•Only few in vivo toxicity and epidemiological studies focused specifically on non-exhaust sources.•Further experiments are needed to better separate individual contributions and health effects.•Need ...of understanding of the interaction between road surface texture, moisture, chemistry, dust load and dust emission.•Poor emission inventorying on resuspension and heavy metals.•The optimal mitigation strategy for each climatic region is still unknown.
About 400,000 premature adult deaths attributable to air pollution occur each year in the European Region. Road transport emissions account for a significant share of this burden. While important technological improvements have been made for reducing particulate matter (PM) emissions from motor exhausts, no actions are currently in place to reduce the non-exhaust part of emissions such as those from brake wear, road wear, tyre wear and road dust resuspension. These “non-exhaust” sources contribute easily as much and often more than the tailpipe exhaust to the ambient air PM concentrations in cities, and their relative contribution to ambient PM is destined to increase in the future, posing obvious research and policy challenges.
This review highlights the major and more recent research findings in four complementary fields of research and seeks to identify the current gaps in research and policy with regard to non-exhaust emissions. The objective of this article is to encourage and direct future research towards an improved understanding on the relationship between emissions, concentrations, exposure and health impact and on the effectiveness of potential remediation measures in the urban environment.
Source apportionment of organic aerosols (OAs) is of great importance to
better understand the health impact and climate effects of particulate matter
air pollution. Air quality models are used as ...potential tools to identify
OA components and sources at high spatial and temporal resolution; however,
they generally underestimate OA concentrations, and comparisons of their
outputs with an extended set of measurements are still rare due to the lack
of long-term experimental data. In this study, we addressed such challenges
at the European level. Using the regional Comprehensive Air Quality Model
with Extensions (CAMx) and a volatility basis set (VBS) scheme which was
optimized based on recent chamber experiments with wood burning and diesel
vehicle emissions, and which contains more source-specific sets compared to
previous studies, we calculated the contribution of OA components and defined
their sources over a whole-year period (2011). We modeled separately the
primary and secondary OA contributions from old and new diesel and gasoline
vehicles, biomass burning (mostly residential wood burning and agricultural
waste burning excluding wildfires), other anthropogenic sources (mainly
shipping, industry and energy production) and biogenic sources. An important
feature of this study is that we evaluated the model results with
measurements over a longer period than in previous studies, which
strengthens our confidence in our modeled source apportionment results.
Comparison against positive matrix factorization (PMF) analyses of aerosol
mass spectrometric measurements at nine European sites suggested that the
modified VBS scheme improved the model performance for total OA as well as
the OA components, including hydrocarbon-like (HOA), biomass burning (BBOA)
and oxygenated components (OOA). By using the modified VBS scheme, the mean
bias of OOA was reduced from −1.3 to −0.4 µg m−3
corresponding to a reduction of mean fractional bias from −45 % to
−20 %. The winter OOA simulation, which was largely underestimated in
previous studies, was improved by 29 % to 42 % among the evaluated
sites compared to the default parameterization. Wood burning was the dominant
OA source in winter (61 %), while biogenic emissions contributed
∼ 55 % to OA during summer in Europe on average. In both seasons,
other anthropogenic sources comprised the second largest component (9 %
in winter and 19 % in summer as domain average), while the average
contributions of diesel and gasoline vehicles were rather small
(∼ 5 %) except for the metropolitan areas where the highest
contribution reached 31 %. The results indicate the need to improve the
emission inventory to include currently missing and highly uncertain local
emissions, as well as further improvement of VBS parameterization for winter
biomass burning. Although this study focused on Europe, it can be applied in
any other part of the globe. This study highlights the ability of long-term
measurements and source apportionment modeling to validate and improve
emission inventories, and identify sources not yet properly included in
existing inventories.
