Air Pollution Modeling and its Application XXIV

We present our recent studies with global modeling and analysis of atmospheric aerosols. We have used the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model and satellite and in situ data to investigate (1) long-term variations of aerosols over polluted and dust source regions and downwind ocean areas in the past three decades and the cause of the changes and (2) anthropogenic and volcanic contributions to the sulfate aerosol in the upper troposphere/lower stratosphere.

In the framework of EURODELTAIII model inter-comparison exercise (Bessagnet et al. 2014), we performed preliminary tests with a volatility basis set (VBS) scheme in the CAMx model to predict the fine organic aerosol concentrations in Europe during one of the EMEP measurement campaigns (February–March 2009). In addition to the traditional (no VBS) approach for organic aerosol modelling, two different assumptions regarding the volatility distribution of primary organic particles were simulated and results compared against aerosol mass spectrometer (AMS) measurements. For the period studied in this work, using the volatility proposed by Robinson et al. (2007) decreased OA concentrations while volatility distribution according to Tsimpidi et al. (2010), Shrivastava et al. (2011) improved the results by about 27 %.

Airborne particles of anthropogenic and/or natural origin have certain direct and indirect effects in the atmosphere. Radiative transfer is the category of processes related to aerosols and clouds (direct effects). Indirect effects are always associated with condensates at various atmospheric layers. Condensation within the tropospheric layers is mainly related to aerosol physical and chemical properties, thermodynamical and dynamical processes. As it was found in various studies, there is a strong relationship between aerosols and extreme weather events such as deep convection and extreme rainfall. Low-level condensation is associated with low-cloud formation and fog. In this presentation we will discuss the condensation processes within the lower and upper troposphere and how they are affected by the various types of aerosols. New model development related to nucleation processes and condensation at different levels is discussed. The model used for development and application is the fully-coupled atmospheric modeling system RAMS/ICLAMS. Model simulations have been performed for selected cases related to (i) extreme precipitation events and (ii) low-level condensation and fog formation in the Euro-Mediterranean and Arabian Peninsula. The sensitivity tests showed that the explicit activation of aerosols as CCN and IN causes changes in the precipitation distribution as well as in its spatiotemporal patterns. Fog formation near the coastline and low-cloud formation mechanisms are controlled by the thermal cooling and moisture evaporation by the surface. The accurate simulation of the microphysical processes involved in formation and dissipation of fog depends on several variables.

The importance of different processes and feedbacks in online coupled chemistry-meteorology models for air quality simulations and weather prediction was investigated in COST Action ES1004 (EuMetChem). Case studies for Europe were performed with different models as a coordinated exercise for two episodes in 2010 in order to analyse the aerosol direct and indirect radiative effect and the response of different models to aerosol-radiation interactions.

High concentrations of particles pose a threat to human health and the environment. In this study the influence of ammonia (NH₃) emissions on aerosol concentration in central Europe is investigated. Depending on crop growth, temperature and local legislation individual temporal profiles for fertilizer and manure application are calculated for each model grid cell of the SMOKE-EU emission model. The emission data was used as input for the CMAQ chemical transport model. Comparisons to EMEP observations indicate that the new ammonia emission module leads to a better agreement of modeled and observed concentrations. The model was used then to assess the influence of emission changes. It was found that a reduction of ammonia emissions by 50 % lead to a 24 % reduction of total PM_2.5 concentrations in the model domain during winter, mainly driven by reduced formation of ammonium nitrate.

The majority of the organic fraction of aerosols is suspected to be of secondary origin. However, the sources, chemical composition and formation mechanisms of secondary organic aerosols (SOA) remain one of the least understood processes relevant to the atmosphere. Laboratory experiments, in situ measurements and numerical simulations recently highlighted the formation of SOA through aqueous chemistry in humid aerosol particles and in cloud droplets. However, there is still a need to evaluate the relative relevance of SOA formation through aqueous chemistry in comparison to classical gas to particles conversion pathways. Cloud resolving model (CRM) allows simulating the complex aerosols-cloud-chemistry interactions. Meso-NH (Mesoscale Non-Hydrostatic atmospheric model) CRM model includes a cloud chemistry module and an aerosol module, which allows representing the formation of SOA due to aqueous phase reactivity. The model is applied on a cloud event observed at the puy de Dôme Mountain. Comparing simulations with or without the cloud chemistry module activated assesses the contribution of the cloud chemistry in the SOA formation. Results show a significant contribution of aqueous phase reactivity in the formation of SOA downstream the Mountain.

This study presents the development of a new 3D size-composition resolved aerosol model (SCRAM). It solves the aerosol dynamic evolution for external mixtures taking into account the processes of coagulation, condensation/evaporation and nucleation. Both the size of particles and the mass fraction of each chemical compound are discretised. For a given particle size, particles of different chemical compositions may co-exist. Chemical components can be grouped into aggregates to reduce computational cost. SCRAM is coupled to the air quality model Polair3d/Polyphemus, and its performance to model air quality over Greater Paris is evaluated, as well as the consequences of the internally mixed assumption on aerosol distribution, composition and optical properties.

Organic aerosols (OA) represent a large fraction (from 20 to 90 %) of the submicron particulate mass and it is mainly composed of secondary organic aerosols (SOA). Despite the ubiquity of OA in the atmosphere, there are still large uncertainties in understanding the formation pathways of SOA. Consequently, OA sources and physico-chemical transformations during their transport are poorly represented in chemistry-transport models and large gaps still remain between simulated and measured OA concentrations. The ability of the WRF-CHEM model to reproduce the organic aerosol mass concentration originated from anthropogenic or/and biogenic emissions is evaluated. From this perspective, simulations for two contrasted air masses are performed with WFR-Chem using the Volatility Basis Set (VBS) approach dedicated to the formation of SOA. Simulations results are compared to aerosol measurements performed at the puy de Dôme station with a compact Time-of-Flight Aerosol Mass Spectrometer for two episodes in autumn 2008 and in summer 2010. Moreover, measurements of both anthropogenic and biogenic VOCs are used to access the capacity of the WRF-Chem model to correctly simulate the concentrations levels of the gas precursors of the SOA.

The North Sea region is characterised by several anthropogenic activities such as shipping, agriculture, industry and tourism. These activities go along with emissions of air pollutants such as NO _X , NH₃, and SO₂ leading to the formation of HNO₃, H₂SO₄, and particulate matter. Gaseous bases and acids (mainly HNO₃, H₂SO₄ and NH₃) tend to form new particles or to condense on existing ones. Meteorological conditions and size distribution of existing particles affect partitioning of these substances between gas and particle phase and between particle modes. In the marine troposphere, sea salt particles (mainly Cl–, Na+ and $${\text{SO}}_{4}^{2 - }$$) account for a considerable amount of fine and coarse particles providing surface for condensation of above mentioned substances. The presence of sea salt may also affect N deposition because dry deposition velocities of gaseous substances and different particle modes vary considerably. In the presented study, the effect of sea salt emissions on atmospheric air pollution in the North Sea region was analysed by the means of the Community Multiscale Air Quality (CMAQ) Model. We simulated on a $$24 \times 24\;\text{km}^{2}$$ grid including the North and Baltic Sea. It was found, that the presence of sea salt increases coarse mode $${\text{NH}}_{4}^{ + }$$ and $${\text{NO}}_{3}^{ - }$$ concentrations considerably while fine mode concentrations are decreased. This leads to increased total N deposition in coastal regions. At the same time, the deposition distant to the shore on the land as well as into the ocean decreases. However, this study shows that on spatial average only about 5 % of N deposition into the North Sea is caused by sea salt particles. Locally, the effect of sea salt on N deposition is partly higher. Therefore, sea salt emissions in regional air quality models are important for predicting the partitioning of anthropogenic pollutants between gas and particle phase and their deposition patterns correctly.

We carry out high-resolution idealized tropical simulations in a general circulation model with interactive aerosol microphysics to investigate aerosol variations relevant to operational weather forecasting and convection permitting sub-climate scales. We quantify the temporal and spatial variations in aerosol properties. The aerosol structure is driven by aerosol microphysics processes and dynamical processes which influence aerosol properties, emission and transport on scales not resolved by climate models.

The paper investigates PM_2.5 levels and composition in 3 different locations in Poland for cold and warm period of 2013. The highest share of SOC and POC in PM_2.5 was found in heating season, which was probably due to an increase in the activity of local emission sources of PM, especially biomass burning and fossil fuel combustion in residential sector, while SIA contribution in PM_2.5 mass was relatively constant during heating and non-heating period at all 3 sites. During non-heating season air mass back trajectories were grouped into 5 clusters representing mostly westerly flows (50–72 %). During heating season the trajectories were grouped into 6 clusters coming mostly from eastern directions (54–64 %).

The Mediterranean region is characterized by the accumulation of aerosols from numerous and various sources, which have very important effects on its climate. Therefore, the evolution of aerosols in future is expected to influence climate projections over this region which is already seen as a climate change “hotspot”. In this study realized in the framework of the ChArMEx and HyMeX programmes of the MISTRALS initiative, we consider a regional climate modelling approach, using the ALADIN-climate model which includes an interactive aerosol scheme, in order to better understand the future evolution of aerosols and their impact on future climate. Simulations have been carried out over past (1971–2000) and future (2071–2100) periods. The evolution of aerosols is driven both by changes in anthropogenic emissions, and the changes in climate variables, such as surface wind, that drive natural emissions such as dust uplift. Both a decrease in sulfate aerosols coming from Europe and an increase of dust emissions in northern Africa are expected by the end of the 21st Century. The decrease in sulfate concentrations could contribute to about 10 % of the expected warming over Europe.

The present analysis focuses on the model capability to properly simulate long-range Saharan dust transport for summer 2012 in the Western Mediterranean. The present contribution shows an intercomparison of a set of 9 European regional dust model simulations. An exhaustive comparison of model outputs against other models and observations can reveal weaknesses of individual models, provide an assessment of uncertainties in simulating the dust cycle and give additional information on sources for potential model improvement. The model outputs are compared against a variety of both ground-based and airborne in situ and remote sensing measurements performed during the pre-ChArMEx/TRAQA field campaign. For this kind of study, multiple and different observations are combined to deliver a detailed idea of the structure and evolution of the dust cloud and the state of the atmosphere at the different stages of the event.

