predicted versus observed copper concentrations in ambient air. Brake wear emissions may be responsible for 50–75% of the total copper emissions to air for most of Western Europe. The hypothesis that road transport is an important source of copper emissions is tested and confirmed by (1) reviewing available literature data of chemically speciated PM data from road tunnel studies and (2) the gradient observed in copper concentrations from ambient PM monitoring going from rural sites to street stations. The literature review and observational data suggest that the majority of the emitted PM10 brake wear particles is in the PM2.5–10 size range. The results of this study indicate that modification of brake lining composition is an important mitigation option to reduce copper exposure of the population in Western Europe.     

Use of Subsampled Traffic Data to Estimate Roadway Emissions,Including Conversion to MOBILE6 Vehicle Classifications

Abstract

Air quality is degraded by many factors, among which the emissions from on‐road vehicles play a significant role. Timely and accurate estimate of such emissions becomes very important for policy‐making and effective control measures. However, lack of traffic data and outdated emission software make this task difficult. This research has demonstrated a new method that facilitates the vehicular emission inventories at the local level by using shorter-time Highway Performance Monitoring System (HPMS) traffic data along with the latest U.S. Environment Protection Agency (EPA) emission modeling software, MOBILE6. The conversion methodology was developed for converting readily available HPMS traffic volume data into EPA MOBILE-based traffic classifications, and a corresponding software program was written for automating the process. EPA MOBILE6 model was used to obtain emissions of nitrogen oxides (NO_x), volatile organic compound (VOC), and cabon monoxide (CO) emitted by the parent traffic and subsampled traffic data, and these emissions were additionally compared. The case study has shown that the difference of the magnitude between the emission estimates produced by certain subsampled and parent traffic data are minor, indicating that subsampled HPMS data can be used for reporting parent traffic emissions. It was also observed that traffic emissions follow a Weibull distribution, and NO_x emissions were more sensitive to the traffic data composition than VOC and CO. Lastly, use of average emission values of 20 or 30 consecutive minutes appears to be valid for representing hourly emissions.     

Atmospheric emissions of mercury from Australian point sources

The UN Global Mercury Assessment (GMA) estimates that atmospheric emissions of mercury from Australian stationary combustion sources were 97.0 tonnes for the year of 1995. This is more than 90% of the estimated emissions from stationary combustion for the whole of North America, and seems abnormally high for a country with a population of around 20 million, in spite of the fact that most of Australia's stationary energy supply is provided by coal. It is also significantly larger than previous estimates of mercury emissions from Australian sources. New estimates of Australian mercury emissions from stationary energy sources, based on both a top down and bottom up approach, are presented. These estimates can be reconciled for black coal fired power stations, but suggest that the bottom up approach (the Australian National Pollutant Inventory) significantly under-estimates emissions from brown coal fired plant, if mercury capture efficiencies in these plants are low, as observed for lignite-fired plant. The major uncertainties in these estimates are the coal mercury content in coals burnt in Australian power stations, and the mercury capture efficiency in particulate control devices used at these stations. Based on these estimates, Australian emissions of mercury from stationary energy are currently 2–8 tonnes/year, significantly lower than the GMA estimate.     

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT

This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO_2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation.

Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .     

Estimated long-term outdoor air pollution concentrations in a cohort study

Several recent studies associated long-term exposure to air pollution with increased mortality. An ongoing cohort study, the Netherlands Cohort Study on Diet and Cancer (NLCS), was used to study the association between long-term exposure to traffic-related air pollution and mortality. Following on a previous exposure assessment study in the NLCS, we improved the exposure assessment methods.Long-term exposure to nitrogen dioxide (NO₂), nitrogen oxide (NO), black smoke (BS), and sulphur dioxide (SO₂) was estimated. Exposure at each home address (N=21 868) was considered as a function of a regional, an urban and a local component. The regional component was estimated using inverse distance weighed interpolation of measurement data from regional background sites in a national monitoring network. Regression models with urban concentrations as dependent variables, and number of inhabitants in different buffers and land use variables, derived with a Geographic Information System (GIS), as predictor variables were used to estimate the urban component. The local component was assessed using a GIS and a digital road network with linked traffic intensities. Traffic intensity on the nearest road and on the nearest major road, and the sum of traffic intensity in a buffer of 100 m around each home address were assessed. Further, a quantitative estimate of the local component was estimated.The regression models to estimate the urban component explained 67%, 46%, 49% and 35% of the variances of NO₂, NO, BS, and SO₂ concentrations, respectively. Overall regression models which incorporated the regional, urban and local component explained 84%, 44%, 59% and 56% of the variability in concentrations for NO₂, NO, BS and SO₂, respectively.We were able to develop an exposure assessment model using GIS methods and traffic intensities that explained a large part of the variations in outdoor air pollution concentrations.     