Atmospheric aerosol particle number concentrations impact our climate and health in ways different from those of aerosol mass concentrations. However, the global, current and future anthropogenic ...particle number emissions and their size distributions are so far poorly known. In this article, we present the implementation of particle number emission factors and the related size distributions in the GAINS (Greenhouse Gas–Air Pollution Interactions and Synergies) model. This implementation allows for global estimates of particle number emissions under different future scenarios, consistent with emissions of other pollutants and greenhouse gases. In addition to determining the general particulate number emissions, we also describe a method to estimate the number size distributions of the emitted black carbon particles. The first results show that the sources dominating the particle number emissions are different to those dominating the mass emissions. The major global number source is road traffic, followed by residential combustion of biofuels and coal (especially in China, India and Africa), coke production (Russia and China), and industrial combustion and processes. The size distributions of emitted particles differ across the world, depending on the main sources: in regions dominated by traffic and industry, the number size distribution of emissions peaks in diameters range from 20 to 50 nm, whereas in regions with intensive biofuel combustion and/or agricultural waste burning, the emissions of particles with diameters around 100 nm are dominant. In the baseline (current legislation) scenario, the particle number emissions in Europe, Northern and Southern Americas, Australia, and China decrease until 2030, whereas especially for India, a strong increase is estimated. The results of this study provide input for modelling of the future changes in aerosol–cloud interactions as well as particle number related adverse health effects, e.g. in response to tightening emission regulations. However, there are significant uncertainties in these current emission estimates and the key actions for decreasing the uncertainties are pointed out.
We quantify the reductions in primary emissions due to the COVID-19 lockdowns in Europe. Our estimates are provided in the form of a dataset of reduction factors varying per country and day that will ...allow the modelling and identification of the associated impacts upon air quality. The country- and daily-resolved reduction factors are provided for each of the following source categories: energy industry (power plants), manufacturing industry, road traffic and aviation (landing and take-off cycle). We computed the reduction factors based on open-access and near-real-time measured activity data from a wide range of information sources. We also trained a machine
learning model with meteorological data to derive weather-normalized electricity consumption reductions. The time period covered is from 21 February, when the first European localized lockdown was implemented in the region of Lombardy (Italy), until 26 April 2020. This period includes 5 weeks (23 March until 26 April) with the most severe and relatively unchanged restrictions upon mobility and socio-economic activities across Europe. The computed reduction factors were combined with the Copernicus Atmosphere Monitoring Service's European emission inventory using adjusted temporal emission profiles in order to derive time-resolved emission reductions per country and pollutant sector. During the most severe lockdown period, we estimate the average emission reductions to be −33 % for NOx, −8 % for non-methane volatile organic compounds (NMVOCs), −7 % for SOx and −7 % for PM2.5 at the EU-30 level (EU-28 plus Norway and Switzerland). For all pollutants more than 85 % of the total reduction
is attributable to road transport, except SOx. The reductions reached −50 % (NOx), −14 % (NMVOCs), −12 % (SOx) and −15 % (PM2.5) in countries where the lockdown restrictions were more severe such as Italy, France or Spain. To show the potential for air quality modelling, we simulated and evaluated NO2 concentration decreases in rural and urban background regions across Europe (Italy, Spain, France, Germany, United-Kingdom and Sweden). We found the lockdown measures to be responsible for NO2 reductions of up to −58 % at urban background locations (Madrid, Spain) and −44 % at rural background areas (France), with an average contribution of the traffic sector to total reductions of 86 % and 93 %, respectively. A clear improvement of the modelled results was found when considering the emission reduction factors, especially in Madrid, Paris and London where the bias is reduced by more than 90 %. Future updates will include the extension of the COVID-19 lockdown period covered, the addition of other
pollutant sectors potentially affected by the restrictions (commercial and residential combustion and shipping) and the evaluation of other air quality pollutants such as O3 and PM2.5. All the emission reduction factors are provided in the Supplement.