During summers 2013 and 2014, two three weeks intensive campaign took place over the western Mediterranean basin in order to investigate photo-oxidant and aerosol sources over the region. Within the frame of the MISTRALS/ChArMEx (Chemistry-Aerosol Mediterranean Experiment) program and the ANR/SAFMED (Secondary Aerosol Formation in the MEDiterranean) project, this campaign included an extensive experimental set-up based on ground-based, balloon-borne, aircraft and satellite measurements. In this paper, a modeling perspective of the campaign is given, based on simulations with the regional chemistry-transport model, CHIMERE, in a configuration shaped for the Mediterranean region. Major sources of photo-oxidants, and aerosols are addressed: long range transport from continental Europe, pollution build-up from shipping emissions, marine emissions, organic aerosol formation from biogenic and anthropogenic VOC emissions, dust emissions.

The Mediterranean region is characterized by the accumulation of aerosols from numerous and various sources, which show a strong spatio–temporal variability and a resulting large variety in aerosol optical properties over this basin. This study realized in the framework of the ChArMEx initiative aims at explaining this aerosol variability and the relationship between aerosol loads and weather conditions. From a regional simulation carried out with the ALADIN-climate model including an interactive aerosol scheme for the main species present in this region (desert dust, sea-salt, sulfates and carbonaceous particles), we have identified typical synoptic conditions that favour high aerosol loads over the Mediterranean, or on the contrary that are opposed to these high aerosol loads. These weather regimes are based on a statistical method of automated classification. This method enables to characterize the effects of aerosols on climate for each weather regime, and the links between aerosol variability and climate oscillations such as the North-Atlantic Oscillation (NAO).

Most epidemiological studies have been conducted based on relations between pollution concentrations measured at fixed ambient air quality monitoring sites, or modelled values using land-use regression models, and various health indicators. However, such simplistic modelling ignores several crucial factors, such as, (i) the activity patterns of individuals, i.e. people’s day-to-day movements, and (ii) the differences between indoor and outdoor air. We have developed a mathematical model for the determination of human exposure to ambient air pollution in an urban area, called EXPAND (EXposure model for Particulate matter And Nitrogen oxiDes). The model combines (i) predicted concentrations, and (ii) information on people’s activities and location of the population, to evaluate the spatial and temporal variation of average exposure of the urban population to ambient air pollution in different microenvironments. In particular, the model takes into account the movements of the population and the infiltration from outdoor to indoor air. We present fine-resolution numerical results on annual spatial concentration, time activity and population exposures to PM_2.5 in London and in the Helsinki Metropolitan Area, for 2008 and 2009. We have shown that the effect of neglecting the movements of the population, which is the currently commonly applied procedure, can result in an underprediction of exposure by several tens of per cent.

A computationally efficient atmospheric chemical transport model (FRAME) was used to generate source-receptor concentration and deposition data from a variety of spatially distributed and point sources of nitrogen and sulphur emissions. The model was evaluated by comparison with measurements of nitrogen compounds in the gaseous, particulate and aqueous phase and found to be fit for purpose as a policy tool for assessing the effects of future emissions controls. A scenario for the year 2030 predicted that 64 % of the area of natural ecosystems in the UK would be subject to deposition of nitrogen exceeding critical loads.

Attributing nitrogen (N) in the environment to emissions from agricultural management practices is difficult because of the complex and inter-related chemical and biological reactions associated with N and its cascading effects across land, air and water. Such analyses are critical, however, in understanding the benefits and disbenefits associated with environmental management options. Coupled physical models present new opportunities to understand relationships among environmental variables across multiple sources, pathways and scenarios. Because they trace the environmental fate of pollutant concentrations found in the environment through first-principle physical and chemical processes, they shed new light on these complex interactions and how they will respond under various management scenarios. In this study, we use a coupled modeling system to holistically assess the impacts of increased corn production on groundwater and air quality. In particular, we show how the models provide new information on the drivers for contamination in groundwater and air, and then relate pollutant concentration changes attributed to potential changes in corn production between 2002 and 2022 to health and cost outcomes.

Future changes in population exposures to ambient air pollution are inherently linked with long-term trends in outdoor air quality, but also with changes in the building stock. Moreover, the burden of disease is further driven by the ageing of the European populations. This study aims to assess the impact of changes in climate, emissions, building stocks and population on air pollution related human health impacts across Europe in the future. Therefore an integrated assessment model combining atmospheric models and health impacts has been setup for projections of the future developments in air pollution related premature mortality. The focus is here on the regional scale impacts of exposure to surface ozone (O₃), Secondary Inorganic Aerosols (SIA) and primary particulate matter (PPM).

EVA (Economic Valuation of Air pollution), a multi-scale and high-resolution integrated model system for assessing health impacts and related external costs from air pollution), has been further developed by implementing the air quality model, UBM, with a 1 km × 1 km resolution covering the whole of Denmark. The high-resolution model is coupled to the long-range chemistry-transport model DEHM (Danish Eulerian Hemispheric Model) in a configuration with four nested domains. By using this system, a high-resolution assessment of health impacts from air pollution and related external cost has been conducted for Denmark for the year 2012.

Semi-volatile organic pollutants (SVOCs) have a recognized impact on the environment and some of them have been banned in many countries for that reason. Others are not regulated in air quality legislation, except for benzo(a)pyrene (BaP), a polyaromatic hydrocarbon resulting from natural and anthropogenic combustion processes. The EU set an annual average limit of 1 ng/m3 in air and some health parameters can also be inferred from the correspondent ecotoxicity thresholds established for this chemical. With the help of chemistry transport models, the evolution of these parameters can be assessed under climate change scenarios, along with the BaP concentrations themselves. Hence, a scenario with MM5-CHIMERE driven by ECHAM5 SRES A2 forcing were calculated for the years 1991 to 2050, in a European domain with a spatial resolution of 25 km. An enhanced risk of lung cancer was registered throughout the covered area, deriving from a parallel increase in the BaP levels in air.

According to the Residential Energy Consumption Survey (RECS), homes in the United States consume approximately 10 quadrillion BTUs of energy each year, including electricity consumption for cooling, various fuels utilized for space heating, and other end uses. Electricity consumption will influence emissions from power plants, and these along with direct residential fuel combustion will also contribute to emissions of multiple key pollutants, with corresponding air quality and health impacts. We have developed models to quantify the energy savings associated with increased residential insulation and to estimate in monetary terms the environmental and public health benefits. We are considering both retrofits to existing housing and new construction, focusing on the 2012 International Energy Conservation Code (IECC), which specifies R-values and U-factors by climate zone and a number of other structural components and design specifications. We are applying EnergyPlus to a series of template files to estimate energy savings by fuel type and state, for both retrofits and new construction. To determine the emissions reductions related to reduced electricity generation, we used EPA’s AVERT tool. AVERT uses the basic attributes of electricity dispatch modeling to determine the power plants most likely influenced by energy efficiency programs, and provides the direct nitrogen oxide (NOx), sulfur dioxide (SO₂), and carbon dioxide (CO₂) emissions reductions on a plant-by-plant basis. For residential combustion, we used EPA’s AP-42 database and other resources to quantify direct emissions by fuel type (including natural gas, fuel oil, and wood). To model the health benefits of the criteria pollutant emissions, we linked the emissions reductions due to increased energy efficiency with the Community Multiscale Air Quality (CMAQ) model. We developed a series of simulations using CMAQ v4.7.1 instrumented with the Decoupled Direct Method (DDM), an advanced sensitivity analysis technique that allows us to estimate the influence of individual pollutants from individual sources or regions. We considered direct residential combustion by state, leveraging Census and housing start data to determine spatial patterns of emissions within states, and modeled individual power plants in geographic groupings using a design of experiments that allow us to estimate the impacts for all major power plants on the grid. We focused on fine particulate matter and ozone concentrations, as the key drivers of monetized health impacts. As CMAQ provides concentration estimates by grid cell, we are able to determine total public health benefits in terms of avoided mortality and morbidity as well as the distribution of those benefits for directly modeled facilities and locations. We estimate 19,000 premature deaths per year associated with EGU emissions, with more than half of the EGU-related health impacts attributable to emissions from seven states with significant coal combustion. We also estimate 10,000 premature deaths per year associated with residential combustion emissions, driven by primary PM_2.5 emissions. In general, primary PM_2.5 health damage functions are an order of magnitude larger than those of secondary PM_2.5 precursors. Our findings reinforce the significance of source-specific assessment of air quality and health impacts for developing public health policies.

This work describes the development and application of a statistical model that links electricity generating unit (EGU) and mobile source emissions with a city center monitoring cite. The model uses estimated emissions and measured concentrations over the period 2000–2012 in Atlanta, GA, USA to develop counterfactual time series of daily ozone concentrations. Further, the model estimates the sensitivity of observed ozone to each emissions sector. Results show that emissions control policies have had little effect on annual median ozone, have decreased 90th percentile ozone, and have increased 10th percentile ozone. Sensitivities to EGU and mobile emissions are compared and agree well with similar sensitivities calculated using a first-principles chemical transport model.

Elevated tropospheric ozone concentrations are not only harmful for human health, but they may also have detrimental effects on vegetation. Currently, an ozone exposure index (AOT40) and critical levels for the protection of vegetation have been agreed upon at European level based on the atmospheric ozone concentration and exposure time. Plants are however mainly affected by the ozone actually interacting with the plant tissue which could well be only a small part of the available ozone. Methods have therefore been developed for estimating ozone uptake by plants and to obtain reliable dose-response relationships based on the ozone flux. In this chapter we present a modelling procedure that integrates the DO3SE model for calculating the phytotoxic ozone dose with ECMWF meteorology and ground level ozone concentration maps to produce estimates of the ozone impact on yield and biomass accumulation of crops, forest trees and grassland. The procedure is applied to a Belgium for the year 2009 and compared with the results published for EMEP and used to quantify the impact of ozone on potato and wheat crop yields in 2009.

We create a streamlined approach for estimating the U.S. public health benefits of emission control, on a per-ton basis. We do so by incorporating epidemiological and economic valuation data into the adjoint of an atmospheric chemical transport model. We estimate benefits-per-ton of emission control for chronic mortality and acute morbidity endpoints. Our results indicate that benefits-per-ton of NO_x reduction are highly variable from source-to-source or location-to-location. We find that mortality due to long-term exposure comprises a significant portion of the total benefits of NO_x control for ozone air quality management.