Ammonia emission, deposition and impact assessment at the field scale: a case study of sub-grid spatial variability

A local ammonia (NH3) inventory for a 5x5 km area in central England was developed, to investigate the variability of emissions, deposition and impacts of NH3 at a field scale, as well as to assess the validity of the UK 5-km grid inventory. Input data were available for the study area for 1993 and 1996 on a field by field basis, allowing NH3 emissions to be calculated for each individual field, separately for livestock grazing, livestock housing and manure storage, landspreading of manures and fertiliser N application to crops and grassland. An existing atmospheric transport model was modified and applied to model air concentrations and deposition of NH3 at a fine spatial resolution (50 m grid). From the mapped deposition estimates and land cover information, critical loads and exceedances were derived. to study the implications of local variability for regional NH3 impacts assessments. The results show that the most extreme local variability in NH3 emissions, deposition and impacts is linked to housing and storage losses. However, landspreading of manures and intensive cattle grazing are other important area sources, which vary substantially in the landscape. Overall, the range of predicted emissions from agricultural land within the study area is 0-2000 kg N ha(-1) year(-1) in 1993 and 0-8000 kg N ha(-1) year(-1) in 1996, respectively, with the peak at a poultry farm located in the study area. On average, the estimated field level NH3 emissions over the study area closely match the emission for the equivalent 5-km grid square in the national inventory for 1996. Deposition and expected impacts are highly spatially variable, with the edges of woodland and small "islands" of semi-natural vegetation in intensive agricultural areas being most at risk from enhanced deposition. Conversely the centres of larger nature reserves receive less deposition than average. As a consequence of this local variability it is concluded that national assessments at the 5 km grid level underestimate the occurrence of critical loads exceedances due to NH3 in agricultural landscapes.     

Estimating national landfill methane emissions: an application of the 2006 Intergovernmental Panel on Climate Change Waste Model in Panama

This paper estimates national methane emissions from solid waste disposal sites in Panama over the time period 1990-2020 using both the 2006 Intergovernmental Panel on Climate Change (IPCC) Waste Model spreadsheet and the default emissions estimate approach presented in the 1996 IPCC Good Practice Guidelines. The IPCC Waste Model has the ability to calculate emissions from a variety of solid waste disposal site types, taking into account country- or region-specific waste composition and climate information, and can be used with a limited amount of data. Countries with detailed data can also run the model with country-specific values. The paper discusses methane emissions from solid waste disposal; explains the differences between the two methodologies in terms of data needs, assumptions, and results; describes solid waste disposal circumstances in Panama; and presents the results of this analysis. It also demonstrates the Waste Model's ability to incorporate landfill gas recovery data and to make projections. The former default method methane emissions estimates are 25 Gg in 1994, and range from 23.1 Gg in 1990 to a projected 37.5 Gg in 2020. The Waste Model estimates are 26.7 Gg in 1994, ranging from 24.6 Gg in 1990 to 41.6 Gg in 2020. Emissions estimates for Panama produced by the new model were, on average, 8% higher than estimates produced by the former default methodology. The increased estimate can be attributed to the inclusion of all solid waste disposal in Panama (as opposed to only disposal in managed landfills), but the increase was offset somewhat by the different default factors and regional waste values between the 1996 and 2006 IPCC guidelines, and the use of the first-order decay model with a time delay for waste degradation in the IPCC Waste Model.     