The development and application of chemistry transport models has a long tradition. Within the Netherlands the LOTOS–EUROS model has been developed by a consortium of institutes, after combining its ...independently developed predecessors in 2005. Recently, version 2.0 of the model was released as an open-source version. This paper presents the curriculum vitae of the model system, describing the model's history, model philosophy, basic features and a validation with EMEP stations for the new benchmark year 2012, and presents cases with the model's most recent and key developments. By setting the model developments in context and providing an outlook for directions for further development, the paper goes beyond the common model description.With an origin in ozone and sulfur modelling for the models LOTOS and EUROS, the application areas were gradually extended with persistent organic pollutants, reactive nitrogen, and primary and secondary particulate matter. After the combination of the models to LOTOS–EUROS in 2005, the model was further developed to include new source parametrizations (e.g. road resuspension, desert dust, wildfires), applied for operational smog forecasts in the Netherlands and Europe, and has been used for emission scenarios, source apportionment, and long-term hindcast and climate change scenarios. LOTOS–EUROS has been a front-runner in data assimilation of ground-based and satellite observations and has participated in many model intercomparison studies. The model is no longer confined to applications over Europe but is also applied to other regions of the world, e.g. China. The increasing interaction with emission experts has also contributed to the improvement of the model's performance. The philosophy for model development has always been to use knowledge that is state of the art and proven, to keep a good balance in the level of detail of process description and accuracy of input and output, and to keep a good record on the effect of model changes using benchmarking and validation. The performance of v2.0 with respect to EMEP observations is good, with spatial correlations around 0.8 or higher for concentrations and wet deposition. Temporal correlations are around 0.5 or higher. Recent innovative applications include source apportionment and data assimilation, particle number modelling, and energy transition scenarios including corresponding land use changes as well as Saharan dust forecasting. Future developments would enable more flexibility with respect to model horizontal and vertical resolution and further detailing of model input data. This includes the use of different sources of land use characterization (roughness length and vegetation), detailing of emissions in space and time, and efficient coupling to meteorology from different meteorological models.
Urban greenhouse gas emissions monitoring is essential to assessing the impact of climate mitigation actions. Using atmospheric continuous measurements of air quality and carbon dioxide (CO2), we ...developed a gradient-descent optimization system to estimate emissions of the city of Paris. We evaluated our joint CO2–CO–NOx optimization over the first SARS-CoV-2 related lockdown period, resulting in a decrease in emissions by 40% for NOx and 30% for CO2, in agreement with preliminary estimates using bottom-up activity data yet lower than the decrease estimates from Bayesian atmospheric inversions (50%). Before evaluating the model, we first provide an in-depth analysis of three emission data sets. A general agreement in the totals is observed over the region surrounding Paris (known as Île-de-France) since all the data sets are constrained by the reported national and regional totals. However, the data sets show disagreements in their sector distributions as well as in the interspecies ratios. The seasonality also shows disagreements among emission products related to nonindustrial stationary combustion (residential and tertiary combustion). The results presented in this paper show that a multispecies approach has the potential to provide sectoral information to monitor CO2 emissions over urban areas enabled by the deployment of collocated atmospheric greenhouse gases and air quality monitoring stations.
Carbon monoxide (CO) is an air pollutant that plays an important role in atmospheric chemistry and is mostly emitted by forest fires and incomplete combustion in, for example, road transport, ...residential heating, and industry. As CO is co-emitted with fossil fuel CO2 combustion emissions, it can be used as a proxy for CO2. Following the Paris Agreement, there is a need for independent verification of reported activity-based bottom-up CO2 emissions through atmospheric measurements.
CO can be observed daily at a global scale with the TROPOspheric Monitoring Instrument (TROPOMI) satellite instrument with daily global coverage at a resolution down to 5.5 × 7 km2. To take advantage of this unique TROPOMI dataset, we develop a cross-sectional flux-based emission quantification method that can be applied to quantify emissions from a large number of cities, without relying on computationally expensive inversions. We focus on Africa as a region with quickly growing cities and large uncertainties in current emission estimates. We use a full year of high-resolution Weather Research and Forecasting (WRF) simulations over three cities to evaluate and optimize the performance of our cross-sectional flux emission quantification method and show its reliability down to emission rates of 0.1 Tg CO yr−1.
Comparison of the TROPOMI-based emission estimates to the Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa (DACCIWA) and Emissions Database for Global Atmospheric Research (EDGAR) bottom-up inventories shows that CO emission rates in northern Africa are underestimated in EDGAR, suggesting overestimated combustion efficiencies. We see the opposite when comparing TROPOMI to the DACCIWA inventory in South Africa and Côte d'Ivoire, where CO emission factors appear to be overestimated. Over Lagos and Kano (Nigeria) we find that potential errors in the spatial disaggregation of national emissions cause errors in DACCIWA and EDGAR respectively. Finally, we show that our computationally efficient quantification method combined with the daily TROPOMI observations can identify a weekend effect in the road-transport-dominated CO emissions from Cairo and Algiers.
Methane emissions due to accidents in the oil and natural gas sector are very challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is ...the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio’s reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions.