In this work a multi-objective approach to define air quality policies is proposed based on the RIAT+ (Regional Integrated Assessment Modelling Tool) system. The solutions of the decision problem represent cost-effective policies at the sectorial level. The methodology is being applied to the Porto urban area, one of the most polluted areas in Portugal, and optimal control policies up to 2020 will be selected.

In this work we use the FRAME model to assess the changes in sulphur and nitrogen deposition for the 2000–2030 period in the Poland-Saxony transboundary area. The results show that the sulphur and oxidised nitrogen deposition was significantly decreased (by 60 and 45 % respectively). This resulted in significant reduction of the ecosystems affected by acidification. Deposition of reduced nitrogen resulting in eutrophication is currently, and forecast to remain, the main threat for the ecosystems of this area.

Black carbon (BC; a good tracer of road traffic emissions) concentrations from personal monitoring of 46 school children showed the highest levels in comparison with the corresponding fixed stations in schools and in a reference urban background station. Commuting periods showed extreme concentration peaks and the concentration was 2.9 times higher than at home. Children spent only 5.6 % of their time on commuting but received 19.6 % of their daily dose. Moreover, they received 36.5 % of the dose at schools. Therefore, traffic density around schools and typical school commuting routes should be reduced.

New agricultural technologies can reduce the emissions of ammonia associated with e.g. manure spreading. Reduced emissions to the atmosphere have the potential to limit the negative impacts of reactive nitrogen (N_r) on terrestrial ecosystems and human health. But could the new technologies transfer more N_r to the watershed instead and hence lead to increased eutrophication in the aquatic environment? In order to answer questions like this a holistic approach is necessary. Therefore a new management tools is under development at the Danish Center for Energy and Environment (DCE), Aarhus University, where models describing the fate of N_r in the relevant compartments (atmosphere, watershed and aquatic systems) are linked.

With advancing evidence of long-term health risks of ozone exposure, cumulative exposures are of interest for air quality regulation. The current form of the ozone air quality standard in the United States pertains to an extreme value (the design value) of the ozone distribution. Using atmospheric chemical transport modeling, we examine how well attainment metrics correlate with average exposure levels. We use forward sensitivity analysis to contrast the responses of two types of ozone metrics to widespread emission reductions. One such metric is based on extreme values of the ozone distribution used for attainment designation, while the other is the seasonal average ozone concentration indicative of long-term exposure levels. We find that in locations that have high day-to-day variability in ozone concentrations, design values are more sensitive to emission reductions and are least indicative of changing exposure levels with emission reductions.

Earth system modelling will be the kind of modelling that will be performed more and more. After some philosophical remarks, an overview will be given of some science questions that can only be properly treated by earth system models. The focus of these science questions will be on emissions. Concerning meteorology, the problems still existing with the atmospheric boundary layer will be presented. Data-assimilation will be addressed as an application issue, which can only be carried out more objectively with earth system models. The paper ends with ten rules for correct modelling.

Despite the substantial progress in addressing air quality problems, air pollution is still a serious concern in the developing and developed countries. There is now scientific consensus that atmospheric loading of greenhouse gases has been contributing to climate change. The 2014 IPCC report reiterated the need to address climate change on a global basis. It is also recognized that atmospheric composition can profoundly influence weather and climate directly by changing the atmospheric radiation budget or indirectly by affecting cloud formation and precipitation. Given the ever increasing computational power and ground and satellite-based observations, it is feasible to conduct observational and modeling investigations to improve our understanding of the role of aerosols on the monsoon dynamics and extreme events under changing climate. To address the above mentioned challenges, a three-day workshop was held during January 12–15, 2015 in Delhi, India bringing together scientists in air quality, weather, and climate fields from India, Europe, Japan, and North America to discuss the current state-of-science, identify research gaps, and prepare a research agenda to help improve our understanding of air quality and climate change interactions, and operationalize atmospheric modeling methods to better forecast the monsoon dynamics. Following the workshop, a small team of scientists from India, USA, Canada, and Europe met for a day to prepare an action plan for implementing recommendations of the workshop. It is envisioned that lead scientists identified from different countries will coordinate this research effort. This paper presents a summary of the recommendations made by the workshop participants and actions being taken at the national and international levels.

Within the framework of the ACCEPTED project (an Assessment of Changing Conditions, Environmental Policies, Time-activities, Exposure and Disease), a high-resolution urban dynamical downscaling technique has been applied for the Brussels Capital Region. First, regional climate simulations were performed with a new version of the limited-area model of the ARPEGE-IFS system running at 4-km resolution called ALARO coupled with the Town Energy Balance scheme (TEB). Then, to further downscale the regional climate projections to an urban scale, at 1 km resolution, a stand-alone surface scheme was employed in offline mode. Downscaling simulations of present and future urban climate of the Brussels areas are conducted. The downscaling strategy was first evaluated for a 10-years period [2001–2010] using ERA-INTERIM re-analysis data. In a next step, a downscaling simulation for the period 2046−2055 under the A1B scenario was performed. Results from our simulations indicate that while both urban and rural areas warm substantially for the 2050s horizon (1.5 °C), climate change will have a neutral impact on annual mean urban heat island (UHI) intensity. The most important increase is noted for the nocturnal UHI during the winter (+15 %) and the most important decrease is noted for the daytime UHI during the summer (−43 %). However, the model projects an increase of stable situations in the lower atmosphere during winter which may tend to keep pollutants concentrated over urban areas, with the associated health effects. Two approaches have been used to examine meteorological conditions that are unfavorable for the dispersion of air pollution under climate change conditions: (i) a transport index, based on the wind speed and Brunt-Väisälä frequency, that characterizes a typical length of horizontal and vertical transport, (ii) state-of-the-art chemistry transport models CHIMERE coupled to the 4 km regional climate simulations. The results from both approaches will be compared to assess future concentrations at the urban scale.

The impact of climate change on sea salt concentration over the European seas is studied using four CTMs driven by the same global projection of future climate over the control (1990–2009) and future (2040–2059) period. This study shows how model formulation for sea salt production dependency on water surface temperature and salinity can influence surface concentrations estimations. According to this study, sea salt concentration is overall increasing between control and future periods due to expected increase of water temperature and more frequent local storms, mostly visible over the Mediterranean and Black Seas.

In the context of the IMPACT2C project, one of the objectives is to estimate the pan-European impacts of a global 2-degree increase in temperature on human health, including change in air pollution. Climate change will affect atmospheric dispersion, biogenic and fire emissions, chemistry, and the frequency of extreme weather situations such as heat waves. These changes will have an impact on air quality with subsequent health consequences that must be evaluated. In order to evaluate how climate change will potentially affect the efficiency of emission abatement policies and how this will potentially affect health, several simulations have been conducted using different chemistry-transport models (CTMs): CHIMERE (IPSL), EMEP MSC-W (MET.NO), MATCH (SMHI), and MOCAGE (Météo-France). The use of four CTMs provide an estimate of the uncertainty in projections with the spread between models and driving meteorological data. To compare with future climate, the first step is to perform air quality simulations for the current climate: HINDCAST (CTMs forced by reanalysis boundary forcing ERA Interim) and HISTORICAL (global climate model boundary forcings). The comparisons between HINDCAST and HISTORICAL simulations allow to evaluate how global climate models modify climate hindcasts by boundary conditions inputs. In this study, we focus on particulate matter (PM10 and PM2.5) and its chemical composition. We first analyze whether the chemical composition of PM is affected by the use of climate models. We then investigate the contributions of the changes in meteorological parameters (frequency of precipitation, 2-m temperature, etc.) as well as emissions and depositions processes on surface PM. These results are the basis for analyzing future 2° warming climates. Under the RCP4.5 scenario, simulations have been performed in order to calculate the effect of climate change on emission reduction scenarios, the climate penalty, as well as the effect of emission mitigation. This analysis also provide uncertainties associated to future AQ projections.

Are clouds able to modify the ambient aerosol particle spectrum so that the number concentration is reduced and the size and chemical composition of the particles is changed? Can they do it in such a way that the cloud forming on these particles will finally be able to precipitate and remove the water and pollution from the atmosphere?

This work aims to assess the impact of climatological increment of temperature on the tropospheric ozone concentration in the Po Valley (Italy). Creation, destruction, and transport of ozone is not only governed by the sun, via photochemical reactions, but also by atmospheric conditions. Air quality is therefore significant, and its connection with climate change important. With a statistical downscaling of data from different General Circulation Models (GCMs), and the application of a Weather Generator (WG), it was possible to generate data series of daily temperature in the future (2021–2050). These were compared to data from the past (1961–1990, from the Agroscenari project), and the present (2000–2013, measured in six stations), showing how temperatures are bound to increase. We calibrated a simple statistical model based on a stratified sampling technique over a dataset of measured ozone and temperature, predicting the summer ozone daily maximum distribution. This allowed us to determine changes in ozone concentration over the years as a consequence of temperature increase. The results suggest that the last decade can be viewed as a projection of the future “ozone climate” in the Po Valley.

Volatile Organic Compound (VOC) reactivity scales are used to compare the ozone-forming potentials of various compounds. The comparison allows for substitution of compounds to lessen formation of ozone from paints, solvents, and other products. Current reactivity scales for VOC compounds were first developed using 1-D trajectory/box models for short pollution episodes several decades ago. In this study, they are updated using the 3-D air quality model CMAQ instrumented with DDM-3D. DDM-3D sensitivities are used to update relative reactivity metrics of a number of VOCs over more meaningful timescales. Using sensitivity calculation in the context of an air quality model for reactivity calculations avoids the issues of trajectory assumptions inherent in the 1-D calculations and allow for calculation of regional representative metrics.

Multi-decadal model calculations for the 1990–2010 period are performed with the coupled WRF-CMAQ modeling system over a domain encompassing the northern hemisphere and a nested domain over the continental U.S. Simulated trends in ozone and precursor species concentrations across the U.S. over the past two decades are compared with those inferred from available measurements during this period. The model results suggest large and contrasting changes in tropospheric composition over the northern hemisphere with significant reductions in air pollution over North America and Western Europe and increase in large portions of Asia. The model is able to capture the changing seasonal distributions in surface O₃ in the U.S. during 1990–2010 arising from changing emissions and long-range transport.