Estimating historical anthropogenic global sulfur emission patterns for the period 1850–1990

It is important to establish a reliable regional emission inventory of sulfur as a function of time when assessing the possible effects of global change and acid rain. This study developed a database of annual estimates of national sulfur emissions from 1850 to 1990. A common methodology was applied across all years and countries allowing for global totals to be produced by adding estimates from all countries. The consistent approach facilitates the modification of the database and the observation of changes at national, regional, or global levels. The emission estimates were based on net production (i.e., production plus imports minus exports), sulfur content, and sulfur retention for each country's production activities. Because the emission estimates were based on the above considerations, our database offers an opportunity to independently compare our results with those estimates based on individual country estimates. Fine temporal resolution clearly shows emission changes associated with specific historical events (e.g., wars, depressions, etc.) on a regional, national, or global basis. The spatial pattern of emissions shows that the US, the USSR, and China were the main sulfur emitters (i.e., approximately 50% of the total) in the world in 1990. The USSR and the US appear to have stabilized their sulfur emissions over the past 20 yr, and the recent increases in global sulfur emissions are linked to the rapid increases in emissions from China. Sulfur emissions have been reduced in some cases by switching from high- to low-sulfur coals. Flue gas desulfurization (FGD) has apparently made important contributions to emission reductions in only a few countries, such as Germany.     

Quality considerations of European PM emission inventories

This paper reviews information on emission inventories of particulate matter (PM) in Europe. A large body of scientific literature is available to cover many different aspects. Studies focus on specific sources or source sectors (road transport as well as off-road machinery, domestic heating, industry, agriculture, and natural sources), among which especially road transport emissions are clearly best established. Emission inventories comprising all sources are available for specific European regions, often pointing out regional differences, but also for the entire continent. Still these inventories often are not able to satisfy the needs. Due to PM specific circumstances such as the large number of sources, very different release pathways and differences of the individual particles in terms of chemical composition or size, it is very difficult to appropriately handle measurement conditions to arrive at adequate emission factors, especially when emission points cannot be defined clearly. Information on fugitive emissions (caused by wind shear, material transfer processes or other mechanical forces from non-point sources) is sparse, except for road traffic emissions where recent data seems to converge. The problem of data gaps concerns activities in industry (quarries), agriculture, but also natural particles like sea salt and wind-blown dust. Comparing complete inventories to independent efforts in assessing emissions, e.g. atmospheric measurements combined with source apportionment, allows to better understand and quantify the reliability of inventory data. Methodological improvements and harmonization currently under way in Europe will focus efforts and allow for more reliable PM inventories in the near future.     

Stochastic analysis to assess the spatial distribution of groundwater nitrate concentrations in the Po catchment (Italy)

A large database including temporal trends of physical, ecological and socio-economic data was developed within the EUROCAT project. The aim was to estimate the nutrient fluxes for different socio-economic scenarios at catchment and coastal zone level of the Po catchment (Northern Italy) with reference to the Water Quality Objectives reported in the Water Framework Directive (WFD 2000/60/CE) and also in Italian legislation. Emission data derived from different sources at national, regional and local levels are referred to point and non-point sources. While non-point (diffuse) sources are simply integrated into the nutrient flux model, point sources are irregularly distributed. Intensive farming activity in the Po valley is one of the main Pressure factors Driving groundwater pollution in the catchment, therefore understanding the spatial variability of groundwater nitrate concentrations is a critical issue to be considered in developing a Water Quality Management Plan. In order to use the scattered point source data as input in our biogeochemical and transport models, it was necessary to predict their values and associated uncertainty at unsampled locations. This study reports the spatial distribution and uncertainty of groundwater nitrate concentration at a test site of the Po watershed using a probabilistic approach. Our approach was based on geostatistical sequential Gaussian simulation used to yield a series of stochastic images characterized by equally probable spatial distributions of the nitrate concentration across the area. Post-processing of many simulations allowed the mapping of contaminated and uncontaminated areas and provided a model for the uncertainty in the spatial distribution of nitrate concentrations.     

Estimation of economic costs of particulate air pollution from road transport in China

Valuation of health effects of air pollution is becoming a critical component of the performance of cost–benefit analysis of pollution control measures, which provides a basis for setting priorities for action. Beijing has focused on control of transport emission as vehicular emissions have recently become an important source of air pollution, particularly during Olympic games and Post-games. In this paper, we conducted an estimation of health effects and economic cost caused by road transport-related air pollution using an integrated assessment approach which utilizes air quality model, engineering, epidemiology, and economics. The results show that the total economic cost of health impacts due to air pollution contributed from transport in Beijing during 2004–2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively. The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of annual Beijing GDP from 2004 to 2008. Average cost per vehicle and per ton of PM₁₀ emission from road transport can also be estimated as 106 US $/number and 3584 US $ t^?1, respectively. These findings illustrate that the impact of road transport contributed particulate air pollution on human health could be substantial in Beijing, whether in physical and economic terms. Therefore, some control measures to reduce transport emissions could lead to considerable economic benefit.     