Heavy metals constitute an important group of persistent toxic pollutants occurring in ambient air and other media. One of the suspected sources of these metals in the atmosphere is combustion of ...transport fuels in road vehicles. However, estimates of the emissions of these metals from road vehicles as reported in national emission inventories show a very high variability in emission factors used. This paper provides high quality data on concentrations of heavy metals in fuels and derives default emission factors from these. The paper discusses these values against the emission estimates presently reported by the Parties to the LRTAP Convention.
The measured concentrations of heavy metals in petrol and diesel fuel show a high variability between different samples taken at gas stations throughout Europe. Metal concentrations in road transport fuels vary over two orders of magnitude, but all remain in the ppb region (a few tenths of a ppb to a few hundred ppb for all metals). The frequency distributions of the measurements could be approximated by lognormal distributions. The emission factors, including 95 percent confidence intervals were derived from a statistical analysis of the survey data. We could not detect a significant difference between samples from different countries.
The fuel based emission factors as derived in this study are complemented with those related to unintentional lubricant oil combustion. This allowed an estimation of total exhaust heavy metal emissions for UNECE Europe, indicating that As, Hg and Se exhaust emissions were dominated by fuel combustion while Cd, Cr, Cu, Ni, Pb, and Zn exhaust emissions were dominated by lubricant oil combustion.
The proposed emission factors were generally lower than previously published emission factors. National emissions of heavy metals from vehicle exhaust, estimated in this study are in many cases considerably lower than those reported by the countries for this source.
► Heavy metal (HM) concentrations in European road transport fuels were measured. ► They all vary over more than two orders of magnitude between different samples. ► Fuel based HM tailpipe emission factors and uncertainty ranges are derived. ► Our data are compared with official national HM emission inventories. ► Significant differences show incomparable reporting by countries.
This paper highlights the development of the emission inventories and emission processing for Europe (EU) and North America (NA) in the second phase of the Air Quality Model Evaluation International ...Initiative (AQMEII) project. The main purpose of the second phase of the AQMEII project is to understand the importance of coupled meteorological-chemical models in our understanding of the feedback of chemistry on the meteorology. A second purpose of the second phase of the AQMEII project is to explore the differences between EU and NA in a dynamic evaluation of two modeling years (2006 and 2010). The first phase of AQMEII also considered the modeling year 2006. Comparing the two AQMEII phases, for the EU domain, there were substantial decreases in CO (−19%), NH3 (−11%), and SO2 (−12%) emissions between the phase 2 and phase 1 emissions used for 2006. For the NA domain, there were decreases in CO (−10%), non-methane hydrocarbons (−5%), PM2.5 (−8%), PM10 (−18%), SO2 (−12%), with an increase of 4% in NOx. For the 2010 modeling year, 2009 emissions were used as a proxy for 2010 emissions in the EU domain. Between 2006 and 2009, considerable emission reductions were achieved for 17 EU countries, Norway and Switzerland as well as EU-Non-Member States, for all emitted species aside from NH3, which remained almost stable. Non-EU countries showed little change in emissions levels, though this may be a result of poor data quality. Shipping emissions decreased for PM and SO2 due to Sulfur Emission Control Areas on the North Sea and the Baltic Sea, while increasing for other species. Overall for the EU domain between 2006 and 2009, estimated NOx emissions decreased by 10%, SO2 by 18%, CO by 12%, PM2.5 by 5%, PM10 by 6%, NMVOC by 11%, and NH3 by 1%. Between the 2006 and 2010 modeling years, estimated US NOx emissions decreased by 17%, SO2 by 29%, CO by 21%, PM2.5 by 12%, PM10 by 7%, NMHC by 4% and NH3 by 2% while Canadian and Mexican emissions were assumed to remain constant. These changes in US emissions, however, only reflect changes in sectors for which we had year-specific information. In terms of natural emissions, climatic differences between 2006 and 2010 caused the European emissions of isoprene to peak earlier in the year 2006 than in 2010, and overall achieved higher levels in 2010. Biogenic emissions in North America increased in the east and decreased in the west between 2006 and 2010, due to regional temperature differences between the years from natural variation in solar radiation reaching the ground.
•Comparison 2006 anthropogenic modeling emission estimates for EU & NA.•Comparison 2006 & 2010 anthropogenic modeling emission estimates for EU & NA.•Comparison biogenic emissions estimates LOTOS_EUROS (EU) & BEIS (NA) for 2006 & 2010.
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