In this study, we compare air quality over North America simulated by the C-IFS global model and the CMAQ regional model driven by boundary conditions from C-IFS against surface and upper air observations. Results indicate substantial differences in model performance for surface ozone between the two models. Above the boundary layer, differences are least pronounced in the free troposphere but increase in the upper troposphere and lower stratosphere. In addition, we also compare the impacts of perturbed emissions in East Asia and North America on air quality over North America simulated by CMAQ and C-IFS.

Previous estimates of aviation contributions to ground-level ozone and fine particulate matter concentrations have either offered domain- and sector-wide estimates or focused on a few airports. Using the decoupled direct method (DDM), an advanced sensitivity analysis module for the CMAQ air quality modeling suite, we calculate per-airport sensitivity coefficients allowing quantification of 66 individual airports’ impact on air quality in the United States. Preliminary results show that these airports, collectively representing about 76 % of aviation activity by fuel burn in the US, are responsible for about 0.04 % of nationwide PM_2.5 concentrations; near-airport concentrations are proportionately much higher. Peak annual average contributions from individual airports vary from 0.018 to 0.0001 μg/m3; secondary PM_2.5 has effects at distances of up to 700 km downwind while primary PM_2.5 affects only the immediate vicinity of the airport. Complete results detailing specific air quality and health impacts of each airport will be presented at the ITM conference in May.

Both detailed chemical process model studies are performed in order to develop a reduced chemical mechanism for MEA and complex 3D dispersion model investigations with COSMO-MUSCAT were carried out focusing mainly on the chemical fate and lifetime of MEA and its reaction products such as nitramines and nitrosamines as well as their removal. In conclusion, the present dispersion model study has revealed that based on the available emissions and the meteorological conditions the proposed guidelines for long-term exposure in air should be not exceeded. Overall, the model results might allow future evaluations of possible environmental impacts and human health effects of pollutants emitted from CCS processes.

We studied the effects of ship emissions on the annual and seasonal concentrations and depositions of pollutants in Europe using the regional air quality model CAMx with and without ship emissions for 2006. Ship emissions cause a decrease in annual ozone mixing ratios around the English Channel and the North Sea, whereas they lead to an increase (4–14 %) in the Mediterranean region. They also cause an increase in PM_2.5 concentrations over the Mediterranean Sea (up to 80 %) and in the North Sea and Baltic Sea as well as along the coastal areas (10–15 %). Increased concentrations of the primary particles are predicted only along the shipping routes, whereas concentrations of the secondary pollutants are affected over a larger area. Particulate sulfate concentrations increase in the Mediterranean and the North Sea while elevated particulate nitrate levels are found especially around the Benelux area and northern Italy where there are high NH₃ land-based emissions. Our model results show that not only the air concentrations of pollutants are affected by ship emissions, but also depositions of nitrogen and sulfur compounds increase significantly along the shipping routes, especially in the Mediterranean Sea. The effects are found to be larger in summer.

Although advection equation is linear in time and space, positive definite and mass conservative advection schemes in chemical transport models (CTMs) behave nonlinearly mostly due to discontinuous (conditional) operations in discretization. A new approach is developed to indirectly simulate the advection process in CTMs. Instead of integrating the continuity equation directly for various species, advection equations for various pollutants are solved in relations to advected air densities. Our approach relies on the run-time estimation of the Jacobian of the advection operator, and can be based on the native advection algorithm in any CTM. The estimated transport Jacobian for air densities are then applied to other species with corrections that would ensure consistency with the underlying scheme. Since our proposed method is truly linear in species concentration, it is not suspect to complications associated with advecting sensitivities as experienced with nonlinear advection schemes. We implement our advection method in CMAQ 5.0 using its native Piecewise Parabolic Method (PPM) advetion scheme. Our preliminary results show good consistency with the PPM scheme, and potential for truly linear advection of sensitivity fields.

The Lower Fraser Valley (LFV) B.C., a largely deindustrialized region with emissions dominated by the transportation sector, has experienced large reduction in precursor emissions over the last 20 years. While these reductions have resulted in concomitant changes in summertime ambient ozone concentrations, the changes have not been uniform across the region, with trends in long term behavior differing noticeably between the eastern and western portions of the valley. In this work, we draw upon previous modeling, observational and experimental studies to infer how the sensitivity of ground-level ozone to precursor emissions has changed over the last two decades. This work is notable for several reasons. First we establish not only VOC- and NO_x-ozone sensitivities during summertime ozone episodes, but across the full range of summertime meteorological conditions. Next, we examine how these sensitivities have changed over the last two decades. We also examine how these sensitivities vary spatially across the LFV and finally we use the above findings to explain observed summertime ozone trends in terms of changing ozone sensitivities and changing emission rates.

Though the Lower Fraser Valley (LFV) has generally good air quality (AQ), ozone episodes may occur under a narrow set of synoptic and mesoscale conditions. These conditions give rise to complex flow systems, which further complicate the chemical sensitivity of the airshed. In this study, we use the MLDP0 dispersion model to investigate source-receptor relationships between NO_x emissions and receptor locations (AQ stations) subject to high ozone concentrations in the NO_x-limited portion of the valley.

In order to assess the potential change in ambient concentrations of ozone and its precursors that may arise from the construction of large industrial facilities in the Terrace-Kitimat valley (TKV), we conducted a study using the WRF, SMOKE and CAMx models for two periods in 2010. We developed and applied control and test cases for each period, the former for model evaluation and the latter to assess pollutant change. Model evaluation showed that CAMx is able to emulate O₃ peaks in an adjacent valley (where monitoring occurred) for both the spring and summer periods. Results for the spring period suggest that the addition of NO_X from industrial sources may lead to modest O₃ production outside of the main plume trajectory and on valley walls during afternoon hours as well as overnight O₃ titration along low elevations of the TKV upwards of 80 km downwind of sources. Results from the summer period suggest that the addition of O₃ precursors may at times contribute to a greater than 100 % increase in O₃ production under certain meteorological conditions up to 50 km downwind of sources.

Vet and Ro (Atmos Environ 42:2518–2529, 2008) developed an observation-based acid deposition model consisting of a conceptual regional mass-balance framework and inputs of mean area-integrated emissions data and wet deposition measurements over eastern North America. They then applied this model to estimate multi-year transboundary mass fluxes of sulphur and oxidized nitrogen between eastern Canada and the eastern United States during the 1990s. In this paper their approach is extended to use gridded emissions and deposition fields obtained from a numerical acid deposition model as inputs in place of measurements in order to diagnose transboundary fluxes of sulphur, oxidized nitrogen, and reduced nitrogen from model-predicted annual wet, dry, and total deposition fields. By following this approach, additional information of interest can also be inferred from an acid deposition model simulation after the simulation has been completed.

Air quality model simulations were carried out for the 2010 northern shipping season over a regional Arctic domain. Preliminary evaluation of the base model simulation shows that the model is able to capture the general trends of the observed ambient ozone and PM_2.5 in the northern region. Analysis on relative contributions from North American wildfires and Arctic marine/shipping to ambient concentrations of various pollutants and their depositions in the Canadian Arctic and northern regions was conducted.

We present the chemistry transport model LEO: a hybrid model, which combines the Eulerian grid model LOTOS-EUROS with the Gaussian plume model OPS. LEO is a new model under development in the Netherlands, which combines high resolution plume calculations around sources with large-scale transport and chemistry from a Eulerian grid model. In this way, we combine the best of both worlds into a new state-of-the-art model. The aim of LEO is to have a new calculation tool available in the Netherlands at a high resolution (typically 1 × 1 km2), which can (among others) deliver data input for the yearly produced maps of concentrations and deposition over the Netherlands. In the presentation a brief overview of the LEO model is given, and results of calculations are presented for calculating annual averaged concentrations of pollutants and deposition of acidifying and eutrophying components over the Netherlands. Furthermore, preliminary results are shown for LEO in its Plume-in-Grid (PinG) configuration, i.e., the transport of the mass released from a number of point sources is initially calculated with a plume description, after which the mass of the plumes is dumped into the grid. We will illustrate the performance of LEO by analysing existing multi source situations for ammonia with high time resolution measurements of ammonia.

SILAM has been among the first regional dispersion models to develop the ability to compute pollen emission and dispersion. For previous seasons forecasts have been provided for birch, grasses, olive and ragweed. For the current season this list has been extended by alder and mugwort. The growing number of modelled species makes it possible to draw conclusions from the existing experiences, related to the features of the input data and algorithms applied to describe the pollen emission. We demonstrate, that the currently available options in SILAM allow adding new pollen species with minimal development effort and limited input data, to allow forecasts with comparable quality to the previously developed taxa.

Very high particulate matter (PM) and black carbon (BC) concentrations are observed in the Kathmandu Valley, Nepal. With an aim of gaining a better understanding of the dynamics of these air pollutants, PM and BC simulations with the WRF-Chem model have been performed. The comparison of the simulation results with measurements shows that the model strongly underestimated the measured PM and BC levels. It is concluded that this is mainly due to missing up-to-date high resolution information on emissions in the valley.

In the framework of global atmospheric composition and air quality forecasting, accurate modeling of atmospheric chemical species redistribution and evolutions in numerical weather and air quality prediction models has become an important challenge in terms of research and developments. Such model developments are a part of the MACCII and MACCIII projects (EU-FP7) done to extend ECMWF’s integrated forecast system (IFS) by adding modules for chemistry, deposition and emission of reactive gases. It is now well known that deposition of chemical species, and especially dry deposition, is a major sink of pollutant in the atmospheric boundary layer. Several parameterizations have been developed in the last decades, while the resistances approach proposed by Wesely (1989) is the most commonly used. However, modeling of dry deposition is confronted to a lack of validation data. Indeed, this kind of parameterization is highly sensible to physical inputs like meteorological and surface fields. Currently, most of models use monthly-means values for dry deposition. It is thus impossible to take into account rapid transitions that can be observed on this inputs and can maybe leads to large biases on the short term forecasts. To investigate impacts of temporal resolution of dry deposition fields on air quality modeling, long simulations were done with the MOCAGE CTM developed at Météo-France, four type of temporal resolution (monthly-means, daily-means, mean daily-cycle, dynamic velocities) have been used, and results have been evaluated at regional and global scale. They shows important impacts in low-levels atmospheric composition locally, but also at global scale.