Impacts of boundary layer mixing on pollutant vertical profiles in the lower troposphere: Implications to satellite remote sensing

Mixing in the planetary boundary layer (PBL) affects vertical distributions of air tracers in the lower troposphere. An accurate representation of PBL mixing is critical for chemical-transport models (CTMs) for applications sensitive to simulations of the vertical profiles of tracers. The full mixing assumption in the widely used global CTM GEOS-Chem has recently been supplemented with a non-local PBL scheme. This study analyzes the impact of the non-local scheme on model representation of PBL mixing, consequences for simulations of vertical profiles of air tracers and surface air pollution, and implications for model applications to the interpretation of data retrieved from satellite remote sensing. The non-local scheme significantly improves simulations of the vertical distributions for NO₂ and O₃, as evaluated using aircraft measurements in summer 2004. It also reduces model biases over the U.S. by more than 10 ppb for surface ozone concentrations at night and by 2–5 ppb for peak ozone in the afternoon, as evaluated using ground observations. The application to inverse modeling of anthropogenic NO_x emissions for East China using satellite retrievals of NO₂ from OMI and GOME-2 suggests that the full mixing assumption results in 3–14% differences in top–down emission budgets as compared to the non-local scheme. The top–down estimate combining the non-local scheme and the Lin et al. inverse modeling approach suggests a magnitude of 6.6 TgN yr^?1 for emissions of NO_x over East China in July 2008 and 8.0 TgN yr^?1 for January 2009, with the magnitude and seasonality in good agreement with bottom–up estimates.     

Meteorologically adjusted trends of daily maximum ozone concentrations in Taipei,Taiwan

The meteorological conditions exert large impacts on ozone concentrations, and may mask the long-term trends in ozone concentrations resulting from precursor emissions. Estimation of long-term trends of ozone concentrations due to the changes in precursor emissions is important for corresponding control strategy. Multiple linear regression (method I), multilayer perceptron (MLP) neural network (method II) and Komogorov-Zurbenko (KZ) filter method plus MLP methodology (method III), are used to estimate the meteorologically adjusted long-term trends of daily maximum ozone concentrations by removing the masking effects of meteorological conditions in this study. The daily maximum ozone concentrations and relative meteorological variables were extracted from six air-monitoring stations in Taipei area from 1994 to 2001. The data collected during 1994–2000 period were used as modeling set and utilized to estimate the meteorologically adjusted trends, and the data of 2001 were used as the validation data. The meteorologically adjusted trends of ozone for these three methods were calculated and compared. The results show that both MLP and KZ filter +MLP models are more suitable than multiple linear regression for estimating the long-term trends of ozone in Taipei, Taiwan. The long-term linear trends of meteorologically adjusted ozone concentrations due to the precursor emissions show an increase trend at all stations, and the percent changes per year range from 1.0% to 2.25% during the modeling period in Taipei area.     

Trend analysis of urban NO2 concentrations and the importance of direct NO2 emissions versus ozone/NOx equilibrium

The annual air quality standard of NO₂ is often exceeded in urban areas near heavy traffic locations. Despite significant decrease of NO_x emissions in 1986–2005 in the industrial and harbour area near Rotterdam, NO₂ concentrations at the urban background remain at the same level since the end of the nineties. Trend analysis of monitoring data revealed that the ozone/NO_x equilibrium is a more important factor than increasing direct NO₂ emissions by traffic. The latter has recently been identified as an additional NO₂ source due to the introduction of oxy-catalytic converters in diesel vehicles and the growing number of diesel vehicles. However, in Rotterdam over the period 1986–2005 direct NO₂ emissions by road traffic only increased 3–4%. Due to the importance of the ozone/NO_x equilibrium, it is concluded that local NO_x emissions in Rotterdam need substantial reduction to achieve lower NO₂ urban background levels. This is a relatively costly abatement strategy and, therefore, a “hotspot” approach aiming at reducing NO_x emissions by local traffic measures is more effective to meet European air quality standards.