The number of patients of pollinosis caused by airborne pollen is increasing rapidly in the world. From a global perspective, pollinosis from birch is a serious problem in Europe, North America, and Northeast Asia. Our aim in this research is to generalize the principle of our pollen emission model developed for cedar. We observed temporal variations in airborne pollen concentrations of birch in Hokkaido, Japan, by using a Hirst-type pollen sampler. Relationship between airborne birch pollen and meteorological conditions during the main dispersal period was analyzed. Our results indicate that airborne pollen concentrations are related to changes in air temperature and solar radiation. The derivative of air temperature is most important and effective factor than the air temperature itself. The air temperature change for shorter time was related to the pollen emission of birch compared with the case of cedar. The amount of birch pollen emitted from the flowering birch forest was formulated using the algorithm summarized from the analysis.

Aim of this work is to better understand the connections between synoptic circulation patterns, local wind regimes and air pollution in the Po Valley which is a densely populated and heavily industrialized area. In this study we present a classification of weather types (WTs) in the Alps region performed with an objective method (COST Action 733 “Harmonization and Applications of Weather Type Classification for European regions”) based on hierarchical cluster analysis followed by a k-means cluster analysis, which is applied to the daily 500hPa time series from ERA INTERIM reanalysis. In order to take in account the strong influence of the regional wind regime on the local air quality, a classification of surface wind pattern (WPs) is performed as well with a cluster analysis technique. The link between WTs and WPs is investigated, and the statistical properties of pollutants concentration, aerosol chemical composition and dimensional distribution are analyzed in connection with WTs and WPs.

Sensitivity of ozone formation to NO_x and VOC emission is important in terms of implementing control strategies for photochemical pollutants, helping to determine the most effective way to limit O₃ concentrations and population exposure to high levels of O₃. In this study, the role of NO_x emissions on hourly O₃ concentrations is examined in order to determine whether NO_x is the driver or limiting factor of ozone formation in SW Poland in high-ozone situations. In order to assess that, four scenarios are analyzed: baseline, with no modification of the available emission inventory, a 10, 20 and 30 % decrease in anthropogenic emission of NO_x. The simulations were performed for an episode with high O₃ concentrations. Both spatial and temporal changes in ozone concentrations for all scenarios have been analyzed. Preliminary results show that reducing NO_x emission is associated with higher O₃ concentrations, however, further research is needed to assess the role of VOC.

This paper presents the influence of an emission approach applied (fixed and dynamical) on calculated ammonia concentration and model performance. We simulated with the WRF-Chem model a winter (January) and an early spring period (March) of 2012 for the entire Europe. Regardless of a relatively coarse resolution (36 km × 36 km) of the WRF-Chem simulations and the Europe-wide default settings applied in the dynamic ammonia model, the modelled concentrations are improved significantly by applying a dynamical emission instead of using a fixed emission profile.

This paper presents the first step in the development of a forecasting system for air pollution concentrations for the south-west region of Poland. We simulated a winter (January) and a summer (July) period of 2014 with WRF-Chem. The focus has been on studying different chemical options during the two simulation periods, therefore keeping emissions constant, except for natural sources. The authors have found that different model setups increase model performance for gases and particulate matter and that there is no clear dependence between forecast lead time and model performance.

A source attribution tool, based on a Lagrangian dispersion model, is proposed for an intermediate scale, when turbulent, small-scale and also synoptic phenomena are relevant for the fate of the emitted pollutant. The tool is applied to better understand the causes of a peak of fine particulate, rich in ammonium nitrate, observed in the Po Valley.

In order to study pollutants dispersion in a stable atmospheric boundary layer, an experimental program consisting in measuring turbulence fluctuations and pollutants dispersion in a stratified surface layer and in near field is being conducted on the site ‘SIRTA’ in the southern suburb of Paris. Turbulence is characterized to be strongly anisotropic in a stable surface layer. Forest to the north of experiment field induces a strong wind directional shear between 3 m and 30 m levels and a wind speed decrease. Numerical simulation in RANS mode with k-ε turbulence closure and a canopy model enables to reproduce the observed wind channeling effect of forest on the mean flow and turbulence kinetic energy. A preliminary simulation taking into account the real pollutants flow rate has been performed and compared with measurements.

Cities are the predominant places for human beings to settle down, thus becoming more vulnerable to extreme weather events aggravating phenomena like heat stress and decreasing air quality aroused by inner city pollution. The excessive warming of impervious surfaces and additional release of anthropogenic heat promotes urban heat island (UHI) formation. Human activities lead to an increase of emissions of air pollutants which in turn influences the chemical composition of urban air. In this study, the mesoscale chemical transport model WRF-Chem is used for the urban area of Stuttgart to simulate the effect of UHI mitigation strategies such as urban greening and high albedo materials on the concentration of primary and secondary pollutants.

Chlorine releases to the atmosphere due to accidents involving railcars can be extremely hazardous to health, the environment, and man-made materials. Since the chlorine is released as a mixture of reactive gas and small (median diameter of 20–100 μm) aerosols, and the initial cloud has a very high concentration (>10,000 ppm), deposition to the surface can be important. The various mechanisms include dry deposition caused by chemical reactions between the gas and the surface (ground, vegetation, or materials), dry or wet deposition of small aerosols, and gravitational settling and impaction of larger aerosols. The state-of-the art in gas deposition modeling is based on the resistance analogy, which has been widely used in deposition modeling of a variety of air pollutants. The resistance formula is reviewed and it is shown that, even though chlorine is relatively reactive, its deposition may be inhibited by the increased aerodynamic resistance in the very stable cloud. A method is suggested for parameterizing the effect of the dense cloud on the aerodynamic resistance. Deposition measurement methods planned for the Jack Rabbit II (JR II) chlorine release field experiments are reviewed, where up to 10 tons of pressurized liquefied chlorine will be released in several field trials.

Near-port air pollution has been identified by numerous organizations as a potential public health concern. Based upon multiple near-road and near-source monitoring studies, both busy roadways and large emission sources at the ports may impact local air quality within several hundred meters of the ports. As the volume of trucking and freight movement increases, near-road air quality along transportation routes could be affected well outside port boundaries. Health effects have been associated with near-road exposures and proximity to large emission sources, so characterizing emission sources is important for understanding potential health effects. To address this need, we have developed a new community-scale tool called C-PORT to model emissions related to all port-related activities—including, but not limited to ships, trucks, cranes, etc.—and predict concentrations at fine spatial scales in the near-source environment. C-PORT represents one of the first efforts to develop a reduce-form modeling system that is optimized for community-scale applications. The modeling system includes dispersion algorithms for area, point, and line sources related to port activities, and emissions from the port terminals. The use of the reduced-form approach in C-PORT enables us to examine what-if scenarios of changes in emission volume, such as due to changes in traffic counts, fleet mix, speed, or in port emissions due to equipment or vehicles in near real-time, using a web-based easy-to-use interface. The C-PORT model can be used to examine different scenarios of air quality impacts in order to identify potentially at-risk populations located near emission sources, and the effects that port expansion may have on them. We present an illustrative example of the near-port modeling assessment focusing on the Port of Charleston in South Carolina, USA, to complement a field-study that was conducted during spring 2014 to take air quality measurements in residential neighborhoods in the port vicinity.

Advances in the land surface model (LSM) and planetary boundary layer (PBL) components of the WRF-CMAQ coupled meteorology and air quality modeling system are described. The aim of these modifications was primarily to improve the modeling of ground level concentrations of trace chemical species such as nitrogen oxides (NO_x), carbon monoxide (CO), particulate matter (PM), and ozone (O₃) during the evening transition and overnight. However, evaluation of both meteorological and air quality quantities shows that the advanced algorithms added to the PX LSM and the ACM2 PBL schemes improved results both day and night. Modeling experiments at various grid spacings (1, 4, and 12 km) and for different years and seasons all show significant improvements. The revised versions of these components have already been released in WRFv3.7 and will be released in the fall of 2015 in CMAQv5.1.

Mobile measurements of black carbon are made in the city center of Liège (Belgium) to evaluate the contribution of this, not yet regulated pollutant, to the ambient air particulate matter level. By means of the CANS_BC model, we intend to determine the part due to traffic and, furthermore, to make the distinction between direct emissions and recirculation, depending on meteorological and geometric parameters, in street canyons. Background concentrations are, on one hand, directly measured at one station, and on the other hand, estimated via the Lagrangian Austal2000 model, fed with domestic heating emissions. Short-term experiments, led at six selected sites, are used to calibrate the model, which will be further extended to the whole city.

Vehicular traffic in Bogota is one of the main causes of air pollution, therefore it is necessary to estimate concentration levels at which the population is exposed near roads. This work summarizes the implementation of the near-road air quality model R-LINE in Bogota. Emissions from vehicles were calculated for 12,725 road links (912 km of principal roads) and assigned to the model in (g m−1s−1) units. Meteorological information was obtained from the Bogota’s Air Quality Monitoring Network (BAQMN) stations. Evaluation of model was performed by comparing modeling outputs against pollutant observations from the BAQMN. Results suggest a good model performance and potential to use the model to evaluate personal exposure and assess/develop emission reduction strategies for improving air quality in a large metropolitan area.

We present a three-dimensional fully-adaptive grid algorithm for chemical transport models. The method is designed to refine vertical and horizontal resolution by dynamically concentrating grid nodes within a region of interest. Exceptionally high grid resolution can be achieved in Eulerian air quality models using the method. Here the algorithm’s main operations are described. In addition, advection tests are used to demonstrate the algorithm’s ability to better capture concentration gradients in atmospheric plumes.

We have presented an overview of the modelling of particle number concentrations (PNC’s) in five major European cities, namely Helsinki, Oslo, London, Rotterdam and Athens, in 2008. We have presented emission inventories of particle numbers both on urban and European scales, atmospheric dispersion modelling of PNC’s in the cities and on a European scale, and evaluated the predicted results against available measured PNC’s. The concentrations of PN in the selected cities were mostly influenced by the emissions from local vehicular traffic. However, harbor and airport activities can also have a substantial influence on the PN concentrations. There was a substantial variation in the performance of the models from site to site, when compared with the available measurements, both regarding the annual average concentrations, and the temporal correlation of the hourly time series. In the future, more accurate emission inventories and emission factors will be needed for the relevant source categories. More long-term measurements of PNC’s will also be needed in various urban locations.

Large mobility projects, for example, construction of new highways, have attracted an increased interest from the public at large. Pressure groups and environmental activists often propose their own alternatives for the planned projects. The vast amount of scenarios that are proposed in this way lead to an enormous increase of work related to the environmental assessment. Therefore, a new screening tool is proposed that can serve as a first phase in the environmental assessment procedure for air pollution. The tool enables users to quickly estimate the impact of a scenario on local air quality. Only scenarios which show promising results in this screening tool are then to be discussed in the normal environmental assessment procedure which is much more time-consuming. There are several requirements in order to have a successful approach: the screening tool needs to be fast, it needs to provide results which are close to the detailed environmental assessment procedure and it needs to enable calculations for the major pollutants (NO₂, PM₁₀, EC/BC, …) including fast ozone chemistry. Such a screening tool is presented here. It determines annual average concentrations of the major pollutants (NO₂, PM₁₀, PM_2.5, EC, C₆H₆) in a short calculation time of only some 10 min on 1 CPU, compared to calculations of 2 days on 24 CPUs for the detailed assessment procedure. The speed-up is obtained by using lookup tables of pre-simulated situations, which are then combined into the large-scale scenario. For instance, the effect of a 100 m road segment from north to south with a unit emission strength is calculated beforehand. If such a segment occurs in the input, this result is then used with the emissions scaled according to need. The tool does not calculate absolute concentrations (therefore, a standard model is still used) but only differences between scenarios. For some scenarios both the screening tool and the regular assessment procedure have been applied and results are compared. Comparing for both methods the screening tool with the full model yields small biases (−0.0022–0.0075 µg/m3), a small RMSE (0.02–0.21 µg/m3), a high R2 of (0.75–0.87) and a slope of the regression curve close to 1 (1.01–1.18), showing that for screening purposes the tool is well capable of making the cut between good and bad scenarios.

The urban heat island effect, in which air temperatures tend to be higher in urban environments than in rural areas, is known to exacerbate the heat impact on population health. We introduce a new urban climate model, further referred to as UrbClim, designed to study the urban heat island effect at a spatial resolution of a few hundred metres. Despite its simplicity, UrbClim is found to be of the same level of accuracy as more sophisticated models, while also being much faster than high-resolution mesoscale climate models. Because of that, the model is well suited for long time integrations, in particular for applications in urban climate projections. In this contribution, we present temperature maps for London, including an assessment of the present-day climate, and projections for the future (2081–2100).

It is now generally admitted by the first responders and decision-makers that atmospheric dispersion modelling and health impact assessment can be of help in an emergency implying the release of Chemical, Biological, Radiological or Nuclear species, possibly preceded by an Explosion (CBRN-E). Such an event may be of accidental or malevolent origin and is likely to occur in a built industrial, harbour or urban environment. Thus, a modelling and decision-support tool should be able to deal with several types of threats and scenarios and give the possibility to run flow and dispersion models adapted to complex environments. Moreover, such a tool should produce in a limited amount of time (maximum of 15 min) 2D/3D simulation results directly useable by the civilian security to protect the population. These are precisely the specifications of CERES® CBRN-E software which is developed at CEA with the aim to transfer R&D advances to operational applications. This paper first comments on the advantages of advanced dispersion models. It presents the main features of CERES® and its use in connection with the civilian security by way of the “Toxic 2014” exercise done in “La Defense” district near Paris. Finally, the paper condenses the major lessons learnt from this exercise regarding the future development of CERES® as a decision-support tool.

Reliable meteorological model results are one pre-condition for a good air quality simulation. The achievable quality of the meteorological information determines how accurate a concentration simulation can be. Many meteorological services as well as research institutions perform model evaluations on a routine basis, but the outcomes are not always published in refereed journals and little is known on typical model performances. This paper summarizes results of quantitative model evaluations that were published in refereed journals by statistically analyzing the published values for bias, root mean square error, rmse, as well as correlation coefficient, r. The 50 percentile of the quality measures rmse and r is used as threshold to derive typical performances. For r the 50 percentile is 0.47, 0.62, 0.89 and 0.87 for wind direction, wind speed, temperature and specific humidity, respectively. While bias values are small compared to their average values, rmse values are large.

Since 2010, new estimations of the particulate matter emissions in Flanders have been made by using a tier-II approach. By means of a survey, the quantity of the wood by households, buildings (services sector), industry and agricultural sector consumption in Flanders has been estimated. A survey is necessary as not all consumed wood is purchased, e.g. some of the wood is sourced locally or waste wood is used. These surveys also provided data on the installation stock in Flanders, such as the type (fireplaces, pellet stoves, …) and age of the heating installation. Besides the wood consumption and use patterns, emission factors of particulate matter per type of wood and per type and age of heating installation were also updated based on international, recent literature on emission factors. Both new estimations resulted in an actualization of the particulate matter emissions of the residential wood burning in Flanders. Overall, the estimations were a factor 13 higher than the old estimations. This factor 13 is due to a factor 4 increase in the average emission factor and a factor of 3.4 in the wood consumption. As a result, heating in residential sector is estimated to be the most important emission source for primary particulate matter (37 % of all primary emissions) in Flanders. To confirm these results a comparison was carried out with the estimates of the contribution of wood burning to ambient PM10 concentrations based on measurements of levoglucosan. It was shown that about 10 % of the particulate matter concentrations in winter were due to wood burning, while in summer this amounted only 2–3 %. In the next step, a dispersion model has been used in order to check the consistency of both results. It is shown that the increased emissions are consistent with the measured particulate matter concentrations due to wood burning. Indeed, the increase in emissions by a factor 13 is needed to understand the high levoglucosan concentrations in ambient air in Flanders. Finally, the large changes that have been made to the Belgian SNAP-2 emission inventory due to the results of this study, raise questions about the homogeneity (both spatial and temporal) of the European emission inventory for this sector. Adjustments to this sector could possibly decrease the modelled PM-gap quite substantially.

Chemistry transport models (CTMs) are a powerful tool to investigate various features of the aerosol pollution in megacities, including its geographical origin or its sensitivity to anthropogenic emissions changes (scenario analysis). However, due to the numerous uncertainties still at stake in CTMs, assessing the reliability of the results obtained in these two common exercises remains a challenging task that usually requires specific observations and methodologies. In our work, we have taken advantage of some recent campaigns in the Paris region—PARTICULES and FRANCIPOL—to run a diagnostic evaluation of the CHIMERE model regarding these two issues. The first substantive point is to assess in what extent the model is able to retrieve the correct share between local production and regional advection of aerosol pollution in the Paris agglomeration. During a whole year, daily measurements of the fine particulate matter (PM_2.5) and its main chemical constituents (elemental and organic carbon, nitrate, sulfate and ammonium) are available at various stations both in and around Paris (PARTICULES project). Based on back-trajectory data, we can locate the upwind station, from which the concentration is identified as the import, the local production being deduced from the urban concentration by subtraction. Uncertainties on these contributions are quantified. Small biases in urban background PM_2.5 simulations (+16 %) hide significant error compensations between local and advected contributions, as well as in PM_2.5 chemical compounds. In particular, wintertime OM imports appear strongly underestimated (potentially explained by uncertain continental woodburning emissions and missing SOA pathways) while local OM and EC production are overestimated all along the year (likely to be related to uncertainties in emissions and dynamics). A statistically significant local formation of nitrate is also highlighted from observations, but missed by the model. Together with the overestimation of nitrate imports, it leads to a bias of +51 % on the local PM_2.5 contribution. In parallel to inorganic aerosols measurements, gaseous nitrate precursors (nitric acid and ammonia) have also been measured (FRANCIPOL project), which offers the opportunity to investigate the regime of nitrate formation in Paris and its sensitivity to precursor changes and to assess, again, the ability of the CHIMERE model to retrieve the observed sensitivity. Experimental data clearly point to NH₃-rich conditions in the city (as indicated by high gas ratio values), but a quite similar sensitivity of nitrate concentrations to changes in nitric acid and ammonia. However, simulation results indicate that the model highly overestimates the sensitivity of nitrate to ammonia changes. Thus, while overall particulate matter levels are well reproduced by the model, differences with observations are much larger for local versus advected contributions, and the sensitivity of nitrate formation with respect to gaseous precursors.

Now that the third model evaluation exercise has been launched, a critical review of the activities performed under the Air Quality Model Evaluation International Initiative (AQMEII) is presented. Attention will be focused on the scientific results obtained by individual modeling groups and by the overall community activity. In particular, we critically review the contributions of AQMEII to operational, diagnostic, dynamic, and probabilistic model evaluation. In addition, the role of community collaborations around coordinated modeling activities will be analyzed. Aspects considered in this analysis are the coverage of multiple topics and research interests, the distribution of the workload among several players, the exploitation of web technology for data exchange, the rationalization of the organization of information, and the exploitation of existing data from emission inventory to niche ad hoc and operational monitoring data. Finally, we discuss the channeling of efforts towards the collaboration with other international activities such as the LTRAP Task Force on Hemispheric Transport of Air Pollutant (TF-HTAP) thus multiplying the benefits for the community.

In this study, the chemistry transport model (CTM) LOTOS-EUROS is used to calculate the deposition fluxes of eutrophying and acidifying reactive nitrogen (Nr) to ecosystems in Germany. In the last years important developments have been made for the modelling of the budget of Nr. The new model version has changed the spatial explicit budget calculations across Germany and was thoroughly evaluated with respect to pollutants concentrations and deposition fluxes. The results revealed that the balance between dry and wet deposition is sensitive to the updates in the process descriptions impacting the pollutants transport distances. A comparison to observations showed an improvement of the new model version compared to the pre-developments model version.

Photochemistry, particles formation and cycling, and aerosol optical properties predicted by a deterministic modeling system have been evaluated through both in-situ and satellite measurements. The three-dimensional air quality modeling system NINFA/AODEM was implemented over the Po valley for the entire year 2012 with the aim to characterize the atmospheric conditions, in terms of meteorological parameters and chemical composition. In addition, NINFA/AODEM has been deeply evaluated by using measurements of size-segregated aerosol samples collected on hourly basis at the 3 different sampling sites representative of urban background (Bologna), rural background (San Pietro Capofiume) and remote high altitude station (Monte Cimone 2165 ma.s.l.).

A novel, hybrid chemical transport/receptor model approach is used to develop spatial fields of daily source impacts. The spatial-hybrid (SH) method uses source impact fields obtained from CMAQ simulations, which are then adjusted to better match species-specific observations. Specifically, the SH method assimilates modeled 36-km source impact estimates from CMAQ and ground observations from the Chemical Speciation Network (CSN) to produce source impacts that better reflect observed data. New source profiles are generated using source impact results from the SH method. The new source profiles reflect modeled and observed concentrations and also reflect secondary formation processes that are captured by CMAQ. Results of the application of this method suggest that the default source profiles used in emissions inventories may be inconsistent with observations. In this work, we present SH source impact fields over the year 2006. These results are then used to develop updated source profiles for fine particulate matter sources for the contiguous U.S. The profiles characterize the composition of 22 particulate matter species, including major ions, carbon species, and 17 metals. Sources analyzed include fossil fuel combustion, mobile sources, sea salt, biogenic emissions, biomass burning, as well as livestock operations, agricultural activities, metals processing, and solvents. Source profiles are evaluated by comparing results for two locations Atlanta, GA, and St. Louis, MO to previous studies.

A main research task of COST Action ES1006 is the evaluation of atmospheric dispersion models by their comparison against test data from qualified field and laboratory experiments and by a model inter-comparison. The model comparison and evaluation carried out for three test cases is presented, addressing the performance of the different modelling approaches, quantifying the scatter of results when different models are applied and assessing the effect of uncertainties.

The governments of Canada and Alberta are implementing a joint plan for oil sands monitoring that includes investigating emissions, transport and downwind chemistry associated with the Canadian oil sands region. As part of that effort, Environment Canada’s Global Environmental Multiscale—Modelling Air-quality And CHemistry (GEM-MACH) system was reconfigured for the first time to create nested forecasts of air quality at model grid resolutions down to 2.5 km, with the highest resolution domain including the Canadian provinces of Alberta and Saskatchewan. The forecasts were used to direct an airborne research platform during a summer 2013 monitoring intensive. Subsequent work with the modelling system has included an in-depth comparison of the model predictions to monitoring network observations, and to field intensive airborne and surface supersite observations. A year of model predictions and monitoring network observations were compared, as were model and aircraft flight track values. The relative impact of different model versions (including modified emissions and feedbacks between weather and air pollution) will be discussed. Model-based predictions of indicators of human-health (i.e., Air Quality Health Index) and ecosystem (i.e. deposition of pollutants) impacts for the region will also be described.

Top-down emission estimation via inverse dispersion modelling is frequently used for estimation of emission from wild-land fires. The approach, efficiently constraining the emission from fires, also has major uncertainties, which are illustrated here with a few examples of the Integrated System for wild-land Fires (IS4FIRES).

It is well known that there are reducible and irreducible uncertainties in both uncoupled and coupled meteorology-atmospheric chemistry models. Reducible (i.e., structural and parametric) uncertainties are attributable to our incomplete or inadequate understanding of the relevant atmospheric processes (e.g. chemical mechanism, PBL evolution, modeling domain, grid resolution, cloud treatment) and errors in model input data (e.g., emissions, boundary conditions). Inherent or irreducible uncertainties stem from our inability to properly characterize the atmosphere with appropriate initial conditions. When the initial state of the atmosphere is unknown, its future state cannot be predicted with great accuracy. There is an emerging need to properly assess these types of modeling uncertainties in order to improve the prediction accuracy of modeling systems. This work focuses on the assessment of inherent uncertainties in atmospheric and air quality modeling systems by estimating the impacts of various options for initial conditions on weather parameters and their consequent effect on atmospheric pollutant concentrations. Support for the modeling efforts is given by data collected from surface measurement networks for the meteorological and air quality parameters. We focus on the changes in atmospheric variables that strongly affect the fate and transport of air pollutants like ozone and aerosols.

This model study about the influence of chemical reactants on the transport of mercury is part of an international mercury model inter-comparison (MMTF) coordinated by the EU-FP7 Research Project GMOS (Global Mercury Observation System). GMOS focuses on the improvement and validation of mercury models to assist establishing a global monitoring network and to support the implementation of the Minamata Convention. For the model inter-comparison, several global and regional Chemistry Transport Models (CTM) were used to simulate the influence of reactants on mercury oxidation. For this, gas and aqueous phase reactions of mercury with bromine were implemented into the models. As reactants, precalculated bromine concentrations were taken from the global bromine models P-TOMCAT and GEOS-CHEM. The modelled concentrations of oxidized mercury were compared to observations from GMOS measurement stations, and air craft campaigns. It was found that, even outside of polar regions, bromine plays an important role in the oxidation of mercury. Moreover, the chosen reactant influenced the vertical distribution of mercury in the atmosphere. While little difference was found for GOM concentrations at the surface level, the bromine reaction was able to explain the elevated concentration of GOM observed in the free troposphere.

We use time series of hourly records of ozone for a whole year (2006) collected by the European AirBase network to analyse the area of representativeness of monitoring stations and find, for similar class of stations (urban, suburban, rural), large heterogeneity and high sensitivity to the density of the network and to the noise of the signal. This suggests the mere station classification to be not a suitable method to help select the pool of stations used in model evaluation. Therefore a novel, more robust technique is developed consisting in studying the spatial properties of the associativity of the spectral component of the ozone time series, in an attempt to determine the level of homogeneity.

A new WRF-CMAQ two-way coupled model was developed to provide a pathway for chemical feedbacks from the air quality model to the meteorological model. The essence of this interaction is focused on the direct radiative effects of scattering and absorbing aerosols in the troposphere that in turn affect radiation calculations in the meteorological model, WRF. We tested this two-way coupled model with a high aerosol loading episode resulting from a wildfire outbreak in California in June 2008. We evaluate various aspects of the model with traditional statistical approaches, a comparison with radiation measurements, and AOD measurements from high resolution (500 m) MODIS data.

A metamodel for ozone is a mathematical relationship between the inputs and outputs of an air quality modeling experiment, permitting calculation of outputs for scenarios of interest without having to run the model again. In this study we compare three metamodel estimation techniques applied to an 18 year long CMAQ simulation covering the Northeastern US (NEUS). The estimation methods considered here include projection onto latent structures, stochastic kriging and a combination of principal components and stochastic kriging.

A study has been done to investigate the difference in the modelled bound water component of PM_2.5 air mass concentration when modelled to correspond to different measurement methods. The study found that the June average PM_2.5 air mass concentration along a transect through southern England for a 2020 emissions scenario differed by 89 % when modelled corresponding to the two different measurement methods. It is, therefore, clear from this study that careful consideration needs to be taken to ensure that the modelled PM_2.5 air mass concentration corresponds to the appropriate measurement method.

This study is focused on a wild-land fire episode in 2010, August 7–9, when the plume of severe wildfires in European part of Russia reached Estonia and southern Finland, thus giving a chance to evaluate the modelling results against a relatively dense network of air quality monitoring stations. The chemistry-transport model SILAM, driven by the ECMWF meteo fields, was run within the European domain of AQMEII2 model validaton exercise with a 0.2-degree grid resolution, applying the AQMEII2 chemical and aerosol boundary conditions. The modelling results were compared with measured three-hourly average concentrations of PM_2.5 in Estonia and Finland. The observed peak values in most of stations reached 60–80 µg/m3 (narrow peaks in the easternmost stations: up to 106 µg/m3 in Estonia and 220 µg/m3 in Finland, whereas SILAM predicted up to 75 µg/m3). Remarkably enhanced particulate matter concentrations were found in all 7 continental (rural and urban) monitoring stations of Estonia, reproduced by the model within 10 % of the peak values in 5 stations. The general shape of the peak was reproduced by SILAM within 3–6 h of a time error. The model runs suggest that the wildfires were not solely responsible for enhanced concentrations: the continental aerosols constituted about a half of the total mass. The westernmost maritime station in Estonia was left almost untouched by the plume. Remarkable direct effect of the aerosols on boundary-layer meteorological conditions, PBL height, near-surface wind and temperature was found during this episode when the aerosol direct radiative effect was considered in numerical weather prediction model HARMONIE.

Ensembles of air quality models have been shown to outperform single models in many cases. Starting from the theoretical evidence behind this empirical ascertainment, we present the conditions granting an ensemble superior to any single model. As those conditions are not systematically met, we also investigate two additional ensemble estimators for which a sound mathematical framework exists. In view of producing a single improved forecast out of the ensemble, the three candidate ensemble estimators, namely the unconditional ensemble mean, the weighted ensemble mean and the mean of the sub-ensemble with the right trade-off between accuracy and diversity, are evaluated against data generated in the context of AQMEII (Air Quality Model Evaluation International Initiative). The pitfalls of training such ensembles are investigated. Overall, following a proper training procedure, the sophisticated ensemble averaging techniques were shown to have higher skill compared to solely ensemble averaging forecasts.

This study presents the continuation and exploration of the research by application of regional and high resolution air quality (AQ) models. Separate ‘offline’ (WRF—CAMx) and ‘online’ (WRF Chem) coupling modeling systems were used to evaluate the contribution of the local anthropogenic sources over the Republic of Croatia. Within the research with the NWP WRF model, various tests were made with the implementation of the new, improved mixing length scale in MYJ PBL scheme. Default and modified setup of the NWP WRF model was tested on different ABL stability conditions and coupled with AQ models. Experiments with different AQ models also encompassed different time periods with relatively high concentrations in rural and urban areas. Using complex atmospheric chemistry models it was possible to analyze the main processes contributing to the relatively high concentration on regional and local scale and to compare the performance of two different coupling modeling systems.

MOCAGE is the 3D global off-line chemistry transport model (CTM) run at Météo-France since 2005 for air quality operational forecasts. Three nested domains are used, with decreasing resolutions (globe, Europe, and France), and 47 vertical levels (from the surface to 5 hPa). For the global and the European domain, input meteorological forcing fields are Météo-France ARPEGE forecasts. For France, and for the first day of forecast, MOCAGE uses the operational outputs of Météo-France non-hydrostatic AROME model. This high-resolution (2.5 km) meteorological model is supposed to better represent urban processes (e.g., the urban heat island), which are of strong interest for air quality applications. The purpose of this study is to test the increase of resolution of the CTM MOCAGE over France from 0.1° to 0.025° (i.e. the native resolution of the input meteorological fields).

The tropical Atlantic is frequently affected by Saharan dust intrusions. Based on MODIS cloud fraction (CF) data during the 10 year study period, we found that these dust intrusions contribute to significant cloud cover along the Saharan Air Layer (SAL). Below the temperature inversion at the SAL’s base, the presence of large amounts of settling dust particles, together with marine aerosols, produces meteorological conditions suitable for the formation of shallow stratocumulus clouds. The significant cloud fraction along the SAL together with clouds over the Atlantic Inter-tropical Convergence Zone contributes to the 20 % hemispheric CF asymmetry between the tropical North and South Atlantic. This leads to the imbalance in strong solar radiation, which reaches the sea surface between the tropical North and South Atlantic, and, consequently, affects climate formation in the tropical Atlantic. Therefore, despite the fact that, over the global ocean, there is no noticeable hemispheric asymmetry in cloud fraction, over the significant area such as the tropical Atlantic the hemispheric asymmetry in CF takes place. Saharan dust is also the major contributor to hemispheric aerosol asymmetry over the tropical Atlantic. The NASA GEOS-5 model with aerosol data assimilation was used to extend the MERRA reanalysis with five atmospheric aerosol species (desert dust, sulfates, organic carbon, black carbon, and sea-salt). The obtained 10 year (2002–2012) MERRA-driven aerosol reanalysis dataset (aka MERRAero) showed that, over the tropical Atlantic, dust and carbonaceous aerosols were distributed asymmetrically relative to the equator, while other aerosol species were distributed more symmetrically.

The newly developed weather-based prescribed burn forecasting capability presents new opportunities for dynamic air quality management. Forecasting of burn emissions has been incorporated into the HiRes-2 Air Quality Forecasting System. Forecasts are being produced daily for air quality and the impacts of power plant, traffic and prescribed burn emissions. The ultimate goal is to integrate these air quality forecasts into the burn permitting operations.

In the past few years a growing amount of space observations focusing on atmospheric composition has become available and this trend will continue with the launch of new satellites (ESA-Sentinels, NASA-TEMPO and JAXA air quality and climate mission) in the near future. To justify the production and launch of these expensive instruments, there is a need for determining the added value of future satellite instruments and their optimal design in an objective way. One methodology that can do so is the OSSE (Observing System Simulation Experiment). Although extensively used in the meteorological community, it’s use in the field of air quality and climate is still limited and a common approach is desirable. Based on existing studies and experience in the meteorological community we have identified requirements for each of the OSSE elements for performing a realistic OSSE. Using illustrative examples from existing air quality OSSEs we will present the methodology and the requirements for the application of OSSEs to satellite observations of atmospheric composition.

This pilot study focusing on Rotterdam and Paris investigates innovative means to use earth observation data as additional constraints to estimate anthropogenic city-scale CO₂ fluxes. NO₂ tropospheric column from OMI is used as a proxy. The LOTOS-EUROS chemistry transport model equipped with a source apportionment module was used to track and label the origin of NO₂ at OMI overpass. Next, this information is combined with source specific reported anthropogenic NO₂/CO₂ ratio to derive co-emitted CO₂ per source sector. In this paper, preliminary results are presented and the added value of the use of remote sensing data and regional air quality models to infer information on anthropogenic emissions is discussed.

NOAA provides operational predictions of ozone and wildfire smoke for the United States (U.S.) and predictions of airborne dust over the contiguous 48 states. Predictions are produced beyond midnight of the following day at 12 km spatial and hourly temporal resolution and are available at http://airquality.weather.gov/. Ozone predictions and testing of fine particulate matter (PM_2.5) predictions combine the NOAA National Centers for Environmental Prediction (NCEP) operational North American Mesoscale (NAM) weather predictions with inventory based emission estimates from the EPA and chemical processes within the Community Multiscale Air Quality (CMAQ) model. Predictions of smoke and dust from dust storms use the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Verification of ozone and developmental aerosol predictions relies on AIRNow compilation of observations from surface monitors. Verification of smoke and dust predictions uses satellite retrievals of smoke and dust.Ozone prediction accuracy is maintained in recent years, while pollution sources are changing, through updates in emission source estimates and updates in the model configuration. Emissions for operational ozone predictions were updated using EPA projections of mobile sources for 2012. Trends in NO_x from satellite and ground observations show a good agreement with emission updates over large U.S. cities. Updated CMAQ model with CB05 mechanism and AERO4 aerosol module was implemented for operational ozone prediction in January 2015. Updates include monthly varying lateral boundary conditions, modified dry deposition, constraints on minimum planetary boundary height, and changes to the lifetime of organic nitrate. Testing of PM_2.5 predictions from the same system modulates soil emissions by snow and ice cover, and includes emissions of windblown dust and particles emitted from forest fires. Further development of PM_2.5 predictions will explore bias correction approaches. Longer-term plans include comprehensive linkages between NAQFC predictions for the U.S. and global atmospheric composition predictions, as resources allow.

Satellite-based and high-altitude airborne remotely sensed air quality data complement land-based and routinely commercial-flight and other measurement-campaign acquired remotely sensed and in situ observations. It is important to optimize the combination and placement of these wide ranges of measurements and data acquisition options for cost-effectiveness. Under this initiative we attempt to quantify the gain by a regional state-of-the-science chemical data assimilation and chemical transport modeling system when incremental sets of observation are acquired into the system. This study represents a first step in a series of steps to ingest such proposed incremental additions of observation. The efficacy of such proposals is quantified systematically by Observation Simulation System Experiments (OSSEs). We compared two end-to-end regional air quality forecasting simulations using: (a) the Weather Forecasting and Research (WRF) regional application initialized by the U.S. national Weather Service (NWS) Global Forecasting System (GFS) coupled with the U.S. Environmental Protection Agency Community Multi-scale Air Quality (CMAQ) chemical model (Byun and Schere 2006), and (b) the same as above but with a new GFS enhanced by assimilating a fictitious addition of Atmospheric Infrared Sounder (AIRS) retrieved radiances at 13 km spatial resolution at nadir from a proposed geostationary satellite positioned over 75oW staring over the U.S. Both sensitivity runs were performed in 12 km horizontal grid resolution and with daily initialization for 12 days between July 29 and August 9 2005. Noticeable forecast skill improvement in surface concentration for O₃ and particulate matter smaller than 2.5 µm in diameter (PM_2.5) was achieved.

This study compares two approaches for data assimilation in forecasting volcanic plumes of sulphuric dioxide released in the eruption of Grimsvötn in 2011. The first data assimilation approach is source term inversion, where the data are used for estimating an effective source term. In the second approach, the assimilation proceeds in cycles, and the observations are used for updating the initial condition. In both cases, the SO2 retrievals by the OMI instrument are assimilated. The results indicate that the source term inversion is more effective at constraining the three-dimensional structure of the plume. However, as the transport distance increases, updating the initial condition provides a better fit to the column observations.

An existing bias correction technique has been extended to intelligently incorporate urban centre and roadside observations by using high (1 km) resolution pollution climatologies. The results show that this can give important improvements in forecast skill, particularly during rush hours where a clear distinction between urban and rural areas becomes more apparent.

MODIS vegetation and albedo products provide a more realistic representation of surface conditions for input to the WRF/CMAQ modeling system. However, the initial evaluation of ingesting MODIS data into the system showed mixed results, with increased bias and error for 2-m temperature and reduced bias and error for 2-m mixing ratio. Recently, the WRF/CMAQ land surface and boundary layer processes have been updated. In this study, MODIS vegetation and albedo data are input to the updated WRF/CMAQ meteorology and air quality simulations for 2006 over a North American (NA) 12-km domain. The evaluation of the simulation results shows that the updated WRF/CMAQ system improves 2-m temperature estimates over the pre-update base modeling system estimates. The MODIS vegetation input produces a realistic spring green-up that progresses through time from the south to north. Overall, MODIS input reduces 2-m mixing ratio bias during the growing season. The NA west shows larger positive O₃ bias during the growing season because of reduced gas phase deposition resulting from lower O₃ deposition velocities driven by reduced vegetation cover. The O₃ bias increase associated with the realistic vegetation representation indicates that further improvement may be needed in the WRF/CMAQ system.

In order to achieve a cost-effective control of air quality in one region and to evaluate effects on population of long term exposure to air pollution, the assessment of spatial representativeness of air quality monitoring stations is of fundamental relevance. In this work, the area of representativeness has been assessed by means of a synthetic indicator describing the dependency of concentration on land cover distribution. The rationale is that, the more variable is the indicator in the surroundings of the station, the less representative are the concentrations measured at the air quality station in the surroundings. Pollutants under investigation were PM2.5 and O₃ and the CORINE land cover map of 2006 was used with ad hoc modifications. The variability of the indicator was explored within circular buffers around the sites, with increasing radii resulting below the established threshold of 20 % for almost all cases. Results showed that the methodology allows an useful and quick assessment of spatial representativeness of a monitoring site, without the need of dedicated measurement campaigns.

Data fusion procedures are developed to fill the gap between monitoring networks and CTMs. However, they often do not account for temporal dynamics, leading to potential inaccurate air quality assessment and forecasting. We propose a statistical data fusion strategy for combing the CTM output with monitoring data in order to improve air quality assessment and forecasting in the Emilia-Romagna region, Italy. We employ a dynamic linear model to accommodate dependence across time and obtain air pollution assessment and forecasting for the current and next two days. Finally, air pollution forecast maps are provided at high spatial resolution using universal kriging and exploiting the CTM output. We apply our strategy to particulate matter (PM₁₀) concentrations during winter 2013.

The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction operates the U.S. Air Quality Forecasting Capability (NAQFC) which uses primarily the U.S Environmental Protection Agency’s Community Multi-Scale Air Quality (CMAQ) model. NAQFC focuses on surface ozone and PM_2.5 (particle matter with diameter <2.5 µm), which impacts human-health. Near surface ozone mainly comes from photochemical reactions of NO_x and volatile organic compounds (VOCs). Its sources in upper layers could come from either long-range transport or stratospheric ozone. Most PM_2.5 comes from near-surface primary emissions or secondary generation from photochemical reactions. During the summer 2014 NASA Discover-AQ-Colorado program, the NOAA Air Resources Laboratory (ARL) provided a real-time forecast in support of aircraft measurements with 12 km CONUS (Contiguous United States) and 4 km nested domains. Here we compare the model results with the aircraft data to investigate our predictions.